Solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5P mission offers nearly global coverage with a fine spectral resolution for reliable SIF retrieval. However, the present satellite-derived SIF datasets are accessible only at coarse spatial resolutions, constraining its applications at fine scales. Here, we utilized a weighted stacking algorithm to generate a high spatial resolution SIF dataset (500 m, 8-day) in China (HCSIF) from 2000 to 2022 from the TROPOMI with a spatial resolution at a nadir of 3.5 km by 5.6–7 km. Our algorithm demonstrated high accuracy on validation datasets (R² = 0.87, RMSE = 0.057 mW/m²/nm/sr). The HCSIF dataset was evaluated against OCO-2 SIF, GOME-2 SIF tower-based measurements of SIF, and gross primary productivity (GPP) from flux towers. We expect this dataset can potentially advance the understanding of fine-scale terrestrial ecological processes, allowing for monitoring of ecosystem biodiversity and precise assessment of crop health, productivity, and stress levels in the long term.

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R²) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.