Surrogate Modeling of MODTRAN Physical Radiative Transfer Code Using Deep-Learning Regression

Abstract

Radiative Transfer Models (RTMs) are one of the major building blocks of remote-sensing data analysis that are widely used for various tasks such as atmospheric correction of satellite imagery. Although high-fidelity physical RTMs such as MODTRAN are considered to offer the best possible modeling of atmospheric procedures, they are computationally demanding and require a lot of parameters that should be tuned by an expert. Therefore, there is a need for surrogate models for the physical RTM codes that can mitigate these drawbacks while offering an acceptable performance. This study aimed to suggest surrogate models for the MODTRAN RTM using deep-learning models. For this purpose, the top of atmosphere (TOA) spectra calculated by the MODTRAN code as well as the bottom of atmosphere (BOA) input spectra and other atmospheric parameters such as temperature and water vapor content observations were collected and used as the training dataset. Two deep-learning regression models, including a fully connected network (FCN) and an auto-encoder (AE), as well as a random forest (RF) machine-learning regression model were trained. The results of these models were assessed using the three evaluation metrics root mean squared error (RMSE), regression coefficient (R2), and spectral angle mapper (SAM). The evaluations indicated that the AE offered the best performance in all the metrics, with RMSE, R2, and SAM scores of 0.0087, 0.9906, and 1.4295 degrees, respectively, in the best-case scenarios. These results showed that deep-learning models can better reproduce results via high-fidelity physical RTMs.

Keywords

• machine learning
• deep learning
• regression
• multispectral remote sensing
• radiative transfer model
• surrogate model