Abstract
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 μ 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 2 ·sr· μ m when compared to actual Terra/MODIS band 31 image data. These studies found that for radiances above 6 W/m 2 ·sr· μ 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.
Highlights
Thermal infrared satellite data have been widely used for cross-calibration studies [1] and climate research [2,3]
This paper describes an approach to predicting TOA thermal infrared radiance using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product (See Figure 1)
The convolutional neural network (CNN) produces estimated radiances that are more averaged over the scene, which can be explained by the use of spatial information around the pixel of interest to train the model
Summary
Thermal infrared satellite data have been widely used for cross-calibration studies [1] and climate research [2,3]. Thermal instruments on geostationary satellites (e.g., The Geostationary Operational Environmental Satellite system (GOES)) have much finer temporal resolution, but have large view angles through the atmosphere for a significant portion of the disk, limiting their utility for cross-calibration. Atmospheric reanalysis data is available on a global spatial grid at three hour time intervals and could potentially be utilized to derive top-of-atmosphere (TOA) thermal radiance data at timescales that are finer than current satellite data. This paper describes an approach to predicting TOA thermal infrared radiance using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product (See Figure 1). A recent study has used MERRA-2 atmospheric data together with Landsat [6] radiance data to predict surface temperature, but no studies have used MERRA-2 to predict TOA radiance
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