The derivation of an Arctic sea ice normal through temporal mixture analysis of satellite imagery

Abstract

The use of temporal mixture analysis (TMA) for creating a long-term baseline, or environmental “normal” is described. TMA is a promising analysis method derived from the hyperspectral image processing technique of spectral mixture analysis (SMA). TMA is algebraically identical to SMA, except that it is applied to temporal spectra rather than to electromagnetic spectra. TMA has particular potential to extract climate signals from long image sequences.To demonstrate the utility of TMA, this paper documents its use to isolate nine fundamental temporal signals (“endmembers”) from a 20-year Northern Hemisphere sea ice concentration image time series. The temporal endmembers establish a baseline of temporal variability that can be treated as an environmental normal. The “fraction images” produced by the analysis highlight the regions where the temporal signals are strongest and provide new insights into the dynamics of the Arctic sea ice cover.