Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models

Atmospheric radiative transfer models (RTMs) are widely used in satellite data processing to correct for the scattering and absorption effects caused by aerosols and gas molecules in the Earth's atmosphere. As the complexity of RTMs grows and the requirements for future Earth Observation missions become more demanding, the conventional lookup-table (LUT) interpolation approach faces important challenges. Emulators have been suggested as an alternative to LUT interpolation, but they are still too slow for operational satellite data processing. Our research introduces a solution that harnesses the power of multifidelity methods to improve the accuracy and runtime of Gaussian process (GP) emulators. We investigate the impact of the number of fidelity layers, dimensionality reduction, and training dataset size on the performance of multifidelity GP emulators. We find that an optimal multifidelity emulator can achieve relative errors in surface reflectance below 0.5% and performs atmospheric correction of hyperspectral PRISMA satellite data (one million pixels) in a few minutes. Additionally, we provide a suite of functions and tools for automating the creation and generation of atmospheric RTM emulators.