Predicting Top-of-Atmosphere Thermal Radiance Using MERRA-2 Atmospheric Data with Deep Learning†
Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623, USA
This paper is an extended version of conference paper published in the Proceedings of the SPIE Defense and Commercial Sensing, Anaheim, CA, USA, 3 May 2017, Volume 10178.
Author to whom correspondence should be addressed.
Received: 9 September 2017 / Revised: 19 October 2017 / Accepted: 2 November 2017 / Published: 7 November 2017
Image data from space-borne thermal infrared (IR) sensors are used for a variety of applications, however they are often limited by their temporal resolution (i.e., repeat coverage). To potentially increase the temporal availability of thermal image data, a study was performed to determine the extent to which thermal image data can be simulated from available atmospheric and surface data. The work conducted here explored the use of Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) developed by The National Aeronautics and Space Administration (NASA) to predict top-of-atmosphere (TOA) thermal IR radiance globally at time scales finer than available satellite data. For this case study, TOA radiance data was derived for band 31 (10.97
m) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. Two approaches have been followed, namely an atmospheric radiative transfer forward modeling approach and a supervised learning approach. The first approach uses forward modeling to predict TOA radiance from the available surface and atmospheric data. The second approach applied four different supervised learning algorithms to the atmospheric data. The algorithms included a linear least squares regression model, a non-linear support vector regression (SVR) model, a multi-layer perceptron (MLP), and a convolutional neural network (CNN). This research found that the multi-layer perceptron model produced the lowest overall error rates with an root mean square error (RMSE) of 1.36 W/m
m when compared to actual Terra/MODIS band 31 image data. These studies found that for radiances above 6 W/m
m, the forward modeling approach could predict TOA radiance to within 12 percent, and the best supervised learning approach can predict TOA to within 11 percent.
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MDPI and ACS Style
Kleynhans, T.; Montanaro, M.; Gerace, A.; Kanan, C. Predicting Top-of-Atmosphere Thermal Radiance Using MERRA-2 Atmospheric Data with Deep Learning. Remote Sens. 2017, 9, 1133.
Kleynhans T, Montanaro M, Gerace A, Kanan C. Predicting Top-of-Atmosphere Thermal Radiance Using MERRA-2 Atmospheric Data with Deep Learning. Remote Sensing. 2017; 9(11):1133.
Kleynhans, Tania; Montanaro, Matthew; Gerace, Aaron; Kanan, Christopher. 2017. "Predicting Top-of-Atmosphere Thermal Radiance Using MERRA-2 Atmospheric Data with Deep Learning." Remote Sens. 9, no. 11: 1133.
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