Using Sentinel-1 Imagery for Soil Salinity Prediction Under the Condition of Coastal Restoration
文献类型：期刊论文
| 作者 | Yang, Ren-Min3; Guo, Wen-Wen1,2 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 2019-05-01 |
| 卷号 | 12期号:5页码:1482-1488 |
| 关键词 | Coastal soil electrical conductivity (EC) invasive species quantitative prediction remote sensing (RS) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2019.2906064 |
| 通讯作者 | Yang, Ren-Min(yangrenmincs@163.com) |
| 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. |
| WOS关键词 | SPARTINA-ALTERNIFLORA ; ORGANIC-CARBON ; INVASIONS |
| 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] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000470830400013 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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 |
| 源URL | [http://ir.ieecas.cn/handle/361006/13787]  |
| 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.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Shaanxi, Peoples R China 2.Zaozhuang Univ, Dept Tourism Resources & Environm, Zaozhuang 277160, Peoples R China 3.Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, 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]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,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. |
入库方式： OAI收割
来源：地球环境研究所
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