Spatiotemporally Seamless GLASS-Based Synthesis Vegetation Index for the Yellow River Basin

To comprehensively capture vegetation growth conditions, we developed a novel Synthesis Vegetation Index (SVI) by dynamically integrating four key indicators closely related to vegetation health and dynamics—greenness (NDVI), canopy structure (LAI), fractional vegetation cover (FVC), and productivity (NPP)—based on the spatiotemporally seamless GLASS long-term vegetation parameter dataset. To effectively capture spatial heterogeneity and enhance the ecological interpretability of the model, we propose a Geographically Weighted Synthesis (GWS) method. The GWS approach integrates spatial heterogeneity modeling with multivariate dynamic weighting, combining global trends with local adaptation. This method addresses the limitations of traditional approaches in handling spatial heterogeneity, ecological interpretability, and management applicability by dynamically adjusting weights based on local environmental and vegetation conditions. The dataset contains the monthly average of the Vegetation Synthesis Index (SVI) for the Yellow River Basin during the growing season (March to November) from 2000 to 2021. It is stored in .tif format with a spatial resolution of 500m × 500m, uses the WGS84 projection, and has a total size of 4.76 GB.