Validation of the GlobColour Full product set ( FPS ) over open ocean Case 1 waters

GlobColour / Medspiration user consultations, Nov 20-22, 2007, Oslo

Validation of the GlobColour Full product

set (FPS) over open ocean Case 1 waters

David AntoineLaboratoire

d’Océanographie de Villefranche

Inputs fromthe GlobColour team

Special thanks to Gilbert Barrot,Julien Demaria and Christophe Lerebourg


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 Examination of global composites

Validation against field data (OBPG, NOMAD, BOUSSOLE)

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) Aerosol optical thickness Cloud fraction Data quality flags


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) Aerosol optical thickness Cloud fraction Data quality flags


1st step: looking over some global compositesExamples 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

Kd(490) = 0.0166 + 0.0835[Chl]0.633 (Morel et al., 2007)


2nd step: match ups with field data(NOMAD + OBPG + BOUSSOLE)


Parameters

Chl = 69 L443 = 54 L531 = 01

KD = 25 L490 = 41 L555 = 54

L412 = 47 L510 = 26 L670 = 49

Matchups with the merged products1 – NASA’s OBPG data set

Location of the OBPG validation dataset used for the GlobColour Level-3 validation


Matchups with the merged products2 – 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)


Matchups with the merged products3 – BOUSSOLE data set

3 years of dataFrom Sept. 2003 to Sept 2006nLw’s 412, 443, 490, 510, 560, 665, 681 nmHPLC TChl-a during monthly servicing cruises


Matchups using all three data sets






Matchups using all three data sets: overall statistics

Parameter Sensor N Slope Intercept r2 MeanRatio MedianRatio Mean%Diff Median%Diff Bias RMSChl1 SeaWiFS 1070 0.77 -0.0944 0.7917 1.1306 1.0191 44.7718 33.4213 -0.0150 0.2513Chl1 MODIS 547 0.68 -0.1381 0.6610 1.2034 1.1038 53.5118 39.9684 -0.0136 0.3122Chl1 MERIS 189 0.74 -0.0394 0.6905 1.4201 1.1788 62.7152 40.9814 0.0762 0.2888K490 SeaWiFS 548 0.60 -0.4878 0.7290 1.1133 1.1002 28.0719 21.9824 0.0234 0.1516K490 MODIS 333 0.57 -0.5359 0.6749 1.0768 1.0452 28.6777 24.4501 0.0048 0.1630K490 MERIS 113 0.48 -0.6512 0.6560 1.1057 1.0813 26.4867 19.1565 0.0207 0.1525T865 SeaWiFS 595 0.80 0.0551 0.5331 1.7531 1.6769 79.6996 68.7373 0.0390 0.0599T865 MODIS 534 0.89 0.0457 0.5823 1.7884 1.6147 81.9891 61.5017 0.0379 0.0556T865 MERIS 121 0.70 0.0538 0.5010 1.8154 1.7234 85.9599 72.3354 0.0332 0.0549L412 SeaWiFS 1120 0.90 0.0630 0.7064 1.5162 0.9505 79.5135 20.2672 -0.0282 0.2999L412 MODIS 684 0.65 0.1534 0.4914 1.1561 0.8508 55.4052 24.2179 -0.1194 0.3123L412 MERIS 247 0.74 0.4223 0.4491 2.5474 1.2828 163.6197 31.9603 0.2220 0.4039L443 SeaWiFS 1630 0.84 0.1597 0.6886 1.3968 0.9896 58.7705 16.1477 0.0050 0.2536L443 MODIS 982 0.69 0.2282 0.5974 1.1935 0.9649 42.0515 16.8536 -0.0394 0.2477L443 MERIS 323 0.76 0.3370 0.5690 1.8440 1.1397 93.2329 20.2611 0.1299 0.2949L490 SeaWiFS 1256 0.80 0.1641 0.7139 1.5518 0.9743 72.5980 12.7811 -0.0128 0.2141L490 MODIS 541 0.73 0.2124 0.6794 1.3525 0.9850 51.5188 11.3867 -0.0140 0.2071L490 MERIS 194 0.63 0.3764 0.5579 2.4202 1.0950 150.5663 15.7299 0.0823 0.2287L510 SeaWiFS 1027 0.74 0.1576 0.7077 1.4117 0.9640 59.9263 13.4798 -0.0229 0.1934L510 MERIS 158 0.19 0.5607 0.0997 2.7149 1.1489 179.9892 20.1114 0.0821 0.2252L555 SeaWiFS 1524 0.83 0.0712 0.7972 1.6217 0.9563 83.4673 16.2600 -0.0241 0.1998L555 MODIS 849 0.82 0.0936 0.8098 1.6927 0.9733 87.8850 13.8554 -0.0246 0.2020L555 MERIS 270 0.76 0.1222 0.8691 2.1410 0.9926 131.0835 17.6238 -0.0295 0.2046L670 SeaWiFS 350 0.66 0.0296 0.8109 2.0609 1.0848 134.8845 41.6199 -0.0080 0.0695L670 MODIS 400 0.86 0.0070 0.7559 1.8320 1.1801 107.4643 41.9583 -0.0023 0.0441L670 MERIS 97 0.73 0.0449 0.8427 2.7201 1.8677 179.7845 86.7665 0.0240 0.0466

Parameter Method N Slope Intercept r2 MeanRatio MedianRatio Mean%Diff Median%Diff Bias RMSChl1 AVW 1192 0.76 -0.0696 0.7457 1.2699 1.1489 51.8350 37.3139 0.0303 0.2714Chl1 GSM 853 0.73 -0.2364 0.8051 0.9139 0.8698 35.3721 28.1239 -0.1021 0.2799

K490 GSM 327 0.71 -0.3686 0.8369 1.0923 1.0999 23.2435 18.0391 0.0216 0.1283CDM GSM 108 0.63 -0.5062 0.6220 1.9028 1.0457 120.7475 38.9621 0.0733 0.3672T865 AVW 618 0.57 0.0727 0.4373 1.7634 1.6505 80.5013 65.5102 0.0376 0.0665L412 AVW 1239 0.93 0.0206 0.7269 1.2062 0.9396 50.9981 20.7646 -0.0416 0.2881L443 AVW 1791 0.85 0.1327 0.7105 1.2298 0.9873 42.2296 16.0691 -0.0012 0.2392L490 AVW 1347 0.81 0.1639 0.7264 1.4794 0.9805 64.2441 12.1851 -0.0100 0.2062L510 AVW 1041 0.72 0.1736 0.6986 1.3886 0.9684 57.0755 13.4798 -0.0220 0.1962L555 AVW 1388 0.85 0.0755 0.8293 1.6687 0.9871 82.9289 14.3763 -0.0059 0.1760


Slope Intercept R2 Mean ratio

Median ratio

Mean % diff

Median % diff Bias RMS

L(412) ++ ++ ++ + + ++ + + + L(443) ++ ++ ++ + + + ++ + ++ L(490) ++ + ++ + + + + = = L(555) ++ + + + - ++ + ++ ++ L(670) - + - + - + - = -

Chl (AVW) + + + - - + + = + Kd(490) ++ ++ ++ + = ++ ++ = ++

T(865) - - - = + + + = -

Do we perform better after the merging process ?

The answer is definitely YES

++ 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

Evolution of the statistical indicators from the individual-sensors’ products to the merged products


3rd step: 9-year time seriesOverall 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 m-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 Chl “anomalies”


Global chlorophyll distributions

Oligotrophic Mesotrophic Eutrophic

51%43%

38%

52%44%

38%


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 youfor yourattention


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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


Documents

Validation of the GlobColour Full product set ( FPS ) over open ocean Case 1 waters