Two Machine Learning Based Schemes for Solving Direct and Inverse Problems of Radiative Transfer Theory

Abstract

Artificial neural networks (ANNs) are used to substitute computationally expensive radiative transfer models (RTMs) and inverse operators (IO) for retrieving optical parameters of the medium. However, the direct parametrization of RTMs and IOs by means of ANNs has certain drawbacks, such as loss of generality, computations of huge training datasets, robustness issues etc. This paper provides an analysis of different ANN-related methods, based on our results and those published by other authors. In particular, two techniques are proposed. In the first method, the ANN substitutes the eigenvalue solver in the discrete ordinate RTM, thereby reducing the computational time. Unlike classical RTM parametrization schemes based on ANN, in this method the resulting ANN can be used for arbitrary geometry and layer optical thicknesses. In the second method, the IO is trained by using the real measurements (preprocessed Level-2 TROPOMI data) to improve the stability of the inverse operator. This method provides robust results even without applying the Tikhonov regularization method.