# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## SONAR
### Fishery  
Fishery is one of the primary sources of food production and is vital for the functionality of the marine ecosystems and sustainable development [367]. Generally, fishery management consists of monitoring the fish stocks and fishing fleets [368]. Monitoring fishing fleets is mainly related to SD, which was discussed in more detail in Part I of this review paper. Thus, in this section, only the fish stock mapping and monitoring using RS methods are explained. Two important aspects should be considered in monitoring fish stock: (1) analyzing fish distribution to find potential places where fish is available to help fishing fleet reach a higher catch rate; and (2) assessing habitat suitability for different typesof fish to ensure sustainable development. Fish distribution is related to finding potential fishing zones. RS is an excellent tool for fish stock mapping and monitoring because it provides NRT and large-scale imagery. For example, RS helps in generating fish abundance maps for fishermen to assist them to reduce fuel usage and search-time during their exploration for fish, modeling different environmental variables to produce fisheries forecasts, and habitat suitability analysis to develop policies for sustainable fisheries management [369; 370; 371]. Other than two main applications of RS in fishery (i.e., fish stock and fishing fleets monitoring), RS can help in the following fishery-related applications: AV mapping (Section 2.8.2) for freshwater fisheries [372], generating the Digital Elevation Model (DEM) for watershed topography and hydrological analysis [373], and monitoring natural disasters, such as floods that can impact fishery ecosystems [374]. Different RS systems can directly/indirectly support fishery activities by estimating various oceanographic parameters, the most important of which are SST (Section 2.3), OSS (Section 2.4), OC (Section 2.5), Chl-a (Section 2.6), and OWH (Part I of this review paper). Since these parameters have already been discussed in previous subsections, several studies that have used these parameters for fishery applications are discussed in this section. Chl-a, derived from optical imagery and SST estimated by the TIR and microwave radiometers, are the most important fish distribution indicators. Thus, several studies have so far investigated the capability of optical, TIR, and microwave radiometer datasets to predict Chl-a and SST to help fisheries. For example, [PERSON] et al. (2017) [375] used catch data of pelagic Rastrelliger kanagurta fishes along with satellite-based Chl-a and SST, to determine the distribution and abundance of fishes. They obtained Chl-a and SST from MODIS satellite data and used the Generalized Additive Model (GAM) to identify the relationship between these parameters and fish presence. Their results showed that areas with high catch probability were located near the coast to offshore (3-20 m) with relatively acceptable accuracy. Moreover, regarding habitat suitability, [PERSON] et al. (2017) investigated the preferred habitats of Yellowfin Tuna in the Tropical Pacific Ocean. They used SST data, derived from the AVHRR and AMSR satellite data. The Chl-a concentration was generated from MODIS data, and a combined sea surface height anomaly was also produced from the TOPEX Poseidon, ERS, and Jason-1 satellites. They used GAM analysis to predict patterns of Yellowfin Tuna habitat based on the relationships determined between oceanographic parameters and catch rates. Moreover, [PERSON] et al. (2020) [376] developed an empirical habitat suitability model for determining optimal habitats for the Albacore Tuna in the North Pacific Ocean. They compared two Habitat Suitability Index (HSI) models, called the Arithmetic Mean Model (AMM) and GAM, along with six satellite-based oceanographic parameters, including SST, Chl-a, OWH, OSS, Mixed Layer Depth (MLD), and Eddy Kinetic Energy (EKE). The results indicated that the AMM HSI model with five variables of the SST, OWH, MLD, Chl-a, and EKE was the important feature.