A dataset of MuSyQ 500 m and 4-day spatiotemporally continuous leaf area index product (2000–2020)

The Leaf Area Index (LAI), defined as half the total green leaf surface area per unit ground area, is a key parameter for characterizing vegetation canopy structure and ecological function. This dataset provides a spatiotemporally continuous global LAI remote sensing product with a spatial resolution of 500 meters at a 4-day interval from 2000 to 2020. The product was generated based on MODIS reflectance data and moderate-to-high resolution land cover classification datasets. Specifically, dedicated retrieval algorithms were developed for water-vegetation mixed pixels and ecotone regions, significantly reducing the impact of surface heterogeneity on LAI estimation accuracy. Moreover, vegetation phenology information and meteorological data were incorporated to reconstruct the time series, thereby further improving the spatiotemporal continuity of the product. Validation against global in-situ LAI observations, as well as direct and indirect comparisons with MODIS and GLASS LAI products, demonstrate that the dataset performs reliably in heterogeneous land cover regions, with an overall Root Mean Square Error (RMSE) of 0.725 m²/m². This dataset has undergone strict quality control and standardization procedures, enabling direct application by end users. It provides reliable and consistent data support for studies of global vegetation dynamics, ecological monitoring, and related decision-making applications.