Validation of Satellite-Derived Green Canopy Cover in Rubber 2 Plantations: Integrating UAV, Ground Observations, and Machine Learning for Monitoring Leaf Fall Dynamics

Accurate estimating green canopy cover in rubber plantations is crucial for monitoring vegetation health and assessing stress impacts. This study validates satellite-derived canopy cover estimates by integrating UAV-based measurements, ground observations, remote sensing, and machine learning approaches. Sentinel-2 and Landsat imagery were utilized to derive spectral vegetation indices (SVIs) under varying stress conditions, while UAV-based canopy cover assessments provided high-resolution reference data for validation. The findings revealed that while certain SVIs exhibited strong cor-relations with canopy density under stable conditions, their predictive accuracy declined significantly during extreme stress events, such as Pestalotiopsis outbreaks and seasonal leaf fall periods. To improve estimation accuracy, machine learning models were developed, with Random Forest (RF) outperforming Support Vector Machines (SVM), Classification and Regression Trees (CART), and Linear Regression (LR). RF achieved the highest predictive accuracy (R² = 0.82, RMSE = 6.48, MAE = 4.97), demonstrating its reliability in capturing non-linear interactions between canopy heterogeneity and environmental stressors. These results highlight the limitations of traditional vegetation indices and emphasize the importance of multi-sensor integration and advanced modeling techniques for more precise canopy monitoring. This study contributes to developing robust remote sensing frameworks for early stress detection and sustainable plantation management in tropical rubber ecosystems.