ADAPTIVE LEAST-SQUARES METHOD FOR ESTIMATION OF PARTIAL CLOUD COVERAGE WITHIN A PIXEL

A study of the estimation of partial cloud cover within a pixel has be en conducted in order to be able to use pixels partially contaminated with cloud in sea surface temperature determination. The existing esti mation methods based on the least squares method with constraints of m inimizing the mixing ratio and observation vector, are theoretically c ompared and then an adaptive least squares method is proposed. In a co mparative study the estimation accuracies for the proposed and other e xisting methods, including the maximum likelihood method, are compared with simulated and real satellite image data of NOAA AVHRR and MOS-1 VTIR. The results with the simulation data show that the maximum likel ihood method is best followed by the adaptive least squares method, th e least squares method and the observation vector, while the results w ith the real VTIR data show that the proposed adaptive least squares m ethod is best followed by the least squares method, the maximum likeli hood method and the observation vector but there is no significant dif ferences between all these methods.