vicarious calibration of gli by global datasets -...

ADEOS-2 PI workshop March 2004 1

Vicarious calibration of GLI by global datasets

Calibration 5th GroupHiroshi Murakami (JAXA EORC)


0. Contents1. Background2. Operation flow3. Results4. Temporal change5. Mirror-angle dependency 6. Possibility of this scheme7. Summary8. Future works9. Lessons learned


1. Background• Ground observation may be better for the vicarious

calibration, but has problems; – In the early phase, we cannot obtain enough number of ground

observations. – Ground measurement error (surface and atmospheric) can be

one of the serious problem.– Sub-pixel spatial structure can be an serious error source.

• As a kind of provisional adjustment, we tried to derive vicarious coefficients using global GLI LTOA, SeaWiFS nLw (8 days mean), and GLI atmospheric correction look-up tables (Rayleigh, ozone, solar irradiance, aerosol by RSTAR5b).

• Each grid may have large error, but the large number (more than 100,000) can be reduce it statistically.


2. Operation flowProcessed 16 days; 02/06, 03/20, 04/06, 04/22, 05/08, 05/24, 06/09, 06/25, 07/11, 07/27, 08/12, 08/28, 09/13, 09/29, 10/15, 10/24 in 2003

Fix two NIR bands for aerosol optical thickness (τa) and model selection

Aerosol model selectionby GLI atmos corr. Look Up Table (τa→LTOA)

SeaWiFS 8-day binned nLwinterpolated to GLI bands through an

in-water model (Tanaka et al.)Pressure (and water vapor)

by JMA objective analysisColumn ozone by TOMS

LUTs

Simulated LTOA

1-day L1BLTOA and geometry

Derive correction coefficients by comparison between GLI L1B and simulated LTOA.


2. Operation flowSelected aerosol models

We can use several model patterns; CH19-13 or CH19-29 are used in this analysis, because CH13 is used in the ocean color atmospheric correction and longer wavelength looks temporally stable.

2003/03/20 around Japan


2. Operation flowComparison between simulated CH01 and observed GLI CH01 LTOA in 2003/07/11 (by CH13-19)

Simulation by GLI NIR & GANAL & SeaWiFS GLI LTOA

GLI/SimulationDistribution by mirror incident angle

Compare → Vicarious coefficients


2. Operation flowscatter diagram

•An example in 2003/07/11

• Water vapor and air-mass dependency for absorption bands

used for aerosol estimation in this case


2. Operation flow; nLw confirmation

• We checked the operation by comparing outputs of ocean algorithm and simulation inputs.

• Calculated nLws, CHLA and Tau are agree with the input SeaWiFS (band-interpolated) ones. nLw, CHLA and Tau in

2003/07/11Atmospheric correction by CH16-19


3. Results; vicarious coefficientsCoefficients based on CH13A-19A•About 5% change at CH01.•Unrealistic large scatter on SWIR channels

global 0508-0711 avg

0.975 0.995 NA

0.945 NA

0.883 1.000 0.988 NA

0.995 1.057 1.023

292827262524191817161514

CH

1.000 131.003 120.999 110.996 101.072 91.024 81.041 71.044 61.082 51.095 41.008 31.082 21.073 1

global 0508-0711 avgCH

Temporal change of the model selection


4. Temporal change (1)Coefficients based on CH19B-29B•On-board calibration and stripe noise correction indicated A-side is changed during the mission period.

•New candidate for bright targets

Scatter on SWIR can be small.

Temporal change of the model selection can be stable


4. Temporal change (2)

CH13A and 19A changes based on the CH19B’-29B’(19B and 29B are tuned to be agree with the results of 13A and 19A)

•CH13A and 19A are about 5% decreased (coefficients are increased) during eight months.•This is consistent with A/B side difference in the stripe-noise analysis in dark areas. (maybe caused by the stray light)

5%


5. Mirror-angle dependency (1)

Scan-mirror incident angle dependency based on CH13A’-19A’ (considering temporal changes of CH13A and 19A)

•CH1-3 show angle dependencies.

•Only mirror-side difference is changed (no mirror angle dependency) in other channels. We can identify the angle

dependency by the large sample number in this scheme.


5. Mirror-angle dependency (2)Example for Level-2 operation: MOBY & GLI nLwBy considering the mirror incident angle dependency, nLw estimation can be improved in UV and blue bands.


6. Possibility of this scheme (1)MODIS Level-1B

This scheme can apply to other satellite data

Terra/ MODIS CH01-03

Aqua/ MODIS CH01-03

Due to the MODIS algorithm version up in Nov 2003


6. Possibility of this scheme (2)GLI thermal channels

Similar scheme can be used to GLI thermal infrared channels (Reynolds SST instead of SeaWiFS nLw)

Thermal infrared simulated by LOWTRAN-7, Reynolds SST and atmospheric profile by JMA objective analysis data

Coefficients in Apr, May, Jun, Aug, Oct, 2003


7. Summary1) We could derive vicarious coefficients, which have

enough accuracy for Level-2 processing, (for dark target) using global data sets and radiative transfer model.

2) The coefficients show,• Band characteristics for all VNIR and SWIR channels

except for strong absorption channels.• Scan-angle dependency and its temporal change for

CH01-03.• Scan-mirror side difference and its temporal change for

the mirror-side A.3) We set the coefficients to “1.0” in Ver.1, and recommend

the CH13A-19A-base ones for level-2 applications.Candidates: http://suzaku.eorc.jaxa.jp/GLI/cal/vcoef/index.html


8. Future works1) Set calibration table for considering mirror-

angle, mirror-side, and their temporal changes in the level-1 (or 2) processing

2) Integrate all results of this analysis, ground match-up, stripe noise, and onboard calibrations

3) Investigate the reasons of the above characteristics


9. Lessons learned1) We should better to have the way to know the

NIR bands, which are used for aerosol estimation, by some independent ways, e.g., ground observation, global statistics, moon observation, or on-orbit calibrations.

2) One (or some) well-calibrated sensor(s) should be operated continuously. (we hope our future sensor to be one of them.)

3) After all, we should better to re-evaluate solar irradiance data set, especially from UV to blue bands.


10. Acknowledgement• This work is doing in JAXA GLI calibration team and GLI calibration

working group.• MOBY data used in “5.” is provided by Dr. Dennis Clark and Dr.

Stephanie Flora of MODIS, NOAA.• SeaWiFS level-3 8-day binned data is provided by NASA Goddard

Space Flight Center.• Objective analysis data and Earth Probe TOMS ozone data used in

the LTOA calculation is provided by Japan Meteorological Agency and NASA Goddard Space Flight Center, respectively.

• Ocean-color atmospheric correction algorithm is provided by Prof. Hajime Fukushima.

• RSTAR5b making the look-up table is constructed by Prof. TeruyukiNakajima and his laboratory.

• In-water algorithm for the channel interpolation is provided by Dr.Akihiko Tanaka.

• We thank very much above data providers and collaborators.

vicarious calibration of gli by global datasets -...

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