Enhanced Remote Sensing of Inland Water Surface Elevation Using Sentinel-3 Radar Altimeter (SRAL)
Rezapour, Mahdis ; Zoej, Mohammad Javad Valadan ; Dehkordi, Alireza Taheri LU ; Khesali, Elahe ; Mehran, Ali ; Farahmand, Alireza ; Naghibi, Amir LU and Hashemi, Hossein LU
(2025)
2025 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2025
p.8328-8331
- Abstract
Inland surface waters are vital for the global carbon, energy, and water cycles; however, they are affected by climate change and human activities. Continuous monitoring of Water Surface Elevation (WSE) is essential for assessing water quantity and ensuring sustainable management. Traditional in-situ WSE measurements often face high costs and accessibility challenges. This study examines the use of satellite altimetry, specifically from the Sentinel-3 (S3) Radar Altimeter (SRAL), to enhance WSE estimates through Machine Learning (ML) techniques, the Random Forest (RF) algorithm. By applying the RF algorithm to SRAL data over Lake Michigan, we corrected altimeter-derived WSE values against in-situ data. Our results show a reduction in... (More)
Inland surface waters are vital for the global carbon, energy, and water cycles; however, they are affected by climate change and human activities. Continuous monitoring of Water Surface Elevation (WSE) is essential for assessing water quantity and ensuring sustainable management. Traditional in-situ WSE measurements often face high costs and accessibility challenges. This study examines the use of satellite altimetry, specifically from the Sentinel-3 (S3) Radar Altimeter (SRAL), to enhance WSE estimates through Machine Learning (ML) techniques, the Random Forest (RF) algorithm. By applying the RF algorithm to SRAL data over Lake Michigan, we corrected altimeter-derived WSE values against in-situ data. Our results show a reduction in the Root Mean Squared Error (RMSE)—from 35 cm for the mean WSE across all Virtual Station (VS) points to 9 cm for the RF-corrected WSE—when compared against in-situ measurements using a Leave-One-Date-Out (LODO) approach. Additionally, the R-squared (R2) improved from 0.88 to 0.96, indicating a stronger correlation between the corrected WSE and in-situ measurements. These findings highlight the effectiveness of the ML method in improving the accuracy of altimetry-based WSE estimation. Moreover, this research underscores the potential of integrating ML methods with remote sensing techniques for enhanced inland water resource management.
(Less)
- author
- Rezapour, Mahdis
; Zoej, Mohammad Javad Valadan
; Dehkordi, Alireza Taheri
LU
; Khesali, Elahe
; Mehran, Ali
; Farahmand, Alireza
; Naghibi, Amir
LU
and Hashemi, Hossein
LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Machine Learning, Random Forest, Remote Sensing, Satellite Altimetry, Sentinel-3
- host publication
- IGARSS 2025 : 2025 IEEE International Geoscience and Remote Sensing Symposium - 2025 IEEE International Geoscience and Remote Sensing Symposium
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2025 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2025
- conference location
- Brisbane, Australia
- conference dates
- 2025-08-03 - 2025-08-08
- external identifiers
-
- scopus:105033914706
- DOI
- 10.1109/IGARSS55030.2025.11243573
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 IEEE.
- id
- 8effe2e9-6898-4099-8b62-917895c9c503
- date added to LUP
- 2026-05-30 19:13:51
- date last changed
- 2026-06-02 14:26:24
@inproceedings{8effe2e9-6898-4099-8b62-917895c9c503,
abstract = {{<p>Inland surface waters are vital for the global carbon, energy, and water cycles; however, they are affected by climate change and human activities. Continuous monitoring of Water Surface Elevation (WSE) is essential for assessing water quantity and ensuring sustainable management. Traditional in-situ WSE measurements often face high costs and accessibility challenges. This study examines the use of satellite altimetry, specifically from the Sentinel-3 (S3) Radar Altimeter (SRAL), to enhance WSE estimates through Machine Learning (ML) techniques, the Random Forest (RF) algorithm. By applying the RF algorithm to SRAL data over Lake Michigan, we corrected altimeter-derived WSE values against in-situ data. Our results show a reduction in the Root Mean Squared Error (RMSE)—from 35 cm for the mean WSE across all Virtual Station (VS) points to 9 cm for the RF-corrected WSE—when compared against in-situ measurements using a Leave-One-Date-Out (LODO) approach. Additionally, the R-squared (R<sup>2</sup>) improved from 0.88 to 0.96, indicating a stronger correlation between the corrected WSE and in-situ measurements. These findings highlight the effectiveness of the ML method in improving the accuracy of altimetry-based WSE estimation. Moreover, this research underscores the potential of integrating ML methods with remote sensing techniques for enhanced inland water resource management.</p>}},
author = {{Rezapour, Mahdis and Zoej, Mohammad Javad Valadan and Dehkordi, Alireza Taheri and Khesali, Elahe and Mehran, Ali and Farahmand, Alireza and Naghibi, Amir and Hashemi, Hossein}},
booktitle = {{IGARSS 2025 : 2025 IEEE International Geoscience and Remote Sensing Symposium}},
keywords = {{Machine Learning; Random Forest; Remote Sensing; Satellite Altimetry; Sentinel-3}},
language = {{eng}},
pages = {{8328--8331}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Enhanced Remote Sensing of Inland Water Surface Elevation Using Sentinel-3 Radar Altimeter (SRAL)}},
url = {{http://dx.doi.org/10.1109/IGARSS55030.2025.11243573}},
doi = {{10.1109/IGARSS55030.2025.11243573}},
year = {{2025}},
}