Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series
Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/20/4986 |
| _version_ | 1797572378952204288 |
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| author | Haohai Jin Shiyu Fang Chao Chen |
| author_facet | Haohai Jin Shiyu Fang Chao Chen |
| author_sort | Haohai Jin |
| collection | DOAJ |
| description | Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km<sup>2</sup> occurred in 2005, and the maximum water area of 2614.96 km<sup>2</sup> occurred in 2020, with an annual average variation of 193.92 km<sup>2</sup>. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture. |
| first_indexed | 2024-03-10T20:55:26Z |
| format | Article |
| id | doaj.art-3050a05a29c94f818e907ff1dd6de417 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:55:26Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3050a05a29c94f818e907ff1dd6de4172023-11-19T17:59:13ZengMDPI AGRemote Sensing2072-42922023-10-011520498610.3390/rs15204986Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time SeriesHaohai Jin0Shiyu Fang1Chao Chen2Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, ChinaMarine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, ChinaSchool of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSurface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km<sup>2</sup> occurred in 2005, and the maximum water area of 2614.96 km<sup>2</sup> occurred in 2020, with an annual average variation of 193.92 km<sup>2</sup>. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture.https://www.mdpi.com/2072-4292/15/20/4986GEELandsatEGS operational surface water mapping algorithmsurface waterwater quality parameter inversionlong sequence time series remote sensing monitoring |
| spellingShingle | Haohai Jin Shiyu Fang Chao Chen Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series Remote Sensing GEE Landsat EGS operational surface water mapping algorithm surface water water quality parameter inversion long sequence time series remote sensing monitoring |
| title | Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series |
| title_full | Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series |
| title_fullStr | Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series |
| title_full_unstemmed | Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series |
| title_short | Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series |
| title_sort | mapping of the spatial scope and water quality of surface water based on the google earth engine cloud platform and landsat time series |
| topic | GEE Landsat EGS operational surface water mapping algorithm surface water water quality parameter inversion long sequence time series remote sensing monitoring |
| url | https://www.mdpi.com/2072-4292/15/20/4986 |
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