Introduction

1. Introduction • A goal of biological remote sensing is the to use observations of ocean color in models to retrieve estimates of PP • But ocean color is really only used to derive chl-a concentrations that are proportional to biomass bestrafe_mich19

2. Why are estimates of PP important? • Phytoplankton “fix” inorganic carbon into organic carbon (e.g. CO2  carbohydrates) • i.e. Convert solar energy into chemical energy • Rate of conversion called primary production (PP) • Phytoplankton die and sink to the deep ocean, thus reducing CO2 in the atmosphere and sequester carbon to the deep ocean, termed the “biological pump” • Important b/c we like to dig up old CO2 to drive our cars

3. Why Chl-a? • Photosynthetic pigments consist of chl-a, accessory pigments chl b and c, and carotenoids. • Only chl- a is present in all phytoplankton

4. Difficulties in estimating PP • Pigment packaging, response to incident light can vary from species to species even for same chl concentrations so biomass hard to determine • Other input variables include daily solar irradaince, ocean optical depth, and physiological variables governing the ability of organism to take up carbon • Aerosols and molecular scattering dominate atmospheric attenuation, water-leaving radiance only 10% of received signal • Chapter only focuses on biomass estimates not on PP

5. Color of water depends on what’s in the water • Phytoplankton • Dissolved organic matter (I think they mean CDOM) • Terrestrial and oceanic sources • Suspended particulate matter • detritus: phytoplankton and zooplankton cell fragments and fecal pellets • Inorganic particles: sand, dust • Sources: river runoff, deposition of wind-blown dust events, wave and current suspension of bottom sediments • Both CDOM and particulates absorb in blue, yielding brownish yellow water

6. Different sizes scatter differently • Viruses: (10-100 nm, 10^12 – 10^15 m^-3) – Rayleigh scattering • Bacteris: (0.1 – 1 micron, 10^13 m^-3) – absorb light in the blue • Phytoplankton: (2-200 micron) – larger than visible wavelenght, Mie scattering • Zooplankton: (100 micon to 20 mm) – graze on phytoplankton • Whales and fish don’t matter • Inorganic particles (1 – 10 microns)

7. Survey of Ocean Color Satellites • Satellite observation began with Coastal Zone Color Scanner (CZCS) in 1978, lested until 1986 • Followed by the Japanese Ocean Color and Temperature Sensor (OCTS), 1996-97, and the German Modular Optical Scanner (MOS), 1996 • SeaWiFS, 1997-present • MODIS, Terra 1999, Aqua 2002 • MERIS (Europe), 2002 • OCM (Germany) 1999 • COCTS (Japan) 2002, with 2 other satellites scheduled to follow (should be up by now)

8. MODIS vs SeaWiFS • See table 6.1, pg 134 • MODIS bands narrower than SeaWiFS by ½ to ¼ • MODIS data 12-bit, SeaWiFS 10-bit digitized • MODIS has twice then signal to noise ratio • 510 nm band moved to 531 nm for MODIS to improve response to acessory pigments and match aircraft remote sensing • MODIS also has a fluorescence band at 678 nm

9. Purpose of the bands (MODIS and SeaWiFS) • 412 nm – Used for the detection of CDOM • 443, 490, 510, 555 nm – used to determine chl concentrations • For MODIS 670, 678, 765 nm – Used to determine chl-a fuorescence peak (683nm) • Note both MODIS (678) and SeaWiFS (670) fluorescence peaks are slightly shorter than actual value to avoid oxygen absorption at 687nm • 765 and 865 – used for atmospheric aerosol correction (CZCS only had 670 for this purpose)

10. SeaWiFS • Sun-synchronous orbit • Altitude 705 km • 1200 descending crossing time • Cross-track scanning, swathwidth of 2800km, scan angle range +- 58.3 degrees • Resolution: 1.6 mrads, surface resolution at nadir 1.1 km • Global coverage at two-day intervals

11. SeaWiFS Calibration • 1) Daily sun observations • While passing over south pole • Solar diffuser plates detriorates over time so cannot be used for long term calibration • 2) Monthly Lunar Observations • Rolls over 180 deg on full moon during the night time segment of its orbit • Lunar radiances similar in magnitude to the daytime upwelled ocean radiance • 3) MOBY

12. MODIS • Two sensors on two different satellites, TERRA and AQUA • Hybrid cross-track scanner, swathwidth of 2300km • Scan angle +- 55 • Global coverage 1-2 days • Both ocean color and SST • Resolution 1-6 km • Uses a mirro paddle wheel instead of a rotating telescope • Uses sensor strips instead of single sensors, gives better signal to noise ratio • Uses Lunar calibration technique similar to SeaWiFS

13. Step 1 Cloud Detection SeaWIFS uses the 870 nm band – water leaving radiance is zero in this band Step 2 Ozone attenuation Sun glint and foam Rayleigh path radiances Aeresols LT = tDLw + tDLF + tLG + LR + LA + LRA Figure 6.11 Atmospheric Correction

14. Seasonal Dependence on ozone Spatial and temporal distribution of ozone is determine by Total Ozone Mapping Spectrometer (TOMS) <= 0.035 Ozone Attenuation

15. Sun Glint and Foam • Sun glint is a function of sun angle and wind speed • Examine NIR radiances: if these radiances are above a threshold apply the sun glint mask • Foam depends on wind speed and assumed to be uniform across an image • If LF is too large image is discarded

16. Rayleigh Path Radiances • Radiance from molecular scatter • Largest term in received radiance for shorter wavelengths

17. Aerosol Path Radiance • Step 1: Remove ozone attenuation, sun glint, foam, and Rayleigh path radiances • Step 2: Assume Lw is zero in NIR and measure reflectance at LA(865) and LA(765) • Step 3: Compare measured values of LA(865) and LA(765) to look up table of known aerosol models • Step 4: If you find a match in Step 3, extrapolate Aerosol irradiance in the visible band and subtract

18. Algorithms make use of reflectance behavior for < 550 nm where reflectance increase with decreasing concentrations Algorithms make use of fluorescence peak at 683 nm – determination of fluorescence magnitude requires radiance measurements at 667, 678, and 748 nm. Independent of CDOM Chlorophyll Reflectance and Fluorescence

19. Semi analytic vs. Empirical Algorithms • Both for wavelengths of 400-550 nm • Empirical derived from regression of coincident ship and satellite observations of LW and shipboard observations of [chl]. • Restricted to Case 1 waters because output is only Chl • Use ratios based on wavelength pairs 443/555, 490/555, 510/555 • Semi analytic relate Rrs to backscatter/absorption ratio to determine water consitutents

20. A maximum band ratio empirical algorithm Uses whichever Rrs ratio is largest (443/555, 490/555, 510/555) SEAWiFS OC4

21. MODIS OC3M • Also maximum band ratio empirical algorithm • Uses whichever Rrs ratio is largest (443/551, 490/551, 510/551) • Statistics are about the same as those of OC4 • Overestimates chl in case 2 waters • Underestimates chl below 1 mg

22. Semi Analytical Algorithms • Inputs: SST, NDT, and RRS at 412, 443, 488, 531, and 551 • SST and NDT are used to classify the ocean in three regimes • A. warm regime with unpackaged chlorphyll • B. transition regime • C. cold upwelling regime where phytoplankton consist of fast growing diatoms with packaged chlorphyll • Outputs: ARP (absorbed radiation by phytoplankton), aCDOM(400), aP(675) • Formulas exist to extend aCDOM(400), aP(675) across the visible spectrum to yeild aT. Components can then be inverted to yield [chl] and [CDOM].

23. Species Specific Algorithms • Coccolithophores, dinoflagellate Karenia brevis, and phycoerythrin-containing species such as Trichodesmium, have unique absorption curves

24. Satellite Images

25. Satellite Images

26. Modis Retrievals