Du et al. (2026) Large-scale water depth reconstruction of Tibetan Plateau lakes using ICESat-2 photon data: Performance, limitations, and environmental drivers

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-03-30
• Authors: Tianjiao Du, J. L. Li, Jian Ju, Liping Zhu, Baojin Qiao
• DOI: 10.1016/j.rse.2026.115390

Research Groups

• School of Geoscience and Technology, Zhengzhou University, Zhengzhou, China
• 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

Short Summary

This study reconstructs water depths for 350 Tibetan Plateau lakes using ICESat-2 photon data, achieving a root mean square error of 0.45 m, and identifies lake area, longitude span, and water clarity as key influencing factors.

Objective

• To comprehensively investigate the performance, limitations, and environmental drivers of ICESat-2 photon data for large-scale water depth reconstruction of Tibetan Plateau lakes.

Study Configuration

• Spatial Scale: 350 Tibetan Plateau lakes larger than 10 square kilometers.
• Temporal Scale: ICESat-2 data collected from 2018 to 2025, with optimal performance noted during post-monsoon months (October and November).

Methodology and Data

• Models used: A quality-controlled, manually assisted workflow involving track selection, photon classification, and refraction correction for water depth reconstruction.
• Data sources: ICESat-2 photon data; in situ bathymetric data (for validation across ten lakes).

Main Results

• Water depth was successfully reconstructed for 350 out of 431 targeted lakes.
• Over 2 million valid water depth points were generated along a total length of 42,640 kilometers.
• The maximum reconstructed water depth was approximately 40 meters.
• The ICESat-2 water depth reconstruction showed good performance with a Root Mean Square Error (RMSE) of 0.45 meters.
• Performance was particularly strong in shallow lakes (less than 10 meters deep) during the post-monsoon months (October and November).
• Lake area, longitude span, and water clarity were identified as the primary environmental drivers influencing the success of water depth reconstruction.

Contributions

• Delivers a comprehensive, large-scale water depth dataset for Tibetan Plateau lakes derived from ICESat-2.
• Provides critical insights into the capabilities and limitations of ICESat-2 for lake depth reconstruction in complex inland water environments.
• Establishes a valuable foundation for future research on lake water storage estimation, hydrological modeling, and remote sensing applications in high-altitude regions.

Funding

• [No specific funding projects or programs were mentioned in the provided text.]

Citation

@article{Du2026Largescale,
  author = {Du, Tianjiao and Li, J. L. and Ju, Jian and Zhu, Liping and Qiao, Baojin},
  title = {Large-scale water depth reconstruction of Tibetan Plateau lakes using ICESat-2 photon data: Performance, limitations, and environmental drivers},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115390},
  url = {https://doi.org/10.1016/j.rse.2026.115390}
}