{"type": "FeatureCollection", "features": [{"id": "10.1016/j.rse.2023.113621", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:17:08Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "In 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 ( ) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the 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 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 holds better for VV than for VH. 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As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud model (WCM) and the support vector regression (SVR). With SVR, a closer fit between backscatter observations and simulations was achieved. The impact on the correlation between modeled and in situ soil moisture measurements was similar when assimilating the Sentinel data using WCM (\u0394 R = +0.037) or SVR (\u0394 R = +0.025).", "keywords": ["Vegetation mapping", "support vector regression (SVR)", "Technology and Engineering", "Data models", "0211 other engineering and technologies", "Computational modeling", "02 engineering and technology", "15. 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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. 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