The time-series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.Using the MOD13Q1 and GIMMS NDVI 3g vegetation index datasets, combined with digital elevation models and other data sources, a random forest downscaling model was employed to generate a 250 m spatial resolution NDVI data product for the Tibetan Plateau from 1982 to 2020, which was subsequently validated and evaluated.Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7. This data can provide essential support for research on the grassland ecosystem of the Tibetan Plateau.
The GIMMS NDVI 3g product is from the AVHRR instrument positioned on the NOAA polar‐orbiting meteorological satellite (https://ecocast.arc.nasa.gov/data/pub/gimms). This product spans the period from 1982 to 2015 with a temporal resolution of 15 days and a spatial resolution of 8 km. It has been widely used globally, eliminating the effects of factors such as volcanic eruptions, solar zenith angle, and sensor sensitivity changes over time.
The MODIS NDVI data (MOD13Q1) originate from the NASA MODIS Land Product suite, developed using a unified algorithm for MODIS Vegetation Index products. The data set, with a temporal resolution of 16 days and a spatial resolution of 250 m, was downloaded from the MODIS Web (https://modis.gsfc.nasa.gov/). The product's time frame spans from February 28, 2000 to December 31, 2020.
The DEM data used in this study are provided by the Shuttle Radar Topography Mission (SRTM) operated by the National Geospatial‐Intelligence Agency and the National Aeronautics and Space Administration. The available options for spatial resolution is 90 m, with the latter being accessible at http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp.
The "overlay and allocate" method was used, integrating landcover-specific biomass maps with percent tree cover and landcover maps via a rule-based decision tree. Belowground biomass was estimated using root-to-shoot ratios. Uncertainty was propagated using error propagation techniques.
(1) Spatial integration and polygon attribute calculations using ArcGIS 10.0;
(2) Gap-filling with pedo-transfer functions (e.g., regression between BD and OC%);
(3) Extrapolation or default values for unsampled depths (e.g., C-horizon SOC density);
(4) Classification of pedons by soil type and region (North America/Eurasia) for mean SOC storage;
(5) Generation of vector polygon and raster formats (TIFF, NetCDF).
Data quality is controlled through pedon completeness, gap-filling methods, and extrapolation strategies. Statistical tests (t-test) validate class independence of SOC storage. Uncertainties include uneven spatial coverage (e.g., Greenland). All gap-filled or extrapolated pedons are flagged for user discretion.
| # | number | name | type |
| 1 | 2020YFA0608501 | Research on Arctic Terrestrial Environmental Change and Its Effects | National key R & D plan |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ARCTIC-CHANGE.DB7139.2026
10.12072/ncdc.arctic-change.db7139.2026
