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Salinization information extraction model based on VI-SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image

文献类型：期刊论文

作者Guo, Bing1,2,3,4,5; Han, Baomin2; Yang, Fei6; Fan, Yewen3; Jiang, Lin2; Chen, Shuting2; Yang, Wenna2; Gong, Rui2; Liang, Tian2
刊名GEOMATICS NATURAL HAZARDS & RISK
出版日期2019
卷号10期号:1页码:1863-1878
关键词Soil salinization feature space Landsat8 OLI monitoring model Yellow River Delta
ISSN号1947-5705
DOI10.1080/19475705.2019.1650125
通讯作者Yang, Fei(guobingjl@163.com)
英文摘要The interference of soil salt content, vegetation, and other factors greatly constrain soil salinization monitoring via remote sensing techniques. However, traditional monitoring methods often ignore the vegetation information. 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 three categories of soil salinization monitoring index were established; then, the inversion accuracies among all the indices were compared. The experiment results showed that remote sensing monitoring index based on MSAVI-SI1 with SDI2 had the highest inversion accuracy (R-2 = 0.876), while that based on the ENDVI-SI4 feature space with SDI1 had the lowest (R-2 = 0.719). The reason lied in the fact that MSAVI fully considers the bare soil line and thus effectively eliminates the background influence of soil and vegetation canopy. Therefore, the remote sensing monitoring index derived from MSAVI-SI1 can greatly improve the dynamic and periodical monitoring of soil salinity in the Yellow River Delta.
WOS关键词SOIL-SALINITY ; CHINA
资助项目Natural Science Foundation of Shandong Province[ZR2018BD001] ; Project of Shandong Province Higher Educational Science and Technology Program[J18KA181] ; Key Research Program of Frontier Science of Chinese Academy of Sciences[QYZDY-SSW-DQC007] ; 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] ; Open fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation[2016NGCM02] ; Project of Hubei Key Laboratory of Regional Development and Environmental Response (Hubei University)[2017(B) 003] ; National Key R&D Program of China[2017YFA0604804]
WOS研究方向Geology ; Meteorology & Atmospheric Sciences ; Water Resources
语种英语
WOS记录号WOS:000480551900001
出版者TAYLOR & FRANCIS LTD
资助机构Natural Science Foundation of Shandong Province ; Project of Shandong Province Higher Educational Science and Technology Program ; Key Research Program of Frontier Science of Chinese Academy of Sciences ; 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 ; Open fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation ; Project of Hubei Key Laboratory of Regional Development and Environmental Response (Hubei University) ; National Key R&D Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/68716]  
专题中国科学院地理科学与资源研究所
通讯作者Yang, Fei
作者单位1.Key Lab Geomat & Digital Technol Shandong Prov, Qingdao, Shandong, Peoples R China
2.Shandong Univ Technol, Sch Civil Architectural Engn, Zibo, Shandong, Peoples R China
3.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Hubei, Peoples R China
4.East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai, Peoples R China
5.Hubei Univ, Hubei Key Lab Reg Dev & Environm Response, Wuhan, Hubei, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Bing,Han, Baomin,Yang, Fei,et al. Salinization information extraction model based on VI-SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image[J]. GEOMATICS NATURAL HAZARDS & RISK,2019,10(1):1863-1878.
APA Guo, Bing.,Han, Baomin.,Yang, Fei.,Fan, Yewen.,Jiang, Lin.,...&Liang, Tian.(2019).Salinization information extraction model based on VI-SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image.GEOMATICS NATURAL HAZARDS & RISK,10(1),1863-1878.
MLA Guo, Bing,et al."Salinization information extraction model based on VI-SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image".GEOMATICS NATURAL HAZARDS & RISK 10.1(2019):1863-1878.

入库方式： OAI收割

来源：地理科学与资源研究所

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