# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Approaches
#### 3.1.6 Unobstructed, Beneath-Vegetation Flood Versus Urban Flood  
Inundation detection over vegetated areas, partially submerged wetlands, and urban areas has gained attention recently. Theoretically, scattering is enhanced during flood time if a trunk structure exists. [PERSON] et al. [70] evaluated the backscattering difference caused by flooding under canopy. [PERSON] and [PERSON] [54] analyzed the sensitivity of multi-temporal/multi-frequency SAR data to different land covers and concluded that X-band can only be used to detect inundation beneath sparse vegetation or forest during the leaf-off period, while L-band, though characterized by better penetration, has a very wide range of backscattering enhancement, which also obscures the reliability of the classification. [PERSON] [44] utilized ground data to train a decision tree to identify flooding beneath a forest using Radarsat-1 ([[http://www.asc-csa.gc.ca/eng/satellites/radarsat1/Default.asp](http://www.asc-csa.gc.ca/eng/satellites/radarsat1/Default.asp)]([http://www.asc-csa.gc.ca/eng/satellites/radarsat1/Default.asp](http://www.asc-csa.gc.ca/eng/satellites/radarsat1/Default.asp))). [PERSON] et al. [68] input the enhanced backscattering at C-band and phase difference of co-polarizations (HH-VV) to the ACM to generate water lines from selected known open water (ocean) and coastal dryland pixels. The area between the dry contour and open water was considered as flooded vegetation. [PERSON] et al. [42] trained a set of rules using visually interpreted ROIs to extract flooded forest and urban areas from COSMO-SkyMed SAR data. [PERSON] et al. [69] combined a fuzzy logic classifier [47] and segmentation method [63] to monitor flood evolution in vegetated areas using COSMO-SkyMed ([[http://www.e-geos.it/cosmo-skymed.html](http://www.e-geos.it/cosmo-skymed.html)]([http://www.e-geos.it/cosmo-skymed.html](http://www.e-geos.it/cosmo-skymed.html))). Given its potential for flood detection under vegetation, most studies have adopted a supervised classification, which can hardly be automated. Specifically, the enhanced dihedral scattering of vegetation cannot be considered as a single class because of different vegetation species, structure, and leaf-off and leaf-on conditions. Such heterogeneity makes it difficult to determine automatically a threshold of backscattering enhancement. In other words, the issue in detecting floods beneath vegetation is identifying multiple classes from an image, the automation of which is more challenging than identifying a single class. Until now, few studies have been made available on flood mapping in urban areas [33; 34; 35; 36; 71; 72], and only a handful [33; 34; 35; 73] investigated the use of dihedral scattering to extract flooded buildings using either the intensity enhancement or the loss coherence. Because of the vertical structure of many buildings, the challenges in detecting urban inundation using intensity enhancement share some similarities with those of identifying floods under vegetation. An additional challenge arises from asymmetric building structure as compared to the symmetric structure of vegetation. Such enhancement only occurs when the radar line of sight (LoS) is orthogonal to the horizontal alignment of the building face [33; 35]. Consequently, utilizing intensity enhancement cannot guarantee complete detection of flooded buildings and requires to know the geometry, orientation, and even material of buildings, and the direction of illumination [74]. Such more ancillary data may not always be available. In contrast, the loss of coherence, though can be created by the dihedral scattering, may still work when the LoS moves away from the orthogonal direction. Either the enhancement or the presence of water, breaks the original backscattering arrangement of building pixels, causing the decrease of coherence. Moreover, smooth artificial surfaces and shadowing areas may also create over-detection which need to be masked out from detection.