|Copyright||Author(s) 2012. CC Attribution 3.0 License.|
|Author(s)||Heygster G.1, Alexandrov V.2, 3, Dybkjaer G., Von Hoyningen-Huene W.1, Ardhuin Fanny4, Katsev I. L.5, Kokhanovsky A.1, Lavergne Thomas6, Malinka A. V.5, Melsheimer C.1, Pedersen L. Toudal, Prikhach A. S.5, Saldo R.7, Tonboe Rasmus, Wiebe H.1, Zege E. P.5|
|Affiliation(s)||1 : Univ Bremen, Inst Environm Phys, Bremen, Germany.
2 : NIERSC, St Petersburg, Russia.
3 : Nansen Environm & Remote Sensing Ctr, Bergen, Norway.
4 : IFREMER, Plouzane, France.
5 : Natl Acad Sci Belarus IP NASB, BI Stepanov Phys Inst, Minsk, Byelarus.
6 : Norwegian Meteorol Inst Met No, Oslo, Norway.
7 : DNSC, Copenhagen, Denmark.
|Source||Cryosphere (1994-0416) (Copernicus Gesellschaft Mbh), 2012 , Vol. 6 , N. 6 , P. 1411-1434|
|WOS© Times Cited||11|
|Abstract||In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a resume of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.|
Heygster G., Alexandrov V., Dybkjaer G., Von Hoyningen-Huene W., Ardhuin Fanny, Katsev I. L., Kokhanovsky A., Lavergne Thomas, Malinka A. V., Melsheimer C., Pedersen L. Toudal, Prikhach A. S., Saldo R., Tonboe Rasmus, Wiebe H., Zege E. P. (2012). Remote sensing of sea ice: advances during the DAMOCLES project. Cryosphere, 6(6), 1411-1434. Publisher's official version : http://doi.org/10.5194/tc-6-1411-2012 , Open Access version : http://archimer.ifremer.fr/doc/00130/24174/