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.
Highlights
Machine learning techniques have become of paramount importance in the geosciences, lighting engineering and remote sensing [22]
By substituting the eigenvalue computations in the discrete ordinate radiative transfer models by neural networks, we obtain a flexible tool with moderate performance enhancement
To parameterize the inverse Radiative transfer models (RTMs) operator, the Artificial neural networks (ANNs) can be trained in the backward direction
Summary
Machine learning techniques have become of paramount importance in the geosciences, lighting engineering and remote sensing [22]. Radiative transfer models (RTMs) are key components of the algorithms designed for the retrieval of atmospheric constituents from remote sensing data. To process a big amount of data comping from the state-of-the-art atmospheric composition sensors, acceleration techniques for RTMs are required. ANNs are used to parameterize an inverse operator in retrieval problems The advantage of this approach is that the explicit inversion of the RTM is avoided, while the resulting ANN operator is fast. The ANN inverse operator is trained on real measurements (namely, preprocessed TROPOMI data) to improve the robustness of retrievals
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