基于3种地理加权回归方法的安徽省土壤pH空间预测制图对比研究  

Comparison and analysis of spatial prediction and variability of soil pH in Anhui Province based on three kinds of geographically weighted regression

作　　者：陈宣强 赵明松[1,2,3] 卢宏亮 徐少杰 邱士其 胡克宏 Chen Xuanqiang;Zhao Mingsong;Lu Hongliang;Xu Shaojie;Qiu Shiqi;Hu Kehong(School of Geomatics,Anhui University of Science and Technology,Huainan 232001,Anhui,China;Key Laboratory of Aviation-Aerospace-Ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes,Huainan 232001,Anhui,China;Coal Industry Engineering Research Center of Collaborative Monitoring of Mining Area's Environment and Disasters,Huainan 232001,Anhui,China)

机构地区：[1]安徽理工大学空间信息与测绘工程学院,安徽淮南232001 [2]矿山采动灾害空天地协同监测与预警安徽省教育厅重点实验室,安徽淮南232001 [3]矿区环境与灾害协同监测煤炭行业工程研究中心,安徽淮南232001

出　　处：《地理科学》2023年第1期173-183,共11页Scientia Geographica Sinica

基　　金：国家自然科学基金项目(41501226);安徽省自然科学基金项目(2208085MD88);安徽省高校自然科学研究项目(KJ2015A034);安徽理工大学人才引进项目(ZY020)资助。

摘　　要：基于安徽省140个采样点的土壤pH数据,综合考虑土壤、地形、气候、生物等因子对土壤pH的影响,采用地理加权回归(Geographically Weighted Regression,GWR)、主成分地理加权回归(Principal Component Geographically Weighted Regression,PCA-GWR)和混合地理加权回归(Mixed Geographically Weighted Regression,M-GWR)3种模型对安徽省土壤pH空间分布进行建模预测,揭示环境因子对土壤pH的影响在空间上的差异,最后以多元线性回归模型(Multiple Linear Regression,MLR)为基准比较3种GWR模型的精度。研究表明:①安徽省土壤pH具有空间异质性,且集聚特征明显。②3种GWR模型中M-GWR模型略优,GWR、PCA-GWR和M-GWR的建模集调整后决定系数(Radj 2)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的Radj 2分别提升了23%、31%和35%。M-GWR生成的预测图在空间上过渡平滑,建模效果稳定,其预测结果表明安徽省淮河以北地区多为碱性土壤,长江以南多为中性或酸性土壤,符合“南酸北碱”特征。③GWR及其改进模型可以有效地预测土壤pH属性,反映环境因子在不同空间位置上对土壤pH的影响程度,而M-GWR兼具变量作用的全局性和和局部性,进而提升了模型解释能力,为大区域数字土壤制图提供了重要的参考方法。Geographically weighted regression(GWR),principal component geographically weighted regression(PCA-GWR)and mixed geographically weighted regression(M-GWR)were used to model and map the spatial distribution of soil pH in Anhui Province.Based on the soil pH data from 140 sampling sites in Anhui Province,and the effects of macroscopic factors such as soil,topography,climate and biology on soil pH were also taken into consideration.And then,the spatial distribution characteristics of the effects of environmental factors on soil pH were explored.Finally the accuracy of three GWR models was compared based on multiple linear regression model(MLR).The results showed that:Soil pH in Anhui Province has spatial heterogeneity and obvious agglomeration characteristics.Among the three GWR models,the M-GWR model is slightly better,and the modeling sets Radj 2 of GWR,PCA-GWR and M-GWR are 0.59,0.62 and 0.63,respectively.Compared with the MLR model,the Radj 2 of the three GWR models increases by 23%,31%and 35%,respectively.The prediction map generated by M-GWR is smooth in space,and the modeling effect is stable.The prediction results show that the area north of the Huaihe River in Anhui Province is mostly alkaline soil,and the south of the Yangtze River is mostly neutral or acidic soil,which accords with the characteristics of"southern acid and northern alkali".The results show that GWR and its improved model can effectively predict soil pH attributes and reflect the influence of environmental factors on soil pH in different spatial locations,while M-GWR has both global and local effects of variables,which improves the interpretation ability of the model and provides an important reference method for digital soil mapping in large areas.

关 键 词：土壤PH 地理加权回归 数字土壤制图 安徽省 

分 类 号：S159.9[农业科学—土壤学]