SASYA: An integrated framework for crop biophysical parameter retrieval and within-season crop yield prediction with SAR remote sensing data

Food security can be assured with a reasonable crop yield forecast at a national and regional scale. The agencies require advance estimates of production of major crops at a regional scale for taking various policy decisions. Hence, it is necessary to develop operational systems for crop monitoring and yield forecasting. Unlike the traditional annual crop yield estimates based on the statistics-based framework, remote sensing techniques are recognized as an effective approach for yield forecasting. In particular, the microwave remote sensing data have gained importance due to its all-weather imaging capability and sensitivity to crop phenological changes. In this present work, an integrated framework `SASYA-Space technology for Agricultural Systems and Yield forecasting Algorithm' is introduced for crop yield forecasting using Synthetic Aperture Radar (SAR) remote sensing images. SASYA incorporates a model inversion scheme to estimate crop biophysical parameters (Leaf Area Index-LAI and wet biomass) simultaneously using a multi-output support vector regression (MSVR). The framework cross-link the MSVR based inversion module with a yield prediction module, which utilizes the WOrld FOod STudies (WOFOST) crop simulation model. The robustness of the framework is assessed for estimation of winter barley yield using L-band NISAR data simulated using full-pol L-band E-SAR airborne SAR data acquired during Agri-SAR 2006 campaign over DEMMIN test site in Germany. The simulated NISAR data in HH-HV-VV, HH-HV, and VV-HV modes are first compared by investigating accuracies to estimate crop biophysical parameters with the inversion of Water Cloud Model. These LAI and wet biomass estimates are then used in SASYA framework to assimilate with WOFOST crop yield model to estimate grain yield. Different polarization modes HH-HV-VV, HH-HV, and VV-HV are also assessed to investigate best performance in grain yield estimation. Also, different test cases are considered to find the suitable acquisition dates based on crop phenological stages, which returns the best potential yield estimation. The SAR images during heading and early fruit development stage are found to be most important for yield prediction with lowest RMSE error in the range of 0.57-1.612 ton/ha. The LAI, biomass and potential yield map indicate promising results, which can be used further for crop risk assessment for that region.