Published April 22, 2026 | Version v1
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Spatiotemporal dynamics and quantitative attribution of vegetation net primary productivity in China: climate change contributes more than human activities

Authors/Creators

  • 1. ROR icon Shanxi Agricultural University

Description

This repository contains the codes and processed datasets used to support the findings of the study titled “Spatiotemporal dynamics and quantitative attribution of vegetation net primary productivity in China: climate change contributes more than human activities.” It is designed to ensure reproducibility of analyses and results reported in the manuscript.

Specifically, the repository includes: (1) processed datasets used for spatiotemporal trend analysis, spatial autocorrelation analysis, Hurst exponent estimation, XGBoost-SHAP attribution framework, and the quantitative decomposition of contributions from climate change and human activities; and (2) scripts and code required to reproduce figures and analyses presented in the study.

No raw restricted or sensitive data are included in this repository. All datasets are derived from publicly available sources, with no access restrictions, embargoes, or permission requirements.

This repository enables reproducibility of the statistical analyses, machine learning modeling, and attribution results reported in the manuscript.                                                               

These datasets were used to analyze the spatiotemporal dynamics and quantitative attribution of vegetation net primary productivity in China from 2001 to 2022, including

(1) The NPP data (tif), MOD17A3HGF Version 6.1 product of NASA EOS/MODIS (https://lpdaac.usgs.gov/), is a spatial resolution of 500m and annual temporal resolution.

(2) The dataset of Planted and Natural Forests in China (tif), produced by Cheng et al. (2024) (https://www.3decology.org/2024/04/15/chinas-planted-forest-maps-from-1990-to-2020/) with a spatial resolution of 1km and a temporal resolution of every 5 years.

(3) The China Land Cover Dataset (CLCD) (tif) produced by Yang and Huang (2023) (https://doi.org/10.5281/ZENODO.8176941) with a spatial resolution of 30 m and annual temporal resolution.

(4) The Precipitation and Temperature datasets (nc) produced by Peng et al. (2019) (https://doi.org/10.5194/essd-11-1931-2019) with a spatial resolution of 1 km and monthly temporal resolution.

(5) The SRTM DEM data (tif) produced by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/) with a spatial resolution of 90 m.

(6) The global GDP dataset (tif) produced by Kummu et al. (2025) (https://doi.org/10.5281/zenodo.10976733) with a spatial resolution of 5arcmin and annual temporal resolution.

(7) The global Population dataset (tif) produced by the Oak Ridge National Laboratory (https://landscan.ornl.gov/) with a spatial resolution of 1 km and annual temporal resolution.

(8) The global Human Footprint dataset (tif) produced by Mu et al. (2021) (https://doi.org/10.6084/m9.figshare.16571064.v8) with a spatial resolution of 1km and annual temporal resolution.

(9) The Vapor Pressure Deficit (VPD) data (nc) produced by the TerraClimate dataset (https://climate.northwestknowledge.net/TERRACLIMATE/) (Abatzoglou et al., 2018) with a spatial resolution of 4.6km and annual temporal resolution.

(10) The Root-Zone Soil Moisture data (nc) produced by Miralles et al. (2025) (https://doi.org/10.5281/zenodo.14056593) with a spatial resolution of 0.1° and annual temporal resolution.

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Additional details

References

  • Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C., 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191. https://doi.org/10.1038/sdata.2017.191
  • Cheng, K., Yang, H., Tao, S., Su, Y., Guan, H., Ren, Y., Hu, T., Li, W., Xu, G., Chen, M., Lu, X., Yang, Z., Tang, Y., Ma, K., Fang, J., Guo, Q., 2024. Carbon storage through china's planted forest expansion. Nat. Commun. 15, 4106. https://doi.org/10.1038/s41467-024-48546-0
  • Kummu, M., Kosonen, M., Masoumzadeh Sayyar, S., 2025. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Sci. Data 12, 178. https://doi.org/10.1038/s41597-025-04487-x
  • Miralles, D.G., Bonte, O., Koppa, A., Baez-Villanueva, O.M., Tronquo, E., Zhong, F., Beck, H.E., Hulsman, P., Dorigo, W., Verhoest, N.E.C., Haghdoost, S., 2025. GLEAM4: global land evaporation and soil moisture dataset at 0.1° resolution from 1980 to near present. Sci. Data 12, 416. https://doi.org/10.1038/s41597-025-04610-y
  • Mu, H., Li, X., Wen, Y., Huang, J., Du, P., Su, W., 2021. An annual global terrestrial Human Footprint dataset from 2000 to 2018. figshare. https://doi.org/10.6084/m9.figshare.16571064.v8
  • Peng, S., Ding, Y., Liu, W., Li, Z., 2019. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data 11, 1931–1946. https://doi.org/10.5194/essd-11-1931-2019
  • Yang, J., Huang, X., 2023. The 30 m annual land cover datasets and its dynamics in China from 1985 to 2022. https://doi.org/10.5281/ZENODO.8176941