# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Oil Spill (OOS)
#### 2.7.1 SAR  
OOS makes the oil-covered surface smoother than its surroundings by diminishing the Bragg waves of the ocean surface. Consequently, as illustrated in Figure 10, the oil-covered areas appear as dark spots in a bright background of seawater in SAR images [222]. In fact, the contrast between OOS and its surrounding is a crucial factor for the successful detection of OOS using SAR data. The contrast depends on wind speed, the type and amount of pollution, OWH, and two important sensor parameters of polarization and wavelength [222]. Although OOS is detectable in wind speed values between 2 and 12 m/s, the ideal range is 5-6 m/s [237]. Ocean surface relatively appears smoother for areas with low wind speed in SAR images. In this condition, there will be no backscattering from the ocean surface. This makes OOS detection impossible because there will be no contrast between the spill and its clutter (i.e., water) [222]. On the other hand, spills, especially thin ones, are  
Figure 10: An OOS caused by the explosion of Sabity Oil tanker in the Red Sea. The spill looks darker than its surrounding because it has lower backscattering values. The image was acquired by Sentinel-1 on 11 October 2019.  
invisible in high wind speed conditions. This is because the spills are sunk into the water for regions with high wind speed values [224; 238; 239]. SAR images with X (2.4-3.75 cm) and C (3.75-7.5 cm) bands are the most appropriate datasets for OOS detection. This is because they have the same scale as the Bragg waves. Additionally, the spills are more detectable in VV polarization because it has a larger backscatter from the ocean surface than the other polarizations [222; 227; 240]. Several research works have studied the feasibility of dual-pol and quad-pol SAR images for OOS detection. Although it is argued that SAR polarimetry improves the accuracy of the discrimination [224], processing polarimetric images is computationally expensive and costly. Quad-pol features, including a polarimetric entropy, anisotropy and span [241], mean scattering angle [224], and conformity coefficient [242], were more utilized for OOS detection. Regarding dual-pol images, the standard deviation of co-polarized phase difference [243; 244; 245], co-polarized correlation coefficient [246], and the degree of polarization [247] have been widely applied to detect OOS. The major drawback of SAR images for OOS detection is the presence of other dark structures called lookalikes. The lookalikes are phenomena with similar behavior as OOS. These phenomena are usually caused by various factors, such as low wind speed, wind-sheltered by land, rain cells, zones of upwelling, ocean or atmospheric fronts, and grease ice [244; 248]. Therefore, the most critical challenge of employing SAR data for OOS detection is accurately discriminating the OOS from OOS lookalikes. It is worth noting that there is also a similar challenge for optical sensors. For instance, sun glints and wind sheen may cause lookalikes in the optical sensor [226]. OOS mapping using SAR images generally contains three steps: (1) segmentation; (2) feature extraction; and (3) classification. More details of each step are provided below. In the first step, the dark regions which contain the OOS and lookalikes are extracted from images after a series of preprocessing steps, including land masking, radiometric and geometric corrections, speckle reduction, and incidence angle correction. The thresholding methods are generally applied to separate the dark area from the SAR images. Adaptive thresholding was utilized in several studies [239; 249] and found to be a better option than global thresholding methods, such as the Otsu algorithm. For instance, several studies employed a segmentation method based on the Laplace and difference of Gaussian [250; 251]. The feasibility of the wavelet transforms and a hidden Markov chain model was also studied in [252; 253]. Additionally, a well-known constant false alarm rate algorithm was applied to identify dark regions [254].