The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in support of research, survey and monitoring programmes. The ARSF is a unique service providing environmental researchers, engineers and surveyors with synoptic analogue and digital imagery of high spatial and spectral resolution.The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.

This dataset contains optical ice velocity time series and seasonal product of the Upernavik Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-15 and 2017-08-14. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway.

This dataset provides monthly Sea Surface Salinity (SSS) data derived as part of the European Space Agency (ESA) Climate Change Initiative (CCI) programme. In this product the data has been produced at a spatial resolution of 25km and a time resolution of 1 month. This has then been spatially resampled on a 25km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A monthly product is also available. This first version of the CCI+SSS products is a preliminary version issued for evaluation purposes by voluntary scientists and for framing future CCI+SSS products. This product has not been fully validated yet and may contain flaws. In case you discover some, the CCI salinity team (Mngt_CCI-Salinity@argans.co.uk) are very keen to get your feedback. In case you would like to use them in a presentation or publication, please be aware of the following caveats: CAVEATS - The SSS random error in the weekly product is overestimated by a factor ~1.4. - The Number of outliers is wrongly set to 'NaN' in the case where it is equal to zero. - Products have not yet been not optimised for some issues encountered at high latitudes (i.e. remaining ice, RFI pollution, biases due to land-sea contamination and dielectric constant in cold waters). - The criteria for flagging data close to land (including islands) are conservative and likely to be too restrictive in places. - There is a systematic global underestimation (-0.08) of SSS starting at the beginning of the data set, and gradually disappearing at the end of 2010. - There is a seasonal varying bias (~0.1, peaking in the middle of the year) in the Pacific North of 25°N. Acknowledgements: The authors thank the CCI+ SSS validation team, in particular S. Guimbard (ODL) and A. Martin (NOC), for their feedback on the products, R. Catany (ARGANS) for managing the project and P. Cipollini and C. Donlon (ESA) for their sound advice.

The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created. The v05.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website. The data set should be cited using all three of the following references: 1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019 2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070

ARSF project 03/24 led by G.Ferrier. Site: Ythan Estuary.

The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in support of research, survey and monitoring programmes. The ARSF is a unique service providing environmental researchers, engineers and surveyors with synoptic analogue and digital imagery of high spatial and spectral resolution.The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.

This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.

These data are a copy of MODIS data from the NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC). The copy is potentially only a subset. Below is the description from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD14A1 MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of a fire (when the fire strength is sufficient to detect), and on detection relative to its background (to account for variability of the surface temperature and reflection by sunlight). Numerous tests are employed to reject typical false alarm sources like sun glint or an unmasked coastline. MOD14A1 is produced every 8 days at 1-kilometer resolution as a gridded level-3 product in the Sinusoidal projection. This product is unique in that it has three dimensions: fire-mask (1D) and a maximum fire-radiative-power (2D) are provided for each day (3D) in the 8-day period. For example, the fire-mask contains eight, band sequential (day) 1200 x 1200 images of fire data representing consecutive days of data collection. The Terra MODIS instrument acquires data twice daily (10:30 AM and PM), as does the Aqua MODIS (1:30 PM and AM). These four daily MODIS fire observations serve to advance global monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate. Collection-5 MODIS/Terra Thermal Anomalies/Fire products are Validated Stage 3, meaning that uncertainties in the product and its associated structure are well quantified from comparison with reference in situ or other suitable reference data. These data are ready for use in scientific publications. Shortname: MOD14A1 , Platform: Terra , Instrument: MODIS , Processing Level: Level-3 , Spatial Resolution: 1 km , Temporal Resolution: daily , ArchiveSets: 6 , Collection: MODIS Collection 6 (ArchiveSet 6) , PGE Number: PGE29 , File Naming Convention: MOD14A1.AYYYYDDD.hHHvVV.CCC.YYYYDDDHHMMSS.hdf YYYYDDD = Year and Day of Year of acquisition hHH = Horizontal tile number (0-35) vVV = Vertical tile number (0-17) CCC = Collection number YYYYDDDHHMMSS = Production Date and Time , Citation: Louis Giglio, Chris Justice - University of Maryland and MODAPS SIPS - NASA. (2015). MOD14A1 MODIS/Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid. NASA LP DAAC. http://doi.org/10.5067/MODIS/MOD14A1.006 , Keywords: Climate Change, Land Surface Temperature, Fires

The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in support of research, survey and monitoring programmes. The ARSF is a unique service providing environmental researchers, engineers and surveyors with synoptic analogue and digital imagery of high spatial and spectral resolution.The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.

The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. This dataset contains the Version 5.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). Please note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d) Version 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0