Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast China
Wetlands are vital ecosystems that support regional ecological balance and require efficient, accurate, and cost-effective monitoring approaches. This study enhances wetland mapping in the Sanjiang Plain using multi-source (Sentinel-1 SAR and Sentinel-2 optical) and multi-temporal (2018–2021) satell...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2538210 |
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| _version_ | 1849224282160234496 |
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| author | Wenqi Zhang Chong Luo Zongming Wang Dehua Mao |
| author_facet | Wenqi Zhang Chong Luo Zongming Wang Dehua Mao |
| author_sort | Wenqi Zhang |
| collection | DOAJ |
| description | Wetlands are vital ecosystems that support regional ecological balance and require efficient, accurate, and cost-effective monitoring approaches. This study enhances wetland mapping in the Sanjiang Plain using multi-source (Sentinel-1 SAR and Sentinel-2 optical) and multi-temporal (2018–2021) satellite data on the Google Earth Engine platform. A Random Forest classifier was applied with training samples from high-resolution imagery and field surveys. Input features included spectral bands, vegetation indices, and radar backscatter coefficients. Preprocessing involved cloud masking for Sentinel-2 and speckle filtering for Sentinel-1. Classification accuracy was evaluated using independent validation samples, overall accuracy, and Kappa coefficient. Results indicate October images provide the highest single-year mapping accuracy, with Sentinel-1 and Sentinel-2 data from October 2021 achieving 85.4% accuracy and a Kappa of 0.669. Multi-temporal data improved accuracy to 88.9% (Kappa = 0.749). Sentinel-1 showed greater annual variation in wetland distribution compared to the stable Sentinel-2. Precipitation impacted accuracy, reducing Sentinel-2 performance while variably affecting Sentinel-1. Combining radar and optical data with multi-temporal analysis enhances wetland monitoring, offering guidance for data acquisition in large-scale conservation. Challenges include misclassifications like water-shadow confusion in optical imagery and backscatter interference from vegetation in radar data, requiring further research. |
| format | Article |
| id | doaj-art-3f34542c77b9469689138eae3ec4a1d9 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-3f34542c77b9469689138eae3ec4a1d92025-08-25T11:31:36ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2538210Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast ChinaWenqi Zhang0Chong Luo1Zongming Wang2Dehua Mao3State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, People’s Republic of ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, People’s Republic of ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, People’s Republic of ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, People’s Republic of ChinaWetlands are vital ecosystems that support regional ecological balance and require efficient, accurate, and cost-effective monitoring approaches. This study enhances wetland mapping in the Sanjiang Plain using multi-source (Sentinel-1 SAR and Sentinel-2 optical) and multi-temporal (2018–2021) satellite data on the Google Earth Engine platform. A Random Forest classifier was applied with training samples from high-resolution imagery and field surveys. Input features included spectral bands, vegetation indices, and radar backscatter coefficients. Preprocessing involved cloud masking for Sentinel-2 and speckle filtering for Sentinel-1. Classification accuracy was evaluated using independent validation samples, overall accuracy, and Kappa coefficient. Results indicate October images provide the highest single-year mapping accuracy, with Sentinel-1 and Sentinel-2 data from October 2021 achieving 85.4% accuracy and a Kappa of 0.669. Multi-temporal data improved accuracy to 88.9% (Kappa = 0.749). Sentinel-1 showed greater annual variation in wetland distribution compared to the stable Sentinel-2. Precipitation impacted accuracy, reducing Sentinel-2 performance while variably affecting Sentinel-1. Combining radar and optical data with multi-temporal analysis enhances wetland monitoring, offering guidance for data acquisition in large-scale conservation. Challenges include misclassifications like water-shadow confusion in optical imagery and backscatter interference from vegetation in radar data, requiring further research.https://www.tandfonline.com/doi/10.1080/17538947.2025.2538210Wetland mappingSentinel imagemulti-temporalmulti-sourceNortheast China |
| spellingShingle | Wenqi Zhang Chong Luo Zongming Wang Dehua Mao Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast China International Journal of Digital Earth Wetland mapping Sentinel image multi-temporal multi-source Northeast China |
| title | Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast China |
| title_full | Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast China |
| title_fullStr | Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast China |
| title_full_unstemmed | Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast China |
| title_short | Temporal variability in remote sensing accuracy for wetland mapping: a case study from Sentinel-1 and Sentinel-2 in Northeast China |
| title_sort | temporal variability in remote sensing accuracy for wetland mapping a case study from sentinel 1 and sentinel 2 in northeast china |
| topic | Wetland mapping Sentinel image multi-temporal multi-source Northeast China |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2538210 |
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