Detection and classification of sea ice from spaceborne multi-frequency synthetic aperture radar imagery and radar altimetry

The sea ice cover in the Arctic is undergoing drastic changes. Since the start of satellite observations by microwave remote sensing in the late 1970\u27s, the maximum summer sea ice extent has been decreasing and thereby causing a generally thinner and younger sea ice cover. Spaceborne radar remote sensing facilitates the determination of sea ice properties in a changing climate with the high spatio-temporal resolution necessary for a better understanding of the ongoing processes as well as safe navigation and operation in ice infested waters.The work presented in this thesis focuses on the one hand on synergies of multi-frequency spaceborne synthetic aperture radar (SAR) imagery for sea ice classification. On the other hand, the fusion of radar altimetry observations with near-coincidental SAR imagery is investigated for its potential to improve 3-dimensional sea ice information retrieval.Investigations of ice/water classification of C- and L-band SAR imagery with a feed-forward neural network demonstrated the capabilities of both frequencies to outline the sea ice edge with good accuracy. Classification results also indicate that a combination of both frequencies can improve the identification of thin ice areas within the ice pack compared to C-band alone. Incidence angle normalisation has proven to increase class separability of different ice types. Analysis of incidence angle dependence between 19-47\ub0 at co- and cross-polarisation from Sentinel-1 C-band images closed a gap in existing slope estimates at cross-polarisation for multiyear sea ice and confirms values obtained in other regions of the Arctic or with different sensors. Furthermore, it demonstrated that insufficient noise correction of the first subswath at cross-polarisation increased the slope estimates by 0.01 dB/1\ub0 for multiyear ice. The incidence angle dependence of the Sentinel-1 noise floor affected smoother first-year sea ice and made the first subswath unusable for reliable incidence angle estimates in those cases.Radar altimetry can complete the 2-dimensional sea ice picture with thickness information. By comparison of SAR imagery with altimeter waveforms from CryoSat-2, it is demonstrated that waveforms respond well to changes of the sea ice surface in the order of a few hundred metres to a few kilometres. Freeboard estimates do however not always correspond to these changes especially when mixtures of different ice types are found within the footprint. Homogeneous ice floes of about 10 km are necessary for robust averaged freeboard estimates. The results demonstrate that multi-frequency and multi-sensor approaches open up for future improvements of sea ice retrievals from radar remote sensing techniques, but access to in-situ data for training and validation will be critical

Sensitivity of radar altimeterwaveform to changes in sea ice type at resolution of synthetic aperture radar

Radar altimetry in the context of sea ice has mostly been exploited to retrieve basin-scale information about sea ice thickness. In this paper, we investigate the sensitivity of altimetric waveforms to small-scale changes (a few hundred meters to about 10 km) of the sea ice surface. Near-coincidental synthetic aperture radar (SAR) imagery and CryoSat-2 altimetric data in the Beaufort Sea are used to identify and study the spatial evolution of altimeter waveforms over these features. Open water and thin ice features are easily identified because of their high peak power waveforms. Thicker ice features such as ridges and multiyear ice floes of a few hundred meters cause a response in the waveform. However, these changes are not reflected in freeboard estimates. Retrieval of robust freeboard estimates requires homogeneous floes in the order of 10 km along-track and a few kilometers to both sides across-track. We conclude that the combination of SAR imagery and altimeter data could improve the local sea ice picture by extending spatially scarce freeboard estimates to regions of similar SAR signature

Landsat-8 Sea Ice Classification Using Deep Neural Networks

Abstract: Knowing the location and type of sea ice is essential for safe navigation and route op-timization in ice-covered areas. In this study, we developed a deep neural network (DNN) for pixel-based ice Stage of Development classification for the Baltic Sea using Landsat-8 optical sat-ellite imagery to provide up-to-date ice information for Near-Real-Time maritime applications. In order to train the network, we labeled the ice regions shown in the Landsat-8 imagery with classes from the German Federal Maritime and Hydrographic Agency (BSH) ice charts. These charts are routinely produced and distributed by the BSH Sea Ice Department. The compiled data set for the Baltic Sea region consists of 164 ice charts from 2014 to 2021 and contains ice types classified by the Stage of Development. Landsat-8 level 1 (L1b) images that could be overlaid with the available BSH ice charts based on the time of acquisition were downloaded from the United States Geological Survey (USGS) global archive and indexed in a data cube for better handling. The input variables of the DNN are the individual spectral bands: aerosol coastal, blue, green, red and near-infrared (NIR) out of the Operational Land Imager (OLI) sensor. The bands were selected based on the reflectance and emission properties of sea ice. The output val-ues are 4 ice classes of Stage of Development and Free Ice. The results obtained show significant improvements compared to the available BSH ice charts when moving from polygons to pixels, preserving the original classes. The classification model has an accuracy of 87.5% based on the test data set excluded from the training and validation process. Using optical imagery can there-fore add value to maritime safety and navigation in ice- infested waters by high resolution and real-time availability. Furthermore, the obtained results can be extended to other optical satel-lite imagery such as Sentinel-2. Our approach is promising for automated Near-Real-Time (NRT) services, which can be deployed and integrated at a later stage at the German Aerospace Center (DLR) ground station in Neustrelitz

First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea

Automatic and visual sea ice classification of SAR imagery is impeded by the incidence angle dependence of backscatter intensities. Knowledge of the angular dependence of different ice types is therefore necessary to account for this effect. While consistent estimates exist for HH polarization for different ice types, they are lacking HV polarization data, especially for multiyear sea ice. Here we investigate the incidence angle dependence of smooth and rough/deformed first-year and multiyear ice of different ages for wintertime dual-polarization Sentinel-1 C-band SAR imagery in the Beaufort Sea. Assuming a linear relationship, this dependence is determined using the difference in incidence angle and backscatter intensities from ascending and descending images of the same area. At cross-polarization rough/deformed first-year sea ice shows the strongest angular dependence with -text{0.11} dB/1{circ } followed by multiyear sea ice with -text{0.07} dB/text{1}{circ }, and old multiyear ice (older than three years) with -text{0.04} dB/text{1}{circ }. The noise floor is found to have a strong impact on smooth first-year ice and estimated slopes are therefore not fully reliable. At co-polarization, we obtained slope values of -0.24, -0.20, -text{0.15}, and -text{0.10} dB/text{1}{circ } for smooth first-year, rough/deformed first-year, multiyear, and old multiyear sea ice, respectively. Furthermore, we show that imperfect noise correction of the first subswath influences the obtained slopes for multiyear sea ice. We demonstrate that incidence angle normalization should not only be applied to co-polarization but should also be considered for cross-polarization images to minimize intra ice type variation in backscatter intensity throughout the entire image swath

Comparison of ice/water classification in Fram Strait from C- A nd L-band SAR imagery

In this paper an algorithm for ice/water classification of C- A nd L-band dual polarization synthetic aperture radar data is presented. A comparison of the two different frequencies is made in order to investigate the potential to improve classification results with multi-frequency data. The algorithm is based on backscatter intensities in co- A nd cross-polarization and autocorrelation as a texture feature. The mapping between image features and ice/water classification is made with a neural network. Accurate ice/water maps for both frequencies are produced by the algorithm and the results of two frequencies generally agree very well. Differences are found in the marginal ice zone, where the time difference between acquisitions causes motion of the ice pack. C-band reliably reproduces the outline of the ice edge, while L-band has its strengths for thin ice/calm water areas within the icepack. The classification shows good agreement with ice/water maps derived from met.no ice-charts and radiometer data from AMSR-2. Variations are found in the marginal ice zone where the generalization of the ice charts and lower accuracy of ice concentration from radiometer data introduce deviations. Usage of high-resolution dual frequency data could be beneficial for improving ice cover information for navigation and modelling

Sea Ice Concentration Estimation and Ice Type Classification from Dual-Frequency Satellite Synthetic Aperture Radar

The sea ice cover in the Arctic has undergone dramatic changes in recent years. The perennial sea ice extent is decreasing by 12.2 % per decade while annual mean sea ice thickness has decreased by more than 2 m for the central Arctic Basin from 1975 to 2012. High resolution information of the ice cover is necessary for a better understanding of the involved processes. Furthermore increased economic, scientific and touristic activities in the Arctic demand ice information for safer navigation in ice infested waters.Satellite synthetic aperture radar facilitates year round monitoring of the sea ice cover with high spatial and temporal coverage. High resolution is a requirement to capture small scale sea ice features like leads and the dynamics of the ice cover driven by the atmosphere and ocean.This thesis presents investigations on sea ice characterization from multi-spectral SAR imagery. Dual-polarization C- and L-band images from Sentinel-1 and ALOS PALSAR-2 have been used to derive sea ice concentration, for creation of ice-water maps and ice type classification. The developed algorithms for sea ice concentration estimation and ice/water classification use spatial autocorrelation as a texture feature to improve the discrimination of ice and water. The mapping between image features and the output variable is realized with a neural network. The proposed algorithms show good performance when evaluated against manually derived ice charts and radiometer data. We demonstrate that C- and L-band contain complementary data and a combination of these frequencies could achieve more robust classification results.Furthermore the separability and signatures of ice types in different ice regimes, i.e. marginal ice zone, pack ice and areas containing fast ice, have been investigated. Classification only based on backscatter intensities has been carried out by means of a support vector machine on selected examples of the same C- and L-band dataset. The results indicate that also for ice type classification a combination of frequencies can improve the classification accuracy

Near-Real Time Detection of the Re-Opening of the Weddell Polynya, Antarctica, from Spaceborne Infrared Imagery

A hole in the Antarctic sea ice cover, the Weddell Polynya, unexpectedly re-opened in winter 2017 for the first time since 1976. Models suggest that the polynya opened because warm oceanic water moved up to the surface, melting the ice from below. Here three temperature thresholds applied to near-hourly spaceborne infrared imagery (AVHRR) successfully detect the appearance of a warm spot up to five days before the polynya opened in June and September 2017. Traditional sea ice concentration and thickness criteria could only detect the polynya once it was open. An automatised warning system, using near-real time passive monitoring of warm spots, would allow researchers to reroute vessels or autonomous sensors in order to finally study the polynya as a whole when it opens again, from its preconditioning to its impacts on the climate system

Sea ice concentration estimation from Sentinel-1 Synthetic Aperture Radar images over the Fram Strait

\ua9 2016 IEEE.In this paper we present an algorithm for sea ice concentration estimation in the Arctic from C-band dual polarization Sentinel-1A SAR images. The algorithm is based on spatial autocorrelation and utilizes an artificial neural network to map the image information to sea ice concentration. The cross-polarization channel facilitates the improvement of concentration estimates of images with high backscatter over open water in the normally used co-polarization channel. Ice charts from the Norwegian meteorological institute are used for the training of the network and as a reference. A mean absolute error of 14.55 (ice concentration is given in the range from 0 to 100) of a test data set consisting of 20 images underlines the capabilities of the proposed algorithm

Comparison of Sentinel-1 Sar and Sentinel-3 Altimetry Data for Sea Ice Type Discrimination

In this paper near co-incidental Sentinel-1 C-band SAR imagery and Sentinel-3 SRAL Ku-band altimeter data are compared for their capabilities of sea ice type discrimination. Knowledge of sea ice type is important for climate research and safety in Arctic offshore operations.First-year ice is characterised by a low SAR backscatter intensity in both HH and HV polarisation compared to multi-year ice, while the altimeter waveform parameters show high pulse peakiness and peak power compared to multi-year ice.Thus SAR imagery and altimetry can principally discriminate different ice types. The complexity of the backscattered radar signal however impedes a clear separation of the two types for all cases. Cross comparison of the two sensors offers an opportunity of high resolution validation data, which is often lacking for sea ice studies