干旱区生态环境知识资源中心(Arid): Using Sentinel-1 Imagery for Soil Salinity Prediction Under the Condition of Coastal Restoration

Arid

DOI	10.1109/JSTARS.2019.2906064
	Using Sentinel-1 Imagery for Soil Salinity Prediction Under the Condition of Coastal Restoration
	Yang, Ren-Min 1; Guo, Wen-Wen 2,3
通讯作者	Yang, Ren-Min
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
EISSN	2151-1535
出版年	2019
卷号	12 期号:5 页码:1482-1488
英文摘要	Soil salinity is a major cause of land degradation in coastal environments and arid lands; in the first case due to sea water, and the second case due to precipitation/evaporation relationship. In coastal wetlands, soil salinity is very sensitive to plant invasion. In this context, it is necessary to obtain a better understanding of soil salinity variation to improve the management of coastal land resources. In this study, we explored the potential of Sentinel-1 data in predicting electrical conductivity (EC) at three depths. Also, we assessed the usefulness of the knowledge of the invasion process in EC prediction by comparing structural equation modeling (SEM), that included such knowledge, and linear regression model (LM), that simply modeled the relationships between EC and predictors. The case study was conducted in an invaded coastal wetland dominated by Spartina alterniflora Loisel in the east-central China coast. Before modeling, principal component analysis was used to reduce the multidimensionality of time series images. In SEM, the model explained 82% of EC variation in 0-30 cm, 99% in 30-60 cm, and 71% in 60-100 cm. The cross validation showed the SEM model provided good accuracy, with RPD (a ratio of performance to deviation) values of 1.41 in 0-30 cm, 1.51 in 30-60 cm, and 1.43 in 60-100 cm. In comparison to the poorer accuracy of LM models, we argued that modeling the relationships between the exotic plant and EC at different depths can be treated as a substantial advantage of the approach. These results provided useful indications about the strong potentials of Sentinel-1 imagery in quantitative prediction of soil salinity.
英文关键词	Coastal soil electrical conductivity (EC) invasive species quantitative prediction remote sensing (RS)
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000470830400013
WOS关键词	SPARTINA-ALTERNIFLORA ; ORGANIC-CARBON ; INVASIONS
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	中国科学院地球环境研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216249
作者单位	1.Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China; 2.Zaozhuang Univ, Dept Tourism Resources & Environm, Zaozhuang 277160, Peoples R China; 3.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Yang, Ren-Min,Guo, Wen-Wen. Using Sentinel-1 Imagery for Soil Salinity Prediction Under the Condition of Coastal Restoration[J]. 中国科学院地球环境研究所,2019,12(5):1482-1488.
APA	Yang, Ren-Min,&Guo, Wen-Wen.(2019).Using Sentinel-1 Imagery for Soil Salinity Prediction Under the Condition of Coastal Restoration.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,12(5),1482-1488.
MLA	Yang, Ren-Min,et al."Using Sentinel-1 Imagery for Soil Salinity Prediction Under the Condition of Coastal Restoration".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 12.5(2019):1482-1488.