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.
Agam, et al., A vegetation index based technique for spatial sharpening of thermal imagery, Remote Sensing of Environment 107 (2007) 545-558
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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.