Globally, flooding is the leading cause of natural disaster related deaths, especially in Bangladesh where approximately one third of national area gets flooded annually by overflowing rivers during the monsoon season, which drastically affects paddy rice agriculture and food security. However, existing studies on the pattern of floods and their impact on agriculture in Bangladesh are limited. Here we examined the spatiotemporal pattern of floods for the country during 2014–2018 using all the available Sentinel-1 Synthetic Aperture Radar (SAR) images and the Google Earth Engine (GEE) platform. We also identified the flood-affected paddy rice fields by integrating the flooding areas and remote sensing-based paddy rice planting areas. Our results indicate that flooding is frequent in northeastern Bangladesh and along the three major rivers, the Ganges, Brahmaputra, and Meghna. Between 2014 and 2018, the flood-affected paddy rice areas accounted for 1.61–18.17% of the total paddy rice area. The satellite-based detection of floods and flood-affected paddy rice fields advance our understanding of the annual dynamics of flooding in Bangladesh, which is essential for adaptation and mitigation strategies where severe annual floods threaten human lives, properties, and agricultural production.

Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine

Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 SAR (Synthetic Aperture Radar) images as a powerful tool for flood mapping applied in the event of extraordinary floods that occurred during the month of April 2018 in the Ebro (Spain). More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation. Evaluating the different Sentinel-1 parameters, our analysis shows that the best results on the final flood mapping for this study area were obtained using VH (Vertical-Horizontal) polarization configuration and Lee filtering 7 × 7 window sizes. Two methods were applied to flood maps from Sentinel-1 SAR images: (1) RGB (Red, Green, Blue color model) composition based on the differences between the pre- and post-event images; and (2) the calibration threshold technique or binarization based on histogram backscatter values. When comparing our flood maps with the flood areas digitalized from vertical aerial photographs, done by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were coincident. The result of the flooding map obtained with the RADAR (Radio Detection and Ranging) image were compared with the layers with different return periods (10, 50, 100, and 500 years) for a selected zone of the study area of SNCZI (National Flood Zone Mapping System in Spain). It was found that the images are consistent and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.

Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study

River flood mapping in urban areas combining Radarsat-2 data and flood return period data

A multi-index assessment method for evaluating coverage effectiveness of remote sensing satellite

Evaluation of SAR speckle filter technique for inundation mapping

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

/pdf/integration-of-speckle-de-noising-and-image-segmentation-msofvsu5y6.pdf

Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction

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Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction