Park-IGARSS2011.pptx
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The document discusses an inter-slot radiance discrepancy issue observed in data from the Geostationary Ocean Color Imager (GOCI) sensor. It finds variability in radiances within and across imaging slots, as well as with observation hour. Radiative transfer simulations show differences in sun/sensor geometry across slots can cause discrepancies. An image smoothing technique is presented and future work is outlined to develop a model to correct the inter-slot issues, which impact atmospheric correction using critical bands.
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resolved with the interferometry technique. However, the cross-track resolution is still far from satisfactory, because of the limited aperture size of antennas. Therefore, the usual three-antenna array cannot resolve multiple terrain points in a same range bin, effectively. In this paper, we
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What to measure:
Proprioceptive sensors
measure values internally to the system (robot),
e.g. motor speed, wheel load, heading of the robot, battery status
Exteroceptive sensors
information from the robots environment
distances to objects, intensity of the ambient light, unique features.
How to measure:
Passive sensors
energy coming for the environment
Active sensors
emit their proper energy and measure the reaction
better performance, but some influence on environment
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Park-IGARSS2011.pptx
- 1. Preliminary After-launch GOCI Characterization: Inter-slot radiance discrepancy issue Young-Je Park*, Hee-Jeong Han, Seongick Cho, Joo-HyungRyu, Jae-Hyun Ahnand Yu-WhanAhn Korea Ocean Satellite Center, Korea Ocean Research and Development Institute Presented at IGARSS 2011, Vancouver, Canada
- 2. Objectives To understand the inter-slot radiance discrepancy issue To seek ideas/suggestions on how to approach this GOCI specific issue
- 3. Sensors have specific issues MODIS stripe noise detector calibration difference in mirror side characteristics Sensitivity to polarization state MERIS SMILE effects: wavelength variation Discontinuity at some camera interface
- 4. Outline Overview 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
- 5. GOCI sensor
- 6. GOCI optical layout Three Mirror Anastigmatic Telescope
- 7. GOCI slots imaging sequence 1 2 3 4 5 6 8 7 9 12 11 10 16 15 14 13
- 8. Imaging procedure for a GOCI slot
- 9. Nominal time intervals for GOCI operation Interval 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??
- 10. Requirements for comparing radiances from two slots Accurate 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
- 12. Variability within a slot 20110330_0h image: slot 3-6 border
- 20. Variability across different slot boundaries 20110330-3h
- 21. Inter-slot discrepancy: spectral aspect(033003) Slot #2-7 border
- 22. Inter-slot discrepancy: spectral aspect(033003) Slot #3-6 border
- 23. Slot #4-5 border
- 27. Slot #8-9 border
- 30. Variability with observation hour
- 34. Slot border reflectance change Within 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.
- 37. GOCI data
- 38. An image smoothing technique Distance-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 processingincluding the atmospheric correction. Smoothing the geophysical parameters would make sense.
- 39. Distance-to-border weighted average Slot i Slot j Slot i d1 wi d4 d2 wj d3 wi= min(d1,d2,d3,d4) where is number of pixels to the k-th border N’=∑(wiⅹNi)/∑wi N’: weighted average Ni: reading from the ith slot
- 40. Example 1 (original)GOCI 20110412-07h, South Japan
- 41. Example 1 (weighted average)GOCI 20110412-07h, South Japan
- 42. Future work Clarify 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.
- 43. Thank you!Please contact us if you have any idea on this issue.youngjepark@kordi.re.kr