# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Surface Temperature (SST)
#### 2.3.1 TIR Radiometer  
TIR radiation does not penetrate through clouds. Even in cloud-free conditions, the atmosphere scattering and absorption can negatively affect TIR radiation due to haze and aerosols [125; 126; 127]. The transmissivity of the clear-sky atmosphere in the TIR range of the electromagnetic spectrum depends on the wavelength and absorptions of atmospheric gases [124]. Consequently, the TIR wavelength intervals in an RS system should be carefully determined based on the atmospheric window where the atmosphere is more transparent [98]. The TIR radiometers measure SST within two atmospheric windows at \begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS System (Passive/Active)** & **RS System (Type)** & **Advantage** & **Disadvantage** \\ \hline \multirow{8}{*}{Passive} & \multirow{8}{*}{TIR radiometers in LEO} & Provides high spatial resolution, frequent & Limited by cloud cover and \\  & & revisit times, global coverage, existing & atmospheric aerosols, sensitivity of \\  & & retrieval methods, and sensor & calibration to input parameters., hard \\  & & technologies enable relatively high & to characterize the SST diurnal cycle \\  & & accuracy for SST retrievals & \\  & & Views a large portion of the Earth from a & \\  & & fixed point with a wide field of view, & \\  & TIR & ability to capture high temporal & \\  & & resolution (e.g., 15 min) data, which is & \\  & & useful for clear-sky masking and & \\  & & characterization of the SST diurnal cycle & \\  & & & Multi-frequency/multi-polarization \\  & & observations, SST retrievals capability & \\  & & under cloudy conditions and through & \\  & & atmospheric aerosols, frequent revisit & \\  & & times, global coverage & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different RS systems for SST estimation along with their advantages and disadvantages. \(\lambda\) = 3.5-4.1 \(\upmu\)m and \(\lambda\) = 9.5-12.5 \(\upmu\)m [128]. However, measurements at the \(\lambda\) = 9.5-12.5 \(\upmu\)m window are negatively affected by solar effects, making them usable only at nighttime [98]. The presence of clouds is one of the most important challenges in measuring SST using Equation (1), which requires an accurate scheme for removing cloud-contaminated and weather-contaminated pixels [98; 129; 130; 131]. Identifying the clear sky pixels is a fundamental step for achieving an accurate time-series SST estimation [131]. In this regard, many algorithms have been proposed for cloud screening in TIR measurements, including BT minima, binary tests in a decision tree based on BT uniformity, and comparisons with lower-resolution gap-free reference fields [130]. However, these methods depend on the selected threshold values, resulting in potential errors in SST estimation at high latitudes and near ocean thermal fronts at semi-transparent clouds [98; 129; 132]. To address these limitations, a Bayesian probabilistic approach was proposed in [133] for cloud screening of TIR imagery, which was widely used for operational SST estimation in several satellite missions, such as the Advanced Along-Track Scanning Radiometer (AATSR) [134], the Geostationary Operational Environmental Satellite (GOES) [135], and the Japanese geostationary meteorological Himawari-8 satellite [136]. Moreover, alternating decision tree [137; 138] methods were identified to improve the performance of the decision-tree approaches in cloud screening, where instead of trial-and-error methods, ML algorithms are applied to determine threshold values and their weights. After cloud screening, SST can be mainly obtained using the Single-Channel (SC) and Multi-Channel (MC) approaches.