Estimation of Aerosol Properties from CHRIS-PROBA Data

1. Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading ESTEC July 2000

2. Atmospheric Correction of Images Aerosols and Climate Aerosols and Air Quality The Importance of Aerosols ESTEC July 2000

3. Reflection properties of the surface depend on position of the sun, and the geometry of sensing. Multi-temporal data can be properly evaluated only if they are normalised for these directional effects. Albedo is determined accurately only by integrating incoming and outgoing flux over all directions. Information on the structure of vegetation canopies may be retrievable by inversion of directional reflectance Data driven atmospheric correction is possible The Need for Directional Measurements ESTEC July 2000

4. The Need for Atmospheric Correction Ground and TOA Reflectance Values in Green Light Ground and TOA NDVI values The main source of error in atmospheric correction is uncertain knowledge of aerosol loading ESTEC July 2000

5. Aerosols have direct and indirect effects on atmospheric radiation Direct They scatter and absorb radiation Indirect They act as cloud condensation nuclei, and affect the microphysical structure of the clouds formed Interaction between aerosols and clouds a major source of uncertainty Aerosols and Climate ESTEC July 2000

6. Sulphate Aerosols and Radiative Forcing of the Climate ESTEC July 2000

7. Aerosols are highly variable in space and time: concentrations vary by a factor ~1000. Global climatologies are model based, or extrapolations from a small number of observations. Aerosol models exist, limited validation. Observational network (Aeronet) highly skewed “…tropospeheric aerosol loading is very poorly measured” (NASA 1993, Modeling the Earth System in the Mission to Planet Earth Global Aerosol Data ESTEC July 2000

8. Aeronet Network ESTEC July 2000

9. Aeronet Sites, Quality Assured ESTEC July 2000

10. ATSR2 characteristics 1 km pixel size 2 view angles (0-20 and 50-55) 4 spectral channels (555, 655, 870, 1600 nm) Correction approach based on premise that surface reflectance is of the form (shape function) x (spectral function) Correction of ATSR2 Images ESTEC July 2000

11. Methodology The essential method is inversion of a radiative transfer model for the TOA radiance field. The inversion is constrained by requiring the surface reflectance field to follow a certain generic pattern. A simpler version has been used successfully on ATSR2 data (2 view directions, 4 wavelength channels). It is robust to the aerosol optical depth. The method is described in North et al (1999) (IEEE Trans. Geoscience and Remote Sensing, 37(1) pp 526-537) ESTEC July 2000

12. ATSR-2 Atmospheric Correction (With thanks to Peter North, ITE) Green Channel Correction Before Correction After Correction ESTEC July 2000

13. ATSR-2 Atmospheric Correction (With thanks to Peter North, ITE) NDVI Correction Before Correction After Correction ESTEC July 2000

14. ATSR-2 Atmospheric Correction BOREAS SSA, 25-9-95 (With thanks to Peter North, ITE) Top of atmosphere Corrected image False colour composite: r=1630nm (nadir), g=870nm (nadir), b=555nm (along-track) ESTEC July 2000

15. Validation of AATSR atmospheric correction (with thanks to Peter North, ITE) Aerosol optical thickness Validation against sun photometer data ESTEC July 2000

16. Sites to be Used in this Study ESTEC July 2000

17. WavelengthCalibrationofCHRIS CHRIS has no spectral calibration device on board so we need to find an ‘external’ method of spectral calibration. We aim to determine the spectral displacement dlof the spectral response curve resulting from launch conditions to within an accuracy of 0.5 nm. Method: Observe a scene that is spectrally ‘bland’, and preferably dark, through the atmosphere and use observations of a prominent atmospheric absorption feature, matching observed and expected profiles. The atmospheric absorption feature used is the O2 absorption at 762 nm, The ocean surface is effectively black over the wavelength range 750 - 780 nm. ESTEC July 2000

18. Wavelength Calibration of CHRIS Data Within a spectral region encompassing just the O2 absorption, locate the detector ‘j’ recording the lowest observed signal and read the signals from adjacent detectors ‘j-2’,’j-1’ and ‘j+1’,’j+2’. This dip is due to the O2 absorption Observed Detector Signal Compare the observed signals with those predicted using Radiative Transfer Theory and the known CHRIS Spectral Response Curves Ri(l) shifted by a range of possible dl between ±3.5 nm (See the figure).This is done for a typical range of atmospheric optical depths t (i.e. visibilities) - the instrument signal is effectively independent of moisture and ozone content in this spectral range. The predicted signals constitute a Look-Up Table (LUT). j-2 j+1 j-1 j+2 j CCD detector cells about the minimum signal cell ‘j’ - aligned in the spectral direction ESTEC July 2000

19. Simulated detector signals for an increasing spectral shift dl at 2 different atmospheric visibilities dl= 3.0 nm Solar zenith is 40 degrees View zenith is 45 degrees dl= - 3.0 nm Visibility 17 km dl= 0.0 nm ±NEdL dl= 0.0 nm Detector Radiance mW/cm2/sr/nm dl= - 3.0 nm Increasing dl dl= 3.0 nm ±NEdL Increasing dl =-1.5 to 1.5 nm step 0.5 nm Visibility 26 km CCD detector index ESTEC July 2000

20. Results The mean rms retrieval accuracy (over all wavelength shifts) of the dl was found to be better than 0.53 nm in the presence of detector noise. Worst case 1.3 nm (50km visibility). We found that the method was robust to uncertainties in the (unknown) surface albedo and atmospheric optical depth. Averaging the darkest pixels in a calibration image will reduce the uncertainty. The method will be extended to include the water vapour absorption profile at 900-1000 ESTEC July 2000

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22. Bands at Full Spectral Resolution ESTEC July 2000

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