The retrieval of sea ice concentration (SIC) using passive microwave satellite sensors has become a well-established field over several decades. Various algorithms employing slightly different approaches, such as utilizing diverse microwave satellite channels with the ability to combine frequencies and polarizations, have been tested in previous studies. This paper further explores the possibility of using the Radiative Transfer Model (RTM) to remove atmospheric effects on Brightness Temperature (TB) to improve SIC retrievals. This study specifically falls within the framework of the OSI SAF Near Real Time (NRT) SIC products, actively developed and maintained by the Danish Meteorological Institute (DMI). The findings indicated the feasibility of using the RTTOV package of NWP SAF for the atmospheric corrections. The RTTOV-based atmospheric correction could remove warm TB signals over the ocean due to atmospheric effects, resulting in the removal of false sea ice signatures without using empirical filters. The proposed atmospheric correction scheme is straightforward to adopt for the upcoming passive microwave sensors without intensive sensor-specific calibration (e.g., EUMETSAT’s MWI, JAXA’s AMSR-3, and ESA’s CIMR).

The Arctic is currently facing unprecedented changes due to anthropogenic warming, leading to an accelerated melting of sea ice and an increase in human activity in the region. The expanding maritime community accessing wider areas of the Arctic emphasizes the need for detailed and timely information on the state of Arctic sea ice for maritime safety and planning. Synthetic Aperture Radar (SAR) imagery have been playing a crucial role in providing year-round mapping of sea ice conditions due to its high spatial resolution (approx. 100m), independent from solar illumination, and the ability to penetrate cloud cover. National Ice Centers worldwide primarily rely on SAR imagery, where ice analysts manually interpret the data to produce sea ice charts for maritime users. However, the task becomes laborious and time-consuming with the growing availability of satellite imagery. To address this challenge, there is a move towards partial automation of the process, aiming to assist ice analysts in delivering high-resolution sea ice products in near-real-time. A fully automated sea ice mapping system is also envisioned to integrate high-resolution sea ice products into forecast models, potentially improving forecast quality. We compiled a vast matched dataset of Sentinel-1 HH/HV imagery and AMSR2 brightness temperatures to train a Convolutional Neural Network-based algorithm with regional ice charts as labels. The retrieval methodology, denoted ASIP, achieves an R-score of 95.5% against a held-out test dataset of regional ice concentration charts.

Automated solutions for sea ice-type classification from synthetic aperture radar (SAR) imagery offer an opportunity to monitor sea ice, unimpeded by cloud cover or the arctic night. However, there is a common struggle to obtain accurate classifications year round, particularly in the melt and freeze-up seasons. During these seasons, the radar backscatter signal is affected by wet snow cover, obscuring information about underlying ice types. By using additional spatiotemporal contextual data and a combination of convolutional neural networks and a dense conditional random field, we can mitigate these problems and obtain a single classifier that is able to classify accurately at 3.5-m spatial resolution for five different classes of sea ice surface from October to May. During the near year-long drift of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition, we collected satellite scenes of the same patch of Arctic pack ice with X-band SAR with a revisit time of less than a day on average. Combined within situobservations of the local ice properties, this offers up the unprecedented opportunity to perform a detailed and quantitative assessment of the robustness of our classifier for level, deformed, and heavily deformed ice. For these three classes, we can perform accurate classification with a probability >95% and calculate a lower bound for the robustness between 85% and 88%.

The research vessel ‘Polarstern’, moored to an ice floe, completed a year long drift with Arctic pack ice in the autumn of 2020. During that expedition, named MOSAiC, a comprehensive data set of airborne laser scanner (ALS) and spaceborne X-band SAR images in the area of the research vessel was acquired. With successful fusion of these two measurements, we can extrapolate sea ice features from the ALS data to the entire SAR scene using a convolutional neural network (CNN). From two preliminary scenes of ALS data we are able to show this for classes of sea ice roughness. This will be the basis for more comprehensive research, once the complete data set is available.

In September 2019, the German research icebreaker Polarstern started the largest multidisciplinary Arctic expedition, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to ice floes at high Arctic for a whole year, thus including the winter season, the main goal of the expedition is to better understand and quantify relevant processes within the atmosphere-ice-ocean system that impact the sea ice, ultimately leading to improved climate models. Satellite remote sensing, specially multi-frequency synthetic aperture radar (SAR) plays a major role to achieve this goal. Two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage, and on the other hand to obtain a radar response that carries as much information as possible. A comprehensive set of C- and L- band SAR images were acquired during the course of MOSAiC. In this initial study we evaluate the effects of seasonal changes on C- and L-band backscatter in respect to three different sea ice types, i.e., Young Ice, Smooth Ice and Rough/Deformed Ice along with the performance of sea ice type retrieval of a established algorithm. Areas of deformed, smooth and young sea ice were observed in the vicinity of R/V Polarstern and these areas are included whenever possible in the yearlong time series. For both frequencies a change in all backscatter channels values can be observed during the early melt season. This is first noticeable in the C-band images and later followed by a change in the L-band images, probably caused by their different penetration depth and volume scattering sensitivities.

The Eastern Mediterranean Sea is known as oil pollution hotspot because of high marine traffic and a growing number of oil and gas industrial activities inside, which makes efficient monitoring oil spills important in this area. Spaceborne Synthetic Aperture Radar (SAR) plays an important role for oil spill detection with its advantage of wide coverage and all-weather observations. However, discriminating whether the dark formations in the SAR imagery are from actual oil spills or look-alikes has been a challenging part. This study applied You Only Look Once version 4 (YOLOv4) object detection algorithm as an one-class (i.e. oil spill) object detector for learning oil spill features inside the Region of Interests (ROIs) and the background information from the rest of the image. The preliminary results pointed out that the pixel threshold for removing some tiny oil spills is suggested as they appeared regularly in the study area but are hardly visible. The average precisions (AP) of the trained model on validation and test sets are 67.80% and 65.37%, showing that the model is not overfitting on our training and validation sets. In addition, this study recommended some data augmentation strategies which might help improve the results.

In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern’s (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions.

Operational ice services around the world have recognized the economic and environmental benefits that come from the increased capabilities and uses of space-borne Synthetic Aperture Radar (SAR) observation system. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large areal coverage, and on the other hand to obtain a radar response that carries as much information as possible. Although until now, L-Band SAR sensors are rarely used in an operational context, it offers greater capabilities for sea ice type retrieval and is more robust during the melt season compared to higher frequency bands. With the help of JAXA's ALOS-2 PALSAR-2 sensor, we are able to explore the potential of polarimetric L-band acquisitions for sea ice analysis and classification in an operational environment. In this study we investigated the incidence angle related variation on the L-band backscatter and recommended optimal scenarios for Artificial Neural Network based sea ice type retrieval schemes.

Using sea ice information generated from Synthetic Aperture Radar (SAR) products can increase the safety and efficiency of ship operations in ice-infested waters. For the purpose of operational sea ice classification, the results need to be highly reliable. The combination of multiple SAR acquisitions can be used to estimate the reliability of sea ice classification and to overcome existing limitations. In this paper, we present a new approach for comparing sea ice classification results from pairs of independent TerraSAR-X acquisitions and additionally overlay it with information on sea ice movement. For this purpose, we combine our processors for estimating sea ice drift and classification. The sea ice drift field is used to compensate the ice movement between two SAR acquisitions and differentiate areas of homogeneous and inhomogeneous ice zones. The processing chain is developed for operational usage in near real-time.

Operational detection and discrimination of oil spills over oceans has received considerable attention due to its impact on marine ecosystem from environmental and political points of view. Synthetic Aperture Radar (SAR) is a valuable instrument for maritime pollution monitoring. The three main requirements for effective operational oil spill detection using SAR are: 1) low noise floor, 2) large area coverage, and 3) maximizing detection and discrimination of pollution and `lookalike' features, by polarization diversity, multiple frequency, etc. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP from three RCM SAR modes for oil spill detection. Results indicate that the RCM MR30 SAR mode has promising oil spill detection performance.

Synthetic Aperture Radar (SAR) satellites are able to observe small and large scale structures in sea ice - in any weather, through clouds and darkness. In order to assist ship navigation during polar campaigns, we acquired SAR images along the ship course and provided them to navigators on board in near real time, utilizing the operational data processing chain of DLR ground station Neustrelitz. These “exclusive” acquisitions already helped to optimize the routes. SAR data, however, contain more information that is not easily visible, e.g. information about the local sea ice drift. In this paper, we explore the capabilities of a new software processor that is intended to retrieve high resolution sea ice drift fields from pairs of colocated SAR images, combining TerraSAR-X and Radarsat-2 images. The processor is foreseen to be integrated into the operational data processing chain at DLR ground station network sites.

SAR Polarimetry has become a valuable tool in spaceborne SAR based sea ice analysis. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage of the imaged ground area, and on the other hand to obtain a radar response that carries as much information as possible. Whereas single-polarimetric acquisitions of existing sensors offer a wide coverage on the ground, dual polarimetric, or even better fully polarimetric data offer a higher information content which allows for a more reliable automated sea ice analysis. In order to reconcile the advantages of fully polarimetric acquisitions with the higher ground coverage of acquisitions with fewer polarimetric channels, compact polarimetric acquisitions offer a trade-off between the mentioned objectives. With the advent of the RISAT-l satellite platform, we are able to explore the potential of compact polarimteric acquisitions for sea ice analysis and classification in operational environment. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. We present our results on datasets acquired over both Arctic and Antarctic sea ice.

In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for maritime pollution monitoring with three major requirements: 1) low noise floor, 2) large area coverage, and 3) polarization diversity to maximize detection and discrimination of pollution features. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP for four RCM SAR modes for oil spill detection. These modes have different spatial resolutions and noise floors. An initial visual interpretation of the results indicates potential of some CP features for the discrimination between oil spills and lookalike.

High resolution sea ice drift fields, the location and extend of converging and diverging zones as well as ice ridges are most important parameters for ship navigation in ice infested waters. In this paper, we present the prototype of a new processor which is aimed to derive the surface ice parameters on the basis of pairs of space-borne Synthetic Aperture Radar (SAR) data of the same and of different sensors, i.e. from data of different bands, resolutions, and orbits. The study is carried out on image data collected during a joint campaign with the Office of Naval Research (ONR) in the western Arctic in 2015. The algorithm proposed is foreseen to be integrated into near-real time (NRT) processing chain at DLR ground stations in order to provide time-critical information as soon as possible to users and stakeholders.

The number of offshore wind facilities is increasing with a proportionate decline in fossil and nuclear power production. The study of turbulent wakes inside a turbine cluster is a very important topic in order to optimize cluster layout for power production. With an increasing density of wind farms in the exclusive economic zone (EEZ) of a country, shadowing effects of wind farms on adjacent clusters are becoming an important issue for wind farm performance and need to be investigated to improve power harvest predictions. We present a comparative study of wind fields of different resolutions and coverages derived from TerraSAR-X and Sentinel-1 images. We elucidate the benefits of certain data for particular applications.

In recent years SAR Polarimetry has become a valuable tool in space-borne SAR based sea ice analysis. This work compares the polarimetric backscatter behavior of sea ice in space-borne X-band C-band and L-band Synthetic Aperture Radar (SAR) imagery. Two sets of spatially and temporally near coincident fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X, RADARSAT-2 and ALOS-2 satellites are investigated. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixel-wise supervised classification. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies and slightly different for L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected.

SAR Polarimetry has become a valuable tool in spaceborne SAR based sea ice analysis. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage of the imaged ground area, and on the other hand to obtain a radar response that carries as much information as possible. Whereas single-polarimetric acquisitions of existing sensors offer a wide coverage on the ground, dual polarimetric, or even better fully polarimetric data offer a higher information content which allows for a more reliable automated sea ice analysis. In order to reconcile the advantages of fully polarimetric acquisitions with the higher ground coverage of acquisitions with fewer polarimetric channels, hybrid polarimetric acquisitions offer a trade-off between the mentioned objectives. With the advent of the RISAT-1 satellite platform, we are able to explore the potential of hybrid dual pol acquisitions for sea ice analysis and classification. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. We present first results on a dataset acquired off the eastern Greenland coast.

The use of fully polarimetric SAR data for oil spill detection is relatively new and shows great potential for operational off-shore platform monitoring. Greater availability of these kind of SAR data calls for a development of time critical processing chain capable of detecting and distinguishing oil spills from `look-alikes'. This paper describes the development of an automated Near Real Time (NRT) oil spill detection processing chain based on quad-pol RADARSAT-2 (RS-2) and quad-pol TerraSAR-X (TS-X) images, wherein we use polarimetric features (e.g. Lexicographic and Pauli Based features) to characterize oil spills and look-alikes. Numbers of TS-X and RS-2 images have been acquired over known off-shore platforms along with some near coincident (spatially and temporally) acquisition. Ten polarimetric feature parameters were extracted from different types of oil (e.g. crude oil, emulsion etc) and `look-alike' (e.g. plant oil, met-oceanic phenomenon etc) spots and divided into training and validation dataset seperately for TerraSAR-X RADARSAT-2. Extracted features were then used for training and validation of a pixel based Artificial Neural Network (ANN) classifier. Initial performance estimation was carried out for the proposed methodology in order to evaluate its suitability for NRT operational service. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spills and look- alikes. Polarimetric features such as Scattering Diversity and Pauli-based features proved to be more discriminative than other polarimetric features.

Oil spill detection using SAR imagery is well established and currently used operationally over European waters. However, adaptation of oil spill detection methodologies exploiting polarimetric features is still in research phase and until recently those properties have not been used for operational services. Proposed methodology introduces for the first time a combination of traditional and polarimetric features for object-based oil spill detection and look-alike discrimination in a Near Real Time environment. A total number of 35 feature parameters were extracted from 225 oil spill and 26 look-alikes and divided into training and validation dataset. Extracted features have been assessed and ranked according to their ability to discriminate between oil spill and `look-alike'. Extracted features are used for training and validation of a Support Vector Machine based classifier. Performance estimation was carried out for the proposed methodology on a large dataset with overall classification accuracy of 90% oil spill and 91% for look-alike. Polarimetric features such as Geometric Intensity, Co-Polarization Power Ratio, Span proven to be more discriminative than other polarimetric and traditional features.

Oil spills represent a major threat to ocean ecosystems and their health. The recent incident in the Gulf of Mexico demonstrates the potentially catastrophic nature of offshore oil spills. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection using two different Artificial Neural Networks (ANN). The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and divided into subsets to facilitate sensitivity analyses. The second ANN classifies objects into oil spills or look-alikes according to their feature parameters. A pilot study employed sixty-two ERS-2 SAR and ENVSAT ASAR images of verified oil spills or look-alikes to train and evaluate the algorithm. Overall accuracies of 96.52 % were obtained for pixel segmentation and 95.2 % for feature classification. The segmentation approach outperformed established edge detection and adaptive thresholding techniques. An analysis of feature descriptors in the classification stage highlighted the importance of image gradient information.