Crop Yield Prediction Using Integration Of Polarimteric Synthetic Aperture Radar And Optical Data

In this study, double-bounce parameter derived from Sentinel-1 was integrated with Difference vegetation index (DVI) derived from Landsat-8 for prediction of soybean yield at field level over central Argentina. Artificial Neural Network (ANN) model was trained using time series of Synthetic Aperture Radar (SAR) and optical features during the growing season. For comparison of SAR versus optical versus their integration for soybean yield prediction, the ANN model was trained and tested for three scenarios of SAR-only, optical-only and SAR-optical integration. Accuracies of yield prediction including correlation of determination (R
²
), root mean square error (RMSE) and mean absolute error (MAE) are 0.80, 0.589 t/ha, 0.445 t/ha for SAR-only; 0.65, 0.800 t/ha, 0.546 t/ha for optical-only; and 0.85, 0.554 t/ha, 0.389 t/ha for SAR-optical integration scenarios, respectively. These accuracies demonstrate of high potential of SAR and SAR-optical integration for soybean yield prediction at field level.