Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery
文献类型：期刊论文
| 作者 | Yang, Ren-Min2; Guo, Wen-Wen1,3 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2019-10-01 |
| 卷号 | 82页码:8 |
| 关键词 | Soil carbon Soil-vegetation relationship Synthetic aperture radar Temporal variation Cause-effect modeling |
| ISSN号 | 0303-2434 |
| DOI | 10.1016/j.jag.2019.101906 |
| 通讯作者 | Yang, Ren-Min(yangrenmincs@163.com) |
| 英文摘要 | 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. |
| WOS关键词 | FIT INDEXES ; RADAR |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000484871800022 |
| 出版者 | ELSEVIER |
| 资助机构 | 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 |
| 源URL | [http://ir.ieecas.cn/handle/361006/13457]  |
| 专题 | 地球环境研究所_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.Zaozhuang Univ, Dept Tourism Resources & Environm, Zaozhuang 277160, Peoples R China 2.Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, 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. |
入库方式： OAI收割
来源：地球环境研究所
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