[04-Dec-2012] Masuelli, S., Labanda, M.F., Marenchino, M., and Jacob, M.M.
Presented at the 2012 AGU Fall MeetingThe National Space Agency of Argentina (CONAE, Comisión Nacional de Actividades Espaciales) developed the SAC-D/Aquarius science mission (launched in June 2011), together with the National Aeronautics and Space Administration of the USA (NASA). The main Argentinean sensor aboard the SAC-D is the MWR (Micro Wave Radiometer). This instrument is a three channel push-broom microwave radiometer with 8 antenna beams per channel and two different incident angles (52 and 58 degrees), that provides a measurement swath of approximately 380 km. These channels provide 36.5 GHz dual horizontal and vertical polarized and 23.8 GHz horizontal polarized radiance measurements in an overlapping swath with the L-band Aquarius radiometer/scatterometer. The main objective of this instrument is to retrieve sea geophysical variables such as columnar water vapor, wind speed, sea ice concentration and rain detection.

On an average summer, about half of Greenland's surface ice melts, but during the summer of 2012 about 90 percent of the ice sheet thawed in mid-July, as was observed first by NASA Satellites. This kind of comprehensive surface melting is the biggest since satellites started monitoring the Arctic in 1979. However, some researchers believe that it might happen about every 150 years or so in Greenland, which would make this event unusual but not unprecedented (NASA, 2012).

Snow cover and snow water equivalent (SWE) were estimated by passive microwave sensors for more than 30 years (Foster et al 2008). Usually, algorithms exploit the strong response of the difference between 37 and 19 GHz V-pol bands to SWE for dry snow conditions, but the presence of liquid water within the snowpack alters strongly the microwave emission, given erroneous estimations (Walker and Goodison, 1993). Thus, the last operational algorithm for AMSR-E adds other microwave bands (23 and 89 GHz) in a more complex estimation method (Tedesco, 2012). Therefore, it is possible to use the algorithm with the MWR bands, but it remains the problem of bad classification for wet condition. For this reason, we try to determine areas having dry and deep snow and areas where snow melting or freezing is probably occurring.