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Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County,Three Gorges Reservoir

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Geoscience Frontiers 2023年第2期14卷 47-58页

作者： Ting Xiao Samuele Segoni Xin Liang Kunlong Yin Nicola Casagli Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of EducationSchool of Geosciences and Info-PhysicsCentral South UniversityChangsha 410083China Faculty of Engineering China University of GeosciencesWuhan 430074China Department of Earth Sciences University of FlorenceFlorence 50121Italy

soil thickness,intended as depth to bedrock,is a key input parameter for many environmental ***,it is often difficult to obtain a reliable spatially exhaustive soil thickness map in widearea applications,and existing prediction models have been extensively applied only to test sites with shallow soil *** study addresses this limitation by showing the results of an application to a section of Wanzhou County(Three Gorges Reservoir Area,China),where soil thickness varies from 0 to40 *** different approaches were used to derive soil thickness maps:a modified version of the geomorphologically indexed soil thickness(GIST)model,purposely customized to better account for the peculiar setting of the test site,and a regression performed with a machine learning algorithm,i.e.,the random forest,combined with the geomorphological parameters of GIST(GIST-RF).Additionally,the errors of the two models were quantified,and validation with geophysical data was carried *** results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area:the mean absolute error was 10.68 m with the root-mean-square error(RMSE)of 12.61 m,and the frequency distribution residuals showed a tendency toward *** contrast,GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 *** derived soil thickness map could be considered a critical fundamental input parameter for further analyses.

关键词： soil thickness soil thickness mapping Geomorphologically indexed soil thickness Random forest

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Assessing soil thickness in a black soil watershed in northeast China using random forest and field observations

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International soil and Water Conservation Research 2021年第1期9卷 49-57页

作者： Shuai Zhang Gang Liu Shuli Chen Craig Rasmussen Baoyuan Liu Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China State Key Laboratory of soil Erosion and Dryland Farming on the Loess Plateau Institute of Soil and Water ConservationChinese Academy of Sciences and Ministry of Water ResourcesYangling 712100China Department of Environmental Science University of Arizona1177 E.Fourth StTucsonAZ 85721-0038USA

soil thickness determines the soil productivity in the black soil region of northeast China,which is important for national food *** information on the spatial variation of black soil thickness is *** this paper,we propose a model framework for spatial estimation of the black soil thickness at the watershed scale by integrating field observations,unmanned aerial vehicle variations of topography,and satellite variations of vegetation with the aid of random *** sampled 141 sample profiles over a watershed and identified the black soil thickness based on indices of the mollic *** variables were derived from a digital elevation model and vegetation variables were derived from Landsat 8 *** forest was used to determine the relationship between black soil thickness and environmental *** resulting model explained 61%of the black soil thickness spatial variation,which was more than twice that of traditional interpolation methods(ordinary kriging,universal kriging and inverse distance weighting).Topographic variables contributed the most toward explaining the thickness,followed by vegetation *** black soil thickness over the watershed had a clear catenary soil pattern,with thickest black soil in the low depositional areas and thinnest at the higher elevations that drain into the low *** proposed model framework will improve estimates of soil thickness in the region of our study.

关键词： soil thickness Random forest Black soils Northeast China soil geomorphology

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