An Interpretable Multi-Source Data Integration Framework for Prior-Guided Decametric-Resolution LAI Estimation

作者：Meng Ke, Zhewei Zhang, Qi Wang, Tongzhou Wu, SONG Zhubeijia, Haodong Wei, C W Wang, Gaofei Yin, Baodong Xu · 发表于：Remote Sensing · 年份：2026 · DOI：10.3390/rs18132137 · 研究领域：Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics、Remote Sensing and LiDAR Applications

Decametric-resolution leaf area index (LAI) is an essential parameter for fine-scale crop growth monitoring and ecosystem modeling. Prior-guided approaches using existing hectometric-resolution LAI products have demonstrated potential in large-scale decametric-resolution LAI estimation. However, within such approaches, the impacts of algorithm selection and band combination on retrieval accuracy remain insufficiently quantified, and the lack of model interpretability limits methodological transferability. To address these challenges, a multi-source data integration (MSDI) framework is developed to systematically assess the sensitivity of prior-guided LAI estimation to retrieval algorithms and spectral bands using Sentinel-2 imagery. In addition, Shapley Additive Explanations (SHAP) is employed to quantify the contributions of individual bands and interpret model behavior. The MSDI LAI was evaluated using ground LAI measurements and compared with Simplified Level 2 Product Prototype Processor (SL2P)-derived LAI and MODIS LAI products. The results indicated that Support Vector Regression (SVR) achieved the best performance in LAI estimation among six machine learning algorithms, likely due to its robustness in modeling nonlinear relationships across different training samples. Band optimization further reduced estimation uncertainty by >24% and increased R2 by >44% for SVR-derived LAI estimates. Moreover, MSDI outperformed SL2P, especially at 20 m resolution, with Bias, R...