Learning to Estimate Sea Ice Concentration from SAR Imagery

Through the growing interest in the Arctic for shipping, mining and climate research, large-scale high quality ice concentration is of great interest. Due to the unavailability of suitable ice concentration estimation algorithms, ice concentration maps are interpreted from synthetic aperture radar (SAR) images manually by ice experts for operational uses. An automatic ice concentration estimation algorithm is required for accurate large-scale ice mapping. In this thesis, a set of algorithms are developed aiming at operational ice concentration estimation from SAR images. The major difficulty in designing a robust algorithm for ice concentration estimation from SAR images is the constantly changing SAR image features of ice and water in time and location. This difficulty is addressed by learning features instead of designing features from SAR images. A set of convolutional neural network based ice concentration estima- tion algorithms are developed to learn multi-scale SAR image features and simultaneously regress ice concentration from the learned image features. We first demonstrated the capa- bility of CNNs in ice concentration estimation from SAR images when trained using image analysis charts as ground truth. Then the model is further improved by taking into account the errors in the image analysis charts. Ice concentration estimates with improved robust- ness to training samples errors, accuracy and scale of details are obtained. The robustness of the developed methods are further demonstrated in the melt season of the Beaufort Sea, where reasonable ice concentration estimates are acquired. In order to reduce the model training time, it is desired to reuse existing models. The model transferability is evaluated and suggestions on using existing models to accelerate the training process are given, which is shown to reduce the training time by over 10 times in our case

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

Spatiotemporal variability and decadal trends of snowmelt processes on Antarctic sea ice observed by satellite scatterometers

The timing and intensity of snowmelt processes on sea ice are key drivers determining the seasonal sea-ice energy and mass budgets. In the Arctic, satellite passive microwave and radar observations have revealed a trend towards an earlier snowmelt onset during the last decades, which is an important aspect of Arctic amplification and sea-ice decline. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here we compile time series of snowmelt onset dates on seasonal and perennial Antarctic sea ice from 1992 to 2014/15 using active microwave observations from the European Space Agency's (ESA) European Remote Sensing (ERS) 1 and 2 missions (ERS-1 and ERS-2), Quick Scatterometer (QSCAT), and Advanced Scatterometer (ASCAT) radar scatterometers. We define two snowmelt transition stages: a weak backscatter rise, indicating the initial warming and destructive metamorphism of the snowpack (pre-melt), followed by a rapid backscatter rise, indicating the onset of thaw–freeze cycles (snowmelt). Results show large interannual variability, with an average pre-melt onset date of 29 November and melt onset of 10 December, respectively, on perennial ice, without any significant trends over the study period, consistent with the small trends of Antarctic sea-ice extent. There was a latitudinal gradient from early snowmelt onsets in mid-November in the northern Weddell Sea to late (end of December) or even absent snowmelt conditions in the southern Weddell Sea. We show that QSCAT Ku-band-derived (13.4 GHz signal frequency) pre-melt and snowmelt onset dates are earlier by 20 and 18 d, respectively, than ERS and ASCAT C-band-derived (5.6 GHz) dates. This offset has been considered when constructing the time series. Snowmelt onset dates from passive microwave observations (37 GHz) are later by 14 and 6 d than those from the scatterometers, respectively. Based on these characteristic differences between melt onset dates observed by different microwave wavelengths, we developed a conceptual model which illustrates how the seasonal evolution of snow temperature profiles may affect different microwave bands with different penetration depths. These suggest that future multi-frequency active and passive microwave satellite missions could be used to resolve melt processes throughout the vertical snow column of thick snow on perennial Antarctic sea ice

NASA Sea Ice Validation Program for the Defense Meteorological Satellite Program Special Sensor Microwave Imager

The history of the program is described along with the SSM/I sensor, including its calibration and geolocation correction procedures used by NASA, SSM/I data flow, and the NASA program to distribute polar gridded SSM/I radiances and sea ice concentrations (SIC) on CD-ROMs. Following a discussion of the NASA algorithm used to convert SSM/I radiances to SICs, results of 95 SSM/I-MSS Landsat IC comparisons for regions in both the Arctic and the Antarctic are presented. The Landsat comparisons show that the overall algorithm accuracy under winter conditions is 7 pct. on average with 4 pct. negative bias. Next, high resolution active and passive microwave image mosaics from coordinated NASA and Navy aircraft underflights over regions of the Beaufort and Chukchi seas in March 1988 were used to show that the algorithm multiyear IC accuracy is 11 pct. on average with a positive bias of 12 pct. Ice edge crossings of the Bering Sea by the NASA DC-8 aircraft were used to show that the SSM/I 15 pct. ice concentration contour corresponds best to the location of the initial bands at the ice edge. Finally, a summary of results and recommendations for improving the SIC retrievals from spaceborne radiometers are provided

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Sea-ice habitat preference of the Pacific walrus (Odobenus rosmarus divergens) in the Bering Sea: a multiscaled approach

Thesis (M.S.) University of Alaska Fairbanks, 2015The goal of this thesis is to define specific parameters of mesoscale sea-ice seascapes for which walruses show preference during important periods of their natural history. This research thesis incorporates sea-ice geophysics, marine-mammal ecology, remote sensing, computer vision techniques, and traditional ecological knowledge of indigenous subsistence hunters in order to quantitatively study walrus preference of sea ice during the spring migration in the Bering Sea. Using an approach that applies seascape ecology, or landscape ecology to the marine environment, our goal is to define specific parameters of ice-patch descriptors and mesoscale seascapes in order to evaluate and describe potential walrus preference for such ice and the ecological services it provides during an important period of their life-cycle. The importance of specific sea-ice properties to walrus occupation motivates an investigation into how walruses use sea ice at multiple spatial scales when previous research suggests that walruses do not show preference for particular floes. Analysis of aerial imagery, using image processing techniques and digital geomorphometric measurements (floe size, shape, and arrangement), demonstrated that while a particular floe may not be preferred, at larger scales a collection of floes, specifically an ice-patch (< 4 km²), was preferred. This shows that walruses occupy ice patches with distinct ice features such as floe convexity, spatial density, and young ice and open water concentration. Ice patches that are occupied by adult and juvenile walruses show a small number of characteristics that vary from those ice patches that were visually unoccupied. Using synthetic aperture radar imagery, we analyzed co-located walrus observations and statistical texture analysis of radar imagery to quantify seascape preferences of walruses during the spring migration. At a coarse resolution of 100-9,000 km², seascape analysis shows that, for the years 2006-2008, walruses were preferentially occupying fragmented pack ice seascapes range 50-89% of the time, when, all throughout the Bering Sea, only range 41-46% of seascapes consisted of fragmented pack ice. Traditional knowledge of a walrus' use of sea ice is investigated through semi-directed interviews conducted with subsistence hunters and elders from Savoonga and Gambell, two Alaskan Native communities on St. Lawrence Island, Alaska. Informants were provided with a large nautical map of the land and ocean surrounding St. Lawrence Island and 45 printed largeformat aerial photographs of walruses on sea ice to stimulate discussion as questions were asked to direct the topics of conversation. Informants discussed change in sea ice conditions over time, walrus behaviors during the fall and spring subsistence hunts, and sea-ice characteristics that walruses typically occupy. These observations are compared with ice-patch preferences analyzed from aerial imagery. Floe size was found to agree with remotely-sensed ice-patch analysis results, while floe shape was not distinguishable to informants during the hunt. Ice-patch arrangement descriptors concentration and density generally agreed with ice-patch analysis results. Results include possible preference of ice-patch descriptors at the ice-patch scale and fragmented pack ice preference at the seascape scale. Traditional knowledge suggests large ice ridges are preferential sea-ice features at the ice-patch scale, which are rapidly becoming less common during the fall and spring migration of sea ice through the Bering Sea. Future work includes increased sophistication of the synthetic aperture radar classification algorithm, experimentation with various spatial scales to determine the optimal scale for walrus' life-cycle events, and incorporation of further traditional knowledge to investigate and interface crosscultural sea-ice observations, knowledge and science to determine sea ice importance to marine mammals in a changing Arctic

Development of an ENVISAT altimetry processor providing sea level continuity between open ocean and Arctic leads

Over the Arctic regions, current conventional altimetry products suffer from a lack of coverage or from degraded performance due to the inadequacy of the standard process- ing applied in the ground segments. This paper presents a set of dedicated algorithms able to process consistently returns from open ocean and from sea ice leads in the Arctic Ocean (detection of water surfaces and derivation of water levels using returns from these surfaces). This processing extends the area over which a precise sea level can be com- puted. In the frame of the ESA Sea Level Climate Change Initiative (CCI, http://cci.esa.int), we have first developed a new surface identification method combining two complementary solutions, one using a multiple criteria approach (in particular the backscattering coefficient and the peakiness coefficient of the waveforms) and one based on a supervised neural net- work approach. Then, a new physical model has been developed (modified from the Brown model to include anisotropy in the scattering from calm protected water surfaces) and has been implemented in a Maximum Likelihood Estimation retracker. This allows us to process both sea-ice lead waveforms (characterized by their peaky shapes) and ocean waveforms (more diffuse returns), guaranteeing, by construction, continuity between open ocean and ice-covered regions. This new processing has been used to produce maps of Arctic sea level anomaly from 18Hz ENVISAT/RA-2 dat

Geostatistical and statistical classification of sea-ice properties and provinces from SAR data

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line

Dual-polarization (HH/HV) RADARSAT-2 ScanSAR Observations of New, Young and First-year Sea Ice

Observations of sea ice from space are routinely used to monitor sea ice extent, concentration and type to support human marine activity and climate change studies. In this study, eight dual-polarization (dual-pol) (HH/HV) RADARSAT-2 ScanSAR images acquired over the Gulf of St. Lawrence during the winter of 2009 are analysed to determine what new or improved sea ice information is provided by dual-pol C-band synthetic aperture radar (SAR) data at wide swath widths, relative to single co-pol data. The objective of this study is to assess how dual-pol RADARSAT-2 ScanSAR data might improve operational ice charts and derived sea ice climate data records. In order to evaluate the dual-pol data, ice thickness and surface roughness measurements and optical remote sensing data were compared to backscatter signatures observed in the SAR data. The study found that: i) dual-pol data provide improved separation of ice and open water, particularly at steep incidence angles and high wind speeds; ii) the contrast between new, young and first-year (FY) ice types is reduced in the cross-pol channel; and iii) large areas of heavily deformed ice can reliably be separated from level ice in the dual-pol data, but areas of light and moderately ridged ice cannot be resolved and the thickness of heavily deformed ice cannot be determined. These results are limited to observations of new, young and FY ice types in winter conditions. From an operational perspective, the improved separation of ice and open water will increase the accuracy of ice edge and total ice concentration estimates while reducing the time required to produce image analysis charts. Further work is needed to determine if areas of heavily ridged ice can be separated from areas of heavily rafted ice based on knowledge of ice conditions in the days preceding the formation of high backscatter deformed ice. If rafted and ridged ice can be separated, tactical ridged ice information should be included on image analysis charts. The dual-pol data can also provide small improvements to ice extent and concentration data in derived climate data records. Further analysis of dual-pol RADARSAT-2 ScanSAR data over additional ice regimes and seasons is required

Exploring the potential of high temporal resolution X-band SAR time series for various permafrost applications with ground truth observations in the Lena River Delta, Siberia.

Permafrost is a subsurface phenomenon that cannot be directly monitored with satellite remote sensing. A variety of indirect approaches are currently being developed which aim to measure permafrost-related processes and environmental variables. Results of these studies aid the planning of future satellite missions which will allow large-scale permafrost monitoring. This thesis contributes to this ongoing effort by assessing the potential of repeat-pass TerraSAR-X (TSX) time series for permafrost-related applications. For the first time, multi-year Synthetic Aperture Radar (SAR) data with high temporal (11 days) and spatial (3 m) resolution was analysed for a region characterized by continuous permafrost in the Siberian Arctic. Extensive in situ data was collected during three summer and winter expeditions to validate and interpret remote sensing results. Three case studies were carried out: (i) the detection of land surface changes (e.g. ground freezing and thawing, surface wetness variations, snow cover onset and melt); (ii) monitoring bedfast lake ice and ice phenology (freeze-up, melt onset, break-up); and (iii) differential SAR interferometry (DInSAR) for thaw subsidence monitoring. For the first two case studies, time series of both backscatter intensity and 11-day interferometric coherence (i.e. a measure of phase stability between two SAR images) were investigated. Backscatter intensity was generally shown to be insensitive to the land surface changes but responded to events that occurred at the time of TSX acquisition (rain, snow shower, melt/freeze crust on snow). Interferometric coherence decreased dramatically across the entire image upon snow cover onset and melt, permitting the possible use of coherence for the monitoring of these events. Backscatter intensity was found to be an excellent tool for the detection and monitoring of bedfast lake ice due in part to improved temporal resolution compared to previously used SAR systems. Ice phenology was mostly well tracked with backscatter intensity. Interferometric coherence was found to be sensitive to the lake ice grounding and to the onset of surface melt on the lakes with bedfast ice. The investigation of coherence was a useful preparative step for the following DInSAR analysis. For the third case study, coherent 11-day and 22-day interferograms were available only for one summer of the two-year TSX time series. The cumulative DInSAR displacement strongly underestimated the subsidence observed on the ground. In situ observations revealed high variability of subsidence, which likely caused errors in phase unwrapping. Conventional DInSAR processing might therefore not be suitable for the accurate representation of permafrost thaw subsidence. This study highlights the importance of field measurements for the quantification of thaw subsidence with DInSAR, which were mostly omitted in the previous studies. All in all, this thesis shows the limitations and potential of TSX time series to spatially and temporally monitor permafrost. It thus provides an important contribution to the methodological development of a long-term permafrost monitoring scheme