Optimal estimation of spectral surface reflectance in challenging atmospheres

Abstract

Optimal Estimation (OE) methods can simultaneously estimate surface and atmospheric properties from remote Visible/Shortwave imaging spectroscopy. Simultaneous solutions can improve retrieval accuracy with principled uncertainty quantification for hypothesis testing. While OE has been validated under benign atmospheric conditions, future global missions will observe environments with high aerosol and water vapor loadings. This work addresses the gap with diverse scenes from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) India campaign. We refine atmospheric models to represent variable aerosol optical depths and properties. We quantify retrieval accuracy and information content for both reflectance and aerosols over different surface types, comparing results to in situ and remote references. Additionally, we assess uncertainty of maximum a posteriori solutions using linearized estimates as well as sampling-based inversions that more completely characterize posterior uncertainties. Principled uncertainty quantification can combine multiple spacecraft data products while preventing local environmental biases in future global investigations.