Estimation of corn yield by assimilating SAR and optical time series into a simplified agro-meteorological model: From diagnostic to forecast

The estimation of crop yield plays a major role in decision making and management of food supply. This paper aims to estimate corn dry masses and grain yield at field scale using an agro-meteorological model. The SAFY-WB model (simple algorithm for yield model combined with a water balance) is controlled by green area index (GAI) derived from optical satellite images (GAI opt ), and the GAI derived from synthetic aperture radar (SAR) satellite images (GAI sar ) acquired over two crop seasons (2015 and 2016) in the south-west of France. Landsat-8 mission provides the optical data. SAR information ( $\sigma _{{\rm{VV}}}^\circ $ , $\sigma _{{\rm{VH}}}^\circ $ , and $\sigma _{{\rm{VH/VV}}}^\circ $ ) is provided by Sentinel-1A mission through two angular normalized orbits (30 and 132) allowing a repetitiveness from 12 to 6 days. $\sigma _{{\rm{VH}}/{\rm{VV}}}^\circ $ is successfully used to derive GAI sar (R 2 = 0.72, relative root mean square error (rRMSE) = 10.4%) over the leaf development stages of the crop cycle from a nonlinear function. Others SAR signal ( $\sigma _{{\rm{VV}}}^\circ $ and $\sigma _{{\rm{VH}}}^\circ $ ) are too much related to soil moisture changes. At the opposite of GAI opt , GAI sar cannot be used alone in the model to accurately estimate vegetation parameters. Finally, the robustness of the results comes from the combination of GAI derived from SAR and optical data. In this condition, the model is able, thanks to the inclusion of a new “production module,” to simulate dry masses and yield (R 2 > 0.75 and rRMSE < 12.75%) with good performances in the diagnostic approach. In the context of forecast, results offer lower performances but stay acceptable, with relative errors inferior to 13.95% (R 2 > 0.69).