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Deriving inherent optical properties from decomposition of hyperspectral non-water absorption

Author:
Grunert, Brice K., Mouw, Colleen B., Ciochetto, Audrey B.
Source:
Remote sensing of environment 2019 v.225 pp. 193-206
ISSN:
0034-4257
Subject:
absorption, algorithms, biogeochemistry, color, models, phytoplankton, reflectance, remote sensing, satellites, spectral analysis, uncertainty
Abstract:
Semi-analytical algorithms (SAAs) developed for multispectral ocean color sensors have benefited from a variety of approaches for retrieving the magnitude and spectral shape of inherent optical properties (IOPs). SAAs generally follow two approaches: 1) simultaneous retrieval of all IOPs, resulting in pre-defined bio-optical models and spectral dependence between IOPs and 2) retrieval of bulk IOPs (absorption and backscattering) first followed by decomposition into separate components, allowing for independent retrievals of some components. Current algorithms used to decompose hyperspectral remotely-sensed reflectance into IOPs follow the first strategy. Here, a spectral deconvolution algorithm for incorporation into the second strategy is presented that decomposes at-w(λ) from in situ measurements and estimates absorption due to phytoplankton (aph(λ)) and colored detrital material (adg(λ)) free of explicit assumptions. The algorithm described here, Derivative Analysis and Iterative Spectral Evaluation of Absorption (DAISEA), provides estimates of aph(λ) and adg(λ) over a spectral range from 350 to 700 nm. Estimated aph(λ) and adg(λ) showed an average normalized root mean square difference of <30% and <20%, respectively, from 350 to 650 nm for the majority of optically distinct environments considered. Estimated Sdg median difference was <20% for all environments considered, while distribution of Sdg uncertainty suggests that biogeochemical variability represented by Sdg can be estimated free of bias. DAISEA results suggest that hyperspectral satellite ocean color data will improve our ability to track biogeochemical processes affiliated with variability in adg(λ) and Sdg free of explicit assumptions.
Agid:
6335234