Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30\u2009arcsec (\u223c1\u2009km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active\u2013Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.
", "keywords": ["YIELD RESPONSE", "2. Zero hunger", "LAND", "QE1-996.5", "Science & Technology", "PRODUCTIVITY", "04 Earth Sciences", "0207 environmental engineering", "UNCERTAINTY", "Geology", "02 engineering and technology", "15. Life on land", "7. Clean energy", "01 natural sciences", "WHEAT YIELD", "37 Earth sciences", "DATA ASSIMILATION", "13. Climate action", "ASSESSMENTS", "Physical Sciences", "IMPLEMENTATION", "FAO CROP MODEL", "Geosciences", " Multidisciplinary", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/7309/2021/gmd-14-7309-2021.pdf"}, {"href": "https://doi.org/10.5194/gmd-2021-98"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gmd-2021-98", "name": "item", "description": "10.5194/gmd-2021-98", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-2021-98"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "10261/220255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:25:50Z", "type": "Journal Article", "created": "2020-01-09", "title": "SHui, an EU-Chinese cooperative project to optimize soil and water management in agricultural areas in the XXI century", "description": "Open AccessThis work has been supported by Project SHui which is co-funded by the European Union Project GA 773903 and the Chinese MOST. This work has been supported by P12-AGR-0931 (Andalusian Government), RTA2014-00063- C04-03 (Spanish government), SHui (European Commission Grant Agreement number: 773903) and EU\u2012FEDER funds", "keywords": ["Yield", "550", "EROSION", "FLOW", "Cropping", "SIMULATE YIELD RESPONSE", "Soil Science", "Environmental Sciences & Ecology", "RICE YIELDS", "01 natural sciences", "630", "12. Responsible consumption", "4104 Environmental management", "4105 Pollution and contamination", "DRYING IRRIGATION", "11. Sustainability", "FAO CROP MODEL", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "1. No poverty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Engineering (General). Civil engineering (General)", "6. Clean water", "4106 Soil sciences", "Cooperation", "Sustainability", "13. Climate action", "Physical Sciences", "Water Resources", "0401 agriculture", " forestry", " and fisheries", "TA1-2040", "Life Sciences & Biomedicine", "Environmental Sciences"]}, "links": [{"href": "https://doi.org/10261/220255"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Soil%20and%20Water%20Conservation%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/220255", "name": "item", "description": "10261/220255", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/220255"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:26:28Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "Open AccessIn preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014\u20132018) with 1-km Sentinel-1 backscatter (\u03d2\u00b0) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the \u03d2\u00b0 simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 \u03d2\u00b0 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil \u03d2\u00b0 holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.", "keywords": ["Agriculture and Food Sciences", "Technology", "ASSIMILATION", "Sentine;-1", "Environmental Sciences & Ecology", "Geological & Geomatics Engineering", "BIOMASS", "Remote Sensing", "SAR BACKSCATTER", "SURFACE SOIL-MOISTURE", "SUPPORT", "0909 Geomatic Engineering", "WATER", "FAO CROP MODEL", "Imaging Science & Photographic Technology", "crop biomass", "Crop biomass", "YIELD RESPONSE", "Science & Technology", "backscatter modelling", "Backscatter modeling", "LEAF-AREA INDEX", "RADAR BACKSCATTER", "37 Earth sciences", "AquaCrop optimisation", "13. Climate action", "Earth and Environmental Sciences", "Sentinel-1", "Soil moisture", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "Environmental Sciences"]}, "links": [{"href": "https://biblio.vub.ac.be/vubirfiles/112110259/108189295.pdf"}, {"href": "https://doi.org/1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "name": "item", "description": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:26:48Z", "type": "Journal Article", "created": "2021-11-30", "title": "Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations", "description": "Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30\u2009arcsec (\u223c1\u2009km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active\u2013Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.
", "keywords": ["YIELD RESPONSE", "2. Zero hunger", "LAND", "QE1-996.5", "Science & Technology", "PRODUCTIVITY", "04 Earth Sciences", "0207 environmental engineering", "UNCERTAINTY", "Geology", "02 engineering and technology", "15. Life on land", "7. Clean energy", "01 natural sciences", "WHEAT YIELD", "37 Earth sciences", "DATA ASSIMILATION", "13. Climate action", "ASSESSMENTS", "Physical Sciences", "IMPLEMENTATION", "FAO CROP MODEL", "Geosciences", " Multidisciplinary", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/7309/2021/gmd-14-7309-2021.pdf"}, {"href": "https://doi.org/20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "name": "item", "description": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "3000130041", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:27:28Z", "type": "Journal Article", "created": "2020-01-10", "title": "SHui, an EU-Chinese cooperative project to optimize soil and water management in agricultural areas in the XXI century", "description": "Open AccessThis work has been supported by Project SHui which is co-funded by the European Union Project GA 773903 and the Chinese MOST. This work has been supported by P12-AGR-0931 (Andalusian Government), RTA2014-00063- C04-03 (Spanish government), SHui (European Commission Grant Agreement number: 773903) and EU\u2012FEDER funds", "keywords": ["Yield", "550", "EROSION", "FLOW", "Cropping", "SIMULATE YIELD RESPONSE", "Soil Science", "Environmental Sciences & Ecology", "RICE YIELDS", "01 natural sciences", "630", "12. Responsible consumption", "4104 Environmental management", "4105 Pollution and contamination", "DRYING IRRIGATION", "11. Sustainability", "FAO CROP MODEL", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "1. No poverty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Engineering (General). Civil engineering (General)", "6. Clean water", "4106 Soil sciences", "Cooperation", "Sustainability", "13. Climate action", "Physical Sciences", "Water Resources", "0401 agriculture", " forestry", " and fisheries", "TA1-2040", "Life Sciences & Biomedicine", "Environmental Sciences"]}, "links": [{"href": "https://doi.org/3000130041"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Soil%20and%20Water%20Conservation%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3000130041", "name": "item", "description": "3000130041", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3000130041"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FAO+CROP+MODEL&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FAO+CROP+MODEL&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FAO+CROP+MODEL&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FAO+CROP+MODEL&offset=6", "hreflang": "en-US"}], "numberMatched": 6, "numberReturned": 6, "distributedFeatures": [], "timeStamp": "2026-07-28T05:14:42.759485Z"}