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  • Soil Moisture data (version 08.1) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments. The homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2022-12-31 and the ACTIVE product covering 1991-08-05 to 2022-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide. The data set should be cited using all 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. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.

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    The FGGD severe environmental constraints map is a global raster datalayer with a resolution of 5 arc-minutes. Pixels with no severe environmental constraints contain a value of zero. Each other pixel contains a cumulative class value that shows which environmental constraint is binding in the pixel area. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

  • IMERG (Integrated MultisatellitE Retrievals from GPM) precipitation product was developed by NASA in collaboration with JAXA

  • Corine Land Cover 2018 (CLC2018) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/.

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    WRIA_DEM: Hydrologically filled GTopo30 DEM with the main stems of cartographic rivers "burned-in" for irrigation analysis. GT30/GTopo30 is Global Topographic 30 arc second DEM database, nominal 1km postings; DEM is Digital Elevation Model. The WRIALAEA grid data layer is comprised of 9194x8736 derivative raster hydrologically filled dem features derived based on 1 kilometer data originally from FAO.

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    Derived from the Global Agro-Ecological Zones Study, Food and Agriculture Organization of the United Nations (FAO), Land and Water Development Division (AGL) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), 2000. Two sources of geo-referenced terrain slopes were available for use in the Global AEZ assessment: (i) terrain slopes indicated in the mapping unit expansion tables of the respective soil maps, and (ii) terrain slopes derived from GTOPO30 data (EROS Data Center, 1998). The latter terrain-slope database was established at IIASA using a rule-based algorithm to calculate slope distributions in terms of seven slope classes per 5 minute grid-cell of the DSMW soil data based on neighborhood relationships among grid-cells in the 30 arc-second GTOPO30 database. Slopes derived from the 30 arc-second DEM were allocated to soil units occurring within individual soil associations. This involved five steps: (i) Determination of slope classes for each 30 arc-second grid-cell of GTOPO30. Results are grouped in the following seven classes: 0-2%, 2-5%, 5-8%, 8-16%, 16-30%, 30-45% and > 45%; (ii) Aggregation of the results respectively to 5 minute latitude/longitude DSMW grid-cells, and to individual soil association map units resulting in a slope class distribution for each grid-cell and map unit; (iii) Defining priority classes of soil unit/slope relationships; (iv) Establishing for each soil association consistent rankings of slopes/soil units; (v) Allocation of individual soil units within a particular soil association map unit to 5 min grid-cells of the DSMW, according to calculated slope distributions.

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    Silt content (2-50/63 micro meter) mass fraction in ‰ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. This map is the result of resampling the mean SoilGrids 250 m predictions (Poggio et al. 2021) for each 5000 m cell.

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    The FGGD CV of LGP map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains an average coefficient of variation of LGP for the pixel area over the period 1901-1996. The data are from FAO and IIASA, 2000, Global agro-ecological zones, as reported in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

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    Derived from the Global Agro-Ecological Zones Study, Food and Agriculture Organization of the United Nations (FAO), Land and Water Development Division (AGL) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), 2000. Data averaged over a period of 37 years. Raster data-set has been exported as ASCII raster file type.

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    Grid with average Wet Day Frequency (rain days per month) for the period 1961-1990. This dataset is constructed from observations, backed with synthetic data derived from precipitation. Source: New, M., Lister, D., Hulme, M. and Makin, I., 2002: A high-resolution data set of surface climate over global land areas. Climate Research 21:1-25