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基于MGWR的滨海区土壤盐渍化分布空间预测及影响因素分析
摘要点击 395  全文点击 104  投稿时间:2023-07-23  修订日期:2023-10-16
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中文关键词  土壤盐渍化  地理加权回归(GWR)  影响因素  数字土壤制图  河口区
英文关键词  soil salinization  geographically weighted regression(GWR)  influencing factors  digital soil mapping  Hekou District
作者单位E-mail
宋颖 山东农业大学资源与环境学院, 泰安 271018 yings9821@163.com 
高明秀 山东农业大学资源与环境学院, 泰安 271018 mxgao@sdau.edu.cn 
王佳凡 国家农机装备创新中心, 洛阳 471934  
徐帻欣 山东鲁研农业良种有限公司, 济南 250100  
中文摘要
      定量分析土壤盐渍化影响因素的空间非平稳性特征,预测其空间分布,对于合理利用滨海盐渍土资源和制定局部针对性防控措施具有重要意义. 以山东省东营市河口区为研究区,通过经典统计学方法对土壤盐渍化状况进行描述性统计分析;利用空间自相关理论探讨研究区土壤盐渍化全局与局部空间结构特征;选取与土壤盐渍化相关的影响因素,采用多元线性回归(MLR)、地理加权回归(GWR)和多尺度地理加权回归(MGWR)方法对研究区土壤盐渍化空间分布进行建模预测,分析不同影响因素与土壤盐渍化的空间非平稳性特征. 结果表明:① 研究区土壤含盐量均值为5.84 g·kg-1,整体表现为重度盐渍化,全局Moran's I指数为0.19 (P<0.00),空间聚集特征明显;② 3种模型中,MGWR模型建模精度最高. 与MLR模型相比,GWR和MGWR的Radj2分别提高了0.05和0.07,RSS分别减少210.13和179.95;③ MGWR回归结果表明,从不同影响因素标准化回归系数均值看,表层土壤盐渍化空间分布主要受中层土壤含盐量、黏粒含量和植被覆盖度影响. 不同影响因素对土壤盐渍化的空间非平稳性特征较为显著;④ MGWR土壤盐渍化空间分布预测结果表明,土壤含盐量高值区域(≥6 g·kg-1)主要分布于研究区北部,空间上整体呈现从沿海向内陆降低的趋势. 研究结果可为县区及更大范围利用MGWR开展土壤盐渍化影响因素分析与预测制图提供参考.
英文摘要
      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 Radj2 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.

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