Inundation Extent Mapping by Synthetic Aperture Radar: A Review

Shen, Xinyi; Wang, Dacheng; Mao, Kebiao; Anagnostou, Emmanouil N.; Hong, Yang

doi:10.3390/rs11070879

Cited by 175 publications

(103 citation statements)

References 74 publications

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“…Sentinel-2 optical imagery has the capability to provide a distinct view of flood extent over a wide area. One major issue with the detection of flooded areas from optical imagery however, is the interference by clouds (Shen et. al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Extent Mapping of a Major Flooding Event on the Island of Trinidad Using Space-Borne Synthetic Aperture Radar

Ramsewak

Maharaj

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Flooding events around the world have been increasing both in their occurrence and their intensities within recent decades. Studies have shown that this is most likely linked to climate change effects and anthropogenic activities that lead to pollution. Irrespective of the cause, floods incur massive economic and human losses. Synoptic data on flooding events help to support the planning and management efforts during this disaster event. Remotely sensed data, particularly from satellites is useful for mapping and monitoring large scale flooding events. More specifically, synthetic aperture radar (SAR) allows for data acquisition despite the interference of clouds and other atmospheric elements such as fog, light rain and mist. This study utilized SAR data from the Sentinel–1 satellite to map a major flooding event on the island of Trinidad which occurred during October 18–21, 2018. The peak of the flooding was estimated to have occurred on October 20, 2018. The SAR images were first calibrated then geometrically corrected and filtered. A threshold method was then applied to extract the inundated areas. A proprietary algorithm implemented by Geospatial Enabling Technologies (GET) and based on SNAP software, was used for processing Sentinel-1 imagery to separate the open water and non-water (land) areas from the images. Outputs were then integrated into ArcGIS 10.6 mapping software and the extents of the flooded areas were delineated based on the available data. By applying this method to a Sentinel–1 image captured on October 19, 2018 it was revealed that the total flooded area on that date was 9.94 square kilometres. This study provides a brief illustration of the value of SAR data for flood delineation and mapping but also highlights some of the limitations that can be involved when using such technology.

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Section: Methodsmentioning

confidence: 99%

Extent Mapping of a Major Flooding Event on the Island of Trinidad Using Space-Borne Synthetic Aperture Radar

Ramsewak

Maharaj

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Synthetic aperture radar (SAR) sensors offer less sensitivity to cloud cover, allowing for a better frequency of images [9]. Hence, SAR is widely used for mapping water and flooded areas [8,[10][11][12][13][14]. New satellites such as the Sentinel constellation and particularly Sentinel-1 A and B (launched in 2014 and 2016 respectively) provide interesting repetitiveness to measure the impact of natural disasters and study the resilience of environments through time series.…”

Section: Introductionmentioning

confidence: 99%

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

et al. 2020

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In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

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“…This results from the fact that the necessary algorithms have limitations in terms of robustness, accuracy and automation. For instance Shen et al (2019) comment on the fact that automation and robustness have not been achieved yet for vegetated areas when using L-band observations. This means that misclassifications and missing data will be present in this type of retrievals.…”

Section: Introductionmentioning

confidence: 99%

“…• and geometric correction: Due to the limited accuracy of input elevation data and orbit accuracy, it is frequent to see location errors at the level of a few pixels. So far, algorithms have only partially addressed these issues and human intervention to reduce our-detection as well as filtering to reduce under-detection are needed (Shen et al, 2019). Therefore, techniques to interpolated missing areas and to reduce noise on retrieved maps need to be used as a post-processing step to improve quality of retrieval results.…”

Section: Introductionmentioning

confidence: 99%

Surface water maps de-noising and missing-data filling using determinist spatial filters based on several a priori information

Aires¹

2020

Remote Sensing of Environment

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Satellite observations are used to detect surface waters but uncertainties such as instrument noise or retrieval errors can introduce noise or missing-data in the resulting water maps, especially for datasets at the global scale. In this study, spatial filters based on several a priori information are proposed to reduce noise and perform spatial interpolation to fill missing-data in satellite-based surface water maps such as wetlands, rivers, lakes. Four main sources of a priori of information are considered: (1) historical information at the pixel level, (2) neighbouring information constraints based on a historical record, (3) constraints based on topography, and (4) hydrological constraints based on a floodability index. Experiments are conducted over synthetic but realistic data, as well as over real Sentinel 1 (SAR) and 2 (visible) water map retrievals. Mis-classification quantitative results over these three types of data show that simple determinist spatial filters allow reducing noise and filling missingdata. The four sources of a priori information can be exploited and combined to improve observed water maps. This opens some ways to develop post-processing tools for improving surface water maps at high spatial resolution from missions such as SWOT (Surface Water and Ocean Topography) to be launched in 2020.

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Inundation Extent Mapping by Synthetic Aperture Radar: A Review

Cited by 175 publications

References 74 publications

Extent Mapping of a Major Flooding Event on the Island of Trinidad Using Space-Borne Synthetic Aperture Radar

Extent Mapping of a Major Flooding Event on the Island of Trinidad Using Space-Borne Synthetic Aperture Radar

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

Surface water maps de-noising and missing-data filling using determinist spatial filters based on several a priori information

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