[Spatial Prediction Modeling for Soil pH Based on Multiscale Geographical Weighted Regression(MGWR) and Its Influencing Factors]

Ming-Song Zhao; Xuan-Qiang Chen; Shao-Jie Xu; Shi-Qi Qiu; Shi-Hang Wang

doi:10.13227/j.hjkx.202212031

[Spatial Prediction Modeling for Soil pH Based on Multiscale Geographical Weighted Regression(MGWR) and Its Influencing Factors]

Huan Jing Ke Xue. 2023 Dec 8;44(12):6909-6920. doi: 10.13227/j.hjkx.202212031.

[Article in Chinese]

Authors

Ming-Song Zhao^{1

2

3}, Xuan-Qiang Chen^{1

2

3}, Shao-Jie Xu¹, Shi-Qi Qiu¹, Shi-Hang Wang^{1

2

3}

Affiliations

¹ School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China.
² Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Huainan 232001, China.
³ Coal Industry Engineering Research Center of Collaborative Monitoring of Mining Area's Environment and Disasters, Huainan 232001, China.

PMID: 38098414
DOI: 10.13227/j.hjkx.202212031

Abstract

Anhui, Henan, Jiangsu, and Shandong provinces were selected as the study area. A total of 599 soil samples and nine environmental factors of soil pH were collected. The spatial distribution of soil pH was modeled based on multi-scale geographically weighted regression(MGWR), mixed geographically weighted regression(Mixed GWR), geographically weighted regression(GWR), and multiple linear regression(MLR) models. Then, the spatial difference in the effect of environmental factors on soil pH was revealed using MGWR and quantile regression models. The results showed that:① soil pH showed significant global and local spatial autocorrelation at different spatial distances, and the clustering characteristics were obvious. ② The MGWR model was the best among the four models, and the R_adj² of MGWR, Mixed GWR, GWR, and MLR were 0.64, 0.62, 0.59, and 0.48, respectively. The residual of MGWR had the strongest independent distribution and the weakest spatial autocorrelation with a global Moran's I of 0.07. ③ Three types of GWR predictions showed that the spatial distribution of soil pH decreased gradually from north to south in the study area, with the highest in northern Henan and the lowest in southern Anhui. ④ MGWR modeling results showed that there was strong spatial heterogeneity of mean annual precipitation(MAP), multi-resolution valley bottom flatness(MRVBF), and elevation affecting soil pH. MAP had a stronger effect on soil pH in northern Jiangsu and most parts of Shandong. The positive effect of MRVBF on soil pH was stronger in northern Jiangsu and western Shandong. The negative effect of elevation on soil pH was stronger in northern and central Jiangsu. ⑤ The quantile regression analysis showed that the mean annual precipitation had a significant negative effect on soil pH at different quantile levels of soil pH, and influence intensity decreased with the increase in pH quantile level. MRVBF had a significant negative effect on soil pH at a low quantile level(θ=0.1 to 0.4) but had no significant effect on soil pH at a high quantile level(θ=0.5 to 0.9). These results can provide an important reference for mapping soil properties and analyzing its influence factors based on the MGWR model in large regions.

Keywords: digital soil mapping; mixed geographically weighted regression; multi-scale geographically weighted regression(MGWR); quantile regression; soil pH.

Publication types

English Abstract