Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques
Coastal regions are vulnerable ecosystems where monitoring soil salinity and vegetation coverage dynamics is critical for environmental management and conservation. This study introduces a multisensor data fusion approach, integrating Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispe...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10930794/ |
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| author | Wen Liu Tiezhu Shi Zhinian Zhao Chao Yang |
| author_facet | Wen Liu Tiezhu Shi Zhinian Zhao Chao Yang |
| author_sort | Wen Liu |
| collection | DOAJ |
| description | Coastal regions are vulnerable ecosystems where monitoring soil salinity and vegetation coverage dynamics is critical for environmental management and conservation. This study introduces a multisensor data fusion approach, integrating Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery with advanced machine learning techniques, specifically a convolutional neural network (CNN) based classification model. This approach enables the precise mapping of coastal salt-affected soil and vegetation patterns, addressing spatial heterogeneity and dynamic environmental conditions. The analysis has been conducted for a coastal region in China, where derived features, such as normalized difference vegetation index (NDVI), salinity indices, and SAR-based soil moisture proxies, have been used as inputs to the CNN model. The model achieved an overall accuracy of 87% and a kappa coefficient of 0.82, outperforming traditional classification methods by leveraging spatial feature learning and data augmentation. Temporal NDVI trends revealed seasonal vegetation dynamics, while predicted soil moisture patterns showed strong alignment with observed ecological conditions. The results indicate that saline soils dominate the study area, with nonsaline soils and vegetated areas exhibiting scattered and localized distributions. These findings demonstrate the potential of integrating multisensor remote sensing with advanced machine learning techniques for coastal monitoring, providing a robust framework for sustainable land-use planning and ecological management. |
| format | Article |
| id | doaj-art-976db1751fcc4e0c85464e1c9d3f3d9e |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-976db1751fcc4e0c85464e1c9d3f3d9e2025-08-20T03:44:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118142031421410.1109/JSTARS.2025.355243610930794Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning TechniquesWen Liu0https://orcid.org/0000-0003-0684-854XTiezhu Shi1https://orcid.org/0000-0001-7045-281XZhinian Zhao2https://orcid.org/0009-0004-8294-4896Chao Yang3https://orcid.org/0000-0001-7724-0385Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaResearch Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaResearch Center for Digital City, School of Urban Design, Wuhan University, Wuhan, ChinaResearch Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, ChinaCoastal regions are vulnerable ecosystems where monitoring soil salinity and vegetation coverage dynamics is critical for environmental management and conservation. This study introduces a multisensor data fusion approach, integrating Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery with advanced machine learning techniques, specifically a convolutional neural network (CNN) based classification model. This approach enables the precise mapping of coastal salt-affected soil and vegetation patterns, addressing spatial heterogeneity and dynamic environmental conditions. The analysis has been conducted for a coastal region in China, where derived features, such as normalized difference vegetation index (NDVI), salinity indices, and SAR-based soil moisture proxies, have been used as inputs to the CNN model. The model achieved an overall accuracy of 87% and a kappa coefficient of 0.82, outperforming traditional classification methods by leveraging spatial feature learning and data augmentation. Temporal NDVI trends revealed seasonal vegetation dynamics, while predicted soil moisture patterns showed strong alignment with observed ecological conditions. The results indicate that saline soils dominate the study area, with nonsaline soils and vegetated areas exhibiting scattered and localized distributions. These findings demonstrate the potential of integrating multisensor remote sensing with advanced machine learning techniques for coastal monitoring, providing a robust framework for sustainable land-use planning and ecological management.https://ieeexplore.ieee.org/document/10930794/Coastal monitoringconvolutional neural networks (CNN)multisensor data fusionsaline soil classificationsatellite imagessoil moisture estimation |
| spellingShingle | Wen Liu Tiezhu Shi Zhinian Zhao Chao Yang Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coastal monitoring convolutional neural networks (CNN) multisensor data fusion saline soil classification satellite images soil moisture estimation |
| title | Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques |
| title_full | Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques |
| title_fullStr | Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques |
| title_full_unstemmed | Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques |
| title_short | Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques |
| title_sort | mapping coastal soil salinity and vegetation dynamics using sentinel 1 and sentinel 2 data fusion with machine learning techniques |
| topic | Coastal monitoring convolutional neural networks (CNN) multisensor data fusion saline soil classification satellite images soil moisture estimation |
| url | https://ieeexplore.ieee.org/document/10930794/ |
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