Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China
Mapping high-spatial-resolution surface water bodies in urban and suburban areas is crucial in understanding the spatial distribution of surface water. Although Sentinel-2 images are popular in mapping water bodies, they are impacted by the mixed-pixel problem. Sub-pixel mapping can predict finer-sp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/15/8/1446 |
| _version_ | 1797603151202746368 |
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| author | Lai Jiang Chi Zhou Xiaodong Li |
| author_facet | Lai Jiang Chi Zhou Xiaodong Li |
| author_sort | Lai Jiang |
| collection | DOAJ |
| description | Mapping high-spatial-resolution surface water bodies in urban and suburban areas is crucial in understanding the spatial distribution of surface water. Although Sentinel-2 images are popular in mapping water bodies, they are impacted by the mixed-pixel problem. Sub-pixel mapping can predict finer-spatial-resolution maps from the input remote sensing image and reduce the mixed-pixel problem to a great extent. This study proposes a sub-pixel surface water mapping method based on morphological dilation and erosion operations and the Markov random field (DE_MRF) to predict a 2 m resolution surface water map for heterogeneous regions from Sentinel-2 imagery. DE_MRF first segments the normalized difference water index image to extract water pixels and then detects the mixed pixels by using combined morphological dilation and erosion operations. For the mixed pixels, DE_MRF considers the intra-pixel spectral variability by extracting multiple water endmembers and multiple land endmembers within a local window to generate the water fraction images through spectral unmixing. DE_MRF was evaluated in the Jinshui Basin, China. The results suggested that DE_MRF generated a lower commission error rate for water pixels compared to the comparison methods. Because DE_MRF considers the intra-class spectral variabilities in the unmixing, it is better in mapping sub-pixel water distribution in heterogeneous regions where different water bodies with distinct spectral reflectance are present. |
| first_indexed | 2024-03-11T04:26:23Z |
| format | Article |
| id | doaj.art-a68098f0176243099276dc805f35206f |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T04:26:23Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a68098f0176243099276dc805f35206f2023-11-17T21:47:17ZengMDPI AGWater2073-44412023-04-01158144610.3390/w15081446Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, ChinaLai Jiang0Chi Zhou1Xiaodong Li2Hubei Water Resources Research Institute, Wuhan 430070, ChinaHubei Water Resources Research Institute, Wuhan 430070, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaMapping high-spatial-resolution surface water bodies in urban and suburban areas is crucial in understanding the spatial distribution of surface water. Although Sentinel-2 images are popular in mapping water bodies, they are impacted by the mixed-pixel problem. Sub-pixel mapping can predict finer-spatial-resolution maps from the input remote sensing image and reduce the mixed-pixel problem to a great extent. This study proposes a sub-pixel surface water mapping method based on morphological dilation and erosion operations and the Markov random field (DE_MRF) to predict a 2 m resolution surface water map for heterogeneous regions from Sentinel-2 imagery. DE_MRF first segments the normalized difference water index image to extract water pixels and then detects the mixed pixels by using combined morphological dilation and erosion operations. For the mixed pixels, DE_MRF considers the intra-pixel spectral variability by extracting multiple water endmembers and multiple land endmembers within a local window to generate the water fraction images through spectral unmixing. DE_MRF was evaluated in the Jinshui Basin, China. The results suggested that DE_MRF generated a lower commission error rate for water pixels compared to the comparison methods. Because DE_MRF considers the intra-class spectral variabilities in the unmixing, it is better in mapping sub-pixel water distribution in heterogeneous regions where different water bodies with distinct spectral reflectance are present.https://www.mdpi.com/2073-4441/15/8/1446sub-pixel surface water mappingSentinel-2morphological dilation and erosionintra-class spectral variability |
| spellingShingle | Lai Jiang Chi Zhou Xiaodong Li Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China Water sub-pixel surface water mapping Sentinel-2 morphological dilation and erosion intra-class spectral variability |
| title | Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China |
| title_full | Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China |
| title_fullStr | Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China |
| title_full_unstemmed | Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China |
| title_short | Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China |
| title_sort | sub pixel surface water mapping for heterogeneous areas from sentinel 2 images a case study in the jinshui basin china |
| topic | sub-pixel surface water mapping Sentinel-2 morphological dilation and erosion intra-class spectral variability |
| url | https://www.mdpi.com/2073-4441/15/8/1446 |
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