Surface reflectivity in polar regions retrieved from TDS-1 mission data

In current times of a changing global climate, a special interest is focused on the
large-scale recording of sea ice. Among the existing remote sensing methods, bi-
statically reflected signals of Global Navigation Satellite Systems (GNSS) could
play an important role in fulfilling the task. Within this project, sensitivity of
GNSS signal reflections to sea ice properties like its occurrence, sea ice thick-
ness (SIT) and sea concentration (SIC) is evaluated. When getting older, sea
ice tends go get thicker. Because of decreasing salinity, i.e. less permittivity,
as well as relatively higher surface roughness of older ice, it can be assumed
that reflected signal strength decreases with increasing SIT. The reflection data
used were recorded in the years 2015 and 2016 by the TechDemoSat-1 (TDS-1)
satellite over the Arctic and Antarctic. It includes a down-looking antenna for
the reflected as well as an up-looking antenna dedicated to receive the direct sig-
nal. The raw data, provided by the manufacturer SSTL, were pre-processed by
IEEC/ICE-CSIC to derive georeferenced signal power values. The reflectivity
was estimated by comparing the power of the up- and down-looking links. The
project focuses on the signal link budget to apply necessary corrections. For this
reason, the receiver antenna gain as well as the Free-Space Path Loss (FSPL)
were calculated and applied for reflectivity correction. Differences of nadir and
zenith antenna FSPL and gain show influence of up to 6 dB and −9 dB to 9 dB
respectively on the recorded signal strength. All retrieved reflectivity values are
compared to model predictions based on Fresnel coefficients but also to avail-
able ancillary truth data of other remote sensing missions to identify possible
patterns: SIT relations are investigated using Level-2 data of the Soil Moisture
and Ocean Salinity (SMOS) satellite. The SIC comparison was done with an
AMSR-2 product. The results show sensitivity of the reflectivity value to both
SIT and SIC simultaneously, whereby the surface roughness is also likely to
have an influence. This on-going study aims at the consolidation of retrieval
algorithms for sea-ice observation. The resolution of different ice types and the
retrieval of SIT and SIC based on satellite data is a challenge for future work
in this respect.