1. SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Satellite observations of ocean color NASA Ocean Biology Processing Group Goddard Space Flight Center, Greenbelt, Maryland, USA SeaDAS Training Material

2. SeaDAS Training ~ NASA Ocean Biology Processing Group 2 Ocean color: monitoring the oceans in the visible range of the electromagnetic spectrum Primary (historical) goal: to extract concentrations of marine phytoplankton Phytoplankton: fix carbon dioxide into organic material play a profound role in the global carbon cycle and climate responsible for ~half of Earth net primary production form the basis of the marine food chain support various industries, primarily fisheries Secondary (modern) goals: separate phytoplankton species (e.g. coccolithophore, harmful algae) monitor coastal environments Satellite ocean color

3. SeaDAS Training ~ NASA Ocean Biology Processing Group 3 ( play MODIS-Terra swath movie ) Satellite ocean color

4. SeaDAS Training ~ NASA Ocean Biology Processing Group 4 Near-polar orbits enable low-altitude imaging and global daily coverage Orbital plane crosses the poles and is situated at high inclination to the Earth's rotation Sun-synchronous orbits cross the equator at the same local time Pass over any given latitude at almost the same local time during each orbital pass Near-polar sun-synchronous orbits

5. SeaDAS Training ~ NASA Ocean Biology Processing Group 5 Ocean color instruments are passive sensors They measure electromagnetic radiation reflected or emitted by the Earth surface ( Compare to an active sensor, such as a LIDAR ) Reflective solar bands (MODIS 20 bands: 0.41 – 2.1  m) Thermal emissive bands (MODIS 16 bands: 3.7 – 14.4  m) MODIS-Aqua passive sensor Passive sensors

6. SeaDAS Training ~ NASA Ocean Biology Processing Group 6 primary optical variable: normalized water-leaving radiances (nLw) –the subsurface upwelled radiance which propagates through the sea-air interface; –units:  W cm -2 sr -1 nm -1 primary bio-optical variable: chlorophyll-a concentration (Chl) –main photosynthetic pigment of phytoplankton, used as index of phytoplankton biomass; –units: mg m -3 412 670 745865 555510 490 443 Primary products

7. SeaDAS Training ~ NASA Ocean Biology Processing Group 7 visible light wavelength (nm) near-infraredultra-violet 443412490510555670765865 8 SeaWiFS channels radiance, L, in units of  W cm -2 nm -1 sr -1 surface 0º  reflectance, R = L incident irradiance, E Satellite ocean color

8. SeaDAS Training ~ NASA Ocean Biology Processing Group 8 Chesapeake Bay Program

9. SeaDAS Training ~ NASA Ocean Biology Processing Group 9 photosynthetic pigment reflectance hinge point absorption peak for photosynthetic pigment (medium-high concentrations) absorption peak for particles, detritus, and dissolved substances case-1/2 separation, absorbing aerosols sediments, turbidity atmospheric correction absorption peak for photosynthetic pigment, fluorescence of elevated chlorophyll absorption peak for photosynthetic pigment (low- medium concentrations) 350 400 450 500 550 600 650 700 750 800 850 900 Spectral characteristics of oceanic waters

10. SeaDAS Training ~ NASA Ocean Biology Processing Group 10 IOP (Inherent Optical Properties) Medium properties that depend only on the composition of this medium, regardless of light conditions. Examples are scattering (b), absorption (a), and fluorescence. AOP (Apparent Optical Properties) Characteristics of the medium dependent on geometric distribution of the light field and on the medium IOPs. They change with varying illumination conditions, such as solar zenith and azimuth angles. Examples are irradiance (E), radiance (L), reflectance (R), diffuse attenuation coefficient (K), which depend on the surface boundary conditions. IOPs and AOPs photons have two fates when they travel through a medium: (1) absorbed, a (2) scattered, b (backwards, b b )

11. SeaDAS Training ~ NASA Ocean Biology Processing Group 11 photons have two fates when they travel through a medium: (1) absorbed, a (2) scattered, b (backwards, b b ) Relative concentrations of water-column constituents Reflectance chlorophyll-a concentrations pure sea water phytoplankton CDOM pure sea water Absorption [arbitrary units] Absorption pure sea water particulate material n backscattering

12. SeaDAS Training ~ NASA Ocean Biology Processing Group 12 Case 1 water where the optical properties are determined primarily by phytoplankton and their derivative products Case 2 everything else, namely water where the optical properties are significantly influenced by other constituents, such as mineral particles, CDOM, or microbubbles, whose concentrations do not covary with the phytoplankton concentration Morel Case-1 versus Case-2 water

13. SeaDAS Training ~ NASA Ocean Biology Processing Group 13 SEA SURFACE TOP-OF-THE-ATMOSPHERE the satellite views the spectral light field at the top-of-the-atmosphere SATELLITE PHYTOPLANKTON 1. remove atmosphere from total signal to derive estimate of light field emanating from sea surface (water-leaving radiance, Lw) 2. relate spectral Lw to Chl (or geophysical product of interest) 3. spatially / temporally bin and remap satellite Chl observations Satellite ocean color

14. SeaDAS Training ~ NASA Ocean Biology Processing Group 14 L1A uncalibrated raw digital counts L1B calibrated and geolocated radiances L2 normalized water-leaving radiances From digital counts to radiances

15. SeaDAS Training ~ NASA Ocean Biology Processing Group 15  F0F0 nL w n  w = wavelength (nm) chlorophyll reflectance 412 nm443 nm488 nm531 nm551 nm667 nm678 nm Water-leaving radiances

16. SeaDAS Training ~ NASA Ocean Biology Processing Group 16 SeaWiFS observes El Niño / La Niña transition January 1998 July 1998 chlorophyll-a concentration 0.01-64 mg m -3 Local coverage chlorophyll-a map provided to fishermen MODIS-Aqua daily global coverage, 1 August 2007 chl-a SST

17. SeaDAS Training ~ NASA Ocean Biology Processing Group 17 ( insert biosphere movie here ) Satellite ocean color

18. SeaDAS Training ~ NASA Ocean Biology Processing Group 18 atmosphere is 80-90% of the total top-of-atmosphere signal in blue- green wavelengths (400-600 nm) Satellite ocean color

19. SeaDAS Training ~ NASA Ocean Biology Processing Group 19 different water masses, different Lw … one Chl algorithm? one atmospheric correction approach? Satellite ocean color

20. SeaDAS Training ~ NASA Ocean Biology Processing Group 20 1. empirical (statistical) algorithms 2. semi-analytical algorithms photons have two fates when they travel through a medium: (1) absorbed, a (2) scattered, b (backwards, b b ) least squares empirical Bio-optical algorithms

21. SeaDAS Training ~ NASA Ocean Biology Processing Group 21 general form of algorithm log 10 ( C a ) = ( c 0 + c 1 R + c 2 R 2 + c 3 R 3 + c 4 R 4 ) where R is log 10 ( R rs / R rs 555) wavelengths used OC4= 443 > 490 > 510 / 555 OC3 = 443 > 490 / 555 OC2 = 490 / 555 Clark = 490 / 555 Carder = 490 / 555 principle differences development data set ( R rs and C a ) coefficients / regression Empirical algorithms

22. SeaDAS Training ~ NASA Ocean Biology Processing Group 22 2 R rs == remote sensing reflectance a == absorption coefficient b b == backscattering coefficient g == constant a separated into contributions by: water ( w ), dissolved + non-algal detrital material ( dg ), and phytoplankton (  ) b b separated into contributions by: water ( w ), and particles ( p ) (simplification of the radiative transfer equation) Semi-analytical algorithms

23. SeaDAS Training ~ NASA Ocean Biology Processing Group 23 a  = a w  + a dg  e -S(     a  *   Chl b b  = b bw  + b bp   2 R rs  S, , g 0, g 1, & a  *   from satellite(s) are constants a dg  b bp    Chl are unknown Semi-analytical algorithms

24. SeaDAS Training ~ NASA Ocean Biology Processing Group 24 some challenges to remote sensing of coastal and inland waters: temporal and spatial variability limitations of satellite sensor resolution and repeat frequency validity of ancillary data (reference SST, wind) varied resolution requirements and binning options straylight contamination from land non-maritime aerosols (dust, pollution) region-specific models required absorbing aerosols suspended sediments and CDOM complicates estimation of L w (NIR), model not a function of C a complicates correction for non-uniform subsurface light field (f/Q) saturation of observed radiances anthropogenic emissions (NO 2 absorption) Ocean color on regional scales

25. SeaDAS Training ~ NASA Ocean Biology Processing Group 25 In-water algorithms... Ocean color on regional scales

26. SeaDAS Training ~ NASA Ocean Biology Processing Group 26 1. empirical (statistical) algorithms 2. semi-analytical algorithms photons have two fates when they travel through a medium: (1) absorbed, a (2) scattered, b (backwards, b b ) least squares empirical regional tuning? Bio-optical algorithms

27. SeaDAS Training ~ NASA Ocean Biology Processing Group 27 Regional chlorophyll algorithms chl-a IFREMER Centre de Brest, France F. GOHIN, J.N. DRUON, and L. LAMPERT modified algorithmSeaWiFS OC4 imaged aerial transect

28. SeaDAS Training ~ NASA Ocean Biology Processing Group 28 L w (412) 1 2 -2 0 negative Does coverage vary by algorithm?

29. SeaDAS Training ~ NASA Ocean Biology Processing Group 29 O 100 10 1 0.1 empirical semi-analytical regional semi-analytical Coverage varies by algorithm

30. SeaDAS Training ~ NASA Ocean Biology Processing Group 30 Atmosphere issues … Satellite ocean color

31. SeaDAS Training ~ NASA Ocean Biology Processing Group 31 OMI-Aura Tropospheric NO 2 Other unaccounted atmospheric gasses: CO 2, sulfates MODIS-Aqua RGB Correction for NO 2 absorption

32. SeaDAS Training ~ NASA Ocean Biology Processing Group 32 Global aerosol models NIR (765 and 865-nm) used to estimate aerosol contributons to the total top- of-atmosphere radiance. Is the operational suite of aerosol models sufficient to describe all potential geophysical scenarios? models defined using: scattering phase function single-scattering albedo aerosol optical thickness relative humidities (particle size)

33. SeaDAS Training ~ NASA Ocean Biology Processing Group 33 Single scattering albedo Maritime and absorbing dust aerosols Operational aerosol models −purely reflective or very weakly absorbing −overestimate the atmospheric contribution in the VIS when absorbing aerosols are present Absorbing aerosols −most are eliminated by the cloud albedo threshold on band 869nm −the ones which pass the test cause negative water leaving radiances and increased chlorophyll levels Absorbing aerosol flagging  0 = b / ( a + b ) Absorbing aerosols

34. SeaDAS Training ~ NASA Ocean Biology Processing Group 34 nLw(412) and in situ measurementsChl and in situ measurements The scene passed 869nm cloud threshold criterion nLw(412) is decreased and Chl is elevated compared to in situ measurements Effects of absorbing aerosols

35. SeaDAS Training ~ NASA Ocean Biology Processing Group 35 Location of AERONET CIMEL sun-photometers Coastal aerosols

36. SeaDAS Training ~ NASA Ocean Biology Processing Group 36 Satellite vs. In Situ AERONET sites operational aerosol models Ångström exponent – spectral shape of aerosols Operational models do not exhibit the spectral distributions exhibited by coastal aerosols Large share of coastal aerosols are composed of small particles (soot, biomass burning) Large share of coastal aerosols are moderately and strongly absorbing Coastal aerosols

37. SeaDAS Training ~ NASA Ocean Biology Processing Group 37 Satellite vs. in situ AOT time-series in Chesapeake Bay

38. SeaDAS Training ~ NASA Ocean Biology Processing Group 38 Satellite vs. in situ AOT match-ups in Chesapeake Bay

39. SeaDAS Training ~ NASA Ocean Biology Processing Group 39 Case-1 waters and absorbing aerosols * application of the entire VIS and NIR spectrum (412-865 nm) * simultaneous derivation of ocean optical properties and a set of aerosol models including weakly and strongly absorbing types Gordon, Du, Zhang, Applied Optics, vol. 36, no. 33, 1997 (best fit for A, ta, chl-a, bb) Chomko and Gordon, Applied Optics, vol. 37, no. 24, 1998 (nonlinear spectral optimization based on simplified aerosol models) Chomko and Gordon, Applied Optics, vol. 40, no. 18, 2001 (nonlinear spectral optimization applied to SeaWiFS data) Chomko, Gordon, Maritorena, Siegel, Remote Sensing of Environment, vol. 5775, 2002 (spectral optimization based on simplified aerosol models and complex water- reflectance models) Turbid coastal waters sequential atmospheric correction and water-leaving radiance retrieval Gao, Montes, Ahmad, Davis, Applied Optics, vol. 39, no. 6, 2000 Simultaneous retrieval of atmospheric and oceanic properties

40. SeaDAS Training ~ NASA Ocean Biology Processing Group 40 Satellite ocean color