# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## SONAR
### Sonar  
Single-beam, multi-beam, and side-scan SONAR systems have been frequently applied to AV and CR mapping [359, 360]. Generally, in SONAR aquatic macrophyte estimation, the acoustic signals are transmitted through submerged vegetation canopies on the way to bottom sediments. Considering that the substratum has a sharper echo-return than the AV, analyzing the return signals makes it possible to determine the presence of aquatic macrophytes [361]. As such, SONAR sensors can provide acoustic images over large spatial scales at an appropriate spatial and temporal resolution. SONAR systems have been effectively employed for AV and CR studies even in deep and turbid waters [361, 362, 363]. Using SONAR systems, AV and CR could be spatially located and classified by the specific characteristics of the captured echoes (e.g., intensity, time lag) from the seafloor. In this regard, [PERSON] et al. (2009) compared and evaluated the performance of airborne LiDAR and ship-based multi-beam SONAR in monitoring CR ecosystems using surface fractal dimensions and fast Fourier transformations methods. They found that since both LiDAR and SONAR can collect seafloor intensity, LiDAR provided better results in relatively shallow waters (depth <50 m). However, due to higher spatial resolutions, SONAR can provide more details about CR by differentiating between fine and coarse sediments.  
It should be noted although SONAR systems have significantly improved underwater aquatic macrophytes monitoring speeds, their accuracy is directly dependent on the movements of vessels carrying the hydroacoustic sensing equipment, study objectives, and environmental conditions of the study area [361].  
Overall, experimental and semi-empirical techniques have gained considerable attention in underwater studies. However, only a few studies have employed advanced ML techniques to assess the underwater features of oceans and shallow waters. For example, [PERSON] et al. (2021) [364] used WorldView-3 and Landsat-8 data to monitor suitable habitats for the occurrence of a model species, the hydrocoral Millepora alicornis, in CRs of Northeast Brazil's marine protected areas. They used three ML techniques: geographically weighted regressions for bathymetry, SVM for habitat mapping, and MaxEnt to build the species distribution model for Millepora alicornis. Moreover, [PERSON] et al. (2019) [365] used in situ data and Landsat images along with an ML classifier to identify changes in the coral-dominated benthic. They showed that ML classifiers could provide a generalized average accuracy of 75.3%. [PERSON] et al. (2020) [366] also estimated the depth of water along the shoreline using different multispectral satellite datasets (i.e., Landsat 8, Sentinel 2, and Planetscope imagery). They investigated the performance of two ML methods, SVM and RF, to estimate water depth. The results of bathometry showed that RF provided the highest accuracy, with an RMSE of 0.228 m.  
Despite the benefits of RS techniques for assessing underwater features, multiple restrictions still exist that diminish their applications (e.g., see Table 6). One efficient way to increase the accuracy of RS methods in underwater research is to identify, estimate, and eliminate the parameters that affect the accuracy of RS methods. Water quality, for example, has a significant impact on optical data and, consequently, low water quality could decrease the accuracy of optical passive RS systems in mapping AV and CR. Consequently, estimating the water quality factors and modeling their impacts on RS data could improve the accuracy of RS systems in underwater studies. Combining different RS datasets is another solution to resolve issues with AV/CR mapping.