# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Color (OC)
#### 2.5.1 Optical
In fact, IOP retrieval of each constituent type employs different absorption and backscattering ranges and ratios of specific bands (e.g., reflectance classification algorithms, spectral band-ratios, and spectral band-difference algorithms). More approaches in this category, such as OC Chl algorithms, are widely discussed in [190; 191]. The IOP retrieval algorithms of ocean water can be generally divided into two groups of band arithmetic and spectral inversion algorithms. Researchers have so far made many efforts to develop RS models to define the relationship between \(R_{rs}\) and IOPs. Considering \(R_{rs}\) as a function of IOPs (called forward model, F), after the development of an appropriate forward model, retrieving the IOPs from \(R_{rs}\) is possible by solving a mathematical inverse problem of \(F-1\). To this end, although scalar RT simulations and approximation to the RT [183] are the two main approaches; however, other approaches, such as empirical-statistical regressions and ANNs have also been utilized [192]. Several RT computations, which depend on user input IOP measurements or models as well as approximations with empirical coefficients, have been proposed to obtain \(R_{rs}\)[193]. Semi-analytical inversion algorithms are also a combination of empiricism and RT theory. The Quasi-Single Scattering Approximations (QSSA) as an approximation to the RT, ignores multiple scattering impact as opposed to hydrological RT algorithms and are less accurate than RT codes. The reflectance beneath the sea surface, which can be obtained from \(R_{rs}\), can be approximated as a function of total absorption and backscattering coefficients in many QSSA models used in various OC applications [183; 194]. In this regard, partitioning the total spectral absorption and backscattering coefficients into water constituents' normalized absorption and backscattering coefficients, including shape and magnitude coefficients has been an imperative stage for further processes of IOP retrievals using semi-analytical inversion approaches, look-up-table approaches, empirical methods, and ML algorithms [190]. Over the past decades, many researchers have utilized various optical RS datasets to study OC. For example, temporal dynamics of coastal water turbidity have been mapped by [PERSON] et al. [195] using the Geostationary Ocean Color Imager (GOCI) OC data. The main objective was to investigate the sedimentary processes and environments that were mostly influenced by semidiurnal tides in specific coastal areas. They developed an empirical TSM algorithm using in situ measurements of TSM concentrations and water-leaving reflectance of coastal water surface. This helped them map the temporal transformations of TSM concentrations from GOCI images acquired at hourly intervals. The assessment process was consequently performed by comparing the results with in- situ measurements and TSM concentration results yielded from the MODIS sensor. Furthermore,  
[PERSON] et al. (2014) investigated the dynamics of Dissolved Organic Carbon (DOC) as the major representative of the total organic carbon in the oceans' coastal water and CDOM. Since DOC and CDOM are significantly important in climatic and biogeochemical cycles and have considerable effects on the estuarine ecosystem, they developed new algorithms for DOC and CDOM retrievals. These algorithms were found suitable to be applied to different regions using various RS datasets and over different water conditions ([PERSON] et al., 2014). There have always been several challenges of OC mapping using optical RS imagery. In this regard, it is essential to consider the necessity for documentation of IOPs' output uncertainties and investigate the instabilities of satellite instruments in the prelaunch or on-orbit characterization.