Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine
Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales. However, monitoring water bodies with highly seasonal hydrological variability, particularly using medium-resolution satellite imager...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2024-01-01
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| Series: | Journal of Remote Sensing |
| Online Access: | https://spj.science.org/doi/10.34133/remotesensing.0117 |
| _version_ | 1797301062079610880 |
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| author | Yaotong Cai Qian Shi Xiaoping Liu |
| author_facet | Yaotong Cai Qian Shi Xiaoping Liu |
| author_sort | Yaotong Cai |
| collection | DOAJ |
| description | Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales. However, monitoring water bodies with highly seasonal hydrological variability, particularly using medium-resolution satellite imagery such as Landsat 4-9, presents substantial challenges. This study introduces the Normalized Difference Water Fraction Index (NDWFI) based on spectral mixture analysis (SMA) to improve the detection of subtle and dynamically changing water bodies. First, the effectiveness of NDWFI is rigorously assessed across four challenging sites. The findings reveal that NDWFI achieves an average overall accuracy (OA) of 98.2% in water extraction across a range of water-covered scenarios, surpassing conventional water indices. Subsequently, using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine (GEE) platform, this study generates a high-resolution surface water (SW) map for Jiangsu Province, China, exhibiting an impressive OA of 95.91% ± 0.23%. We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps. This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring, contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes. |
| first_indexed | 2024-03-07T23:15:59Z |
| format | Article |
| id | doaj.art-6ccf4991d14b40f8b7d154f479994bd0 |
| institution | Directory Open Access Journal |
| issn | 2694-1589 |
| language | English |
| last_indexed | 2024-03-07T23:15:59Z |
| publishDate | 2024-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Journal of Remote Sensing |
| spelling | doaj.art-6ccf4991d14b40f8b7d154f479994bd02024-02-21T10:48:49ZengAmerican Association for the Advancement of Science (AAAS)Journal of Remote Sensing2694-15892024-01-01410.34133/remotesensing.0117Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth EngineYaotong Cai0Qian Shi1Xiaoping Liu2School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275,Guangdong Province, China.School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275,Guangdong Province, China.School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275,Guangdong Province, China.Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales. However, monitoring water bodies with highly seasonal hydrological variability, particularly using medium-resolution satellite imagery such as Landsat 4-9, presents substantial challenges. This study introduces the Normalized Difference Water Fraction Index (NDWFI) based on spectral mixture analysis (SMA) to improve the detection of subtle and dynamically changing water bodies. First, the effectiveness of NDWFI is rigorously assessed across four challenging sites. The findings reveal that NDWFI achieves an average overall accuracy (OA) of 98.2% in water extraction across a range of water-covered scenarios, surpassing conventional water indices. Subsequently, using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine (GEE) platform, this study generates a high-resolution surface water (SW) map for Jiangsu Province, China, exhibiting an impressive OA of 95.91% ± 0.23%. We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps. This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring, contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.https://spj.science.org/doi/10.34133/remotesensing.0117 |
| spellingShingle | Yaotong Cai Qian Shi Xiaoping Liu Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine Journal of Remote Sensing |
| title | Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine |
| title_full | Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine |
| title_fullStr | Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine |
| title_full_unstemmed | Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine |
| title_short | Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine |
| title_sort | spatiotemporal mapping of surface water using landsat images and spectral mixture analysis on google earth engine |
| url | https://spj.science.org/doi/10.34133/remotesensing.0117 |
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