Neural network for aerosol retrieval from hyperspectral imagery
Abstract. We retrieve aerosol optical thickness (AOT) independently
for brown carbon, dust and sulfate from hyperspectral image data. The
model, a neural network, is trained on atmospheric radiative transfer
calculations from MODTRAN 6.0 with varying aerosol concentration and type,
surface albedo, water vapor, and viewing geometries. From a set of test
radiative transfer calculations, we are able to retrieve AOT with a standard
error of better than ±0.05 . No a priori information on the surface
albedo or atmospheric state is necessary for our model. We apply the model
to AVIRIS-NG imagery from a recent campaign over India and demonstrate its
performance under high and low aerosol loadings and different aerosol types.