Spatial Prediction and Influencing Factors Analysis of Soil Salinization in Coastal Area Based on MGWR

被引：0
|
作者：
Song Y. [1 ]
Gao M.-X. [1 ]
Wang J.-F. [2 ]
Xu Z.-X. [3 ]
机构：
[1] College of Resources and Environment, Shandong Agricultural University, Tai’an
[2] National Agricultural Machinery and Equipment Innovation Center, Luoyang
[3] Shandong Luyan Agricultural Co.，Ltd., Jinan
来源：
Huanjing Kexue/Environmental Science | 2024年 / 45卷 / 07期
关键词：
digital soil mapping; geographically weighted regression（GWR）; Hekou District; influencing factors; soil salinization;
D O I：
10.13227/j.hjkx.202307195
中图分类号：
学科分类号：
摘要：
Quantitative analysis of the spatial non-stationary characteristics of soil salinization influencing factors and the prediction of its spatial distribution are of great significance for the rational use of coastal saline soil resources and the formulation of local prevention and control measures. In this study，the Hekou District of Dongying City，Shandong Province，was used as the study area，and the descriptive statistics of soil salinization status were conducted using classical statistical methods. Spatial autocorrelation theory was used to explore the characteristics of global and local spatial structure of soil salinization in the study area. Influential factors related to soil salinity were selected，and multivariate linear regression （MLR），geographically weighted regression（GWR），and multi-scale geographically weighted regression（MGWR）methods were used to model and predict the spatial distribution of soil salinity in the study area and to analyze the spatial heterogeneity of the effects of different influencing factors on soil salinity. The results showed that：① The mean value of soil salinity in the study area was 5.84 g·kg−1，indicating severe salinization，with a global Moran’s I index of 0.19（P<0.00）and obvious spatial aggregation characteristics. ② Among the three models，the MGWR model had the highest modeling accuracy. Compared with that of the MLR model，the R2adj of GWR and MGWR improved by 0.05 and 0.07，respectively，and the RSS decreased by 210.13 and 179.95，respectively. ③ The results of MGWR regression showed that the spatial distribution of soil salinity appeared to be mainly affected by the middle soil salinity，soil clay content，and vegetation cover from the mean values of standardized regression coefficients of different influencing factors. Different influencing factors had significant spatial non-stationary characteristics on soil salinization. ④ The results of the spatial distribution prediction of soil salinity in MGWR showed that the areas of high soil salinity（≥6 g·kg−1）were mainly distributed in the northern part of the study area，with an overall spatial trend of decreasing from the coast to the interior. The results of the study can be used as a reference for the analysis and predictive mapping of factors affecting soil salinization in the county and on a larger scale using MGWR. © 2024 Science Press. All rights reserved.
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页码：4293 / 4301
页数：8
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