Producing High-Resolution Martian Surface Temperature Maps Using VIR-TIR Relationships

TL;DR

This paper develops a machine learning model to enhance the spatial resolution of Martian surface temperature maps by integrating thermal infrared and hyperspectral data, revealing finer geological features.

Contribution

It introduces a novel approach to downscale thermal inertia maps on Mars using VIR-TIR relationships, achieving higher resolution than existing data.

Findings

Achieved high accuracy in predicting TI from CRISM spectra (R2 ~ 0.90).

Produced TI maps at 12 m/pixel resolution, ten times finer than previous data.

Revealed decametre-scale surface features previously unresolved.

Abstract

Thermal infrared data (TIR; 8 - 15 $\mu m$) has a wide range of applications in Earth and planetary remote sensing. On Mars, this includes deriving thermal inertia (TI), which describes surface physical characteristics (e.g. particle size, degree of cementation) and is key for understanding geologic processes, assessing in-situ resource utilisation (ISRU) environments, and assisting mission planning. However, TI data from the THEMIS instrument is limited to 100 m/pixel resolution. Hyperspectral visible and near-infrared data (VIR; 0.5 - 5 $\mu m$) compliments TIR data by providing information on surface composition and is provided by the CRISM instrument at 12 m/pixel. In this work, we generate a machine learning regressor-based model to constrain relationships between THEMIS TI and CRISM VIR images at THEMIS resolution, and predict TI values from CRISM spectra with high accuracy (R2 $\sim$ 0.90, RMSE $\sim$ 23.6 TIU). We use the model to produce a downscaled TI map at a spatial resolution of 12 m/pixel, an order of magnitude finer than currently available, revealing decametre-scale features previously unresolved in THEMIS data.