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This dataset was developed based on climate projection data under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), combined with historical temperature, precipitation, and NDVI observations with a spatial resolution of 1 km across the Qinghai-Tibet Plateau from 2000 to 2020. A multiple linear regression model was employed to characterize the relationship between climate factors and vegetation responses, enabling the generation of annual NDVI predictions from 2025 to 2100. To improve spatial accuracy, the original climate simulation data were spatially downscaled using bias correction and bilinear interpolation techniques. Modeling procedures, including NDVI simulation, error control, and regional masking under different scenarios, were implemented in MATLAB. The dataset covers the entire Qinghai-Tibet Plateau, over a 76-year period, with a spatial resolution of 1 km. It consists of 228 GeoTIFF raster files with a total volume of 2.43 GB. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Across all scenarios, R² values exceeded 0.72 and RMSE values remained below 0.13, indicating reliable data quality. This dataset features multi-scenario comparability, long-term temporal continuity, and high spatial detail, providing valuable support for a wide range of ecological and climate-related studies, including ecosystem service simulation, regional carbon sink estimation, and vegetation dynamics, with broad application potential.
ZHANG et al. (Fri,) studied this question.