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Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2

Deutsches Geodätisches Forschungsinstitut, Technische Universität München, Arcisstraße 21, 80333 München, Germany
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Remote Sens. 2019, 11(9), 1010; https://doi.org/10.3390/rs11091010
Received: 7 March 2019 / Revised: 22 April 2019 / Accepted: 23 April 2019 / Published: 28 April 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI). View Full-Text
Keywords: surface area time series; spatial data gaps; lakes; reservoirs; Landsat; Sentinel-2; MNDWI; NWI; AWEIsh; AWEInsh; TCwet; satellite altimetry; DAHITI; AWAX surface area time series; spatial data gaps; lakes; reservoirs; Landsat; Sentinel-2; MNDWI; NWI; AWEIsh; AWEInsh; TCwet; satellite altimetry; DAHITI; AWAX
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Schwatke, C.; Scherer, D.; Dettmering, D. Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2. Remote Sens. 2019, 11, 1010.

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