Observation List
Get a list of Observation objects.
### Available end points:
- `/observations/` - Will list all Results in the database
- `/observations.json` - Will return all Results in json format. This can
also be achieved by using the accept header. `application/json`
- `/observations/<object_id>/` - Returns Results object with that id
### Available Methods:
- `GET`
- `HEAD`
### Available filters:
- `title`
- `uuid`
- `keywords`
- `status`
- `result_field`
- `discoveryKeywords`
- `updateFrequency`
- `nonGeographicFlag`
- `publicationState`
- `permissions`
### How to use filters:
These filters can be used like django query filters using __ for related model relationships.
- `/observations/?uuid=d594d53df2612bbd89c2e0e770b5c1a0`
- `/observations/?status=completed`
- `/observations/?results_field__dataPath__startswith=/neodc/esacci`
- `/observations/?discoveryKeywords__name=ESACCI`
- `/observations/?nonGeographicFlag=True`
Filters can be stacked to build an 'AND' relationship. E.g.
- `/observations/?publicationState__in=published,citable&nonGeographicFlag=True`
- `/observations/?publicationState__in=published,citable&discoveryKeyword__name=NDGO0003`
GET /api/v2/observations/?format=api&offset=6800
{ "count": 10400, "next": "https://catalogue.ceda.ac.uk/api/v2/observations/?format=api&limit=100&offset=6900", "previous": "https://catalogue.ceda.ac.uk/api/v2/observations/?format=api&limit=100&offset=6700", "results": [ { "ob_id": 31204, "uuid": "ef8eb5ff84994f2ca416dbb2df7f72c7", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2019), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 2000 – 2019. \r\n\r\nThe SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe 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. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the 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 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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "dataPublishedTime": "2021-05-10T15:24:50", "doiPublishedTime": "2021-05-12T09:57:35", "updateFrequency": "notPlanned", "status": "superseded", "result_field": { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/32260/?format=api", "dataPath": "/neodc/esacci/snow/data/scfv/MODIS/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 203930783168, "numberOfFiles": 7191, "fileFormat": "Data are netCDF formatted." }, "timePeriod": "https://catalogue.ceda.ac.uk/api/v2/times/8856/?format=api", "geographicExtent": "https://catalogue.ceda.ac.uk/api/v2/bboxes/2614/?format=api", "nonGeographicFlag": false, "phenomena": [ "https://catalogue.ceda.ac.uk/api/v2/phenomona/6021/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/6022/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/32072/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50416/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61128/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61129/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61130/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61131/?format=api" ], "dataLineage": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFV products based on the Land Cover CCI map of the year 2000. All SCFV products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.\r\n\r\nWe acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS.", "removedDataTime": null, "removedDataReason": "", "language": "English", "identifier_set": [ "https://catalogue.ceda.ac.uk/api/v2/identifiers/10872/?format=api" ], "projects": [ "https://catalogue.ceda.ac.uk/api/v2/projects/30229/?format=api" ], "observationcollection_set": [], "responsiblepartyinfo_set": [ "https://catalogue.ceda.ac.uk/api/v2/rpis/136837/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136838/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136839/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136840/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136835/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136836/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136834/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136848/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136847/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144471/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144472/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144473/?format=api" ], "procedureAcquisition": null, "procedureCompositeProcess": "https://catalogue.ceda.ac.uk/api/v2/composites/32517/?format=api", "procedureComputation": null, "permissions": [ { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/2571/?format=api", "useLimitation": null, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": "any" } ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ] }, { "ob_id": 31205, "uuid": "3b3fd2daf3d34c1bb4a09efeaf3b8ea9", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2019), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2000 – 2019. \r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe 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 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 SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) 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 is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on 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 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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nThe SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "dataPublishedTime": "2021-05-10T11:03:49", "doiPublishedTime": "2021-05-12T09:51:29", "updateFrequency": "notPlanned", "status": "superseded", "result_field": { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/32243/?format=api", "dataPath": "/neodc/esacci/snow/data/scfg/MODIS/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 203818851581, "numberOfFiles": 7191, "fileFormat": "Data are netCDF formatted." }, "timePeriod": "https://catalogue.ceda.ac.uk/api/v2/times/8859/?format=api", "geographicExtent": "https://catalogue.ceda.ac.uk/api/v2/bboxes/2614/?format=api", "nonGeographicFlag": false, "phenomena": [ "https://catalogue.ceda.ac.uk/api/v2/phenomona/6021/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/6022/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/32072/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50416/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61130/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61131/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61132/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/62644/?format=api" ], "dataLineage": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.\r\n\r\nWe acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS.", "removedDataTime": null, "removedDataReason": "", "language": "English", "identifier_set": [ "https://catalogue.ceda.ac.uk/api/v2/identifiers/10870/?format=api" ], "projects": [ "https://catalogue.ceda.ac.uk/api/v2/projects/30229/?format=api" ], "observationcollection_set": [], "responsiblepartyinfo_set": [ "https://catalogue.ceda.ac.uk/api/v2/rpis/136856/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136850/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136851/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136852/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136854/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136855/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136864/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136853/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136863/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144474/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144475/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144476/?format=api" ], "procedureAcquisition": null, "procedureCompositeProcess": "https://catalogue.ceda.ac.uk/api/v2/composites/32515/?format=api", "procedureComputation": null, "permissions": [ { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/2571/?format=api", "useLimitation": null, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": "any" } ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ] }, { "ob_id": 31206, "uuid": "4710d4bea29648528925576a618fc6f5", "title": "WCRP CMIP6: National Center for Atmospheric Research (NCAR) CESM2 model output for the \"piClim-4xCO2\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the National Center for Atmospheric Research (NCAR) CESM2 model output for the \"effective radiative forcing by 4xCO2\" (piClim-4xCO2) experiment. These are available at the following frequencies: Amon, day and fx. The runs included the ensemble member: r1i1p1f1.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "keywords": "CMIP6, WCRP, climate change, NCAR, CESM2, piClim-4xCO2, Amon, day, fx", "publicationState": "published", "dataPublishedTime": "2020-07-08T13:14:55.499227", "doiPublishedTime": null, "updateFrequency": "asNeeded", "status": "ongoing", "result_field": { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/31207/?format=api", "dataPath": "/badc/cmip6/data/CMIP6/RFMIP/NCAR/CESM2/piClim-4xCO2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5564721726, "numberOfFiles": 25, "fileFormat": "Data are netCDF formatted." }, "timePeriod": "https://catalogue.ceda.ac.uk/api/v2/times/8522/?format=api", "geographicExtent": "https://catalogue.ceda.ac.uk/api/v2/bboxes/529/?format=api", "nonGeographicFlag": false, "phenomena": [ "https://catalogue.ceda.ac.uk/api/v2/phenomona/6023/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/18948/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/18949/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/19456/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50427/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50468/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50509/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50512/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50549/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50554/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50555/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50556/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50557/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50566/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/52664/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/52665/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54373/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54374/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54375/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54392/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54810/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54811/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54812/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/54813/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/59462/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/62525/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/62736/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/71572/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/82762/?format=api" ], "dataLineage": "Data were produced and verified by National Center for Atmospheric Research (NCAR) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataTime": null, "removedDataReason": "", "language": "English", "identifier_set": [], "projects": [ "https://catalogue.ceda.ac.uk/api/v2/projects/28424/?format=api" ], "observationcollection_set": [ "https://catalogue.ceda.ac.uk/api/v2/observationcollections/28466/?format=api" ], "responsiblepartyinfo_set": [ "https://catalogue.ceda.ac.uk/api/v2/rpis/136874/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136868/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136869/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136865/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136866/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136870/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136871/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136872/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136873/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136867/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/167403/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/167404/?format=api" ], "procedureAcquisition": null, "procedureCompositeProcess": null, "procedureComputation": "https://catalogue.ceda.ac.uk/api/v2/computations/31208/?format=api", "permissions": [ { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/2520/?format=api", "useLimitation": null, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": "" } ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ] }, { "ob_id": 31209, "uuid": "d9df331e346f4a50b18bcf41a64b98c7", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2019), version 1.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1982-2019. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\nThe 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-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 SCFV retrieval method is applied. \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.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years.", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "citable", "dataPublishedTime": "2021-05-10T10:59:37", "doiPublishedTime": "2021-05-12T09:49:01", "updateFrequency": "notPlanned", "status": "superseded", "result_field": { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/32261/?format=api", "dataPath": "/neodc/esacci/snow/data/scfv/AVHRR_MERGED/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 35670768784, "numberOfFiles": 13770, "fileFormat": "Data are netCDF formatted." }, "timePeriod": "https://catalogue.ceda.ac.uk/api/v2/times/8858/?format=api", "geographicExtent": "https://catalogue.ceda.ac.uk/api/v2/bboxes/2614/?format=api", "nonGeographicFlag": false, "phenomena": [ "https://catalogue.ceda.ac.uk/api/v2/phenomona/6021/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/6022/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/32072/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/50416/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61128/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61129/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61130/?format=api", "https://catalogue.ceda.ac.uk/api/v2/phenomona/61131/?format=api" ], "dataLineage": "The snow_cci SCFV product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). \r\n\r\nThe final product is quality checked.", "removedDataTime": null, "removedDataReason": "", "language": "English", "identifier_set": [ "https://catalogue.ceda.ac.uk/api/v2/identifiers/10869/?format=api" ], "projects": [ "https://catalogue.ceda.ac.uk/api/v2/projects/30229/?format=api" ], "observationcollection_set": [], "responsiblepartyinfo_set": [ "https://catalogue.ceda.ac.uk/api/v2/rpis/136881/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136882/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136883/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136885/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136886/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144530/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136884/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/136887/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144531/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144532/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144533/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144534/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144535/?format=api", "https://catalogue.ceda.ac.uk/api/v2/rpis/144536/?format=api" ], "procedureAcquisition": null, "procedureCompositeProcess": "https://catalogue.ceda.ac.uk/api/v2/composites/32525/?format=api", "procedureComputation": null, "permissions": [ { "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/2571/?format=api", "useLimitation": null, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": "any" } ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ] }, { "ob_id": 31210, "uuid": "5484dc1392bc43c1ace73ba38a22ac56", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2019), version1.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1982-2019. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe 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.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. 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