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OCEAN COLOUR PRODUCTION CENTRESatellite Observation Global Products
OCEANCOLOUR_GLO_OPTICS_L3_NRT_OBSERVATIONS_009_030
OCEANCOLOUR_GLO_CHL_L3_NRT_OBSERVATIONS_009_032
OCEANCOLOUR_GLO_CHL_L4_NRT_OBSERVATIONS_009_033
OCEANCOLOUR_GLO_OPTICS_L4_REP_OBSERVATIONS_009_081
OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_082
OCEANCOLOUR_GLO_OPTICS_L4_NRT_OBSERVATIONS_009_083
OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_085
OCEANCOLOUR_GLO_OPTICS_L3_REP_OBSERVATIONS_009_086
Issue: 1.9
Contributors: P.Garnesson, A.Mangin, F.Gohin
Approval date by the CMEMS product quality coordination team: dd/mm/yyyy
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CHANGE RECORD
Issue Date § Description of Change Author Validated By
1.0 May 1 2015 all Creation of the CMEMS V1
release P.Garnesson L. Crosnier
1.1 29/10/2015 all Modification about the DT
products P.Garnesson A.Mangin
1.2 15/12/2015 all Modification to fit CMEMS
graphical rules P.Garnesson
1.3 26/1/2016 all
The Copernicus-GlobColourREP is added to the products disseminated including a new SPM parameter.
P.Garnesson
F.GohinA.Mangin
1.4 04/4/2016 all Changes for V2 ARRP.Garnesson
1.5 7/9/2016 all Baltic no more cover by this QUID
P.Garnesson
1.6 18/1/2017 all
Upgrade for V3. The REP is extended from [1997-2015]to [1997-Dec-2016]. New INSITU datasets are introduced.
P.Garnesson A.Mangin
1.7 25/3/2017 all Changes for V3 AR P.Garnesson A.Mangin
1.8 25/9/2017 all Changes for V3.2 AR P.Garnesson A.Mangin
1.9 21/11/2017
all Changes for V3.3: the REP is extended to Dec-2016 and a new dataset about
P.Garnesson A.Mangin
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climatology is provided.
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TABLE OF CONTENTS
I.1Products covered by this document..............................................................................................................5
I.2Summary of the results..................................................................................................................................7
IIProduction system description..............................................................................................................................9
I.3Production Centre Name: OCTAC.............................................................................................................10
I.4Production System Name: OC-ACRI-NICE-FR.......................................................................................11
I.5The Copernicus-GlobColour Processor.....................................................................................................12
II.1.1Product processing mode and temporal coverage..................................................................................13
IIIValidation framework........................................................................................................................................14
I.6Validation metric..........................................................................................................................................15
I.7In-situ Measurements used for the validation...........................................................................................16
I.8Off-line validation.........................................................................................................................................18
I.9OLCI datasets qualification........................................................................................................................19
I.10On-line validation.......................................................................................................................................20
I.11Upstreams used by OC-ACRI-NICE_FR.................................................................................................21
IVValidation results................................................................................................................................................22
I.12Estimated Accuracy Numbers...................................................................................................................23
I.13Validation of Chlorophyll results..............................................................................................................24
I.14Validation of RRS results...........................................................................................................................25
I.15Validation of the non-algal SPM product.................................................................................................26
I.16Validation of the other Optic products.....................................................................................................27
I.17On-line validation.......................................................................................................................................28
VSystem’s Noticeable events, OUTAGEs or changes...........................................................................................30
VIQuality changes since previous version............................................................................................................31
VIIReferences.........................................................................................................................................................32
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I.1 Products covered by this document
This document covers the Global NRT (Near Real Time) and REP (Reprocessed) Ocean Colourproducts provided by ACRI-ST Company (Sophia Antipolis, France). These products are basedon the Copernicus-GlobColour processor. ACRI-ST also provides a complementary “cloudfree” chlorophyll product (Optimal interpolation approach) which is described in the CMES-OC-QUID-009--033-037-082-098_document.
Main characteristics of the CMEMS products covered by this document are resumed in theTable 1 and Table 2 below.
CMEMS product reference
(link to the CMEMS catalogue)
Temporalresolution
Period Validation
GLO
BAL
NRT
4km
OCEANCOLOUR_GLO_OPTICS_L3_NRT_OBSERVATIONS_009_030 daily [Jan-2017-present[ on/off-line
OCEANCOLOUR_GLO_CHL_L3_NRT_OBSERVATIONS_009_032 daily [Jan-2017-present[ on/off-line
OCEANCOLOUR_GLO_CHL_L4_NRT_OBSERVATIONS_009_033 daily [Jan-2017-present[ on/off-line
OCEANCOLOUR_GLO_OPTICS_L4_NRT_OBSERVATIONS_009_083 8 days,monthly
[Jan-2017-present[ on/off-line
GLO
BAL
REP
4km
OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_085 daily [Sep-1997-Dec-2016] off-line
OCEANCOLOUR_GLO_OPTICS_L3_REP_OBSERVATIONS_009_086 daily [Sep-1997-Dec-2016] off-line
OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_082 daily [Sep-1997-Dec-2016] off-line
OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_081 8 days,monthly
[Sep-1997-Dec-2016] off-line
Table 1 : List of the CMEMS datasets covered by this document. The product category isdetailed in the Table 2. The off-line validation refers to results obtained via off-line validation
assessment; the on-line validation refers to indicators computed/associated with the newproducts that are delivered each day to the end-users (e.g. consistency with climatology,
timeliness, availability of the upstream…)
Products delivered by ACRI-ST are obtained by merging different sensors: SeaWIFS, MODISAqua, MERIS and VIIRS NPP. In the OCTAC CMEMS catalogue, the products based on amerging approach use the label “Multi” for the file naming. The products based on the newESA Sentinel-3A OLCI sensor, are at present delivered as a “single sensor” (using the ESAalgorithm).
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Products delivered by OC-ACRI-NICE-FR
Parameter Description Algorithm
CHL Chlorophyll concentration (mg/m3) GSM [RD3] (for Multisensors)
OC4ME, Morel (for OLCI-S3A)
Optics
KD Diffuse attenuation coefficient at 490 nm (m-1) ofthe downwelling irradiance at 490nm. It is oneindicator of the turbidity of the water column.
Morel [RD5] (for Multisensors)
OK2-560 (for OLCI-S3A)
BBP Particulate backscattering coef. at 443 nm (m-1) GSM [RD3] (for Multisensors)
ADG
Absorption coef. due to cdom and non-pigmented particles at 443 nm (m-1). ADG issometimes also called CDM.
GSM [RD3] (for Multisensors)
ZSD Secchi disk depth Transparency (m) Morel [RD5] (for Multisensors)
SPM
Non Algal Suspended Particle Matter (g/m3) Gohin[RD8] (for Multisensors)
Table 2 : List of products delivered by OC-ACRI-NICE-FR
• The CHL, BBP, ADG multi-sensors products are obtained by the merging of MERIS,MODIS/AQUA and SeaWiFS data using an advanced retrieval based on fitting an in-water bio-optical model to the merged set of observed normalised water-leavingradiances. This technique is termed GSM [RD2, RD4] because it originates from theGarver-Siegel-Maritorena Model bio-optical model, 2005. For OLCI, the GSMapproach is not applied at present, the chlorophyll is retrieved using the standardOC4ME algorithm.
• The RRS are the Fully Normalised remote sensing reflectance coming from the level2products. The merged of the sensor is done using an average of the single sensorpondered by coefficient depending a sensor characterisation.
• The ZSD and KD multi-sensors products are derived from the CHL product using theMorel algorithm [RD5][RD6]. The ZSD retrieval is very interesting, because it can be
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compared to in situ observation base on the Secchi disk (it has been created in 1865by Angelo Secchi, it is a plain white, circular disk 30 cm in diameter used to measurewater transparency in bodies of water).
• The SPM product is based on the Gohin algorithm [RD8]. It corresponds to a non-algal(Inorgarnic) Suspended matter. It has been developed to provide an estimation of the mineralsuspended matter in coastal zone. The non-algal SPM product is of great utility for validatinghydro-sedimentary models. It can also be transformed in KPAR (the attenuation of the diffusedescending light in the water column in the photosynthesis domain) for input in biologicalmodels for coastal seas.
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I.2 Summary of the results
The quality of the OC products delivered by OC-ACRI-NICE-FR is obtained using thereprocessed Copernicus-GlobColour data set (period Sep-1997-Dec-2016) and in-situdescribed in section I.8. The Table 3provide a synthesis of results obtained (details areprovided in Table 6) The overall products presented in this document present an acceptablequality in terms of both their temporal consistency and with respect to their in situcounterparts.
VariablesMetrics (see Table 4)
r2 RMSE
CHL 0.79 0.28
RRS 412 0.86 0.004
RRS 443 0.81 0.004
RRS 490 0.77 0.004
RRS 555 0.84 0.004
RRS 670 0.74 0.0028
KD 490 0.80 0.18
BBP 443 0.42 0.1053
ADG 443 0.61 0.328
ZSD 0.60 5.64
SPM 0.34 0.57
Table 3 : Main Estimated Accuracy Numbers (EAN) as defined in section I.6
Chlorophyll-a (CHL): The algorithm validation shows a reasonable relationship between in-situ measurements and chlorophyll concentration. The statistics show a low bias with acoefficient of determination (r2) of 0.79.
Reflectance (RRS): a good relationship is obtained for all bands (r2 greater than 0.7) with alow bias.
For other optic products (KD, BBP, ADG, ZSD , SPM) the number of matchup with in-situ isless significant. It means results of validation should be carefully considered except for Kdwhich demonstrates a good correlation (R2 is 0.8) but with a slope/offset showing somelimitations.
In complement to this off-line validation, on-line validations are automatically generatedduring operational processing.
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II PRODUCTION SYSTEM DESCRIPTION
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I.3 Production Centre Name: OCTAC
The OCTAC (Ocean Colour Thematic Assembly Centre) is the CMEMS component providingOcean Colour products based on satellite observations.
The main objective of OCTAC is to build and operate a European Ocean Colour Service formarine applications providing Global, Pan-European and Regional (NW Shelves, Arctic,Baltic, Mediterranean, Iberian-Biscay-Ireland (IBI) and Black Seas) high-quality ocean colourcore products.
ACRI-ST Company is part of the OCTAC and provides the Global products.
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I.4 Production System Name: OC-ACRI-NICE-FR
In the OC TAC operations, ACRI-ST Company provides near real-time (NRT), delayed mode(DT) and Reprocessed (REP) Global products.
Global products (see Table 2) are based on the Copernicus-GlobColour processing chain (seesection I.5).
The processing chain starts from official level2 products (SeaWIFS, MODIS, VIIRS, MERIS,OLCI-S3A) of spatial agencies (NASA, ESA). After the interruption of SeaWiFS in late 2010 andMERIS in April 2012, the NRT production relies at present on VIIRS, MODIS and OLCI-S3Asince beginning of July 2017. During 2018, it will benefit of the Sentinel-3 program withOLCI-S3B.
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I.5 The Copernicus-GlobColour Processor
The GlobColour project has been funded by the ESA Data User Element Program to develop asatellite based ocean colour data service to support global carbon-cycle research. TheGlobColour dataset has been then improved in the frame of the OSS2015 FP7 R&D projectand is continuing in the frame of CMEMS.
The Copernicus-GlobColour (release 2017.0) processor is the computation element of theGlobal CMEMS-OCTAC system. Its function is the transformation of EO level 2 products fromindependent instrument/missions into a single or merged level 3 product.
The level-2 products are transformed after the sensor-specific preprocessing to a global ISINgrid with a resolution of 1/24° at the equator (i.e. around 4.63 km). This binning is separatelyapplied to each level-2 input product for each instrument. Outputs are intermediate spatiallybinned level-3 products for each instrument, also called level 3 at track level.
The term binning refers to the process of distributing the contributions of the level-2 pixels insatellite coordinates to a fixed level-3 grid using a geographic reference system.
When images of different resolutions are to be accumulated together, if the spatial coverageof each pixel is not taken into account, the importance of the image of the highest resolutionis largely predominant over the images of smaller resolutions; this may result in introducing abias in the final product.
Computing a flux value associated to each pixel may solve that problem. Assuming that thedata flux for each input pixel is constant, the resampling problem is actually reduced to theproblem of finding the set of pixels overlapping each level-3 bin, and then calculating therelative overlapped area.
This approach not only allows to properly mix data of various resolutions together, it alsoallows to distribute data properly among different level-3 bins as the input image pixel isusually overlapping several of them. This also makes it possible to produce level-3 data at ahigher resolution than the input data with no "holes".
The algorithm implemented in the Copernicus-GlobColour processing chain uses the fastSutherland-Hodgeman area clipping. For more information on the algorithm used refer to["A fast flux-conserving resampling algorithm", available athttp://skyview.gsfc.nasa.gov/polysamp/].
The same binning algorithm is applied to each kind of input variables. Only the flags takeninto account when filtering the data are different. These flags are listed in the next sub-section.
Following this logic, the Copernicus-GlobColour processor is mainly composed of 4 separatemodules, namely:
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1. a pre-processor module2. a spatial binning module3. a merging module4. a temporal binning module
For each sensor, a pre-processing is foreseen just after extraction of the L2. This pre-processor serves for example in the case of MERIS and wherever requested to transform theL2 normalised water leaving reflectances into fully normalised remote sensing reflectance. Itcould be used also to apply cross calibration LUT to be in position to merge equivalent data.
The complete binning scheme for the production of the Copernicus-GlobColour ocean colourproducts is a three steps approach comprising spatial binning, data merging and temporalbinning.
In the frame of CMEMS, following spatial/temporal binning products are delivered:
- Global daily products at 4km (also at 25km and 100km for the Chlorophyll coreproduct)
- Global 8-days product at 4km. A year is split in periods of 8 days (46 for one year, thefirst one is covering 1st January to 8th of January, the last 8-days period is composedof 5 days (or 6 days for bissextile year). The 8-days product corresponds to the meanvalue of the daily products.
- Global monthly product at 4km (also at 25km and 100km for the Chlorophyll coreproduct). The monthly product corresponds to the mean value of the daily products(28 to 31 days).
At present all available sensors are merged, except OLCI-S3A which is delivered as a singlesensor product in a first step.
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II.1.1 Product processing mode and temporal coverage
In the frame of CMEMS, Copernicus-GlobColour products distributed by ACRI-ST are:
• Reprocessing (REP) products covering the period [Sep-1997-Dec-2016]
o The REP is extended each year, of one year to consolidate the time series andto benefit of inputs based on the last reprocessing of the agencies. Forinstance the the 4th MERIS reprocessing planed by ESA during 2017 or theSentinel 3A (first acquisition in March 2016) will be used for the next releaseof the REP. The REP is the guaranty that the same Copernicus-GlobColourprocessor (with same parameters) has been used to process the time series.
o The Copernicus-GlobColour REP is at present based on SeaWIFS, MODISAqua, MERIS and VIIRS NPP sensors.
• Near real Time (NRT) product covering [Jan-2017-present]
o The NRT products are operationally produced every day and provide the bestestimate of the ocean colour variables at the time of processing. Theseproducts are generated using the best auxiliary data available (meteorologicaldata) at the time of the processing. The NRT products are based at present onMODIS Aqua (named QL processing), VIIRS-NPP (named QLP or QL processing)and OLCI-S3A (named NRT processing).
o Two versions of NRT products are produced by OCTAC: 1) a first version isproduced soon after the satellite path; this version is meant for providing theuser with products as soon as possible (defined NRT, in the product file name);2) the NRT products are then updated 31 days later, using updated ancillaryinformation, The new files (defined DT, in the product file names) supersedethe previous NRT files as soon as are available. It corresponds to “RF” NASAprocessing or “NTC” ESA Copernicus processing.
o First version of the NRT products (NRT files) may be affected by errors withrespect to DT files (second version). Nevertheless, these NRT data are veryuseful for NRT coastal application, water quality monitoring, fisheryapplication, and in situ data acquisition. This version of the product isdelivered within one day from the satellite acquisition, compatible with theless-than-24 hours requirement.
o The first version of NRT products are understood to be of reduced quality dueto unavailability of ancillary data (precision orbital data and meteorologicalfields used for atmospheric correction) used to produce L2 data. Therefore OCdata are re-processed in delayed mode using consolidated L2 products toobtain consolidated products 31 days later. The error associated to the DT filesshould be lower than the NRT one. However the Figure 1 shows that the
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difference at global level is quite small except if there is a change at the levelof upstream (the 1st of September NASA has adopted a new processing calledR2014.0.1 which explains difference observed).
Figure 1 : The graphic shows the difference observed between NRT & DT chlorophyllproducts. The red lines (median, Percentil 16 & 84) correspond to the NRT products and blue
line to the DT products. During July small differences are observed. During September thedifference observed are due to a NASA processing change (MODIS from R2014.0 to
R2014.0.1)
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III VALIDATION FRAMEWORK
The assessment of ocean colour product uncertainties faces several particularities. Onespecificity is the production of a broad set of quantities of various natures, some of themspectral in character (water leaving radiance or reflectance, inherent optical properties ofbackscattering and absorption, diffuse attenuation coefficients, chlorophyll-a concentrationetc …). Another specificity is that the current data base of associated field observations isfairly restricted in size (with the notable exception of chlorophyll-a), even more ifobservations obtained in near-real time are considered (this is partly due to methodologicalreasons). Moreover, the uncertainties associated with the field data depend on the quantityitself as well as from the sources, and are not always well constrained.
Two core products have been identified: remote sensing reflectance (R rs or its normalisedversion nRrs) and chlorophyll-a concentration (Chla), for which assessment is particularlymandatory. The assessment of other products depends on available (and scarser) data setsand is conducted on a best-effort (BE) basis. Due to the considerations given above, theassessment of the OCTAC products is essentially based on an off-line context (assessment offull time series; i.e., Class 4), and not in a real-time framework (with some exceptions).
The OC-ACRI-NICE-FR subsystem performance and the associated product quality arescientifically assessed in different ways:
1. The off-line validation refers to results obtained via assessment of an archive ofproducts corresponding to a long-term series (Sep-1997-Dec-2016) . It should benoted that this time series is fully consistent in terms of processor with the NRTprocessing
2. The on-line validation refers to indicators computed for the new products that aredelivered each day. The indicators are based on NRT product and updated whenconsolidated DT product are available (31 days after).
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I.6 Validation metric
Offline validation activity results are here provided in terms of the metrics defined in Table 4used to compare the estimated (satellite-based) dataset XE to a reference (in-situ) dataset XM.For log-normally distributed variables (ie. Chl-a, bbp; adg, kd) both datasets are log-transformed prior to computing the metrics.
Metric Name anddescription
Definition
CHL-SURF-D-NC-INS-AVAIL-GLOB1
Number of matchup (N)N = Number of matchups
CHL-SURF-D-CLASS4-INS-MEAN-GLOB1
Estimated dataset mean (XE) X
E=
1
NXi
E
i=1
N
∑CHL-SURF-D-CLASS4-INS-MEAN-GLOB1
Reference dataset mean (XM) X
M=
1
NXi
M
i=1
N
∑
CHL-SURF-D-CLASS4-INS-LR_SLOPE-GLOB1
Type-2 slope (S)
CHL-SURF-D-CLASS4-INS-LR_OFFSET-GLOB1
Type-2 intercept (I)
MEXSX=I ⋅−
CHL-SURF-D-CLASS4-INS-LR_CORR-GLOB1
Determination coefficient (r2)
CHL-SURF-D-CLASS4-INS-RMSD-GLOB1
Root Mean Square Error () ( )
2/1
1
21
−∑
N
=i
Mi
Ei XX
N=Ψ
CHL-SURF-D-CLASS4-INS-CRMSD-GLOB1
Centre-pattern (or unbiased) Root Mean
Square Error(∆)
CHL-SURF-D-CLASS4-ALL-BIAS-GLOB1
Bias (δ) ( )∑ −
N
=i
Mi
Ei XX
N=δ
1
1
1 The variable name (CHL) vary according the products (see Error: Reference source not found)
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Table 4: List of metrics (Type-2 regression)
Notes:
Type-2 regression (called also orthogonal regression) is used instead of minimisingthe vertical distance between independent data and linear fit (as in Type-1regression). It minimises the perpendicular distance between independent data andlinear fit. Type-1 regression typically assumes the dependent variable (in situ data) isknown infinitely well, when in reality, the in situ data are also affected byuncertainties (e.g. problems with in situ data sampling techniques) that are difficultto quantify.
A slope(S) close to one and an intercept(I) close to zero is an indication that themodel compares well with the in situ data.
The Centre-pattern (or unbiased) Root Mean Square Error(∆) describes the error ofthe estimated values with respect to the measured ones, regardless of the averagebias between the two distributions.
the RMSE is thus the distance, on average, of a data point from the fitted line,measured perpendicular to the regression line. The RMSE is directly interpretable interms of measurement units, and so is a better measure of goodness of fit than acorrelation coefficient.
The bias indicator is also directly interpretable in terms of measurement units. Thesquaring is not done so negative values can cancel positive values. The bias is not agood indicator of average model performance and might be a misleading indicator ofaverage error. It should be carefully considered taking into account the scatter plot.
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I.7 In-situ Measurements used for the validation
The Copernicus-GlobColour products include a comparison of MERIS, SeaWiFS and MODIS-Aqua, VIIRS compared to the following in-situ databases available in MERMAID (see Table 5).The Principle Investigators (PI) are here duly acknowledged for providing these in-situmeasurements.
Dataset PI Location PeriodAAOT (AERONET-OC)
Giuseppe Zibordi North Adriatic Sea 45.314N 12.508E
2002041920150720
Abu Al-Bukhoosh (AERONET-OC)
Giuseppe Zibordi Arabian Gulf25.49N 53.14E
2004092720080608
Algarve John Icely Sagres, Portugal36/37N 8W
2008100420101015
BioOptEuroFleets Giuseppe Zibordi (ρw)J.F Berthon (IOP)Elisabetta Canuti (Chl)
Black Sea 42/45N 37/31E
2011070120110712
BOUSSOLE David Antoine W. Mediterranean 43.367N 7.9E
2003090620121231
Bristol Channel andIrish Sea
David McKee Bristol Channel &Irish Sea51/54N -3/-4E
2001080620060810
BSH BSH North Sea 2003072820040818
BSHSummerSurvey Holger Klein North sea English Channel 49.0/62.5N -6.0/8.25E
2008072220110825
CALCOFI CALCOFI PI2 California 2002070220080126
CaliforniaCurrentGreg Mitchell Mati Kahru
California 32.2/34.8N120.3/123.8W
2006051120081027
CASESSimon belangerSelima Ben Mustapha
Beaufort Sea69.52/71.96N 123.22/138.93W
2004060420040802
ChesapeakeBayMichael Ondrusek Chesapeake Bay
38.70/39.00N -76.30/.76.50W
2008010420081023
CoveSEAPRISM(AERONET-OC)
Greg SchusterBrent Holben
Chesapeake Lighthouse36.90N 75.71W
2006042020140402
DYFAMED Funding projects: EUROSITES (grant 202955), MOOSE, EMSO and FIXO3 (grant312463).
Mediterranean NWPacific Tropical South
2002012020111211
EastEngChannel Hubert Loisel Eastern English channel49.4/51.4N 0.0/3.0E
2004031620040630
FrenchGuiana Hubert Loisel French Guiana4.7/5.0S 51.9/52.3W
2006071020060711
2 CALCOFI PI : The California State Department of Fish and Wildlife, NOAA,National Marine Fisheries Service, SouthwestFisheries Science Center, and the University of California, Integrative Oceanography Division at the Scripps Institution ofOceanography, UCSD.
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Dataset PI Location PeriodAAOT (AERONET-OC)
Giuseppe Zibordi North Adriatic Sea 45.314N 12.508E
2002041920150720
Abu Al-Bukhoosh (AERONET-OC)
Giuseppe Zibordi Arabian Gulf25.49N 53.14E
2004092720080608
Algarve John Icely Sagres, Portugal36/37N 8W
2008100420101015
BioOptEuroFleets Giuseppe Zibordi (ρw)J.F Berthon (IOP)Elisabetta Canuti (Chl)
Black Sea 42/45N 37/31E
2011070120110712
BOUSSOLE David Antoine W. Mediterranean 43.367N 7.9E
2003090620121231
Bristol Channel andIrish Sea
David McKee Bristol Channel &Irish Sea51/54N -3/-4E
2001080620060810
BSH BSH North Sea 2003072820040818
BSHSummerSurvey Holger Klein North sea English Channel 49.0/62.5N -6.0/8.25E
2008072220110825
CALCOFI CALCOFI PI California 2002070220080126
CaliforniaCurrentGreg Mitchell Mati Kahru
California 32.2/34.8N120.3/123.8W
2006051120081027
CASESSimon belangerSelima Ben Mustapha
Beaufort Sea69.52/71.96N 123.22/138.93W
2004060420040802
Gageocho(AERONET-OC)
Jae-Seol ShimJoo-Hyung Ryu
China Sea 2011101720120517
Galata (AERONET-OC)
Giuseppe Zibordi Black Sea 2014041220150513
Gloria(AERONET-OC)
Giuseppe Zibordi Black Sea44.60N 29.36E
2011012520150624
GotSeaprism(AERONET-OC)
Brent Malaka straight 2012031620141210
Gustav Dalen Tower(AERONET-OC)
Giuseppe Zibordi Baltic Sea 58.59N 17.47E
2005060920151005
Helgoland(AERONET-OC)
Roland Doerffer North Sea54N 7.5/8.5E
2005042020060726
Helsinki Lighthouse(AERONET-OC)
Giuseppe Zibordi Baltic Sea59.95N 24.93E
2006051720150815
Ieodo(AERONET-OC)
Youngje ParkJoo-Hyung Ryu
China Sea 2013120120141130
IMR IMR Baltic 2004041420040429
KAUST(AERONET-OC)
Kaust 22.30N 39.10E 2012072820120728
LISCO(AERONET-OC)
Sam AhmedAlex Gilerson
Long Island Sound40.95N 73.34W
2009102220150526
LJCOVittorio Brando Lucinda Australia
18.52S 146.38E2009102820100613
Lucinda(AERONET-OC)
Thomas Schroeder Queensland, Australia 2009120320150820
MAREL Catherine Belin French Coast4 buoys at :
2000072020110529
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Dataset PI Location PeriodAAOT (AERONET-OC)
Giuseppe Zibordi North Adriatic Sea 45.314N 12.508E
2002041920150720
Abu Al-Bukhoosh (AERONET-OC)
Giuseppe Zibordi Arabian Gulf25.49N 53.14E
2004092720080608
Algarve John Icely Sagres, Portugal36/37N 8W
2008100420101015
BioOptEuroFleets Giuseppe Zibordi (ρw)J.F Berthon (IOP)Elisabetta Canuti (Chl)
Black Sea 42/45N 37/31E
2011070120110712
BOUSSOLE David Antoine W. Mediterranean 43.367N 7.9E
2003090620121231
Bristol Channel andIrish Sea
David McKee Bristol Channel &Irish Sea51/54N -3/-4E
2001080620060810
BSH BSH North Sea 2003072820040818
BSHSummerSurvey Holger Klein North sea English Channel 49.0/62.5N -6.0/8.25E
2008072220110825
CALCOFI CALCOFI PI California 2002070220080126
CaliforniaCurrentGreg Mitchell Mati Kahru
California 32.2/34.8N120.3/123.8W
2006051120081027
CASESSimon belangerSelima Ben Mustapha
Beaufort Sea69.52/71.96N 123.22/138.93W
2004060420040802
43.32N, 4.85E40.74N, 1.57E47.46N, 2.57W48.36N, 4.55W
MOBY Kenneth VossKent Hughes
Lanai, Hawaii20.822N, 157.187W
2002012320140114
MUMMTriOS Kevin Ruddick European Waters 27.35N/53.83N -11.98E/12.50E
2010091620010618
MVCO(AERONET-OC)
Hui FengHeidi Sosik
Massachusetts 41.30N 70.55W
2004022620150916
NOMAD NOMAD PI3 World wide 2007090619911203
Pålgrunden (AERONET-OC)
Susanne Kratzer Pålgrunden, Sweden58.75N 13.15E
2008070420151015
PlumesAndBlooms David Siegel California34.9/34.1N119.1/12.1W
2010032420020110
PortCoast Vanda Brotas Portuguese coast38.08/40.69N 8.79/10.50W
2012011920050428
REPHY Catherine Belin French Coast41.53/51.10N 9.79/5.10W
2002042920100125
SIMBADA Pierre-Yves Deschamps World wide 2001021420031015
3 NOMAD PI: Robert Arnone, William Balch, Ken Carder, Richard Gould, Larry Harding, Stan Hooker, Zhongping Lee, RuMorrison, Antonio Mannino, Greg Mitchell, Frank Muller-Karger, Norman Nelson, David Siegel, Dariusz Stramski, AjitSubramaniam, Jeremy Werdell
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Dataset PI Location PeriodAAOT (AERONET-OC)
Giuseppe Zibordi North Adriatic Sea 45.314N 12.508E
2002041920150720
Abu Al-Bukhoosh (AERONET-OC)
Giuseppe Zibordi Arabian Gulf25.49N 53.14E
2004092720080608
Algarve John Icely Sagres, Portugal36/37N 8W
2008100420101015
BioOptEuroFleets Giuseppe Zibordi (ρw)J.F Berthon (IOP)Elisabetta Canuti (Chl)
Black Sea 42/45N 37/31E
2011070120110712
BOUSSOLE David Antoine W. Mediterranean 43.367N 7.9E
2003090620121231
Bristol Channel andIrish Sea
David McKee Bristol Channel &Irish Sea51/54N -3/-4E
2001080620060810
BSH BSH North Sea 2003072820040818
BSHSummerSurvey Holger Klein North sea English Channel 49.0/62.5N -6.0/8.25E
2008072220110825
CALCOFI CALCOFI PI California 2002070220080126
CaliforniaCurrentGreg Mitchell Mati Kahru
California 32.2/34.8N120.3/123.8W
2006051120081027
CASESSimon belangerSelima Ben Mustapha
Beaufort Sea69.52/71.96N 123.22/138.93W
2004060420040802
SHOM Shom Mediterranean NWBiscay Gulf, North Channel
2002100720050617
Thornton(AERONET-OC)
Dimitri Vanderzande Belgian 2015040920151102
UscSEAPRISM(AERONET-OC)
Burton Jones California 2012020920150302
Wadden Sea Annelies Hommersom Wadden Sea52-53N4-6W
2006092120060307
WaveCIS(AERONET-OC)
Bill GibsonAlan Weidemann
Gulf of Mexico28.86N 90.48W
2010050420150520
Zeebrugge(AERONET-OC)
Kevin Rudick & Vanderzande
Belgian 2014030520150123
Table 5 : in-situ database used by Copernicus-GlobColour. Validation is basedon in-situ data and matchups available in the MERMAID database
(http://hermes.acri.fr/mermaid) developed and maintained by ACRI-ST, ARGANS andESA. The Principle Investigators (PI) are here duly acknowledged for providing in-situ
measurements.
Protocol:
To establish the match-up between satellite observation and in-situ the following protocol isused:
1. Time difference between the in situ record and the satellite overpass should notexceed ±12 hours.
2. For buoy/stations (where many in-situ measurements can be available) only the
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nearest in time is kept.3. For the Global product at 4km, a minimum of 5 valid pixels in a 3x3 pixel box centred
on the in situ location is required (pixel resolution at about 4km). Valid means pixelsfor which no “questionable”/confidence flags were raised (including sensor zenithangle greater than 60o or Sun zenith angle greater than 70°).
4. The matchup is based on the median value of the 3x3 window.5. The in-situ are compared to the level 3 satellite observation products in the sinusoidal
projection. The alternative will be to use the level 3 products delivered in CMEMS inthe plate-carre projection, but with drawbacks on high latitude where the 3x3 pixelbox is not well represented by this projection.
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I.8 Off-line validation
Validation/assessment activities can be performed at different levels:
• Make an extensive use of different external data sources for validation (MERMAID,Mazeran et al., 2012 [RD2])
• Assess the reliability of the products by intercomparison between sensors, andbetween various initiatives (GlobColour validation report [RD1], Maritorena et al.,2010 [RD1], Fanton d’Andon et al, 2008, [RD3])
• Assess the uncertainty estimates in addition to the core variables
• Monitor retrieval performance indicators (Maritorena et al., 2010 [RD1]).
• Monitor anomaly wrt climatology
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I.9 OLCI datasets qualification
This section provides an initial qualification for the OLCI datasets introduced in October 2017in the CMEMS catalogue, following the operational release of the L2 data stream byEUMETSAT on 5 July 2017.
The Sentinel-3A Product Notice – OLCI Level-2 Ocean Colour describes the OLCI currentProcessing Baseline (2.16) and Ocean Colour products, their quality, limitations, and productavailability. The Notice also provides guidance on the use of the products, including the useof flags to mask cloudy or unreliable pixels. In this operational data stream, the SystemVicarious Calibration gains has been implemented for OLCI Level-2 products to adjust to theTop-of-the- Atmosphere absolute radiance level for both NIR and VIS band ranges. Theapplication of the SVC gains enabled to reduce OLCI absolute radiometric bias in Level 2products when compared to inset measurements and other ocean colour missions.EUMETSat and ESA will adjust these SVC gains as more insitu data becomes available.
As a matchup analysis with the L2 operational data form 5 July 2017 onwards would not yieldmeaningful EANs, the qualification of the introduced OLCI datasets is based on thecomparison with Multi products currently in the CMEMS catalogue. The followingqualification analysis was thus performed only on data produced in July-September 2017(6/7/2017-15/9/2017 time range) at global level (see section I.17).
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I.10 On-line validation
For a representative set of dataset (see table Table 1, on-line products) the quality ofproducts delivered by OC-ACRI-NICE-FR are characterized by different indicators:
- Consistency of the product compared to a climatology
- Inter-comparison of the different single sensors RRS products.
- Estimation of the uncertainty computed by the GSM model [RD1] or AVW mergemethod
- For Chlorophyll products, comparison with measurement done by the bio-argo float.
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I.11 Upstreams used by OC-ACRI-NICE_FR
The products relies on the official level 2 products of NASA & ESA agencies (see Table 1). Dueto the degradation of the instruments (MODIS and VIIRS) the corresponding NASA processinghas been updated several times. At the date of this report, the current NASA release used forVIIRS is called R2014.0.2, and for MODIS R2014.0.1. The last release of the NASA processing is described here http://oceancolor.gsfc.nasa.gov/WIKI/OCReproc.htmlDuring 2018, it is planned to also ingest the ESA Copernicus OLCI-S3B products.
Sensor Product type Start Date End Date Reprocessing name and date of
availability
SeaWIFS GAC 4km Sep 1997 Dec 2010 NASA R2014 (20 Nov 2015)
MERIS RR 1km April 2002 8 April 2012 ESA 3rd reprocessing (1st July 2011)
MODIS AQUA
RR 1 km July 2002 8 June 2014
NASA R2014.0 (June 2015)
MODIS AQUA 1 km 9 June 2014 present NASA R2014.0.1 (1-Sep-2016)
VIIRS NPP 1 km January 2012
present NASA R2014.0 .2 (April 2016)
OLCI-S3A 1km 6 July 2017 present ESA Baseline 2.16 (6 July 2017)
Table 1: Upstreams level2 data used by ACRI-ST at the date of this report.
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IV VALIDATION RESULTS
The off-line validation results are mainly described in sections I.12 to I.16.
The period considered is 1998-Dec-2016 and products were derived from the official level2 products of agencies (see Table 1).
The On-line validation is described in section I.17.
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I.12 Estimated Accuracy Numbers
The Table below provides a synthesis of off-line validation results performed by OC-ACRI-NICE-FR obtained using the Globcolour REP data set [Sep-1997-Dec-2016] and in-situdescribed in section I.12
These numbers should be representative of the NRT products [Jan-2017-present] which arebased on the same algorithm as the REP products. These numbers are not provided for NRTproduct because only a limited insitu are available for this period. It should be noted also thatthe present NRT products relies on VIIRS, MODIS-A and OLCI-S3A, knowing VIIRS & MODISare both suffering of important degradations (see sectionI.17). It probably means a lowerquality.
Variable Metrics (see Table 4)
Name UnitsDecimalplaces
N r2 Slope Offset Bias RMSE ubRMSE
CHL mg/m3 2 649 0.79 0.93 -0.06 -0.05 0.28 0.28
RRS 412 sr-1 3 7358 0.86 1.04 -0.002 -0.002 0.005 0.004
RRS 443 sr-1 3 8934 0.81 0.99 -0.001 -0.001 0.005 0.004
RRS 490 sr-1 3 8108 0.77 0.92 -0.000 -0.001 0.004 0.004
RRS 555 sr-1 3 8848 0.84 0.94 -0.000 -0.001 0.004 0.004
RRS 670 sr-1 4 7210 0.74 0.85 -0.0001 -0.0006 0.0029 0.0028
KD 490 m-1 2 981 0.80 0.74 -0.28 0.02 0.18 0.18
BBP 443 m-1 4 51 0.42 0.61 -0.9125 0.0036 0.1053 0.1053
ADG 443 m-1 3 282 0.61 0.99 -0.024 -0.005 0.328 0.328
ZSD m 1 303 0.60 1.03 -2.15 -1.72 5.90 5.64
SPM g/m3 2 1586 0.34 0.61 -0.06 -0.18 0.59 0.57
Table 6 : Estimated Accuracy Numbers as defined in section I.6
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I.13 Validation of Chlorophyll results
Both chlorophyll and uncertainties estimates are validated through comparison with in situdata. Results for Chl are displayed on Figure 2.
Figure 2 : Matchups statistics for the GSM merged chlorophyll products.
(see acknowledgement section I.12)
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I.14 Validation of RRS results
Results about remote merged sensing reflectance delivered by OC-ACRI-NICE-FR aredisplayed on Figure 3.
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Figure 3 : Illustration of validation of Copernicus-GlobColour merged radiance at global levelusing in-situ.
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I.15 Validation of the non-algal SPM product
For the non-algal SPM parameter, the in-situ measurements available are in fact TSM (TotalSuspended Matter) measurements which include the algal component. The non-algal SPM isretrieved by the formula non-algal SPM = TSM - 0.234Chl-a 0.57 (in the procedure developedin Gohin (2011) the SPM relative to the chlorophyll is calculated by the formula: biologicalSPM= 0.234Chl-a 0.57).
Insitu Dataset Period
MERMAID PMLNorthSeaWEC 2002-04-17 to 2003-09-17
MERMAID REPHY 2002-05-23 to 2010-01-11
IFREMER Campagnes 1997-09-26 to 2010-06-02
IFREMER Quadridge 2 1998-01-06 to 2015-12-18
SOMLIT-INSU4 1998-01-08 to 2011-06-14
Table 7 : Insitu datasets used for the non-algal SPM product validation.
The following figure shows the scatterplot of satellite-derived SPM versus in situobservations. Most of these observations are from the coastal Ifremer REPHY Phytoplanktonnetwork. These data are mainly located in the Southern North-Sea, the English Channel andthe western Mediterranean Sea.
4 SOMLIT-INSITU stations considered : Wimereux_Point_C, Wimeruex_Point_L, Luc-Sur-Mer,Banyuls_Sola, Marseille_Frioul, Villefranche_Point_B
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The quality of this product through the seasons has also been assessed by comparison to theobservations at the Liverpool mooring station during the 2003-2010 period. Although thesatellite-derived SPM shows an overall good correlation with in-situ data, there is a cleareffect of the seasonal variability of the nature of SPM in its quality.
Mean seasonal cycle of satellite-derived algal SPM, non-algal SPM (CMEMS product), andin-situ filtered SPM for the period 2003-2010 at the Liverpool Bay station. In-situ SPM isestimated by Non-algal + algal SPM
The upper figure shows the mean seasonal cycles of in-situ filtered SPM, satellite-derived
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non-algal SPM (CMEMS product) and algal SPM at the location of the Liverpool station.
The evolution of the Turbidity (in situ): SPM (in situ) ratio through the season and theconsequences of this variability on the satellite SPM product at the Liverpool station hasbeen investigated in a publication submitted to Remote Sensing of Environment (Binti JafarSidik et al). It appeared that this ratio reaches values around 1. in winter (when small mineralparticles are dominant) and about 0.5 in summer (large aggregates occur after thephytoplankton blooms). As the satellite-derived SPM is better related to turbidity (which isan optical property) than to SPM, we logically observe an underestimation in summer.Despite this flaw in the method, the non-algal SPM algorithm used in the OC TAC avoidsseasonal strong bias by switching from the green, in lowly turbid areas, to the redwavelength in moderately turbid waters using two mass-specific backscattering coefficientsadapted to summer and winter waters respectively.
Reference: Binti Jafar Sidik, M., Gohin, F., Bowers, D., Howarth, J., Hull, T., Relationship between Turbidity and Suspended Particulate Matter at a mooring station in a coastal environment: consequences for remote-sensing. Submitted to Remote-Sensing of Environment.
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I.16 Validation of the other Optic products
A validation of the other bio-optical products has been done at global level but the numberof matchup with in-situ is sometimes limited. It means results of validation below should becarefully considered.
Figure 4 : Validation at global level, Bio-Optic variables.
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I.17 On-line validation
The on-line validation is done using NRT products, and refined when consolidated DTproducts are available 31 days after.
The online validation results are daily monitoring for all the OCTAC products. Theclimatology comparison results are regularly reported on the CMEMS web portal. Moredetailed results are also updated at daily basis on the OCTAC Quality/Control website.
The indicators used by OC-ACRI-NICE-FR for on-line validation are based on the followingindicators:
• Consistency of the daily product compared to climatology: a daily climatology hasbeen computed in an initialization step. A pixel at day j, is computed considering allthe day j of the period [1998-2014] plus an additional temporal window of j+-5 days.This sliding window allows obtaining more representative statistics. The spatialresolution of this climatology is 4km and based on the Copernicus-GlobColour REP.This daily climatology (based on the same algorithm as the NRT product) is used tocompare each pixel with the corresponding daily product taking into account theclimatology variability. This allows classifying the pixels according their consistencywith the climatology (see Error: Reference source not found). From these Qualityindex maps, a time-series is derived to show consistency along the time (see Error:Reference source not found).For OLCI-A, the comparison with the climatology shows an acceptable correlationwith the band 412, 443, 490, 620 but large differences with higher reflectances 665,674, 681, 709 (knowing the reference for the last 2 ones should be normalized to beconsidered). For the reflectance 510, 560 and CHL OC4ME a bias is observed.
• Inter-comparison of sensors: maps of relative difference are computed between each pair of sensors (MODIS-1/VIIRS-N, MODIS-A/OLCI-A and VIIRS/OLCI-A). This relative difference is computed on a daily basis and time series of the median value are derived. The plot about MODIS & VIIRS (see Figure 8) illustrates the know issues of these two sensors which are discussed here http://oceancolor.gsfc.nasa.gov/WIKI/OCReproc.html:
• VIRRS-NPP: http://oceancolor.gsfc.nasa.gov/cms/reprocessing/OCReproc20140VN.htm
• MODIS-Aqua (MODIS-AQUA (launched in 2002 and developed for a six-year design life) : http://oceancolor.gsfc.nasa.gov/cms/reprocessing/OCReproc20140MA.html
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As a consequence, the OLCI inter-comparisons with MODIS-A or VIIRS-N are affectedby these issues. It shows reflectances (see Figure 9) which slightly varied for bands412, 443, 490 and 555. For 665 and 674 larger differences are observed. However,these outcomes should be considered very carefully especially for the periodconsidered (July to Mid-September 2017).
• Uncertainties: each pixel product is characterized by its quantity (e.q. chlorophyllconcentration) and an error which characterizes its uncertainty (available for NRT butalso REP products). Uncertainties are provided for all Copernicus-GlobColourproducts (coming from GSM algorithm or coming from sensor/productcharacterisation for others (i.e. RRS, SPM, KD and ZSD)). For GSM, the uncertaintiescomputation has been published and validated (Maritorena & al. 2011). For practicalconsideration about format and compression the error is expressed as a percentageof the product value. If this uncertainty is used, it is recommended to transform theuncertainty in the physical value (e.g an error of 300% with a chlorophyllconcentration of 0.01 mg/l means anl uncertainty 0.005 mg/l).For OLCI, no uncertainty is provided at present.
• Comparison with “NRT” insitu: for Chlorophyll products, the satellite chlorophyllproduct is compared with Chlorophyll fluorescence measurements done by the bio-argo float. Each day, the bio-argo float provides in an automatic way, quality-controlled and consistent biogeochemical data all over the world. These data arecompared on a monthly based to the OCTAC GLOB products using facilities providedby the SeasideRendezVous environment. It is planned to extend this experimentalapproach to compare the OCTAC ACRI reflectance products with the AERONET-OCNRT data.
Figure 5: illustration of the “pixel approach” for a daily product. The difference between the
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daily and the climatology is computed and weighted by the standard deviation of theclimatology.
Figure 6: Example of online validation statistics time series for the 2016-presentperiod, formerged Global CHL product. Upper panel 3D plot with the time on the x axis and the bins ofthe histogram of the Quality Index on the y axis; colour shows the percent occurrence withrespect to the total number of valid pixels (lower panel). Similar plots are daily updated for
all parameters and available on the OCTAC website.
Figure 7: illustration of the comparison of Chlorophyll products with measurement obtainedfrom the bio-Argo float products using facilities provided by the SeasideRendezVous
environment.
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Figure 8: MODIS and VIIRS level2 reflectance products are intercompared and the resultsreported on the OCTAC website. Based on monthly products, differences that can raise more
than 15% are sometime observed (median value of common pixels at global pixels)
Figure 9: MODIS & VIIRS level2 reflectance products are inter-compared with OLCI-S3A(median value of common pixels at global pixels). The results are daily updated and reportedon the OCTAC website. The differences can raise more than 50% for 665 and 674. For others
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RRS, MODIS and VIIRS provide contradictory results making OLCI a potential better reference(except for 490 where MODIS and VIIRS provide the same trend)
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V SYSTEM’S NOTICEABLE EVENTS, OUTAGES OR CHANGES
The table below tracks the main events of the operational system and external events aboutupstream (change in the processing chain of agencies products used as input of theoperational system).
Date Change/Event description Systemversion
other
15-Sep-2014 ESA/VIIRS-N upstream change:processing switch from R2013.1to R2014.0 NRT NASA processing
NASA-VIIRSN-R2014.0
19-March-2015
ESA/VIIRS-N upstream change:processing switch from R2014.0to R2014.0.1 NRT NASA processing
NASA-VIIRSN-R2014.0.1
19-March-2015
ESA/MODIS-A upstream change:processing switch from R2013.1to R2014.0 NRT NASA processing
NASA-MODIS-1 R2014.0
20-Nov-2015 ESA/SeaWIFS upstream change, SeawifsReprocessing R2014
NASA-SWF-R2014
April-2016
(CMEMS V2.0)
The Rep time series [1997-2014] isprovided including 8-days and monthlyproducts.
A non-mineral SPM has been added(IFREMER contribution)
GC-2015.0
16-Feb-2016 ESA has launched Sentinel-3A, firstimages from OLCI the 29 Feb 2016).
1-Sep-2016NASA/MODIS upstream change: reprocessing of the MODIS-Aqua 2014.0.1 [9-June-2014 -30-Dec-2016] (minor calibration update to reduce detector striping artefacts).
NASA-MODIS-A-R2014.0.1
April-2017 The Rep time series is extended to [1997- GC-2016.1 New
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(CMEMS V3.0) Dec-2016]. A first attempt to compareNRT products with NRT insitu (seesection I.17)
AERONET-OCinsitu datasets
are used.
Oct-2017
(CMEMS-V3.2)
6-July-2017: EUMETSAT released theOLCI L2 operational datastream: Sentinel-3A Product Notice –OLCI Level-2 Ocean Colour.
GC-2017.0 OLCI productsavailable as
single sensoronly (not yetused in the
mergedsensors
products)
Dec-2017(CMEMS-V3.3)
The Rep time series is extended to [1997-Dec-2016]. A consistant NRT time seriesis starting in Jan.2017.
Uncertainties data (stored at pixel level)have been updated to fix an encodingissue.
V.1.1.1.1.1.1.1.1A Chlorophyllclimatology dataset isnow provided.
GC-2017.2
Table 8 : main events of the operational system and external events.
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VI QUALITY CHANGES SINCE PREVIOUS VERSION
The REP is now covering the period Sep-1997 to Dec-2016. It is based on the last officialupstream data from agencies and processed with the same configuration. The validationresults have been updated at V3.2 using this REP and a larger dataset of insitu with morerecent data (AERONET, Boussole and Moby). It confirms the quality of the previous validationresults.
The NRT is now covering the period Jan-2017 to present. Since the interruption of SeaWIfs in late 2010 and then interruption of MERIS in April 2012, the NRT processing chain relies then on AQUA/MODIS and VIIRS/NPP data level2 provided by NASA. The quality is highly dependent of the MODIS and VIIRS calibration issues. It means it is difficult to appreciate the quality of the products for the 2 last years, also because a very limited number of insitu are available.
The ESA Sentinel-3A OLCI products is now distributed (6-July 2017, present) providing a new inter-comparison source useful to confirm the degradation of MODIS and VIIRS for some reflectances.
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VII REFERENCES
[RD1] GlobColour Full Validation Report, version 1.1, December 14, 2007,http://www.globcolour.info/validation/report/GlobCOLOUR_FVR_v1.1.pdf
[RD2] Maritorena, S., O. Hembise Fanton d’Andon, A. Mangin & D.A. Siegel. 2010. MergedOcean Color Data Products Using a Bio-Optical Model: Characteristics, Benefits andIssues. Remote Sensing of Environment.
[RD3] Mazeran, C., Barker, K., Lerebourg, C., Kent, C., Huot, J-P. (2012) MERMAID andODESA: Complementary Marine Bio-optical Processing and Validation Facilities.Proceedings of the 21st Ocean Optics conference – October 8-12 – Glasgow, Scotland.
[RD4] Fanton d'Andon O.H., D. Antoine, A. Mangin, S. Maritorena, D. Durand, Y. Pradhan, S.Lavender, A. Morel, J. Demaria, G. Barrot (2008) Ocean colour sensors characterisationand expected error estimates of ocean colour merged products from GlobColour, OceanOptics, Barga.
[RD5] OC3 the official NASA algorithm (O'Reilly, J.E., and 24 Coauthors, 2000: SeaWiFSPostlaunch Calibration and Validation Analyses, Part 3. NASA Tech. Memo. 2000-206892,Vol. 11, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, 49 pp.)
[RD6] Morel, A., Huot, Y., Gentili, B., Werdell, P.J., Hooker, S.B. and B.A. Franz (2007).Examining the consistency of products derived from various ocean color sensors in openocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sensing ofEnvironment, 111, 69-88, doi:10.1016/j.rse.2007.03.012
[RD7] Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon (2006). Estimation of lightpenetration, and horizontal and vertical visibility in oceanic and coastal waters fromsurface reflectance. Journal of Geophysical Research, volume 112, C06003, doi:10.1029/2006JC004007.
[RD8] Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, andturbidity observed from space and in-situ in coastal waters, Ocean Sci., 7, 705-732,doi:10.5194/os-7-705-2011, 2011.
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