Articles | Volume 13, issue 3
https://doi.org/10.5194/essd-13-1135-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1135-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
19 Mar 2021
Data description article |
| 19 Mar 2021
| 19 Mar 2021
Landsat-derived bathymetry of lakes on the Arctic Coastal Plain of northern Alaska
Claire E. Simpson
CORRESPONDING AUTHOR
Department of Geography, University of California, Los Angeles (UCLA),
Los Angeles, California, 90095, USA
Christopher D. Arp
Water and Environmental Research Center, University of Alaska,
Fairbanks, 306 Tanana Loop Rd., Fairbanks, Alaska, 99775, USA
Yongwei Sheng
Department of Geography, University of California, Los Angeles (UCLA),
Los Angeles, California, 90095, USA
Mark L. Carroll
Computational and Information Science and Technology Office,
NASA-GSFC, Greenbelt, Maryland, 20771, USA
Benjamin M. Jones
Water and Environmental Research Center, University of Alaska,
Fairbanks, 306 Tanana Loop Rd., Fairbanks, Alaska, 99775, USA
Laurence C. Smith
Department of Geography, University of California, Los Angeles (UCLA),
Los Angeles, California, 90095, USA
Department of Earth, Environmental and Planetary Sciences, Brown
University, 324 Brook St,
Providence, Rhode Island, 02912, USA
Institute at Brown for the Environment and Society, Brown University,
85 Waterman St,
Providence, Rhode Island, 02912, USA
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Total article views: 3,945 (including HTML, PDF, and XML)
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Cited
16 citations as recorded by crossref.
- Arctic Tundra Lake Drainage Increases Snow Storage in Drifts R. Rangel et al. https://doi.org/10.1029/2023JF007294
- Monitoring coastal shoreline change using PlanetScope imagery B. Tan et al. https://doi.org/10.1016/j.ecss.2026.109783
- A satellite-based ice fraction record for small water bodies of the Arctic Coastal Plain (2017 to 2023) H. Lin et al. https://doi.org/10.5194/essd-18-535-2026
- River Bathymetry Retrieval From Landsat-9 Images Based on Neural Networks and Comparison to SuperDove and Sentinel-2 M. Niroumand-Jadidi et al. https://doi.org/10.1109/JSTARS.2022.3187179
- Remote sensing for shallow bathymetry: A systematic review J. He et al. https://doi.org/10.1016/j.earscirev.2024.104957
- Enhancing water depth inversion accuracy in the Yangtze River's Nantong Channel using random forest and coordinate attention mechanisms Z. Wu et al. https://doi.org/10.1364/OE.538367
- Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea C. Xie et al. https://doi.org/10.1016/j.ecoinf.2025.103121
- China’s freshwater lake storage hotspots revealed by bathymetry and typology mapping C. Song et al. https://doi.org/10.1093/nsr/nwag245
- Satellite-derived bathymetry using Sentinel-2 in mesotidal coasts S. Viaña-Borja et al. https://doi.org/10.1016/j.coastaleng.2024.104644
- Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2 M. Merchant https://doi.org/10.1016/j.asr.2023.07.064
- 主被动遥感融合辐射传输卷积神经网络水深反演方法 谢. XIE Congshuang et al. https://doi.org/10.3788/gzxb20245308.0801002
- Investigating effects of thermokarst lakes on permafrost under equilibrium conditions H. Brisebois et al. https://doi.org/10.1016/j.scitotenv.2024.177921
- Remote Sensing-Based Statistical Approach for Defining Drained Lake Basins in a Continuous Permafrost Region, North Slope of Alaska H. Bergstedt et al. https://doi.org/10.3390/rs13132539
- Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN C. Xie et al. https://doi.org/10.3390/rs16030511
- Discrepancies in Arctic-boreal lake area trends driven by sensitivity to dry conditions E. Webb et al. https://doi.org/10.1038/s41598-026-35981-w
- Multi-resolution satellite images bathymetry inversion of Bangda Co in the western Tibetan Plateau H. Guo et al. https://doi.org/10.1080/01431161.2021.1970271
16 citations as recorded by crossref.
- Arctic Tundra Lake Drainage Increases Snow Storage in Drifts R. Rangel et al. https://doi.org/10.1029/2023JF007294
- Monitoring coastal shoreline change using PlanetScope imagery B. Tan et al. https://doi.org/10.1016/j.ecss.2026.109783
- A satellite-based ice fraction record for small water bodies of the Arctic Coastal Plain (2017 to 2023) H. Lin et al. https://doi.org/10.5194/essd-18-535-2026
- River Bathymetry Retrieval From Landsat-9 Images Based on Neural Networks and Comparison to SuperDove and Sentinel-2 M. Niroumand-Jadidi et al. https://doi.org/10.1109/JSTARS.2022.3187179
- Remote sensing for shallow bathymetry: A systematic review J. He et al. https://doi.org/10.1016/j.earscirev.2024.104957
- Enhancing water depth inversion accuracy in the Yangtze River's Nantong Channel using random forest and coordinate attention mechanisms Z. Wu et al. https://doi.org/10.1364/OE.538367
- Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea C. Xie et al. https://doi.org/10.1016/j.ecoinf.2025.103121
- China’s freshwater lake storage hotspots revealed by bathymetry and typology mapping C. Song et al. https://doi.org/10.1093/nsr/nwag245
- Satellite-derived bathymetry using Sentinel-2 in mesotidal coasts S. Viaña-Borja et al. https://doi.org/10.1016/j.coastaleng.2024.104644
- Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2 M. Merchant https://doi.org/10.1016/j.asr.2023.07.064
- 主被动遥感融合辐射传输卷积神经网络水深反演方法 谢. XIE Congshuang et al. https://doi.org/10.3788/gzxb20245308.0801002
- Investigating effects of thermokarst lakes on permafrost under equilibrium conditions H. Brisebois et al. https://doi.org/10.1016/j.scitotenv.2024.177921
- Remote Sensing-Based Statistical Approach for Defining Drained Lake Basins in a Continuous Permafrost Region, North Slope of Alaska H. Bergstedt et al. https://doi.org/10.3390/rs13132539
- Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN C. Xie et al. https://doi.org/10.3390/rs16030511
- Discrepancies in Arctic-boreal lake area trends driven by sensitivity to dry conditions E. Webb et al. https://doi.org/10.1038/s41598-026-35981-w
- Multi-resolution satellite images bathymetry inversion of Bangda Co in the western Tibetan Plateau H. Guo et al. https://doi.org/10.1080/01431161.2021.1970271
Saved (final revised paper)
Latest update: 03 Jun 2026
Short summary
Sonar depth point measurements collected at 17 lakes on the Arctic Coastal Plain of Alaska are used to train and validate models to map lake bathymetry. These models predict depth from remotely sensed lake color and are able to explain 58.5–97.6 % of depth variability. To calculate water volumes, we integrate this modeled bathymetry with lake surface area. Knowledge of Alaskan lake bathymetries and volumes is crucial to better understanding water storage, energy balance, and ecological habitat.
Sonar depth point measurements collected at 17 lakes on the Arctic Coastal Plain of Alaska are...
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