# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 4 Summary  
This study has reviewed existing principles and methods for inundation mapping using SAR data. As microwave measurements are significantly less disturbed by weather than measurements from optical sensors, SAR has the greater potential for high-resolution flood mapping. Efforts to automate the process leave a number of common errors unaddressed by most of the reviewed studies:  
(1) The argument that flood maps resulting from region growing should include all \"open water\" pixels connected to the seeds may be untrue and lead to instances of under- and over- detection. If captured after the apex, large isolated and scattered flood pockets disconnected from the pre-flooded water bodies can develop at times due to variability in surface elevation and barriers. Limited by image resolution, narrow water paths or paths covered by vegetation may also appear isolated from known core water zones. Water-like areas can be connected to real water bodies as well; airports built, respectively, in Boston along the Atlantic Ocean and in San Francisco along the Pacific Ocean, for example, may be identified as water areas because they are water-like (smooth) and connected to real water bodies. (2) Change detection at pixel level can eliminate false positives caused by water-like surfaces, but it is sensitive to noise-like speckle and geometric errors. In SAR applications, change detection approaches need to be applied with caution. Change in shadow areas, for instance, can be caused by change in the looking direction. Most studies that have used change detection models (including those reviewed here) have compared backscattering of collocated SAR pixels within and outside the flood period. Backscattering exhibits large heterogeneity in space and time, however, indicating that in a single object (water or non-water), the direct difference in backscattering might have led to erroneous interpretations. In addition, change detection was carried out from the output of RGA. Consequently, isolated inundation areas could not be detected. (3) Although a speckle model has been taken into account by most studies, false positives and negatives caused by speckle pixels are not effectively reduced by other than brutal filtering techniques. More advanced approaches target on reducing the noisy effects without damaging image  
Figure 4: During Harvey, (**a**) Sentinel-1 SAR data availability days and coverage in Texas and (**b**) the inundated times of the Seabrook area. details, including selecting the optimal filter, simple morphological operators, multi-thresholding compensation, machine learning refinement and utilization of big EO data. (4) Most approaches have not been tested in large areas, and their validation is usually limited to small areas along rivers. In particular, the limit of SAR-based mapping in terms of river width has not been reported. Limited by the 5-10 m resolution of noncommercial SAR satellites and the presence of speckle, one should not expect a successful rate in delineating inundation in headwater regions. In the past decades, measurements based on satellite SAR have shifted from data scarcity to abundance. At present, Sentinel-1 enables new approaches taking advantage of big EO data by offering free and global SAR measurements from a large number of repeated observations. With its six-day revisiting intervals, however, Sentinel-1 alone cannot provide sufficiently frequent revisits for floods of short duration. More frequent scanning of the earth will be implemented by the constellation of SAR satellites (e.g., the four-satellite constellation of Cosmos SkyMed can provide revisits within a few hours) and planned SAR satellites (e.g., the NASA-ISRO SAR Satellite Mission (NISAR), planned to launch in 2020, is designed to provide spatiotemporal resolution of 5-10 m and four to six times per month). Consequently, newly emerging studies utilizing the big EO data have advanced to reduce the summarized error to a great extent.