Preliminary After-launch GOCI Characterization: - PowerPoint Presentation

Slide1

Preliminary After-launch GOCI Characterization: Inter-slot radiance discrepancy issue

Young-Je

Park*,

Hee-Jeong

Han,

Seongick

Cho,

Joo-Hyung

Ryu

, Jae-Hyun

Ahn

and Yu-

Whan

Ahn

Korea Ocean Satellite Center,

Korea Ocean Research and Development Institute

Presented at IGARSS 2011, Vancouver, Canada

Slide2

ObjectivesTo understand the inter-slot radiance discrepancy issueTo seek ideas/suggestions on how to approach this GOCI specific issue

Slide3

Sensors have specific issuesMODIS stripe noise

detector calibration

difference in mirror side characteristics

Sensitivity to polarization state

MERIS

SMILE effects: wavelength variation

Discontinuity at some camera interface

Slide4

OutlineOverview of the GOCI optical system and image acquisition sequence

Inter-slot discrepancy:

variability

within a

slot

variability across different slot boundaries

variability with observation hours (0, 3, 7 hours)

How RT simulations show

Image smoothing technique

Future directions

Slide5

GOCI sensor

Slide6

GOCI optical layout

Three Mirror Anastigmatic Telescope

Slide7

GOCI slots imaging sequence

1

2

3

4

5

6

16

9

8

7

15

14

13

12

11

10

Slide8

Imaging procedure for a GOCI slot

Slide9

Nominal time intervals for GOCI operationInterval between bands = ~ 8 seconds

Interval between consecutive L1a slots = ~ 103 seconds

Duration

for acquiring one

GOCI image

= ~

103*16 seconds = 27 minutes

Interval between consecutive GOCI images = one hour

Interval between the adjacent slots in L1B scene = up to

~103*7 seconds or 12 minutes

=> sun angle difference

??

Slide10

Requirements for comparing radiances from two slotsAccurate

geometric registration

Spatially homogenous conditions for the

atmosphere

and

water

are preferred, which is to avoid seeing different air/water mass from two different slots

Slide11

Inter-slot discrepancy

Slide12

Variability within a slot20110330_0h image: slot 3-6 border

Slide13

Slide14

Slide15

Slide16

Slide17

Slide18

Slide19

Slide20

Variability across different slot boundaries 20110330-3h

Slide21

Inter-slot discrepancy: spectral aspect(033003)Slot #2-7 border

Slide22

Inter-slot discrepancy: spectral aspect(033003)Slot #3-6 border

Slide23

Slot #4-5 border

Slide24

Slot #5-12 border

Slide25

Slot #6-11 border

Slide26

Slot #7-10 border

Slide27

Slot #8-9 border

Slide28

Slot #9-16 border

Slide29

Slot #10-15 border

Slide30

Variability with observation hour

Slide31

Slide32

Slide33

Slide34

Slot border reflectance changeWithin a slot border: moderately variable with consistent difference spectra

For different slot borders: variable magnitude, moderately variable spectra

For different observation hours: larger difference (lower reflectance for the upper slot) in the 7h image

Bands 7 & 8 reflectance ratio changes significantly, which has a serious effect on atmospheric correction that uses those bands.

Slide35

How does RT code simulate the discrepancies?

uslot=3,lslot=6: 3099,1584

3099,1585

lat,lon= 41.0020866 131.8832245

sunz= 54.8425102 54.0042229

suna= 121.0095673 122.2263718

senz= 47.5428658 47.5378571

sena= 185.6037445 185.6040649

uslot=3,lslot=6: 3099,1592

3099,1593

lat,lon= 40.9659882 131.8822021

sunz= 37.4894829 37.5132179

suna= 180.6660614 182.7969666

senz= 47.5027428 47.4977188

sena= 185.6062317 185.6065369

uslot=3,lslot=6: 3099,1597

3099,1598

lat,lon= 40.9434242 131.8815613

sunz= 65.5979691 66.5323486

suna= 252.3380127 253.3214874

senz= 47.4776611 47.4726372sena= 185.6077881 185.6080933

0h: 9hr local time3h: 12hr local time

7h: 17hr local time

Slide36

Simulation with AOT550=0.1

Slide37

Simulation with AOT550=0.5

Slide38

GOCI data

Slide39

An image smoothing techniqueDistance-to-border weighted average

Applied to overlapped area

Simple and good for image generation

Smoothing the TOA reflectance data will not be good for downstream data processing

including the atmospheric correction.

Smoothing the geophysical parameters would make sense.

Slide40

Distance-to-border weighted average

d1

Slot

i

d2

d3

d4

Slot

i

w

i

w

i

= min(d1,d2,d3,d4)

w

here is number of pixels to the k-

th

border

Slot j

w

j

N’=∑(

w

i

ⅹN

i

)/∑

w

i

N’: weighted averageNi: reading from the ith slot

Slide41

Example 1 (original)GOCI 20110412-07h, South Japan

Slide42

Example 1 (weighted average)GOCI 20110412-07h, South Japan

Slide43

Future workClarify questions of Is it an issue of the GOCI radiometric calibration?

Is it an issue of the band filter properties?

Is it an issue of the ghost image?

Develop a scientifically based model to correct the inter-slot discrepancy. Bands6,7,8 are critical for atmospheric correction.

Slide44

Thank you!Please contact us if you have any idea on this issue.

youngjepark@kordi.re.kr