Current remote sensing capabilities have allowed us to estimate the global biomass of plankton (Behrenfeld et al., 2005), how these stocks are changing (Behrenfeld et al., 2006), and the influ- ence that mesoscale eddies and meanders have on the distribution of phytoplankton (Chelton et al., 2011; Gaube et al., 2014) in the world's ocean, amongst a multitude of other valuable findings. Some efforts have been made to parse the reflectance signatures detected by existing satellite-based radiometers to try to identify the dominant phytoplankton groups present (e.g., Alvain et al., 2005, 2008; Hirata et al., 2011). These early estimates, however, are limited in the amount of information they can provide by the multispectral nature of the data used in analysis. The upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will put into orbit a hyperspectral radiometer, the Ocean Color Instrument (OCI), that will span the ultraviolet to near- infrared region of the electromagnetic spectrum at 5 nm resolution, thus providing information on spectral features in reflectance signatures that result, in part, from the variable light absorption and scattering by different phytoplankton groups, which are often collectively referred to as phytoplankton community composition (PCC). We propose to further refine methodologies to derive the concentration of various phytoplankton pigments using hyperspectral observations and to link these to PCC, thus developing a validated and robust PCC algorithm to be applied to hyperspectral measurements from PACE OCI reflectances.
The proposed project will address the following objectives:
- Refine hyperspectral pigment identification algorithm for application to PACE OCI measurements
- Use high-throughput microscopy collected concurrently with hyperspectral optical measurements to develop algorithms for phytoplankton community composition (PCC) detection
- Define algorithm product uncertainties
The anticipated result of the work proposed here is a set of algorithms that will provide validated estimates of the phytoplankton pigments Chlorophyll-a, b, and c along with photoprotective and photosynthetic carotenoids. This pigment algorithm will be the base of the method by which we will estimate PCC using hyperspectral data from PACE along with physical parameters that can be estimated from satellite data and ocean data- assimilating models. Both the predictive capabilities and uncertainties for the estimation of several major phytoplankton groups along with pigments will be quantified in the proposed work. The resulting algorithms will have quantified uncertainties and will allow for a pixel-by-pixel analysis of PCC using PACE hyperspectral reflectance data.
