Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques Article Swipe
Wen Liu
,
Tiezhu Shi
,
Zhengyu Zhao
,
Chao Yang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3552436
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3552436
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2025.3552436
- OA Status
- gold
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4408564682Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/jstars.2025.3552436Digital Object Identifier
- Title
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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Wen Liu, Tiezhu Shi, Zhengyu Zhao, Chao YangList of authors in order
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https://doi.org/10.1109/jstars.2025.3552436Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/jstars.2025.3552436Direct OA link when available
- Concepts
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Vegetation (pathology), Remote sensing, Sensor fusion, Environmental science, Computer science, Geology, Artificial intelligence, Pathology, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.derived | 84 |
| abstract_inverted_index.dynamic | 69 |
| abstract_inverted_index.enables | 54 |
| abstract_inverted_index.feature | 130 |
| abstract_inverted_index.imagery | 37 |
| abstract_inverted_index.machine | 40, 187 |
| abstract_inverted_index.mapping | 57 |
| abstract_inverted_index.methods | 126 |
| abstract_inverted_index.network | 47 |
| abstract_inverted_index.overall | 113 |
| abstract_inverted_index.precise | 56 |
| abstract_inverted_index.regions | 1 |
| abstract_inverted_index.results | 155 |
| abstract_inverted_index.sensing | 184 |
| abstract_inverted_index.spatial | 66, 129 |
| abstract_inverted_index.Temporal | 135 |
| abstract_inverted_index.accuracy | 114 |
| abstract_inverted_index.achieved | 111 |
| abstract_inverted_index.advanced | 39, 186 |
| abstract_inverted_index.analysis | 73 |
| abstract_inverted_index.aperture | 31 |
| abstract_inverted_index.approach | 53 |
| abstract_inverted_index.coverage | 11 |
| abstract_inverted_index.critical | 14 |
| abstract_inverted_index.dominate | 160 |
| abstract_inverted_index.dynamics | 12 |
| abstract_inverted_index.findings | 176 |
| abstract_inverted_index.indicate | 156 |
| abstract_inverted_index.indices, | 94 |
| abstract_inverted_index.land-use | 199 |
| abstract_inverted_index.learning | 41, 131, 188 |
| abstract_inverted_index.moisture | 98, 145 |
| abstract_inverted_index.observed | 151 |
| abstract_inverted_index.patterns | 146 |
| abstract_inverted_index.planning | 200 |
| abstract_inverted_index.proxies, | 99 |
| abstract_inverted_index.revealed | 138 |
| abstract_inverted_index.salinity | 8, 93 |
| abstract_inverted_index.seasonal | 139 |
| abstract_inverted_index.SAR-based | 96 |
| abstract_inverted_index.alignment | 149 |
| abstract_inverted_index.approach, | 27 |
| abstract_inverted_index.conducted | 76 |
| abstract_inverted_index.dynamics, | 141 |
| abstract_inverted_index.features, | 85 |
| abstract_inverted_index.framework | 196 |
| abstract_inverted_index.localized | 173 |
| abstract_inverted_index.nonsaline | 165 |
| abstract_inverted_index.patterns, | 64 |
| abstract_inverted_index.potential | 179 |
| abstract_inverted_index.predicted | 143 |
| abstract_inverted_index.providing | 193 |
| abstract_inverted_index.scattered | 171 |
| abstract_inverted_index.synthetic | 30 |
| abstract_inverted_index.vegetated | 168 |
| abstract_inverted_index.Sentinel-1 | 29 |
| abstract_inverted_index.Sentinel-2 | 35 |
| abstract_inverted_index.addressing | 65 |
| abstract_inverted_index.difference | 89 |
| abstract_inverted_index.ecological | 152, 202 |
| abstract_inverted_index.ecosystems | 4 |
| abstract_inverted_index.exhibiting | 170 |
| abstract_inverted_index.introduces | 22 |
| abstract_inverted_index.leveraging | 128 |
| abstract_inverted_index.management | 17 |
| abstract_inverted_index.monitoring | 6 |
| abstract_inverted_index.normalized | 88 |
| abstract_inverted_index.techniques | 189 |
| abstract_inverted_index.vegetation | 10, 63, 90, 140 |
| abstract_inverted_index.vulnerable | 3 |
| abstract_inverted_index.coefficient | 120 |
| abstract_inverted_index.conditions. | 71, 153 |
| abstract_inverted_index.demonstrate | 177 |
| abstract_inverted_index.integrating | 28, 181 |
| abstract_inverted_index.management. | 203 |
| abstract_inverted_index.monitoring, | 192 |
| abstract_inverted_index.multisensor | 24, 182 |
| abstract_inverted_index.sustainable | 198 |
| abstract_inverted_index.techniques, | 42 |
| abstract_inverted_index.traditional | 124 |
| abstract_inverted_index.specifically | 43 |
| abstract_inverted_index.augmentation. | 134 |
| abstract_inverted_index.conservation. | 19 |
| abstract_inverted_index.convolutional | 45 |
| abstract_inverted_index.environmental | 16, 70 |
| abstract_inverted_index.heterogeneity | 67 |
| abstract_inverted_index.multispectral | 36 |
| abstract_inverted_index.outperforming | 123 |
| abstract_inverted_index.salt-affected | 60 |
| abstract_inverted_index.classification | 50, 125 |
| abstract_inverted_index.distributions. | 174 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.86735959 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |