Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin

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).

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

“…If various RADAR sensors with different sensor specifics are implemented, then more fluvial traits can be investigated and the weaknesses of the sensors can offset each other. Hong Quang et al [199] used hydrological/hydraulic modeling-based thresholding of multi SAR RS sensors (Sentinel-1) to monitor floods in regions of Vietnam's Lower Mekong River Basin. Alsdorf et al [200] used InSAR technologies to measure water level changes on the Amazon floodplain.…”

Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam

“…The district is home to 220,000 people (2019 statistics), who live in an area of 474 km 2 . The average rainfall range varies from 1170 to 1520 mm, and 90-95% of the total annual rainfall is concentrated in the rainy season (May to November) (Quang et al, 2019). The typical crop is rice, which is divided into 2-(no irrigation) or 3-season rice (with irrigation and not flooded in the flooding season).…”

“…An optimal thresholding algorithm called Hammock Swing Thresholding (HST) was developed, which was applied in Quang et al (2019) for both Sentinel-1 and AlOS-2 images because it has shown its high accuracy and consistency with different SAR inputs. The concept of HST is simple: it minimizes the total absolute values of the difference layer between the SAR and reference water extent in order to obtain an optimal Vegetation type S1 AL2 S1 Scatters AL2 Scatters…”

“…In a previous study by Quang et al (2019), although the authors tried to map flood inundation using optimism Hammock Swing Thresholding (HST) algorithms of matching SAR-RS-based water masks with a well-calibrated hydraulic model output, the difference remained at a rate of 11.7% for the use of Sentinel-1 images. It is considered that the accuracy of the inundation map could be improved if the inundated vegetation areas were classified and accounted for in the flooded areas.…”

Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study

“…Remote sensing-based flood-detection methods can be divided into two categories: optical and radar sensor. Optical data such as WorldView2 [4], Landsat [5][6][7], Spot-5 [8], sentinel [9], Moderate-Resolution Imaging Spectroradiometer (MODIS) [7,10] and RADAR [8,[11][12][13] had been explored for flood detection. In the meanwhile, machine-learning algorithms including decision tree algorithm [4,14], SVM [6,8], MLP [8] had been developed for flood detection.…”