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

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

ESTEC July 2000 The Need for Directional Measurements 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

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

ESTEC July 2000 Aerosols and Climate 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

ESTEC July 2000 Sulphate Aerosols and Radiative Forcing of the Climate

ESTEC July 2000 Global Aerosol Data 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

ESTEC July 2000 Aeronet Network

ESTEC July 2000 Aeronet Sites, Quality Assured

ESTEC July 2000 Correction of ATSR2 Images 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)

ESTEC July 2000 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 )

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

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

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

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

ESTEC July 2000 Sites to be Used in this Study

ESTEC July 2000 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  of 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 O 2 absorption at 762 nm, The ocean surface is effectively black over the wavelength range nm. Wavelength Calibration of CHRIS

ESTEC July 2000 Within a spectral region encompassing just the O 2 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’. Compare the observed signals with those predicted using Radiative Transfer Theory and the known CHRIS Spectral Response Curves R i ( ) shifted by a range of possible  between ±3.5 nm (See the figure).This is done for a typical range of atmospheric optical depths  (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 j+1 j+2 j-2 j-1 Observed Detector Signal This dip is due to the O 2 absorption CCD detector cells about the minimum signal cell ‘j’ - aligned in the spectral direction Wavelength Calibration of CHRIS Data

ESTEC July 2000 Increasing  to  nm step 0.5 nm  nm Simulated detector signals for an increasing spectral shift  at 2 different atmospheric visibilities Increasing   nm  nm  nm ±NEdL  nm  nm Visibility 26 km Visibility 17 km CCD detector index Detector Radiance  W/cm 2 /sr/nm Solar zenith is 40 degrees View zenith is 45 degrees

ESTEC July 2000 The mean rms retrieval accuracy (over all wavelength shifts) of the  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 Results

ESTEC July 2000

Bands at Full Spectral Resolution

ESTEC July 2000