design justification v2 overview samantha lavender

GlobColour CDR Meeting – July 10-11, 2006, ESRIN

Design Justification v2 overview

Samantha Lavender


Work Packages

15 Jan2006

CDR


Design Justification v2

• 5.2 In situ Characterisation

• 5.3 Coastal Waters

• 5.4 Sensor Cross Characterisation

• 6.3 Merging Algorithm Sensitivity Analysis


In situ characterisation Samantha Lavender and Yaswant Pradhan


Characterisation review

• In situ data

• MERIS

• MODIS

• SeaWiFS

• Parasol: data not available at present for characterisation

• Overall Conclusions


In situ data sets

• NOMAD (2002 onwards)• Publicly available SeaBASS (2002 onwards)• NILU database• Boussole buoy


On-line Database


Spatial Coverage

NOMAD SeaBASS

NILU


NOMAD In Situ Data Conversion to Fully Normalised Water Leaving

Radiance #412nm

y = 0.6918x + 0.0576

R2 = 0.9383

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5

OBPG nLw

Glo

bC

OL

OU

R n

Lw

#443nmy = 0.7558x + 0.0349

R2 = 0.9544

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5

OBPG nLw

Glo

bC

OL

OU

R n

Lw

#490nmy = 0.8278x + 0.0012

R2 = 0.9671

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5

OBPG nLw

Glo

bC

OL

OU

R n

Lw

#510nmy = 0.8361x - 0.0034

R2 = 0.9859

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5

OBPG nLw

Glo

bC

OL

OU

R n

Lw

#555nmy = 0.8351x - 0.0042

R2 = 0.9922

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5

OBPG nLw

Glo

bC

OL

OU

R n

Lw

#670nmy = 0.8221x - 0.0027

R2 = 0.9976

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2 2.5

OBPG nLw

Glo

bC

OL

OU

R n

Lw

OBPG nLw

Glo

bCO

LO

UR

nL

w

412 443 490

510 555 670


Data Processing L2 (M)LAC

L3-DDS Generator

In-situ meta

L3 DDS In-situ dataIn GC-NOMAD template

DDS Match-UpTimediff

<24 hrs

Locationdiff

<=0.02°NO

match-up

NO match-up

Extract 3x3 kernel

L3-DDS Reader GC in-situ Reader

Y N

Import to ExcelStat Template

N

Preparation/GenerationExtraction

Statistics/Result

Tdiff< 24 hrsFLAG !=NoData

Tpix > 5

Match-up Result

Y

Exclude

N


MERIS MODIS SeaWiFS Total BOUSSOLE 244 210 311 765 NILU 32 41 47 120 NOMAD 151 107 244 502 Total 427 358 602 1387

Data Processing

Number of generated DDS


MERIS


MODIS


SeaWiFS


Normalised Water Leaving Radiance

• Further discussion and analysis is occurring with respect to the derivation of in-situ normalised water leaving radiances as this is a key step in the characterisation process.

• Propose that this work should be ongoing and the characterisations will be updated as additional insitu data becomes available.

• The results presented so far indicate that it is particularly important to seek out datasets with high normalised water leaving radiances.


Chlorophyll & GlobCOLOUR Kd

MERIS MODIS-Aqua (Literature)

SeaWiFS

(Literature)

Chl

Bias and RMS

-0.077

0.479

-0.093

(-0.084)

2.551

(0.644)

-0.112

(0.006)

1.214

(0.657)

Kd (Literature is sensor

algorithms)

Bias and RMS

0.025

0.017

0.065

(-0.018)

0.103

(0.046)

0.051

(0.001)

0.063

(0.520)


DJF V2.0 Coastal Waters Kai Sørensen and Jo Høkedal


Coastal waters - Guianas CoastMERSEA-IP

• The provinces, Guianas Coastal (GUIA) and Guinea Current Coastal (GUIN) are both coastal stripes influenced by land and river inputs.

• On the African side (GUIN) there is also a strong impact of atmospheric conditions (cloud coverage, biomass burning and desert dust aerosols) on the ocean colour products.

• The two provinces are characterized by the largest differences of the provinces (in this study) between sensor products.

• Between SeaWiFS and MODIS–Aqua the differences (defined as the root mean square relative difference) was as high a 21.3 % and 24.7 % on average for GUIA and GUIN, respectively.

• The differences compared to MERIS are 3-4 % higher.


Coastal water - Baltic SeaMERSEA-IP and FerryBox-EU

• An optically complex water with a high load of CDOM, and summer blooming of Cyanobacteria causing large changes in the IOPs.

• An average difference of MERIS vs SeaWiFS or MODIS-Aqua of around 25%, while between SeaWiFS and MODIS-Aqua of 19.2 %.

• MERIS Algal_1 and Algal_2 show erroneous data in the bloom, but Algal_2 after the 2nd processing gave better agreement.

• Even if the MERIS Neural Network Case 2 products can be trained for this area it will be problematic due to the high IOP variability.

• The validation will also be a challenge during such extreme blooms.


North Sea – Skagerrak Case1 Chl-a Algorithms, Folkestad, 2005

R2

Sensors compared (small areas of 25 pixels)

All stations

Without #7

MODIS/Aqua vs MERIS 0.60 0.76

SeaWiFS vs MERIS 0.15 0.44

SeaWiFS vs MODIS/Aqua 0.82 0.91

MODIS/Aqua vs MERIS SeaWiFS vs MERIS SeaWiFS vs MODIS/Aqua


MERIS Skagerrak (2nd processing)Sørensen, 2006.

57.5 58 58.5 59 59.5 600

1

2

3

4

5

6

7

8

9

10200305

KLA

mg/

m- 3

Breddegrad

algal2fluor

MERIS Algal_2 vs Chl-a_HPLC MERIS Algal_2 binned one month vs Chl-a fluorescence from the Ferrybox systems (+/- 1. Stdev.dev.)

Central Skagerrak

Danish Coast

Oslo Fjord


Coast and Open Sea – Spatial variability

400 450 500 550 600 650 700 750 800 850 9000

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Wavelength

w

(sr

-1)

2002-07-29 10:07:00, lat=51.3083, lon=2.85

in situ1 of11 of19 of99 of922 of2522 of2537 of4937 of4956 of8156 of81

400 450 500 550 600 650 700 750 800 850 9000

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Wavelength

w

(sr

-1)

2002-07-29 10:22:36, lat=63.92, lon=0.7

1 of11 of19 of99 of925 of2525 of2549 of4949 of4981 of8181 of81

Vertical bars: Max-min

Vertical bars: Max-min


Summary• It is clear from the findings by many authors that SeaWiFS and MODIS do not

resolve the true values in Case 2 water and that multivariate complex Case 2 waters need to have complex algorithms e.g. MERIS NN.

• It is presently difficult to give any recommendation on how to solve the issue of combining data from different sensors in coastal water without dealing with all the Case 2 problems.

• The only combining possibilities is then to merge MERIS Case 2 products with Case 1 products, but boundaries will probably be present.

• Alternative are to use Case 1 algorithms into the coast and flag Case2 water. To be discussed.


Sensor Cross Characterisation

Antoine Mangin


Cross characterisation

Cross comparison between MERIS/MODIS/SeaWifs – attempt to detect systematic biases: At global scale and regional scale

Check of the consistency with JRC results

Harmonisation of Kd algorithm


Cross comparison between MERIS/MODIS/SeaWifs – attempt to detect systematic biases: At global scale and

regional scale

comparison


0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5

SW:MO

SW:ME

MO:ME

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5

SW:MO

SW:ME

MO:ME

March03 06 09 12

03 06 09 12

Slope of the regression

Determination coeff. r2

Med

iter

rane

anSummary for Mediterranean


0.840.860.880.9

0.920.940.960.98

11.021.04

0 1 2 3 4 5

SW:MO

SW:ME

MO:ME

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0 1 2 3 4 5

SW:MO

SW:ME

MO:ME

03 06 09 12

03 06 09 12

Slope of the regression

Determination coeff. r2

Glo

bal

Summary for Global results


S:A S:M M:A RMS % 11/13/15 14/16/18 14/15/17

Bias 3/5/9 -2/0/4 1/5/9

From JRC’s assessment:

Global

Regional: very fluctuant, seasonal dependency – in agreement with our daily results

Confrontation with other sources

There is a bias between sensors


Either…. …or….

We get a faithful caracterisation of bias wrt season and region and correct for it prior to merging.

We anticipate the impact of using biased data.We apply inputs as is.The impact will be reflected into the error bar estimates wrt to season/region

Not mature enough Recommended


Harmonisation of Kd algorithm

Kd


Overall Conclusions

• Used some large databases and produced a large number of DDS files (1387), but as is often the case with ocean colour data the number of match-up points is considerably smaller than the number of original insitu points. • The characterisation will undergo additional work within the next couple of months to tie up the loose ends and come to a final set of conclusions.• For now the merging will use the following characterisation results:

• normalised water leaving radiance: GlobCOLOUR• chlorophyll: NASA (will split GlobCOLOUR into low/high groupings)• diffuse attenuation coefficient: GlobCOLOUR

•For Case 2 waters, a decision on the alternatives of using (1) MERIS Case2 products for the coast or (2) using Case1 products only with flagging information must be taken.

design justification v2 overview samantha lavender

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