A method to improve high-resolution sea ice drift retrievals in the presence of deformation zones

Source at: http://doi.org/10.3390/rs9070718 Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based on correlation and feature identification techniques. Here, we propose two extensions to a pattern matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed in an earlier study, which is based on analyzing texture and correlation parameters of SAR image pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field, and detects and replaces outliers after considering the presence of such LDFs in their neighborhood. We validate the improvement of our pattern matching algorithm by comparing the automatically retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice covered regions

Impact of Sea Ice Drift Retrieval Errors, Discretization and Grid Type on Calculations of Sea Ice Deformation

We studied two issues to be considered in the calculation of parameters characterizing sea ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the influence of the type of drift vector grid. Sea ice deformation changes the local ice mass balance and the interaction between atmosphere, ice, and ocean, and constitutes a hazard to marine traffic and operations. Due to numerical effects, the results of deformation retrievals may predict, e.g., openings and closings of the ice cover that do not exist in reality. We focus specifically on fields of ice drift obtained from synthetic aperture radar (SAR) imagery and analyze the Propagated Drift Retrieval Error (PDRE) and the Boundary Definition Error (BDE). From the theory of error propagation, the PDRE for the calculated deformation parameters can be estimated. To quantify the BDE, we devise five different grid types and compare theoretical expectation and numerical results for different deformation parameters assuming three scenarios: pure divergence, pure shear, and a mixture of both. Our findings for both sources of error help to set up optimal deformation retrieval schemes and are also useful for other applications working with vector fields and scalar parameters derived therefrom

A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data

Sea-ice thickness on a global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the sensitivity to certain sea-ice types and thickness ranges. Satellite altimeters, such as CryoSat-2 (CS2), sense the height of the ice surface above the sea level, which can be converted into sea-ice thickness. Relative uncertainties associated with this method are large over thin ice regimes. Another retrieval method is based on the evaluation of surface brightness temperature (TB) in L-band microwave frequencies (1.4 GHz) with a thickness-dependent emission model, as measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. While the radiometer-based method looses sensitivity for thick sea ice (> 1 m), relative uncertainties over thin ice are significantly smaller than for the altimetry-based retrievals. In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean (WM) and an optimal interpolation (OI) scheme. While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arctic-wide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic (AEM) induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged product has a root mean square deviation (RMSD) of about 0.7 m less than the CS2 product and therefore demonstrates the capability to enhance the CS2 product in thin ice regimes. However, in mixed first-year (FYI) and multiyear (MYI) ice regimes as in the Beaufort Sea, the CS2 retrieval shows the lowest bias

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

Dynamics of the Terra Nova Bay Polynya: The potential of multi-sensor satellite observations

Research on processes leading to formation, maintenance, and disappearance of polynyas in the Polar Regions benefits significantly from the use of different types of remote sensing data. The Sentinels of the European Space Agency (ESA), together with other satellite missions, provide a variety of data from different parts of the electromagnetic spectrum, at different spatial scales, and with different temporal resolutions. In a case study we demonstrate the advantage of merging data from different spaceborne instruments for analysing ice conditions and ice dynamics in and around the frequently occurring Terra Nova Bay Polynya (TNBP) in the Ross Sea in the Antarctic. Starting with a list of polynya parameters that are typically retrieved from satellite images, we assess the usefulness of different sensor types. On regional scales (several 100 km), passive microwave radiometers provide a view on the mutual influence of the three Ross Sea polynyas on sea ice drift and deformation patterns. Optical sensors with meter-scale resolution, on the other hand, allow very localized analyses of different polynya zones. The combination of different ranges of the electromagnetic spectrum is essential for recognition and classification of ice types and structures. Radar images together with data from thermal infrared sensors, operated at tens to hundreds of meters resolution, improve the separation of the outlet zone of the polynya from the adjacent pack ice. The direct comparison of radar and passive microwave images reveals the visibility of deformed ice zone in the latter. A sequence of radar images was employed to retrieve ice drift around the TNB, which allows analysing the temporal changes of the polynya area and the extension and structure of the outlet zone as well as ice movements and deformation that are influenced by the katabatic winds

Correcting Multiyear Sea Ice Concentration Estimates from Microwave Satellite Observations with Air Temperature, Sea Ice Drift and Dynamic Tie Points

Arctic sea ice cover is a sensitive climate indicator. Due to the warming climate, it has decreased dramatically in the Arctic over the past three decades. Moreover, multiyear ice (MYI), ice which has survived at least one summer, is decreasing at a much higher rate. MYI concentration can be retrieved from microwave remote sensing data. However, the retrieval shows flaws under specific weather conditions. The current thesis is motivated by the need of better estimates of MYI distribution. It introduces three methods to improve/correct the MYI concentration estimates from microwave satellite observations. The first method builds upon the NASA Team algorithm and uses dynamic tie points to compensate the temporal variations of tie points (typical brightness temperatures of each surface type at all the channels). The MYI retrievals in winters (Oct-May) of the years 1989-2012 show that the method with dynamic tie points yields higher estimates than the original method in most years. Both methods show clear declining trends of the MYI area from 1989 to 2012, which is consistent with the sea ice extent minimum. The MYI concentration retrieval with the NASA Team algorithm is most sensitive to the tie points of MYI and FYI at 19 GHz vertical polarized channel. These tie points should be treated with more caution when dynamic tie points are used. The second and third methods are two correction schemes used to account for radiometric anomalies that trigger the erroneous MYI concentration retrievals from microwave satellite observations. The correction based on air temperature is introduced to restore the underestimated MYI concentration under warm conditions. It utilizes the fact that the warm spell in autumn lasts for a few days and replaces the erroneous MYI concentrations with interpolated ones. It is applied to MYI retrievals from the Environment Canada Ice Concentration Extractor (ECICE) using inputs from QuikSCAT and AMSR-E data, acquired over the Arctic in a series of autumn seasons (Sep-Dec) from 2003 to 2008. The correction works well by identifying and correcting the anomalous MYI concentrations. For September of the six years, it introduces over 1.0x105 km2 MYI area, except for 2005. The correction based on ice drift is designed to correct the overestimated MYI concentrations that are impacted by factors such ice deformation, snow wetness and metamorphism. It utilizes ice drift records to constrain the MYI changes within a predicted contour and uses two thresholds of passive microwave radiometric parameters to account for snow wetness and metamorphism. It is applied to the MYI concentration retrievals from ECICE in winters (Oct-May) from 2002 to 2009. Qualitative comparison with Radarsat-1 SAR images and quantitative comparison against results from previous studies show that the correction works well by removing the anomalous high MYI concentrations. On average, the correction reduces 5.2x105 km2 of the estimated MYI area in Arctic except for the April-May time frame, when the reduction is larger as the warmer weather prompts the condition of the anomalous snow radiometric signatures. Both corrections can be used as post-processings to all the microwave-based MYI concentration retrieval algorithms. Due to the regional effect of weather conditions, they could be important in the operational applications. In addition, both corrections take the spatial and temporal continuity of MYI into account, which gives a new insight that instantaneous observations alone of sea ice may lead to ambiguities in determination of partial ice concentrations. This approach may be applicable to the retrieval of other sea ice parameters as well