[Temporal mixture analysis application in monitoring the Antarctic sea ice concentration variability].

Temporal mixture analysis (TMA) is deduced from spectral mixture analysis (SMA). They are algebraically identical except for that TMA is applied to temporal spectra and thus can extract the temporal characteristics of features. The ice concentration is diverse across the Antarctic sea through different periods, and TMA has a great potential to obtain this variability as an environmental normal. In the present study, sea ice concentration data remotely sensed by AMSR-E from 2003 to 2010 were used and seven typical endmembers were captured, standing for temporally different sea ice classification. TMA can also be utilized in change analysis of Antarctic sea ice concentration for its capability to record the spatial distribution of temporal characteristics, allowing further study of regional or global climatic variations. In short, TMA supplies a new method for researchers to investigate the spatial and temporal variability of polar sea ice.

[The arctic sea ice refractive index retrieval based on satellite AMSR-E observations].

The refractive index of sea ice in the polar region is an important geophysical parameter. It is needed as a vital input for some numerical climate models and is helpful to classifying sea ice types. In the present study, according to Hong Approximation (HA), we retrieved the arctic sea ice refractive index at 6.9, 10.7, 23, 37, and 89 GHz in different arctic climatological conditions. The refractive indices of wintertime first year (FY) sea ice and summertime ice were derived with average values of 1.78 - 1.75 and 1.724 - 1.70 at different frequencies respectively, which are consistent with previous studies. However, for multiyear (MY) ice, the results indicated relatively large bias between modeled results since 10.7 GHz. At a higher frequency, there is larger MY ice refractive index difference. This bias is mainly attributed to the volume scattering effect on MY microwave radiation due to emergence of massive small empty cavities after the brine water in MY ice is discharged into sea. In addition, the retrieved sea ice refractive indices can be utilized to classify ice types (for example, the winter derivation at 89 GHz), to identify coastal polynyas (winter retrieval at 6.9 GHz), and to outline the areal extent of significantly melting marginal sea ice zone (MIZ) (summer result at 6.9 GHz). The investigation of this study suggests an effective tool of passive microwave remote sensing in monitoring sea ice refractive index variability.