Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data
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 image...
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/12/2893 |
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| author | Tao Zhang Hongxing Wang Shanshan Hu Shucheng You Xiaomei Yang |
| author_facet | Tao Zhang Hongxing Wang Shanshan Hu Shucheng You Xiaomei Yang |
| author_sort | Tao Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:36:00Z |
| format | Article |
| id | doaj.art-a76ca902e0774acfa6c547a70c6f06be |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T22:36:00Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a76ca902e0774acfa6c547a70c6f06be2023-11-23T18:48:31ZengMDPI AGRemote Sensing2072-42922022-06-011412289310.3390/rs14122893Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat DataTao Zhang0Hongxing Wang1Shanshan Hu2Shucheng You3Xiaomei Yang4Land Satellite Remote Sensing Application Center (LASAC), Beijing 100048, ChinaLand Satellite Remote Sensing Application Center (LASAC), Beijing 100048, ChinaBeijing Laboratory of Water Resources Security, College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaLand Satellite Remote Sensing Application Center (LASAC), Beijing 100048, ChinaState Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaLakes 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.https://www.mdpi.com/2072-4292/14/12/2893lakesurface waterLandsatGEETSIRBBSWD |
| spellingShingle | Tao Zhang Hongxing Wang Shanshan Hu Shucheng You Xiaomei Yang Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data Remote Sensing lake surface water Landsat GEE TSIRB BSWD |
| title | Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data |
| title_full | Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data |
| title_fullStr | Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data |
| title_full_unstemmed | Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data |
| title_short | Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data |
| title_sort | long term and bimonthly estimation of lake water extent using google earth engine and landsat data |
| topic | lake surface water Landsat GEE TSIRB BSWD |
| url | https://www.mdpi.com/2072-4292/14/12/2893 |
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