Abstract
Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water and provide useful information regarding operational flood monitoring, in particular for the improvement of rapid flood assessments. However, this application frequently requires standard and simple, yet robust, algorithms. Although thresholding approaches meet these requirements, limitations such as data inequality over large spatial regions and challenges in estimating optimal threshold values remain. Here, we propose a new method for SAR water extraction named Hammock Swing Thresholding (HST). We applied this HST approach to four SAR remote sensing datasets, namely, Sentinel-1, ALOS-2, TerraSAR-X, and RadarSAT-2 for flood inundation mapping for a case study focusing on the Tam Nong district in the Vietnam Mekong delta. A 2D calibrated Hydrologic Engineering Centers River Analysis System (HEC-RAS) model was coupled with the HST outputs in order to estimate the optimal thresholds (OTs) where the SAR-based water masks fitted best with HEC-RAS’s inundation patterns. Our results showed that water levels extracted from Sentinel-1 data best agreed with the HEC-RAS water extent (88.3%), following by ALOS-2 (85.9%), TerraSAR-X (77.2%). and RadarSAT-2 (72%) at OTs of −15, 68, 21, and 35 decibel (dB), respectively. Generated flood maps indicated changes in the flood extent of the flooding seasons from 2010 and 2014–2016 with variations in spatial extent appearing greater in the TerraSAR-X and RadarSAT-2 higher resolution maps. We recommend the use of OTs in applications of flood monitoring using SAR remote sensing data, such as for an open data cube (ODC).
Highlights
Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water [1,2,3] and provide useful information for flood monitoring [4,5] due to its large temporal scale [6]
The main reason for the popular use of SAR images for flood studies is that water surfaces are able to be classified robustly due to the lower backscatter returned to the SAR sensors compared to dry areas [8]
As the Sa Rai and Thuong Thoi Tien were newly installed stations, the observed data were only available for these short periods of time and were compared with the model simulated data independently
Summary
Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water [1,2,3] and provide useful information for flood monitoring [4,5] due to its large temporal scale (e.g., decadal periods) [6]. The main reason for the popular use of SAR images for flood studies is that water surfaces are able to be classified robustly due to the lower backscatter returned to the SAR sensors compared to dry areas [8]. Fewer studies have inverted this process and used the results of hydrological/hydraulic models to calibrate remote sensing-based flood extractions. Hydrological and hydraulic modeling commonly require extensive data inputs [12,13] based around four key groupings, namely, topographic, hydro-meteorological, soil, and land cover data
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