Arid
DOI10.1080/22797254.2021.1888657
Comparisons of random forest and stochastic gradient treeboost algorithms for mapping soil electrical conductivity with multiple subsets using Landsat OLI and DEM/GIS-based data at a type oasis in Xinjiang, China
Wei, Yang; Ding, Jianli; Yang, Shengtian; Yang, Xiaodong; Wang, Fei
Corresponding AuthorYang, XD ; Wang, F (corresponding author), Xinjiang Univ, Coll Resource & Environm Sci, Common Univ, Key Lab Smart City & Environm Stimulat, Urumqi, Peoples R China.
JournalEUROPEAN JOURNAL OF REMOTE SENSING
EISSN2279-7254
Year Published2021
Volume54Issue:1Pages:158-181
Abstract in EnglishAccurate assessment of the spatial distribution and severity of soil salinization has long plagued local governments and researchers in the arid parts of Xinjiang Uygur Autonomous Region (XJUAR). The emergence of machine learning has brought hope to this research field, such as Random Forest (RF) and Stochastic Gradient Treeboost (SGT),however, which are few applications to the quantitative assessment of soil salinization. Therefore, in order to evaluate the accuracy level of the two algorithms for predicting soil salinity, twenty-seven environmental subsets were designed. Each data set is calculated using both RF and SGT to produce an optimal set of variables. The simulation results from 70.37% (19/27) of the subsets showed that the predicted value of soil salinity from SGT is closer to the observed value than is that from RF. The statistics of all datasets showed that the average values of R-2 value for RF and SGT were 0.38 and 0.40, the average Root Mean Squared Error (RMSE) value were 28.59 and 27.46, and the Ratio of Prediction to Deviation (RPD) averages were 1.20 and 1.24, respectively. The important dominant factor were topographic variables with coarse resolution, temperature and vegetation indices, land use and landform.
Keyword in EnglishSoil salinity machine learning arid regions Landsat OLI spatial heterogeneity
SubtypeArticle
Language英语
OA Typegold
Indexed BySCI-E
WOS IDWOS:000630976800001
WOS KeywordBOOSTED REGRESSION TREE ; VEGETATION INDEX ; SPATIAL PREDICTION ; ORGANIC-CARBON ; RIVER-BASIN ; SALINITY ; WATER ; CLASSIFICATION ; SALINIZATION ; DEGRADATION
WOS SubjectRemote Sensing
WOS Research AreaRemote Sensing
Document Type期刊论文
Identifierhttp://119.78.100.177/qdio/handle/2XILL650/350198
Affiliation[Wei, Yang; Ding, Jianli; Yang, Shengtian; Wang, Fei] Xinjiang Univ, Coll Resource & Environm Sci, Xinjiang Common Univ Key Lab Smart City & Environ, Urumqi, Peoples R China; [Wei, Yang; Ding, Jianli; Yang, Shengtian; Wang, Fei] Minist Educ, Lab Oasis Ecosyst, Urumqi, Peoples R China; [Yang, Xiaodong] Ningbo Univ, Ningbo, Peoples R China; [Yang, Xiaodong] Univ Newcastle, Global Ctr Environm Remediat GCER, ATC Bldg, Callaghan, NSW, Australia
Recommended Citation
GB/T 7714
Wei, Yang,Ding, Jianli,Yang, Shengtian,et al. Comparisons of random forest and stochastic gradient treeboost algorithms for mapping soil electrical conductivity with multiple subsets using Landsat OLI and DEM/GIS-based data at a type oasis in Xinjiang, China[J],2021,54(1):158-181.
APA Wei, Yang,Ding, Jianli,Yang, Shengtian,Yang, Xiaodong,&Wang, Fei.(2021).Comparisons of random forest and stochastic gradient treeboost algorithms for mapping soil electrical conductivity with multiple subsets using Landsat OLI and DEM/GIS-based data at a type oasis in Xinjiang, China.EUROPEAN JOURNAL OF REMOTE SENSING,54(1),158-181.
MLA Wei, Yang,et al."Comparisons of random forest and stochastic gradient treeboost algorithms for mapping soil electrical conductivity with multiple subsets using Landsat OLI and DEM/GIS-based data at a type oasis in Xinjiang, China".EUROPEAN JOURNAL OF REMOTE SENSING 54.1(2021):158-181.
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