Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale  

作　　者：Mohammad Sajjad GHAVAMI Shamsollah AYOUBI Mohammad Reza MOSADDEGHI Salman Naimi 

机构地区：[1]Department of Soil Science,College of Agriculture,Isfahan University of Technology,Isfahan 8415683111,Iran

出　　处：《Journal of Mountain Science》2023年第10期2975-2992,共18页山地科学学报（英文）

基　　金：the Iranian National Science Foundation(INSF)for the financial support of this research under Project Number 4004169;Isfahan University of Technology。

摘　　要：Knowledge about the spatial distribution of the soil physical and mechanical properties is crucial for soil management,water yield,and sustainability at the watershed scale;however,the lack of soil data hinders the application of this tool,thus urging the need to estimate soil properties and consequently,to perform the spatial distribution.This research attempted to examine the proficiency of three machine learning methods(RF:Random Forest;Cubist:Regression Tree;and SVM:Support Vector Machine)to predict soil physical and mechanical properties,saturated hydraulic conductivity(Ks),Cohesion measured by fall-cone at the saturated(Psat)and dry(Pdry)states,hardness index(HI)and dry shear strength(SS)by integrating environmental variables and soil features in the Zayandeh-Rood dam watershed,central Iran.To determine the best combination of input variables,three scenarios were examined as follows:scenarioⅠ,terrain attributes derivative from a digital elevation model(DEM)+remotely sensed data;scenarioⅡ,covariates of scenarioⅠ+selected climatic data and some thematic maps;scenarioⅢ,covariates in scenarioⅡ+intrinsic soil properties(Clay,Silt,Sand,bulk density(BD),soil organic matter(SOM),calcium carbonate equivalent(CCE),mean weight diameter(MWD)and geometric weight diameter(GWD)).The results showed that for Ks,Psat Pdry and SS,the best performance was found by the RF model in the third scenario,with R2=0.53,0.32,0.31 and 0.41,respectively,while for soil hardness index(HI),Cubist model in the third scenario with R2=0.25 showed the highest performance.For predicting Ks and Psat,soil characteristics(i.e.clay and soil SOM and BD),and land use were the most important variables.For predicting Pdry,HI,and SS,some topographical characteristics(Valley depth,catchment area,mltiresolution of ridge top flatness index),and some soil characteristics(i.e.clay,SOM and MWD)were the most important input variables.The results of this research present moderate accuracy,however,the methodology employed provides quick and costeffectiv

关 键 词：Machine learning Soil physical property Soilmechanical property Saturatedhydraulic conductivity Soil cohesion Soil shear strength. 

分 类 号：S152[农业科学—土壤学]