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Article

Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece

by
Nikolaos Gourgouletis
1,*,
Georgios Bariamis
1,
Marios N. Anagnostou
2,3 and
Evangelos Baltas
1
1
Department of Water Resources & Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Str. Iroon Politexniou 9, 157 80 Zografou, Greece
2
Department of Informatics, School of Information Science & Informatics, Ionian University, Plateia Tsirigoti 7, 491 00 Corfu, Greece
3
Institute of Environmental Research and Sustainable Development, National Observatory of Athens, I. Metaxas and Vassilis Pavlou, 152 36 Penteli, Greece
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(8), 1860; https://doi.org/10.3390/rs14081860
Submission received: 22 February 2022 / Revised: 24 March 2022 / Accepted: 10 April 2022 / Published: 12 April 2022

Abstract

Inland water resources are facing increasing quantitative and qualitative pressures, deriving from anthropogenic causes and the ongoing climate change. The monitoring of reservoirs is essential for sustainable management and preparation against water scarcity and extreme events, such as droughts. This research, relying on the Sentinel-2 and 3 missions, attempts to demonstrate the efficiency of combining remotely sensed water level and water area estimations, in order to estimate the water storage variation of Yliki reservoir. The case study is conducted in one of the few sufficiently monitored reservoirs in Greece, enabling a direct comparison of the proposed methodology results with in situ observations. Moreover, this research work proposes a weekly time interval for pairing level and area estimations, instead of shorter time intervals. The results strongly demonstrate the efficiency of remote sensing in the production of empirical level–area–storage (L–A–S) curves. Correlation to in situ monitored storage- and satellite-derived water level, area stand for 98.81% and 99.27% respectively. Water storage variation is estimated and compared to the observed time series, resulting in an RMSE of 1.28% of the reservoir capacity and a correlation of 96.14%. The empirical L–S relationship underestimates storage, while the A–S relationship overestimates storage when compared to the existing L–A–S curve.
Keywords: reservoir storage; remote sensing; satellite water level monitoring; water area reservoir storage; remote sensing; satellite water level monitoring; water area
Graphical Abstract

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MDPI and ACS Style

Gourgouletis, N.; Bariamis, G.; Anagnostou, M.N.; Baltas, E. Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece. Remote Sens. 2022, 14, 1860. https://doi.org/10.3390/rs14081860

AMA Style

Gourgouletis N, Bariamis G, Anagnostou MN, Baltas E. Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece. Remote Sensing. 2022; 14(8):1860. https://doi.org/10.3390/rs14081860

Chicago/Turabian Style

Gourgouletis, Nikolaos, Georgios Bariamis, Marios N. Anagnostou, and Evangelos Baltas. 2022. "Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece" Remote Sensing 14, no. 8: 1860. https://doi.org/10.3390/rs14081860

APA Style

Gourgouletis, N., Bariamis, G., Anagnostou, M. N., & Baltas, E. (2022). Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece. Remote Sensing, 14(8), 1860. https://doi.org/10.3390/rs14081860

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