Han et al. (2026) SWOT performance in monitoring water level of high-mountain lakes on the Tibetan Plateau

Identification

• Journal: International Journal of Applied Earth Observation and Geoinformation
• Year: 2026
• Date: 2026-03-14
• Authors: Xiaoran Han, Guoqing Zhang, Jean-François Crétaux, Jida Wang, Christian Schwatke, Menger Peng, Xue Wang, C.K. Shum, R.Iestyn Woolway, Yinghai Ke, Yiming Wang, Tao Zhou, Fenglin Xu
• DOI: 10.1016/j.jag.2026.105236

Research Groups

• State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
• University of Chinese Academy of Sciences, Beijing, China
• Key Laboratory of Eco-Environment and Population Health of Xizang Autonomous Region, School of Ecology and Environment, Xizang University, Lhasa, China
• LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
• Department of Geography and Geographic Information Science, Department of Earth Science and Environmental Change, University of Illinois Urbana-Champaign, Urbana, IL, USA
• Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA
• Deutsches Geodätisches Forschungsinstitut der Technischen Universität München (DGFI-TUM), Munich, Germany
• Division of Geodetic Science, School of Earth Sciences, The Ohio State University, Columbus, OH, USA
• School of Ocean Sciences, Bangor University, Bangor, UK
• College of Resource Environment and Tourism, Capital Normal University, Beijing, China
• Laboratory Cultivation Base of Environment Process and Digital Simulation, Capital Normal University, Beijing, China
• Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China
• Department of Geography, McGill University, Montreal, Québec, Canada

Short Summary

This study introduces a novel Gaussian kernel density estimation approach to retrieve water levels of high-mountain lakes from SWOT observations, demonstrating that SWOT reliably captures variations in water level (average r = 0.72, RMSE = 0.29 m) and has transformative potential for monitoring global small water bodies.

Objective

• To explore the capability of the Surface Water and Ocean Topography (SWOT) mission to observe lakes in complex terrain on the Tibetan Plateau, with a particular emphasis on improving data usability for small water bodies.

Study Configuration

• Spatial Scale: Nine high-mountain lakes (tectonic, wetland, glacial) across the Qiangtang region and the southeast of the Tibetan Plateau. Broader assessment of SWOT's potential for over 24,000 Tibetan Plateau lakes smaller than 1 km² and 2,924 glacial lakes across the Himalaya.
• Temporal Scale: SWOT observations from 29 August 2023 to 9 October 2025. In-situ validation data collected from August 2023 to October 2025.

Methodology and Data

• Models used:
  • ATBD-standard approach for SWOT Water Surface Elevation (WSE) retrieval (quality-weighted average).
  • Novel Gaussian Kernel Density Estimation (Gaussian KDE) for statistical WSE retrieval (peak density).
  • Layover Intersection Model (LIM) to quantify potential terrain-induced layover effects.
• Data sources:
  • Satellite: SWOT Level-2 High-Rate Pixel Cloud (L2HRPIXC, Version D) product, SWOT L1BHRSLC data, PlanetScope imagery (Dove Classic and Dove-R), Advanced Land Observing Satellite (ALOS) PALSAR DEM (12.5 m resolution).
  • Observation: In-situ Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) surveys (vertical accuracy ~1.5 cm), water-level gauge records (4-hour interval), Uncrewed Aerial Vehicle (UAV) measurements (orthophotos and DEMs, 12.3 to 17.1 cm resolution).
  • Geoid Model: EGM2008.

Main Results

• SWOT reliably captures water level variations in high-mountain lakes with an average Pearson's r of 0.72 and an average Root Mean Square Error (RMSE) of 0.29 m.
• For small wetlands (~1 km²), SWOT-derived WSEs show significant accuracy with an RMSE of 0.09 m (mean r = 0.7), with peak performance reaching r = 0.94 (p < 0.01) and RMSE = 0.09 m for Eluba.
• The proposed Gaussian KDE method significantly improves WSE retrieval accuracy for glacial lakes in complex terrain (e.g., RMSE for Guangxie Co decreased from 0.61 m to 0.34 m; Yanong Co RMSE decreased from 5.25 m to 1.41 m).
• Water level errors are primarily attributed to KaRIn retrieval degradations, including phase-unwrapping errors, low Signal-to-Noise Ratio, and a lack of coherent gain, rather than terrain-induced layover effects.
• Across all PIXC pixels over selected lakes, retrieved elevations capture WSE accurately with a negligible bias (average Cumulative Distribution Function 50% signed error = 0.03 m).
• SWOT demonstrates the observational potential to retrieve WSE for more than 2,924 glacial lakes across the Himalaya (34% of inventoried glacial lakes) and over 24,000 Tibetan Plateau lakes smaller than 1 km².
• SWOT-derived lake areas show good agreement with reference areas, with relative errors smaller than 15%, meeting mission requirements.

Contributions

• Introduces a novel Gaussian kernel density estimation approach for robust Water Surface Elevation (WSE) retrieval, particularly enhancing the usability of SWOT data for small glacial lakes in complex high-mountain environments.
• Provides practical guidance for future SWOT hydrologic data processing and applications, including recommendations for using PIXC quality flags to mitigate errors.
• Demonstrates SWOT's transformative potential for monitoring small water bodies globally and advancing cryospheric research, especially for lakes previously unobservable by conventional altimetry missions.
• Identifies KaRIn retrieval degradations (e.g., phase-unwrapping errors, low Signal-to-Noise Ratio) as the primary factors limiting data accuracy in glacial lake environments, rather than terrain-induced layover effects.
• Releases open-source code for SWOT L2HRPIXC processing (for lake WSE and area retrieval) and the Layover Intersection Model (LIM), promoting reproducibility and broader scientific application.

Funding

• Department of Science and Technology of the Tibet Autonomous Region (XZ202403ZY0028)
• National Natural Science Foundation of China (grant no. 42571153)
• Basic Excellent Research Group for Tibetan Plateau Earth System, National Natural Science Foundation of China (NSFC ERGTPES project No. 42588201)
• Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0201)
• High-Level Talent Program of Xizang University (xzdxdr202501)
• NASA SWOT Science Team grant (80NSSC20K1143)
• UKRI Natural Environment Research Council (NERC) Independent Research Fellowship (NE/T011246/1)

Citation

@article{Han2026SWOT,
  author = {Han, Xiaoran and Zhang, Guoqing and Crétaux, Jean-François and Wang, Jida and Schwatke, Christian and Peng, Menger and Wang, Xue and Shum, C.K. and Woolway, R.Iestyn and Ke, Yinghai and Wang, Yiming and Zhou, Tao and Xu, Fenglin},
  title = {SWOT performance in monitoring water level of high-mountain lakes on the Tibetan Plateau},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  year = {2026},
  doi = {10.1016/j.jag.2026.105236},
  url = {https://doi.org/10.1016/j.jag.2026.105236}
}