Thermal infrared satellite images have been widely used in environmental studies. However, satellites have limited temporal resolution, e.g., 16 day Landsat or 1 to 2 day Terra MODIS. This paper investigates the use of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product, produced by NASA’s Global Modeling and Assimilation Office (GMAO) to predict global topof-atmosphere (TOA) thermal infrared radiance. The high temporal resolution of the MERRA-2 data product presents opportunities for novel research and applications. Various methods were applied to estimate TOA radiance from MERRA-2 variables namely (1) a parameterized physics based method, (2) Linear regression models and (3) non-linear Support Vector Regression. Model prediction accuracy was evaluated using temporally and spatially coincident Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data as reference data. This research found that Support Vector Regression with a radial basis function kernel produced the lowest error rates. Sources of errors are discussed and defined. Further research is currently being conducted to train deep learning models to predict TOA thermal radiance
Tania Kleynhans, Matthew Montanaro, Aaron Gerace, and Christopher Kanan, "Estimating top-of-atmosphere thermal infrared radiance using MERRA-2 atmospheric data," Proc. SPIE 10178, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVIII, 101780R (Presented at SPIE Defense + Security: April 12, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2262571.
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