Rezapour et al. (2026) Enhanced remote sensing of water surface elevation through fusion of Sentinel-3 altimeter data and climate variables using machine learning

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Authors: Mahdis Rezapour, mohammad javad valadan zoej, Alireza Taheri Dehkordi, Elahe Khesali, Amir Naghibi, Hossein Hasehmi
• DOI: 10.1016/b978-0-443-36394-8.00016-9

Research Groups

• Department of Photogrammetry and Remote Sensing, Faculty of Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
• Division of Water Resources Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
• United Nations University Hub on Water in a Changing Environment (WICE) at Lund University, United Nations University Institute for Water, Environment and Health (UNU-INWEH).
• Centre for Advanced Middle Eastern Studies, Lund University, Lund, Sweden.

Short Summary

This research develops a framework to enhance the monitoring of inland Water Surface Elevation (WSE) by fusing Sentinel-3 satellite altimetry data with climate variables using machine learning. The approach aims to overcome the spatial and temporal limitations of traditional in situ gauging stations for effective water resource management.

Objective

• To improve the accuracy and reliability of inland water surface elevation (WSE) monitoring by integrating satellite radar altimetry with environmental climate drivers through machine learning fusion techniques.

Study Configuration

• Spatial Scale: Regional to Global (focused on inland surface water bodies/SWRs).
• Temporal Scale: Multitemporal, aligned with the repeat cycles of the Sentinel-3 satellite mission.

Methodology and Data

• Models used: Machine Learning (ML) algorithms for data fusion; waveform retracking techniques for processing altimetry signals.
• Data sources: Sentinel-3 satellite radar altimeter data (utilizing Ku-band and C-band frequencies), climate variables, and historical hydrological records.

Main Results

• Identification of satellite altimetry as a transformative tool for tracking SWR dynamics in remote or inaccessible regions where traditional gauging is unfeasible.
• Determination that radar altimeters (operating in microwave frequencies) provide superior reliability for WSE monitoring compared to laser altimeters due to their ability to function under all weather conditions and penetrate cloud cover.
• Conceptual validation of a fusion-based approach that utilizes climate variables to complement satellite-derived elevation measurements, addressing gaps in traditional profiling techniques.

Contributions

• Proposes a novel integration of machine learning with Sentinel-3 altimetry specifically tailored for inland water bodies.
• Advances the field of satellite hydrology by moving beyond simple profiling to a multi-source data fusion framework.
• Provides a scalable solution for monitoring water storage changes, which is critical for flood forecasting, irrigation planning, and hydroelectric power generation under the pressures of climate change.

Funding

• Not specified in the provided text.

Citation

@article{Rezapour2026Enhanced,
  author = {Rezapour, Mahdis and zoej, mohammad javad valadan and Dehkordi, Alireza Taheri and Khesali, Elahe and Naghibi, Amir and Hasehmi, Hossein},
  title = {Enhanced remote sensing of water surface elevation through fusion of Sentinel-3 altimeter data and climate variables using machine learning},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-36394-8.00016-9},
  url = {https://doi.org/10.1016/b978-0-443-36394-8.00016-9}
}

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