Refined Leaf Area Index Retrieval in Yellow River Delta Coastal Wetlands: UAV-Borne Hyperspectral and LiDAR Data Fusion and SHAP–Correlation-Integrated Machine Learning

Abstract

Highlights: What are the main findings? A SHAP–correlation feature selection strategy (aggregated mean absolute SHAP values with Pearson analysis) enhanced robustness and identified critical predictive variables. Multi-source feature fusion significantly improved LAI retrieval accuracy across models, and LAI showed a distinct coastal-to-inland spatial gradient. What are the implications of the main findings? Fusing hyperspectral and LiDAR with SHAP–correlation selection provides a robust, generalizable pathway for high-precision LAI mapping in heterogeneous wetlands. The mapped coastal–inland LAI gradient offers a quantitative basis for ecological assessment, supporting vegetation succession monitoring, and water–salt regulation practices in the Yellow River Delta. The leaf area index (LAI) serves as a critical parameter for assessing wetland ecosystem functions, and accurate LAI retrieval holds substantial significance for wetland conservation and ecological monitoring. To address the spatial constraints of traditional ground-based measurements and the limited accuracy of single-source remote sensing data, this study utilized unmanned aerial vehicle (UAV)-borne hyperspectral and LiDAR sensors to acquire high-quality multi-source remote sensing data of coastal wetlands in the Yellow River Delta. Three machine learning algorithms—random forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were employed for LAI retrieval modeling. A total of 38 vegetation indices (VIs) and 12-point cloud features (PCFs) were extracted from hyperspectral imagery and LiDAR point cloud data, respectively. Pearson correlation analysis and the Shapley Additive Explanations (SHAP) method were integrated to identify and select the most informative VIs and PCFs. The performance of LAI retrieval models built on single-source features (VIs or PCFs) or multi-source feature fusion was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). The main findings are as follows: (1) Multi-source feature fusion significantly improved LAI retrieval accuracy, with the RF model achieving the highest performance (R2 = 0.968, RMSE = 0.125). (2) LiDAR-derived structural metrics and hyperspectral-derived vegetation indices were identified as critical factors for accurate LAI retrieval. (3) The feature selection method integrating mean absolute SHAP values (|SHAP| values) with Pearson correlation analysis enhanced model robustness. (4) The intertidal zone exhibited pronounced spatial heterogeneity in the vegetation LAI distribution.