PROGRESS IN GEOGRAPHY ›› 2022, Vol. 41 ›› Issue (12): 2327-2341.doi: 10.18306/dlkxjz.2022.12.011

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Estimation of soil organic carbon and its uncertainty in Qinghai Province

ZHOU Peifang1,2,3(), ZHOU Tao1,2,3,*(), LIU Xia1,2,3, ZHANG Yajie1,2,3, XU Yixin1,2,3, LUO Hui1,2,3, YU Peixin1,2,3, ZHANG Jingzhou1,2,3   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
    2. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing NormalUniversity, Beijing 100875, China
    3. Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2022-03-07 Revised:2022-10-25 Online:2022-12-28 Published:2022-12-31
  • Contact: ZHOU Tao;
  • Supported by:
    The Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0405);National Natural Science Foundation of China(42277206)


Soil organic carbon plays a critical role in regional carbon balance. Quantifying the spatial pattern and dynamic changes of soil organic carbon can lay a solid foundation for a realistic assessment of the ecosystem carbon sink potential. Different methods and samples for the estimation of soil organic carbon can lead to dramatic differences and uncertainties in the estimated results, particularly in the Qinghai-Tibet Plateau area with complex terrain and sensitive to climate changes. In order to quantitatively assess differences of the soil organic carbon density spatial distribution pattern obtained by different methods in the Qinghai-Tibet Plateau area, this study used Qinghai Province as its research area, and collected data from 806 soil organic carbon density sampling sites in the province. Explanatory variables, including climate, vegetation, terrain, and soil conditions, and four different methods, including stepwise regression, inverse distance weighted interpolation, ordinary kriging interpolation, and random forest model, were used to investigate the surface (0-30 cm) soil organic carbon density spatial distribution in Qinghai Province and its influencing factors. The results suggest that the normalized difference vegetation index, photosynthetically active radiation, total nitrogen, annual mean temperature, elevation, annual precipitation, and net primary productivity are the major factors that influence the soil organic carbon density estimation. The average of the soil organic carbon density in Qinghai Province estimated by the four methods ranges from 5.14 to 5.62 kg C·m-2. But their variation range is significantly different, being 0.17-23.25, 0.34-46.61, 0.56-35.08, and 0.62-24.85 kg C·m-2, respectively. Simulation precision assessment revealed that the root-mean-square error of the results obtained by the four methods is 3.93, 3.37, 3.48, and 3.19 kg C·m-2, and their mean standard deviation is 0.12, 0.51, 0.61, and 0.27 kg C·m-2, respectively. Among the results, those obtained by the random forest model are relatively stable and of high precision, and can more accurately reflect the spatial distribution pattern of the soil organic carbon in Qinghai Province. The current products—SoilGrids250m 2.0 and HWSD v1.2—show relatively large differences in reflecting the distribution of the soil organic carbon in Qinghai Province. Comparatively, among these two soil carbon products, the results obtained by SoilGrids250m 2.0 and the random forest simulation results are more similar to each other.

Key words: soil organic carbon, random forest model, spatial interpolation, regression model, Qinghai Province