Mean Annual Forest Gross Primary Productivity in China (1990-2018)

<p dir="ltr">The mean annual forest GPP in China from 1990 to 2018 (gC m^-2 y^-1) was calculated using a random forest model, which had a R² of 0.73, a mean absolute error (MAE) of 208.13, and a root mean square error (RMSE) of 325.97, based on the variables listed in Table 1. All the datasets were extracted from open data sources available in the National Tibetan Plateau Scientific Data Center (<a href="https://data.tpdc.ac.cn/" target="_blank">https://data.tpdc.ac.cn/</a>). All the raster data was upscaled to 0.1º with a bilinear resample function and used a WGS 1984 World Mercator projection.</p><p dir="ltr">The dataset has the following files:</p><p dir="ltr">· Shapefile with dependent (mean forest GPP) and independent variables for China between 1990 and 2018 using data described in Table 1.</p><p dir="ltr">· Tif file with mean forest GPP for China between 1990 and 2018 using data from (<a href="https://www.sciencedirect.com/science/article/pii/S0959652625009667#bib73" target="_blank">Wang et al., 2021</a>).</p><p><br></p><p dir="ltr">Details about the methodology to build this dataset can be found in:</p><p dir="ltr">Zhu, C., Wang, G., Shao, Y., Dai, W., Liu, Q., Wang, S., Costa, A. C., & Cabral, P. (2025). Disentangling Gross Primary Productivity drivers of forested areas in China and its climate zones from 1990 to 2018. <i>Journal of Cleaner Production</i>, 145616. <a href="https://doi.org/10.1016/j.jclepro.2025.145616 " rel="noreferrer" target="_blank">https://doi.org/10.1016/j.jclepro.2025.145616 </a></p><p dir="ltr"><br></p><p dir="ltr">Table 1. Variables used in the Random Forest Model</p><table><tr><td><p dir="ltr"><b>Variable</b></p></td><td><p dir="ltr"><b>Units</b></p></td><td><p dir="ltr"><b>Original data source</b></p></td></tr><tr><td><p dir="ltr">Mean Forest Gross Primary Productivity (dependent variable)</p></td><td><p dir="ltr">gC m^-2 d^-1</p></td><td><p dir="ltr">(Wang et al., 2021)</p></td></tr><tr><td><p dir="ltr">Temperature</p></td><td><p dir="ltr">ºC</p></td><td><p dir="ltr">(Peng et al., 2019)</p></td></tr><tr><td><p dir="ltr">Precipitation</p></td><td><p dir="ltr">0.1mm</p></td><td><p dir="ltr">(Peng et al., 2019)</p></td></tr><tr><td><p dir="ltr">Downward shortwave radiation</p></td><td><p dir="ltr">W m⁻²</p></td><td><p dir="ltr">(He et al., 2020)</p></td></tr><tr><td><p dir="ltr">Soil moisture</p></td><td><p dir="ltr">m³ m⁻³</p></td><td><p dir="ltr">(Zhang et al., 2024)</p></td></tr><tr><td><p dir="ltr">Nighttime light</p></td><td><p dir="ltr">Digital Number</p></td><td><p dir="ltr">(L. Zhang et al., 2024)</p></td></tr><tr><td><p dir="ltr">Forest fragmentation index</p></td><td><p>[0, 1]</p></td><td><p dir="ltr">Derived from landcover map (Yang & Huang, 2021)</p></td></tr><tr><td><p dir="ltr">Digital Elevation Model (DEM)</p></td><td><p dir="ltr">m</p></td><td><p dir="ltr">(CGIAR-CSI, 2022)</p></td></tr><tr><td><p dir="ltr">Aspect</p></td><td><p dir="ltr">Degree</p></td><td><p dir="ltr">Derived from DEM(CGIAR-CSI, 2022)</p></td></tr><tr><td><p dir="ltr">Slope</p></td><td><p dir="ltr">Degree</p></td><td><p dir="ltr">Derived from DEM(CGIAR-CSI, 2022)</p></td></tr><tr><td><p dir="ltr">Climate zones (vector file)</p></td><td><p><br></p></td><td><p dir="ltr">(Kottek et al., 2006)</p></td></tr></table><p dir="ltr"><br></p><p dir="ltr"><b>References</b></p><p dir="ltr">CGIAR-CSI. (2022). SRTM DEM dataset in China (2000). In <i>National Tibetan Plateau Data Center</i>. National Tibetan Plateau Data Center. https://dx.doi.org/</p><p dir="ltr">He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., & Li, X. (2020). The first high-resolution meteorological forcing dataset for land process studies over China. <i>Scientific Data</i>, <i>7</i>(1), 25. https://doi.org/10.1038/s41597-020-0369-y</p><p dir="ltr">Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. <i>Meteorologische Zeitschrift</i>, <i>15</i>(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130</p><p dir="ltr">Peng, S., Ding, Y., Liu, W., & Li, Z. (2019). 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. <i>Earth System Science Data</i>, <i>11</i>(4), 1931–1946. https://doi.org/10.5194/essd-11-1931-2019</p><p dir="ltr">Wang, S., Zhang, Y., Ju, W., Qiu, B., & Zhang, Z. (2021). Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. <i>Science of The Total Environment</i>, <i>755</i>, 142569. https://doi.org/10.1016/j.scitotenv.2020.142569</p><p dir="ltr">Yang, J., & Huang, X. (2021). The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. <i>Earth System Science Data</i>, <i>13</i>(8), 3907–3925. https://doi.org/10.5194/essd-13-3907-2021</p><p dir="ltr">Zhang, K., Chen, H., Ma, N., Shang, S., Wang, Y., Xu, Q., & Zhu, G. (2024). A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020. <i>Scientific Data</i>, <i>11</i>(1), 445. https://doi.org/10.1038/s41597-024-03271-7</p><p dir="ltr">Zhang, L., Ren, Z., Chen, B., Gong, P., Xu, B., & Fu, H. (2024). A Prolonged Artificial Nighttime-light Dataset of China (1984-2020). <i>Scientific Data</i>, <i>11</i>(1), 414. https://doi.org/10.1038/s41597-024-03223-1</p><p dir="ltr"><br></p>