Nonlinear Multichannel Algorithms for Estimating Sea Surface Temperature with AVHRR Satellite Data

Abstract

The familiar linear multichannel sea surface temperature algorithms (MCSST) for estimating sea surface temperature with AVHRR satellite data describe the solution in terms of a constant gamma parameter multiplied by the measured brightness temperature difference of two of the window channels. A nonlinear algorithm is developed in this paper which is similar in form to the MCSST algorithm but with the gamma parameter having a specific two- or three-channel temperature dependence. Simulation studies show that the linear and nonlinear algorithms provide nearly identical accuracies for a wide range of atmospheric conditions if the satellite data are error free. When random or noncorrelative error sources are present in the multichannel AVHRR data, it is found that the nonlinear algorithms significantly reduce their effect upon the final solution relative to the linear MCSST solution. These results are verified with actual AVHRR data obtained in January-March 1982 and 1983. The algorithm estimates of surface temperature are compared with buoy measurements in the North Atlantic and North Pacific.