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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.

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