Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery

doi:10.1016/j.jag.2019.101906

IEECAS OpenIR > 黄土与第四纪地质国家重点实验室（2010~）

	Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery
	Yang, Ren-Min 1; Guo, Wen-Wen 2,3
通讯作者	Yang, Ren-Min(yangrenmincs@163.com)
	2019-10-01
发表期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	0303-2434
卷号	82 页码:8
摘要	Soil monitoring information is important to improve our understanding of the role of soil on global environment change such as invasion of foreign species. For regions with dense vegetation cover the use of remote sensing data provides an attractive solution to soil prediction through the relationship between soil and remotely sensed information of vegetation, especially considering the availability of multi-temporal series of synthetic aperture radar (SAR) data such as Sentinel-1. In this study, we used a structural equation model (SEM) to link soil organic carbon (SOC) and bulk density (BD) with temporal variation of SAR signals, taking into account possible interacting relationships of the soil-vegetation system. The test area is in the coastal wetlands of east-central China, where Sentinel-1 data were acquired during the vegetation growing season in 2017. A total of fifteen sites were sampled at three depths: 0-30 cm, 30-60 cm, and 60-100 cm. Predictive accuracy was assessed using leave-oneout cross-validation (LOOCV). Results showed that SE models successfully predicted SOC (RMSE = 1.63 g kg(-1), RPD = 1.22) and BD (RMSE = 0.14 g cm(-3), RPD = 1.25) at three depths. We found that SEM supported the idea that the interrelationships exist among soil, vegetation, and remotely sensed information, and improved our ability to investigate relationships between SAR backscatters and soil attributes. The use of time series Sentinel-1 data allowed capturing characteristics of vegetation dynamics and the possible relationships between soil attribute and vegetation. The findings from this study highlight the usefulness of dense temporal SAR data and SEM in soil prediction.
关键词	Soil carbon Soil-vegetation relationship Synthetic aperture radar Temporal variation Cause-effect modeling
DOI	10.1016/j.jag.2019.101906
关键词[WOS]	FIT INDEXES ; RADAR
收录类别	SCI ; SCI
语种	英语
资助项目	National Natural Science Foundation of China[41701236] ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China[17KJB210004] ; Open Fund of State Key Laboratory of Loess and Quaternary Geology[SKLLQG1810] ; Priority Academic Program Development of Jiangsu Higher Education Institutions
WOS研究方向	Remote Sensing
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; Open Fund of State Key Laboratory of Loess and Quaternary Geology ; Priority Academic Program Development of Jiangsu Higher Education Institutions
WOS类目	Remote Sensing
WOS记录号	WOS:000484871800022
出版者	ELSEVIER
引用统计	被引频次：27[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ieecas.cn/handle/361006/13458
专题	黄土与第四纪地质国家重点实验室（2010~）
通讯作者	Yang, Ren-Min
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 Envirornm, 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. Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2019,82:8.
APA	Yang, Ren-Min,&Guo, Wen-Wen.(2019).Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,82,8.
MLA	Yang, Ren-Min,et al."Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 82(2019):8.