Coastline extraction by exploiting optical images is challenging during adverse weather conditions. This letter proposes coastline extraction from synthetic aperture radar (SAR) data. Since collecting in-situ data is expensive and not always possible, the Doppler parameter is used to delineate coastlines when neither in-situ data nor cloud-free optical images are available. We propose a novel coastline extraction method based on classic coastal dynamic variation, such as Doppler centroid ( f_DC ), since coastline is static and has zero Doppler with respect to the dynamic sea-state. The results of the Doppler-based novel technique allow us to investigate the impact of natural hazards on coastline degradation. We compare the proposed method to state-of-the-art (SOA) coastline extraction methods based on polarimetric correlations and the reference method from Sentinel-2. The results show that using scattering from dual and cross-polarization for coastline extraction is more reliable than using co-polarization. Based on empirical distributions and using the constant false alarm rate (CFAR) method, the relevant threshold has been adapted to distinguish land and sea in an unsupervised manner. We compare the results of polarimetric and Sentinel-2 with Doppler-based coastline extraction, which emphasizes the accuracy of the proposed f_DC method for extracting coastlines at full resolution.

Oil spills impose significant environmental challenges, leading to critical consequences for marine ecosystems and sea habitant’s health. Early delineatin and efficient surveillance are absolutely important to prevent more contamination and support quick hazards reduction. This study focuses on detecting oil spills using satellite imagery and deep learning models, specifically Convolutional Neural Networks (CNN). The dataset used to train the CNN comprised 695 images extracted from Sentinel-1 Synthetic Aperture Radar (SAR) data over the Mediterranean Sea. In particular, 486 images (70%) were allocated for training, 139 images (20%) for validation, and 70 images (10%) for testing. Preprocessing involved a thresholding technique to enhance feature extraction and improve classification precision. The CNN model achieved a high test accuracy of 98.57%, with perfect precision (1.0000), recall of 96.43%, and F1 score of 0.9818, demonstrating strong performance and reliability. These high accuracy levels underscore the model’s efficiency in identifying oil spills and its soundness in handling unseen data. The significance of this work is in using satellite-based deep learning models for scalable and automated oil spill detection, therefore providing a reliable and effective substitute for more traditional monitoring systems. The model may be applied over large oceanic areas by using satellite images, thereby supporting marine ecosystem preservation and enhancing environmental risk management connected with oil pollution.

Synthetic aperture radar (SAR) is the most efficient tool to provide high-resolution data for Earth Observation (EO). Doppler centroid (DC) estimation is indispensable for high precision SAR data analysis such as extracting the ocean surface current, which is important for scientific pursuits. Correlation doppler estimation (CDE), and energy balancing (EB) based DC methods are implemented in this paper. A 2-D sliding window is deployed to estimate DC on small blocks of data while covering the whole scene so that all parts of the scene are potentially represented. We analyzed Sentinel-1 single look complex (SLC) data from the coastline of a non-homogeneous scene. The CDE method utilizes the azimuth shift in the time domain which is associated with the DC, and this $f_{Dc}$ history is used to extract ocean surface current. We find the results of DC estimates are confined to primitive baseband (PRF/2). Moreover, the corresponding retrieved ocean surface current component is reasonable, particularly values vary within the limit of error bounds. Finally, the parameters of ocean surface current are compared with ocean wave models reported in the literature. Efficacy and simulation of implemented methods are good fit for Sentinel-1 SAR data.

The application of deep learning methods in various fields is rapidly increasing. The development of complex-valued (CV) networks that can process CV data has provided many opportunities for utilizing the immense capabilities of deep networks for CV data, including Synthetic Aperture Radar (SAR). However, the physical model and basic properties of the original SAR data must be preserved in the CV architecture. Without these properties, the physical parameters cannot be accurately retrieved from the SAR data. This study evaluates the competency of CV deep architectures to preserve the properties of the original SAR data and how it affects the retrieval of physical parameters. Ocean Surface Current (OSC) is an important parameter for ocean circulation and plays a vital role globally. In this work, the correlation Doppler estimation (CDE) method is used to estimate the OSC from SAR data before and after reconstruction with the CV autoencoder. The obtained OSCs are compared, and we demonstrate the ability of the CV deep architectures to learn the data model and preserve the original Doppler centroid (fDC) information in the SAR data. This research paves the way for the development of CV deep architectures for physical parameter retrieval and prediction from CV SAR data in future studies.