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

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, RMSE, and R2 values of 0.26, 0.76, and 0.71, respectively. Meanwhile, MSDI LAI exhibited a similar spatial distribution to MODIS LAI while providing substantially enhanced spatial detail and accuracy. SHAP analysis revealed that red-edge (RE) and shortwave-infrared (SWIR) bands contributed the most to LAI prediction, consistent with their sensitivity to vegetation canopy biophysical properties. Overall, this study highlights the importance of retrieval strategy optimization and model interpretability for improving prior-guided decametric-resolution LAI estimation and offers practical guidance for generating consistent LAI estimations across various scales.