This study aims to monitor and map the oil spills which occurred from 2019 to 2021 along the northeastern portion of the Nile Delta using Sentinel-1 (SAR) and Sentinel-2 (MSI) data. The examination of VV polarized SAR-C images displayed the presence of the oil spills as dark spots of different sizes. These images were processed using the oil spills detection model in SNAP Toolbox. The oceanographic parameters that may influence the dispersal of oil spills were mapped using GIS technique. This study identified 29 oil spills during the study period in the research area. The largest spill was detected on February 23, 2019, and covered an area of about 10.5 km². The band ratios and decorrelation stretch methods of available Sentinel-2 data confirmed the results of SAR-C data. The accuracy assessment of spills was achieved using Parallelepiped supervised classification model. The results demonstrated that the overall accuracy (OA) and Kappa coefficient (KC) for seawater, land, and oil spills classes were between 86% and 98% and 0.73% and 0.97%, respectively. The sensitivity zone of oil spills was higher in winter than in summer. This study proved the efficiency of VV polarized data of Sentinel-1 sensor for detection and mapping of oil spills. Several management strategies are needed in the offshore zone of the Nile Delta to limit oil pollution effects on the marine environment.

Increased frequency and enhanced damage to the marine environment and to human society caused by green macroalgae blooms demand improved high-resolution early detection methods. Conventional satellite remote sensing methods via spectra radiometers do not work in cloud-covered areas, and therefore cannot meet these demands for operational applications. We present a methodology for green macroalgae bloom detection based on RADARSAT-2 synthetic aperture radar (SAR) images. Green macroalgae patches exhibit different polarimetric characteristics compared to the open ocean surface, in both the amplitude and phase domains of SAR-measured complex radar backscatter returns. In this study, new index factors are defined which have opposite signs in green macroalgae-covered areas, compared to the open water surface. These index factors enable unsupervised detection from SAR images, providing a high-resolution new tool for detection of green macroalgae blooms, which can potentially contribute to a better understanding of the mechanisms related to outbreaks of green macroalgae blooms in coastal areas throughout the world ocean.

In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle – synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case.