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

Junjun Fang, Xiaomei Li (), Jinming Sha (), Taifeng Dong, Jiali Shang, Eshetu Shifaw, Yung-Chih Su and Jinliang Wang
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Junjun Fang: College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, 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
Yung-Chih Su: College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China
Jinliang Wang: Faculty of Geography, Yunnan Normal University, Kunming 650500, China

Sustainability, 2023, vol. 15, issue 11, 1-18

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.

Keywords: digital soil organic matter mapping; influencing factors; geostatistics; geodetector; remote sensing; arenosols (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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