Mapping water bodies under cloud cover using remotely sensed optical images and a spatiotemporal dependence model
文献类型：期刊论文
| 作者 | Li, Xinyan1,3; Ling, Feng3,4; Cai, Xiaobin3; Ge, Yong2; Li, Xiaodong3; Yin, Zhixiang1,3; Shang, Cheng1,3; Jia, Xiaofeng1,3; Du, Yun3 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 103页码:13 |
| 关键词 | Water body mapping Cloud contamination Water map reconstruction Remotely sensed optical images Spatiotemporal dependence model |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2021.102470 |
| 通讯作者 | Ling, Feng(lingf@whigg.ac.cn) |
| 英文摘要 | Optical remote sensing imagery is commonly used to monitor the spatial and temporal distribution patterns of inland waters. Its usage, however, is limited by cloud contamination, which results in low-quality images or missing values. Selecting cloud-free scenes or combining multi-temporal images to produce a cloud-free composite image can partially overcome this problem at the cost of the monitoring frequency. Predicting the spectral values of cloudy areas based on the spectral characteristics is a possible solution; however, this is not appropriate for water because it changes rapidly. Reconstructing cloud-covered water areas using historical water-distribution data has good performance, but such methods are typically only suitable for lakes and reservoirs, not over vast and complex terrain. This paper proposes a category-based approach to reconstruct the water distribution in cloud-contaminated images using a spatiotemporal dependence model. The proposed method predicts the class label (water or land) of a cloudy pixel based on the neighboring pixel labels and those at the same position in images acquired on other dates according to historical spatiotemporal water-distribution data. The method was evaluated through eight experiments in different study regions using Landsat and Sentinel-2 images. The results demonstrated that the proposed method could yield high-quality cloud-free classification maps and provide good water-extraction accuracy and consistency in most hydrological conditions, with an overall accuracy of up to 98%. The accuracy and practicality of the method render it promising for applications across a wide range of future research and monitoring efforts. |
| WOS关键词 | ETM PLUS DATA ; SURFACE-WATER ; TIME-SERIES ; LANDSAT TM ; INDEX NDWI ; DYNAMICS ; REMOVAL ; LAKE |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000706391000002 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/167061]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ling, Feng |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Innovat Acad Precis Measurement Sci & Technol, Chinese Acad Sci, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan 430077, Peoples R China 4.Chinese Acad Sci, Sino Africa Joint Res Ctr, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xinyan,Ling, Feng,Cai, Xiaobin,et al. Mapping water bodies under cloud cover using remotely sensed optical images and a spatiotemporal dependence model[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2021,103:13. |
| APA | Li, Xinyan.,Ling, Feng.,Cai, Xiaobin.,Ge, Yong.,Li, Xiaodong.,...&Du, Yun.(2021).Mapping water bodies under cloud cover using remotely sensed optical images and a spatiotemporal dependence model.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,103,13. |
| MLA | Li, Xinyan,et al."Mapping water bodies under cloud cover using remotely sensed optical images and a spatiotemporal dependence model".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 103(2021):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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