Thickness retrieval and emissivity modeling of thin sea ice at L-band for SMOS satellite observations

In this study we have developed an empirical retrieval for thickness of young and first-year ice during the freeze up period for the L-band passive microwave radiometer Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) on the Soil Moisture and Ocean Salinity (SMOS) satellite. The retrieval is based on intensity and polarization difference using the incidence angle range of 40° to 50° and is validated using data from airborne EM-Bird, Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery, and self consistency checks for ice thicknesses up to 50 cm with an error of 30 % on average. In addition, we modeled the microwave emission for Arctic first-year ice using the sea ice version of the Microwave Emission Model of Layered Snowpacks (MEMLS). The sea ice conditions used as input for MEMLS were generated using a thermodynamic energy balance model (based on the Crocus model) driven by reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF). From unexpected features in the modeled microwave emission and disagreements with the empirically trained SMOS retrieval several shortcomings of the energy balance model and MEMLS were identified and corrected. The corrections include a treatment of mismatch of layer definition between the energy balance model and MEMLS, an adaptation of the reflection coefficient for lossy media in MEMLS, and several smaller corrections. For comparison, two simple models ignoring volume scattering, one incoherent and one coherent, were set up and were found to be able to reproduce the results of the more complex MEMLS model on average. With the simple models, the effects of thin coherent layers, the snow cover, the interface roughness and three different dielectric mixture models for sea ice were explored. It was found that the choice of the mixture model is essential for the relation of sea ice thickness to brightness temperatures in L-band, suggesting sea ice thickness sensitivities from few centimeters to several meters for salinity conditions of the global oceans. The interface properties, especially at the sea ice bottom, were found to be a major uncertainty source when modeling the microwave emission of thin sea ice. In addition, the variability in snow depth, the interface roughness, and the ice surface salinity and temperature were found to have a similar influence on the resulting brightness temperatures, with a strong effect on horizontally (up to 30 K) and weak effect on vertically polarized radiation (up to 10 K) for temperatures below 260 K. A model for simulating coherent microwave emission for thickness distributions of ice and snow was prepared to overcome weaknesses from the single thickness coherent and incoherent models. Comparison to the incoherent model showed that for realistic snow depth distributions obtained from Operation IceBridge (OIB) coherence effects can change the brightness temperatures on the scale of a SMOS footprint up to 10 K in horizontal polarization. These findings suggest that the retrieval for the thickness of thin sea ice with satellite based L-band sensors yield higher uncertainties than expected from earlier studies

Method for detection of leads from Sentinel-1 SAR images

Source at https://doi.org/10.1017/aog.2018.6.The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. Here, an algorithm providing an automatic lead detection based on synthetic aperture radar images is described that can be applied to a wide range of Sentinel-1 scenes. By using both the HH and the HV channels instead of single co-polarised observations the algorithm is able to classify more leads correctly. The lead classification algorithm is based on polarimetric features and textural features derived from the grey-level co-occurrence matrix. The Random Forest classifier is used to investigate the importance of the individual features for lead detection. The precision–recall curve representing the quality of the classification is used to define threshold for a binary lead/sea ice classification. The algorithm is able to produce a lead classification with more that 90% precision with 60% of all leads classified. The precision can be increased by the cost of the amount of leads detected. Results are evaluated based on comparisons with Sentinel-2 optical satellite data

Sea ice surface temperatures from helicopter-borne thermal infrared imaging during the MOSAiC expedition

The sea ice surface temperature is important to understand the Arctic winter heat budget. We conducted 35 helicopter flights with an infrared camera in winter 2019/2020 during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The flights were performed from a local, 5 to 10 km scale up to a regional, 20 to 40 km scale. The infrared camera recorded thermal infrared brightness temperatures, which we converted to surface temperatures. More than 150000 images from all flights can be investigated individually. As an advanced data product, we created surface temperature maps for every flight with a 1 m resolution. We corrected image gradients, applied an ice drift correction, georeferenced all pixels, and corrected the surface temperature by its natural temporal drift, which results in time-fixed surface temperature maps for a consistent analysis of one flight. The temporal and spatial variability of sea ice characteristics is an important contribution to an increased understanding of the Arctic heat budget and, in particular, for the validation of satellite products

Monitoring Arctic Sea Ice During One Year: Linearly Polarized GNSSReflectometry at the MOSAiC Campaign

Ponencia expuesta online en el 41st annual IGARSS (2021) celebrado en Bruselas del 12-16 de juli

Snow Depth on Arctic and Antarctic Sea Ice Derived from Snow Buoys

The snow cover on sea ice received more and more attention in recent sea ice studies and model simulations, because its physical properties dominate many sea ice and upper ocean processes. In particular; the temporal and spatial distribution of snow depth is of crucial importance for the energy and mass budgets of sea ice, as well as for the interaction with the atmosphere and the oceanic freshwater budget. Snow depth is also a crucial parameter for sea ice thickness retrieval algorithms from satellite altimetry data. Recent time series of Arctic sea ice volume only use monthly snow depth climatology, which cannot take into account annual changes of the snow depth and its properties. For Antarctic sea ice, no such climatology is available. With a few exceptions, snow depth on sea ice is determined from manual in-situ measurements with very limited coverage of space and time. Hence the need for more consistent observational data sets of snow depth on sea ice is frequently highlighted. Here, we present time series measurements of snow depths on Antarctic and Arctic sea ice, recorded by an innovative and affordable platform. This Snow Buoy is optimized to autonomously monitor the evolution of snow depth on sea ice and will allow new insights into its seasonality. In addition, the instruments report air temperature and atmospheric pressure directly into different international networks, e.g. the Global Telecommunication System (GTS) and the International Arctic Buoy Programme (IABP). We introduce the Snow Buoy concept together with technical specifications and results on data quality, reliability, and performance of the units. We highlight the findings from four buoys, which simultaneously drifted through the Weddell Sea for more than 1.5 years, revealing unique information on characteristic regional and seasonal differences. Finally, results from seven snow buoys co-deployed on Arctic sea ice throughout the winter season 2015/16 suggest the great importance of local effects, weather events, and potential influences of dynamic sea ice processes on snow accumulation

Snow depth on Antarctic sea ice from autonomous measurements

The snow cover on sea ice received more and more attention in recent sea ice studies and model simulations, because its physical properties dominate many sea ice and upper ocean processes. In particular; the temporal and spatial distribution of snow depth is of crucial importance for the energy and mass budgets of sea ice, as well as for the interaction with the atmosphere and the oceanic freshwater budget. Snow depth is also a crucial parameter for sea ice thickness retrieval algorithms from satellite altimetry data. Recent time series of Arctic sea ice volume only use monthly snow depth climatology, which cannot take into account annual changes of the snow depth and its properties. For Antarctic sea ice, no such climatology is available. With a few exceptions, snow depth on sea ice is determined from manual in-situ measurements with very limited coverage of space and time. Hence the need for more consistent observational data sets of snow depth on sea ice is frequently highlighted. Here, we present time series measurements of snow depths on Antarctic sea ice, recorded by an innovative and affordable platform. This Snow Buoy is optimized to autonomously monitor the evolution of snow depth on sea ice and will allow new insights into its seasonality. In addition, the instruments report air temperature and atmospheric pressure directly into different international networks, e.g. the Global Telecommunication System (GTS). We introduce the Snow Buoy concept together with technical specifications and results on data quality, reliability, and performance of the units. We highlight the findings from four buoys, which simultaneously drifted through the Weddell Sea for more than 1.5 years, revealing unique information on characteristic regional and seasonal differences

Retrieval of Snow Depth on Arctic Sea Ice From Surface‐Based, Polarimetric, Dual‐Frequency Radar Altimetry

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters