(1)We established linear relationships between GLASS LAI and MODIS NDVI products (MOD13A1/MYD13A1) through the following methodology: For each coincident scene of GLASS LAI, MOD13A1, and MYD13A1 NDVI data during 2014-2018, we selected temporally matched sample points, extracted corresponding pixel values, and developed LAI-NDVI regression models at 30m spatial resolution.

   (2) The Normalized Difference Vegetation Index (NDVI) was calculated using preprocessed red and near-infrared (NIR) bands from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) data.    (3) The Landsat-derived NDVI values were input into the pre-established LAI-NDVI linear regression model to estimate high-resolution Leaf Area Index (LAI) values.    (4) The three-period GLASS LAI data and two-period Landsat LAI data were input into the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to generate fused ESTARFM-LAI products with 8-day/30m spatiotemporal resolution.   (5) The 8-day/30m LAI product was temporally downscaled to daily (1d/30m) resolution using piecewise linear interpolation, generating continuous Leaf Area Index (LAI) time series while preserving the original spatial fidelity.

  The GLASS (Global LAnd Surface Satellite) LAI product exhibits superior accuracy and data integrity with lower uncertainty relative to other remote sensing-derived LAI products, establishing it as a high-accuracy reference dataset. Accordingly, we conducted a spatiotemporal feature comparison between our high spatiotemporal resolution LAI dataset (1-day/30m) and GLASS LAI to validate data precision.

  The high spatial and temporal resolution LAI shows more spatial details (e.g., small river valleys) than the original GLASS LAI, and the contour and texture features are more obvious, and the boundaries of the features are clearer. The high spatial and temporal resolution LAI reflects the different seasons well, and the LAI values change dynamically with the plant growth cycle in space, which shows that the LAI values in the study area are the lowest in winter (December, January, and February) - rising in spring (March, April, and May) - the highest in summer (June, July, and August) - and decreasing in fall (September, October, and November), and this change characteristic is most obvious in the eastern part of the study area where there is high vegetative cover. This variation was most obvious in the eastern part of the study area with higher vegetation cover. There is a significant linear relationship between the high temporal and spatial resolution LAI and the monthly and 8-day mean LAI values of the original GLASS LAI.