Machine learning regressor predicts Martian thermal inertia at 12 m/pixel from CRISM VIR data with R² ≈ 0.90 and RMSE ≈ 23.6 TIU, producing downscaled maps an order of magnitude finer than THEMIS.
Murchie, et al., Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO), Journal of Geophysical Research: Planets 112 (2007)
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Producing High-Resolution Martian Surface Temperature Maps Using VIR-TIR Relationships
Machine learning regressor predicts Martian thermal inertia at 12 m/pixel from CRISM VIR data with R² ≈ 0.90 and RMSE ≈ 23.6 TIU, producing downscaled maps an order of magnitude finer than THEMIS.