Partial cloud cover estimation within a pixel based on the category decompositions

Abstract

A comparative study was conducted for estimation of the sea surface temperature of the pixel suffered from partial cloud cover within a pixel. Three methods for estimation of partial cloud cover within a pixel, based on well known least square method and maximum likelihood method, were compared. It was found that around 9% of RMS error can be achieved. Also it was found that estimation accuracy highly depends on variance of representative vectors for cloud and the ocean, or observed noise. The experimental results with simulated data show RMS error of Generalized Inverse Matrix Method is highly dependent to the noise followed by Maximum Likelihood Method and Least Square Method. The results also show the best estimation accuracy can be achieved for Maximum Likelihood Method followed by Least Square Method and Generalized Inverse Matrix Method.