Crop production has grown exponentially over the past centuries, mostly due to scientific innovations and mechanical automation. The more recent developments in technology have led to a growing interest of upscaling process-based crop models, which typically calculate crop production for a field under homogeneous soil, crop and management conditions, to assess agriculture from a regional perspective. A limitation of these upscaled crop models is generalized information of spatial input data. One way to try to correct for model errors is the use of independent observations to update (state) variables in a data assimilation system.

In my doctoral research, I focused on the potential of using Synthetic Aperture Radar (SAR) observations from the microwave satellite Sentinel-1 to correct for regional AquaCrop soil moisture and biomass simulations over Europe via data assimilation. The first phase consisted of the performance evaluation of a spatially distributed version of the field-scale AquaCrop model. In the second phase, the regional AquaCrop model simulations of soil moisture and biomass were translated to backscatter, to facilitate evaluation with Sentinel-1 observations in preparation for Sentinel-1 based data assimilation system. Finally, the evaluated regional AquaCrop model was implemented into a data assimilation framework; NASA's Land Information System (LIS).

With the regional model set-up (using generic input data) soil moisture and biomass productivity patterns over diverse regions in Europe could be captured. The data assimilation system showed potential for improving soil moisture and biomass simulations, but needs more development to be fully optimal.