A dataset of 5-day GPP with a resolution of 30 meter for the Hulunbuir grasslands in Inner Mongolia (2023)

<p indent="0mm">Gross Primary Productivity (GPP) is a key indicator of ecosystem productivity, reflecting the amount of organic matter generated through photosynthesis. A decline in GPP often indicates ecological degradation. To enable precise monitoring and quantification of GPP, this study developed a dataset of 5-day GPP with a resolution of 30 meter, based on Landsat-Sentinel harmonized data (HLS), ERA5-Land meteorological reanalysis data, and global grassland flux site data. First, the Savitzky-Golay filter was applied to reconstruct the time series of the Normalized Difference Vegetation Index (NDVI), resulting in continuous and seamless vegetation dynamic data. Subsequently, four GPP inversion models (MOD17, CFIX, CILUE, and EC-LUE) were compared to assess their performance in simulating GPP in the Hulunbuir grassland ecosystem. The results showed that the improved EC-LUE model outperformed others, achieving a correlation coefficient of 0.80 between model simulations and flux station observations. Based on this model, a GPP dataset with a high spatiotemporal resolution was generated for the Hulunbuir grasslands from March to October 2023. This dataset provides valuable support for dynamic monitoring of grassland ecosystems, carbon cycle research, grassland resource management, and livestock applications.