Procedure Composite Process List
Get a list of ProcedureComputation objects. ProcedureComputations have a 1:1 mapping with Observations where used.
These may have a number of 2 or more components made up of combinations of Computation and Acquisition records.
The details of the underlying records have been serialised.
### Available end points:
- `/ProcedureComputations/` - Will list all ProcedureComputations in the database
- `/ProcedureComputations.json` - Will return all ProcedureComputations in json format
- `/ProcedureComputations/<object_id>/` - Returns ProcedureComputations object with that id
### Available Methods:
- `GET`
- `HEAD`
### Available filters:
None
### How to use filters:
None
GET /api/v2/composites/?format=api&offset=500
{ "count": 662, "next": "https://api.catalogue.ceda.ac.uk/api/v2/composites/?format=api&limit=100&offset=600", "previous": "https://api.catalogue.ceda.ac.uk/api/v2/composites/?format=api&limit=100&offset=400", "results": [ { "ob_id": 38837, "computationComponent": [ { "ob_id": 38838, "uuid": "83b75e6d88a2425cbb13909b5afd7a57", "title": "Level 2 Formaldehyde (HCHO) total column processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data", "abstract": "The general method used for the derivation of HCHO VCDs from UV spectral measurements is the Differential Optical Absorption Spectroscopy method (DOAS; Platt and Stutz, 2008) which involves two main steps. First, the effective slant column amount (corresponding to the integrated HCHO concentration along the mean atmospheric optical path: Ns) is derived through a least-squares fit of the measured Earth reflectance spectrum by laboratory absorption cross-sections and a low order polynomial. Subsequently, a correction is applied to the slant column values to correct for appearing biases that may be of known or unknown origin. Finally, slant columns are converted into vertical columns by means of air mass factors (AMF) obtained from suitable radiative transfer calculations, accounting for the presence of clouds, surface properties, and best-guess HCHO vertical profiles.\r\n\r\nIn the UV, the sensitivity to HCHO concentrations in the boundary layer is intrinsically limited from space due to the combined effect of Rayleigh and Mie scattering that limits the radiation fraction scattered back toward the satellite. In addition, ozone absorption reduces the number of photons that reaches the lowest atmospheric layers. Furthermore, the absorption signatures of HCHO are weaker than those of other UV Vis absorbers, such as e.g. NO2. As a result, the retrieval of formaldehyde from space is noise sensitive and error prone. While the precision (or random error uncertainty) is driven by the signal to noise ratio of the recorded spectra and by the width of the retrieval interval, the trueness (or systematic error uncertainty) is limited by the current knowledge of the external parameters needed in the different retrieval steps.\r\n\r\nThe selection of the optimal retrieval interval must maximize the sensitivity of the inversion to the HCHO absorption signatures while minimizing errors from geophysical and instrument related spectral features. The retrieval interval should be chosen as wide as possible to maximize the number of sampling points while avoiding overlap with strong atmospheric spectral features from interfering species (mainly O3, BrO, and O4). The DOAS algorithm intrinsically assumes that the atmosphere is optically thin so that the optical light path is independent of wavelength within the fitting window. Hence the method is accurate only for modest ozone absorption (i.e., for small to medium solar zenith angles). Generally, the effect of ozone misfit on the retrieval can be handled by introducing wavelength dependent-AMF in the fit, and by applying appropriate background corrections on the columns. The correlation with BrO absorption can be reduced by using two different wavelength intervals to fit BrO and HCHO (see section 5.3: Formaldehyde slant column retrieval).\r\n\r\nThe Sentinel-5P sensor TROPOMI samples the Earth’s surface with a revisit time of one day and with an unprecedented spatial resolution of 7x3.5 km2 (5.5x3.5 km2 from 6 August 2019). Furthermore, the signal to noise ratio of TROPOMI is required to be equivalent to OMI in the UV-Visible range. This allows the resolution of fine details and S5P will arguably be a valuable tool to better study NMVOC emissions. Nevertheless, it poses additional constraints on the retrieval code for several reasons:\r\n1. Computational speed: the Level 1b data flow delivers spectral measurements for band 3 with a size of 4 gigabytes per orbit (15 orbits daily).\r\n2. Precision: given the required signal to noise ratio of measured spectra, the air quality requirements for HCHO [RD04] will not be met for individual ground pixels. Spatial and temporal averages of the HCHO columns will have to be considered in order to provide useful constraints on human and natural NMVOC emissions.\r\n3. Trueness: currently, the spatial resolution of global-scale external parameters needed in the AMF calculation (e.g. albedo or a priori HCHO profiles) is much coarser than the resolution of TROPOMI. This introduces errors in the final vertical columns, mostly for coastal regions and localized sources (Heckel et al., 2011) \r\n\r\nTo fully exploit the potential of satellite data, applications relying on tropospheric HCHO observations require high quality long-term time series, provided with well characterized errors and averaging kernels, and consistently retrieved from the different sensors. Furthermore, as the HCHO observations are aimed to be used synergistically with other species observations (e.g. for air quality applications), it is essential to homogenize as far as possible the retrieval methods as well as the external databases, in order to minimize systematic biases between the observations.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 26443, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11891, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26439/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26444/?format=api", "relatedTo": { "ob_id": 26443, "uuid": "929d929b043242e69de7b5373acfb611", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/186566/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/186567/?format=api" ] }, { "ob_id": 38858, "computationComponent": [ { "ob_id": 38859, "uuid": "87fe4f2cf868471d87a3a843cde2f667", "title": "Level 2 processing algorithm applied to Sentinel 3 Ocean and Land Colour Instrument (OLCI) raw data", "abstract": "In addition, a common pre-processing and product formatting process aims to read, check the input, and define and write the outputs.\r\n\r\nIt is important to note that, for each geophysical parameter included in the OLCI Level-2 product, a switch has been defined in the OLCI Level-2 configuration file. Each parameter and its associated flags are produced only if the associated switch is set to '1'. As a consequence, the module generating each parameter is triggered only if the appropriate switch is set to '1'.\r\n\r\nThe pre-processing module, starting from the Level-1B TOA radiances, derives reflectances corrected for gaseous absorption. The consolidation of pixel classifications from Level-1B and the definition of water vapour retrieval are included in this module.\r\n\r\nThe algorithm is divided into five successive steps:\r\n\r\n1. The conversion from radiances to reflectances step-checks the Level-1B products and converts radiances into reflectances (also known as first instrumental correction).\r\n2. To be correctly taken into account or to be rejected from the algorithm, pixels have to be differentiated according to four criteria: cloud, land, water and invalid pixels. The first pixel classification focuses on identification of cloudy pixels. This Cloud masking will be improved in the frame of coming evolutions, including the improvement tested and validated during the MERIS 4th reprocessing.\r\n3. Gaseous correction: correcting reflectances for gaseous absorption (i.e. O2, H2O and O3). Five OLCI bands are dedicated to this correction and are not used after this step: Oa13 to Oa15, Oa19 and Oa20.\r\n4. The second-pixel classification estimates glint reflectance and completes pixel classification starting at the second step by consolidating the classification of land and water pixels.\r\n5. The water vapour process retrieves atmospheric water vapour content from clear sky pixels.\r\n\r\nThe land processing module consists of two independent sections (one for each product):\r\n\r\n1. The Green Instantaneous Fraction of Absorbed Photosynthetically Active Radiation (GI-FAPAR) section combines the information contained in the blue band with that contained in the bands at 681 and 865 nm to generate \"rectified channels\" at these latter two wavelengths.\r\n2. The OLCI Terrestrial Chlorophyll Index (OTCI) section uses Rayleigh correction to produce the necessary index. This step accounts for the spectral smile i.e. the in-FOV variation of the central wavelengths of OLCI channels.\r\n\r\nFor more information please see the OLCI user guide in the docs tab.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 19028, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 10049, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 19028, "uuid": "4b9669233ac644fa853788607d6ca1b2", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/186908/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/186909/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/186910/?format=api" ] }, { "ob_id": 38860, "computationComponent": [ { "ob_id": 38861, "uuid": "7f3ded66e9234b08969ce6b799357d87", "title": "LiCSAR processing", "abstract": "COMET LiCSAR processing using Gamma SAR and Interferometry software on Sentinel 1 IW SLC products.\r\n\r\nFor the details of the LiCSAR methodology [LiCSAR: An Automatic InSAR Tool for Monitoring Tectonic and Volcanic Activities paper in the docs tab]. In summary, once a new acquisition arrives, it is logically decomposed into pre-defined burst units and registered in the LiCSInfo database that handles burst and frame definitions. Images including bursts that form a given frame are extracted and merged into frame images. These are coregistered towards a primary frame image (a master image) that was set during the initialization of a frame, beforehand. The coregistration process includes spectral diversity and other necessary corrections. Once coregistered, the interferograms are formed by combining the new image with three chronologically previous ones. This way is suitable for interpretation and for further use of the interferograms in multitemporal InSAR processing methods based on small baselines strategy (e.g. NSBAS approach currently implemented into custom LiCSBAS chain [ LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor paper in the docs tab]). The interferogram unwrapping is performed using optimised SNAPHU approach. All the LiCSAR products are multilooked by factors of 5 in the range and 20 in the azimuth directions to achieve a resolution of around 100×100 m per pixel.\r\n\r\nLiCSAR processing chain consists of:\r\n1. Preparation of Frame Epoch SLC\r\n2. Resampling to RSLC\r\n3. Formation of Differential Interferograms\r\n4. Unwrapping Interferograms\r\n5. LiCSInfo Metadata Database\r\n6. LiCSAR FrameBatch Processing\r\n7. FrameBatch Processing Chain\r\n8. FrameBatch Post-Processing\r\n\r\nFor more information please see the LiCSAR: An Automatic InSAR Tool for Monitoring Tectonic and Volcanic Activities paper in the docs tab.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 12318, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4316, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq" } } ] }, { "ob_id": 20018, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 10863, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 20018, "uuid": "c28a3a6627354dd19363ac971116b0d8", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/186919/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/186923/?format=api" ] }, { "ob_id": 39208, "computationComponent": [ { "ob_id": 39207, "uuid": "a3aa8304674943f788a95d442155520e", "title": "Derivation of the CH4_S5P_WFMD v1.5 product from the WFM-DOAS Retrieval algorithm", "abstract": "The Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) algorithm is a least-squares retrieval method based on scaling (or shifting) pre-selected atmospheric vertical profiles. The column-averaged dry air mole fractions of methane (denoted XCH4) are derived from the vertical column amounts of methane by normalising with the dry air column, which is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). The corresponding vertical columns of CH4 are retrieved from the measured sun-normalised radiance using spectral fitting windows in the SWIR spectral region (2311-2315.5 nm and 2320-2338 nm).\r\n\r\nFor further details see the documentation section.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 26443, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11891, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26439/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26444/?format=api", "relatedTo": { "ob_id": 26443, "uuid": "929d929b043242e69de7b5373acfb611", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/190891/?format=api" ] }, { "ob_id": 39281, "computationComponent": [ { "ob_id": 707, "uuid": "fea5a699972c4649a95d53d0cb611044", "title": "Met Office operational unified model (UM) deployed on Keflavik, Iceland", "abstract": "This computation involved: Met Office operational unified model (UM) deployed on Keflavik, Iceland. Base for the GFDex measurement campaign", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/458/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/459/?format=api" ] } ], "acquisitionComponent": [ { "ob_id": 39280, "independentInstrument": [ 2010 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/191655/?format=api" ] }, { "ob_id": 39316, "computationComponent": [ { "ob_id": 39311, "uuid": "a0dd77615c424b50b314d6c30e17a0bf", "title": "BRDF/Albedo Inversion Model.", "abstract": "Computation for the Globalbedo BRDF products.\r\nFor more information, please see the ATBD document in the related docs tab.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39315, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13456, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/11888/?format=api", "relatedTo": { "ob_id": 39315, "uuid": "85a81af3fa474c7bb11d45843c1eeb9f", "short_code": "acq" } }, { "ob_id": 13457, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39314/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/39313/?format=api", "relatedTo": { "ob_id": 39315, "uuid": "85a81af3fa474c7bb11d45843c1eeb9f", "short_code": "acq" } }, { "ob_id": 13458, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39318/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/39317/?format=api", "relatedTo": { "ob_id": 39315, "uuid": "85a81af3fa474c7bb11d45843c1eeb9f", "short_code": "acq" } }, { "ob_id": 13459, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/847/?format=api", "relatedTo": { "ob_id": 39315, "uuid": "85a81af3fa474c7bb11d45843c1eeb9f", "short_code": "acq" } }, { "ob_id": 13461, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/5566/?format=api", "relatedTo": { "ob_id": 39315, "uuid": "85a81af3fa474c7bb11d45843c1eeb9f", "short_code": "acq" } }, { "ob_id": 13460, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8063/?format=api", "relatedTo": { "ob_id": 39315, "uuid": "85a81af3fa474c7bb11d45843c1eeb9f", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/191779/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/191780/?format=api" ] }, { "ob_id": 39507, "computationComponent": [ { "ob_id": 39503, "uuid": "a08cc459df1945b29f1bd5a67eaee9ca", "title": "DTU Space ESMR NIMBUS-5 algorithm", "abstract": "Sea ice concentration is obtained from ESMR passive microwave satellite data over the polar regions. The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, and 4) one channel sea ice concentration algorithm.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39502, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13488, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/5512/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/39501/?format=api", "relatedTo": { "ob_id": 39502, "uuid": "0ced6ecb04824c1399cec262e6dfce52", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/192680/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/192681/?format=api" ] }, { "ob_id": 39524, "computationComponent": [ { "ob_id": 39525, "uuid": "28a24e2f4a1e4b50962b3378afabc980", "title": "Computation Component: Level 2 Land processing algorithm applied to Sentinel 3 SRAL raw data.", "abstract": "There are three main steps in the Level-2 processing chain:\r\n\r\n1. Compute time-derived geophysical/environmental parameters.\r\n2. Perform re-tracking and compute physical parameters.\r\n3. Compute Level-2 altimeter/radiometer geophysical processing. \r\n\r\nComputing time-derived geophysical parameters involves:\r\n\r\n* re-computing altitude, orbital altitude rate, location and Doppler correction, accounting for updated orbit data\r\n* computing ionospheric corrections\r\n*computing non-equilibrium and equilibrium ocean tide heights, tidal loading, solid earth tide height, equilibrium long period ocean tide height and pole tide height (using pole locations)\r\n*computing the height of the mean sea surface above the reference ellipsoid\r\n*computing the mean dynamic topography, the height of the geoid and the ocean depth/land elevation.\r\n\r\nPerforming retracking and computing physical parameters (both SAR and LRM modes) involves:\r\n\r\n*discriminating echoes (ocean/lead, sea-ice, ice sheet margin or ocean/coastal)\r\n*performing retracking (ocean/lead, sea-ice, ice sheet margin or ocean/coastal)\r\n*computing physical parameters\r\n*merging snow depth (ocean/lead and sea-ice only)\r\n*performing a short-arc, along track ocean interpolation (ocean/lead and sea-ice only)\r\n*estimating freeboards (ocean/lead and sea-ice only)\r\n*performing a latitude limit check (ocean/lead and sea-ice only)\r\n\r\n(For SAR mode) performing modified slope correction (ice sheet margin and ocean/coastal only).\r\nLevel-2 altimeter/radiometer geophysical processing involves:\r\n\r\n*inputting and checking Level-1 MWR products\r\n*computing and correcting physical parameters according to platform data\r\n*interpolating MWR data and computing MWR geophysical parameters\r\n*computing altimeter wind speed and rain/ice flags\r\n*computing wind, tropospheric corrections and inverted barometer according to meteorological data\r\n*computing HF fluctuations of the atmospheric effect on the surface (dynamic atmosphere correction)\r\n*computing sea state bias\r\n*computing dual frequency ionospheric corrections\r\n*building and checking Level-2 SRAL/MWR products.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 19015, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 10047, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19016/?format=api", "relatedTo": { "ob_id": 19015, "uuid": "1c1605795ae247c28a64d003c686bdb2", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/192734/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/192736/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/192735/?format=api" ] }, { "ob_id": 39608, "computationComponent": [ { "ob_id": 39609, "uuid": "aaa20cc705654b399a314bf2fa15c294", "title": "BRDF/Albedo Inversion Model computation for QA4ECV broadband albedo.", "abstract": "More information needed", "keywords": "QA4ECV", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 1667, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 689, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1667, "uuid": "4c43f22544cd42f9bb50a7cca0f34dc3", "short_code": "acq" } } ] }, { "ob_id": 1674, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4729, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1672/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1674, "uuid": "031d90075a0a48d0b698953cb83ffeef", "short_code": "acq" } } ] }, { "ob_id": 1681, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4730, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1681, "uuid": "810deaaa6e464bf090d402c88cc584a2", "short_code": "acq" } } ] }, { "ob_id": 1688, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4731, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1686/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1688, "uuid": "42b66b70f48b47689c0fbce02a161a97", "short_code": "acq" } } ] }, { "ob_id": 1695, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4734, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1695, "uuid": "5388228bda17496eabd2b9566d296d58", "short_code": "acq" } } ] }, { "ob_id": 1703, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 694, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1700/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1703, "uuid": "2b32db20c0e6496988e69dc4be760e27", "short_code": "acq" } } ] }, { "ob_id": 1710, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4740, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1708/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1710, "uuid": "4f6ca70d15dd462e84156fabbc20441b", "short_code": "acq" } } ] }, { "ob_id": 1717, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4735, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1715/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1717, "uuid": "b58f0465305d4a369b9c19c077611515", "short_code": "acq" } } ] }, { "ob_id": 1746, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4738, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1744/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1746, "uuid": "219d5d1cca634426b9fd16c8708578f3", "short_code": "acq" } } ] }, { "ob_id": 1753, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4737, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1751/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1753, "uuid": "cb42b02517cb4e3393c05c7e3094a3e9", "short_code": "acq" } } ] }, { "ob_id": 1760, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4741, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1758/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1760, "uuid": "6363a453bc2c4a599d7c77033655c03b", "short_code": "acq" } } ] }, { "ob_id": 1767, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4733, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1765/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1767, "uuid": "39da0f08844a4366860772ce197f9ee8", "short_code": "acq" } } ] }, { "ob_id": 1774, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 704, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1772/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1774, "uuid": "602bb7b29a48430b946b46e8498d5318", "short_code": "acq" } } ] }, { "ob_id": 1781, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 705, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1779/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1781, "uuid": "64ea51e989064feb98f59c1fd3a3ba2d", "short_code": "acq" } } ] }, { "ob_id": 1789, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 706, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1786/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1787/?format=api", "relatedTo": { "ob_id": 1789, "uuid": "7cb84752296e46ff8b023f8bb6449ae9", "short_code": "acq" } } ] }, { "ob_id": 1796, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 707, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1794/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1787/?format=api", "relatedTo": { "ob_id": 1796, "uuid": "9ea2d9ecd0514c4381450e57c251eb31", "short_code": "acq" } } ] }, { "ob_id": 1840, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 713, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1838/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 1840, "uuid": "572bcedd329c4c3ba6609b55369e1f8a", "short_code": "acq" } } ] }, { "ob_id": 1848, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 714, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1845/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1846/?format=api", "relatedTo": { "ob_id": 1848, "uuid": "a96e534233464a98900d3d7314807d8c", "short_code": "acq" } } ] }, { "ob_id": 1855, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 715, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1853/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1846/?format=api", "relatedTo": { "ob_id": 1855, "uuid": "14d79434ab5445838f0d7221761d3873", "short_code": "acq" } } ] }, { "ob_id": 1862, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4742, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1860/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1846/?format=api", "relatedTo": { "ob_id": 1862, "uuid": "1098651b533940a6bbf7a4ae322f55b9", "short_code": "acq" } } ] }, { "ob_id": 2653, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 895, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2650/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2651/?format=api", "relatedTo": { "ob_id": 2653, "uuid": "c35bb3f64f9d46caa38cf0cd9d8eab77", "short_code": "acq" } } ] }, { "ob_id": 4054, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 1221, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 4054, "uuid": "3c4fdf3f53974540a9462acf006af98d", "short_code": "acq" } } ] }, { "ob_id": 4058, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 1222, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1686/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 4058, "uuid": "fac8996e6be5412cb7a286d73acdae34", "short_code": "acq" } } ] }, { "ob_id": 4063, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 1223, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 4063, "uuid": "ca82e75052d64303abed3e456f59d199", "short_code": "acq" } } ] }, { "ob_id": 11083, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4803, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1838/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 11083, "uuid": "9ac98c23b6f549fa82e55f7f1bba70fc", "short_code": "acq" } } ] }, { "ob_id": 11094, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4802, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1772/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 11094, "uuid": "9f274f31e54c4a6aa688158bbc9d182c", "short_code": "acq" } } ] }, { "ob_id": 11474, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 716, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1860/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1846/?format=api", "relatedTo": { "ob_id": 11474, "uuid": "e3cd0fe6b5b54fbe8ca43893ff85f721", "short_code": "acq" } }, { "ob_id": 3862, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1772/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 11474, "uuid": "e3cd0fe6b5b54fbe8ca43893ff85f721", "short_code": "acq" } }, { "ob_id": 3953, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1779/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 11474, "uuid": "e3cd0fe6b5b54fbe8ca43893ff85f721", "short_code": "acq" } }, { "ob_id": 3960, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1860/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1846/?format=api", "relatedTo": { "ob_id": 11474, "uuid": "e3cd0fe6b5b54fbe8ca43893ff85f721", "short_code": "acq" } }, { "ob_id": 3860, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1838/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1701/?format=api", "relatedTo": { "ob_id": 11474, "uuid": "e3cd0fe6b5b54fbe8ca43893ff85f721", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193062/?format=api" ] }, { "ob_id": 39619, "computationComponent": [ { "ob_id": 39620, "uuid": "4257ffdb796e49a7b46edeb9db5350c6", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AATSR v1 product.", "abstract": "The retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39618, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13506, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8076/?format=api", "relatedTo": { "ob_id": 39618, "uuid": "038e91b2492e4a61a6dd0fd8ae42d27f", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193085/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193086/?format=api" ] }, { "ob_id": 39621, "computationComponent": [ { "ob_id": 39622, "uuid": "7e310b21fb4e45eea77f188dd85b06df", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG ATSR-2 v1 product.", "abstract": "The retrieval method of the snow_cci SCFG product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39623, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13507, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8097/?format=api", "relatedTo": { "ob_id": 39623, "uuid": "f214ca1d7e794daeb2ea528eb35e484d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193090/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193091/?format=api" ] }, { "ob_id": 39625, "computationComponent": [ { "ob_id": 39626, "uuid": "f4d9f525a03f4db7b6eea9f2f003068b", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AATSR v1 product.", "abstract": "The retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39624, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13508, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8076/?format=api", "relatedTo": { "ob_id": 39624, "uuid": "acdf9a1322dd4c5ebbc5ae484c6a948d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193102/?format=api" ] }, { "ob_id": 39627, "computationComponent": [ { "ob_id": 39629, "uuid": "51ce66d2c6804ed6a14c768e4768e2e8", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV ATSR-2 v1 product.", "abstract": "The retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39628, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13509, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8097/?format=api", "relatedTo": { "ob_id": 39628, "uuid": "06b041234b5b4794a2af6cbae37ba298", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193106/?format=api" ] }, { "ob_id": 39708, "computationComponent": [ { "ob_id": 39706, "uuid": "13477dbb98d4424a94d308b710944fee", "title": "ESA Snow Climate Change Initiative: Derivation of Fractional Snow Cover in CryoClim, v1.0", "abstract": "The snow_cci CryoClim FSC products are based on fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017). These have been used to assure having the best possible temporal quality of input data. The FCDRs are based on optical data from AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19, and PMR data from SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F11, DMSP F13 and DMSP F17 satellites, respectively. \r\n\r\nThe snow_cci CryoClim FSC processing chain includes retrieval of fractional snow cover per grid cell for all grid cells. Permanent snow and ice areas as well as water bodies are masked in the FSC products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All FSC products are prepared according to the CCI data standards.\r\n\r\nThe processing chain was developed by Norwegian Computing Center (Norsk Regnesentral, NR) and Norwegian Meteorological Institute (MET Norway), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39709, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13512, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13513, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13514, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13515, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1809/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13516, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1824/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13517, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27174/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13518, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27175/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13519, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39737/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13520, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27698/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13521, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39738/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13522, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39739/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13524, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13525, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39788/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13526, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39789/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13527, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39790/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13528, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39791/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } }, { "ob_id": 13529, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39792/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 39709, "uuid": "b1d1e7437c9e4cd69f39ba5a65264dd4", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193413/?format=api" ] }, { "ob_id": 39767, "computationComponent": [ { "ob_id": 39768, "uuid": "2a7d2d2f8a6c40598620fdfdb1a4a41c", "title": "Computation for the 3D displacements and strain from the 2023 February Turkey Earthquakes", "abstract": "The Sentinel-1 azimuth and range offsets came from the COMET-LiCS portal (https://comet.nerc.ac.uk/comet-lics-portal/) and are processed using Gamma package from Level 1 (L1) data from the European Space Agency (https://scihub.copernicus.eu/dhus/#/home) The offsets are from 4 tracks of interferometric pairs which are provided in links in the docs tab. These are results of processing by GAMMA command offset_pwr_tracking, cross-correlating 128x64 pixel windows (range x azimuth) over 2x oversampled deramped low-pass filtered intensity data, selecting data of cross-corr. coeff. over 0.1. Upsampled by linear interpolation. The optical pixel tracking east and north offsets are derived from the Sentinel-2 data using the ENVI COSI-Corr plugin and are also provided in the docs tab. Images: L1C 25th Jan and 9th Feb. Parameters: window size of 64 down to 32 with step of 4. 2 iterations, 0.9 mask threshold, no resampling and gridded output. All the offset data are referenced to a distribution of dummy zero points away from the coseismic ruptures by removing a planar ramp. The uncertainties of the offset data are empirically estimated from the offset data themselves by evaluating the absolute deviation of each pixel value from its local mean averaged across a 4x4 pixel window, assuming nan-values are zeros. These uncertainties are used to weight the 3D motion inversion and are propagated to the uncertainties of the decomposed displacements through a model covariance matrix. The motion magnitude field is a vector combination of the east and north motion fields, each masked by respective uncertainties. The arrows represent average east and north motions from 30 km windows evaluated at 0.2 degree intervals. The second invariant of horizontal strain is calculated from the horizontal displacement gradients of east and north motions after median filter with 30 km windows at 0.01 degree intervals.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 12318, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4316, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq" } } ] }, { "ob_id": 13191, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4334, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/13187/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 13191, "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6", "short_code": "acq" } } ] }, { "ob_id": 25439, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11412, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25277/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 25439, "uuid": "18f84df32d934058862f2c3990885a4c", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193698/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193702/?format=api" ] }, { "ob_id": 39809, "computationComponent": [ { "ob_id": 39808, "uuid": "4a2d0f1a95b4454889d91cc4fa878855", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps for Africa at 30m spatial resolution, 1990-2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover and Land Cover Change Maps for Africa at 30m spatial resolution, 1990-2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39810, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13530, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8581/?format=api", "relatedTo": { "ob_id": 39810, "uuid": "d154c605e9794e63808ba045a9db2dcd", "short_code": "acq" } }, { "ob_id": 13531, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8256/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8257/?format=api", "relatedTo": { "ob_id": 39810, "uuid": "d154c605e9794e63808ba045a9db2dcd", "short_code": "acq" } }, { "ob_id": 13532, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12365/?format=api", "relatedTo": { "ob_id": 39810, "uuid": "d154c605e9794e63808ba045a9db2dcd", "short_code": "acq" } }, { "ob_id": 13533, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 39810, "uuid": "d154c605e9794e63808ba045a9db2dcd", "short_code": "acq" } }, { "ob_id": 13534, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 39810, "uuid": "d154c605e9794e63808ba045a9db2dcd", "short_code": "acq" } }, { "ob_id": 13535, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7820/?format=api", "relatedTo": { "ob_id": 39810, "uuid": "d154c605e9794e63808ba045a9db2dcd", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193913/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193915/?format=api" ] }, { "ob_id": 39813, "computationComponent": [ { "ob_id": 39811, "uuid": "ac560d881750438fad472505ab0ed74e", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Map for Africa at 10m spatial resolution for 2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover Map for Africa at 10m spatial resolution for 2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 39812, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13536, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 39812, "uuid": "4aaa9de24d6e4c98bb03dc2aea7b9733", "short_code": "acq" } }, { "ob_id": 13537, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 39812, "uuid": "4aaa9de24d6e4c98bb03dc2aea7b9733", "short_code": "acq" } }, { "ob_id": 13538, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/13187/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 39812, "uuid": "4aaa9de24d6e4c98bb03dc2aea7b9733", "short_code": "acq" } }, { "ob_id": 13539, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25277/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 39812, "uuid": "4aaa9de24d6e4c98bb03dc2aea7b9733", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193919/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/193920/?format=api" ] }, { "ob_id": 39977, "computationComponent": [ { "ob_id": 11010, "uuid": "2d125dda9d6e44ed804191a3b7b41bc5", "title": "CCI SST Processor", "abstract": "This computation involved: CCI SST Processor. This processor was developed in the ESA Climate Change Initiative, Sea Surface Temperature Project", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/7351/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/7352/?format=api" ] } ], "acquisitionComponent": [ { "ob_id": 39976, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13575, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27175/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 39976, "uuid": "a2a99cad71a040308f6139c76ec07c98", "short_code": "acq" } }, { "ob_id": 13576, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 39976, "uuid": "a2a99cad71a040308f6139c76ec07c98", "short_code": "acq" } }, { "ob_id": 13577, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 39976, "uuid": "a2a99cad71a040308f6139c76ec07c98", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/194851/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/194852/?format=api" ] }, { "ob_id": 39983, "computationComponent": [ { "ob_id": 11010, "uuid": "2d125dda9d6e44ed804191a3b7b41bc5", "title": "CCI SST Processor", "abstract": "This computation involved: CCI SST Processor. This processor was developed in the ESA Climate Change Initiative, Sea Surface Temperature Project", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/7351/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/7352/?format=api" ] } ], "acquisitionComponent": [ { "ob_id": 39982, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13581, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 39982, "uuid": "789cda09b4354defa607c7c437054e36", "short_code": "acq" } }, { "ob_id": 13582, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 39982, "uuid": "789cda09b4354defa607c7c437054e36", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/194869/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/194870/?format=api" ] }, { "ob_id": 40002, "computationComponent": [ { "ob_id": 39999, "uuid": "0ea3c51424374e1b8de4c59d295ff6a6", "title": "CCI+ Phase 1 SIC: N90LIN and pan-sharpening", "abstract": "The CCI+ Phase 1 Sea Ice Concentration (SIC) production has two streams: N90LIN, and pan-sharpening. The N90LIN step prepares SICs relying mostly on the near-90 GHz channels (higher resolution, higher noise). The pan-sharpening step prepares SICs using the results of both the OSI SAF “19/37 GHz” algorithm (SICCI3LF) and N90LIN (higher resolution, higher noise). The result of the pan-sharpening step (named “resolution enhanced” SICCI3LF, reSICCI3LF) is a main SIC outcome from CCI+ Phase 1, while N90LIN is more seen as a by-product for expert users (because of the increased noise).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40001, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13583, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39788/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40000/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } }, { "ob_id": 13584, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27698/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40000/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } }, { "ob_id": 13586, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39738/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40000/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } }, { "ob_id": 13587, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39789/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40000/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } }, { "ob_id": 13588, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39790/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40000/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } }, { "ob_id": 13589, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39791/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } }, { "ob_id": 13590, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39739/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } }, { "ob_id": 13591, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39792/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 40001, "uuid": "f7725768381b40e5bf93c67ee7eaba81", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/194933/?format=api" ] }, { "ob_id": 40008, "computationComponent": [ { "ob_id": 40006, "uuid": "d2bc57f26d58470d8ec94dc0616c367d", "title": "MIXCRA: the mixed-phase cloud property retrieval algorithm", "abstract": "mixcra2.pro,v 1.12 2022/03/16 00:32:15 dave.turner", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40007, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13592, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30506/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40009/?format=api", "relatedTo": { "ob_id": 40007, "uuid": "cf1ed35c025543fe9a1d324e8b90a482", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/194949/?format=api" ] }, { "ob_id": 40087, "computationComponent": [ { "ob_id": 8096, "uuid": "3758814994634374bd1b958a8877e882", "title": "deployed on ERS-2", "abstract": "This computation involved: deployed on ERS-2.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40151, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13601, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8097/?format=api", "relatedTo": { "ob_id": 40151, "uuid": "b41b4f70d57c46d4a51ddec6d6fc411a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/195306/?format=api" ] }, { "ob_id": 40104, "computationComponent": [ { "ob_id": 40103, "uuid": "bace7ed22c804ae2850b1e63dfb76304", "title": "DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE.", "abstract": "Computation for ozone from GOME-2 deployed on Metop-A.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 8210, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 2564, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8208/?format=api", "relatedTo": { "ob_id": 8210, "uuid": "eec8d2e5b2f14fd7b69e8f209b23ac4a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/195412/?format=api" ] }, { "ob_id": 40150, "computationComponent": [ { "ob_id": 40149, "uuid": "bc00289ce807473da5a54a9914f93fcd", "title": "Derivation of the combined high resolution global TCWV product from microwave and near infrared imagers - COMBI", "abstract": "Details of the retrieval are described in Andersson et al. (2010) and ATBD HOAPS for the microwave imagers as well as in Lindstrot et al. (2012), Diedrich et al. (2015) and ABTD NIR Level 2 for the near-infrared imagers. The water vapour of the atmosphere is vertically integrated over the full column and given in units of kg/m². The microwave and near-infrared data streams are processed independently and combined afterwards by not changing the individual TCWV values and their uncertainties.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40148, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13594, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 40148, "uuid": "335891bfa0ce4851b1e2aa5f09d24b98", "short_code": "acq" } }, { "ob_id": 13595, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 40148, "uuid": "335891bfa0ce4851b1e2aa5f09d24b98", "short_code": "acq" } }, { "ob_id": 13596, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 40148, "uuid": "335891bfa0ce4851b1e2aa5f09d24b98", "short_code": "acq" } }, { "ob_id": 13597, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 40148, "uuid": "335891bfa0ce4851b1e2aa5f09d24b98", "short_code": "acq" } }, { "ob_id": 13598, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/11888/?format=api", "relatedTo": { "ob_id": 40148, "uuid": "335891bfa0ce4851b1e2aa5f09d24b98", "short_code": "acq" } }, { "ob_id": 13599, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10897/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 40148, "uuid": "335891bfa0ce4851b1e2aa5f09d24b98", "short_code": "acq" } }, { "ob_id": 13600, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 40148, "uuid": "335891bfa0ce4851b1e2aa5f09d24b98", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/195642/?format=api" ] }, { "ob_id": 40162, "computationComponent": [ { "ob_id": 8096, "uuid": "3758814994634374bd1b958a8877e882", "title": "deployed on ERS-2", "abstract": "This computation involved: deployed on ERS-2.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 10924, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 3845, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8097/?format=api", "relatedTo": { "ob_id": 10924, "uuid": "1344b11f69ae43019882a84e0471191b", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/195838/?format=api" ] }, { "ob_id": 40164, "computationComponent": [ { "ob_id": 8096, "uuid": "3758814994634374bd1b958a8877e882", "title": "deployed on ERS-2", "abstract": "This computation involved: deployed on ERS-2.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 13643, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4457, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8097/?format=api", "relatedTo": { "ob_id": 13643, "uuid": "7ad73ff4c72d48e29650dce5488d095d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/195840/?format=api" ] }, { "ob_id": 40341, "computationComponent": [ { "ob_id": 40340, "uuid": "677c25df9f324ca3a9e1920ea3c330b1", "title": "TLS2trees: a semi-automated processing pipeline", "abstract": "Plot-level point clouds were processed using TLS2trees which is a set of Python command line tools & designed to be horizontally scalable, e.g., on a High Performance Computing (HPC) facility. Pipeline steps: 1) Point cloud re-processing, 2) semantic segmentation into wood & leaf point classes, 3) instance segmentation into sets of point clouds representing individual trees, 4) Quantitative structural models (QSMs) of individual tree point clouds, & 5) Plot biophysical & AGB estimates.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40338, "independentInstrument": [ 40339 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/196616/?format=api" ] }, { "ob_id": 40343, "computationComponent": [ { "ob_id": 40340, "uuid": "677c25df9f324ca3a9e1920ea3c330b1", "title": "TLS2trees: a semi-automated processing pipeline", "abstract": "Plot-level point clouds were processed using TLS2trees which is a set of Python command line tools & designed to be horizontally scalable, e.g., on a High Performance Computing (HPC) facility. Pipeline steps: 1) Point cloud re-processing, 2) semantic segmentation into wood & leaf point classes, 3) instance segmentation into sets of point clouds representing individual trees, 4) Quantitative structural models (QSMs) of individual tree point clouds, & 5) Plot biophysical & AGB estimates.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40342, "independentInstrument": [ 40339 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/196618/?format=api" ] }, { "ob_id": 40345, "computationComponent": [ { "ob_id": 40340, "uuid": "677c25df9f324ca3a9e1920ea3c330b1", "title": "TLS2trees: a semi-automated processing pipeline", "abstract": "Plot-level point clouds were processed using TLS2trees which is a set of Python command line tools & designed to be horizontally scalable, e.g., on a High Performance Computing (HPC) facility. Pipeline steps: 1) Point cloud re-processing, 2) semantic segmentation into wood & leaf point classes, 3) instance segmentation into sets of point clouds representing individual trees, 4) Quantitative structural models (QSMs) of individual tree point clouds, & 5) Plot biophysical & AGB estimates.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40344, "independentInstrument": [ 40339 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/196620/?format=api" ] }, { "ob_id": 40617, "computationComponent": [ { "ob_id": 40616, "uuid": "b53ab401d94b4ba0896bc1b17a320bbe", "title": "GlobAlbedo processing chain", "abstract": "The GlobAlbedo project aims at generating multi-sensor multi-annual global land surface albedo products. MERIS and SPOT-VGT level-1 data serves as input data to a processing chain. The function of the processing chain can be summarised as:\r\n processes every single input to some level in several processing steps.\r\n accumulates at least one year of data for each 10º x 10º tile and then calculates products every 8 days as well as every month, as well as seasonal and annual products.\r\n finally retrieves BRDF and albedo for each composite reporting time-step (every 8 days and every month, as well as seasonal and annual products).\r\nFirstly, all inputs are systematically processed from L1 to surface directional reflectances\r\n(SDR). The processing steps to retrieve SDRs are:\r\n pixel classification for cloud, water, etc.. detection\r\n retrieval of aerosol optical thickness\r\n atmospheric correction using retrieved aerosol optical thickness\r\nThe results are SDR values in the same granularity as the inputs. They are not written for distinct products, but just kept in memory and used as input to the next part of the processing chain. The processing steps are\r\n broad-band integration of these SDRs\r\n re-projection of these broad-band SDRs onto the MODIS Sinusoidal grid\r\nThe results of this step are re-projected broadband directional reflectances (BBDR) still in the granularity of the input products. During implementation, it has been decided to store this intermediate product of BBDR tiles on disk, but to remove them subsequently after the BRDF computation (accumulation/inversion). The steps are:\r\n computation of BRDFs every 8-days and every month of the broad-band BBDRs from all sensors by accumulation method, using an additional land cover mask information from Idepix\r\n computation of the albedo from these BRDFs\r\nThe final results are 8-day, monthly, seasonal, and annual composites of broadband\r\nalbedo. \r\nFor more information on the processing please see the product user guide and the ATBD in the docs tab.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 8283, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 2580, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8280/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8281/?format=api", "relatedTo": { "ob_id": 8283, "uuid": "14d30db63bb043cfa8693b8e02c6f556", "short_code": "acq" } } ] }, { "ob_id": 8344, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 2588, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8342/?format=api", "relatedTo": { "ob_id": 8344, "uuid": "c1311786388a421fa0675d8b69258757", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/197611/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/197612/?format=api" ] }, { "ob_id": 40637, "computationComponent": [ { "ob_id": 40634, "uuid": "048e6ac7c94f41c99164b19127855ca0", "title": "Yellowscan CloudStation computation for UAV Paracou", "abstract": "Export of raw LAS points with Yellowscan CloudStation software, with line adjustment option.Improvement of inter-line matching using BayesMap software (to account for a defect in roll angle of the scanner). Merging and processing of each flight with Lastools software (PC classification with lasground using options -step 15 -wilderness, generation of DTM, DSM and CHM at 1m resolution)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40635, "independentInstrument": [ 27217 ], "instrumentplatformpair_set": [ { "ob_id": 13686, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40636/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40633/?format=api", "relatedTo": { "ob_id": 40635, "uuid": "f12e72bdbbca4db0b0223048a5ef6b7c", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/197721/?format=api" ] }, { "ob_id": 40641, "computationComponent": [ { "ob_id": 40640, "uuid": "361d0f25744840e5bfec5838e2787ace", "title": "JRC-TOC FAPAR Computation", "abstract": "Look Up Table (LUT) of bidirectional reflectance factors (BRF) representing the AVHRR\r\nNOAA like data are created using the physically-based semi-discrete model of Gobron\r\net al. (1997) to represent the spectral and directional reflectance of horizontally homogeneous plant canopies, as well as to compute the values of FAPAR in each of them.\r\nThe sampling of the vegetation parameters and angular values were chosen to cover a\r\nwide range of environmental conditions. These simulations constitute the basic information used to optimise the formulae. The sampling selected to generate the LUT has\r\nbeen chosen so as to generate a robust global FAPAR algorithm.\r\nOnce this LUT was created, the design of the algorithm consisted in defining the mathematical combination of spectral bands which will best account for the variations of\r\nthe variable of interest (here, FAPAR) on the basis of (simulated) measurements, while\r\nminimising the effect of perturbing factors such as angular effects.\r\nIn the case of bare soil simulations, the Hapke modified soil model of Pinty et al. (1989)\r\nis used with a fixed hot-spot parameter equal to 0.2 and an asymmetry factor equal to\r\n-0.1. The soil data required to specify the lower boundary condition in this model were\r\ntaken from Price (1995). The value of single albedo, for each spectral bands, have been\r\ninverted to obtain the same albedo value as the lambertian assumption is made.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 1667, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 689, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1667, "uuid": "4c43f22544cd42f9bb50a7cca0f34dc3", "short_code": "acq" } } ] }, { "ob_id": 1681, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4730, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1681, "uuid": "810deaaa6e464bf090d402c88cc584a2", "short_code": "acq" } } ] }, { "ob_id": 1695, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4734, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1665/?format=api", "relatedTo": { "ob_id": 1695, "uuid": "5388228bda17496eabd2b9566d296d58", "short_code": "acq" } } ] }, { "ob_id": 1811, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 709, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1809/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 1811, "uuid": "15390f2f1f4e4b70bb05335ac7d8ea87", "short_code": "acq" } } ] }, { "ob_id": 1826, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 711, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1824/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 1826, "uuid": "a4e2d6b37bea4e4e87b48d731fce6e35", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/197735/?format=api" ] }, { "ob_id": 40768, "computationComponent": [ { "ob_id": 40769, "uuid": "0177eeaec34b43a381345b398f491607", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v08.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40770, "independentInstrument": [ 27121, 27122 ], "instrumentplatformpair_set": [ { "ob_id": 13715, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40770, "uuid": "c922d295908c4bd1bfddf02f3b61aa7e", "short_code": "acq" } }, { "ob_id": 13716, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40770, "uuid": "c922d295908c4bd1bfddf02f3b61aa7e", "short_code": "acq" } }, { "ob_id": 13717, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40770, "uuid": "c922d295908c4bd1bfddf02f3b61aa7e", "short_code": "acq" } }, { "ob_id": 13718, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40770, "uuid": "c922d295908c4bd1bfddf02f3b61aa7e", "short_code": "acq" } }, { "ob_id": 13719, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40770, "uuid": "c922d295908c4bd1bfddf02f3b61aa7e", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198346/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198347/?format=api" ] }, { "ob_id": 40771, "computationComponent": [ { "ob_id": 40769, "uuid": "0177eeaec34b43a381345b398f491607", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v08.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40772, "independentInstrument": [ 2630, 2636, 14485, 25272, 27124, 27125, 27126, 29938, 32305, 40773 ], "instrumentplatformpair_set": [ { "ob_id": 13720, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13721, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13722, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32306/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32305/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13726, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13727, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13728, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13729, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13731, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13732, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13723, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40775/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13724, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40774/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } }, { "ob_id": 13725, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32304/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40772, "uuid": "2d90a918c9d84a01a2c5091c5d710fcb", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198353/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198352/?format=api" ] }, { "ob_id": 40776, "computationComponent": [ { "ob_id": 40769, "uuid": "0177eeaec34b43a381345b398f491607", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v08.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40777, "independentInstrument": [ 2630, 2636, 14485, 25272, 27121, 27122, 27124, 27125, 27126, 29938, 32305, 40773 ], "instrumentplatformpair_set": [ { "ob_id": 13734, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40774/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13735, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40775/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13736, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13737, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13738, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13739, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13740, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13741, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13742, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13743, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32306/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32305/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13733, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32304/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13744, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13745, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13746, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13747, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13748, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } }, { "ob_id": 13749, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40777, "uuid": "cdeb44fdae2b4f48820df5b97f84e177", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198360/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198359/?format=api" ] }, { "ob_id": 40778, "computationComponent": [ { "ob_id": 40782, "uuid": "5d12b39a20894334b676bfd237d53236", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v07.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40779, "independentInstrument": [ 27121, 27122 ], "instrumentplatformpair_set": [ { "ob_id": 13750, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40779, "uuid": "a057a1b217ed4c2ebc396b0a00bc6707", "short_code": "acq" } }, { "ob_id": 13751, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40779, "uuid": "a057a1b217ed4c2ebc396b0a00bc6707", "short_code": "acq" } }, { "ob_id": 13752, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40779, "uuid": "a057a1b217ed4c2ebc396b0a00bc6707", "short_code": "acq" } }, { "ob_id": 13753, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40779, "uuid": "a057a1b217ed4c2ebc396b0a00bc6707", "short_code": "acq" } }, { "ob_id": 13754, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40779, "uuid": "a057a1b217ed4c2ebc396b0a00bc6707", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198363/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198364/?format=api" ] }, { "ob_id": 40783, "computationComponent": [ { "ob_id": 40782, "uuid": "5d12b39a20894334b676bfd237d53236", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v07.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40780, "independentInstrument": [ 2630, 2636, 14485, 25272, 27124, 27125, 27126, 29938, 32305, 40773 ], "instrumentplatformpair_set": [ { "ob_id": 13755, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32304/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13756, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40774/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13766, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13757, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40775/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13758, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13759, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13767, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13761, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13762, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13763, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13764, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } }, { "ob_id": 13765, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32306/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32305/?format=api", "relatedTo": { "ob_id": 40780, "uuid": "c1b406feacdf48f5a94a131ceb042310", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198373/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198374/?format=api" ] }, { "ob_id": 40784, "computationComponent": [ { "ob_id": 40782, "uuid": "5d12b39a20894334b676bfd237d53236", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v07.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40781, "independentInstrument": [ 2630, 2636, 14485, 25272, 27121, 27122, 27124, 27125, 27126, 29938, 32305, 40773 ], "instrumentplatformpair_set": [ { "ob_id": 13773, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13776, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13768, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13769, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13780, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13770, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13777, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13771, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13772, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13774, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32306/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32305/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13778, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13775, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13783, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40774/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13779, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13781, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13782, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40775/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } }, { "ob_id": 13784, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32304/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 40781, "uuid": "c13aaa02dc9349a19b5315821b7bcd33", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198375/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/198376/?format=api" ] }, { "ob_id": 41200, "computationComponent": [ { "ob_id": 41201, "uuid": "c6d943bdefe34a19a1518dce9a6843e2", "title": "TCCON GGG2020 Retrieval algorithm", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. The TCCON GGG2020 Data Version is produced using the algorithm as described in the preprint 'The Total Carbon Column Observing Network's GGG2020 Data Version'. Please see the following for more information: https://essd.copernicus.org/preprints/essd-2023-331/.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41202, "independentInstrument": [ 41203 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/200325/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/200326/?format=api" ] }, { "ob_id": 41219, "computationComponent": [ { "ob_id": 41215, "uuid": "63273e9e0da44216ba0e6323dc0ef9b8", "title": "OptiSAIL retrieval", "abstract": "The retrieval model OptiSAIL is built around the established components 4SAILH (Scattering of Arbitrarily Inclined Leaves, with 4-stream extension and hot-spot), PROSPECT-D (simulation of leaf spectra, version D including senescence; Féret et al., 2017), TARTES (Two-streAm Radiative Transfer in Snow; Libois et al., 2013), with the addition of an empirical soil reflectance model, a semi-empirical soil moisture model (Philpot, 2010), the Ross-Thick-Li-Sparse BRDF model, and a cloud contamination simulation.", "keywords": "OptiSAIL, canopy, LAI, fAPAR", "inputDescription": null, "outputDescription": null, "softwareReference": 41220, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41218, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13831, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39314/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/39313/?format=api", "relatedTo": { "ob_id": 41218, "uuid": "722a08d1f95149e2a3e4cf691090a80d", "short_code": "acq" } }, { "ob_id": 13832, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/39318/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/39317/?format=api", "relatedTo": { "ob_id": 41218, "uuid": "722a08d1f95149e2a3e4cf691090a80d", "short_code": "acq" } }, { "ob_id": 13833, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/41217/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/41216/?format=api", "relatedTo": { "ob_id": 41218, "uuid": "722a08d1f95149e2a3e4cf691090a80d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/200382/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/200383/?format=api" ] }, { "ob_id": 41255, "computationComponent": [ { "ob_id": 41254, "uuid": "8f43f9ab978a47aabf828e68bc4f5806", "title": "Processing chain for the GERB-HR-ED01-1-1 rsut 1hrCM Obs4MIPS v1.1 product,", "abstract": "The GERB TOA reflected shortwave radiation (RSW) Obs4MIPs product has been generated from GERB level 2 HR RSW fluxes. There are five main stages. The first is to take the Edition 1 GERB HR SW flux data with the user corrections applied and generate an albedo using knowledge of the incoming SW flux for each location and time. The albedo is then area weighted to create a 1° by 1° spatial resolution albedo for every 15-minute time interval. These data are then averaged over time to produce an hourly averaged albedo product for each hour of each day. Each hour interval is defined as the time-centred mean at half-past the hour, e.g. 1030 UTC comprises the mean of albedos from 1000 UTC, 1015 UTC, 1030 UTC and 1045 UTC (or from those slots in this window that are available). An hourly average albedo is produced for a 1° by 1° grid cell if there is one or more observation from the four observation times available for that grid cell. The averaged albedos are converted back to RSW flux using the incoming solar flux at the midpoint of the hour and 1° cell. The fourth stage identifies days where there are no observations available for the hour and fills them with broadband RSW fluxes derived from the narrowband SEVIRI data, averaged to the daily hourly scale in the same way as the GERB data and corrected to the GERB observations at the monthly hourly 1° scale1. Finally, a monthly mean for each hourly time step is calculated encompassing the full data. The number of filled data points (days per monthly hourly average) contributing to each monthly hourly mean are used to estimate the uncertainties associated with the product. For more detailed information please see: Bantges, R., Russell, J., & Brindley, H. (2023). GERB-HR-ED01 rsut 1hrCM v1.1. Zenodo. https://doi.org/10.5281/zenodo.10034410.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 25280, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11379, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2658/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/3853/?format=api", "relatedTo": { "ob_id": 25280, "uuid": "8b1f29db1c304ce2a849cc727f64ff0a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/200804/?format=api" ] }, { "ob_id": 41256, "computationComponent": [ { "ob_id": 41253, "uuid": "6c4085cf959b40d193f7152f0d3b22d5", "title": "Processing chain for the GERB-HR-ED01-1-1 rlut 1hrCM Obs4MIPS v1.1 product,", "abstract": "The GERB TOA outgoing longwave radiation (OLR) Obs4MIPs product has been generated from GERB level 2 HR OLR fluxes. There are four main stages. The first ingests the Edition 1 GERB HR OLR data and generates an area weighted flux product at 1° by 1° spatial resolution for every 15-minute time interval. In the second stage these data are averaged over time to produce an hourly averaged product. Each hour interval is defined as the time-centred mean at half-past the hour, e.g. 1030 UTC, comprises the mean of data from 1000 UTC, 1015 UTC, 1030 UTC and 1045 UTC (or from those slots in this window that are available). An hourly average is produced for a 1° by 1° grid cell if there is at least one of the four observation times available for that grid cell. If no available data are available for a particular hour of a day, then the third stage is implemented, and these hours are filled using broadband fluxes derived from the narrowband SEVIRI data corrected to the GERB observations at the monthly hourly 1° scale1. The final processing stage averages the hourly mean fluxes over the days in the month to produce a one degree monthly hourly mean product. The number of filled data points (days per monthly hourly mean) contributing to each monthly mean are used to estimate the uncertainties associated with the product. For more detailed information please see: Bantges, R., Russell, J., & Brindley, H. (2023). GERB-HR-ED01 rlut 1hrCM v1.1. Zenodo. https://doi.org/10.5281/zenodo.10034430.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 25280, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11379, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2658/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/3853/?format=api", "relatedTo": { "ob_id": 25280, "uuid": "8b1f29db1c304ce2a849cc727f64ff0a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/200805/?format=api" ] }, { "ob_id": 41373, "computationComponent": [ { "ob_id": 41369, "uuid": "8fb75140be014c36923b9307335fc48f", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Map for Amazonia at 10m spatial resolution for 2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover Map for Amazonia at 10m spatial resolution for 2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41365, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13851, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 41365, "uuid": "c046b7e6fa8e43a09a2b3d0f5cb2f23e", "short_code": "acq" } }, { "ob_id": 13852, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 41365, "uuid": "c046b7e6fa8e43a09a2b3d0f5cb2f23e", "short_code": "acq" } }, { "ob_id": 13853, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/13187/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 41365, "uuid": "c046b7e6fa8e43a09a2b3d0f5cb2f23e", "short_code": "acq" } }, { "ob_id": 13854, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25277/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 41365, "uuid": "c046b7e6fa8e43a09a2b3d0f5cb2f23e", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201370/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201371/?format=api" ] }, { "ob_id": 41374, "computationComponent": [ { "ob_id": 41370, "uuid": "0d3896db10ac4e659db5115b8fa645f3", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Map for Siberia at 10m spatial resolution for 2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover Map for Siberia at 10m spatial resolution for 2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41366, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13855, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 41366, "uuid": "64d5cf8a552647b4b16ff7d1c4ae2621", "short_code": "acq" } }, { "ob_id": 13856, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 41366, "uuid": "64d5cf8a552647b4b16ff7d1c4ae2621", "short_code": "acq" } }, { "ob_id": 13857, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/13187/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 41366, "uuid": "64d5cf8a552647b4b16ff7d1c4ae2621", "short_code": "acq" } }, { "ob_id": 13858, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25277/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 41366, "uuid": "64d5cf8a552647b4b16ff7d1c4ae2621", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201372/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201373/?format=api" ] }, { "ob_id": 41375, "computationComponent": [ { "ob_id": 41371, "uuid": "9c29d5c4fdca44eba4122417a28ad211", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps for Amazonia at 30m spatial resolution, 1990-2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover and Land Cover Change Maps for Amazonia at 30m spatial resolution, 1990-2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41367, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13859, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8581/?format=api", "relatedTo": { "ob_id": 41367, "uuid": "5ece13aea08247f2945579c11d30919d", "short_code": "acq" } }, { "ob_id": 13860, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8256/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8257/?format=api", "relatedTo": { "ob_id": 41367, "uuid": "5ece13aea08247f2945579c11d30919d", "short_code": "acq" } }, { "ob_id": 13861, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12365/?format=api", "relatedTo": { "ob_id": 41367, "uuid": "5ece13aea08247f2945579c11d30919d", "short_code": "acq" } }, { "ob_id": 13862, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 41367, "uuid": "5ece13aea08247f2945579c11d30919d", "short_code": "acq" } }, { "ob_id": 13863, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 41367, "uuid": "5ece13aea08247f2945579c11d30919d", "short_code": "acq" } }, { "ob_id": 13864, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7820/?format=api", "relatedTo": { "ob_id": 41367, "uuid": "5ece13aea08247f2945579c11d30919d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201374/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201375/?format=api" ] }, { "ob_id": 41376, "computationComponent": [ { "ob_id": 41372, "uuid": "c64308936d94431daef88d04f8f38b13", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps for Siberia at 30m spatial resolution, 1990-2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover and Land Cover Change Maps for Siberia at 30m spatial resolution, 1990-2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41368, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13865, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8581/?format=api", "relatedTo": { "ob_id": 41368, "uuid": "b0d7e8aba72f4aa98b7436a6ced802ad", "short_code": "acq" } }, { "ob_id": 13866, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8256/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8257/?format=api", "relatedTo": { "ob_id": 41368, "uuid": "b0d7e8aba72f4aa98b7436a6ced802ad", "short_code": "acq" } }, { "ob_id": 13867, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12365/?format=api", "relatedTo": { "ob_id": 41368, "uuid": "b0d7e8aba72f4aa98b7436a6ced802ad", "short_code": "acq" } }, { "ob_id": 13868, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 41368, "uuid": "b0d7e8aba72f4aa98b7436a6ced802ad", "short_code": "acq" } }, { "ob_id": 13869, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 41368, "uuid": "b0d7e8aba72f4aa98b7436a6ced802ad", "short_code": "acq" } }, { "ob_id": 13870, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7820/?format=api", "relatedTo": { "ob_id": 41368, "uuid": "b0d7e8aba72f4aa98b7436a6ced802ad", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201376/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201377/?format=api" ] }, { "ob_id": 41415, "computationComponent": [ { "ob_id": 41417, "uuid": "722a203405214ccfb77e27ff9b307801", "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The\r\nmethod involves separating the radar echoes returning from the ice floes from those\r\nreturning from the sea surface in the leads between the floes. This step of a surface-type\r\nclassification is crucial and allows for a separate determination of the ice floe and\r\nsea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow\r\ninterface) above the sea level can then be computed by deducting the interpolated\r\nsea-surface height at the floe location from the height of the floe. Sea-ice thickness can then\r\nbe calculated from the sea-ice freeboard with the additional information of the snow load.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 26734, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11941, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26733/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26732/?format=api", "relatedTo": { "ob_id": 26734, "uuid": "eda0664efbab45bf82345cd5d257d7df", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201788/?format=api" ] }, { "ob_id": 41416, "computationComponent": [ { "ob_id": 41417, "uuid": "722a203405214ccfb77e27ff9b307801", "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The\r\nmethod involves separating the radar echoes returning from the ice floes from those\r\nreturning from the sea surface in the leads between the floes. This step of a surface-type\r\nclassification is crucial and allows for a separate determination of the ice floe and\r\nsea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow\r\ninterface) above the sea level can then be computed by deducting the interpolated\r\nsea-surface height at the floe location from the height of the floe. Sea-ice thickness can then\r\nbe calculated from the sea-ice freeboard with the additional information of the snow load.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41418, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13872, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13692/?format=api", "relatedTo": { "ob_id": 41418, "uuid": "5d16bccb807e4ed4b11d8001e60c0f19", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/201789/?format=api" ] }, { "ob_id": 41443, "computationComponent": [ { "ob_id": 33276, "uuid": "47f37aea9c174706a348b692669e8b02", "title": "Marine phytoplankton carbon", "abstract": "A spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020 and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data. Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v5). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. Mixed Layer Depth (MLD) was obtained from the French Research Institute for Exploration of the Sea (Ifremer). In situ datasets of chlorophyll-a profile parameters and P-I parameters were incorporated as described in Kulk et al. (2020)\r\n\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252\r\n\r\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovac, Z.; Lutz, V.A.; Marañón,\r\nE.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12, 826. doi:10.3390/rs12050826", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41441, "independentInstrument": [ 41442 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202026/?format=api" ] }, { "ob_id": 41447, "computationComponent": [ { "ob_id": 41446, "uuid": "793181872832428784ce64c01fc0f2da", "title": "Derivation of the CH4_S5P_WFMD v1.8 product from the WFM-DOAS Retrieval algorithm", "abstract": "The Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) algorithm is a least-squares retrieval method based on scaling (or shifting) pre-selected atmospheric vertical profiles. The column-averaged dry air mole fractions of methane (denoted XCH4) are derived from the vertical column amounts of methane by normalising with the dry air column, which is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). The corresponding vertical columns of CH4 are retrieved from the measured sun-normalised radiance using spectral fitting windows in the SWIR spectral region (2311-2315.5 nm and 2320-2338 nm).\r\n\r\nFor further details see the documentation section.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 26443, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11891, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26439/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26444/?format=api", "relatedTo": { "ob_id": 26443, "uuid": "929d929b043242e69de7b5373acfb611", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202053/?format=api" ] }, { "ob_id": 41451, "computationComponent": [ { "ob_id": 41450, "uuid": "a56cd2b6603f4c9c97c70da8d5d4e9e4", "title": "Derivation of the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, v1.1", "abstract": "For more information see the documentation at https://climate.esa.int/en/projects/fire/", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41452, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13877, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 41452, "uuid": "02c3a6274e8846d8a6b84389acfde987", "short_code": "acq" } }, { "ob_id": 13878, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 41452, "uuid": "02c3a6274e8846d8a6b84389acfde987", "short_code": "acq" } }, { "ob_id": 13879, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 41452, "uuid": "02c3a6274e8846d8a6b84389acfde987", "short_code": "acq" } }, { "ob_id": 13880, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 41452, "uuid": "02c3a6274e8846d8a6b84389acfde987", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202089/?format=api" ] }, { "ob_id": 41467, "computationComponent": [ { "ob_id": 41463, "uuid": "a9f25aea12554048bdc4953587accf32", "title": "Derivation of the ESA Climate Change Initiative River Discharge Water Level product, v1.1", "abstract": "For information on the derivation of the Water Level dataset see the project documentation \r\n(https://climate.esa.int/projects/river-discharge)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41464, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13886, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30014/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30015/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13887, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30018/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30019/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13888, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30020/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30021/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13889, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30022/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30023/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13890, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/41465/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/41466/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13891, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13692/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13892, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30024/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13893, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26738/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26737/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13894, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19016/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } }, { "ob_id": 13895, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19016/?format=api", "relatedTo": { "ob_id": 41464, "uuid": "6b870fe612644bfc878bc7605d22bdf8", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202125/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202126/?format=api" ] }, { "ob_id": 41490, "computationComponent": [ { "ob_id": 41489, "uuid": "151eb837a9464275b8c54f158f8bc13b", "title": "The SRON-RemoTeC algorithm used to generate the CO2_GO2_SRFP and CH4_GO2_SRFP (SRON Full Physics) v2.0.2 products.", "abstract": "The SRON-RemoTeC retrieval algorithm retrieves column-averaged methane and carbon dioxide using a 'Full Physics' retrieval technique. \r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32858, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12590, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32856/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32857/?format=api", "relatedTo": { "ob_id": 32858, "uuid": "74166c97d5e74d51ac946aa36431ae95", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202167/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202168/?format=api" ] }, { "ob_id": 41491, "computationComponent": [ { "ob_id": 41492, "uuid": "a572a6dd688e4d5c8e03318eb9c1bdae", "title": "The SRON-RemoTeC algorithm used to generate the CH4_GO2_SRPR (SRON Proxy) v2.0.2 product.", "abstract": "The SRON-RemoTeC retrieval algorithm retrieves column-averaged methane using a 'Proxy' retrieval technique. \r\n\r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32858, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12590, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32856/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32857/?format=api", "relatedTo": { "ob_id": 32858, "uuid": "74166c97d5e74d51ac946aa36431ae95", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202169/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202172/?format=api" ] }, { "ob_id": 41576, "computationComponent": [ { "ob_id": 41575, "uuid": "008b9792168a4cb087393d56aad63e16", "title": "Derivation of the ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Microwave Scanning Radiometer (AMSR) Level 2 Pre-processed (L2P) product, version 3.0", "abstract": "For information on the derivation of the SST CCI AMSR L2P product, see the SST CCI project documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41574, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13903, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 41574, "uuid": "6adb7ea8330f4428832ca3309601b7b6", "short_code": "acq" } }, { "ob_id": 13904, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 41574, "uuid": "6adb7ea8330f4428832ca3309601b7b6", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202652/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202653/?format=api" ] }, { "ob_id": 41622, "computationComponent": [ { "ob_id": 41621, "uuid": "6666ae0ed0e94bfda0c302f0fe5bec1b", "title": "The ESA Biomass Climate Change Initiative above ground biomass retrieval algorithm, v5.0", "abstract": "For information on the derivation of the Biomass CCI data, please see the ATBD (Algorithm Theoretical Baseline Document).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41623, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13910, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 41623, "uuid": "18c73f89a9a94c859a096fafb983d30d", "short_code": "acq" } }, { "ob_id": 13911, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29959/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29958/?format=api", "relatedTo": { "ob_id": 41623, "uuid": "18c73f89a9a94c859a096fafb983d30d", "short_code": "acq" } }, { "ob_id": 13912, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7820/?format=api", "relatedTo": { "ob_id": 41623, "uuid": "18c73f89a9a94c859a096fafb983d30d", "short_code": "acq" } }, { "ob_id": 13913, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8197/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8198/?format=api", "relatedTo": { "ob_id": 41623, "uuid": "18c73f89a9a94c859a096fafb983d30d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202881/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202882/?format=api" ] }, { "ob_id": 41633, "computationComponent": [ { "ob_id": 8062, "uuid": "f338d413b2ad4b8091d77da723385d1d", "title": "deployed on ERS-1", "abstract": "This computation involved: deployed on ERS-1.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41634, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13914, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8063/?format=api", "relatedTo": { "ob_id": 41634, "uuid": "0a6841f065614070a8fd2a4c70c59c3b", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202902/?format=api" ] }, { "ob_id": 41650, "computationComponent": [ { "ob_id": 41648, "uuid": "c7177c0c7a5448bbbb76dd71bf04509a", "title": "CCI SST Processor v3", "abstract": "For more information on the derivation of the CCI SST v3 datasets see the Algorithm Theoretical Baseline Document (ATBD) at https://climate.esa.int/documents/2367/SST_CCI_D2.1_ATBD_v3.1-signed.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41649, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13915, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 41649, "uuid": "40cd29ca020c4f0b84802a6a4d299b1c", "short_code": "acq" } }, { "ob_id": 13916, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 41649, "uuid": "40cd29ca020c4f0b84802a6a4d299b1c", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202962/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202963/?format=api" ] }, { "ob_id": 41655, "computationComponent": [ { "ob_id": 41648, "uuid": "c7177c0c7a5448bbbb76dd71bf04509a", "title": "CCI SST Processor v3", "abstract": "For more information on the derivation of the CCI SST v3 datasets see the Algorithm Theoretical Baseline Document (ATBD) at https://climate.esa.int/documents/2367/SST_CCI_D2.1_ATBD_v3.1-signed.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41656, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13917, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13918, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13919, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13920, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1801/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13921, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1809/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13922, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1816/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13923, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1824/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13924, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1831/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13925, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27174/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13926, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27175/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13927, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13928, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1817/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13989, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43015/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43014/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13990, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43016/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43014/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } }, { "ob_id": 13991, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43014/?format=api", "relatedTo": { "ob_id": 41656, "uuid": "2dcdb1e197d34d9081a42a52a7f93c81", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202964/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/202965/?format=api" ] }, { "ob_id": 41689, "computationComponent": [ { "ob_id": 45172, "uuid": "58962da25319475cb57ad9641bb05ef2", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41690, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13933, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 41690, "uuid": "7fd82940bde54cf7bb9911cb5503818c", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/203134/?format=api" ] }, { "ob_id": 41960, "computationComponent": [ { "ob_id": 41961, "uuid": "cefe18e177704799b6f8b8171057704e", "title": "ESA Snow Climate Change Initiative (snow_cci): SWE, v3", "abstract": "The snow_cci SWE product has been based on the ESA GlobSnow SWE retrieval approach (Takala et al. 2011). The retrieval is based on passive microwave radiometer (PMR) data considering the change of brightness temperature due to different snow depth, snow density, grain size and more. The retrieval algorithm handles data from the sensors SMMR, SSM/I, SSMIS, AMSR-E and AMSR-2. The retrieval methodology combines the satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme. The GlobSnow algorithm implemented for snow_cci version 3 includes the utilisation of an advanced emission model with an improved forest transmissivity module and treatment of sub-grid lake ice. Because of the importance of the weather station snow-depth observations on the SWE retrieval, there is improved screening for consistency through the time series.\r\n\r\nPassive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data/) Algorithm improvements relative to snow_cci v2.0 include improved dry snow detection due to an update of the dry snow detection algorithm, improved SWE retrieval due to implementation of dynamic snow densitites into the retrieval, and improved snow masking, due to an update of the snow mask used for post-processing. The time series has been extended from snow_cci version 2 by two years with data from 2020 to 2022 added.\r\n\r\nSWE products are based on SMMR, SSM/I and SSMIS passive microwave radiometer data for non-alpine regions of the Northern Hemisphere.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 41962, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13961, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 41962, "uuid": "a549c571b5684a7c8495919b88083f8e", "short_code": "acq" } }, { "ob_id": 13962, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 41962, "uuid": "a549c571b5684a7c8495919b88083f8e", "short_code": "acq" } }, { "ob_id": 13963, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 41962, "uuid": "a549c571b5684a7c8495919b88083f8e", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/203907/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/203908/?format=api" ] }, { "ob_id": 41963, "computationComponent": [ { "ob_id": 42315, "uuid": "142bd054c1544b1698077ccf8904d273", "title": "Computation for: C3S: Obs4MIPs format GOME-type Total Ozone Essential Climate Variable (GTO-ECV), Version 9.0", "abstract": "The C3S: Obs4MIPs format GOME-type Total Ozone Essential Climate Variable (GTO-ECV), Version 9.0 dataset was generated by combining measurements from several nadir-viewing satellite sensors (GOME/ERS-2, SCIAMACHY/Envisat, OMI/Aura, GOME-2/MetOp-A, GOME-2/MetOp-B, TROPOMI/Sentinel-5P, and GOME-2/MetOp-C) into one single cohesive record. This was done using a merging approach developed in the framework of the European Space Agency's (ESA's) Climate Change Initiative Ozone project. Firstly, the separate pixel-based level-2 observation - produced by the GOME-type Direct FITting v4 retrieval algorithm (GODFIT) are converted into level-3 products per sensor i.e. daily and monthly averages on a regular grid of 1x1 degree in latitude and longitude. Before the individual level-3 data records are finally merged into one single product, adjustments are applied in order to minimize possible inter-sensor biases and/or temporal drifts. Due to its notable long-term stability one sensor (OMI/Aura) was selected to serve as a reference for the other instruments, which are then adjusted in terms of a correction that depends on latitude and time. A detailed description of the merging approach, the generation, and the geophysical validation of GTO-ECV is provided in Coldewey-Egbers et al. (2015, 2022), Garane et al. (2018) and Lambert et al. (2022).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 42314, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13964, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2747/?format=api", "relatedTo": { "ob_id": 42314, "uuid": "65cdb261d5714d62a1267132a797cdf2", "short_code": "acq" } }, { "ob_id": 13965, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8029/?format=api", "relatedTo": { "ob_id": 42314, "uuid": "65cdb261d5714d62a1267132a797cdf2", "short_code": "acq" } }, { "ob_id": 13966, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/3883/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/4119/?format=api", "relatedTo": { "ob_id": 42314, "uuid": "65cdb261d5714d62a1267132a797cdf2", "short_code": "acq" } }, { "ob_id": 13967, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8208/?format=api", "relatedTo": { "ob_id": 42314, "uuid": "65cdb261d5714d62a1267132a797cdf2", "short_code": "acq" } }, { "ob_id": 13968, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8208/?format=api", "relatedTo": { "ob_id": 42314, "uuid": "65cdb261d5714d62a1267132a797cdf2", "short_code": "acq" } }, { "ob_id": 13969, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8208/?format=api", "relatedTo": { "ob_id": 42314, "uuid": "65cdb261d5714d62a1267132a797cdf2", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/203914/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/203915/?format=api" ] }, { "ob_id": 42320, "computationComponent": [ { "ob_id": 42319, "uuid": "66b1679c46ef49fdadc0c0c1a590ce63", "title": "Computation for Greenland 1980 and 2010s landcover grids from Landsat 5 and Landsat 8", "abstract": "the tif grids were produced using Google Earth Engine. All summer Landsat imagery was filtered by metadata, followed by topographical correction, resulting in a best-pixel mosaic for Greenland's periphery. Band ratios (NDSI, NDVI, NDWI) were computed and stacked with visible, NIR, and SWIR bands. A principal component analysis was conducted, retaining the first six principal components as bands, which were subsequently classified using a K-means clusterer and refined with a supervised random-forest classifier and a slope threshold was applied to discriminate shadows from dark water bodies more effective.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 12363, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4806, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12365/?format=api", "relatedTo": { "ob_id": 12363, "uuid": "6b99193d62cd44fa9239d3b02991887d", "short_code": "acq" } } ] }, { "ob_id": 27068, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11996, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7872/?format=api", "relatedTo": { "ob_id": 27068, "uuid": "812b78639c2740f98090d73e5a8f938e", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/203926/?format=api" ] }, { "ob_id": 42340, "computationComponent": [ { "ob_id": 42339, "uuid": "6f52e38ef1c84008b5f135015f870b35", "title": "RAL Ozone Profile Algorithm", "abstract": "The RAL retrieval scheme derives profiles of number density on a set of pressure levels, spaced approximately every 4-6 km in altitude (taken from the SPARC-DI grid). The optimal estimation method is used. Averaging kernels are provided on this basis. It is noted that the vertical resolution of the retrieval is relatively coarse compared to the vertical grid and that tropospheric levels in particular have significant bias towards the assumed a priori state. It is therefore important to take account of the characterisation of the retrieval provided by the averaging kernels when comparing these results to other data-sets, particularly where those are more highly vertically resolved.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 42338, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13970, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2747/?format=api", "relatedTo": { "ob_id": 42338, "uuid": "09b4dd6109334531a715550e2fbcf5d8", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204114/?format=api" ] }, { "ob_id": 43035, "computationComponent": [ { "ob_id": 43039, "uuid": "960ba0d151c048b5bd3fa19d0f274390", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v09.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43036, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13992, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 43036, "uuid": "adcfb7247fb84548a903bc965ea5383d", "short_code": "acq" } }, { "ob_id": 13993, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 43036, "uuid": "adcfb7247fb84548a903bc965ea5383d", "short_code": "acq" } }, { "ob_id": 13994, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 43036, "uuid": "adcfb7247fb84548a903bc965ea5383d", "short_code": "acq" } }, { "ob_id": 13995, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 43036, "uuid": "adcfb7247fb84548a903bc965ea5383d", "short_code": "acq" } }, { "ob_id": 13996, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 43036, "uuid": "adcfb7247fb84548a903bc965ea5383d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204469/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204470/?format=api" ] }, { "ob_id": 43040, "computationComponent": [ { "ob_id": 43039, "uuid": "960ba0d151c048b5bd3fa19d0f274390", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v09.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43037, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13997, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 13998, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 13999, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32306/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32305/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14000, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14001, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14002, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14003, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14004, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14005, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14006, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40775/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14007, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40774/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } }, { "ob_id": 14008, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32304/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 43037, "uuid": "55fea2d8ca504a8eb1e689f35ab72e24", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204479/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204480/?format=api" ] }, { "ob_id": 43041, "computationComponent": [ { "ob_id": 43039, "uuid": "960ba0d151c048b5bd3fa19d0f274390", "title": "Algorithm for the ESA Soil Moisture Climate Change Initiative, v09.1", "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors. For information on the algorithm see the Algorithm Theoretical Baseline Document.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43038, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14009, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40774/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14010, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40775/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14011, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14012, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14013, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14014, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14015, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14016, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14017, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14018, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32306/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32305/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14019, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32304/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40773/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14020, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14021, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14022, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14023, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14024, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } }, { "ob_id": 14025, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 43038, "uuid": "c85b7a41528c47c19078f090e565c65d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204481/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204482/?format=api" ] }, { "ob_id": 43063, "computationComponent": [ { "ob_id": 43082, "uuid": "85d99c0af1e74b43bd2a02c11cb33a40", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV MODIS v3.0 product.", "abstract": "The retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3/) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFV version 3.0 compared to the Snow_cci version 2.0 include (i) an update in the pre-classification of snow free areas and (ii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. A new mask for salt lakes derived from manual delineation based on Terra MODIS data is added in the version 3.0 products from Terra MODIS. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable and has been adjusted to account for updates in the retrieval algorithm. Two additional variables are provided for each daily product: the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the MODIS granule used for the classification.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32512, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12550, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10897/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 32512, "uuid": "b7f993e0c3e745dc9975da8aa580a654", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204544/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204551/?format=api" ] }, { "ob_id": 43064, "computationComponent": [ { "ob_id": 43081, "uuid": "ed1d0074bf064f319a9acc78e4236435", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG MODIS v3.0 product.", "abstract": "The retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFG version 3.0 compared to the Snow_cci version 2.0 include (i) an updated classification of snow free areas and (ii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. \r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. A new mask for salt lakes derived from manual delineation based on Terra MODIS data is added in the version 3.0 products from Terra MODIS. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable variable and has been adjusted to account for updates in the retrieval algorithm. Two additional variables are provided for each daily product: the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the MODIS granule used for the classification.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32512, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12550, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10897/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 32512, "uuid": "b7f993e0c3e745dc9975da8aa580a654", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204545/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204550/?format=api" ] }, { "ob_id": 43065, "computationComponent": [ { "ob_id": 33466, "uuid": "c89218fb8dc045b2875c06e8042f25a5", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AVHRR v2.0 product.", "abstract": "The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale.\r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32518, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12553, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12555, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12556, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12557, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1809/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12558, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1824/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12559, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27174/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12560, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27175/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204546/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204549/?format=api" ] }, { "ob_id": 43066, "computationComponent": [ { "ob_id": 33467, "uuid": "0b0e05e096b24d4a92edac27d43fb6cc", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AVHRR v2.0 product.", "abstract": "The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32518, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12553, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12555, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12556, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12557, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1809/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12558, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1824/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12559, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27174/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12560, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27175/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204547/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204548/?format=api" ] }, { "ob_id": 43119, "computationComponent": [ { "ob_id": 43118, "uuid": "5f4a954afcc34c099445058b6f8fc657", "title": "PROFFAST retrieval code for trace gas concentration", "abstract": "PROFFAST is a software package for retrieving trace gas concentrations from interferograms measured with Bruker EM27/SUN solar absorption FTIR spectrometer by using Bruker OPUS software. The PROFFAST software package is developed at the Karlsruhe Institute of Technology (KIT) and funded by the European Space Agency (ESA). Recently, PROFFASTpylot was created to run PROFFAST under Python.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43116, "independentInstrument": [ 43117 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/204706/?format=api" ] }, { "ob_id": 43210, "computationComponent": [ { "ob_id": 43209, "uuid": "686e9eebf27340159d6d5dd8def14672", "title": "Derivation of the ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2", "abstract": "The multispectral indices-based river discharge data has been computed at different locations from several satellite multispectral missions. At each location, time series are provided for each available single sensor and then merged in a unique time series. \r\n\r\nThe river discharges are derived following several approaches:\r\n\r\nBestFit: by non-linear regression relationship between the multi-mission time series and the ground observed river discharge;\r\n\r\nCopula: by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution;\r\n\r\nuncalCDF: by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43206, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14071, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8248/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14072, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8256/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8257/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14073, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43205/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14074, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43204/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43205/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14075, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14076, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10897/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14077, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/13187/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14078, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25277/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14079, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } }, { "ob_id": 14080, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 43206, "uuid": "b6a3a04f2ed84d2b9e9d4edf9c8667d0", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205532/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205533/?format=api" ] }, { "ob_id": 43213, "computationComponent": [ { "ob_id": 43212, "uuid": "44b4a239478248978d6b4089d8f2e538", "title": "Derivation of the ESA River Discharge Climate Change Initiative (RD_cci): combined river discharge product, v1.0", "abstract": "These river discharge time series have been computed at different locations by the combination of data derived from satellite altimeters and multispectral sensors. Two levels of combination are implemented based on the original products: Level-2, in which the data has been derived by merging multi-mission multispectral time series and radar altimeters water level product and Level-3, in which the river discharge products obtained from altimeters and multispectral imagers are used. The river discharges are derived following several approaches:\r\n\r\n1) L2 Merged river discharge:\r\n\r\na) COPULA Altimetry – CM: by a bivariate cumulative distribution function (CDF) which is applied between the multispectral indices and the water level from altimetry to get their joint probability distribution.\r\n\r\nb) RIDESAT Altimetry - CM: by a three-parameter non-linear relationship that merges the multispectral indices and the water level from altimetry\r\n\r\n2) L3 Merged river discharge:\r\n\r\na) Altimetry - CM cal_BestFIT: by the combination of river discharges obtained by the procedure of BestFIT applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nb) Altimetry – CM cal_Copula: by the combination of river discharges obtained by the procedure of Copula applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nc) Altimetry – CM uncal_CDF: by the combination of river discharges obtained by the procedure of CDF applied to the multispectral and the altimetry through a weighted approach", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43211, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14081, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8248/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14082, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8256/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8257/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14083, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43205/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14084, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43204/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43205/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14085, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14086, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10897/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14087, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/13187/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14088, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25277/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14089, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14090, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14091, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30024/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14092, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13692/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14093, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30018/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30019/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14094, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30020/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30021/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14095, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30022/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30023/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14096, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26738/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26737/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14097, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30014/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30015/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14098, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19016/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14099, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19016/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } }, { "ob_id": 14100, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/41465/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/41466/?format=api", "relatedTo": { "ob_id": 43211, "uuid": "283dc6501e0b43e9b3c91187ba1bcd16", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205542/?format=api" ] }, { "ob_id": 43233, "computationComponent": [ { "ob_id": 43231, "uuid": "65ad73a5dc2242f8960b76b9f838445a", "title": "ESA Fire_cci Burned Area SFDL algorithm", "abstract": "Fore further details regarding the Burned Area (BA) algorithm used for producing the Fire_CCI Burned Area product, please see the Fire_CCI Algoirthm Theoretical Basis Document (Bastarrika and Roteta, 2018), available at https://climate.esa.int/en/projects/fire/", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43232, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14101, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8245/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8248/?format=api", "relatedTo": { "ob_id": 43232, "uuid": "73e06acfa99442a8a06e8630e86c32b2", "short_code": "acq" } }, { "ob_id": 14102, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8248/?format=api", "relatedTo": { "ob_id": 43232, "uuid": "73e06acfa99442a8a06e8630e86c32b2", "short_code": "acq" } }, { "ob_id": 14103, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8256/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8257/?format=api", "relatedTo": { "ob_id": 43232, "uuid": "73e06acfa99442a8a06e8630e86c32b2", "short_code": "acq" } }, { "ob_id": 14104, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43205/?format=api", "relatedTo": { "ob_id": 43232, "uuid": "73e06acfa99442a8a06e8630e86c32b2", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205647/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205648/?format=api" ] }, { "ob_id": 43246, "computationComponent": [ { "ob_id": 43224, "uuid": "b76749a181e04042b4d0ef79e60b7dd4", "title": "Altimetry", "abstract": "", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205704/?format=api" ] }, { "ob_id": 43249, "computationComponent": [ { "ob_id": 43248, "uuid": "850720a2b3854f3ba5f073e83c406df1", "title": "Derivation of the EOCIS: Ice Sheet Mass Balance, V1.00", "abstract": "Mass balance calculated from radar altimetry measurements from ERS-1, ERS-2, ENVISAT, and CryoSat-2, using the method from Shepherd et al. (2019).\r\n\r\nFor more information on the EOCIS Ice Surface Elevation project see the documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205707/?format=api" ] }, { "ob_id": 43272, "computationComponent": [ { "ob_id": 43271, "uuid": "e533e4eb715e4f26a350a31258a0f820", "title": "Calimnos", "abstract": "Calimnos is a multi-sensor satellite processing chain developed at Plymouth Marine Laboratory to observe lakes and other isolated waterbodies. It is built around per-pixel dynamic algorithm selection and blending to accommodate the wide optical diversity of lakes. Processor versions >= 2 include per-pixel product uncertainty for key variables. Calimnos calls several external libraries including modules for pixel identification (SNAP Idepix) and atmospheric correction using Polymer by Hygeos.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43270, "independentInstrument": [ 13182 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205789/?format=api" ] }, { "ob_id": 43276, "computationComponent": [ { "ob_id": 43274, "uuid": "8d6e1f181fb643f5a64f2a37d31c70fd", "title": "Computation for Lava Aerosol Gas and Trace Element data from the Fagradalsfjall 2021-2023 eruption, Iceland", "abstract": "Thermochemical equilibrium modelling performed on HSC Chemistry software for Lava Aerosol Gas and Trace Element data from the Fagradalsfjall 2021-2023 eruption, Iceland case study - See the paper https://essopenarchive.org/users/814040/articles/1215204-trace-element-emissions-vary-with-lava-flow-age-and-thermal-evolution-during-the-fagradalsfjall-2021-2023-eruptions-iceland for details", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43275, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14105, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43278/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43277/?format=api", "relatedTo": { "ob_id": 43275, "uuid": "8d7117e260404891a85387f9a4652eb9", "short_code": "acq" } }, { "ob_id": 14106, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43280/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/43279/?format=api", "relatedTo": { "ob_id": 43275, "uuid": "8d7117e260404891a85387f9a4652eb9", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/205808/?format=api" ] }, { "ob_id": 43297, "computationComponent": [ { "ob_id": 43296, "uuid": "bf23c3ea947a4cd6a4822e1bd656d8a1", "title": "Derivation of the Swansea University Global Aerosol Optical Depth products from the Along-Track Scanning Radiometers and Sea and Land Surface Temperature Radiometers", "abstract": "For information on the derivation of the Swansea University Global Aerosol Optical Depth products see the associated documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43261, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14107, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/5566/?format=api", "relatedTo": { "ob_id": 43261, "uuid": "dad8ba529e9a431e8818b21ab5e3f360", "short_code": "acq" } }, { "ob_id": 14108, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/847/?format=api", "relatedTo": { "ob_id": 43261, "uuid": "dad8ba529e9a431e8818b21ab5e3f360", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206131/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206132/?format=api" ] }, { "ob_id": 43299, "computationComponent": [ { "ob_id": 43296, "uuid": "bf23c3ea947a4cd6a4822e1bd656d8a1", "title": "Derivation of the Swansea University Global Aerosol Optical Depth products from the Along-Track Scanning Radiometers and Sea and Land Surface Temperature Radiometers", "abstract": "For information on the derivation of the Swansea University Global Aerosol Optical Depth products see the associated documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43261, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14107, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/5566/?format=api", "relatedTo": { "ob_id": 43261, "uuid": "dad8ba529e9a431e8818b21ab5e3f360", "short_code": "acq" } }, { "ob_id": 14108, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/847/?format=api", "relatedTo": { "ob_id": 43261, "uuid": "dad8ba529e9a431e8818b21ab5e3f360", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206133/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206134/?format=api" ] }, { "ob_id": 43301, "computationComponent": [ { "ob_id": 43296, "uuid": "bf23c3ea947a4cd6a4822e1bd656d8a1", "title": "Derivation of the Swansea University Global Aerosol Optical Depth products from the Along-Track Scanning Radiometers and Sea and Land Surface Temperature Radiometers", "abstract": "For information on the derivation of the Swansea University Global Aerosol Optical Depth products see the associated documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43302, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14109, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 43302, "uuid": "540ddb41ff10439994305b2c934076ea", "short_code": "acq" } }, { "ob_id": 14110, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 43302, "uuid": "540ddb41ff10439994305b2c934076ea", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206135/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206136/?format=api" ] }, { "ob_id": 43304, "computationComponent": [ { "ob_id": 43296, "uuid": "bf23c3ea947a4cd6a4822e1bd656d8a1", "title": "Derivation of the Swansea University Global Aerosol Optical Depth products from the Along-Track Scanning Radiometers and Sea and Land Surface Temperature Radiometers", "abstract": "For information on the derivation of the Swansea University Global Aerosol Optical Depth products see the associated documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43302, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14109, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 43302, "uuid": "540ddb41ff10439994305b2c934076ea", "short_code": "acq" } }, { "ob_id": 14110, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 43302, "uuid": "540ddb41ff10439994305b2c934076ea", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206139/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206140/?format=api" ] }, { "ob_id": 43315, "computationComponent": [], "acquisitionComponent": [], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206208/?format=api" ] }, { "ob_id": 43341, "computationComponent": [ { "ob_id": 43325, "uuid": "f1c83717268241b48ca60519d844fc5b", "title": "Derivation of the EOCIS: Time Series of Sea Ice Arctic Thickness, Volume and Mass, V1.00 product", "abstract": "For information on the derivation of this dataset see the related documents section", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43340, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14128, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26733/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26732/?format=api", "relatedTo": { "ob_id": 43340, "uuid": "a53c92885dd944a88eac297c18d5b98a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206285/?format=api" ] }, { "ob_id": 43343, "computationComponent": [ { "ob_id": 43331, "uuid": "bfcb9dc8593b4fb49bd068db6ed5fc77", "title": "Derivation of the EOCIS gridded Sea Ice Arctic Thickness V1.00 product", "abstract": "For information on the derivation of this dataset see the related documents section", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43342, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14126, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26733/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26732/?format=api", "relatedTo": { "ob_id": 43342, "uuid": "db7f1161a0cb45a5a88673a6f79d5372", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206287/?format=api" ] }, { "ob_id": 43377, "computationComponent": [ { "ob_id": 43585, "uuid": "c8effc64e12a4f51b8e303706540e4d0", "title": "ESA SAFE processor for generating AATSR multimission data into AT_1_RBT in SAFE format", "abstract": "The datasets have been fully reprocessed into a new product which aligns them with the format adopted by the instruments’ successors, Sentinel-3 SLSTR, and includes the following updates from the former L1B datasets [AT1_TOA_1P / AT2_TOA_1P] generated by the ERS ATSR 3rd Reprocessing in 2013:\r\n\r\nImproved and extended Level 1B datasets, beginning with recovery of Level 0 data, via the reprocessing of Level 0 data to ungridded brightness temperature (UBT) scenes to Level 1A to retain extra instrument information.\r\n\r\nLevel 1B products in a Sentinel-SAFE/NetCDF format: a product folder containing NetCDF files and an XML manifest file, holding extensive metadata. This means new information is now available, such as per-pixel uncertainty estimates, associated meteorological data from ECMWF ERA-Interim, instrument scan and pixel quality information, and channel-specific exception and flag values.\r\n\r\nGeolocation improvement via orthogeolocation to a Digital Elevation Model.\r\n\r\nImproved surface classification via use of the Sentinel-3 Land Water Masks.\r\n\r\nImproved cloud information via a latitude correction within cloud look-up tables.", "keywords": "SAFE ATSR1 ATSR2 AATSR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43595, "independentInstrument": [ 847 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206407/?format=api" ] }, { "ob_id": 43378, "computationComponent": [ { "ob_id": 43585, "uuid": "c8effc64e12a4f51b8e303706540e4d0", "title": "ESA SAFE processor for generating AATSR multimission data into AT_1_RBT in SAFE format", "abstract": "The datasets have been fully reprocessed into a new product which aligns them with the format adopted by the instruments’ successors, Sentinel-3 SLSTR, and includes the following updates from the former L1B datasets [AT1_TOA_1P / AT2_TOA_1P] generated by the ERS ATSR 3rd Reprocessing in 2013:\r\n\r\nImproved and extended Level 1B datasets, beginning with recovery of Level 0 data, via the reprocessing of Level 0 data to ungridded brightness temperature (UBT) scenes to Level 1A to retain extra instrument information.\r\n\r\nLevel 1B products in a Sentinel-SAFE/NetCDF format: a product folder containing NetCDF files and an XML manifest file, holding extensive metadata. This means new information is now available, such as per-pixel uncertainty estimates, associated meteorological data from ECMWF ERA-Interim, instrument scan and pixel quality information, and channel-specific exception and flag values.\r\n\r\nGeolocation improvement via orthogeolocation to a Digital Elevation Model.\r\n\r\nImproved surface classification via use of the Sentinel-3 Land Water Masks.\r\n\r\nImproved cloud information via a latitude correction within cloud look-up tables.", "keywords": "SAFE ATSR1 ATSR2 AATSR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43586, "independentInstrument": [ 8097 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206408/?format=api" ] }, { "ob_id": 43379, "computationComponent": [ { "ob_id": 43585, "uuid": "c8effc64e12a4f51b8e303706540e4d0", "title": "ESA SAFE processor for generating AATSR multimission data into AT_1_RBT in SAFE format", "abstract": "The datasets have been fully reprocessed into a new product which aligns them with the format adopted by the instruments’ successors, Sentinel-3 SLSTR, and includes the following updates from the former L1B datasets [AT1_TOA_1P / AT2_TOA_1P] generated by the ERS ATSR 3rd Reprocessing in 2013:\r\n\r\nImproved and extended Level 1B datasets, beginning with recovery of Level 0 data, via the reprocessing of Level 0 data to ungridded brightness temperature (UBT) scenes to Level 1A to retain extra instrument information.\r\n\r\nLevel 1B products in a Sentinel-SAFE/NetCDF format: a product folder containing NetCDF files and an XML manifest file, holding extensive metadata. This means new information is now available, such as per-pixel uncertainty estimates, associated meteorological data from ECMWF ERA-Interim, instrument scan and pixel quality information, and channel-specific exception and flag values.\r\n\r\nGeolocation improvement via orthogeolocation to a Digital Elevation Model.\r\n\r\nImproved surface classification via use of the Sentinel-3 Land Water Masks.\r\n\r\nImproved cloud information via a latitude correction within cloud look-up tables.", "keywords": "SAFE ATSR1 ATSR2 AATSR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 10928, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 3846, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8063/?format=api", "relatedTo": { "ob_id": 10928, "uuid": "c83c56aeecc0489082083d8dab9397e9", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206409/?format=api" ] }, { "ob_id": 43392, "computationComponent": [ { "ob_id": 45099, "uuid": "fa02152813cb4a988a2bfbbb9e37a2f3", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative: Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A and Sentinel 3B, level 3 collated (L3C) global product, version 4.00", "abstract": "For more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 45101, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14403, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 45101, "uuid": "c542218cdb3c425eb88860da1074982a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206522/?format=api" ] }, { "ob_id": 43394, "computationComponent": [ { "ob_id": 43393, "uuid": "3e26006a87a24c419cba05f9eab2e4b9", "title": "Derivation of EOCIS: Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A and Sentinel 3B level 3 collated (L3C) global products, v4.00", "abstract": "For more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43395, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14121, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 43395, "uuid": "c97688056f4a4289bc63bbe0feac9c60", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206525/?format=api" ] }, { "ob_id": 43406, "computationComponent": [ { "ob_id": 43407, "uuid": "b72285dc95484e039334713dee387ac3", "title": "TLS2trees processing pipeline for FBRMS-03: Kabili-Sepilok, Malaysian Borneo 1ha plots", "abstract": "Data for each of the three French Guiana FBRMS plots is found within plot directories: SEP-11; Sep-12 and SEP-30. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2017-03-14.001.riproject) with nine data subdirectories, a tile_index.dat file as shown in the attached ForestScan_example_directory_structure.pdf document.\r\n\r\nThe raw project subdirectory contains all registered scans for each FBRMS 1ha plot. The matrix project subdirectory contains each scan’s Sensor's Orientation and Position (SOP) matrix.\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the three ForestScan FBRMS plots in French Guiana, the step-by-step processing summary below provides details for these files.\r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply.\r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat.\r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project subdirectory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports. \r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project subdirectory, e.g. figures/SEP_12.0.png.", "keywords": "Terrestrial LiDAR Scanning (TLS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, TLS2trees processing pipeline", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 26679, "independentInstrument": [ 27223 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206580/?format=api" ] }, { "ob_id": 43412, "computationComponent": [ { "ob_id": 40340, "uuid": "677c25df9f324ca3a9e1920ea3c330b1", "title": "TLS2trees: a semi-automated processing pipeline", "abstract": "Plot-level point clouds were processed using TLS2trees which is a set of Python command line tools & designed to be horizontally scalable, e.g., on a High Performance Computing (HPC) facility. Pipeline steps: 1) Point cloud re-processing, 2) semantic segmentation into wood & leaf point classes, 3) instance segmentation into sets of point clouds representing individual trees, 4) Quantitative structural models (QSMs) of individual tree point clouds, & 5) Plot biophysical & AGB estimates.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 40635, "independentInstrument": [ 27217 ], "instrumentplatformpair_set": [ { "ob_id": 13686, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/40636/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/40633/?format=api", "relatedTo": { "ob_id": 40635, "uuid": "f12e72bdbbca4db0b0223048a5ef6b7c", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206615/?format=api" ] }, { "ob_id": 43421, "computationComponent": [ { "ob_id": 43420, "uuid": "032147637bad4ee9b3d0488a58daaeae", "title": "Computation to derive surface turbulence from FAAM aircraft measurements", "abstract": "Computation to derive surface turbulence from FAAM aircraft measurements", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 659, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4847, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/51/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/357/?format=api", "relatedTo": { "ob_id": 659, "uuid": "3638b6f9ef924cad847ddec52cb52bfc", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206713/?format=api" ] }, { "ob_id": 43422, "computationComponent": [ { "ob_id": 43420, "uuid": "032147637bad4ee9b3d0488a58daaeae", "title": "Computation to derive surface turbulence from FAAM aircraft measurements", "abstract": "Computation to derive surface turbulence from FAAM aircraft measurements", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 659, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4847, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/51/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/357/?format=api", "relatedTo": { "ob_id": 659, "uuid": "3638b6f9ef924cad847ddec52cb52bfc", "short_code": "acq" } } ] }, { "ob_id": 14777, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 5573, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7575/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14773/?format=api", "relatedTo": { "ob_id": 14777, "uuid": "370d881708674f82a9da163fb81e8657", "short_code": "acq" } }, { "ob_id": 5574, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7575/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14774/?format=api", "relatedTo": { "ob_id": 14777, "uuid": "370d881708674f82a9da163fb81e8657", "short_code": "acq" } }, { "ob_id": 5575, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7575/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7747/?format=api", "relatedTo": { "ob_id": 14777, "uuid": "370d881708674f82a9da163fb81e8657", "short_code": "acq" } }, { "ob_id": 5576, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7575/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/11781/?format=api", "relatedTo": { "ob_id": 14777, "uuid": "370d881708674f82a9da163fb81e8657", "short_code": "acq" } }, { "ob_id": 5577, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7575/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14775/?format=api", "relatedTo": { "ob_id": 14777, "uuid": "370d881708674f82a9da163fb81e8657", "short_code": "acq" } }, { "ob_id": 5578, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7575/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14776/?format=api", "relatedTo": { "ob_id": 14777, "uuid": "370d881708674f82a9da163fb81e8657", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206714/?format=api" ] }, { "ob_id": 43426, "computationComponent": [ { "ob_id": 13192, "uuid": "74e36a898d694a70bc8b0412720741e3", "title": "Level 1C processing algorithm applied to Sentinel 2 raw data", "abstract": "This computation involves the Level 1 processing algorithm applied to raw Multispectral Instrument (MSI) data. Level-1C processing includes radiometric and geometric corrections including ortho-rectification and spatial registration on a global reference system with sub-pixel accuracy. This processing produces the level 1C data as well as quicklook images for the user.", "keywords": "Multispectral Instrument, Sentinel 2, level 1C, algorithm, MSI", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43427, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14123, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/43428/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/13182/?format=api", "relatedTo": { "ob_id": 43427, "uuid": "636c6974c4034d0ca3bbe6661ac03128", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206715/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206723/?format=api" ] }, { "ob_id": 43471, "computationComponent": [ { "ob_id": 33426, "uuid": "f0adab0324864be387e64fc53fba2641", "title": "RAL extended Infrared Microware Sounder IMS retrieval scheme", "abstract": "For information on the RAL extended Infrared Microwave Sounder (IMS) retrieval scheme see the ATBD in the linked docmentation", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43470, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14125, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8300/?format=api", "relatedTo": { "ob_id": 43470, "uuid": "b4923792e87a4851ad6beed84151e379", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206953/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/206954/?format=api" ] }, { "ob_id": 43559, "computationComponent": [ { "ob_id": 43557, "uuid": "91b3899a155146a8be7bc528cfe9bc1e", "title": "TLS2trees processing pipeline for ForestScan 1ha plots FG5c1, FG6c2 and FG8c4 in FBRMS-01: Paracou, French Guiana", "abstract": "Data for each of the four Gabon FBRMS plots is found within plot directories: OKO-01; OKO-02; OKO-03 and LPG-01. Plot directories contain a main project directory (named using the starting date of data collection and the plot ID, e.g. 2022-06-24_LPG-01_PROJ) with nine data sub-directories, a tile_index.dat file and a 2022-06-24_LPG-01.kmz file as shown in the archived ForestScan_example_data_directory_structure.pdf document.\r\n\r\nThe raw project sub-directory contains all registered scans for each FBRMS 1ha plot. The matrix project sub-directory contains each scan’s Sensor's Orientation and Position (SOP) matrix with the GNSS coordinates (geographical coordinate system: WGS84 Cartesian) for all scans saved separately and made available via a .kmz file under the project main directory, e.g. 2022-06-24_LPG-01.kmz.\r\n\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the four ForestScan FBRMS plots in Gabon, the step-by-step processing summary below provides details for these files.\r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply.\r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat.\r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project sub-directory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports.\r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project sub-directory, e.g. figures/lpg_01.0.png.", "keywords": "Terrestrial LiDAR Scanning (TLS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, TLS2trees processing pipeline", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43558, "independentInstrument": [ 43569 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/207987/?format=api" ] }, { "ob_id": 43565, "computationComponent": [ { "ob_id": 43564, "uuid": "af3b26877032453d9f95ca9bb842f941", "title": "TLS2trees processing pipeline for FBRMS-02: Lopé, Gabon 1ha plots", "abstract": "Data for each of the four Gabon FBRMS plots is found within plot directories: OKO-01; OKO-02; OKO-03 and LPG-01. Plot directories contain a main project directory (named using the starting date of data collection and the plot ID, e.g. 2022-06-04_OKO-01.PROJ) with nine data subdirectories, a tile_index.dat file and a 2022-06-04_OKO-01.kmz file as shown in the attached ForestScan_example_data_directory_structure.pdf document.\r\n\r\nThe raw project subdirectory contains all registered scans for each FBRMS 1ha plot. The matrix project subdirectory contains each scan’s Sensor's Orientation and Position (SOP) matrix with the GNSS coordinates (geographical coordinate system: WGS84 Cartesian) for all scans saved separately and made available via a .kmz file under the project main directory, e.g. 2022-06-04_OKO-01.kmz.\r\n\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the four ForestScan FBRMS plots in Gabon, the step-by-step processing summary below provides details for these files.\r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply.\r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat.\r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project subdirectory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports. \r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project subdirectory, e.g. figures/oko_01.0.png.", "keywords": "Terrestrial LiDAR Scanning (TLS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, TLS2trees processing pipeline", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43563, "independentInstrument": [ 43569 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/208002/?format=api" ] }, { "ob_id": 43581, "computationComponent": [ { "ob_id": 43580, "uuid": "1cef7aff4035496cbde00291b498c0fd", "title": "Derivation of the EOCIS: University of Leicester GOSAT and GOSAT-2 Proxy XCH4 v9.0_eocis data", "abstract": "The GOSAT and GOSAT-2 EOCIS datasets were derived using the UoL-FP retrieval algorithm using a proxy retrieval approach. See the linked documentation for further information", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 43584, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 14130, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/28024/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/28023/?format=api", "relatedTo": { "ob_id": 43584, "uuid": "8b377fefcfae43c8aec0d6202bcb61b8", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/208079/?format=api" ] } ] }