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A model for the rapid monitoring of soil salinization in the Yellow River Delta using Landsat 8 OLI imagery based on VI-SI feature space
Guo, Bing1,2,3,4; Yang, Fei5; Han, Baomin1; Fan, Yewen3; Chen, Shuting1; Yang, Wenna1; Jiang, Lin1
2019-08-03
Source PublicationREMOTE SENSING LETTERS
ISSN2150-704X
Volume10Issue:8Pages:796-805
Corresponding AuthorYang, Fei(1468007871@qq.com)
AbstractTraditional monitoring methods often ignore the vegetation information, which has significantly indirect influence on the process of soil salinization. In this study, the vegetation indices-salinity indices (VI-SI) feature space was utilized to improve the inversion accuracy of soil salinity, while considering the bare soil and vegetation information. By fully considering the surface vegetation landscape in the Yellow River Delta, twelve VI-SI feature spaces were constructed, and two categories of soil salinization monitoring index were established. The experiment results showed that remote sensing monitoring index based on MSAVI-SI1 had the highest inversion accuracy (coefficient of determination (R-2) = 0.912), while that based on the ENDVI-SI4 feature space had the lowest (R-2 = 0.664). Therefore, the remote sensing monitoring index derived from MSAVI-SI can greatly improve the dynamic and periodical monitoring of soil salinity in the Yellow River Delta.
DOI10.1080/2150704X.2019.1610981
WOS KeywordSALINITY ; INDEX
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFA0604804] ; Natural Science Foundation of Shandong Province[ZR2018BD001] ; Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University[KLGIS2017A02] ; Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University[17I04] ; Project of Shandong Province Higher Educational Science and Technology Program[J18KA181]
Funding OrganizationNational Key R&D Program of China ; Natural Science Foundation of Shandong Province ; Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University ; Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University ; Project of Shandong Province Higher Educational Science and Technology Program
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000466114500001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68617
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang, Fei
Affiliation1.Shandong Univ Technol, Sch Civil Architectural Engn, Zibo, Shandong, Peoples R China
2.East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai, Peoples R China
3.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Hubei, Peoples R China
4.Hubei Univ, Hubei Key Lab Reg Dev & Environm Response, Wuhan, Hubei, Peoples R China
5.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Guo, Bing,Yang, Fei,Han, Baomin,et al. A model for the rapid monitoring of soil salinization in the Yellow River Delta using Landsat 8 OLI imagery based on VI-SI feature space[J]. REMOTE SENSING LETTERS,2019,10(8):796-805.
APA Guo, Bing.,Yang, Fei.,Han, Baomin.,Fan, Yewen.,Chen, Shuting.,...&Jiang, Lin.(2019).A model for the rapid monitoring of soil salinization in the Yellow River Delta using Landsat 8 OLI imagery based on VI-SI feature space.REMOTE SENSING LETTERS,10(8),796-805.
MLA Guo, Bing,et al."A model for the rapid monitoring of soil salinization in the Yellow River Delta using Landsat 8 OLI imagery based on VI-SI feature space".REMOTE SENSING LETTERS 10.8(2019):796-805.
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