Snow-covered Sea Ice Research Articles

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

Passive microwave Arctic sea ice melt onset dates from the advanced horizontal range algorithm 1979–2022

The onset of the summer melt season is a key stage of the Arctic sea ice seasonal cycle and is an indicator of climate change. Surface melting of the bare or snow-covered sea ice is detected using passive microwave satellite observations. The data set presented here is a 44 year record of Arctic sea ice annual melt onset (MO) dates for 1979–2022 produced using an updated version of the Advanced Horizontal Range Algorithm (AHRA). This data product contains annual maps of the sea ice MO date and a set of descriptive statistics summarizing the data. This paper describes a new update of the AHRA methodology, now AHRA V5, including key changes to the algorithm starting date and sea ice mask methodology to improve estimates of early-season MO dates especially near the sea ice periphery. AHRA V5 data are suitable for monitoring trends in Arctic and regional sea ice MO dates and for process studies of atmosphere-sea ice interactions during the early spring and summer months.

Relevance of warm air intrusions for Arctic satellite sea ice concentration time series

Abstract. Winter warm air intrusions entering the Arctic region can strongly modify the microwave emission of the snow-covered sea ice system due to temperature-induced snow metamorphism and ice crust formations, e.g., after melt–refreeze events. Common microwave radiometer satellite sea ice concentration retrievals are based on empirical models using the snow-covered sea ice emissivity and thus can be influenced by strong warm air intrusions. Here, we carry out a long-term study analyzing 41 years of winter sea ice concentration observations from different algorithms to investigate the impact of warm air intrusions on the retrieved ice concentration. Our results show that three out of four algorithms underestimate the sea ice concentration during (and up to 10 d after) warm air intrusions which increase the 2 m air temperature (daily maximum) above − 5 ∘C. This can lead to sea ice area underestimations in the order of 104 to 105 km2. If the 2 m temperature during the warm air intrusions crosses − 2 ∘C, all algorithms are impacted. Our analysis shows that the strength of these strong warm air intrusions increased in recent years, especially in April. With a further climate change, such warm air intrusions are expected to occur more frequently and earlier in the season, and their influence on sea ice climate data records will become more important.

Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter

The onset of melt ponds on landfast first-year and multiyear sea ice is detected using dual co-polarized Ku-band spaceborne scatterometer data. Twenty-nine locations in the Canadian Arctic Archipelago are used to develop and test the methodology. The daily ScatSat-1 sigma-nought backscatter composite product with a spatial resolution of 2.216 km is used. Vertical (VV) and horizontal (HH) polarizations of these data are used to derive the co-polarized ratio (VV/HH). An increase in VV/HH is associated with an increase in melt pond fraction on the sea ice. This occurs because there is a larger increase in resonant Bragg scattering for VV than for HH when melt pond surfaces exhibit wind-generated capillary waves. VV backscatter magnitude is evaluated in time series using a peak-finding methodology to initiate detection of melt ponds with VV/HH. The peak-finding is based on geophysical controls on backscatter from snow-covered sea ice during the melt season. Daily time-series data from ScatSat-1 are used in conjunction with ERA-5 reanalysis data (2-m air temperature and wind) and a semi-empirical snowmelt model. Air temperature data are used to identify snow melt onset and to force the snowmelt model. Wind data are used to evaluate if the wind speed is sufficient for capillary wave formation. The NSCAT-4DS geophysical model function is used to model Ku-band backscatter from water, which is then used in a sea ice mixture model to simulate the VV/HH magnitude for a range of pond fractions and wind speeds. The potential for detecting pond onset is evaluated in relation to the noise inherent in the ScatSat-1 VV/HH sea ice measurements, which is found to be approximately 0.16 dB. When the melt pond fraction is sufficiently large to cause VV/HH to exceed the noise, pond onset is detected. Cloud-free Sentinel-2 optical satellite imagery is used for validation; specifically, the near-infrared albedo. We find an overall mean bias of −0.5 days, and an absolute deviation of 1.4 days, when comparing our method to a near-infrared method for detecting pond onset. The mean biases (absolute deviations) for first-year and multiyear sea ice are −0.3 (1.5) and −0.8 (1.2) days, respectively. Results show that the temporal resolution, spatial resolution, and efficacy of this method can provide robust sea ice melt pond onset timing for landfast sea ice regions of the Arctic.

Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice

Abstract. Wind-driven redistribution of snow on sea ice alters its topography and microstructure, yet the impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution over Arctic sea ice on radar waveforms and backscatter signatures obtained from a surface-based, fully polarimetric Ka- and Ku-band radar at incidence angles between 0∘ (nadir) and 50∘. Two wind events in November 2019 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coefficients at nadir are observed, coincident with surface topography changes measured by a terrestrial laser scanner. At both frequencies, redistribution caused snow densification at the surface and the uppermost layers, increasing the scattering at the air–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous air–snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air–snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite altimeters. With increasing incidence angles, the relative scattering contribution of the air–snow interface decreases, and the snow–sea ice interface scattering increases. Relative to pre-wind event conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan area at all incidence angles and polarizations, in response to increasing wind speed and changes in wind direction. Our results show that snow redistribution and wind compaction need to be accounted for to interpret airborne and satellite radar measurements of snow-covered sea ice.

Airborne Investigation of Quasi-Specular Ku-Band Radar Scattering for Satellite Altimetry Over Snow-Covered Arctic Sea Ice

Airborne Investigation of Quasi-Specular Ku-Band Radar Scattering for Satellite Altimetry Over Snow-Covered Arctic Sea Ice

Walking on snow-covered Arctic sea ice to infer ice thickness.

The ice-covered Arctic Ocean constitutes a unique underwater acoustic waveguide; it is a half-channel, upward refracting environment possessing a rough upper boundary consisting of sea ice of varying thickness. The sea ice itself is an acoustic waveguide, capable of supporting the propagation of compressional and shear waves. In particular, the ice supports compressional wave resonances created by impulsive forces on the upper surface of the ice. During ICEX20 and ICEX22, observations were made of compressional wave resonances excited by hammer drops, as well as by near-impulsive signals generated from the compression of dry snow underfoot while walking on the ice. Results demonstrate that ice thickness can be inferred from compressional wave resonances in the sea ice waveguide using signals generated by walking on the snow-covered ice. Inferred ice thickness estimates were consistent with observations made by magnetic induction and physical measurements in holes drilled through the ice. Average first- and multi-year ice thicknesses were inferred to be 1.1-1.3 m and 2.4-2.5 m, respectively.

Simulated Geophysical Noise in Sea Ice Concentration Estimates of Open Water and Snow-Covered Sea Ice

Sea ice concentration algorithms using brightness temperatures (<i>T_B</i>) from satellite microwave radiometers are used to compute sea ice concentration (<i>c_ice</i>), sea ice extent, and generate sea ice climate data records (CDRs). Therefore, it is important to minimize the sensitivity of <i>c_ice</i> estimates to geophysical noise caused by snow/sea ice thermal microwave emission signature variations, and presence of water vapor and clouds in the atmosphere and/or near-surface winds. In this study, we investigate the effect of geophysical noise leading to systematic <i>c_ice</i> biases and affecting <i>c_ice</i> standard deviations (STD) using simulated top of the atmosphere (TOA) <i>T_B</i>s over open water and 100 % sea ice. We consider three case studies for the Arctic and the Antarctic and eight different <i>c_ice</i> algorithms, representing different families of algorithms based on the selection of channels and methodologies. Our simulations show that, over open water and low <i>c_ice</i>, algorithms using gradients between V-polarized 19 GHz and 37 GHz <i>T_B</i>s shows the lowest sensitivity to the geophysical noise, while the algorithms exclusively using near 90 GHz channels have by far the highest sensitivity. Over sea ice, the atmosphere plays a much smaller role than over open water and the <i>c_ice</i> STD for all algorithms is smaller than over open water. The hybrid and low frequency (6 GHz) algorithms have the lowest sensitivity to noise over sea ice, while the polarization type of algorithms have the highest noise levels.

Passive Microwave Signatures and Retrieval of High-Latitude Snowfall Over Open Oceans and Sea Ice: Insights From Coincidences of GPM and CloudSat Satellites

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> database populated with the active–passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mmh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> .

Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry

Abstract. Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEMs) are measuring the radar scattering center height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for thick and deformed sea ice with snow cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer-plus-volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The experiments are performed across four polarizations: HH, VV, Pauli 1 (HH + VV), and Pauli 2 (HH − VV). The model-retrieved performance is validated with the optically derived DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.26 m in Pauli-1 (HH + VV) polarization.

Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the “track point” (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (&gt;0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions.

Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.

Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients.

Under-Sea Ice Diffusing Optical Communications

In this paper, we propose a novel approach to establish a reliable high-speed broadcast communication link between a group of autonomous underwater vehicles (AUVs) swarm under-sea ice. We utilize the fact that sea ice exists above the AUVs to diffuse the optical beam sent from AUV transmitter. We model this channel using a new seawater-sea ice cascaded layers (SSCL) model in which the vertical channel is divided into multiple layers based on their optical characteristics. The diffusing pattern of the SSCL model is computed using Monte Carlo numerical ray-tracing technique. We derive a quasi-analytic equation for the channel impulse response (CIR) which is valid for AUV receivers with different configurations, locations and orientations. The communication performance of underwater sea ice diffusing system is quantified via bit error rate performance, power penalty and and maximum achievable bit rate. Our results reveal that, for a snow-covered sea ice sheet with thickness of 36 cm and bare sea ice sheet with thickness 12 cm, the proposed system can achieve a broadcast communication rate of 100 Mbps with ranges up to 3.5 meters and 3 meters, respectively, with BER less than 10-3 and average transmitted power of 100 mW.

Feasibility study of miniature near-infrared spectrometer for the measurement of solar irradiance within Arctic snow-cover sea ice

The extremely low temperature, high humidity and limited power supply pose considerable challenges when using spectrometers within the Arctic sea ice. The feasibility of using a miniature low-power near-infrared spectrometer module to measure solar radiation in Arctic sea ice environments was investigated in this study. Temperature and integration time dependences of the spectrometer module were examined over the entire target operating range of −50°C to 30°C, well below the specified operating range of this spectrometer. Using these observations, a dark output prediction model was developed to represent dark output as a function of temperature and integration time. Temperature-induced biases in the saturation output and linear operating range of the spectrometer were also determined. Temperature and integration time dependences of the signal output were evaluated. Two signal output correction models were developed and compared, to convert the signal output at any temperature within the operating temperature range and integration time to that measured at the reference temperature and integration time. The overall performance of the spectrometer was evaluated by integrating it into a refined fiber optic spectrometry system and measuring solar irradiance distribution in the ice cover with thickness of 1.85 m in the Arctic during the 9th Chinese National Arctic Research Expedition. The general shape of the measured solar irradiance above the snow surface agreed well with that measured by other commercial oceanographic spectroradiometers. The measured optical properties of the sea ice were generally comparable to those of similar ice measured using other instruments. This approach provides a general framework for assessing the feasibility of using spectrometers for applications in cold environments.

Seasonal evolution of L-band SAR backscatter over landfast Arctic sea ice

We construct and present the first baseline, time series evolution of L-band SAR microwave backscatter (σ0HH) for landfast Arctic first-year (FYI) and multi-year (MYI) sea ice from ALOS PALSAR. We compare the winter to freeze-up seasonal L-band σ0HH signatures with C-band RADARSAT-2 SAR imagery and Ku-band QuikSCAT scatterometer data. We also explored the utility of L-band σ0HH to detect FYI and MYI melt onset dates. For both ice types, L-band exhibited a similar σ0HH evolution to C- and Ku-bands. A rise in L-band σ0HH was observed from winter to melt onset, however, with a lower magnitude attributed to the larger penetration of L-band microwaves through the snow-covered sea ice medium. Over FYI, L-band σ0HH exhibited the most robust response and greater diversity in melt onset signature, compared to C- and Ku-bands. Over MYI, L-band σ0HH declined during the winter to melt onset transition contrasting the C- and Ku-band evolution of σ0HH during melt onset. Although, a distinct L-band σ0HH response was not found during pond onset, a very strong σ0HH response was found during sea ice break-up, which could operationally be utilized to identify sea ice break-up timing. During freeze-up, L-band σ0HH showed greater diversity compared to C- and Ku-bands. Our study also found that, L-band detected melt onset dates were up to 6 days later than C- and Ku-band detected dates, for both ice types. The melt response was the strongest at L-band for FYI, where C-band detected a robust melt onset date for MYI. Therefore, for operational sea ice monitoring, L-band suits better for FYI, whereas C-band is still a better choice for MYI. In the warming Arctic, where FYI has become the dominant ice type, L-band demonstrates promise to be the optimal choice for sea ice operational monitoring with high spatiotemporal data acquisition from existing, upcoming and proposed L-band SAR missions.

Evaluating the impact of blowing-snow sea salt aerosol on springtime BrO and O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; in the Arctic

Abstract. We use the GEOS-Chem chemical transport model to examine the influence of bromine release from blowing-snow sea salt aerosol (SSA) on springtime bromine activation and O3 depletion events (ODEs) in the Arctic lower troposphere. We evaluate our simulation against observations of tropospheric BrO vertical column densities (VCDtropo) from the GOME-2 (second Global Ozone Monitoring Experiment) and Ozone Monitoring Instrument (OMI) spaceborne instruments for 3 years (2007–2009), as well as against surface observations of O3. We conduct a simulation with blowing-snow SSA emissions from first-year sea ice (FYI; with a surface snow salinity of 0.1 psu) and multi-year sea ice (MYI; with a surface snow salinity of 0.05 psu), assuming a factor of 5 bromide enrichment of surface snow relative to seawater. This simulation captures the magnitude of observed March–April GOME-2 and OMI VCDtropo to within 17 %, as well as their spatiotemporal variability (r=0.76–0.85). Many of the large-scale bromine explosions are successfully reproduced, with the exception of events in May, which are absent or systematically underpredicted in the model. If we assume a lower salinity on MYI (0.01 psu), some of the bromine explosions events observed over MYI are not captured, suggesting that blowing snow over MYI is an important source of bromine activation. We find that the modeled atmospheric deposition onto snow-covered sea ice becomes highly enriched in bromide, increasing from enrichment factors of ∼5 in September–February to 10–60 in May, consistent with composition observations of freshly fallen snow. We propose that this progressive enrichment in deposition could enable blowing-snow-induced halogen activation to propagate into May and might explain our late-spring underestimate in VCDtropo. We estimate that the atmospheric deposition of SSA could increase snow salinity by up to 0.04 psu between February and April, which could be an important source of salinity for surface snow on MYI as well as FYI covered by deep snowpack. Inclusion of halogen release from blowing-snow SSA in our simulations decreases monthly mean Arctic surface O3 by 4–8 ppbv (15 %–30 %) in March and 8–14 ppbv (30 %–40 %) in April. We reproduce a transport event of depleted O3 Arctic air down to 40∘ N observed at many sub-Arctic surface sites in early April 2007. While our simulation captures 25 %–40 % of the ODEs observed at coastal Arctic surface sites, it underestimates the magnitude of many of these events and entirely misses 60 %–75 % of ODEs. This difficulty in reproducing observed surface ODEs could be related to the coarse horizontal resolution of the model, the known biases in simulating Arctic boundary layer exchange processes, the lack of detailed chlorine chemistry, and/or the fact that we did not include direct halogen activation by snowpack chemistry.

Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR

ABSTRACT Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer.

Effects of microbial processes and CaCO3 dynamics on inorganic carbon cycling in snow-covered Arctic winter sea ice

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 611:31-44 (2019) - DOI: https://doi.org/10.3354/meps12868 Effects of microbial processes and CaCO3 dynamics on inorganic carbon cycling in snow-covered Arctic winter sea ice Dorte Haubjerg Søgaard1,2,*, Jody W. Deming3, Lorenz Meire1,4, Søren Rysgaard1,2,5 1Greenland Climate Research Centre (C/O Greenland Institute of Natural Resources), Kivioq 2, PO Box 570, 3900 Nuuk, Greenland 2Arctic Research Centre, Department of Bioscience, Aarhus University, Ny Munkegade 116, building 1540, 8000 Aarhus C, Denmark 3School of Oceanography, University of Washington, Seattle, WA 98195, USA 4Royal Netherlands Institute for Sea Research, and Utrecht University, 4401 NT Yerseke, Netherlands 5Centre for Earth Observation Science, CHR Faculty of Environment Earth and Resources, University of Manitoba, 499 Wallace Building, Winnipeg, MB R3T 2N2, Canada *Corresponding author: doso@natur.gl ABSTRACT: Few combined measurements of primary and bacterial productivity exist for Arctic sea ice, particularly during winter, making it difficult to assess the relative importance of these microbial processes for carbon cycling in sea ice. Furthermore, the occurrence of calcium carbonate (CaCO3), though well-documented in sea ice, is poorly described for the overlying snow. To address these gaps, we investigated primary and bacterial productivity and carbon dynamics at 2 contrasting locations: (1) a landfast site, with thick snow-covered first-year sea ice, and (2) a polynya site, with thin snow-covered young (<1 wk) sea ice. Comparisons of bacterial carbon demand and primary production indicated net heterotrophy in the sea ice at both locations, with a net carbon consumption rate of 0.87 to 1.86 mg C m-2 d-1 derived from sea ice bacterial carbon demand of 0.93 to 2.00 mg C m-2 d-1 and gross primary production of 0.06 to 0.14 mg C m-2 d-1. As these microbial rates are very low, physical processes largely account for the observed CO2 depletion in the ice. High CaCO3 concentrations of 250 to 430 µmol kg-1 were measured in the snow covers which, though similar to concentrations in the underlying ice, are orders of magnitude higher than those reported from the few studies available on CaCO3 in snow. Together these results suggest that the role of biology in modulating inorganic carbon cycling in ice, which can be important in spring, is minor as compared to abiotic processes. KEY WORDS: High Arctic · First-year sea ice · Polynya ice · Snow cover · Primary production · Bacterial production · CaCO3 Full text in pdf format Supplementary material PreviousNextCite this article as: Søgaard DH, Deming JW, Meire L, Rysgaard S (2019) Effects of microbial processes and CaCO3 dynamics on inorganic carbon cycling in snow-covered Arctic winter sea ice. Mar Ecol Prog Ser 611:31-44. https://doi.org/10.3354/meps12868 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 611. Online publication date: February 14, 2019 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2019 Inter-Research.

A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes From Heterogeneous Sea Ice Surfaces

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multiscale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler synthetic aperture radar (SAR) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facet-based models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, and roll) and sea ice properties (physical properties, roughness, and presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm—potentially underestimating sea ice thickness by around 50 cm. We present a set of “ideal” waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness, and snow depth, from SAR altimeter observations.