Retrieval of rice biophysical parameters from Sentinel‑2 using parsimonious multi‑output machine learning

Abstract Rice ( Oryza sativa L.) crop monitoring in Africa requires efficient retrieval of biophysical parameters such as leaf area index (LAI) and leaf chlorophyll content (LCC), which are essential for crop stress detection and nutrient management. This study aimed at developing a parsimonious modeling approach that overcomes the limitations of existing single‐ and multi‐target models. We developed a novel multi‐output framework that integrates Pareto optimization with adaptive stopping rules to simultaneously retrieve LAI and LCC from Sentinel‐2 imagery. Model performance was evaluated using in situ data ( n  = 177) collected from rice farms in Kenya's Ahero irrigation scheme using coefficient of determination ( R 2 ) and root mean square error (RMSE) metrics. The parsimonious XGBoost (eXtreme gradient boosting) model trained on 11 vegetation indices achieved high accuracies ( R 2  = 0.76 and RMSE = 0.766 m 2 m − 2 for LAI; R 2  = 0.74 and RMSE = 3.590 µg cm − 2 for LCC). These results represent gains of up to 32% for LAI and 47% for LCC compared to prior Sentinel‐2‐based multi‐target retrieval approaches. Notably, the model achieved these improvements with a 54% reduction in vegetation indices and a 28% smaller model size relative to a 24‐vegetation indices baseline model. Therefore, our innovative design results in improved stability, generalization, and computational efficiency. By reducing complexity while improving accuracy, the framework offers a practical pathway for scalable crop monitoring and productivity improvement using freely available satellite data.