Optical constituent concentrations and uncertainties obtained for Case 1 and 2 waters from a spectral deconvolution model applied to in situ IOPs and radiometry

Lo Prejato, M. and McKee, D. (2023) Optical constituent concentrations and uncertainties obtained for Case 1 and 2 waters from a spectral deconvolution model applied to in situ IOPs and radiometry. Earth and Space Science, 10 (12). e2022EA002815. ISSN 2333-5084 (https://doi.org/10.1029/2022ea002815)

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Abstract

A spectral deconvolution model (SDM) for inversion of light absorption, a(λ) and backscattering, bb(λ), to estimate concentrations of chlorophyll (CHL), colored dissolved organic material (CDOM) and non-biogenic mineral suspended solids (MSS) in offshore and shelf waters is presented. This approach exploits the spectral information embedded in the ratio bb(λ)/a(λ), without the need to know each parameter separately. The model has been applied to in situ inherent optical properties (IOPs), a(λ) and bb(λ), and to in situ remote sensing reflectance, rrs(λ). CHL, MSS, and CDOM estimates are provided by propagating uncertainties in input IOPs and material-specific IOPs using a bootstrapping approach. Application of the SDM to a data set collected in the Ligurian Sea provides Mean Average Errors (MAE) of <0.7 mg m−3 for CHL, <0.02 m−1 for CDOM, and <0.2 g m−3 for MSS. The SDM is found to perform as well as, or in some cases better than, single parameter algorithms and other semi-analytical algorithms (SAA) for each parameter for the Ligurian Sea data set. The SDM CHL product is tested using the NOMAD, Case 1 dominated, global data set and found to perform consistently with the quasi-analytical algorithm (Lee et al., 2002, https://doi.org/10.1364/ao.41.005755) but with slightly poorer performance than standard OCx algorithms. However, the additional estimates of CDOM and MSS provided by the SDM suggest that the approach may be particularly useful for Case 2 waters. Successful retrieval of constituent concentrations with uncertainties suggests good potential to adapt this technique for satellite remote sensing.

ORCID iDs

Lo Prejato, M. and McKee, D. ORCID logoORCID: https://orcid.org/0000-0001-8023-5923;
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