Time-series high spatio-temporal resolution vegetation leaf area index estimation based on NDVI trends

• Developed a new data assimilation framework to retrieve time-series high spatio-temporal resolution LAI. • The method reduces the patch effects from data scale differences in LAI estimation. • Used NDVI to construct background field reflecting vegetation trends, reducing reliance on existing LAI products. • Generated 2016-2022 high spatio-temporal resolution LAI dataset for Saihanba Forestry Center, China. Vegetation leaf area index (LAI) data with continuous spatio-temporal coverage are crucial for monitoring vegetation growth, predicting crop yields, and studying global environmental changes. However, due to limitations of recent satellite observations and retrieval algorithms, the prevailing operational LAI product is of low spatial resolution, which is insufficient to support fine-scale detection of land surface vegetation changes. To overcome these challenges, a novel data assimilation approach, the ENKF-NDVI (Ensemble Kalman Filter-NDVI) model, was developed to estimate LAI data with 10-m spatial and 5-day temporal resolution. Time-series normalized difference vegetation index (NDVI) data with 30-m spatial and 5-day temporal resolution were used instead of 500-m resolution LAI data to depict vegetation change trends and solve the patch effects caused by the scale differences. The LAI time series from 2016 to 2022 with high spatio-temporal resolution for the Saihanba Forestry Center (SFC) in China was generated. Validation with ground-based measurements indicates a high accuracy with R 2 of 0.85 and RMSE of 0.39. At the regional scale, the results agree well with other products, and mitigate the patch effects. The proposed algorithm can be used in mapping large-scale, high spatio-temporal resolution LAI data, providing valuable references for forest planning and management in the SFC and globally.