Earth Online - MERIS US Workshopearth.esa.int/workshops/esameris08/presentations/VicCal... · 2018....

MERIS US Workshop, Silver Springs, 14th July 2008

MERIS US WorkshopVicarious Calibration Methods and Results

Steven Delwart


Recent results1. CNES methods

Deserts, Sun Glint, Rayleigh Scattering2. Inter-sensor Uyuni 3. MOBY-AAOT 4. Vicarious Adjustment methodologyOlder results1. Dark Water2. SIMBADA Ocean3. Rail Road Valley Playa4. Comparison with AATSR5. Comparison with SCIAMACHY6. Snow Targets

Presentation Overview


Deserts, Sun Glint, Rayleigh Scattering(Claire Tinel, Patrice Henry, Olivier Hagolle - CNES)

RayleighDeserts Sun glint

Calibration of MERISusing natural targets

These calibration methods are used operationally at CNESfor POLDER 1, 2, 3, VEGETATION 1 and 2,

for SPOT satellites, MERIS, FORMOSAT-2 and KOMPSAT-2


• Definitions– Meris on board calibration is the nominal calibration method– Meris is calibrated against t.o.a. reflectance

• k: Spectral band• DN : Digital Number (Meris Level 0, corrected for

instrumental defects)• r : Reflectance• Ak : Sensitivity of instrument for spectral band k

– Calibration results are expressed this way

(Method)ñ

ñ=

A

A=ÄA

predictedk,

1)measured(Lk,

L1k,

methodk,k

)(èñA=DN skkk .cos.



• Initial objectives :– In orbit vicarious calibration to assess :

• Multiangular calibration (detectors normalization in the f.o.v.)• In time calibration monitoring (on-board calibration verification)• Intercalibration of sensors

• Main characteristics of the requested sites :• Stable in time : no vegetation…• Easy to access : low cloud coverage, good atmospheric conditions• High reflectance : to reduce the impact of atmospheric effects• Low directional effects

⇒ Choice : Desert sites

Desert Calibration


• Sites selection :– Spatial uniformity

• Better than 2% for 100x100 km2 area• Statistics using Meteosat acquisitions

– Stability over time (seasonal effect)• Stability better than 20% (after atmospheric

effect filtering)• 1 year of Meteosat data (1 per day)

– Low directional effect• Directional effects less than 15%• 1 month of AVHRR data completed with

Meteosat data• Sites characterization :

– Ground truth measurements in Algeria (1993)

Desert Calibration Sites


• Systematic collect of satellite acquisitions over the 20 sites :Operational monitoring of CNES sensors calibration (on a monthly basis) :– SPOT(s)/High Resolution– SPOT(s)/Vegetation-1&2– PARASOL

Calibration monitoring and intercalibration of other sensors on a regular basis (through cooperation agreements with international space agencies)

– High resolution : Formosat-2 (Taiwan NSPO), Kompsat-2 (South Korea KARI)– Medium resolution : MERIS (ESA), MODIS (soon to come)

Archive of POLDER1&2, SeaWiFS, AVHRR, MISR, MODIS, ATSR2 data.• Storage in a data base :

• SADE data base : “Structure d’Accueil de Données d’Etalonnage”(Calibration Data Repository)

• Easy data management• Link between satellite measurements and calibration results (traceability)

• Nota : the SADE data base also includes calibration measurements over ocean, sunglint, clouds and snow covered sites.

SADE Data Base


– Compare two sensors :• One sensor as reference• Comparison at TOA level

Reference Sensor

Spectral resampling

Surface reflectancefor sensor 2

Surface reflectancefor reference sensor

Simulated ToAreflectance for sensor 2

Comparison :ΔAk

Sensor 2 MeasurementTOA

SURFACE ♦ Need to account for:• Directional effects• Atmospheric conditions• Spectral discrepancies

Atmospheric correctionto ToA reflectance

Atmospheric correctionto surface reflectance

Desert CalibrationMethod


• Directional effects :– Direct comparison of measurements in the same geometry (qs, qv, f)– Use of reciprocity principle to extend field of matching geometries

Desert CalibrationGeometry


• Atmospheric correction :– Atmospheric correction performed using SMAC and meteo data :

• Rayleigh scattering correction• Water vapour• Ozone• Other contributors : CO2, CO, NO2, CH4 (climatologies)

– Problem : aerosol correction…• Aerosol optical thickness t =0.2• Statistically solved through the use of a lot of data:

no significant bias, but dispersion for short wavelengths

Desert CalibrationProcessing


490

560

665

Cross-calibration with PARASOL as afunction of time (20 sites)

No significant variation with time

2002 2007

POLDER Comparison

865


• Observe the atmosphere over ocean (dark)• Absolute calibration of bands < 700 nm• Rayleigh scattering : > 80% of signal• TOA reflectance well predicted using:

– Successive Orders of Scattering– Smile effect correction

• Main error sources– ocean surface reflectance :

• predicted using climatology derived from SeaWiFS (1998-1999)• only over very stable oceanic zones (oligotrophic zones)

– aerosols : estimated using 865 nm band• Only optical thickness < 0.1 are kept

• Accuracy : 4 to 5% (3s) – 2 to 3 % (RMS)

Rayleigh Calibration


Choice of oligotrophic areas with 2 years of SeaWiFS datamade in 2001 with ACRI and LOV (CLIMZOO zones)

Rayleigh Calibration Sites


No discrepancy greater than 2.5%

0,9

0,92

0,94

0,96

0,98

1

1,02

1,04

1,06

1,08

1,1

400 450 500 550 600 650 700

wavelength

delta_Ak

Rayleigh/M98

Rayleigh CalibrationResults


• Sun glint calibration– observe the white reflection of the sun over the ocean surface– interband calibration w.r.t. a reference spectral band– TOA reflectance predicted using SOS code

– Main error sources• Reference band calibration errors• ocean surface : SeaWiFS climatology• aerosols : fixed model used (M98, AOT :0.08)

– daily SeaWiFS aerosol product used to discard cases when aerosol properties differ from reference model

• Accuracy : 3 to 4.5% (3s) - 1.5 to 2% (RMS)



Comparison of sun glint resultsusing different reference bands

0,985

0,990

0,995

1,000

1,005

1,010

1,015

1,020

400 500 600 700 800 900

Glint / 620 Glint / 665 Glint / 709

Sun Glint Calibration Results

σ∆Akσ∆Ak0.022

0.020

0.016

0.016

0.021

-

0.008

0.008

0.013

0.130

0.011

0.026

0.0240.029

0.031

0.042

0.031

0.013

0.010

0.013

-

0.004

0.007

0.010

0.017

0.019

1.0161.010885

1.0231.012865

1.0151.004778

1.0211.015753

-0.993708

1.0191.010681

1.0171.008665

1.007-620

1.0081.001560

0.9970.990510

1.0131.005490

1.0000.993442

1.0071.001412

Ref Band 708Ref Band 620Band


Still no discrepancy greater than 2.5%

0,9

0,92

0,94

0,96

0,98

1

1,02

1,04

1,06

1,08

1,1

400 500 600 700 800 900 1000

wavelength

delta_ak

glint / (620)

Rayleigh/M98Sun glintReference

spectral band

Error barscorrespondto standarddeviation of

results

Rayleigh / Sun Glint


• Conclusions– MERIS instrument seems well calibrated– Rayleigh + glint + deserts

• measurements agree with MERIS level 1 calibration (within 2 %)– Very good agreement between Rayleigh and sun glint calibration methods– no significant degradation with time– thanks to :

» MERIS calibration device» MERIS good spectral calibration

– Simultaneous validation of :• MERIS calibration• CNES calibration methods

• Perspective– Multitemporal calibration monitoring of 412 and 443 nm bands– MERIS/MODIS intercalibration over deserts



Inter-sensor at Uyuni

Sensor IntercomparisonMERIS, MODIS, AATSR, PARASOL

Salar de Uyuni (Bolivia)Geometric selection criteria 10 deg > √ [ (SZA(i)- SZA(j))2+ (VZA(i)- VZA(j))2+ 1/4x(abs(RAA(i))- abs( RAA(j)))2]

(Equvalent to a difference of 5 deg for VZA & SZA and ±10 for RAA.)

Data Selection Criteria: Reciprocaland identical doublets are kept iffrom the same day or differing by

one day

MODIS vs MERIS Very good agreement

865nmAvrDif=0.38% STD=2.11%

560nmAvrDif=0.22% STD=2.64%


LISE AnalysisMoby, AAOT

Good agreement in the blue,questionable in the NIR do to

dominant backscattering geometryavailable from Moby.


Older results• Dark Water• SIMBADA Ocean• Rail Road Valley Playa• Comparison with AATSR• Comparison with SCIAMACHY• Snow Targets

Older Vicarious Results


Dark Water (LISE)

884 nm-2.2 %

864 nm-1.4 %

778 nm+0.9%

664 nm-2.5%

680 nm-1.0%

753nm+1.4%


bands Ak(mean)

412 0.991

443 0.987

490 0.991

510 0.996

560 0.988

620 0.992

SIMBADA (LOA)

• 23 independant pixels from 14 scenes• AOT max : 0.15• Within 3 hours from satellite overpass• Case 2 waters rejected


Rail Road Valley PlayaCollaboration RSL & UofA


Rail Road Valley PlayaCollaboration JPL & UofA


Surface 560 nm 670 nm 870 nm

Deserts (Smith) 1.041 1.001 1.037

Greenland (Smith) 1.034 1.012 1.037

Clouds (Poulsen) 1.047 1.026 1.054

Longyeardyen 1.026 1.024 1.038

Barrows 1.024 1.001 1.023

MODTRAN 3.5 - Mid Summer

0

0.2

0.4

0.6

0.8

1

1.2

0.500 0.550 0.600 0.650 0.700 0.750 0.800 0.850 0.900

Wavelength !m

Tra

nsm

issio

n

"0 Degrees"

"20 Degrees"

"40 Degrees"

"60 Degrees"

"AATSR SpectralResponse"

"ATSR-2 SpectralResponse"

"MERIS SpectralResponse"

Comparison with AATSR


0.9760.9800.9850.9850.988

0.9870.9880.9900.9880.990

0.9880.9890.9900.9920.993Corr.

0.0130.0100.0020.000-0.004

-0.012-0.020-0.023-0.022-0.022

-0.027-0.023-0.024-0.023-0.026Offset

1.151.121.121.091.10

1.201.191.171.161.15

1.2571.211.191.171.18Slope

• Given the current state of knowledge, we (KNMI) propose that it is the reflectance data of SCIAMACHY instead of MERIS, that should be corrected.

Comparison with SCIAMACHY


-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

400 500 600 700 800 900

Wavelength in nm

(R-R

_M

ER

IS_

11

:26

)/R

_M

ER

IS_

11

:26

(in

%)

MERIS 11:26

MERIS 13:03

SeaWiFS

11:19

SeaWiFS

12:57

SeaWiFS

14:35

SeaWiFS

16:11

AATSR Nadir

11:26

MODIS 8:50

MODIS 10:30

Longyearbyne (ESA) Barrows (JAXA)

Snow Targets


Vicarious AdjustmentPrinciple


Vicarious Gain Computation

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