SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Introduction to ocean color satellite calibration NASA Ocean Biology Processing Group Goddard Space Flight Center, Greenbelt, Maryland, USA SeaDAS Training Material

SeaDAS Training ~ NASA Ocean Biology Processing Group 2 scope of the calibration paradigm: to meet the accuracy goals, top-of-the-atmosphere radiances need to have uncertainties lower than 0.5% uncertainties are present in * instrument characterization and calibration * atmospheric and in-water data processing algorithms Ocean color calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 3 direct calibration pre-launch: sensor is calibrated in a laboratory (thermal vacuum) on-orbit: regular solar, deep-space, and lunar observations track changes in sensor response (possible additional on-board calibrators) vicarious calibration on-orbit: force instrument + atmospheric correction system to agree with sea-truth data (e.g., in situ measurements) Instrument calibration stages

SeaDAS Training ~ NASA Ocean Biology Processing Group 4 photons to data each stage in this sequence contributes to uncertainties every element needs: to be well characterized its calibration parameters derived radiant source (Earth surface and atmosphere) scanning mirror calibrators optics (aperture, mirrors, beam splitters, objectives) filters detectors electronics analog to digital (A/D) converters data formatters and data recorders ground receiving antenna digital count to radiance conversion Elements of instrument operation

SeaDAS Training ~ NASA Ocean Biology Processing Group 5 SeaWiFS (12 noon descending orbit) Rotating telescope 8 bands: 412, 443, 490, 510, 555, 670, 765, 865 nm 12 bit digitization truncated to 10 bits on spacecraft 4 focal planes, 4 detectors/band, 4 gain settings, bilinear gain configuration Polarization scrambler: sensitivity at 0.25% level (for fully polarized light) Solar diffuser (SD) daily observations Monthly lunar views at 7° phase angle via pitch maneuvers MODIS-Aqua (1:30 pm ascending orbit) Rotating mirror 9 OC bands: 412, 443, 488, 531, 551, 667, 678, 748, 869 nm 12 bit digitization 2 VIS-NIR focal planes, 10 to 40 detector arrays depending on band resolution, 0.25 to 1 km No polarization scrambler: sensitivity up to 6% at 412 nm Spectral Radiometric Calibration Assembly (SRCA) Solar diffuser (observations every two weeks), Solar Diffuser Stability Monitor (SDSM) Monthly lunar views at 55° phase angle via space view port NPP/VIIRS (1:30 pm descending orbit) SeaWiFS-like rotating telescope MODIS-like focal plane arrays No polarization scrambler Solar diffuser with stability monitor 7 OC bands: 412, 445, 488, 555, 672, 746, 865 nm differences in sensor design differences in orbits Example sensor specifications

SeaDAS Training ~ NASA Ocean Biology Processing Group 6 MODIS instrument design

SeaDAS Training ~ NASA Ocean Biology Processing Group 7 MODIS pre-launch characterization concerns solar diffuser characterization bidirectional reflectance factor (BRF) impact on calibration Earth shine effect – sunlight reflecting off the Earth and onto the diffuser and adding to the solar irradiance attenuation screen characterization through vignetting function SDSM uncertainty in monitoring SD reflectance changes mirror degradation, response vs. scan-angle (RVS), two mirror sides detector calibration changes polarization sensitivity in-band and out-of-band response instrument and focal plane temperature effects electronic cross-talk stray-light contamination solar diffuser stability stray-light contamination photons in the optical path from Earth coming from bright sources, i.e. clouds, land, and sun glitter (characterized by point spread function)

SeaDAS Training ~ NASA Ocean Biology Processing Group 8 * MODIS solar diffuser calibrations performed at the Pole every 2 weeks * North Pole for Terra and South Pole for Aqua * at the dark side of the terminator to limit the stray light entering the instrument Solar calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 9 Moon acts as an external diffuser Moon is viewed at specific lunar phase angles Lunar calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 10 Lunar calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 11 MODIS absolute radiometric accuracy reflective solar bands (0.41–2.1  m): ±2% in reflectance and ±5% in radiance MODIS relative accuracy over time reflective solar bands (0.41–2.1  m): ±0.2% in reflectance Direct calibration uncertainty limits

SeaDAS Training ~ NASA Ocean Biology Processing Group 12 Vicarious calibration approach on-orbit calibration temporal change through the mission vicarious calibration single radiometric gain adjustment NIR band calibration calibration of the combined instrument + algorithm system

SeaDAS Training ~ NASA Ocean Biology Processing Group 13 cloud-free air mass with low optical thickness (e.g., AOT(865) < 0.1) spatially homogeneous Lw( ) ~ or, L w (NIR) = 0 for NIR calibration) limited solar and sensor geometries, wind speed, stray-light and glint contamination VIS calibration NIR calibration Criteria for vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 14 TARGET SATELLITE TOP OF ATMOSPHERE from the satellite + L r, t d, … L t target Criteria for vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 15 provides a relative calibration between the two NIR bands based on assumptions of the most probable maritime atmosphere assumptions open ocean is black in the NIR, i.e. Lw(748) and Lw(869) = 0 vicarious gain of band 869-nm is fixed at 1 based on on-orbit calibration only maritime aerosol with 90% humidity (M90) is chosen over the calibration sites band 869-nm defines the amount of aerosol, AOT(869) aerosol radiance is tabulated for M90 and any geometry NIR { NIR vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 16 L w (,  0 ) cos(  0 ) t(,  0 ) Visible band vicarious calibration the Marine Optical Buoy (MOBY) alternatives: ocean surface reflectance model alternative buoy accumulated field campaigns

SeaDAS Training ~ NASA Ocean Biology Processing Group 17 target dataextract 5x5 box locate L1A files extract 101x101 pixel box process to L2 Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 18 target dataextract 5x5 box identify flagged pixels: LAND, CLDICE, HILT, HIGLINT, ATMFAIL, STRAYLIGHT, LOWLW require 25 valid pixels calculate g pixel for each pixel in semi-interquartile range; then: g scene =  g pixel / n pixel limit to scenes with average values: < 0.20 C a < 0.15  (865) < 60  sensor zenith < 75  solar zenith locate L1A files extract 101x101 pixel box process to L2 (1)calculate gains for each matchup Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 19 target dataextract 5x5 box identify flagged pixels: LAND, CLDICE, HILT, HIGLINT, ATMFAIL, STRAYLIGHT, LOWLW require 25 valid pixels calculate g pixel for each pixel in semi-interquartile range; then: g scene =  g pixel / n pixel limit to scenes with average values: < 0.20 C a < 0.15  (865) < 60  sensor zenith < 75  solar zenith limit to g scene within semi-interquartile range visually inspect all scenes g =  g scene / n scene locate L1A files extract 101x101 pixel box process to L2 (1)calculate gains for each matchup (2)calculate final, average gain Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 20 Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 21 Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 22 Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 23 changes in g with increasing sample size … standard error of g decreases to 0.2% overall variability (min vs. max g ) approaches 0.5% provides insight into temporal calibration, statistical choices Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 24 future ruminations … … statistical and visual exclusion criteria influence g only slightly, yet … they reduce the standard deviations … can uncertainties be quantified … for the assigned thresholds? … how do the uncertainties of the embedded models (e.g., f / Q, the NIR- … correction, etc.) propagate into the calibration? … what are the uncertainties associated with L w target ? Vicarious calibration

SeaDAS Training ~ NASA Ocean Biology Processing Group 25 Franz et al., Appl. Opt. (2007) ~ vicarious calibration approach, using MOBY Werdell et al., Appl. Opt. (2007) ~ vicarious calibration using an ocean surface reflectance model Bailey et al., Appl. Opt. (in press) ~ vicarious calibration using alternative in situ data sources (e.g., NOMAD, BOUSSOLE) Vicarious calibration references