Optical and Radar Data Synergie to Capture Intra/inter Field Yield Variability

The yield and production forecasting of the main crops are a societal and economic challenge at global, national and farm levels. In the context of climate change, the uncertainty on the future of crop productions is a major issue. The evolution of optical and radar satellites imagery from moderate to very high resolution provides new tools to address yield variability at different spatial scales. The Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) tools have provided significant information in the crop yield status at the national and regional levels (Franch et al, 2019). But to improve local scale forecasting models, it is necessary to integrate high and very high-resolution data. The first part of the study focused on intra-field yield variability using Planet/Dove-Classic, Sentinel-2, Landsat 8 (through Harmonized Landsat Sentinel-2 - HLS), Sentinel-1-SAR and yield data at 30 m resolution. We have estimated different indexes combining spectral bands / polarization backscattering over 30 fields of corn and soybean in Iowa state. The second part of the study was focused on the yield variability study between fields by averaging yields data at field level from 258 fields of Corn, Soybean and Rice in Nebraska. We used optical and radar data (HLS, Planet, UAVSAR, RADARSAT-2, Sentinel 1-SAR). For optical data, we show that the most important spectral bands explaining yield variability were green (0.560 m), red-edge (0.726 m), and near-infrared (NIR - 0.865 m). Overall, we observed mixed performance of models derived from satellites with the coefficient of determination (R2) varying from 0.21 to 0.88 (on average 0.56) for the 30 m HLS, and from 0.09 to 0.77 (on average 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the reflectance characteristics collected by satellites in these cases (Skakun et al, 2021). These results suggest the importance of combining radar data and optical data to integrate other biophysical variables, such as soil moisture and evapotranspiration. For radar data, the best R2 was obtained with Sentinel 1 and RS2 by mixing the different polarization: VH-VV ascendant and descendant for Sentinel 1-SAR with a R2 of 0.34 for corn and HH-HV-VV for RS2 with a R2 of 0.59 for soybean and 0.51 for rice.