Larysa Istomina

Aerosol optical depth retrieval over snow using AATSR data

Aerosol observations over the Arctic are important because of the effects of aerosols on Arctic climate, such as their direct and indirect effects on the Earths radiation balance and on snow albedo. Although information on aerosol properties is available from ground-based measurements, passive remote sensing using satellite measurements would offer the advantage of large spatial coverage with good temporal resolution, even though, due to light limitations, this is only available during the Arctic summer. However, aerosol optical depth (AOD) retrieval over the Arctic region is a great challenge due to the high reflectance of snow and ice and due to the high solar zenith angle. In this article, we describe a retrieval algorithm using Advanced Along-Track Scanning Radiometer (AATSR) data, a radiometer flying on the European Space Agency (ESA) Environmental Satellite (ENVISAT), which offers two views (near nadir and at 55° forward) at seven wavelengths in the visible thermal-infrared (VIS-TIR). The main idea of the Dual-View Multi-Spectral (DVMS) approach is to use the dual view to separate contributions to reflectance measured at the top of the atmosphere (TOA) due to atmospheric aerosol and the underlying surface. The algorithm uses an analytical snow bidirectional reflectance distribution function (BRDF) model for the estimation of the ratio of snow reflectances in the nadir and forward views, as well as an estimate of the atmospheric contribution to TOA reflectance obtained using the dark pixel method over the adjacent ocean surface, assuming that this value applies over nearby land surfaces in the absence of significant sources across the coastline. An iteration involving all four AATSR wavebands in the visible near-infrared (VIS-NIR) is used to retrieve the relevant information. The method is illustrated for AATSR overpasses over Greenland with clear sky in April 2009. Comparison of the retrieved AOD with AErosol Robotic Network (AERONET) data shows a correlation coefficient of 0.75. The AODs retrieved from AATSR using the DVMS approach and those obtained from AERONET data show similar temporal trends, but the AERONET results are more variable and the highest AOD values are mostly missed by the DVMS approach. Limitations of the DVMS method are discussed. The pure-snow BRDF model needs further correction in order to obtain a better estimation for mixtures of snow and ice.

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff,C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff,S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff,C for liquid water clouds. In general, the impact of uncertainties of reff,S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff,C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff,C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ , reff,C, reff,S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff,S), λ2 = 1650 nm (sensitive to τ ), and λ3 = 2100 nm (sensitive to reff,C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ , reff,C, and reff,S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ , and low reff,S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

Living on the edge of a shrinking habitat: the ivory gull, Pagophila eburnea, an endangered sea-ice specialist

The ongoing decline of sea ice threatens many Arctic taxa, including the ivory gull. Understanding how ice-edges and ice concentrations influence the distribution of the endangered ivory gulls is a prerequisite to the implementation of adequate conservation strategies. From 2007 to 2013, we used satellite transmitters to monitor the movements of 104 ivory gulls originating from Canada, Greenland, Svalbard-Norway and Russia. Although half of the positions were within 41 km of the ice-edge (75% within 100 km), approximately 80% were on relatively highly concentrated sea ice. Ivory gulls used more concentrated sea ice in summer, when close to their high-Arctic breeding ground, than in winter. The best model to explain the distance of the birds from the ice-edge included the ice concentration within approximately 10 km, the month and the distance to the colony. Given the strong links between ivory gull, ice-edge and ice concentration, its conservation status is unlikely to improve in the current context of sea-ice decline which, in turn, will allow anthropogenic activities to develop in regions that are particularly important for the species.

Retrieval of sea ice thickness during melt season from in situ, airborne and satellite imagery

Currently available sea ice thickness retrieval algorithms are compromised in summer in the presence of melt. This study presents a new approach to estimate sea ice thickness in summer in the presence of melt ponds. Analysis of field data obtained during RV “Polarstern” cruise ARK27/3 (August - October 2012) has shown a clear connection of ice thickness under melt ponds to their measured spectral albedo and to saturation value in the HSL colour space from field photographs. An empirical function has been derived from the data, which gives a possibility to access sea ice thickness information stored in the historic dataset of in situ imagery. A similar function has been applied to aerial imagery. The presented approach can be used (1) as fast, non-intrusive method to estimate sea ice thickness in situ; (2) for general circulation model input, validation, ice mass balance estimate; (3) for assessing summer sea ice thickness dynamics from historic data to be used in the context of Arctic change.

Clouds discrimination and surface classification for the sea ice albedo retrieval from MODIS data

The effect of Arctic sea ice on the radiative budget is of disproportional importance relative to its areal coverage. High albedo of sea ice is the driving force for a number of climatic feedbacks in the Arctic. In order to retrieve sea ice albedo from satellite and assess the state of the Arctic climate, important preparation steps are needed. They are the surface type classification and cloud clearing of the satellite scene. The MODIS sensor features spectral channels which can be used to distinguish all kinds of surfaces from snow-like surfaces, and further - separate snow, melting snow and ice, melt ponds and leads. In situ data is used to derive threshold values characterizing each family of surface types for MODIS bands 4 and 5. Separation of all the above mentioned snow-like surfaces from clouds is not a trivial task; MODIS bands 4, 5 and 6 are used to enhance the atmospheric reflectance in the NIR and screen out clouds from snow or ice-covered scene.

Cloud filtering with MERIS and AATSR for melt pond detection on Arctic sea ice

Melt ponds cover up to 50% of Arctic summer sea ice and strongly affect the local radiative balance. However, they are currently poorly represented in circulation models. One of the reasons is the lack of reliable large-scale observations. A challenge encountered during melt pond retrieval from optical satellite observations is the filtering of cloud which in the visible range of spectrum look similarly bright as the sea ice surface. Here we demonstrate the influence of undetected cloud on the melt pond retrieval and suggest a masking procedure based on a Bayesian classifier trained using coincident infrared observations of AATSR. The AATSR swath only covers the central half of the MERIS swath but the masking procedure extends the filtering scheme to the outer parts of the MERIS swath so that the full swath can be used for melt pond retrieval.

Aerosol optical depth retrieval over Arctic region using AATSR data

Istomina (2011) presented a dual-view approach using Advanced Along-Track Scanning Radiometer (AATSR) onboard ENVISAT for Arctic AOD retrieval. In this paper, some improvements have been done. One improvement is that we use more physical-based dual-view estimation without assumption. Another improvement is to include the more appropriate snow Bidirectional Reflectance Distribution Function (BRDF) ratio estimation from pure snow BRDF model given by Kokhanovsky et al (2005) together with Snow Cover Fraction (SFC), in order to get more appropriate surface properties during retrieval. Simple compare between Istominas algorithm and the algorithm described in the paper show the approach described in the paper also provides reasonable results without correction from Raditive Transfer Model (RTM) like SCIATRAN.