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Determining both the average temperature and the underlying temperature distribution within a battery system is crucial for system design, control, and operation. Therefore, thermal battery system models, which allow for the calculation of these distributions, are required. In this work, a generic thermal equivalent circuit model for commercial battery modules with passive cooling is introduced. The model approach can be easily adopted to varying system designs and sizes and is accompanied by a corresponding low-effort characterization process. The validation of the model was performed on both synthetic and measured load profiles from stationary and marine applications. The results show that the model can represent both the average temperature and the occurring temperature spread (maximum to minimum temperature) with deviations below 1 K. In addition to the introduced full-scale model, further simplifying assumptions were tested in order to reduce the computational effort required by the model. By comparing the resulting simplified models with the original full-scale model, it can be shown that both reducing the number of simulated cells and assuming electrical homogeneity between the cells in the module offer a reduction in the computation time within one order of magnitude while still retaining a high model accuracy.

This paper performs a study on sea state and wind fields at the ice edge boundary by utilizing information from different sources including synthetic aperture radar (SAR) satellite imagery, weather and sea state analyses from the European Centre for Medium-Range Weather Forecasts, shipborne in-situ measurements, and AMSR2 ice charts. The basis is a Stripmap scene from the TerraSAR-X satellite acquired on October 18, 2015, at ~18 UTC, in support of the cruise of the research vessel R/V Sikuliaq in the Beaufort/Chukchi Sea. This scene covers an area with a length of more than 100 km and comprises both the marginal ice zone and, for the largest part, open water. The wave and wind field is retrieved from satellite at high spatial resolution using empirical retrieval algorithms. These algorithms are XWAVE and XMOD-2 specifically developed for X-Band SAR. XWAVE allows for determining the significant wave height not only for long swell waves, but also for short waves with their wave pattern being hardly visible from SAR. The latter is based on the analysis of image spectrum parameters and spectral noise. As well, the possibility of the imaging quality of longer waves visible from SAR being affected by SAR-specific nonlinear imaging effects is narrowed down. Both the wave and wind field are found to exhibit considerable spatial variability, and their relationship is analyzed. The relevance of the findings of this study with respect to wave/ice modeling is discussed.

Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH-VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to be more useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.

Synthetic aperture radar (SAR) images are operationally used for the detection of oil spills in the marine environment, as they are independent of sun light and weather-induced phenomena. Exploitation of radar polarimetric features for operational oil spill detection is relatively new and until recently those properties have not been extensively exploited. This paper describes the development of a oil spill detection processing chain using coherent dual-polarimetric (copolarized channels, i.e., HH-VV) TerraSAR-X images. The proposed methodology focuses on offshore platform monitoring and introduces for the first time a combination of traditional and polarimetric features for object-based oil spill detection and look-alike discrimination. A total number of 35 feature parameters were extracted from 225 oil spills and 26 look-alikes and divided into training and validation dataset. Mutual information content among extracted features have been assessed and feature parameters are 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 spills and 80% for look-alikes. Polarimetric features such as geometric intensity, copolarization power ratio, span proved to be more discriminative than other polarimetric and traditional features.

The damping of short gravity-capillary waves (Bragg waves) due to surfactant accumulation under low wind speed conditions results in the formation of natural sea slicks. These slicks are detectable visually and in synthetic aperture radar satellite imagery. Surfactants are produced by natural life processes of many marine organisms, including bacteria, phytoplankton, seaweed, and zooplankton. In this work, samples were collected in the Gulf of Mexico during a research cruise on the R/V 'F.G. Walton Smith' to evaluate the relative abundance of 'Bacillus' spp., surfactant-associated bacteria, in the sea surface microlayer compared to the subsurface water at 0.2 m depth. A method to reduce potential contamination of microlayer samples during their collection on polycarbonate filters was implemented and advanced, including increasing the number of successive samples per location and changing sample storage procedures. By using DNA analysis (real-time polymerase chain reaction) to target 'Bacillus' spp., we found that in the slick areas, these surfactant-associated bacteria tended to reside mostly in subsurface waters, lending support to the concept that the surfactants they may produce move to the surface where they accumulate under calm conditions and enrich the sea surface microlayer.

We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting.

Changes in the sea surface roughness are usually associated with a change in the sea surface wind field [...]

New radar satellites image the sea surface with resolutions as high as 1 m. A large spectrum of ocean processes can be estimated using such Earth observation data. These data have been applied to investigations of geophysical processes as well as to forecast model validations and near-real-time services. The numerous processes, parameters, and features observed in high-resolution synthetic aperture radar images include winds, waves (with wavelengths as small as 30 m), oil slicks, waterline changes, changes in seabed morphology in shallow waters, wakes and bow waves of ships, underwater topography, wave energy flux along wave tracks from deep water to the coast, and breaking waves. New algorithms have been developed that are capable of taking into account fine-scale effects in coastal areas.

Spatial distribution and dynamics of intertidal habitats are integral elements of the Wadden Sea ecosystem, essential for the preservation of ecosystem functions and interlocked with geomorphological processes. Protection and monitoring of the Wadden Sea is mandatory and remote sensing is required to survey the extensive, often inaccessible tidal area. Mainly airborne techniques are carried out for decades. High-resolution satellite-borne sensors now enable new possibilities with satellite synthetic aperture radar (SAR) offering high availability of acquisitions during low water time due to independence from daylight and cloud cover. More than 100 TerraSAR-X images from 2009 to 2016 were used to examine the reproduction of intertidal habitats and macrostructures from the flats south of the island of Norderney and comparative areas in the Lower Saxony Wadden Sea. As a non-specific, generic approach to distinguish various and variable surface types continuously influenced by tidal dynamics, visual analysis was chosen which was supported by extensive in situ data. This technically unsophisticated access enabled us to identify mussel beds, fields of shell-detritus, gully structures, mud fields, and bedforms, the latter detected in the upper flats of every East Frisian island. Based on the high frequency of TerraSAR-X recordings for the Norderney area, a bedform shift was observed in a time-series from 2009 to 2015. For the same period, the development of a mud field with an adjoining depression was traced. Beside seasonal variations of the mud field, the formation of a mussel bed settling in the depression was imaged over the years. This study exemplifies the relevance of TerraSAR-X imagery for Wadden Sea remote sensing. Further development of classification methods for current SAR data together with open access availability should contribute to large-scale surveys of intertidal surface structures of geomorphic or biogenic origin and improve monitoring and long-term ecological research in the Wadden Sea and related tidal areas.