Object-based detection of linear kinematic features in sea ice

Source at: https://doi.org/10.3390/rs9050493 Inhomogenities in the sea ice motion field cause deformation zones, such as leads, cracks and pressure ridges. Due to their long and often narrow shape, those structures are referred to as Linear Kinematic Features (LKFs). In this paper we specifically address the identification and characterization of variations and discontinuities in the spatial distribution of the total deformation, which appear as LKFs. The distribution of LKFs in the ice cover of the polar oceans is an important factor influencing the exchange of heat and matter at the ocean-atmosphere interface. Current analyses of the sea ice deformation field often ignore the spatial/geographical context of individual structures, e.g., their orientation relative to adjacent deformation zones. In this study, we adapt image processing techniques to develop a method for LKF detection which is able to resolve individual features. The data are vectorized to obtain results on an object-based level. We then apply a semantic postprocessing step to determine the angle of junctions and between crossing structures. The proposed object detection method is carefully validated. We found a localization uncertainty of 0.75 pixel and a length error of 12% in the identified LKFs. The detected features can be individually traced to their geographical position. Thus, a wide variety of new metrics for ice deformation can be easily derived, including spatial parameters as well as the temporal stability of individual features

Polynya evolution at the Terra Nova Bay Antarctica – Analysis of a multi sensor time series

Coastal polynyas are open water areas in the sea ice cover. They are highly dynamical regions in the sea ice covered oceans of the Polar Regions. Their occurrence is mainly triggered by strong katabatic winds that push the ice offshore. Due to the lack of the insulating sea ice cover, polynyas have a strong impact on the local heat and energy exchange as well as on the ice production. Since the evolution of coastal polynyas is a dynamic event generating visible changes within a few hours, it is a demanding task for satellite remote sensing. The synchronised acquisition with the different sensors is of great importance for any multi sensor analysis over these dynamic regions. On the other hand it offers the chance to study various aspects of ocean - sea ice - atmosphere interactions in a relatively small area. During a recent project, funded by the Federal Ministry for Economic Affairs and Energy, we studied the potential of the Sentinel Constellation missions for Polynya research based on satellite data from present and past missions. In the period from September to November 2014, we acquired an extensive time series of TerraSAR-X ScanSAR wide images for the Terra Nova Bay and MacKenzie Bay Polynya, Antarctica. Both polynyas are known for their regular formation, and both are small enough to fit into a ScanSAR Wide Scene of TerraSAR-X. The TerraSAR-X time series is supplemented by acquisitions from other sensors like Sentinel-1, ALOS-2, RapidEye and Landsat. The combination of these different sensors at the same day is a great opportunity to study dynamic regions such as the polynyas at hand in more detail. We will present first results on the dynamical sea ice regime around the Terra Nova Bay Polynya based on high resolution drift estimation from the acquired TerraSAR-X data and combine it with datasets from different sensors for an improved analysis of the sea ice conditions around the polynya. The results emphasises the potential of multi-sensor approaches for sea ice research

Ableitung von Schneeakkumulationsraten fuer den groenlaendischen und den antarktischen Eisschild aus Mikrowellenfernerkundungsdaten

In order to estimate the impact of climate change it is necessary to monitor the surface mass balance of the ice sheets. Much effort has gone into developing accurate methods for surface mass balance estimation over the past years. Mass balance is defined as the sum of mass gain and mass loss. Still, it remains difficult to quantify surface accumulation, which is the net gain term of the balance equation. This is due to the fact that the polar ice sheets are difficult to access, so in-situ measurements are sparse. Therefore, it is important to employ remote sensing techniques to obtain accumulation data with a better spatial and temporal resolution. However, the resulting datasets still exhibit large amounts of uncertainty. This thesis seeks to improve the currently available methods to derive snow accumulation rates from microwave remote sensing data. For this purpose, different types of microwave data are systematically evaluated with respect to their suitability for accumulation rate retrieval. The approach taken here makes use of the fact that microwaves interact with a volume of dry polar firn and are sensitive to accumulation rate-dependent firn characteristics. For this reason, firn microstructure is examined in more detail. On the basis of this analysis, an improved parameterization of firn properties (grain size and density) is developed, which is valid for the layers of the firn column that interact with the microwave radiation. The improved microstructure model is used in conjunction with a simple radiative transfer model to simulate firn-microwave interaction, resulting in a synthetic microwave signal. Accumulation rates can subsequently be inverted by matching the signal from the model to microwave data measured by satellite sensors. A number of assumptions made in the radiative transfer model have proven to be invalid. In this work, the radiative transfer model is improved by including Mie scattering instead of the widely-used Rayleigh approximation for the combination of scatterer sizes and microwave frequencies investigated. The results from the accumulation retrieval algorithm developed in this work are validated with in-situ data, and limits in its applicability are evaluated. Accumulation rates inverted from the method introduced in this work are found to agree with field data as well as with accumulation maps from external sources within the range of the model's validity

Deriving Snow Accumulation Rates of Greenland and the Antarctic Ice Sheet from Microwave Remote Sensing Data

In order to estimate the impact of climate change it is necessary to monitor the surface mass balance of the ice sheets. Much effort has gone into developing accurate methods for surface mass balance estimation over the past years. Mass balance is defined as the sum of mass gain and mass loss. Still, it remains difficult to quantify surface accumulation, which is the net gain term of the balance equation. This is due to the fact that the polar ice sheets are difficult to access, so in-situ measurements are sparse. Therefore, it is important to employ remote sensing techniques to obtain accumulation data with a better spatial and temporal resolution. However, the resulting datasets still exhibit large amounts of uncertainty. This thesis seeks to improve the currently available methods to derive snow accumulation rates from microwave remote sensing data. For this purpose, different types of microwave data are systematically evaluated with respect to their suitability for accumulation rate retrieval. The approach taken here makes use of the fact that microwaves interact with a volume of dry polar firn and are sensitive to accumulation rate-dependent firn characteristics. For this reason, firn microstructure is examined in more detail. On the basis of this analysis, an improved parameterization of firn properties (grain size and density) is developed, which is valid for the layers of the firn column that interact with the microwave radiation. The improved microstructure model is used in conjunction with a simple radiative transfer model to simulate firn-microwave interaction, resulting in a synthetic microwave signal. Accumulation rates can subsequently be inverted by matching the signal from the model to microwave data measured by satellite sensors. A number of assumptions made in the radiative transfer model have proven to be invalid. In this work, the radiative transfer model is improved by including Mie scattering instead of the widely-used Rayleigh approximation for the combination of scatterer sizes and microwave frequencies investigated. The results from the accumulation retrieval algorithm developed in this work are validated with in-situ data, and limits in its applicability are evaluated. Accumulation rates inverted from the method introduced in this work are found to agree with field data as well as with accumulation maps from external sources within the range of the model's validity

Reliability Measures for Sea Ice Motion Retrieval From Synthetic Aperture Radar Images

Sea ice motion is triggered by wind and ocean currents. Its magnitude and direction can be automatically retrieved using pairs of satellite images acquired over the same area. However, external reference data for validation of drift retrievals, such as tracks from buoys, are sparse. Information about the reliability of the retrieved ice drift field is crucial for applications such as operational sea ice mapping or validation of computer models for simulations of sea ice dynamics. In this paper, we introduce an intrinsic measure based on the properties of radar image pairs to assess the reliability of the retrieved ice drift vectors. The proposed method combines different parameters, e.g., correlation coefficient and two textural quantities, to provide information about the suitability of subimage regions for pattern matching. In this way, we generate a quality parameter [called confidence factor (CFA)] for the calculated ice drift velocities. The CFA is compared to results obtained by “backmatching.” The latter requires that the drift field is computed twice using the image pair, first in sequential and then in reversed order. For stable ice conditions, the results show that areas regarded as unreliable by the CFA compare well with the areas revealing larger differences from backmatching

Retrieval of Accumulation Rates on the Ice Sheets Using SAR

- presentation is attached

Space Missions for the Retrieval of Accumulation Rates in Polar Regions

Recently the actual contribution of the ice sheets to sea level rise, and the interaction of ice sheets with the surrounding seas, has become an important issue in polar research. To determine the effects of the net mass flux from Greenland and Antarctica to the surrounding ocean, accurate data on mass gain (caused by accumulation of snow over the ice sheets and ice shelves) and mass loss (due to melting and iceberg calving) are required. The main factor of uncertainty arises from the difficulty to correctly determine snow accumulation over large areas. Work from various authors demonstrates a potential for mapping snow accumulation based on data from microwave radiometers, scatterometers, altimeters, and SAR systems (including INSAR and polarimetric SAR). These instruments cover a frequency range from about 1 to 40 GHz. Radar altimeters such as ASIRAS/Cryosat seem to be useful to map the upper annual layer(s) in the percolation and dry snow zone, provided the layers are thick enough (and accumulation rates correspondingly high) to be resolved in the measured radar signals. For the dry snow zone, empirical relationships were presented that relate brightness temperatures or radar intensities to accumulation rates. Another approach is to combine a model of firn structure with a scattering model for accumulation rate retrieval. Here, various problems exist, ranging from a realistic representation of the firn structure to an adequate simulation of the interactions between microwaves and the medium firn. The signal sensitivity to varying accumulation rates is only sufficient at low values of accumulation, and the results are integrals over a depth determined by the microwave frequency and environmental conditions. Field data for validation are sparse and often limited to small depths only. The main conclusion for ice sheets from the work presented so far is that no single sensor and retrieval approach can be regarded optimal for the retrieval of accumulation rates. The need to gather information on accumulation rates over the ice sheets is an excellent example for the need to combine different sensors, field measurements and modelling approaches, hence to interpret the term “mission concept” in a broad sense. In our presentation, we will sketch different approaches for accumulation rate retrieval and try to assess which combinations are most promising, including recently presented ideas for new satellite missions

Accounting for the layering of snow and firn - on the link between density and grain size variability

Microwave satellite remote sensing is an important source of information about the polar ice sheets. Radiation in the microwave frequency range interacts with the firn volume, and firn properties such as density and microstructure have been found to strongly influence the scattered or emitted signal. Hence, a correct interpretation of the data is only possible if the interaction of microwave radiation with polar firn is understood sufficiently. In this context, ground truth measurements are indispensable. However, a direct comparison between pointwise field measurements and satellite data is often difficult, since the variability within the satellite footprint needs to be considered. In the case of dry polar firn, the variability of density and grain size over the signal penetration depth also needs to be take into account. In order to investigate the latter effect, we conduct a statistical analysis of the layering properties in a number of polar firn cores. We show that there is a correlation between density variability and grain size variability and introduce a procedure which enables us to use measured densities as a proxy for grain size values. Scattering and emission models often use mean profiles of grain size and density to describe the snow volume. However, these profiles ignore the strongly pronounced layering of the snow and firn, which impacts the dielectric contrast and hence potentially introduces a bias into the model results. Using our improved representation of microstructure variability, we conduct a sensitivity study where we examine the impact of firn layering on the microwave signal