A unified approach to estimate land and water reflectances with uncertainties for coastal imaging spectroscopy

Abstract

Coastal ecosystem studies using remote visible/infrared spectroscopy typically invert an atmospheric model to estimate the water-leaving reflectance signal. This inversion is challenging due to the confounding effects of turbid backscatter, atmospheric aerosols, and sun glint. Simultaneous estimation of the surface and atmosphere can resolve the ambiguity enabling spectral reflectance maps with rigorous uncertainty quantification. We demonstrate a simultaneous retrieval method that adapts the Optimal Estimation (OE) formalism of Rodgers (2000) to the coastal domain. We compare two surface representations: a parametric bio-optical model based on Inherent Optical Properties (IOPs); and an expressive statistical model that estimates reflectance in every instrument channel. The latter is suited to both land and water reflectance, enabling a unified analysis of terrestrial and aquatic domains. We test these models with both vector and scalar Radiative Transfer Models (RTMs). We report field experiments by two airborne instruments: NASA's Portable Remote Imaging SpectroMeter (PRISM) in an overflight of Santa Monica, California; and NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) in an overflight of the Wax Lake Delta and lower Atchafalaya River, Louisiana. In both cases, in situ validation measurements match remote water-leaving reflectance estimates to high accuracy. Posterior error predictions demonstrate a closed account of uncertainty in these coastal observations.

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