典型文献
Mapping soil erodibility in southeast China at 250 m resolution:Using environmental variables and random forest regression with limited samples
文献摘要：
Soil erodibility(K factor)mapping has been accomplished mainly by soil map-linked or geo-statistical interpolation.However,the resulting maps usually have coarse spatial resolution at a regional scale.The objectives of this study were a)to map the K factors using a set of environmental variables and random forest(RF)model,and b)to identify the important environmental variables in the predictive mapping on a regional scale.We collected 101 surface soil samples across southeast China in the summer of 2019.For each sample,we measured the particle size distribution and organic matter content,and calculated the K factors using the nomograph equation.The hyperparameters of RF were optimized through 5-fold cross validation(may=2,ntree=500,p=63),and a digital map with 250 m resolution was generated for the K factor.The lower and upper limits of a 90％prediction interval were also pro-duced for uncertainty analysis.It was found that the important environmental variables for the K factor prediction were relief,climate,land surface temperature and vegetation indexes.Since the existing K factor map has an average polygonal area of 6.8 km2,our approach dramatically improves the spatial resolution of the K factor to 0.0625 km2.The new method captures more distinct differences in spatial details,and the spatial distribution of the K factor derived from RF prediction followed a similar pattern with kriging interpolation.This suggests the presented approach in this study is effective for mapping the K factor with limited sampling data.
文献关键词：
中图分类号：
作者姓名：
Zhiyuan Tian;Feng Liu;Yin Liang;Xuchao Zhu
作者机构：
State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences,Nanjing,210008,China
文献出处：
引用格式：
[1]Zhiyuan Tian;Feng Liu;Yin Liang;Xuchao Zhu-.Mapping soil erodibility in southeast China at 250 m resolution:Using environmental variables and random forest regression with limited samples)[J].国际水土保持研究（英文）,2022(01):62-74
A类：
nomograph,ntree
B类：
Mapping,soil,erodibility,southeast,China,resolution,Using,environmental,variables,random,forest,regression,limited,samples,Soil,mapping,has,been,accomplished,mainly,by,linked,geo,statistical,interpolation,However,resulting,maps,usually,have,coarse,spatial,regional,scale,objectives,this,study,were,factors,using,set,RF,model,identify,important,predictive,We,collected,surface,across,summer,For,each,measured,particle,size,distribution,organic,matter,content,calculated,equation,hyperparameters,optimized,through,fold,validation,may,digital,was,generated,lower,upper,limits,prediction,interval,also,duced,uncertainty,analysis,It,found,that,relief,climate,land,temperature,vegetation,indexes,Since,existing,average,polygonal,area,km2,our,approach,dramatically,improves,new,method,captures,more,distinct,differences,details,derived,from,followed,similar,pattern,kriging,This,suggests,presented,effective,sampling,data
AB值：
0.569846
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