RSCM: A Bayesian remote sensing-integrated crop model software framework for yield estimation

Abstract

RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.

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