# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Chlorophyll (OCH)
#### 2.6.1 Optical  
Chl-a can be studied using optical RS imagery due to its effects on ocean water. For example, the color of ocean waters can be affected by phytoplankton blooms. Phytoplankton blooms either raise light backscattering due to the spectrally localized water-leaving radiance minima of Chl-a or increase especial algal pigments absorption in some of the algal species [191]. Furthermore, similar to the process of IOP retrievals of ocean water constituents from RS measurements, the absorption and backscattering properties of Chl-a as the spectral marine IOPs can be estimated by applying bio-optical algorithms to \(R_{rs}\). Generally, water reflects electromagnetic energy in the visible up to the NIR ranges of the spectrum. As shown in Figure 9, all energy beyond 1.2 \(\upmu\)m is absorbed by water. The spectral signatures of three types of water, including ocean water, turbid water, and water with chlorophyll are provided in Figure 9a. Chl-a mainly reflects green and absorbs most energy from wavelengths of violet-blue and orange-red light whose reflectance makes chlorophyll appear green. Figure 9b illustrates the absorption spectrum of both Chl-a and Chl-b pigments. This pigment characteristic offers a basis for Chl-a estimation from blue-to-green ratios of RS reflectance in clear oceanic waters. Figure 9: (a) Typical effects of chlorophyll and sediments on water reflectance [205]: graph a, b, and c indicate ocean water, turbid water, and water with chlorophyll respectively; (b) the absorption spectrum of both the Chl-a and the Chl-b pigments [185]. The approaches applied by the RS community to estimate Chl-a from \(R_{rs}\) can be categorized into the following groups: empirical, semi-analytical [184], and ML models. Empirical algorithms statistically relate in situ measurements of Chl-a and \(R_{rs}\) (radiance or reflectance in narrow bands) observed by an optical system [182; 190; 191; 200]. The reflectance band-ratio and spectral band difference algorithms are the most common empirical methods for Chl-a estimation. The blue-green (440-550 nm) spectral band ratio, Tassan's approach [206], linear red-edge ratio methods [207], and OC band-ratio algorithms (e.g., OC2 [208] to OC6 [190]) for SeaWiFS and their corresponding versions for other OC sensors are the examples of the reflectance band-ratio algorithms. Among these methods, the empirical blue-green spectral band ratios have been widely utilized. Blue-green ratios are mainly applicable for open ocean waters because they show less sensitivity to changes in Chl-a concentrations in optically complex coastal waters [191]. Regarding the spectral band difference algorithms, diverse band triplets from the red-NIR or the blue-green spectral ranges can be used. The fluorescence line height algorithm [209], maximum Chl index [210], which can be only applied to MEdium Resolution Imaging Spectrometer (MERIS), color index algorithm [211], which is applicable to SeaWiFS, MODIS, and MERIS, are the popular spectral band difference algorithms. Semi-analytical algorithms are developed by a composition of empiricism and simplified RT models [190]. Semi-analytic bio-optical models have allowed us to simultaneously estimate Chl, suspended particulate matter, and absorption by CDOM, which can potentially be more efficient for optically complex waters (e.g., coastal waters) [212]. For this purpose, spectral shape functions have usually been assumed for all constituent absorption and scattering components of ocean water. Additionally, linear or nonlinear inversion methods have been employed to retrieve the magnitudes of constituents, which are required for matching the spectral distribution of \(R_{rs}\)[190]. Multiple ML-based algorithms, such as ANN and SVM, have also been applied to Chl-a concentration estimation [213; 214].