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.

Soil moisture plays a vital role in the water-energy cycle, climate dynamics, and agricultural practices. Conventional approaches for estimating surface soil moisture (SSM) with single-frequency Synthetic Aperture Radar (SAR) often face challenges in accuracy and adaptability due to differences in sensor properties. This study proposes a novel cross-frequency calibration framework for SSM retrieval using non-coincident multi-frequency SAR data, specifically leveraging SAOCOM (L-band) and RADARSAT Constellation Mission (RCM) (C-band) datasets. A domain adaptation approach is employed to transfer knowledge from SAOCOM to RCM, enabling robust SSM estimation without requiring coincident acquisitions. Ensemble Learning Regression (ELR) and Gaussian Process Regression (GPR) models were developed and optimized using Bayesian hyperparameter tuning and k-fold cross-validation. The adapted models achieved RMSE of 0.039 m³/m³ (ELR) and 0.047 m³/m³ (GPR), with corresponding coefficients of determination R² = 0.81 and R² = 0.72, respectively. Minimal bias (0.001) and strong agreement between predicted and observed values were confirmed through Bland-Altman and Q-Q plots. This framework enhances the scalability and generalizability of SSM retrieval, offering practical applications in drought monitoring, flood prediction, and irrigation management. The study highlights the effectiveness of machine learning and domain adaptation techniques in advancing large-scale soil moisture mapping.

THE TOPIC IS INTERESTING BUT THERE ARE SEVERAL ASPECTS THAT SHOULD BE FURTHER ADDRESSED AND EXPLAINED. SOME OBSERVATIONS ARE GIVEN BELOW. The abstract seems to contain too much background information from the existing work which is not much suitable...

STRENGTHS: The abstract effectively highlights the relevance of the study and the system's overall benefits. It clearly defines the research's objective, i.e., the development and evaluation of a cost-effective soil monitoring and irrigatio...