A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)

Park, Edward; Merino, Eder; W. Lewis, Quinn; O. Lindsey, Eric; Yang, Xiankun

doi:10.3390/rs12081353

Open AccessArticle

A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)

by

Edward Park

^1,2,*

,

Eder Merino

³

,

Quinn W. Lewis

⁴,

Eric O. Lindsey

⁵ and

Xiankun Yang

⁶

¹

National Institute of Education, Nanyang Technological University, Singapore 637551, Singapore

²

Asian School of the Environment, Nanyang Technological University, Singapore 637551, Singapore

³

Institute of Energy and Environment, University of Sao Paulo, São Paulo 05508-060, Brazil

⁴

Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada

⁵

Earth Observatory of Singapore, Nanyang Technological University, Singapore 637551, Singapore

⁶

School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2020, 12(8), 1353; https://doi.org/10.3390/rs12081353

Submission received: 1 April 2020 / Revised: 20 April 2020 / Accepted: 21 April 2020 / Published: 24 April 2020

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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Abstract

Global measurements of reservoir water levels are crucial for understanding Earth’s hydrological dynamics, especially in the context of global industrialization and climate change. Although radar altimetry has been used to measure the water level of some reservoirs with high accuracy, it is not yet feasible unless the water body is sufficiently large or directly located at the satellite’s nadir. This study proposes a gauging method applicable to a wide range of reservoirs using Sentinel–1 Synthetic Aperture Radar data and a digital elevation model (DEM). The method is straightforward to implement and involves estimating the mean slope–corrected elevation of points along the reservoir shoreline. We test the model on six case studies and show that the estimated water levels are accurate to around 10% error on average of independently verified values. This study represents a substantial step toward the global gauging of lakes and reservoirs of all sizes and in any location where a DEM is available.

Keywords: reservoir; lakes; water level; remote sensing; sentinel; DEM

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MDPI and ACS Style

Park, E.; Merino, E.; W. Lewis, Q.; O. Lindsey, E.; Yang, X. A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR). Remote Sens. 2020, 12, 1353. https://doi.org/10.3390/rs12081353

AMA Style

Park E, Merino E, W. Lewis Q, O. Lindsey E, Yang X. A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR). Remote Sensing. 2020; 12(8):1353. https://doi.org/10.3390/rs12081353

Chicago/Turabian Style

Park, Edward, Eder Merino, Quinn W. Lewis, Eric O. Lindsey, and Xiankun Yang. 2020. "A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)" Remote Sensing 12, no. 8: 1353. https://doi.org/10.3390/rs12081353

APA Style

Park, E., Merino, E., W. Lewis, Q., O. Lindsey, E., & Yang, X. (2020). A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR). Remote Sensing, 12(8), 1353. https://doi.org/10.3390/rs12081353

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A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)

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