Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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📌Poster Session and Networking Aperitivo 🍷 Location: Lower Lobby | |
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Improving cotton yield and biomass prediction by assimilating SAR data into a modified crop growth model with simple calibration 1: Universitat Politècnica de Catalunya, Barcelona, Spain; 2: Wageningen Environmental Research, Wageningen, Netherlands Aboveground biomass density (AGBD) is a key indicator of crop productivity and carbon storage. However, accurate regional estimation remains challenging because optical remote sensing suffers from cloud contamination, while process-based crop growth models, such as WOFOST, require extensive calibration and often fail to capture spatial heterogeneity. To improve AGBD estimation performance and operational flexibility, this study integrates the complementary strengths of cloud-insensitive microwave remote sensing and crop growth models through a SAR-driven, simple-calibration assimilation framework. AGBD is estimated from dual-polarized Sentinel-1 radar backscatter calibrated with field measurements. A modified and simplified version of the WOFOST potential production (WOFOST-PP) model was proposed, which reduces data requirements and calibration complexity. Pixel-wise WOFOST parameters are updated via data assimilation by minimizing the least-squares difference between SAR-derived AGBD and model outputs, improving their spatial heterogeneity representation and accuracy. Validation with multi-year cotton observations from two contrasting farms in Georgia, USA (rainfed ACF; irrigated TCF) shows that assimilation configuration achieves the best performance compared with both WOFOST-PP and SAR-derived estimates. In WOFOST-PP, assimilated carbon is partitioned among leaves, stems, and storage organs; the dry weight of storage organs, a subset of AGBD, is defined as yield and enables computation of the harvest index (HI). Pixel-level maps of AGBD, yield, and HI capture spatial heterogeneity and maintain operational coverage under frequent cloudiness, where optical data are sparse. Spatial fields of YRF reveal persistent low-efficiency zones suitable for targeted management, enabling earlier stress detection than end-of-season diagnostics. The SAR-driven, simple-calibration assimilation offers a practical pathway to regional AGBD and yield mapping with limited field data. It remains competitive with optical-based systems, maintains accuracy while providing temporal robustness in cloudy conditions. | |
