Dataset on Spatiotemporal Variation Trends of NPP in Southeastern Tibet (2000–2021)

This dataset presents the spatiotemporal trends of forest Net Primary Productivity (NPP) in southeastern Tibet from 2000 to 2021. The spatial coverage encompasses southeastern Tibet (specifically Nyingchi, Chamdo, and Shannan cities), while the temporal scope spans the years 2000 through 2021. The data is provided in GeoTIFF (.tif) format. The raw NPP data was derived from the MOD17A3HGF V6 Net Primary Productivity product. The extent of forest types within the study area was extracted from the *Global 30 m Fine-Resolution Classification and Analysis of Land Cover Dynamics* (GLC_FCS30) product covering the period 1985–2020; this includes three categories: Evergreen Broadleaf Forest (EBF), Deciduous Broadleaf Forest (DBF), and Evergreen Needleleaf Forest (ENF). All data processing was conducted on the Google Earth Engine (GEE) platform.Regarding trend analysis methodology, this dataset employs the Theil-Sen slope estimator and the Mann-Kendall non-parametric trend test to analyze the pixel-wise NPP time series. The Theil-Sen slope is utilized to estimate the rate of NPP change: a slope greater than 0 indicates an upward trend in NPP over time, while a slope less than 0 indicates a downward trend. The Mann-Kendall trend test is applied to assess the statistical significance of long-term NPP changes; based on the Z-statistic, these changes are categorized into three classes: significant increase (Z > 1.65), no significant change, and significant decrease (Z < -1.65).Within the dataset, the numerical value assigned to each pixel represents a specific category of forest NPP change: 0 indicates a non-forest area; values ​​of 1, 2, -1, -2, and 10 indicate no significant change in NPP; values ​​of 3 and 4 indicate a significant increase in NPP; and values ​​of -3 and -4 indicate a significant decrease in NPP.