Transcript Kahru_Report_WorkingGroup_OnDataIntegrationSatelliteAlgorithms_Kahru_Lee.pptx

9/24/2014, 4:00PM, Rapporteur: M. Kahru; participants: ZhongPing Lee,
Rick Reynolds, Greg Mitchell, Oscar Schofield.
Large amounts of satellite data (particularly OC data) already exist at ~ 1
km resolution to assess large-scale distributions and somewhat
inaccurate time series in for the Southern Ocean. The current algorithms
are being improved. The following major gaps exist and improvements
are needed:
•Hyperspectral ocean color data at typical ~1 km resolution would allow
separating Rrs spectra and classify into certain clusters with optimized
inversion algorithms.
•High spatial resolution (~ 1-10 m) multi- or hyperspectral data would allow
assessment of potentially high biomass and insufficiently characterized processes in
marginal ice zones (e.g. intense algal blooms associated with colored “pancake” ice,
colored melt ponds, fronts and eddies, glacier meltwater plumes, etc). While these
data can be occasionally (i.e. very infrequently) obtained from existing satellite
sensors, due to persistent cloudiness, it would be much more effective to deploy these
sensors from sub-orbital platforms, e.g. drones (UAV or UAS), manned aircraft.
Logistics details are highly important and may restrict the use of these resources.
Landing strips at King George Island and at Palmer Station may be used. Aircraft of
opportunity may be used. Due to persistent cloud cover, current satellite time series
may miss important periods in the annual cycle (“missing phenology”).
•Improved products needed from current OC sensors: PAR product in
the vicinity of ice.
•Improved in-water algorithms should be developed for the UV spectral
region, particularly to derive MAAs and phycobiliproteins.
•Detailed optical work on process cruises should derive parameters for
IOP algorithms, e.g. spectral slope of CDOM absorption (S), spectral
slope of particular backscatter, etc.
Data integration is important and must be implemented with care.
Either use a dedicated database with a professional manager or depend
on outside resources, e.g. NASA SeaBASS.