A Dynamical Framework that Integrates Optical and SAR Imagery for Sugarcane Monitoring in the Cauca Valley

Citation: Mestre-Quereda, A., Martinez-Marin, T., Lopez-Sanchez, J. M., Mosquera, C., Pelgrum, H., Skarli, A., Hoekman, D., & van der Sande, C. (2026). A Dynamical Framework that Integrates Optical and SAR Imagery for Sugarcane Monitoring in the Cauca Valley. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2026.3694476

Abstract: In this work, we present a novel dynamical framework for crop monitoring that integrates SAR and optical satellite imagery. The proposed methodology employs two-dimensional state transitions to explicitly model the expected temporal evolution of crops. By combining such crop modeling with multi-sensor observations, including dual-polarimetric (VV and VH) radar images gathered by Sentinel-1 and optical imagery from Sentinel-2, the developed framework produces robust and consistent estimations using the well-established Kalman Filter.  Specifically, the approach is applied to retrieve the biomass values of sugarcane crops located in the Cauca Valley (Colombia), over a full growing season (from summer 2024 to summer 2025). An intensive field campaign was carried out, so that ground-truth measurements in a large number of points, located in multiple fields, were available to develop and validate the proposed integration algorithm. Results show that biomass is accurately estimated especially when all available observations, both SAR and optical, are employed.