Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management

Abstract

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m2. The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.

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

“…Our conclusions are backed by the results of domestic research groups that examined the integration potential of Sentinel-1 C-SAR and Sentinel-2 MSI optical data for mapping inland excess water [54,58,59]. This is a local phenomenon that represents unwanted surface water patches on agricultural land, especially in the melting period at the end of winter and during spring, which has multiple causes [54].…”

Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

“…The difference between the results was the number of cases in the input data; the omission of the water category could also have an effect on the accuracies and the fact that only 50 data were used per category to train the models. Water body is a usually well distinguishable land cover class [84][85][86], but due to the low number of cases, we excluded it from the analysis, which could reduce the OA by having fewer true positive polygons. The other possible reason is that the different spatial resolutions could also have a bias through the number of pixels involved in a given CLC-category: statistical parameters were calculated by polygons with 9 times more data with the Sentinel and with 100 times more with the PlanetScope satellites compared to Landsat.…”

Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery

“…applications (Streutker, 2002;Li et al 2009;Szabó Z. et al 2019;Paramita and Matzarakis, 2019), agriculture (Atzberger, 2013), water quality monitoring (Chebud et al 2012), drought monitoring (Gulácsi and Kovács, 2018) mapping at wetlands (Szabó et al 2020;Van Leeuwen et al 2020) and erosion risk assessment (Bakacsi et al 2019;Phinzi et al 2020).…”