土壤学报2025，Vol.62Issue(1)：40-53,14.DOI:10.11766/trxb202311270498
四川盆地耕地表层土壤容重缺失数据填补方法
Methods of Filling in Bulk Density Gaps of Cropland Topsoil in the Sichuan Basin
摘要
Abstract
[Objective]This study aimed to construct a high precision prediction method for soil bulk density to accurately complete the regional soil attribute database.[Method]Based on the data of 2883 typical cropland samples in the Sichuan Basin(including Sichuan Province and Chongqing Municipality)obtained during the second national soil census,this study used correlation analysis,variance analysis,and regression analysis to reveal the statistical characteristics and main controlling factors of the cropland topsoil bulk density in the Sichuan Basin.The traditional pedotransfer functions(PTFs),multiple linear regression(MLR)models,radial basis function neural network(RBFNN)model,and random forest(RF)models were used to establish a soil bulk density prediction model through three modeling methods:whole region,by river basin and by soil type,to fill the missing value of soil bulk density.[Result]The results show that the cropland topsoil bulk density in the study area ranged from 0.60 to 1.71 g·cn-3,with a mean value of 1.29 g·cm-3.Soil organic matter,soil subgroup,and rainfall in summer were the most important factors influencing bulk density.The RBFNN model constructed by the river basin can better capture the nonlinear relationship between soil bulk density and the influencing factors and the spatial non-stationarity of this relationship.The coefficient of determination(R2)and root mean square error(RMSE)of the 432 independent validation samples were 0.519 and 0.095 g·cm-3,respectively,which were significantly better than those of other methods.[Conclusion]Therefore,the RBFNN prediction model constructed in sub-basin is helpful to improve the imputation accuracy of the missing values of topsoil bulk density in the Sichuan Basin,and also provides a method reference for the imputation of missing values of soil properties in other regions.关键词
土壤容重/传递函数/四川盆地/多元线性回归模型/径向基函数神经网络模型/随机森林模型Key words
Soil bulk density/Pedotransfer functions/Sichuan Basin/Multiple linear regression model/Radial basis function neural network model/Random forest model分类
农业科技引用本文复制引用
李艾雯,李文丹,宋靓颖,冉敏,陈丹,成金礼,齐浩然,郭聪慧,李启权..四川盆地耕地表层土壤容重缺失数据填补方法[J].土壤学报,2025,62(1):40-53,14.基金项目
四川省自然科学基金项目(2022NSFSC0104)资助 Supported by the Natural Science Foundation of Sichuan Province,China(No.2022NSFSC0104) （2022NSFSC0104）
