Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image

doi:10.1080/19475705.2020.1721573

	Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image
	Guo, Bing 1,2,7,8; Zang, Wenqian 3; Luo, Wei 4; Wen, Ye 6; Yang, Fei 5; Han, Baomin 7; Fan, Yewen 1; Chen, Xi 7; Qi, Zhen 7; Wang, Zhen 7
刊名	GEOMATICS NATURAL HAZARDS & RISK
	2020
卷号	11 期号:1 页码:288-300
关键词	Soil salinization feature space surface parameters Landsat 8 OLI Yellow River Delta
ISSN号	1947-5705
DOI	10.1080/19475705.2020.1721573
通讯作者	Guo, Bing(154520807@qq.com)
英文摘要	The Yellow River Delta, with the most typical new wetland system in warm temperate zone of China, is suffering from increasingly serious salinization. The purpose of this study is to utilize five typical surface parameters, including Albedo (the surface Albedo), NDVI (vegetation index), SI (salinity index),WI (humidity index), and (Iron oxide index), to construct 10 different feature spaces and, then, propose two different kinds of monitoring models (point-to-point model and point to line model) of soil salinization. The results showed that the inversion accuracy of the feature space detection index based on the point-to-point model was the highest with R-2=0.86. However, the inversion accuracy of Albedo-NDVI feature space detection index based on the point-to-point model is the lowest with R-2=0.72. This is due to the fact that NDVI is not sensitive enough to indicate the status of vegetation grown in the region with low (disturbance of soil background) and high (influenced by the saturation effect) vegetation coverage. The chemical weathering is also a primary cause of soil salinization, during which Fe2O3 is formed by the reaction of oxygen present in the atmosphere with primary Fe2+ minerals in the soil .Therefore, the feature space detection index based on the point-to-point model has a stronger applicability to monitor the information of soil salinization in the Yellow River Delta. This above point-to-point detection model can be utilized as a better approach to provide data and decision support for the development of agriculture, construction of reservoirs, and protection of natural ecological system in the Yellow River Delta.
资助项目	Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences[2019LDE006] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA2002040203] ; Natural Science Foundation of Shandong Province[ZR2018BD001] ; 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] ; Open Fund of Key Laboratory of Geomatics and Digital Technology of Shandong Province ; National Key R&D Program of China[2017YFA0604804]
WOS关键词	SALINITY ; COMBINATION ; REFLECTANCE ; REGION ; CHINA ; VNIR
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000511285800001
资助机构	Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Natural Science Foundation of Shandong Province ; 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 ; Open Fund of Key Laboratory of Geomatics and Digital Technology of Shandong Province ; National Key R&D Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/132183]
专题	中国科学院地理科学与资源研究所
通讯作者	Guo, Bing
作者单位	1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China 2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China 4.North China Inst Aerosp Engn, Langfang, Hebei, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 6.Shenyang Agr Univ, Coll Land & Environm, Shenyang, Peoples R China 7.Shandong Univ Technol, Sch Civil Architectural Engn, Zibo, Peoples R China 8.Key Lab Geomat & Digital Technol Shandong Prov, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Guo, Bing,Zang, Wenqian,Luo, Wei,et al. Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image[J]. GEOMATICS NATURAL HAZARDS & RISK,2020,11(1):288-300.
APA	Guo, Bing.,Zang, Wenqian.,Luo, Wei.,Wen, Ye.,Yang, Fei.,...&Yang, Xiao.(2020).Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image.GEOMATICS NATURAL HAZARDS & RISK,11(1),288-300.
MLA	Guo, Bing,et al."Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image".GEOMATICS NATURAL HAZARDS & RISK 11.1(2020):288-300.