Iterative estimation of Antarctic sea ice extent using SeaWinds data

Polar sea ice extent is an important input to global climate models and is considered to be a sensitive indicator of climate change. An algorithm has previously been developed for sea ice extent detection using data from the NASA scatterometer (NSCAT). This paper discusses the extension of that algorithm to data from SeaWinds on QuikSCAT. Due to differences in the two instruments, several modifications to the algorithm are needed for adaptation to SeaWinds data. Enhanced resolution, reconstructed SeaWinds imagery is generated using the scatterometer image reconstruction (SIR) algorithm. Images of the modified polarization ratio, h-pol σ⁰, and the hand v-pol σ⁰ estimate error standard deviations are used in the discrimination. An iterative maximum likelihood classifier is developed to segment sea ice and open ocean pixels. Residual classification errors are reduced through binary image processing techniques. The resulting ice extent estimates are highly correlated with the NASA Team algorithm 30% ice concentration edges derived from SSM/I data