A comparison of different optimization algorithms for retrieving aerosol optical depths from satellite data: an example of using a dual-angle algorithm

Abstract

Optimization techniques are often used in remote sensing retrieval of surface or atmospheric parameters. Nevertheless, different algorithms may exhibit different performances for the same optimization problem. Comparison of some classic optimization approaches in this article aims to select the best method for retrieving aerosol opacity, or even for other parameters, from remotely sensed data. Eight frequently used optimization algorithms were evaluated using both simulated data and actual AATSR (advanced along track scanning radiometer) data. Several typical land cover types and aerosol opacity levels were also considered in the simulations to make the tests more representative. It was observed that the absolute error in retrieval would rise after a certain number of iterations due to the round-off error, and the algorithms showed different performances in the inversions without any a priori knowledge. When combined with reasonable a priori knowledge, the selection of various algorithms only slightly affected the retrieval accuracy. Given a summary of all the comparison tests, a special class named ‘trust-region methods’ (TR) was demonstrated to be the optimal choice in general cases. In contrast, some widely used optimization methods in aerosol research, for example, the Levenberg–Marquardt (LM) algorithm, seemed not to display a persuasive performance.