Global mapping of tidal wetlands and adjacent environments using tidal analysis and multi-source Earth observations

Tidal wetlands, located at the dynamic land-sea interface, provide vital ecosystem services, yet face increasing threats from human activities and climate change. Accurate and up-to-date global mapping of tidal wetlands is essential for assessing their status and advancing conservation efforts. However, challenges such as tidal fluctuations and limited data availability lead to inconsistencies across existing datasets. Moreover, previous studies have largely overlooked the adjacent terrestrial environments of tidal wetlands, reducing our understanding of coastal dynamics and associated environmental drivers. To address these critical issues, this study proposes a novel global coastal mapping framework with three key components. First, using the ocean tide model EOT20, we systematically analyzed tidal variations observed in Sentinel-1 and Sentinel-2 imagery between 2019 and 2021, facilitating the development of an adaptive image selection strategy to ensure low-tide coverage. Second, we integrated multi-source global datasets and employed a knowledge-driven, semi-automatic sampling approach to generate training samples for tidal wetlands and adjacent land covers. Third, we iteratively trained and refined random forest models using tide-level and phenological features extracted from composite optical and radar imagery, producing a global coastal dataset centered on 2020 with 11 distinct land cover types. The mapping result was cross-compared with multiple global and regional coastal datasets and validated using the temporally cleaned external dataset, achieving an overall accuracy of 92.7%. By optimizing the selection of Sentinel scenes to approximately 10–50 in time-series composite mapping, this approach balances computational efficiency with intertidal classification accuracy. The dataset delineates the global distribution of tidal wetlands and their adjacent environments at a 10-m resolution; specifically, tidal wetlands–including mangroves, tidal marshes, and tidal flats, amount to 425,509 ± 932 km 2 . This fine-resolution and multi-category mapping framework enables a more precise identification of potential trade-offs between conservation and development, providing valuable references for sustainable coastal management.

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Publication Details

Journal
ISPRS Journal of Photogrammetry and Remote Sensing
Published
2026-03-02
DOI
https://doi.org/10.1016/j.isprsjprs.2026.02.014
Primary Topic
Coastal wetland ecosystem dynamics
Type
article
Field-Weighted Citation Impact
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article

Global mapping of tidal wetlands and adjacent environments using tidal analysis and multi-source Earth observations

Lindsey S. Smart, Mark Spalding, S. Li, Sayam U. Chowdhury et al.
ISPRS Journal of Photogrammetry and Remote Sensing
Coastal wetland ecosystem dynamics
article

Global mapping of tidal wetlands and adjacent environments using tidal analysis and multi-source Earth observations

Lindsey S. Smart, Mark Spalding, S. Li, Sayam U. Chowdhury, Thomas Worthington, Tao Zhang, Dongmei Yan, Bing Xu, Bin Chen, Miao Li, Yuqi Bai
article en

Abstract

Tidal wetlands, located at the dynamic land-sea interface, provide vital ecosystem services, yet face increasing threats from human activities and climate change. Accurate and up-to-date global mapping of tidal wetlands is essential for assessing their status and advancing conservation efforts. However, challenges such as tidal fluctuations and limited data availability lead to inconsistencies across existing datasets. Moreover, previous studies have largely overlooked the adjacent terrestrial environments of tidal wetlands, reducing our understanding of coastal dynamics and associated environmental drivers. To address these critical issues, this study proposes a novel global coastal mapping framework with three key components. First, using the ocean tide model EOT20, we systematically analyzed tidal variations observed in Sentinel-1 and Sentinel-2 imagery between 2019 and 2021, facilitating the development of an adaptive image selection strategy to ensure low-tide coverage. Second, we integrated multi-source global datasets and employed a knowledge-driven, semi-automatic sampling approach to generate training samples for tidal wetlands and adjacent land covers. Third, we iteratively trained and refined random forest models using tide-level and phenological features extracted from composite optical and radar imagery, producing a global coastal dataset centered on 2020 with 11 distinct land cover types. The mapping result was cross-compared with multiple global and regional coastal datasets and validated using the temporally cleaned external dataset, achieving an overall accuracy of 92.7%. By optimizing the selection of Sentinel scenes to approximately 10–50 in time-series composite mapping, this approach balances computational efficiency with intertidal classification accuracy. The dataset delineates the global distribution of tidal wetlands and their adjacent environments at a 10-m resolution; specifically, tidal wetlands–including mangroves, tidal marshes, and tidal flats, amount to 425,509 ± 932 km 2 . This fine-resolution and multi-category mapping framework enables a more precise identification of potential trade-offs between conservation and development, providing valuable references for sustainable coastal management.

ISPRS Journal of Photogrammetry and Remote SensingVol. 234
The Nature Conservancy (US), North Carolina State University (US), Hong Kong Baptist University (HK), University of Cambridge (GB), Beijing Institute of Big Data Research (CN), Istituto di Scienze Marine del Consiglio Nazionale delle Ricerche (IT), China National Commission for Disaster Reduction (CN), Beijing University of Civil Engineering and Architecture (CN), University of Hong Kong (HK), Tsinghua University (CN)
Climate action
Openalex Percentile: Top 46%
Coastal wetland ecosystem dynamics
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