A Global Database of High Horizontal Resolution IOPs for Validation of Remotely Sensed Ocean ColorPI: Emmanuel Boss - University of Maine
Such a dataset will be unique in its global extent, being ideal for validation of remote sensing product and for algorithm development for a global mission such as PACE. Critical evaluation of the in-line IOP acquisition is necessary to assign realistic uncertainties to those IOPs.
Once processing methodology is agreed upon among the collaborators, UMaine will reprocess historical in-line data collected by the collaborators and provide them to SeaBASS with the processing algorithms and source codes for future use by the ocean color community. Efforts will be made such that data generated will have sufficient details so that alternative processing could be applied without the need to reprocess the raw data.
As part of this proposal we will use the data to answer the following SCIENCE question: What are the characteristics of sub-satellite-pixel variability in IOPs in the ocean?
The utility of the in-line dataset goes well beyond the scope of this proposal and can be used to answer other science questions directly relevant to PACE (a global hyperspectral mission), such as:
- What are the deviations of IOPs from published bio-optical relationships and how do they vary with variables such as temperature, salinity, date, distance from land and ocean depth?
- What information is available in hyperspectral IOPs (and hence hyperspectral ocean color) in addition to that currently obtained with spectral sensors (e.g. added pigments in addition to chlorophyll a (e.g. Chase et al., 2014), size information etc.)?
PI Boss also proposes himself to be the IOP Science team lead.