Next Article in Journal
Integrating UAV and TLS Approaches for Environmental Management: A Case Study of a Waste Stockpile Area
Previous Article in Journal
The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China
Open AccessArticle

Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine

Department of Physical Geography and Geoinformatics, University of Szeged, 6722 Szeged, Hungary
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1614; https://doi.org/10.3390/rs12101614
Received: 30 March 2020 / Revised: 12 May 2020 / Accepted: 14 May 2020 / Published: 18 May 2020
Saline wetlands experience large temporal fluctuations in water supply during the year and are recharged only or mainly through precipitation, meaning they are vulnerable to climate-change-induced aridification. Most passive satellite sensors are unsuitable for continuous wetland monitoring due to cloud cover and their relatively low temporal resolution. However, active satellite sensors such as the C-band synthetic aperture radar of Sentinel-1 satellites offer free, cloud-independent data. We examined surface water cover changes from October 2014 to November 2018 in the strictly protected area (13,000 ha) of the Upper-Kiskunság Alkaline Lakes region in the Danube–Tisza Interfluve in Hungary, with the aim of helping with nature protection planning. Changes and sensitivity can be defined based on the knowledge of variability. We developed a method for water cover detection based on automatic classification, applying the so-called WEKA K-Means clustering algorithm. For satellite data processing and analysis, we used the Google Earth Engine cloud processing platform. In terms of validation, we compared our results with the multispectral Modified Normalized Difference Water Index (MNDWI) derived from Landsat 8 and Sentinel-2 top-of-atmosphere reflectance images using a threshold-based binary classifier (receiver operator characteristics) for the MNDWI data. Using two completely distinct methods operating in distinct wavelength ranges, we obtained adequately matching results, with Spearman’s correlation coefficients (ρ) ranging from 0.54 to 0.80. View Full-Text
Keywords: Sentinel-1; synthetic aperture radar; Google Earth Engine; wetlands; surface water cover; cluster analysis Sentinel-1; synthetic aperture radar; Google Earth Engine; wetlands; surface water cover; cluster analysis
Show Figures

Graphical abstract

MDPI and ACS Style

Gulácsi, A.; Kovács, F. Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine. Remote Sens. 2020, 12, 1614.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop