Visible–shortwave infrared imaging spectroscopy provides valuable remote
measurements of Earth's surface and atmospheric properties. These
measurements generally rely on inversions of computationally intensive
radiative transfer models (RTMs). RTMs' computational expense makes them
difficult to use with high-volume imaging spectrometers, and forces
approximations such as lookup table interpolation and surface–atmosphere
decoupling. These compromises limit the accuracy and flexibility of the
remote retrieval; dramatic speed improvements in radiative transfer models
could significantly improve the utility and interpretability of remote
spectroscopy for Earth science. This study demonstrates that nonparametric
function approximation with neural networks can replicate radiative transfer
calculations and generate accurate radiance spectra at multiple wavelengths
over a diverse range of surface and atmosphere state parameters. We also
demonstrate such models can act as surrogate forward models for atmospheric
correction procedures. Incorporating physical knowledge into the network
structure provides improved interpretability and model efficiency. We
evaluate the approach in atmospheric correction of data from the PRISM
airborne imaging spectrometer, and demonstrate accurate emulation of
radiative transfer calculations, which run several orders of magnitude faster
than first-principles models. These results are particularly amenable to
iterative spectrum fitting approaches, providing analytical benefits
including statistically rigorous treatment of uncertainty and the potential
to recover information on spectrally broad signals.</p
