GlobColour / Medspiration user consultations, Nov 20-22, 2007, Oslo Validation of the GlobColour Full product set ( FPS ) over open ocean Case 1 waters

Diapositive 1Validation of the
GlobColour Full product set (FPS) over open ocean Case 1 waters
David Antoine
GlobColour / Medspiration user consultations, Nov 20-22, 2007, Oslo
Objectives - questions
Are the overall geographical distributions valid in the merged data set? (e.g., any artificial boundaries?)
Are the statistics derived from the match up analysis of the FPS with field data at least not worst (and hopefully better) than the individual-sensors’ statistics?
Is the data set usable for delivery of operational services (GMES-MCS) and for “carbon cycle research”?
Recommendations for the next steps
Plan
Looking at time series and histograms over selected areas
A bit more sophisticated analyses as well (distributions, anomalies)
Conclusions
Recommendations
The GlobColour product list
Chlorophyll-a concentration (Chl-a)
derived from reflectance ratios or, for the GSM method, from aph
Error bar on the Chlorophyll concentration (GSM)
Colored dissolved + particulate (“detrital”) organic matter (CDM)
either for MERIS or from the GSM01 method
Particle backscattering at 443 nm (bbp443) from the GSM algorithm
Total suspended matter (TSM)
Diffuse attenuation coefficient for downward irradiance (Kd490)
Chl-based algorithm (original Kd’s from SeaWiFS and MODIS are not used)
Fully normalized water leaving radiances (nLw’s)
Excess of radiance at 560 nm (turbid Case 2 waters)
Photosynthetically available radiation (PAR)
The GlobColour products we have considered in the open ocean validation (in red)
Chlorophyll-a concentration (Chl-a)
derived from reflectance ratios or, for the GSM method, from aph
Error bar on the Chlorophyll concentration (GSM)
Colored dissolved + particulate (“detrital”) organic matter (CDM)
either for MERIS or from the GSM01 method
Particle backscattering at 443 nm (bbp443) from the GSM algorithm
Total suspended matter (TSM)
Diffuse attenuation coefficient for downward irradiance (Kd490)
Chl-based algorithm (original Kd’s from SeaWiFS and MODIS are not used)
Fully normalized water leaving radiances (nLw’s)
Excess of radiance at 560 nm (turbid Case 2 waters)
Photosynthetically available radiation (PAR)
1st step: looking over some global composites
Examples for may 2006
nLw(412), may 2006
nLw(443), may 2006
nLw(490), may 2006
nLw(555), may 2006
Chlorophyll (weighted average), may 2006
Chlorophyll (GSM), may 2006
GSM Bbp(443), may 2006
GSM CDM, may 2006
Kd(490), may 2006
2nd step: match ups with field data
(NOMAD + OBPG + BOUSSOLE)
Matchups with the merged products
1 – NASA’s OBPG data set
Location of the OBPG validation dataset used for the GlobColour Level-3 validation
Parameters
2 – NASA’s NOMAD data set
Werdell and Bailey, 2005: An improved bio-optical data set for ocean color algorithm development and satellite data product validation. Remote Sensing of Environment , 98(1), 122-140.
First contributed by the NASA SIMBIOS Program (NRA-96-MTPE-04 and NRA-99-OES-09)
3 – BOUSSOLE data set
3 years of data
nLw’s 412, 443, 490, 510, 560, 665, 681 nm
HPLC TChl-a during monthly servicing cruises
Matchups using all three data sets
Matchups using all three data sets: overall statistics
CharacterisationSummary
Parameter
Sensor
N
Slope
Intercept
r2
MeanRatio
MedianRatio
The answer is definitely YES
Evolution of the statistical indicators from the individual-sensors’ products to the merged products
++ Better than any of the 3 individual-sensor statistics
+ Better than at least two of the 3 individual-sensor statistics, and similar than (or slightly worst than) the third one
= No significant difference with the three individual-sensor statistics
- Worst than at least 2 of the individual-sensor statistics
3rd step: 9-year time series
Overall consistency, trends? Jumps?
Analysis of time series: the selected areas
Analysis of time series: global ocean (50S-50N, depth>1000m)
Analysis of time series: South east Pacific
Analysis of time series: Mediterranean Sea
Analysis of time series: Chl at the BOUSSOLE site only
Analysis of time series: bbp(443) at the BOUSSOLE site only
Analysis of time series:
Main results from what we have seen and from the other areas as well
- The best agreement between the 3 sensors is for L(490)
GSM Chl is always larger for MERIS than for MODIS-A & SeaWiFS
GSM Chl is often smaller than the weighted average Chl
L(555) is often smaller for MODIS-A than for MERIS and SeaWiFS, and this is due to lower values for clear waters. It is also “flatter” (less seasonality) in many occasions. The average value (0.3 mW cm-2 mm-1 sr-1 is, however, closer to the “clear water radiance” (Gordon and Clark, 1981).
Good results for the preliminary validation of the bbp(443) at BOUSSOLE
The merged data set is close to the SeaWiFS data set
4th (and last) step:
global Chl distributions and
Global chlorophyll distributions
Global chlorophyll “anomalies”
Differences between the global Chl stock (mgChl m-2) of a given month and the same stock for the corresponding “climatological month”, i.e., the average for this month over 9 years (1998-2006)
From the Behrenfeld et al. (2006) paper in Nature
Chl from the weighted average
GSM Chl
Which one is right? Raises the question about the validity of the FPS for long-term analyses
Conclusions (1/3)
- Based on the match up with the global data set of field data (Chl and nLw’s), the GlobColour FPS is validated.
- The statistics favourably evolve for the normalized water-leaving radiance in the blue bands (412 and 443 nm), as compared to what they are for the individual sensors
- In terms of Chl, the statistics for the GSM Chl are a little better than those for the product from the weighted average.
- The normalized water-leaving radiance at 490 nm is by far the most homogeneous product among the 3 sensors, so the confidence in the merged product is higher for this peculiar band
- The MODIS-A L(555) is often smaller than the L(555) for the two other sensors
- The merged product has not degraded the situation as compared to each of the 3 single-sensor data set
Conclusions (2/3)
- The good results in term of global match ups are somewhat fortuitous, however, and may often result from compensating effects, in particular between MODIS-A and MERIS.
- The GlobColour FPS is often close to the SeaWiFS data set alone.
- This is not totally satisfactory: the merged data set would not be validated in case another sensor, with its specific bias, would be added, or if one the presently used sensor would be removed from the merging process.
- This is not due to the merging process, but to remaining uncertainties in the vicarious calibration of the various ocean color sensors, and to differences in algorithms (atmospheric corrections in particular)
Conclusions (3/3)
- The GlobColour FPS is definitely qualified and usable for operational uses, such as assimilation into global models (there is a pixel-by-pixel error bar delivered with the GSM Chl), or delivery of services.
The GlobColour FPS is not yet qualified to perform temporal analysis over the period 1998-2007.
In other words, the GlobColour FPS doesn’t yet meet the standards for being qualified as a “climate quality data record”
Recommendations
- We need several in situ long-term time series, in order to add the temporal dimension to our statistical analyses (match ups).
- An international effort is still needed to standardize & improve the vicarious calibration methodologies. We are still not at the desired level of confidence (see, e.g., Ohring et al., EOS Trans AGU, 88(11), 13 march 2007)
- Establish a collaborative frame between space Agencies (ESA, NASA and others), so that vicarious calibration and related issues (atmospheric corrections) can be standardized. This may need a specific body where these issues are discussed and the methods are implemented (see, e.g., The GHRSST).
- Incorporate new approaches, for instance where the TOA level-1 observations of all instruments are processed the same way (same algorithm), which also means that they are all vicariously calibrated against the same standard. It might become obvious at some point that this approach is mandatory if one thinks to CDRs (“climate quality data records”).
Thank you
for your
Statistical measures (indicators)
Coef. of determination
The full product set “FPS”
10 years of global data from the 3 sensors
Systematic application of the 2 merging methods (weighted average and GSM)
Generation of all products
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(
)
(
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++ Better than any of the 3 individual -sensor statistics
+ Better than at least two of the 3 individual-sensor statistics, and similar than (or slightly worst than) the third one
= No significant difference with t he three individual-sensor statistics
- Worst than at least 2 of the individual-sensor statistics
Slope
Intercept
R2
Mean
ratio
Median
ratio
Mean %
diff
Median
% diff
Bias
RMS
L(412)

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GlobColour / Medspiration user consultations, Nov 20-22, 2007, Oslo Validation of the GlobColour Full product set ( FPS ) over open ocean Case 1 waters