150 shades of green: Using the full spectrum of remote sensing reflectance to elucidate color shifts in the ocean

Highlights

• A simplistic spectral classification of remote sensing reflectance is proposed.

• This algorithm helps conceptualize spectral-spatial-temporal trends in the ocean.

• Shifts in spectral shape over time are detectable on a per-pixel (global) basis.

• Cross-sensor adaptability enables a one-dimensional spectral matching function.

• The approach is applicable to both remotely sensed and in situ ocean color data.

Abstract

This article proposes a simple and intuitive classification system by which to define full spectral remote sensing reflectance (R_rs(λ)) data with a quantitative output that enables a more manageable handling of spectral information for aquatic science applications. The weighted harmonic mean of the R_rs(λ) wavelengths outputs an Apparent Visible Wavelength (in units of nanometers), representing a one-dimensional geophysical metric of color that is inherently correlated to spectral shape. This dimensionality reduction of spectral information combined with the output along a continuum of wavelength values offers a robust and user-friendly means to describe and analyze spectral R_rs(λ) in terms of spatial and temporal trends and variability. The uncertainty in the algorithm's estimation of spectral shape is demonstrated on a global scale, in addition to the utility of the algorithm to discern spectral-spatial-temporal trends in the ocean, on a per-pixel basis for the entire 22 year continuous ocean color (SeaWiFS and MODIS-Aqua) time-series. This technique can be applied to datasets of varying multi- and hyper-spectral resolutions, providing continuity between heritage and future satellite sensors, and further enabling an effective means of elucidating similarities or differences in complex spectral signatures within the constraints of two dimensions. This straightforward means of conceptualizing multi-dimensional variability can help maximize the potential of the spectral information embedded in remote sensing data.