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Page 1 Study of Sensor Inter-calibration Using CLARREO Jack Xiong, Jim Butler, and Steve Platnick NASA/GSFC, Greenbelt, MD 20771 with contributions from.

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Presentation on theme: "Page 1 Study of Sensor Inter-calibration Using CLARREO Jack Xiong, Jim Butler, and Steve Platnick NASA/GSFC, Greenbelt, MD 20771 with contributions from."— Presentation transcript:

1 Page 1 Study of Sensor Inter-calibration Using CLARREO Jack Xiong, Jim Butler, and Steve Platnick NASA/GSFC, Greenbelt, MD 20771 with contributions from MODIS Characterization Support Team (MCST), NASA/GSFC CLARREO Workshop, 21-23 October 2008, Washington, DC

2 Outline Introduction –Applications of CLARREO –Requirements for CLARREO Observations Inter-calibration Approaches Using CLARREO –Lunar observations VIS/NIR/SWIR only –Simultaneous nadir observations (SNO) –Ground observations (Dome C) Inter-comparison of Terra and Aqua MODIS calibration Sensitivity Study (future work) –RSR sensitivity study (using Schiamachy with LaRC) –Spectral and spatial sensitivity studies of Earth view targets (Hyperion and AVIRIS) Summary Page 2

3 Page 3 Introduction Applications of CLARREO –Benchmark Observations Accurate, stable, SI traceable, spectrally resolved –Inter-calibration for Other Sensors Consistent data records for long-term climate change Requirements for CLARREO Observations –General Requirements Spectral; spatial; temporal; orbital –Special Requirements Maneuver (pointing) capability Lunar observations

4 Inter-calibration Approaches Using CLARREO Lunar observations –MODIS (T/A), SeaWiFS, Hyperion, VIRS/TRMM Simultaneous nadir observations (SNO) –MODIS (T/A), AVHRR, AIRS, MISR, ASTR, Landsat, GLI, VIRS/TRMM Ground observations –Dome Concordia, Antarctica –MODIS (T/A), AVHRR, AIRS, MISR, ASTR –Inter-calibration of Terra and Aqua MODIS Page 4 Collaboration with USGS (Moon), NOAA, JPL, JAXA, USGC (SNO/Dome C)

5 Page 5 Inter-calibration Using Lunar Observations MODIS Lunar Observations: via spacecraft maneuvers at fixed phase angle reference to a lunar model (USGS) using integrated irradiance Images form Aqua MODIS (band 1) Lunar Observations (Oct 04 – Jun 05) Xiong and Sun (GRSL in press) Advantages vs disadvantages:

6 Page 6 Inter-calibration Using SNO Terra MODIS Aqua MODIS AVHRR SNO For RSB For TEB Advantages vs disadvantages:

7 Page 7 Aqua MODIS SD Degradation (0.41 to 0.94  m) Any Impact on Calibration? Inter-calibration Using a Ground Target Why using a ground target –Validate on-board calibration; complement other cal/val approaches –Monitor calibration long-term stability –Support sensor inter-calibration Requirements for a ground “calibration” target –Spectral and spatial uniformity and radiometric stability (minimum environmental impact) –Site accessibility and data availability –Ground measurements of radiometric traceability

8 Examples of Using Dome C for Inter-calibration Site Description Data Selection Methodology –Thermal emissive: reference to Automated Weather Station ( AWS) measurements –Solar reflective: BRDF model based on ground measurements over Antarctic snow Results from MODIS –Recent presentation at SPIE Europe Remote Sensing (Xiong et al. 2008) Future Work Page 8

9 Page 9 Site Description Dome Concordia Antarctica Located on Antarctic Plateau (75.1 S, 123.4 E) –One of the most homogeneous land surfaces on earth in terms of surface temperature and emissivity. Uniformity over spatial scales typical of the ground footprint of satellite sensors –High altitude (~3200 m) & minimal slope –Low snow accumulation rate –Extremely dry, cold & rarefied atmosphere Low fractional cloud coverage Low atmospheric aerosol and water vapor content Permanently manned Research Station now operational –AWS data available since 1995 10-minute averages of meteorological parameters (T, RH, WS, WD, P) –Daily radiosonde measurements Frequent satellite overpass CEOS Endorsed Site NASA/NOAA/ESA Effort

10 Page 10 Data Selection MODIS Collection 5 Level 1B data Multiple MODIS observations each day (~8) at different angles of incidence. Only near-nadir overpasses used (nadir track within +/- 50 km of Dome C). One granule every 2-3 days. 20x20 pixel average centered on Dome C No cloud screening applied for TEB. All granules used. Uniformity screening applied for RSB to eliminate any granules showing greater than 2% non-uniformity in reflectance over the 20x20 pixel area

11 Page 11 Methodology and Approach (TEB) AWS surface temperature measurements are used as a proxy to track any trends in the relative bias between MODIS Terra & Aqua.  T MODIS = BT MODIS – T AWS Relative Bias =  T Terra –  T Aqua Relative Bias calculated for each MODIS band and only for days with measurements from both Terra & Aqua. Applications to other sensors: relative spectral response (RSR), spatial resolution (ground footprint)

12 Page 12 Methodology and Approach (RSB) A BRDF model developed by Warren et al. (JGR 1998) based on near- surface reflectance measurements over the Antarctic snow R (θ,ψ,φ) = c 1 + c 2 cos(π- φ)+ c 3 cos[2(π- φ)] c 1, c 2 and c 3 are functions of cos(θ) and cos(ψ) c 1 = a 0 + a 1 [1 - cos(ψ)], c 2 = a 2 [1 - cos(ψ)], c 3 = a 3 [1 - cos(ψ)] a i = b 0i + b 1i cos(θ) + b 2i cos 2 (θ) (i = 0, 1, and 2) where θ is the incident solar zenith angle, ψ is the viewing zenith angle, and φ is the relative azimuth angle. A ratio of the observed reflectance factor r to modeled reflectance factor R is calculated by Δr = r / R Spectral BRDF of Antarctic snow from Hudson, Warren et al (JGR 2006)

13 Page 13 Sensor (11 and 12  m) and AWS Observations Terra: Black diamonds; Aqua: Blue squares Good correlation between sensor and AWS observations (focusing long-term behavior, not individual observations)

14 Page 14 Long-term draft (<10mK) for bands 31 and 32; high quality on-board TEB calibration Excellent calibration consistency (11  m: 0.025±2.984K; 12  m: 0.013±3.010K) No obvious temperature dependent bias (<20mK) Relative Bias =  T Terra –  T Aqua (time)  T MODIS = BT MODIS – T AWS Terra: Black diamonds; Aqua: Blue squares 11  m12  m Relative Bias =  T Terra –  T Aqua (temperature)

15 Page 15 One orbit – June 20, 2006 (near nadir footprints) Dome C data (near nadir) 190K – 330K Inter-comparison of Aqua MODIS and AIRS at 11  m (using Dome C observations) Old version New version

16 Page 16 Sensor (0.65 and 0.86  m) Observations strong correlation between sensor observations and solar zenith angle

17 Page 17 Sensor (0.65 and 0.86  m) Observations versus Modeled Values Averaged fitting residual: 1.3 – 1.9% Model parameters derived using sensor first-year observations

18 Page 18 Terra MODIS observations (0.65  m) over Dome C (2002 - 2003)

19 Page 19 Terra MODIS observations (0.86  m) over Dome C (2002 - 2003)

20 Page 20 Terra and Aqua MODIS observations over Dome C

21 Sensitivity Study (future work) RSR Sensitivity Study –Extend from our previous study reported at April/May CLARREO workshop (e.g., preferred inter-calibration scene types vs. spectral band) –Work with LaRC using Schiamachy data Spectral and Spatial Sensitivity Studies –Hyperion observations, Dome C and other targets 0.4 to 2.5  m, 30m IFOV (@705km altitude) –AVIRIS observations 0.4 to 2.5  m, 1 mrad IFOV Page 21

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24 Page 24 Summary Sensor Inter-calibration Using CLARREO –Improve current sensor inter-calibration approaches with highly accurate, stable, and spectrally resolved observations –Resolve calibration differences or establish calibration consistency among sensors with on-orbit SI traceable measurements Ground Target Characterization Using CLARREO –Extend consistent data records, using observations from previous, current, and future missions/sensors, for studies of long-term climate changes –Dome C site can be used to track sensor long-term stability and calibration consistency among sensors (challenges in VIS/NIR/SWIW) Other Approaches –Lunar observations; SNO

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