Leaf Area Index Estimation of Grassland Based on UAV-Borne Hyperspectral Data and Multiple Machine Learning Models in Hulun Lake Basin

作者：Difu Wu, Saru Bao, Yi Tong, Yifan Fan, L. Lu, Songtao Liu, Wenjing Li, Mengyong Xue, Bingshuai Cao, Quan Li, Muha Cha, Qian Zhang, Nan Shan · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17162914 · 被引用次数：5 · 研究领域：Remote Sensing and Land Use、Remote Sensing in Agriculture、Remote-Sensing Image Classification

Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments to achieving high-precision LAI retrieval. This study used hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to estimate LAI in a typical grassland, Hulun Lake Basin. Multiple machine learning (ML) models were constructed to reveal a relationship between hyperspectral data and grassland LAI using two input datasets, namely spectral transformations and vegetation indices (VIs), while SHAP (SHapley Additive ExPlanation) interpretability analysis was further employed to identify high-contribution features in the ML models. The analysis revealed that grassland LAI has good correlations with the original spectrum at 550 nm and 750 nm–1000 nm, first and second derivatives at 506 nm–574 nm, 649 nm–784 nm, and vegetation indices including the triangular vegetation index (TVI), enhanced vegetation index 2 (EVI2), and soil-adjusted vegetation index (SAVI). In the models using spectral transformations and VIs, the random forest (RF) models outperformed other models (testing R2 = 0.89/0.88, RMSE = 0.20/0.21, and RRMSE = 27.34%/28.98%). The prediction error of the random forest model exhibited a positive correlation with measured LAI magnitude but...