Multi-method approach to quantify uncertainties in the measurements of light absorption by particles

Performance of two semi-analytical algorithms in deriving water inherent optical properties in the Southern Ocean

Remotely sensed inherent optical properties (IOPs) are key proxies for synoptic mapping of primary production and carbon export in the global ocean. However, the IOPs inversion algorithms are scarcely evaluated in the Southern Ocean (SO) because of limited field observations. In this study, the performance of two widely used semi-analytical algorithms (SAAs), i.e., the quasi-analytical algorithm (QAA) and the generalized IOP model (GIOP), were evaluated using a compiled in situ bio-optical dataset in SO, as well as measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS). Evaluations with in situ data show that QAA and GIOP have comparable performance in retrieving the total absorption coefficient (a(λ)), absorption coefficients of phytoplankton (aph(λ)), and that of detritus and colored dissolved organic matter (adg(λ)). Overall, it was found that remotely sensed a(λ) and aph(λ) by both SAAs agreed well with field measurements, with the mean absolute percentage difference (MAPD) of derived a(λ) and aph(λ) in the blue-green bands being ∼20% and ∼40%, respectively. However, derived adg(λ) by both SAAs were higher than the measured values at the lower end (adg(443) < ∼0.01 m-1), but lower at the higher end (adg(443) > ∼0.02 m-1), with MAPD of ∼60%. Results of this effort suggest confident products of a(λ) and aph(λ) from VIIRS in SO, but more dedicated efforts on the measurements and evaluation of adg(λ) in SO would be desired.

Determining the optimal spectral sampling frequency and uncertainty thresholds for hyperspectral remote sensing of ocean color

Using a modified geostatistical technique, empirical variograms were constructed from the first derivative of several diverse Remote Sensing Reflectance and Phytoplankton Absorbance spectra to describe how data points are correlated with "distance" across the spectra. The maximum rate of information gain is measured as a function of the kurtosis associated with the Gaussian structure of the output, and is determined for discrete segments of spectra obtained from a variety of water types (turbid river filaments, coastal waters, shelf waters, a dense Microcystis bloom, and oligotrophic waters), as well as individual and mixed phytoplankton functional types (PFTs; diatoms, eustigmatophytes, cyanobacteria, coccolithophores). Results show that a continuous spectrum of 5 to 7 nm spectral resolution is optimal to resolve the variability across mixed reflectance and absorbance spectra. In addition, the impact of uncertainty on subsequent derivative analysis is assessed, showing that a 3% Gaussian noise (SNR ~66) addition compromises data quality without smoothing the spectrum, and a 13% noise (SNR ~15) addition compromises data with smoothing.

An analytical model for light backscattering by coccoliths and coccospheres of Emiliania huxleyi

We present an analytical model for light backscattering by coccoliths and coccolithophores of the marine calcifying phytoplankter Emiliania huxleyi. The model is based on the separation of the effects of diffraction, refraction, and reflection on scattering, a valid assumption for particle sizes typical of coccoliths and coccolithophores. Our model results match closely with results from an exact scattering code that uses complex particle geometry and our model also mimics well abrupt transitions in scattering magnitude. Finally, we apply our model to predict changes in the spectral backscattering coefficient during an Emiliania huxleyi bloom with results that closely match in situ measurements. Because our model captures the key features that control the light backscattering process, it can be generalized to coccoliths and coccolithophores of different morphologies which can be obtained from size-calibrated electron microphotographs. Matlab codes of this model are provided as supplementary material.

Improved determination of particulate absorption from combined filter pad and PSICAM measurements

Filter pad light absorption measurements are subject to two major sources of experimental uncertainty: the so-called pathlength amplification factor, β, and scattering offsets, o, for which previous null-correction approaches are limited by recent observations of non-zero absorption in the near infrared (NIR). A new filter pad absorption correction method is presented here which uses linear regression against point-source integrating cavity absorption meter (PSICAM) absorption data to simultaneously resolve both β and the scattering offset. The PSICAM has previously been shown to provide accurate absorption data, even in highly scattering waters. Comparisons of PSICAM and filter pad particulate absorption data reveal linear relationships that vary on a sample by sample basis. This regression approach provides significantly improved agreement with PSICAM data (3.2% RMS%E) than previously published filter pad absorption corrections. Results show that direct transmittance (T-method) filter pad absorption measurements perform effectively at the same level as more complex geometrical configurations based on integrating cavity measurements (IS-method and QFT-ICAM) because the linear regression correction compensates for the sensitivity to scattering errors in the T-method. This approach produces accurate filter pad particulate absorption data for wavelengths in the blue/UV and in the NIR where sensitivity issues with PSICAM measurements limit performance. The combination of the filter pad absorption and PSICAM is therefore recommended for generating full spectral, best quality particulate absorption data as it enables correction of multiple errors sources across both measurements.