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. The current intensive use of agricultural land is affecting the land qualityand contributes to climate change. Feeding the world's growing populationunder changing climatic conditions demands a global transition to moresustainable agricultural systems. This requires efficient models and data tomonitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale cropmodel AquaCrop version 6.1 to simulate agricultural biomass production andsoil moisture variability over Europe at a relatively fine resolution of 30 arcsec (∼1 km). A highly efficient parallel processingsystem is implemented to run the model regionally with global meteorologicalinput data from the Modern-Era Retrospective analysis for Research andApplications version 2 (MERRA-2), soil textural information from theHarmonized World Soil Database version 1.2 (HWSDv1.2), and generic cropinformation. The setup with a generic crop is chosen as a baseline for afuture satellite-based data assimilation system. The relative temporalvariability in daily crop biomass production is evaluated with the CopernicusGlobal Land Service dry matter productivity (CGLS-DMP) data. Surface soilmoisture is compared against NASA Soil Moisture Active–Passive surface soilmoisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surfacesoil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ datafrom the International Soil Moisture Network (ISMN). Over central Europe, theregional AquaCrop model is able to capture the temporal variability in bothbiomass production and soil moisture, with a spatial mean temporal correlationof 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). Thehigher performance when evaluating with SMAP-SSM compared to Sentinel-1CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievalsunder growing vegetation. The regional model further captures the short-termand inter-annual variability, with a mean anomaly correlation of 0.46 for dailybiomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM)for soil moisture. It is shown that soil textural characteristics andirrigated areas influence the model performance. Overall, the regionalAquaCrop model adequately simulates crop production and soil moisture andprovides a suitable setup for subsequent satellite-based data assimilation.

Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations