Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

Abstract

Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165 gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

“…Currently, the cartographic monitoring of floods in the Ganges River Delta is mostly based on GIS-based mapping [29][30][31]. Besides the conventional approaches, recent studies in the study area have used alternative data such as Sentinel-1 SAR images and data from the Google Earth Engine [32,33]. Such case studies have raised questions about operative and accurate methods for data processing aimed at flood prediction and management.…”

Optimal Fusion of Multispectral Optical and SAR Images for Flood Inundation Mapping through Explainable Deep Learning

“…Traditional machine learning models have yielded accuracies ranging from 70% to 90%, but deep learning has demonstrated superior performance [18][19][20][21][22]. Deep learning models, particularly Convolutional Neural Networks (CNN), have been widely adopted for flood inundation mapping due to their ability to learn complex, non-linear relationships directly from raw data.…”

A Temporal Deep Convolutional Neural Network Model on Sentinel-1 Image Time Series for Pixel-Wise Flood Classification

“…Core flood detection approaches regardless of number of images are thresholding, change detection [5], change detection and thresholding [6], supervised [7,8], semi-supervised [9,10] and unsupervised [2] image classification based on classical machine and deep learning. Multi-temporal pixelwise time series approaches such as [11,12], despite being lightweight, do not exploit the temporal autocorrelation, as they treat the time series using statistical analysis without any learning procedure.…”