A Leaf Area Index dataset retrieved by benchmark-driven machine learning framework from Chinese Fengyun-3B VIRR data

Leaf Area Index (LAI) serves as a key biophysical parameter for characterizing vegetation canopy structure and ecosystem functions. To address the absence of LAI products for the Fengyun-3B (FY-3B) satellite and the limitations of current satellite LAI products, this study proposes an LAI retrieval framework integrating a high-quality benchmark library with machine learning from Fengyun-3B Visible and Infra-Red Radiometer (VIRR) Data. Under rigorous quality control, we constructed a long-term, high-quality LAI benchmark dataset covering Asia by spatiotemporally fusing and screening the MODIS and GEOV2 products. Random Forest, XGBoost, and MLP regression models were trained and optimized for specific vegetation types, generating an 8-day composite 0.01º LAI product for Asia from 2011 to 2020. Validation against in-situ measurements, MODIS, and GEOV2 LAI products indicates that: (1) the accuracy of the FY-3B LAI product (R = 0.581, RMSE = 1.307) showed improved performance compared to MODIS (R = 0.484, RMSE = 1.831) and GEOV2 (R = 0.391, RMSE = 1.920); and (2) the product achieves seamless spatiotemporal coverage. This dataset provides robust support for ecosystem monitoring in Asia and contributes to the construction of a diversified, multisource, synergistic global satellite observation system. This dataset is publicly available at https://doi.org/10.5281/zenodo.18217405.