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

Christian Schwatke,Daniel Scherer,Denise Dettmering

doi:10.3390/rs11091010

Christian Schwatke, Daniel Scherer + Show 1 more

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https://doi.org/10.3390/rs11091010

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Journal: Remote Sensing	Publication Date: Apr 28, 2019
Citations: 62	License type: CC BY 4.0

Affiliation: Technical University of Munich

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).

Highlights

Monitoring and modeling of the Earth’s water cycle has become increasingly important in the last few years, especially in the context of climate change
In remote areas where no in situ data is available, we are use water level time series from satellite altimetry which are available at the Database of Hydrological Time Series of Inland (DAHITI)
This paper presents a new approach for the automated extraction of consistent time-variable water surfaces of lakes and reservoirs using optical images from Landsat and Sentinel-2

Summary

Introduction

Monitoring and modeling of the Earth’s water cycle has become increasingly important in the last few years, especially in the context of climate change. Though only 0.013% [1] of the Earth‘s water is stored in lakes and reservoirs, the knowledge about storage changes is of great importance for the development of hydrological models. In the 1970s, the first remote sensing satellites such as Landsat-1 for mapping the Earth‘s surface and Seasat for measuring the water level from space were launched. In the following decades until the present day, numerous further remote sensing satellites carrying sensors of improved quality have been launched. The usage of multi-mission approaches allows scientists to create time series of surface areas or water levels over a period of more than 30 years for lakes and reservoirs. Over the last few decades, the number of in situ data used for local monitoring has strongly decreased. An example for decreasing of in situ data is the Global Runoff Data Center (GRDC) where the number of river discharge stations decreased from about 7900 to 600 between 1979 and 2018 [4]

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