Principal component analysis of satellite passive microwave data over sea ice


  • D. A. Rothrock,

  • Donald R. Thomas,

  • Alan S. Thorndke


The 10 channels of scanning multichannel microwave radiometer data for the Arctic are examined by correlation, multiple regression, and principal component analyses. Data from April, August, and December 1979 are analyzed separately. Correlations are greater than 0.8 for all pairs of channels except some of those involving the 37-GHz channels. Multiple regression shows a high degree of redundancy in the data; three channels can explain between 94.0 and 99.6% of the total variance. A principal component analysis of the covariance matrix shows that the first two eigenvalues contain 99.7% of the variance. Only the first two principal components contain variance due to the mixture of surface types. Three component mixtures (water, first-year ice, and multiyear ice) can be resolved in two dimensions. The presence of other ice types, such as second-year ice or wet ice, makes determination of ice age ambiguous in some geographic regions. Winds and surface temperature variations cause variations in the first three principal components. The confounding of these variables with mixture of surface types is a major source of error in resolving the mixture. The variance in principal components 3 through 10 is small and entirely due to variability in the pure type signatures. Determination of winds and surface temperature, as well as other variables, from this information is limited by instrument noise and presently unknown large-scale variability in the emissivity of sea ice.