Ocean Color Remote SensingCurt Davis and Pete Strutton, COAS/OSU

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?

Components of a remote sensing system

source

signalraw data

processing / dissemination

cal/val

sensor

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).

Focus on Chlorophyll / Ocean Photosynthesis

LHC 2 e-

Fluorescencef

heat h

ADP+P ATP

NADP + 2H+

NADPH2

The ocean color measurement and how it’s used

0788.010

)555()490(log

)2972.01416.13534.22733.0(

10

32

RRR

rsrs

d

wrs

Chl

nmRnmRR

ELR

Ed

Lw

Main signals: Atmosphere,reflection from the sea surface and ocean color

Lt

http://www.satlantic.com/cgi-bin/nph-null

Measuring chlorophyll from space

The instrument The satellite

The Sea-viewing Wide Field of view Sensor: SeaWiFS

What SeaWiFS sees in one day

The gap here is caused by the satellite tilting as it passes over the equator

A problem with visible remote sensing: Clouds

1 day

3 days

8 days

14 days

1 month

Clouds and other challenges

Clouds• 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

Global picture of ocean and land pigments

Colored Dissolved Organic Matter

Chlorophyll fluorescence

LHC 2 e-

Fluorescencef

heat h

ADP+P ATP

NADP + 2H+

NADPH2

p + f + h = 1Light energy not used for photosynthesis is lost as heat and fluorescence

Blue light induced chlorophyll fluorescence in Tobacco leaf. A. photographedin white light. B. taken in the low steady state of fluorescence, 5 min after theonset of illumination. The bright red fluorescing upper part of the leaf is wherephotosynthesis has been blocked by the herbicide duiron (DCMU).

(From Krause and Weis, 1988)

LHC PSIe-

L683 heat

(ATP & NADPH2)

LHC PSI

L683 heat

(ATP & NADPH2)

DCMUp + f + h = 1

FLH vs. chlorophyll

FLH vs. CDOM

Sea Surface Temperature Chl a Chl Fluorescence Line Height (°C) (mg m-3) (W m-2 mm-1 sr-1)

MODIS Terra L2 1 km resolution scene from October 3rd 2001

From OSU-COAS EOS DB Station

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

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)

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

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…

Some miscellaneous applications

SeaWiFS 1 km data PHILLS-2 9 m data mosaic

Sand waves inPHILLS-1 1.8 m data

Fronts in AVIRIS 20 m data

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.)

The need for High Spatial Resolution in the Near Coastal Ocean

Resolving the Complexity of Coastal OpticsRequires 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.)

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:

The NRL Portable Hyperspectral Imager for Low-light Spectroscopy (PHILLS)

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, 210--221.)

PHILLS Sensor

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.

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):3576-3592.

Sept 15, 2006: No bloomimages by Maria Kavanaugh

9:11

9:34

Bloom disappears on the 15th

0

0.002

0.004

0.006

400 500 600 700 800 900

in situMERIS

St.10

0

0.002

0.004

0.006

0.008

400 500 600 700 800 900

in situMERIS

St.11

St.10

St.11

Wavelength [nm]

Rrs [

sr-1]

Rrs [

sr-1]

Monterey Bay (CA), Sept. 11, 2006

[Chl] was ~ 500 mg/m3.

MERIS remote sensing reflectance (Rrs) compared with in situ measurements

Data from Z.-P. Lee, NRLSSC

 Atmospheric Correction for Turbid Waters in Coastal Regions:

Menghua WangNOAA/NESDIS/ORA

E/RA3, Room 102, 5200 Auth Rd. Camp Springs, MD 20746, USA

Menghua.Wang@noaa.gov

The Coastal Ocean Applications and Science Team Meeting September 7-8, 2005, Corvallis, Oregon

Solar Irradiance

0

50

100

150

200

250

300 500 700 900 1100 1300

Sol

ar Ir

radi

ance

(m

W/c

m2

m s

r)

Wavelength (nm)

Thuillier (2002)

Passive Remote Sensing: Sensor-measured signals are all originated from the sun!

Ocean Color Remote Sensing

Sensor-Measured

Blue ocean

“Green” ocean

From H. Gordon

Atmospheric Correction (removing >90% signals)Calibration (0.5% error in TOA >>>> 5% in surface)

Atmospheric Windows

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0.8

1

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

TotalH

2O

Ozone

Tran

smitt

ance

Wavelength (m)

UV bands can be used for detecting the absorbing aerosol casesTwo long NIR bands (1000 & 1240 nm) are useful for of the Case-2 waters

The Ocean Radiance Spectrum

0.01

0.1

400 500 600 700 800 900

Case 1, C = 0.1 mg m-3

Case 2, Sediment DominatedCase 2, Yellow Substance

TOA

Ref

lect

ance

t(

)

Wavelength (nm)

M80 model, a(865) = 0.1

= 0o, = 45o, = 90o

TOA Reflectance

Coastal Waters

10-4

10-3

10-2

10-1

300 400 500 600 700 800 900

Mauritania Water (sediment)Alberni Inlet Water (yellow-subs)

[w(

)] N

Wavelength (nm)

10-5

10-4

10-3

10-2

10-1

400 500 600 700 800 900

C = 0.03 mg/m3

C = 0.10 mg/m3

C = 0.30 mg/m3

C = 1.00 mg/m3

[w(

)]N

Wavelength (nm)

Case 1 water: Gordon et al. (1988) and Siegel et al. (2000)

Open Ocean Waters

Atmospheric Correction

w is the desired quantity in ocean color remote sensing. Tg is the sun glint contribution—avoided/masked/corrected. Twc 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, 443-452, 1994.

t r A twc Tg tw, L 0 F0

MODIS and SeaWiFS algorithm (Gordon and Wang 1994)

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

0.01 0.10 1.00 10.0

Chlorophyll-a Concentration (mg/m3)