seadas training ~ nasa ocean biology processing group 1 satellite observations of ocean color nasa...

SeaDAS Training ~ NASA Ocean Biology Processing Group

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Satellite observations of ocean color

NASA Ocean Biology Processing Group

Goddard Space Flight Center, Greenbelt, Maryland, USA

SeaDAS Training Material


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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


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( play MODIS-Terra swath movie )



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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


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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 – 2.1m)

Thermal emissive bands (MODIS 16 bands: 3.7 – 14.4m)

MODIS-Aquapassive sensor

Passive sensors


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• 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

412 670 745 865555510490443 Primary products


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visible light

wavelength (nm)

near-infraredultra-violet

443412 490 510 555 670 765 865

8 SeaWiFS channels

radiance, L, in units of W cm-2 nm-1 sr-1

surface

0º

reflectance, R =

L

incident irradiance, E



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Chesapeake Bay Program


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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


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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 whenthey travel through a medium:

(1) absorbed, a(2) scattered, b (backwards, bb)

€

Rrs(λ ) ≈

bb(λ )

a(λ )


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€

Rrs(λ ) ≈

bb(λ )

a(λ )

Relative concentrations of water-column constituents

Ref

lect

ance

chlorophyll-a concentrations

pure sea water

phytoplankton

CDOM

pure sea water

Abs

orpt

ion

[arb

itrar

y un

its]

Abs

orp

t ion

pure sea water

particulate material

nback

sca

t ter

ing


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Case 1water where the optical properties are determined primarily by phytoplankton and their derivative products

Case 2everything 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


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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



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L1Auncalibrated raw digital counts

L1Bcalibrated and geolocated

radiances

L2normalized water-leaving

radiances

From digital counts to radiances


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F0

nLwnw =

wavelength (nm)

chlo

rop

hyl

l re

flect

ance

412 nm 443 nm 488 nm 531 nm 551 nm 667 nm 678 nm

Water-leaving radiances


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SeaWiFS observes El Niño / La Niña transition

January 1998

July 1998

chlorophyll-a concentration

0.01-64 mg m-3

Local coverage chlorophyll-amap provided to fishermen

MODIS-Aqua daily global coverage, 1 August 2007

chl-a

SST


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( insert biosphere movie here )



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atmosphere is 80-90% of the total top-of-atmosphere signal in blue-green wavelengths (400-600 nm)



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different water masses, different Lw …

one Chl algorithm?

one atmospheric correction approach?



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1. empirical (statistical) algorithms

2. semi-analytical algorithms



€

Rrs(λ ) ≈

bb(λ )

a(λ )

€

Rrs(λ ) → b

b, a → C

a

least squares empirical

Bio-optical algorithms


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general form of algorithm

log10(Ca) = (c0 + c1 R + c2 R2 + c3 R3 + c4 R4)

where R is log10(Rrs / Rrs555)

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 (Rrs and Ca)

coefficients / regression

Empirical algorithms


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⎟⎟⎠

⎞⎜⎜⎝

⎛

++⎟⎟

⎠

⎞⎜⎜⎝

⎛

+=

b

b

b

brs ba

bg

ba

bgR 10

2

Rrs == remote sensing reflectance

a == absorption coefficient

bb == backscattering coefficient

g == constant

a separated into contributions by:

water (w) , dissolved + non-algal detrital material (dg), and phytoplankton ()

bb separated into contributions by:

water (w), and particles (p)

(simplification of the radiative transfer equation)

Semi-analytical algorithms


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a = aw + adg e -S( a* Chl

bb = bbw + bbp ⎟⎠

⎞⎜⎝

⎛

443

⎟⎟⎠

⎞⎜⎜⎝

⎛

++⎟⎟

⎠

⎞⎜⎜⎝

⎛

+=

b

b

b

brs ba

bg

ba

bgR 10

2

Rrs

S, , g0, g1, & a*

from satellite(s)

are constants

adg bbp Chl are unknown

Semi-analytical algorithms


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some challenges to remote sensing of coastal and inland waters:

temporal and spatial variabilitylimitations of satellite sensor resolution and repeat frequencyvalidity of ancillary data (reference SST, wind)varied resolution requirements and binning options

straylight contamination from land

non-maritime aerosols (dust, pollution)region-specific models requiredabsorbing aerosols

suspended sediments and CDOMcomplicates estimation of Lw(NIR), model not a function of Ca

complicates correction for non-uniform subsurface light field (f/Q)saturation of observed radiances

anthropogenic emissions (NO2 absorption)

Ocean color on regional scales


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In-water algorithms ...

Ocean color on regional scales


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1. empirical (statistical) algorithms

2. semi-analytical algorithms



€

Rrs(λ ) ≈

bb(λ )

a(λ )

€

Rrs(λ ) → b

b, a → C

a

least squares empirical

regional tuning?

Bio-optical algorithms


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N

1 km

South Brittany, France

Vilaine river plume – June 99

Spectral bands: 412, 443, 467, 489, 509, 531, 555, 599, 621, 667, 681, 705, 753, 772, 865, 899

RGB (705-555-489 nm)

Noctiluca scintillans Clear water

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Increasing

suspended

matter

conc.

1

3

6

2

4 5

2

3

6

4

5

RGB (620-555-489 nm) Mouth of the Vilaine river

R2b

47°N

2°W Regional chlorophyll algorithms

chl-a

IFREMER Centre de Brest, France F. GOHIN, J.N. DRUON, and L. LAMPERT

modified algorithmSeaWiFS OC4

imaged aerial

transect


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Lw(412)

1

2

-1

-2

0

negative

Does coverage vary by algorithm?


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O

100

10

1

0.1

empirical semi-analyticalregional semi-analytical

Coverage varies by algorithm


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Atmosphere issues …



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OMI-Aura Tropospheric NO2

Other unaccounted atmospheric gasses: CO2, sulfates

MODIS-Aqua RGB

Correction for NO2 absorption


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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 functionsingle-scattering albedoaerosol optical thicknessrelative humidities (particle size)


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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


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nLw(412) and in situ measurements Chl and in situ measurements

The scene passed 869nm cloud threshold criterionnLw(412) is decreased and Chl is elevated compared to in situ measurements

Effects of absorbing aerosols


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Location of AERONET CIMEL sun-photometers

Coastal aerosols


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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 aerosolsLarge share of coastal aerosols are composed of small particles (soot, biomass burning)Large share of coastal aerosols are moderately and strongly absorbing

Coastal aerosols


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Satellite vs. in situ AOT time-series in Chesapeake Bay


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Satellite vs. in situ AOT match-ups in Chesapeake Bay


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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


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seadas training ~ nasa ocean biology processing group 1 satellite observations of ocean color nasa...

Documents

primary biooptical variable

separate phytoplankton

mthermal emissive bands

global carbon cycle

local time pass

carbon dioxide

high inclination

mineral particles