1. Ocean Color Remote Sensing Curt Davis and Pete Strutton, COAS/OSU

2. Presentation Outline What do we mean by ‘Ocean Color’?
How are the measurements made?
What parameters can be derived?
What are these data used for?
Where are the data available?

4. Components of a remote sensing system processing / dissemination
sensor
source
raw data
signal
processing / dissemination
cal/val

6. Ocean color (chlorophyll)
Passive measurement - energy source is the Sun
In contrast to altimetry, SST etc, looks at subsurface, not ‘skin’
Measures light emitted from the ocean (careful to distinguish between ‘emission’ and ‘reflection’)
Actual parameter measured (raw data) is the total radiance at the sensor Lt
Most of the signal (>90%) at the satellite is reflected by the atmosphere and the sea surface – atmospheric correction is performed to remove these signals and obtain the desired signal normalized water leaving radiance, (nLw ) or remote sensing reflectance Rrs
Also interference from other colored material in the ocean, e.g. sediments, ‘colored dissolved organic matter’ (CDOM).

7. Focus on Chlorophyll / Ocean Photosynthesis
LHC
2 e- u
Fluorescence ff
heat fh
ADP+P ATP
NADP + 2H+
NADPH2

9. The ocean color measurement and how it’s used
Main signals: Atmosphere, reflection from the sea surface and ocean color Lt Ed Lw

13. Measuring chlorophyll from space
The Sea-viewing Wide Field of view Sensor: SeaWiFS
The instrument
The satellite

14. What SeaWiFS sees in one day
The gap here is caused by the satellite tilting as it passes over the equator

15. A problem with visible remote sensing: Clouds
1 day

16. 3 days

17. 8 days

18. 14 days

19. 1 month

20. Clouds and other challenges
Severity varies with location and season
When viewing multi-day composites, variable ‘sample size’
Coastal fog can have the same effect as clouds
Other complicating factors
Atmospheric aerosols
Colored dissolved organic matter (CDOM) - mostly breakdown products from phytoplankton and terrestrial sources.
Other components of river runoff such as sediments.
Diminished in open ocean, aka Case I waters

21. Global picture of ocean and land pigments

22. Colored Dissolved Organic Matter

23. Chlorophyll fluorescence
Light energy not used for photosynthesis is lost as heat and fluorescence
Fp + Ff + Fh = 1 u
NADP + 2H+
NADPH2
2 e-
LHC
ADP+P ATP
Fluorescence ff
heat fh

24. Blue light induced chlorophyll fluorescence in Tobacco leaf. A
Blue light induced chlorophyll fluorescence in Tobacco leaf. A. photographed
in white light. B. taken in the low steady state of fluorescence, 5 min after the
onset of illumination. The bright red fluorescing upper part of the leaf is where
photosynthesis has been blocked by the herbicide duiron (DCMU).
(From Krause and Weis, 1988)
LHC
PSI e- u
L683
heat
(ATP & NADPH2)
DCMU
Fp + Ff + Fh = 1

25. FLH vs.
chlorophyll
FLH vs.
CDOM

26. MODIS Terra L2 1 km resolution scene from October 3rd 2001
Sea Surface Temperature Chl a Chl Fluorescence Line Height
(°C) (mg m-3) (W m-2 mm-1 sr-1)
From OSU-COAS EOS DB Station

27. Chlorophyll fluorescence from space
Passive measurement (sun is the initial source)
Offers the possibility of phytoplankton physiology from space
Also potentially a chlorophyll proxy that is unaffected by:
Sediments
Other colored material
However, signal is very small, and our understanding is evolving

28. Satellite-based productivity algorithms
Motivation:
Chlorophyll measurements give biomass, we want productivity (rate)
Global coverage
1000s of 14C measurements of primary productivity have been made and continue to be made
Do not accurately reflect global temporal and spatial variability
Need models of primary productivity - take satellite data as input and provide integrated primary productivity as the output
Allows us to quantify the spatial distribution of productivity, but also…
Temporal changes at time scales from days to decades (NASA's main goal)

29. Other Ocean Color Products
Particulate Organic Carbon (POC): Potentially more useful for carbon budgets than phytoplankton chlorophyll
Particulate Inorganic Carbon (PIC): Indicative of a specific type of phytoplankton (coccolithophorids), common in polar waters.
Photosynthetically Available Radiation (PAR)
Diffuse attenuation coefficient
Terrestrial biosphere products

30. The sensors and data
SeaWiFS: Launched 1997, very successful, well-calibrated and still operating?
MODIS Aqua: Launched 2002, has fluorescence channel that SeaWiFS lacks.
Data available at spatial resolutions from ~1km to 9km
Data available at daily resolution with the caveats previously discussed
Data gateway depends on user: NASA directly, CoastWatch, others…

31. Some miscellaneous applications

32. The need for High Spatial Resolution in the Near Coastal Ocean
SeaWiFS 1 km data
PHILLS-2 9 m data mosaic
Near-simultaneous data from 5 ships, two moorings, three Aircraft and two satellites collected to address issues of scaling in the coastal zone. (HyCODE LEO-15 Experiment July 31, 2001.)
During the Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) conducted at Tuckerton, New Jersey near coincident PHILLS airborne hyperspectral data at 1.8 m and 9 m Ground Sample Distance (GSD), AVIRIS data at 20 m GSD and SeaWiFS and MODIS data at 1 km GSD were all collected during a 2 hour time window on July 31, At the same time 5 research vessels, CODAR, and a variety of in-situ autonomous systems were sampling the environment. This unprecedented data set makes it possible to evaluate the utility of high spectral and spatial resolution data for a variety of coastal environments, and to directly compare it to ocean color measurements from SeaWiFS and MODIS and ship measurements. The offshore end of the study area is a typical continental shelf environment, which is well sampled by the 1 km GSD systems. The inshore end is far more complex and includes salt marshes, barrier islands and complex bottom features in the Mullica River Estuary and Barnegat Bay. This is a preliminary look at these data sets, highlighting the advantages and disadvantages of the different remote sensing data sets for the diversity of environments sampled in this experiment.
Sand waves in
PHILLS m data
Fronts in AVIRIS 20 m data

33. Resolving the Complexity of Coastal Optics Requires Imaging Spectrometry
Extensive studies using shipboard measurements and airborne hyperspectral imaging have shown that visible hyperspectral imaging is the only tool available to resolve the complexity of the coastal ocean from space.
(Lee and Carder, Appl. Opt., 41(12), 2191 – 2201, 2002.)

34. Solving the Shallow Ocean Remote Sensing Problem using Hyperspectral Data
Remote-sensing reflectance (Rrs = Lu/Ed at the sea surface) is a function of properties of the water column and the bottom,
Rrs() = f[a(), bb(), (), H], (1)
where a() is the absorption coefficient, bb() is the backscattering coefficient, () is the bottom albedo, H is the bottom depth.
Where a() is the sum of awater + aphytoplankton + aCDOM + Detritus
And bb() is the sum of bb water + bb phytoplankton + bb detritus + bb sediments
It is desired to simultaneously derive bottom depth and albedo and the optical properties of the water column. This is done by taking advantage of the spectral characteristics of the absorption and reflectance characteristics of the water column constituents and the bottom.
The next three slides show examples of how bottom albedo, water optical properties and depth effect remote sensing reflectance:

35. The NRL Portable Hyperspectral Imager for Low-light Spectroscopy (PHILLS)
AN-2 Aircraft
PHILLS image of shallow water features near Lee Stocking Island, Bahamas used to develop and validate hyperspectral algorithms for bathymetry, bottom type and water clarity.
Ocean PHILLS is a push-broom imager.
f 1.4 high quality lens, color corrected and AR coated for 380 –1000nm.
all reflective spectrograph with a convex grating in an Offner configuration to produce a distortion free image (Headwall, Fitchburg, MA ).
1024 x 1024 thinned backside illuminated CCD camera (Pixel Vision, Inc, Beaverton, OR).
Images 1000 pixels cross track and is typically flown at 3000 m altitude yielding 1.5 m GSD and a 1500 m wide sample swath.
(C. O. Davis, et al., (2002), Optics Express 10:4, )
PHILLS Sensor

36. Bathymetry, Bottom Type and Optical Properties using Look-up Tables
Interpretation of hyperspectral remote-sensing imagery via spectrum matching and look-up tables, Mobley, C. D., et al., 2005, Applied Optics, 44(17):

37. Bloom disappears on the 15th
9:11
9:34
Sept 15, 2006: No bloom
images by Maria Kavanaugh

38. Data from Z.-P. Lee, NRLSSC
MERIS remote sensing reflectance (Rrs) compared with in situ measurements
St.10
St.11
Wavelength [nm]
Rrs [sr-1]
[Chl] was ~ 500 mg/m3.
Monterey Bay (CA), Sept. 11, 2006
Data from Z.-P. Lee, NRLSSC

42. Atmospheric Correction for Turbid Waters in Coastal Regions:
Menghua Wang
NOAA/NESDIS/ORA
E/RA3, Room 102, 5200 Auth Rd.
Camp Springs, MD 20746, USA
The Coastal Ocean Applications and Science Team Meeting
September 7-8, 2005, Corvallis, Oregon

43. Solar Irradiance
Passive Remote Sensing: Sensor-measured signals are all originated from the sun!

44. Ocean Color Remote Sensing
Sensor-Measured
“Green” ocean
Blue ocean
Atmospheric Correction (removing >90% signals)
Calibration (0.5% error in TOA >>>> 5% in surface)
From H. Gordon

45. Atmospheric Windows
UV bands can be used for detecting the absorbing aerosol cases
Two long NIR bands (1000 & 1240 nm) are useful for of the Case-2 waters

46. The Ocean Radiance Spectrum
TOA Reflectance
Open Ocean Waters
Coastal Waters

47. Atmospheric Correction
MODIS and SeaWiFS algorithm (Gordon and Wang 1994)
w is the desired quantity in ocean color remote sensing.
Tg is the sun glint contribution—avoided/masked/corrected.
Twc is the whitecap reflectance—computed from wind speed.
r is the scattering from molecules—computed using the Rayleigh lookup tables (atmospheric pressure dependence).
A = a + ra is the aerosol and Rayleigh-aerosol contributions —estimated using aerosol models.
For Case-1 waters at the open ocean, w is usually negligible at 750 & 865 nm. A can be estimated using these two NIR bands. Ocean is usually not black at NIR for the coastal regions.
Gordon, H. R. and M. Wang, “Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm,” Appl. Opt., 33, , 1994.

48. SeaWiFS Chlorophyll-a Concentration (October 1997-December 2003)