# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Chlorophyll (OCH)
#### 2.6.1 Optical
The non-linear and complex nature of the measurements provided by OC sensors and the need for techniques declining high-dimensional data have recently encouraged the RS community to produce high-dimensional predictors using different models of DL [215]. Due to the complexity of the marine environment, many DL algorithms have been used to ameliorate the precision and reliability of empirical-predictive methods [216]. ML algorithms can tackle the difficulties associated with traditional empirical modelling for Chl-a retrievals, hence modelling non-linear relations between dependent (Chl-a) and independent variables [216]. The popular approach to training ML models is often supervised learning using match-ups from in situ Chl-a measurements and \(R_{rs}\) data (known input-output pairs) [216; 217]. Many studies (e.g., [214; 215; 216; 217; 218]) have so far demonstrated the superiority of various ML methods in comparison with conventional approaches for Chl-a concentration estimation using RS data. There are numerous RS studies focused on ocean Chl-a retrievals. For instance, [PERSON] et al. [219] delineated the spatiotemporal patterns of the Chl-a concentration in the Vietnamese inshore waters using OC RS data which was acquired by MERIS between 2002 and 2012. To this end, a wide range of in situ data of Chl-a, \(R_{rs}\), and IOPs was collected from different parts of the Vietnamese coastal waters. Subsequently, they evaluated different Chl-a inversion algorithms utilizing the aforementioned in situ data. The functionality of the most appropriate bio-optical algorithm was also investigated considering the geographical location and bio-optical environment. After applying the best algorithm to the monthly RS reflectance data of MERIS, they also analyzed the temporal variability trends of Chl-a. Moreover, a comprehensive study to develop the Chl-a algorithms for 25 OC RS systems has been conducted by [PERSON] et al. [190] using 65 different empirical algorithms. They aimed to fulfill consistency and comparability among Chl-a algorithms which is required for synthesizing data from overlapping OC missions of various sensors. This process led to a considerable increase in coverage of accessible data over the global ocean and resulted in expanding the existing time series. They also introduced their expanded versions of the OC algorithms in which the 412 nm band was utilized in the maximum band ratio. Despite the advantages of optical RS systems in estimating Chl-a concentrations, there are still several challenges restricting the application of Chl-a algorithms. For example, A single OC sensor cannot be solely utilized to evaluate long-term Chl-a changes due to its limited operation time. Moreover, combining Chl-a data from multiple sensors augments their global coverage especially in cloudy regions of cloudy seasons. However, this data combination faces several challenges. Inadequate quality of Chl-a multi-source data calibrations can degrade the overall quality of Chl-a estimations. Overall, inconsistent and incomparable atmospheric correction algorithms, and inconsistency of the geophysical algorithms utilized for different sensors can negatively affect the accuracy of multi-instrument Chl-a products [191]. Moreover, considering the dependence of passive OC sensors on daylight measurement of reflectance, multiple regional, seasonal, and latitudinal differences could occur in Chl-a mapping. This can also affect long-term studies of Chl-a. Consequently, most long-term approaches suffer from latitudinal and seasonal biases and contain errors due to coverage gaps [190]. Additionally, estimating Chl-a concentrations in optically complex waters is highly required because the optical properties of these water regions may differ from multiple assumptions in the algorithms. This difference may result in creating large errors in the produced Chl-a maps [212]. Finally, multiple studies (e.g., [191; 220]) have reported that optical RS systems cannot always sense deep Chl maxima.