干旱区生态环境知识资源中心(Arid): 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

Arid

DOI	10.1080/22797254.2021.1888657
	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
通讯作者	Yang, XD ; Wang, F (corresponding author), Xinjiang Univ, Coll Resource & Environm Sci, Common Univ, Key Lab Smart City & Environm Stimulat, Urumqi, Peoples R China.
来源期刊	EUROPEAN JOURNAL OF REMOTE SENSING
EISSN	2279-7254
出版年	2021
卷号	54 期号:1 页码:158-181
英文摘要	Accurate 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.
英文关键词	Soil salinity machine learning arid regions Landsat OLI spatial heterogeneity
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000630976800001
WOS关键词	BOOSTED REGRESSION TREE ; VEGETATION INDEX ; SPATIAL PREDICTION ; ORGANIC-CARBON ; RIVER-BASIN ; SALINITY ; WATER ; CLASSIFICATION ; SALINIZATION ; DEGRADATION
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350198
作者单位	[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
推荐引用方式 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]. 新疆大学,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.