Articles | Volume 12, issue 11
Research article
20 Nov 2019
Research article |  | 20 Nov 2019

Neural network for aerosol retrieval from hyperspectral imagery

Steffen Mauceri, Bruce Kindel, Steven Massie, and Peter Pilewskie

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Cited articles

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., and Kudlur, M.: Tensorflow: a system for large-scale machine learning, OSDI, 16, 265–283, 2016. 
Adler-Golden, S. M., Matthew, M. W., Bernstein, L. S., Levine, R. Y., Berk, A., Richtsmeier, S. C., Acharya, P. K., Anderson, G. P., Felde, J. W., Gardner, J. A., and Hoke, M. L.: Atmospheric correction for shortwave spectral imagery based on MODTRAN4, P. Soc. Photo-Opt. Ins., 3753, 61–70, 1999. 
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230,, 1989. 
Alexander, D. T. L., Crozier, P. A., and Anderson, J. R.: Brown Carbon Spheres in East Asian Outflow and Their Optical Properties, Science, 321, 833–836, 2008. 
Baldridge, A. M., Hook, S. J., Grove, C. I., and Rivera, G.: The ASTER spectral library version 2.0, Remote Sens. Environ., 113, 711–715, 2009. 
Short summary
Aerosols are fine particles that are suspended in Earth’s atmosphere. A better understanding of aerosols is important to lower uncertainties in climate predictions. We propose measuring aerosols from satellites and airplanes equipped with hyperspectral cameras using an artificial neural network, a form of machine learning. We applied our neural network to hyperspectral observations from a recent airplane flight over India and find general agreement with independent aerosol measurements.