# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## SONAR
### Summary and Future Direction
#### 2.8.2 AV and CR Mapping  
Optical, SAR, LiDAR, and SONAR datasets have been widely used for AV and CR mapping. AV and CR mapping methods using RS data can be generally divided into two groups of empirical and physical models [295]. Empirical models focus on the empirical relationship between in situ and RS data [346]. On the other hand, physical approaches utilize prior knowledge about AV and CR based on RT models [295; 347], which simulate the attenuation of the irradiance through the atmosphere. RT models require a considerable amount of input parameters, the optimization of which increases the computational cost and the amount of effort. In the following subsections, the applications of different RS systems for AV and CR mapping are discussed.  
Optical  
The number of extracted AV and CR classes and the level of accuracy of optical RS methods directly depend on the environmental conditions (e.g., water quality, water depth, and sea surface situation) and optical sensor characteristics (spatial, spectral, and temporal). If water quality is suitable and the seabed is visible, AV and CR can be reasonably mapped. However, if the seabed is invisible, mapping is almost impossible, even after the optical imagery is enhanced [295]. Moreover, Due to the air-water interface issues, the spectral differentiation of AV and CR using optical sensors needs specialized strategies [346]. The spatial resolution is also important for accurate AV and CR mapping. For example, very high spatial resolution images acquired by UAVs are commonly used for this purpose over relatively small areas and near coastlines. Moderate and high spatial resolution optical datasets (e.g., those acquired by Landsat and SPOT) are also common for this application over relatively large areas with acceptable accuracies [346; 348]. Furthermore, it has been argued that the spectral resolution of optical sensors plays an important role in the detection and classification of AV and CR types [346; 349]. Therefore, selecting a suitable sensor with a proper number of spectral bands (e.g., hyperspectral sensors), which can effectively identify small spectral differences, is very important for accurate AV and CR classification (see Figure 13).  
Figure 13: Spectral signatures of different (**a**) CRs [349] and (**b**) AVs [295].  
Many studies have employed optical datasets for AV and CR classification. For instance, [PERSON] et al. (2019) [350] mapped the extent of AV in Lake Iseo, Italy, between 2015 and 2017 based on in situ measurements and 22 Sentinel-2 images. For this purpose, a physical inversion model was applied to classify bare sand and AV classes. Inter- and intra-annual fluctuations in lacustrine spatial coverage, patterns of AV, and intense uprooting phenomena were also recorded in this study. The proposed method for mapping AV was concluded to be robust with acceptable accuracy. Moreover, [PERSON] et al. (2018) [351] combined an object-based image classification method and an ecological model for CR habitat mapping in the Great Barrier Reef. It was observed that the combination of Landsat-8 with geographic-ecological models could provide satisfactory results over a large area. Additionally, [PERSON] et al. (2013) [352] discussed the application of optical RS for AV mapping using statistical and ML algorithms. They studied the role of spatial and textural information for improving the discrimination of AV species using very high-resolution optical images. Their results showed that water depth was a limiting factor for AV species mapping from optical RS images. It was also observed that shape and texture information could improve the accuracy of the detection of AV species.