Abstract

An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape factors. Besides remote sensing geometry considerations (solar zenith angle, viewing angle, and relative azimuth), the inputs are Inherent Optical Properties (IOPs) absorption a and backscattering bb coefficients, the shape of the particulate volume scattering function (VSF) in the backward direction, and the particulate backscattering ratio. Model performance (absolute error) is equivalent to full RT simulations for available high quality validation data sets, indicating almost all residual errors are inherent to the data sets themselves, i.e., from the measurements of IOPs and radiometry used as model input and in match up assessments, respectively. Best performance was observed when a constant backward phase function shape based on the findings of Sullivan and Twardowski (2009) was assumed in the model. Critically, using a constant phase function in the backward direction eliminates a key unknown, providing a path toward inversion to solve for a and bb. Performance degraded when using other phase function shapes. With available data sets, the model shows stronger performance than current state-of-the-art look-up table (LUT) based BRDF models used to normalize reflectance data, formulated on simpler first order RT approximations between rrs and bb/a or bb/(a + bb) (Morel et al., 2002; Lee et al., 2011). Stronger performance of ZTT relative to LUT-based models is attributed to using a more representative phase function shape, as well as the additional degrees of freedom achieved with several physically meaningful terms in the model. Since the model is fully described with analytical expressions, errors for terms can be individually assessed, and refinements can be readily made without carrying out the gamut of full RT computations required for LUT-based models. The ZTT model is invertible to solve for a and bb from remote sensing reflectance, and inversion approaches are being pursued in ongoing work. The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable. In summary, this new analytical model shows good potential for future ocean color inversion with low bias, well-constrained uncertainties (including the VSF), and explicit terms that can be readily tuned. Emphasis is put on application to the future NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission.

Highlights

  • Radiative transfer (RT) approximations linking inherent optical properties (IOPs), such as spectral absorption a(λ) (m−1 ) and spectral backscattering bb (λ) (m−1 ) to ocean color remote sensing reflectance Rrs (λ) are vital to interpreting Rrs because it is not possible to analytically invert the fullRT equation [1,2]

  • The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable

  • Nadir viewing was again considered in the simulations for fL, and we again make the assumption from Section 2.1 that fL for other viewing angles that define a specific in-water scattering angle ψ can be approximated by fL observed at the nadir viewing geometry with equivalent ψ

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Summary

Introduction

Radiative transfer (RT) approximations linking inherent optical properties (IOPs), such as spectral absorption a(λ) (m−1 ) and spectral backscattering bb (λ) (m−1 ) to ocean color remote sensing reflectance Rrs (λ) are vital to interpreting Rrs because it is not possible to analytically invert the fullRT equation [1,2]. Ocean color expressions to date have almost exclusively relied on first order approximations of RT relating Rrs to bb /a through a proportionality represented as f /Q [5,6,7,8], or to bb /(a + bb ) with multi-term polynomial expressions based originally on Gordon et al [9] with coefficients represented as l or. The coefficients describing the relationship between Rrs and IOPs are detailed in look-up tables (LUTs), or a neural network in the case of [8], with dependencies on geometry (i.e., solar zenith, viewing angle, relative azimuth) and in some cases wavelength, wind speed, atmospheric conditions, and/or chlorophyll concentration [Chl].

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