1. Vicarious Calibration of Sentinel-3 Toward the Blending of Methods
GSICS Annual Meeting
20-24th March 2017, Madison, Wisconsin
Vicarious Calibration of Sentinel-3
Toward the Blending of Methods
Bertrand Fougnie
Camille Desjardins
+ support from CNES-DNO/OT/PE

2. Summary Sentinel-3 Optical Sensors S3A nominal on-board calibration
The Vicarious Calibration Tool Box
Calibration Results
OLCI and SLSTR
Cross-calibration over desert-sites
Interband and absolute calibration
Assessing the temporal monitoring
Feedbacks – Combination of Methods
Questions and support for discussion

3. Vicarious Calibration of Sentinel-3 Optical Sensors

4. Sentinel-3 Sentinel 3 Sentinel-3A OLCI SLSTR
ESA mission - Operational needs of the European Copernicus Program
Oceanography and land monitoring
S3A : launched in February 2016
S3B : to be launched in November 2017
Altimetry + 2 optical sensors OLCI and SLST (follow-on MERIS and AATSR)
OLCI = 21 bands from 410 to 1020nm, 300m, 1270km
SLSTR = 11 bands from 550 to nm, 500m/1km, 1400km
On-board diffusers + Black body (nominal) + Vicarious calibration methods
Sentinel-3A
OLCI
SLSTR

5. OLCI - Ocean and Land Colour Imager
OLCI is a follow-on of MERIS
21 VISNIR 300m-bands derived from a spectrometer
5 cameras 1270km - reduced sunglint : 12.6º tilt
Calibration wheel : 2 diffuser + spectral

6. SLSTR – Sea and Land Surface Temperature Radiometer
SLSTR is a follow-on of ATSR-1&2 and AATSR instruments
Scanning radiometer
2 views : nadir (1400km, OLCI overlap) and 55º backward (740km)
3 VISNIR + 3 SWIR 500m-bands
3 TIR + 2 fire 1km-bands
Blackbody + VISCAL targets

7. Nominal Calibration Nominal calibration = on-board calibration OLCI
Spectral diffuser (monthly) :
spectral calibration using Erbium-doped “pink” diffuser
spectral calibration using diffuser and Fraunhofer lines
check the spectrometer response, adjust the definition of bands
Primary diffuser (every 2 weeks) :
optical PTFE panel covering the field-of-view
field-of-view characterization + reflectance calibration
Secondary diffuser (every 3 months) :
same optical PTFE
degradation monitoring
SLSTR
VISCAL unit (every orbit at sunrise) :
same optical PTFE as OLCI
calibration and monitoring of visible bands
Black bodies (every scan) :
2 highly stable references hot (305ºK) and cold (265ºK)
calibration of TIR bands
OLCI Calibration wheel

8. Validation of the radiometry
Additional validation will be performed
Secure the nominal calibration
Detect early anomalies on calibration parameter
Detect possible improvement on calibration or radiometry
Validate the accuracy to a wide range of targets
Multi-method approach :
If every method tries to evaluate the same instrumental calibration, each method is also sensitive to different instrumental radiometric behavior
e.g. variation within the field-of-view, variation with time, spectral consistency, linearity behavior, straylight, spectral knowledge, saturation…
A good consistency at the end would be a contribution to the validation of the whole radiometric instrumental behavior, including of course the absolute calibration

9. Operational Configuration for Sentinel-3
S3ETRAC / SADE / MUSCLE
Operational Environment = S3ETRAC + SADE + MUSCLE
1/ S3ETRAC = Extraction and Selection of Measurements = PREPROCESSING
reading of S3 images, selection of relevant data, extraction of data
2/ SADE = Measurement & Calibration Data Repository = DATABASE
3 steps : measurements // elementary calibration and synthesis
Various methods for VIS-NIR-SWIR range
Easy data management & traceability
product identifier, calibration version, SADE identifier
acquisition conditions : dates, geometries, meteorological data
tool version, processing date and parameters…
3/ MUSCLE = Multiple Method Calibration tools (Front-end Graphic) = CALIBRATION
3 steps : insertion // calibration // synthesis
Common calibration tools for all sensors

10. Map of Calibration Approaches
in SADE/Muscle
Pseudo Invariant Calibration Sites (PICS)
use of 20 desert sites in Africa/Arabia [Lachérade et al., 2013]
reference = one sensor (i.e. MODIS or MERIS) or one date
geometrical matching (no simultaneity req.) + spectral interpolation [Lachérade et al., 2013]
cross-calibration/trending for all REFLECTIVE bands
additional use of 4 snowy sites (inc. Dome C) for VIS-NIR bands [Six et al., 2004]
Oceanic Oligotrophic Sites (very clear non-turbid scenes)
Strict selection : very clear + non-turbid situations for atmosphere + surface
Rayleigh [Hagolle et al., 1999, Fougnie et al., 2010]
reference = Rayleigh scattering (~90% of TOA signal after selection - predictable)
absolute calibration over a wide range of the fov (exc. sunglint) for VISIBLE range
Sunglint [Hagolle et al., 2004; Fougnie 2016]
Reference = one spectral band (red band ~ nm)
Interband for all REFLECTIVE bands wrt the reference band
Cloudy Sites (DCC - deep convective clouds)
Strict selection of DCC [Fougnie and Bach, 2009; Fougnie 2016]
Interband for VISNIR bands
All are statistical approaches
Result is Measured / Simulated ratio

11. Validation OLCI and SLSTR OLCI (additional)
Validation of OLCI&SLSTR calibration though SADE/MUSCLE
All methods were already tested over many sensors, including MERIS
OLCI and
SLSTR
Deserts
Sun Glint
Clouds
DCC
OLCI
(additional)
Snow
Rayleigh

12. Chronology Launch in mid-February 2016
First data available for calibration : beginning of March
Mid-Term Review (MTR) in End of April 2016
MTR : Only 1.5 month available for vicarious calibration
A first evaluation was possible
These preliminary results were very relevant
Absolute bias, interband consistency, temporal evolution
These conclusions were confirmed and consolidated at IOCR
In Orbit Calibration Review (IOCR) in 1st July 2016
Confirmation of the results on ~3 months of data
Note : the best time series available for desert sites
Swap of level-1 processing from the prototype (IPFP operated in ESTEC) to the operational level-1 processing chain (IPF operated in EUMETSAT and ESRIN)
Since IOCR
time series are longer
no significant changes on the vicarious results
re-analysis of the early weeks of the mission to track ageing  successful

13. Temporal consistency at IOCR MTR
Stability as seen by cross-calibration over desert sites
Very good stability after adjustment made before Mid-Term Review (dashed line)
Oa8-665
Oa18-885 at
IOCR
S1-555
S3-865
MTR
S6A-2225
S5A-1600

14. Temporal consistency Stability of the radiometry after IOCR MERIS
S2-659nm
MODIS

15. Temporal consistency Derived from the primary on-board diffuser 1%
Ageing observed by the diffuser
Can be trust the diffuser and implement the correction as baseline ?
Noise on the absolute trending (diffuser BRDF)
Bands are increasing, other decreasing
Can we use vicarious calibration to conclude ?
Derived from the primary on-board diffuser 1%

16. Temporal consistency OLCI 7-620 time series
Is it possible to extract very small instrumental drift from desert time-series ?
Usual time series of calibration results : dispersion is too high !
Interband approach wrt a reference band (620nm)
OLCI time series
OLCI is the time reference
Time series are arbitrarily shifted up/down for clarity
Time unit = 4 days

17. Temporal consistency
Is it possible to extract very small instrumental drift from desert time-series
Usual time series of calibration results : dispersion is too high !
Interband approach wrt a reference band (620nm) : limitation
On-going analysis based on an “interband to closest band” approach : spectral correlation

18. Temporal consistency
How to improve the result ? Combination ! (that’s not blending)
MTR = some updates on the CCDB (parameters)
Time series were not homogeneous
Before MTR
Desert : nice cover of the time series  very suitable to assess the trending
DCC : only 2 dates  not possible to assess the trending
 Use of Desert sites
After MTR
Desert : ~no data before October  poor information for the trending
DCC : not so many matchups, but at different dates on the time series
 better for the trending estimation
 Use of DCC

19. Temporal consistency Results : camera#4
Same observation for cameras 3 and 5 19

20. Consistency with other sensors
PICS-Desert sites : References
MERIS (best spectral interpolation for VISNIR) + MODIS (GSICS reference)
S2A-MSI (Sentinel) + AATSR (consistency with Envisat)
Classical approach [Lachérade et al., 2013] + double difference (when few matchups)
VISNIR : bias between OLCI/SLSTR/AATSR and MERIS/MODIS/S2MSI
SWIR : bias between SLSTR and MODIS/S2MSI/AATSR
OLCI
SLSTR

21. Spectral consistency Various methods very well agree OLCI SLSTR
Interband methods (clouds, sunglint), and Desert/Rayleigh : all renormalized
OLCI : perfect spectral consistency <1% (exc. Absorption bands, and perhaps 1020)
SLSTR : within 2% for VISNIR, but very important bias on SWIR bands
OLCI
SLSTR
Normalization
mean[ 7-620; 8-665; ; ]
Normalization
S2-659

22. Consistency between all methods
Multi-method comparison
Interband methods (clouds, sunglint), and Desert/Rayleigh : all renormalized
OLCI : absolute residue ~2% (exc. Absorption bands, and perhaps 1020)
SLSTR : residue of 3% for VISNIR, but very important bias on SWIR bands (10 to 40%)
OLCI
SLSTR
Normalization for interband : 1.025
Normalization for interband : 1.042

23. Consistency within field-of-view
No large variation detected within field-of-view
OLCI = pushbroom with 5 cameras SLSTR = scanner
Interband over white target validated : some residues to be explained
Nevertheless : variation of the ISR with detector number for OLCI
to be considered to explain residues on Rayleigh scattering and DCC
OLCI
OLCI
SLSTR
Oa2-412
Oa8-865
S3-865
DCC
Desert-MERIS
Desert-MODIS
Oa16-779
Oa6-560
S1-555
DCC
Rayleigh
Rayleigh

24. Consistency within field-of-view
Comparison between methods would help to identify errors (need statistics)
8-665
4-490
Rayleigh
8-665
4-490
DCC
8-665
4-490
Desert

25. Combining methods as seen by S3A
Results derived from various methods were important in IOCR for
OLCI&SLSTR :
Confirm a light absolute bias in VISNIR (about 2.5%) [Desert, Rayleigh]
OLCI :
Provide confidence on the on-board diffuser [Desert, DCC]
Check the temporal stability of the instrument [Desert, DCC]
Provide high confidence on the spectral consistency [Desert, DCC, sunglint]
Investigation on the variation within fov [Rayleigh, Desert, DCC]
SLSTR :
Confirm a large absolute bias in SWIR (about 10 to 30%) [Desert, Sunglint]
Additional results are foreseen
Check the temporal stability [Desert, Sunglint]
Check the variation nadir/oblique [Rayleigh, Desert]
But that’s combination, still not merging !

26. Combination of Methods toward the Blending ?

27. Observation Several calibration methods could be available
Cloud-DCC, Moon, PICS-Desert, Rayleigh, Sunglint, PICS-Antarctica, SNO, Ray-matching…
Implement several methods will provide
various results which will in general differ
sometimes consistent, sometimes not at all
What’s the definition of « consistant » for GSICS needs ?
GSICS needs to define the target in term of « self-consistency »
Self-consistency of calibration results will differ because :
theoretical performance of the method : bias and noise
properties /representativity of the matchups
known contribution from the sensor and considered on the calibration method (e.g. SBAF)
known contribution from the sensor but not considered (e.g. homogeneity of ISR)
unknown contribution from the sensor (or level-1)

28. Observation
It is often called “calibration error” a radiometric artifact which is not a calibration error
 Straylight, spectral rejection, non-linearity, variation with scan, polarization…
They are radiometric errors, but not calibration errors
They are radiometric biases, but varying with every different situation
They are not full instrumental biases
 What do we mean by “calibration” ?
For some of us, this means “global adjustment of level-1”
We may empirically correct the bias, but it could not be purely a calibration error
a good instrument : all radiometric artifacts are well controlled
 May often means good engineering
a good calibration : consistency on results wrt the prime calibration
May often means good instrument
GSICS : to be considered when selected a reference (metric and scoring)
Blending is not a fate, if a single method remains the best estimation
GSICS has to face the way to consider this in order to provide to users the most relevant results
 Difficult to adopt a standardized unique method (approach ?)

29. Radiometric artifacts or
DCC
Moon
Rayleigh
Calibration units
GSICS
Blending
Strategy
Evaluation & Scoring
Investigation
Limitations or
discrepancies
Radiometric artifacts or
Method artifacts
Diagnosis
3/ Investigation
Loop
Full
consistency
Limitations
GSICS Standard
Blind blending
GPRC modulated
blending
1/ Blending
Template
2/ Optimized
Blending

30. To be evaluated for each band
The Synergy matrix
Sensor to calibrate
To be evaluated for each band
Uncertainty from implemented method
(depending on data sampling)
Uncertainty from sensor
Characterization
to be addressed
DCC
Moon
PICS-desert
PICS-snow
SNO
Rayleigh
Sunglint
Spectral response knowledge
Straylight
Linearity
Polarization
Radiometric noise …
Trending
Absolute
Interband
Cross-calibration …

31. Best method or weighted mean Radiometric explanation
The Synergy matrix
Sensor to calibrate
To be evaluated for each band
Uncertainty from implemented method
(depending on data sampling)
Uncertainty from sensor
Characterization
to be addressed
DCC
Moon
PICS-desert
PICS-snow
SNO
Rayleigh
Sunglint
Spectral response knowledge
Straylight
Linearity
Polarization
Radiometric noise …
Trending
Absolute
Interband
Cross-calibration … 1
One
calibration coefficient
per band per date
Best method or weighted mean
GSICS Output
Radiometric explanation

32. Combination of Methods

33. Combination of Methods
PICS-Desert
Moon
DCC
SNO
Rayleigh
PICS
Snow
Sunglint
DIY Calibration activities
« Calibration, that’s hobby… » (Dave Doelling)

34. DIY is fine, but GSICS needs operationality Blended Method GSICS
House-blend
DIY is fine,
but GSICS needs
operationality

35. Discussion – Questions
Derive results from various methods
How to define the mean value, or weighted value ? How to define corresponding weights ?
Consider theoretical accuracy of methods
Consider the sensor behavior
If results are consistent between methods
Do we keep the best theoretical method ?
Do we derive a blend through a weighted mean in order to reduce residual bias ?
This is the job of every agency or GPRS
If results are not consistent between methods
How discrepancies will be interpreted ? Ignored ?
Will GSICS try to understand what radiometric artifacts could be ?
If not solved/solvable, do we propose all results and ask users their own selection
Ideally not recommended
But may help various users  rename to GSICS adjustment
instead of calibration (because we do not adjust calibration but something else)

36. Blending methods as seen by S3A
Which calibration result could we recommend based on the analysis of OLCI calibration during the 1st year in orbit ?
Inter-calibration over desert (MERIS) :
If we want to optimize the continuity with MERIS or the historical archive
Calibration over Rayleigh scattering :
If we want to optimize the performance for ocean color application
Calibration over DCC:
If we want to optimize the performance
for clouds retrieval
Blending of ~50% desert and ~50% DCC :
If we want to optimize the level-1 calibration
at the state-of-the-art
Even for OLCI/S3A, which can be considered as a « very good » sensor, the wrap-up is not obvious

37. « This is not Science, this is Art… » (again Dave Doelling)
Wrap-up
In general, derive results from various (accurate) methods = provide different information from the radiometry
In general, results will slightly/largely differ
Probably, no universal method can be defined in advance for blending, but an approach was proposed
At least at the beginning, this would be a case-by-case analysis and conclusion
GSICS must define how the various calibration sets will be used
Unclear information toward users may endanger the future of calibration correction proposed by GSICS
« This is not Science, this is Art… » (again Dave Doelling)

38. That’s all Folks !

39. The GSICS paradigm
Tim’s car
GSICS’s vehicle
when WG in US