Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques
Accurate surface water volume (SWV) estimates are crucial for effective water resource management and for the regional monitoring of hydrological trends. This study introduces a multi-resolution surface water volume estimation framework that integrates ICESat-2 altimetry, Sentinel-1 Synthetic Apertu...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3263/15/7/255 |
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| author | Megan Renshaw Lori A. Magruder |
| author_facet | Megan Renshaw Lori A. Magruder |
| author_sort | Megan Renshaw |
| collection | DOAJ |
| description | Accurate surface water volume (SWV) estimates are crucial for effective water resource management and for the regional monitoring of hydrological trends. This study introduces a multi-resolution surface water volume estimation framework that integrates ICESat-2 altimetry, Sentinel-1 Synthetic Aperture Radar (SAR), and Sentinel-2 multispectral imagery via machine learning to improve the vertical resolution of a digital elevation model (DEM) to improve the accuracy of SWV estimates. The machine learning approach provides a significant improvement in terrain accuracy relative to the DEM, reducing RMSE by ~66% and 78% across the two models, respectively, over the initial data product fidelity. Assessing the resulting SWV estimates relative to GRACE-FO terrestrial water storage in parts of the Amazon Basin, we found strong correlations and basin-wide drying trends. Notably, the high correlation (r > 0.8) between our surface water estimates and the GRACE-FO signal in the Manaus region highlights our method’s ability to resolve key hydrological dynamics. Our results underscore the value of improved vertical DEM availability for global hydrological studies and offer a scalable framework for future applications. Future work will focus on expanding our DEM dataset, further validation, and scaling this methodology for global applications. |
| format | Article |
| id | doaj-art-2630dfb5df5947aaa9ee8b3fa85bade5 |
| institution | DOAJ |
| issn | 2076-3263 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj-art-2630dfb5df5947aaa9ee8b3fa85bade52025-08-20T03:08:01ZengMDPI AGGeosciences2076-32632025-07-0115725510.3390/geosciences15070255Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning TechniquesMegan Renshaw0Lori A. Magruder1Department of Aerospace Engineering and Engineering Mechanics, Cockrell School of Engineering, University of Texas at Austin, Austin, TX 78705, USADepartment of Aerospace Engineering and Engineering Mechanics, Cockrell School of Engineering, University of Texas at Austin, Austin, TX 78705, USAAccurate surface water volume (SWV) estimates are crucial for effective water resource management and for the regional monitoring of hydrological trends. This study introduces a multi-resolution surface water volume estimation framework that integrates ICESat-2 altimetry, Sentinel-1 Synthetic Aperture Radar (SAR), and Sentinel-2 multispectral imagery via machine learning to improve the vertical resolution of a digital elevation model (DEM) to improve the accuracy of SWV estimates. The machine learning approach provides a significant improvement in terrain accuracy relative to the DEM, reducing RMSE by ~66% and 78% across the two models, respectively, over the initial data product fidelity. Assessing the resulting SWV estimates relative to GRACE-FO terrestrial water storage in parts of the Amazon Basin, we found strong correlations and basin-wide drying trends. Notably, the high correlation (r > 0.8) between our surface water estimates and the GRACE-FO signal in the Manaus region highlights our method’s ability to resolve key hydrological dynamics. Our results underscore the value of improved vertical DEM availability for global hydrological studies and offer a scalable framework for future applications. Future work will focus on expanding our DEM dataset, further validation, and scaling this methodology for global applications.https://www.mdpi.com/2076-3263/15/7/255ICESat-2GRACE-FOmachine learninghydrologySentinel-2surface water volume |
| spellingShingle | Megan Renshaw Lori A. Magruder Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques Geosciences ICESat-2 GRACE-FO machine learning hydrology Sentinel-2 surface water volume |
| title | Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques |
| title_full | Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques |
| title_fullStr | Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques |
| title_full_unstemmed | Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques |
| title_short | Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques |
| title_sort | advancing scalable methods for surface water monitoring a novel integration of satellite observations and machine learning techniques |
| topic | ICESat-2 GRACE-FO machine learning hydrology Sentinel-2 surface water volume |
| url | https://www.mdpi.com/2076-3263/15/7/255 |
| work_keys_str_mv | AT meganrenshaw advancingscalablemethodsforsurfacewatermonitoringanovelintegrationofsatelliteobservationsandmachinelearningtechniques AT loriamagruder advancingscalablemethodsforsurfacewatermonitoringanovelintegrationofsatelliteobservationsandmachinelearningtechniques |