Developing Data-driven Artificial Neural Network for a High Throughput Retrieval of Aerosol Optical Depth and Surface Temperature of Mars

Abstract

In this work, we aim to develop artificial neural network (ANN) techniques to reproduce the retrieval results of physical quantities from spacecraft observations of solar system bodies using radiative transfer methods. The particular application here is the retrieval of dust optical depth, water ice optical depth, and surface temperature on Mars using daytime observations obtained by the Thermal Emission Spectrometer on board the Mars Global Surveyor. Compared against the results obtained from traditional radiative transfer retrieval techniques, our ANN successfully recovered the three quantities using daytime observations. The principal advantage of these machinelearning algorithms is their complete automation and high throughput. Therefore, the algorithms presented here would be useful for very large data sets and would make practical the sampling of many different approximations or boundary conditions related to a given observation data set and retrieval problem.