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Regional-Scale Topsoil Organic Matter Estimation Based on a Geographic Detector Model Using Landsat Data, Pingtan Island, Fujian, China

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  • Junjun Fang

    (College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China
    State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xiaomei Li

    (College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350117, China)

  • Jinming Sha

    (College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China)

  • Taifeng Dong

    (Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave, Ottawa, ON K1A 0C6, Canada)

  • Jiali Shang

    (Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave, Ottawa, ON K1A 0C6, Canada)

  • Eshetu Shifaw

    (College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China
    Geography and Environmental Studies, Wollo University, Dessie P.O. Box 1145, Ethiopia)

  • Yung-Chih Su

    (College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China)

  • Jinliang Wang

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China)

Abstract

Understanding the spatial distribution of soil organic matter (SOM) is important for land use management, but conventional sampling methods require significant human and financial resources. How to map SOM and monitor its changes using a limited number of sample points combined with remote sensing techniques that provide long-time series data is crucial. This study aimed to generate a regional-scale near-surface SOM map using 70 soil samples and covariate environmental factors extracted mainly from Landsat 8 OLI. Firstly, the sensitivity of each environmental factor to SOM was tested using a geographic detector model (GDM). Secondly, the tested factors were selected for modeling and mapping by ordinary least squares (OLS) and geographically weighted regression kriging (GWRK). The performance of these two models was compared. Finally, the mapping results of the better model (GWRK) were compared and analyzed with the traditional interpolation results based solely on sampling points to verify the rationality of the proposed method. The results show that three environmental factors, ratio vegetation index (RVI), differential vegetation index (DVI), and terrain roughness (TR), have a strong influence on the spatial variability of SOM. Using these three factors in combination with the GWRK method, a more accurate and refined spatial distribution map of SOM can be obtained. Comparing the SOM maps of GWRK and the traditional interpolation method, the results show that the accuracy of GWRK (R 2 = 0.405; mean absolute error = 0.637, and root mean square error = 0.813) is higher than that of traditional interpolation methods (R 2 = 0.291, MAE = 0.609, and RMSE = 0.863). The spatial recognition rate (fineness) of SOM patches at all levels using the GWRK method increased by more than 73 times compared to the traditional kriging. We conclude that the combination of limited SOM samples, environmental variables, GDM, and GWRK is a pragmatic approach for estimating regional-scale SOM.

Suggested Citation

  • Junjun Fang & Xiaomei Li & Jinming Sha & Taifeng Dong & Jiali Shang & Eshetu Shifaw & Yung-Chih Su & Jinliang Wang, 2023. "Regional-Scale Topsoil Organic Matter Estimation Based on a Geographic Detector Model Using Landsat Data, Pingtan Island, Fujian, China," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8511-:d:1154391
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    References listed on IDEAS

    as
    1. Shifaw, Eshetu & Sha, Jinming & Li, Xiaomei & Bao, Zhongcong & Zhou, Zhenglong, 2019. "An insight into land-cover changes and their impacts on ecosystem services before and after the implementation of a comprehensive experimental zone plan in Pingtan island, China," Land Use Policy, Elsevier, vol. 82(C), pages 631-642.
    2. Min Fu & Lixin Tian & Gaogao Dong & Ruijin Du & Peipei Zhou & Minggang Wang, 2016. "Modeling on Regional Atmosphere-Soil-Land Plant Carbon Cycle Dynamic System," Sustainability, MDPI, vol. 8(4), pages 1-18, March.
    3. Zhongqi Zhang & Dongsheng Yu & Xiyang Wang & Yue Pan & Guangxing Zhang & Xuezheng Shi, 2018. "Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China," Sustainability, MDPI, vol. 10(7), pages 1-12, July.
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