LEO Calibration over Rayleigh Scattering … the ATBD Rayleigh ATBD Web Meeting 3rd July 2013 LEO Calibration over Rayleigh Scattering … the ATBD Bertrand Fougnie Patrice Henry Contributions Lydwine Gross-Colzy and Elise Durant (Cap Gemini, France)

LEO Calibration over Rayleigh Scattering … toward the ATBD Context Method Outlines of the ATBD Selection of scenes Selection of clear pixels Computation of the TOA signal Calibration and matchups A quick look on the error budget Examples of applications Why not immediately applicable to GEO

Context Calibration over Rayleigh scattering appears to be a powerful method to : validate in-flight calibration if an on-board device exists contribute to elaborate the in-flight calibration if no onboard device in addition to other methods using natural targets (“vicarious”) validate the monitoring or temporal drift validate the calibration inside the field of view analyze instrumental behavior (straylight, polarization , spectral…) This method was developed for LEO sensors and intensively used Calibrate various sensors over this “identical” reference provide potentially not a direct cross-calibration, but a calibration over the same reference Nevertheless this method is still not fully mature for GEO An ATBD has to be written A first version based on the LEO experience A second version including extension to GEO

Method Statistical approach over molecular scattering (Rayleigh) : observe the atmosphere above ocean surface (= dark surface) calibration from blue to red 443nm to 670nm contributions to the TOA signal Rayleigh molecular scattering : accurately computed (SOS code) main contributor : ~85/90% of the TOA signal ocean surface : prediction through a climatology no foam because of threshold on wind speed aerosols : rejected using threshold + corrected background residue using 865nm band + Maritime-98 model for POLDER (and in general) criteria : <ta> = 0.025 and max(ta) = 0.05 gaseous absorption : O3 (TOMS), NO2 (climato), H20 (meteo) accuracy : typically 2% (3% in blue) Rayleigh ocean aerosols molecular aerosol marine gaseous I_mean 443 84.25 1.25 14.48 -0.56 0.1177 490 85.25 1.98 12.75 -1.84 0.0842 565 90.56 3.76 5.67 -8 0.04456 670 90.23 7.5 2.25 -3.67 0.02308 Main contributors to TOA reflectance (in %)

Method Analysis over predefined and characterized oceanic sites selection of candidate sites through a climatology based on SeaWiFS data spatial homogeneity for each site limited (=“controlled”) seasonal variation Benefit to calibrate over various oceanic sites 1 site = still a small possible bias due to exact knowledge of rw statistical approach : distinguish sensor from sites behaviors analysis of correlations with various geometrical or geophysical parameters different for each sites (Latitude / monthly variations) strong credible if a phenomenon is observed for each site (ex: time evolution) more accurate analysis 443 – Arbitrary value 443 - Climatology

Dedicated sites Step 1 = geographical selection ClimZOO : Climatology of Oligotrophic Oceanic Zones Measurement Selection (POLDER3 - 14,000 points from Jan 05 to Sep 06) (from Fougnie et al., 2002 and 2010)

Selection stream Step 2 : some sites may be not recommended for some months refer to the climatologic table Step 3 = rough cloud mask typically using level-1 mask or simple threshold : quite easy over ocean Step 4 = cloud mask dilatation reject all pixels in the vicinity of clouds (ideally at least 30km) Step 5 = geometrical selection avoid data on the sunglint region : predictable using the wave angle (<30°) Step 6 = surface wind speed avoid sea surface foam : discard wind speed over 5 m/s (ideally) Step 7 = quality index control discard all pixels corresponding to inappropriate quality index (depending on the sensor) Step 8 = invalid ancillary data discard data corresponding to no or abnormal WV, Ozone, Pressure, wind speed Step 9 = “turbid” pixel rejection threshold on NIR band : radiance(NIR)*cos(VZA)*cos(SZA) < 0.003 (ideally) discard : aerosol load, residual clouds, residual surface foam, UFO

Computation of the TOA signal for the geometrical conditions of the considered pixel (elementary measure) : gaseous transmission atmospheric scattering marine reflectance atmospheric diffuse transmission aerosol scattering + coupling Rayleigh scattering

Computation of the TOA signal Inputs : IRay, Iaer, Ttot are computed using an accurate radiative transfer code Successive Order of Scattering (SOS) : coupling, polarization require the knowledge of the Rayleigh equivalent optical thickness for the considered spectral band Iaer is evaluated using the NIR band after Rayleigh correction (black surface) because turbid cases were rejected, only very small loads are observed = residual background a Maritim-M98% mode is considered to extrapolate to shorter bands if a calibration error is know for the NIR band – an iteration is necessary Gaseous transmission are evaluated using SMAC (simplified model from 6S) Rw is taken from a climatology – interpolation for the spectral bands

Unified computation This is an absolute calibration method means, we don’t use reference band or reference sensor at the first order, the reference is the Rayleigh scattering Rayleigh scattering is not perfectly know : 1% uncertainty on the computation of the molecular optical thickness uncertainty due to the radiative transfer code : multiple scattering, polarization Compare different sensor using Rayleigh scattering requires to use : at least exactly the same “Rayleigh” reference ideally all the same contributors The current reference method is Rayleigh Calibration Version 3.5 See development plan

Development Plan The reference method is Rayleigh Calibration Version 3.5 See development plan (GSICS inspired graphical representation) DEV = study & ATBD first definition [resp. SI/MO] PROTO = prototype on dedicated test environment on MUSCLE – Final ATBD [resp. SI/MO] Pre-OPE = test on the operational MUSCLE [resp. ME/EI] OPE = fully operational method / Traceability guaranteed [resp. ME/EI]

A quick look on the Error Budget The elementary calibration error : the radiative transfer code inaccuracy Rayleigh scattering knowledge inputs / ancillary data uncertainty aerosol contribution (use of NIR info) marine reflectance Statistical approach – Matchups  Robust error budget // uncertainty analysis is under construction Results are very sensitive to : knowledge of the spectral response and its variation inside the field-of-view polarization sensitivity of the instrument possible non-linearity for very dark signal, stray light…  Consequently : it is a very good diagnostic

A quick look on the Error Budget Contributors to the computed calibration SADE

A quick look on the Error Budget Computation of error factors Calibration coefficient : Derivate : Error factor

A quick look on the Error Budget Level-1 Error sources tree Error factors Level-2

A quick look on the Error Budget Error budget construction document the variability range for each source : strongly depend of the considered sensor necessary to compute the error factor for each source document the uncertainty for each source necessary to compute the error for each source On-going Produce the error budget for MERIS : OC profile PARASOL : bidirectional sensor Végétation : broadband not systematic acquisitions SEVIRI : GEO representative

Interpretation of results A comprehensive interpretation of results requires : an accurate knowledge of the spectral response for each band if not, the associated uncertainty 1nm shift error on the ISR = 1% error on calibration consideration of the sensitivity to polarization Rayleigh may be 80% polarized (Sca. Angle 90°) to 0% polarized (backscattering) x% of polarization sensitivity can be ~x% error on the calibration consideration of the straylight level Rayleigh between clouds = very dark targets amongs very bright targets consideration of the non-linearity (especially for red bands) Rayleigh = very dark targets, in the lower part of the dynamic

Examples of Application The Rayleigh scattering method was deployed : historical development with narrow band sensors : POLDER 1&2 and broad band sensors : Végétation 1 & 2 dedicated ocean color sensor : SeaWiFS (US), MERIS (ESA), MODIS-AQUA (prelim.) high-resolution imagery sensor : SPOT6, Pleiades preliminary over Geostationary sensor : SEVIRI

Why not immediately applicable to GEO The principle will be exactly the same for GEO : selection + calibration But this experience cannot be immediately generalized to GEO Statistically : only a few sites are accessible : equilibrium North/South + various oceans range of geometries are not the same : ~fix VZA, mainly variations with solar geomerty influence of NIR band (not designed for very low signal) Need to validate on a large dataset but very promising results for SEVIRI : some signature to understand (backscattering) GEO LEO

Why not immediately applicable to GEO range of geometries are not the same : ~fix VZA, mainly variations with solar geometry LEO - SeaWiFS GEO - SEVIRI Small VZA population Backscattering / principal plane population

References Methodology : Application : Fougnie et al., 2002, Identification and Characterization of Stable Homogeneous Oceanic Zones : Climatology and Impact on In-flight Calibration of Space Sensor over Rayleigh Scattering, Ocean Optics XVI Proceedings Fougnie et al., 2010, Climatology of Oceanic Zones Suitable for In-flight Calibration of Space Sensors, Earth Observing Systems XV, SPIE Optics & Photonics Llido et al., 2010, Climatology of Oceanic Zones Suitable for In-flight Calibration of Space Sensors, Rapport d’étude CNES Application : Fougnie et al., 2007, PARASOL In-flight Calibration and Performance, Applied Optics Hagolle et al., 2006, Meris User Meeting Jolivet et al., 2009, In-flight Calibration of Seviri Solar Channels on board MSG Platforms, Eumetsat User Meeting Fougnie et al., 2012, “Validation of the MERIS 3rd Reprocessing Level-1 Calibration Through Various Vicarious Approaches – Perspectives for OLCI,“ Sentinel-3 OLCI/SLSTR and MERIS/AATSR Workshop Fougnie B. et al., 2012, “In-Flight Calibration of Space Sensors Through Common Statistical Vicarious Methods : Toward an Ocean Color Virtual Constellation,” Ocean Optics XXI