Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data
文献类型：期刊论文
| 作者 | Zhang, Tao1,3; Wang, Hongxing1,2; Hu, Shanshan2; You, Shucheng1; Yang, Xiaomei3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 14期号:12页码:22 |
| 关键词 | lake surface water Landsat GEE TSIRB BSWD bimonthly GSW |
| DOI | 10.3390/rs14122893 |
| 通讯作者 | Hu, Shanshan(hushanshan@cnu.edu.cn) |
| 英文摘要 | Lakes are one of the most important parts of the terrestrial hydrosphere. The long-term series of lake area dynamic data with high spatial-temporal resolution is of great significance to the study of global change of the water environment. Satellite observations (such as Landsat) have provided images since the 1970s, but there were challenges for the construction of long-term sequences of lake area on a monthly temporal scale. We proposed a temporal-spatial interpolation and rule-based (TSIRB) approach on the Google Earth Engine, which aims to achieve automatic water extraction and bimonthly sequence construction of lake area. There are three main steps of this method which include bimonthly image sequence construction, automatic water extraction, and anomaly rectification. We applied the TSIRB method to five typical lakes (covering salt lakes, river lagoons, and plateau alpine lakes), and constructed the bimonthly surface water dataset (BSWD) from 1987 to 2020. The accuracy assessment that was based on a confusion matrix and random sampling showed that the average overall accuracy (OA) of water extraction was 96.6%, and the average Kappa was 0.90. The BSWD sequence was compared with the lake water level observation data, and the results show that the BSWD data is closely correlated with the water level observation sequence, with correlation coefficient greater than 0.87. The BSWD improves the hollows in the global surface water (GSW) monthly data and has advantages in the temporal continuity of surface water data. The BSWD can provide a 30-m-scale and bimonthly series of surface water for more than 30 years, which shows good value for the long-term dynamic monitoring of lakes, especially in areas that are lacking in situ surveying data. |
| WOS关键词 | GLOBAL SURFACE-WATER ; CLIMATE-CHANGE ; DONGTING LAKE ; MAR CHIQUITA ; INDEX NDWI ; AREA ; BASIN ; EXTRACTION ; STREAMFLOW ; CATCHMENT |
| 资助项目 | National Key Research and Development Program of China[2021YFC3000405] ; Ministry of Natural Resources of China[121133000000210013] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000818281800001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; Ministry of Natural Resources of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/180671]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Hu, Shanshan |
| 作者单位 | 1.Land Satellite Remote Sensing Applicat Ctr LASAC, Beijing 100048, Peoples R China 2.Capital Normal Univ, Beijing Lab Water Resources Secur, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Tao,Wang, Hongxing,Hu, Shanshan,et al. Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data[J]. REMOTE SENSING,2022,14(12):22. |
| APA | Zhang, Tao,Wang, Hongxing,Hu, Shanshan,You, Shucheng,&Yang, Xiaomei.(2022).Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data.REMOTE SENSING,14(12),22. |
| MLA | Zhang, Tao,et al."Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data".REMOTE SENSING 14.12(2022):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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