Articles | Volume 27, issue 20
https://doi.org/10.5194/hess-27-3687-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3687-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Oct 2023
Research article |
| 20 Oct 2023
| 20 Oct 2023
The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators
School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
Dan Miller
TerrainWorks, Seattle, WA, USA
Anthony J. Stewart
School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
Amy Yahnke
Washington Department of Ecology, Lacey, WA, USA
Daniel Lorigan
TerrainWorks, Seattle, WA, USA
Tate Brasel
TerrainWorks, Seattle, WA, USA
Ludmila Monika Moskal
School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
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Total article views: 5,288 (including HTML, PDF, and XML)
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Cited
17 citations as recorded by crossref.
- Revealing the hidden carbon in forested wetland soils A. Stewart et al. https://doi.org/10.1038/s41467-024-44888-x
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
- Comparable Hydrologic Dynamics of Wetlands with and without Persistent Surface Water Connections K. Hwang & D. Chandler https://doi.org/10.1007/s13157-026-02052-9
- Deep neural network and transfer learning for annual wetland vegetation mapping using sentinel-2 time-series data in the heterogeneous lake floodplain environment J. Ai et al. https://doi.org/10.1080/01431161.2024.2448309
- Urban Flood Susceptibility Assessment in Arid Environment Using a Novel Hybrid Deep Learning Approach R. Chakrabortty et al. https://doi.org/10.1007/s41748-025-00933-3
- Origin, hydrogeochemical evolution and human health risk assessment of surface and groundwater in the Densu Delta Ramsar site, Ghana Z. Kusi-Afrakoma et al. https://doi.org/10.1016/j.scitotenv.2025.179694
- The shadeshed: A lidar-based variable width shade buffer and riparian core for headwater streams D. Martin et al. https://doi.org/10.1016/j.foreco.2025.123343
- Tropical Wetland Methane Emissions and Trends (2004–2023) Inferred from Landsat-Based Inundated Vegetation Data Z. Chen et al. https://doi.org/10.1021/acs.est.6c05412
- Detecting Tamarix chinensis in the Yellow River Delta Coastal Wetland Using Sentinel-1/2 and Red-Edge–Vegetation-Cover Features J. Guo et al. https://doi.org/10.3390/f17070829
- Where have all the tidal forests gone? Mapping losses of forested and emergent tidal wetlands on the Oregon coast, USA L. Brophy https://doi.org/10.1016/j.ecss.2025.109542
- Defining and assessing the extent of international freshwater wetlands 1 km 2 and larger Z. Rosenblum https://doi.org/10.1080/15715124.2026.2751012
- Surface Water and Ocean Topography satellite data reveals flood depths and environmental flows in wetlands M. Callaway et al. https://doi.org/10.1038/s43247-026-03816-1
- Building new hydrography and virtual watersheds to conserve freshwater fisheries L. Benda et al. https://doi.org/10.1038/s41598-026-37143-4
- Degradation trends in lakes and wetlands of Iran and their contribution to dust pollution S. Samadi-Todar et al. https://doi.org/10.1038/s41598-026-40357-1
- A Murky Ruling Threatens the Fate of Millions of US Wetlands B. Simmons et al. https://doi.org/10.1007/s13157-024-01801-y
- TGB-UNet: Dual-Scale Topographic Gating and Topography-Guided Boundary Learning for Instantaneous Waterline Extraction on Tidal Flats W. Ding et al. https://doi.org/10.3390/jmse14211982
- Conservation easements target high quality lands but do not increase their quality N. Kolarik et al. https://doi.org/10.1016/j.biocon.2025.111234
17 citations as recorded by crossref.
- Revealing the hidden carbon in forested wetland soils A. Stewart et al. https://doi.org/10.1038/s41467-024-44888-x
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
- Comparable Hydrologic Dynamics of Wetlands with and without Persistent Surface Water Connections K. Hwang & D. Chandler https://doi.org/10.1007/s13157-026-02052-9
- Deep neural network and transfer learning for annual wetland vegetation mapping using sentinel-2 time-series data in the heterogeneous lake floodplain environment J. Ai et al. https://doi.org/10.1080/01431161.2024.2448309
- Urban Flood Susceptibility Assessment in Arid Environment Using a Novel Hybrid Deep Learning Approach R. Chakrabortty et al. https://doi.org/10.1007/s41748-025-00933-3
- Origin, hydrogeochemical evolution and human health risk assessment of surface and groundwater in the Densu Delta Ramsar site, Ghana Z. Kusi-Afrakoma et al. https://doi.org/10.1016/j.scitotenv.2025.179694
- The shadeshed: A lidar-based variable width shade buffer and riparian core for headwater streams D. Martin et al. https://doi.org/10.1016/j.foreco.2025.123343
- Tropical Wetland Methane Emissions and Trends (2004–2023) Inferred from Landsat-Based Inundated Vegetation Data Z. Chen et al. https://doi.org/10.1021/acs.est.6c05412
- Detecting Tamarix chinensis in the Yellow River Delta Coastal Wetland Using Sentinel-1/2 and Red-Edge–Vegetation-Cover Features J. Guo et al. https://doi.org/10.3390/f17070829
- Where have all the tidal forests gone? Mapping losses of forested and emergent tidal wetlands on the Oregon coast, USA L. Brophy https://doi.org/10.1016/j.ecss.2025.109542
- Defining and assessing the extent of international freshwater wetlands 1 km 2 and larger Z. Rosenblum https://doi.org/10.1080/15715124.2026.2751012
- Surface Water and Ocean Topography satellite data reveals flood depths and environmental flows in wetlands M. Callaway et al. https://doi.org/10.1038/s43247-026-03816-1
- Building new hydrography and virtual watersheds to conserve freshwater fisheries L. Benda et al. https://doi.org/10.1038/s41598-026-37143-4
- Degradation trends in lakes and wetlands of Iran and their contribution to dust pollution S. Samadi-Todar et al. https://doi.org/10.1038/s41598-026-40357-1
- A Murky Ruling Threatens the Fate of Millions of US Wetlands B. Simmons et al. https://doi.org/10.1007/s13157-024-01801-y
- TGB-UNet: Dual-Scale Topographic Gating and Topography-Guided Boundary Learning for Instantaneous Waterline Extraction on Tidal Flats W. Ding et al. https://doi.org/10.3390/jmse14211982
- Conservation easements target high quality lands but do not increase their quality N. Kolarik et al. https://doi.org/10.1016/j.biocon.2025.111234
Saved (final revised paper)
Latest update: 02 Nov 2026
Short summary
Accurate wetland inventories are critical to monitor and protect wetlands. However, in many areas a large proportion of wetlands are unmapped because they are hard to detect in imagery. We developed a machine learning approach using spatially mapped variables of wetland indicators (i.e., vegetation, hydrology, soils), including novel multi-scale topographic indicators, to predict wetland probability. Our approach can be adapted to diverse landscapes to improve wetland detection.
Accurate wetland inventories are critical to monitor and protect wetlands. However, in many...
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