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MERIS US Workshop, 14 July 2008, Washington (USA)

Samantha Lavender

New directions?

IOP Workshop, October 2008

Some personal thoughts (ramblings) …..

Topics as suggested… Fluorescence, “exact” normalization (f/Q), GlobColour; others

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Model assimilation…..Model assimilation…..

• Do (carbon/climate/operational) modellers prefer radiances to the output of SAAs? Are they in a better place to determine errors?

• From a talk by ECMWF on data assimilation in numerical forecasting at ESA EO Summer School 2008 (presentations are available on-line)….classes of retrieval schemes given as:

- Solutions to reduced inverse problems- Statistical / NNet (statistical) methods- Forecast Background or 1D-var: forecast is used as a source

of prior information (their preferred method)

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Differential optical absorption spectroscopy (DOAS) is a popular technique and this is migrating over to the ocean….They need to know the signal from the ocean if they’re to retrieve parameters such as CO2 and NH4 accurately…

Also neural nets continue to be popular…. atmospheric correction / SAA algorithm together…

What can we learn from the atmospheric community …..What can we learn from the atmospheric community …..

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Should SAAs [in turbid coastal waters] extend into [start in] the NIR? – needs for turbid waters

Should SAAs [in turbid coastal waters] extend into [start in] the NIR? – needs for turbid waters

• If we are looking to derived sediment concentrations (via bb) in turbid waters then we know that the signal will saturate within the visible bands…

• By moving into the NIR we can still see a variation with sediment concentration (at high concentrations). Also in the NIR we have no CDOM signal and a reduced/no signal from the phytoplankton… So should we start with the SAA running in the NIR and then progress to the visible?

• Need the atmospheric correction working properly first.

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NIR signalNIR signal

Lavender (1996, PhD Thesis)

In ARGANS we’re about to start a set of new tank experiments to understand NIR reflectance better.. combined with fieldwork.

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Not trying to derive everything from one sensor…..Not trying to derive everything from one sensor…..

• Should we be making better use of cross-sensor data? On Sentinel-3 there will be both OLCI and SLSTR.

• For atmospheric correction, SLSTR is dual so has improved capabilities for deriving atmospheric properties such as aerosols…. But is not designed for ocean colour. Take aerosols derived from SLSTR and apply to OLCI….

• Could we do the same for SAAs?

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Fluorescence, “exact” normalization (f/Q), GlobColour; others….Fluorescence, “exact” normalization (f/Q), GlobColour; others….

• Fluorescence [and Raman] – at what stage should these be included in our SAA models?

• Normalisation: approach for MERMAID (in situ database)… Morel LUT for case 1 waters with variation based on Chl…starting to test Roland’s Neural Net as an alternative / for case 2 waters…

• GlobColour: has been presented