REMOTE SENSING
PRINCIPLES AND
APPLICATIONS
General principles:
radiative properties of
atmosphere and ocean surface
Infrared Remote Sensing
Passive Microwave Remote Sensing
Wind Scatterometry
Altimetry
Optical Remote Sensing
Frédéric Mélin
Joint Research Centre
frederic.melin@jrc.it
marine.jrc.cec.eu.int
SOLAS summer school, 2005
The Remote Sensing Problem
Satellite in orbit at altitude H:
-Sun-synchronous
-Geo-synchronous
day
night
Field-of
ΔΩ
-view
aerosols
Atmosphere
molecules
Boundary
Condition
[Cox & Munk 1954]
Ocean
clouds
θ0
θ
φ
wind
bubbles/foam
dw: absorption depth
= 1/aw(λ)
SOLAS summer school, 2005
Radiative Transfer Equation (RTE)
the mathematical expression of
the remote sensing problem
scattering with probability β(α)
dΩ
dV
irradiance
E(r)
dL(α)
α
dr
E(r+dr)
attenuation
3 main (spectral) processes:
- absorption
- scattering
- emission
In order to invert the RTE,
you need to know the most on
the propagating medium,
and the boundary conditions.
General expression of the RTE:
SOLAS summer school, 2005
Emission of a ‘grey’ body
Sun
Earth
VIS
TIR
solar irradiance
black body
The Sun acts approximately as a black body and
is a major player of the upper boundary condition.
SOLAS summer school, 2005
The Medium of Propagation:
The Atmosphere
ionosphere
Radiative “agents”:
ionospheric free electrons
water vapor O
O3
10 mbar
100 mbar
1000 mbar
Source:
www.physicalgeography.net
clouds:
liquid droplets
ice crystals
ozone (stratosphere)
H
H
other gases
C.-Labonnote
et al. 2001
aerosols:
tropospheric,
stratospheric
(various shapes, compositions,
mixing states…)
SOLAS summer school, 2005
The Medium of Propagation:
The Atmosphere
Wavelength dependence
of the atmospheric transmittance
O
CO2
O2 2
H2O H2O H2O
O3
CO2
N2O
CO2
H2O
N2O
O2 O3 H2O
H2O
CO2
Source:
Selby & McClatchey 1975
(molecular scattering becomes negligible for λ>λvis)
- O3 blocks radiation with λ<0.3µm;
- in the visible, a few small absorption
bands by O2, O3, H2O;
- for λ>3µm, CO2 and H2O mainly
determine the transmittance;
The atmospheric windows of
absorption and transmission are
an element of the remote sensing
strategy.
The wavelength of the selected
electromagnetic radiation, as well as the
characteristics of the surface, impose
limitations and constraints on the design
of the instrument, its resolution,
its signal-to-noise ratio…
It also leads to different approximations
in the RTE.
SOLAS summer school, 2005
The lower Boundary Condition: the Ocean Surface
Index of refraction of distilled water
Real
Absorption, m-1
aw = 4πni/λ
Penetration depth, m
dw = 1/ aw
nr ~ cst=1.34
in the VIS
Imaginary
VIS is the only domain
where radiation penetrates
to substantial depths.
VIS
10GHz
1 cm
10 nm 1 µm
wavelength
10GHz
1 cm
10 nm 1 µm
wavelength
SOLAS summer school, 2005
The lower Boundary Condition: the Ocean Surface
Radiative interactions at the interface:
θi θr
na
nw
θt
Specular
reflector
Lambert
reflector
[See Morel & Gentili 1993 for definition of
bidirectional aspects in the visible]
SOLAS summer school, 2005
Remote Sensors and Measurements
Passive Sensors
Sensor
Class
Optical
sensors
Sensor
Type
Multi-spectral
scanners, Imaging
spectrometers
Derived
Quantities
ozone,
aerosols
UV
3nm 30nm
wavelength, m
frequency, Hz
3.10-9
Infra-red
sensors
Microwave
sensors
Microwave
Radars
Infra-red
imaging
radiometers
Scanning
microwave
radiometers
Scatterometers,
Altimeters,
SARs
ocean
colour
UV-A, 320-400 nm
UV-B, 280-320 nm
Active Sensors
brightness
temp., SST
brightness
temp., wind,
water vapor,
rain, SST
400-700 nm TIR, 3.5-20 µm
VIS
IR
300nm 3μm
surface roughness, wind vectors,
surface height, slope,
currents, internal waves, slicks
1-90 GHz
MW
30μm
300μm
3.10-6
3mm 3cm
30cm 3m
3.10-3
30m
300m
3km
3.103
3
1THz
1GHz
1MHz
1012
109
106
SOLAS summer school, 2005
Infrared Remote Sensing
SST: what is it ?
“no longer an easy parameter to define”
[Barton 2001]
Ts T11µm T10GHz
Ts
ATSR-2 SST
www.metoffice.com
ΔT=Ts-Tb = skin T – bulk T = SSST-BSST
SSST and BSST are different and
are measured in the field through
different means.
Tb
ΔT
Source: Donlon et al. 2002
Tb
ΔT
“For many purposes, SSST is a more
appropriate ocean surface temperature than
the BSST: it represents a physically definable
quantity that exerts significant control on the
exchange of heat, gas, and moisture between
the atmosphere and the ocean.”
[Donlon et al. 1999]
It is also what the remote IR sensor measures.
SOLAS summer school, 2005
Infrared Remote Sensing
With respect to VIS, IR remote sensing shifts to an absorption/emission regime
Reflectance and emissivity are directly linked: e=1-r ~1 ( for θ<40o)
The atmospheric transmittance is mainly influenced by water vapor,
desert dust and stratospheric aerosols.
10.3-11.3 µm
The necessity (and complexity) of
excluding clouds required the development
of elaborate cloud-masking algorithms
(up to 17 channels for MODIS).
3.55-3.93 µm
example of AVHRR
IR channels
The solar flux reflected by wave facets
is significant around 4 µm
used only for night algorithm.
11.5-12.3 µm
SOLAS summer school, 2005
Infrared Remote Sensing
How is it computed ?
sum of attenuated surface radiance
and atmospheric path radiance
form with Planck’s function,
Ti blackbody temperature associated
with received radiance
after linearization and
spectral combination
[McClain et al. 1985]
many other formulations followed…
Typical accuracies :
±0.3K for (A)ATSR-1,2
www.atsr.rl.ac.uk; envisat.esa.int
±0.5K for AVHRR (1981-)
±0.3K for MODIS
podaac.jpl.nasa.gov/sst; modis.gsfc.nasa.gov
http://www.rsmas.miami.edu/groups/rrsl/
±0.7K for GOES
www.goes.noaa.gov/
The (ci)s are calculated by fitting
match-up data bases (algorithms
constrained by in situ measurements).
[Kilpatrick et al. 2001]
In the case of ATSR: dual-look angle, dual window
(no need for match-ups)
SOLAS summer school, 2005
Infrared Remote Sensing
Source: Walton et al. 1998
Sources of error :
- Clouds (cirrus)
- Short-term solar heating
(differential impact on different channels)
- Volcanic (stratospheric) aerosols
- Desert dust
Estimation of ΔT=SSST-BSST with measurements
in Atlantic transects:
wind speed > 6 m s-1, ΔT~-0.17K bias
wind speed < 6 m s-1, ΔT is more influenced by
thermal stratification and is more variable.
implications for validation!
Source: Donlon et al. 1999
SOLAS summer school, 2005
Infrared Remote Sensing: Example 1
Study of the diurnal warming
at global scale:
particularly noticeable in
the tropics,
influenced by solar insolation,
and wind forcing
Source: Stuart-Menteth et al. 2003
SOLAS summer school, 2005
Infrared Remote Sensing: Example 2
Eddy dynamics
in the lee of Hawaii
using GOES-10 imagery
Loretta and Mikalele eddies formed in 1999,
with a life span 3-6 months,
Depth-integrated N nutrients x 3-to-15.
Source:
Seki et al. 2001
Haulani eddy formed in Sep. 2000,
with a life span ~5 months
Carbon export and heterotrophic biomass
2 to 3 times higher in the eddy versus outside,
Depth-integrated N nutrients x 9.
Source: Bidigare et al. 2003
Vaillancourt et al. 2003
SOLAS summer school, 2005
Microwave Remote Sensing
Atmospheric absorption is mainly due to oxygen and water vapor.
18-22GHz: used for water vapor retrieval.
opaque
atmosphere
Water droplets affect transmissivity
by scattering the incident radiation:
cloud droplet: radius<0.1 mm
(Rayleigh scattering, ~f 4)
rain drops: radius ~ 1mm (Mie scatterers)
creates a dip
in transmissivity
2123.8
Microwave remote sensing (passive and active)
is based on the properties of an antenna,
in the frequency domain 1-90 GHz.
increases
with WV
36.5-37
18-19.3
10.7
6.6-6.9
Source: Wentz & Meissner 1999
85
increases
with T
can be neglected for
f <10GHz, rain rate < 1mm.h-1
for f > 10 GHz, strong decrease in
transmissivity due to clouds.
Note:
- resolution of the order 25-50 km
- sensors are such that θ is constant ~50-55o
(TBV independent of wind speed);
- at these angles, emissivity and reflectivity are
of the same order (≠IR).
SOLAS summer school, 2005
Microwave Remote Sensing
+D/2
idealized
antenna
r
polarization
θ
I(θ,φ)
φ
-D/2
Main microwave imagers
Instrument
Plaform
Life
Frequencies and polarization
SMMR
Nimbus-7
1978-1987
6.6V,H, 10.7V,H, 18.0V.H, 21V,H, 37V,H
SSMI’s
DMSP
1987-
19.3V,H, 22.2V, 37.0V,H, 85.5V,H
TMI
TRMM
1998-
10.7V,H, 19.3V,H, 21.3V, 37.0V,H, 85.5V,H
AMSR
ADEOS-2
AQUA
12/02-10/03
2002-
6.9V,H, 10.7V,H, 18.7V,H, 23.8V,H, 36.5V,H, 89.0V,H
AMSR
Coriolis
2003-
6.6V,H, 10.6(4), 18.7 (4), 23.8V,H, 37.0 (4)
SOLAS summer school, 2005
Microwave Remote Sensing
Text
TA
TD
Tsol
TU
TS, SS
TA is sensitive to:
multifrequency polarized multivariable equations
(and simultaneous retrieval, constrained by
measurements from buoys and radiosondes).
in the atmosphere:
columnar water vapor V (0.03-75mm), cloud liquid water L (0-0.25 mm)
(+rain rate R, Wentz & Spencer 1998)
at the surface:
sea surface temperature and salinity (1.4GHz)
wind vector: dependence (spectral and polarized) of e on waves (speed and azimuthal) and foam
sea ice properties
SOLAS summer school, 2005
Microwave Remote Sensing
Accuracy:
Wentz (1997):
rms accuracy of SSMI products (50 km): 0.9 m s-1 for U, 1.2 mm for V, 0.025 mm for L.
Mears et al. (2001): standard deviation of collocated wind speeds SSMI / buoys: 1.3 m s-1
Meissner et al. (2001): standard deviation of collocated wind speeds
SSMI / ECMWF: 2.1 m s-1 ;
SSMI / NCEP/NCAR: 2.6 m s-1
Gentemann et al. (2004): TRMM TMI SST vs. tropical buoys SST
mean bias of -0.7oC, standard deviation of 0.57oC.
Stammer et al. (2003): TRMM TMI SST vs. Reynolds & Smith SST: standard deviation of 0.54oC.
[also Ricciardulli & Wentz (2004)]
[www.remss.com,
sharaku.eorc.nasda.go.jp/AMSR/index_e.htm]
A note on GHRSST: The existence of long time series of SST satellite products from different
sensors and techniques (IR and MW) and the importance of SST for climate research has prompted
the GODAE High Resolution SST Pilot Project, whose aim is to
www.ghr-sst-pp.org
"Ensure the provision of rapidly and regularly diffused, high-quality
sea surface temperature products at a fine spatial and temporal
resolution that meet the diverse needs of GODAE, the scientific
community, operational users and climate applications
at a global scale."
SOLAS summer school, 2005
Microwave Remote Sensing
V-pol
H-pol
V-pol
H-pol
For sea ice: use of 18, 37, 85 GHz.
In the dry atmosphere of polar regions,
the RTE can be further simplified TA~eTS;
The algorithms use the frequency-dependent
differences in emissivity between open water
and ice categories to determine ice concentration
and type (but ice/water is so multiform!).
nsidc.org , www.ifremer.fr/cersat/en/index.htm,
www.nersc.no, www.osi-saf.org
young ice: high salinity surface layer
FIY: first-year ice, saline, 1-2 m
MIY: multi-year, hard-upper surface, fresh, containing bubbles
frazil, grease ice: first stages
pancake ice
….
FIY
pancakes
FIY
water
MYI
Source: Eppler et al., In Carsey 1992
SOLAS summer school, 2005
polar.jpl.nasa.gov, B. Holt
Microwave Remote Sensing: Example 1
Source: Wentz et al. 2000
seeing through the clouds…
imaging the cold wake
of hurricane (Bonnie) with
TMI derived SST
studying Tropical Instability Waves
in the Equatorial Pacific
3-day SST composites
24-26 Aug. 1998
[also Strutton et al. 2001, Ryan et al. 2002,
Legeckis et al 2004 with GOES-10]
Source: McClain et al. 2002
Source: Chelton et al. 2000
SOLAS summer school, 2005
Microwave Remote Sensing: Example 2
Microwave remote sensing has been instrumental
in monitoring the changes affecting the sea ice regime
in the Arctic Ocean, in terms of extent, thickness and age.
[Cavalieri et al. 1997, Bjørgo etal. 1997,
Serreze et al. 2003, Stroeve et al. 2005 ….]
Source: Johannessen et al. 1999
Source: Rigor & Wallace 2004
SOLAS summer school, 2005
Active Microwave Remote Sensing: Radio Detection And Ranging
Фt transmission
τ
t0
reflection
I0
radar
t
cycle of Pulse Repetition
Frequency (PRF)
R0
dΩ
pulse τ
target dA
properties of the antenna properties of the surface
(transmitting and receiving)
sensitive to the properties of the surface
SOLAS summer school, 2005
Active Microwave Remote Sensing: Radio Detection And Ranging
σ0 is sensitive to the surface roughness, influenced by wind U:
normal incident
normal incident
specular surface roughened surface
σ0 decreases with U
oblique incident
specular surface:
no return to
the sensor
oblique incident
roughened surface;
Bragg scatter
corner
reflector
σ0 increases with U
Altimetry
Wind Scatterometry
[round trip travel time]
[σ0]
thermal noise
surface signal
attenuated by atmosphere
instrument noise +
environmental emissions
SOLAS summer school, 2005
Wind Scatterometry
Geophysical model function for σ0,
that can be expressed for different
look angles and polarizations:
σ0 = F(P, U, θ, ΦR)
U, ΦR
polarization
view
angle
wind speed azimuth relative
to wind direction
F can be written as a Fourier series in ΦR;
ambiguities of ±90o or ±180o are raised by
the available equations and numerical
weather predictions.
10 dyn/cm2
ERS-1 1991-1996
www.ifremer.fr/cersat/en/research/ioa/wnd_monitoring.htm
SOLAS summer school, 2005
Wind Scatterometry
Typical accuracy:
2 m s-1 and 20o.
Bourassa et al. (2003):
QuikSCAT vs. ships
Freilich & Dunbar (1999):
NSCAT vs. buoys
for wind speeds of 1-18 m s-1,
rms acccuracy of ±1.2 m s-1 , bias of 0.3 m s-1
for wind speeds of 2-17 m s-1,
mean difference of direction of 8o,
with rms directional error of 18o.
AMI
Source: Liu 2002
ADEOS-1 SeaWinds-1
Satellite based direction
(middle swath)
Missions:
ship based direction
www.osi-saf.org
www.ifremer.fr/cersat/en/index.htm
podaac.jpl.nasa.gov
www.remss.com
SOLAS summer school, 2005
Wind Scatterometry: Example 1
The high resolution and quality of
wind scatterometry products have benefited
a range of applications:
forcing of numerical models
studies of the tropical dynamics
analysis of transient features
(storms, hurricanes, eddies…)
Some small-scale patterns appear persistent:
- in the wake of islands or continental features
(e.g., Gulf of Tehuantepec)
- in regions of strong SST gradients,
- in regions of strong coupling with oceanic
currents (western boundary, e.g. Gulf Stream)
Source: Chelton et al. 2004
SOLAS summer school, 2005
Wind Scatterometry: Example 2
Dipole vorticity anomalies in
the wake of the islands
m s-1
Source: Xie et al. 2001
The impact on the atmospheric circulation of the
Hawaiian archipelago appears to affect the coupled
ocean atmospheric system very far west in the
subtropical northwest Pacific.
m s-1
wind speed
wind speed
Signature in the coupled ocean-atmosphere system
SOLAS summer school, 2005
Altimetry
Altimetry
±100 m
orbit
H
h
Note: gravity missions (CHAMP, GRACE) will improve
our knowledge of the geoid!
www.gfz-potsdam.de/pb1/op/index_GRAM.html
surface
height
geoid
ref. ellipsoide
centre of mass
gravity field model
www.gfz-potsdam.de/pb1/op/index_GRAM.html
SOLAS summer school, 2005
Altimetry
Altimetry
altimeter
h
Δθ
Δθ
pulse
r1
τ
r
r2
tRT: round-trip
travel time
Фt
2t0+τ
t
2t0 2t0+τ/2
SOLAS summer school, 2005
Altimetry
Altimetry
Sea surface height (dynamic topography):
whose variability is induced by:
atmospheric pressure changes
tides
ocean heating and cooling
geostrophic flows
…
[Fu & Cazenave 2001]
Source of errors:
Missions:
GEOSAT
13.5 GHz
1985-1989
ERS-1,2
13.5 GHz
1991-2004
TOPEX/Poseidon
5.3 , 13.6 GHz
1992-2002
JASON-1
5.3 , 13.6 GHz
2002-
ENVISAT-RA2
3.2 , 13.6 GHz
2002-
instrument noise
atmospheric constituents:
ionospheric free electrons, atmospheric pressure, presence of cloud liquid water and rain
sea state bias:
electromagnetic bias (troughs of waves are better reflectors than crests), skewness bias
orbit determination
On-board microwave radiometer provide ancillary data.
Surface sites provide calibration data.
Accuracy of 2-4 cm.
www.aviso.oceanobs.com
podaac.jpl.nasa.gov
ibis.grdl.noaa.gov/SAT/
SOLAS summer school, 2005
Altimetry:
Altimetry Applications
Applications to the study of global circulation:
sea surface height variability [e.g., North Atlantic, Häkkinen & Rhines 2004, Fu 2004]
heat storage [Chambers et al 1997, 1998, Polito et al. 2000]
wave propagation [e.g., Chelton & Schlax 1996…]
mesoscale variability
TOPEX/Poseidon and ERS-1/2 data
merging resulted in a 1/4o global
distribution of sea level anomaly,
with levels of Eddy Kinetic Energy
30% higher than T/P record alone.
[Ducet et al. 2000,
also Stammer & Wunsch 1999, …]
EKE (cm.s-1)2
Source: Ducet et al. 2000
SOLAS summer school, 2005
Altimetry:
Altimetry Applications
Applications to the sea level trends:
from the global ocean:
[3.2 ± 0.2 mm.a-1
Cabanes et al. 2001]
to basin scales
[ rates up to 20 mm.a-1 SE of Crete,
Cazenave et al. 2001]
or local scales for small water bodies
[large lakes, Birkett al. 1999, Mercier et al. 2002,
Aral Sea, Stanev et al. 2004…]
SOLAS summer school, 2005
Optical Remote Sensing: Ocean Colour
SeaWiFS: An example of ocean colour sensors :
(seawifs.gsfc.nasa.gov)
Comparison:
The human eye has a fov of 0.2 mrad
in the blue, 3-4 channels.
The mantis schrimp is hyperspectral (16),
with UV and polarization!!
Marshall et al. 2002
field of view 1.6 mrad
SOLAS summer school, 2005
Optical Remote Sensing
In the visible, the RTE is dominated by the scattering regime, and thermal emission is negligible.
After ‘bright surface’ masking
(sea ice, clouds, land, thick aerosols):
known +
vicarious calibration
Rayleigh known +
aerosol model
intrinsic colour of the
surface; 10% of Ltoa
modelled (empir.)
masked or corrected
depend on gases and
aerosols
Major sources of uncertainties:
Top-of-atmosphere calibration [Eplee et al. 2001, Sturm & Zibordi 2002]
Aerosol optical properties [non-spherical dust: Mishchenko et al. 1997, Kalashnikova & Sokolik 2002
Foam / bubbles [Zhang et al. 1998, Stramski & Tegowski 2001, Terrill et al. 2001] mixing state: Chandra et al. 2004]
Optical properties of the boundary conditions (water)
Depth of the boundary condition (bottom effect in optically shallow waters) [Ackleson 2003]
Cloud contamination (cirrus, shadows )
SOLAS summer school, 2005
Optical Remote Sensing
classical approach (Gordon 1997)
SeaWiFS, Level-1, 10 Sep. 1998, N. Adriatic
‘true-color’
derivation of aerosol
Ltoa, 412 nm load in the VIS
determination
of aerosol model
derivation of aerosol
load in the NIR
At 765 and 865 nm,
water MAY BE dark
(black pixel assumption)
Ltoa, 555 nm
Ltoa, 865 nm
There are other possible approaches! (but RTE simulations are needed)
NB: few codes are available to solve the RTE
for the coupled ocean-atmosphere system
[Bulgarelli et al.1999, Stamnes et al. 2003]
SOLAS summer school, 2005
Optical Remote Sensing
A by-product of ocean colour atmospheric correction:
the aerosol optical thickness
but aerosol remote sensing uses wavelengths from the UV to IR,
with multi-angle or polarized measurements as well as dedicated processors
[King et al. 1999]
6-year average of aerosol optical thickness from SeaWiFS
Source: Wang et al. 2005
missing data
the water surface
underneath is not visible
Validation, e.g. with AERONET
aeronet.gsfc.nasa.gov
[Holben et al. 1998]
SOLAS summer school, 2005
Optical Remote Sensing
Lwn, 412 nm
absorbing /scattering
waters
Lwn, 555 nm
SeaWiFS, Level-2,
10 Sep. 1998, N. Adriatic
blue waters
Apparent optical properties (AOP) / Inherent optical properties (IOP),a and b
[Preisendorfer 1961]
Seasonal averages of Lwn over Atlantic provinces
SOLAS summer school, 2005
Optical Remote Sensing
Relationship between AOPs and IOPs:
normalization
for incident flux:
few channels
many unknowns !!
total backscattering
(bubbles omitted)
total absorption
Functions of
geometry, wind, IOPs
(0.085-0.10)
Two main approaches to invert this spectral equation:
empirical formulations: depends a lot on the development data.
(semi)analytical models: depends a lot on the parameters.
Classical equations for IOPs parameterizations:
Be careful!!
SOLAS summer school, 2005
Optical Remote Sensing: Optically Significant Constituents
Pure sea water:
data: omlc.ogi.edu/spectra/water
Einstein-Smoluchowski theory
still a subject of measurements, particularly in the UV.
SOLAS summer school, 2005
Optical Remote Sensing: Optically Significant Constituents
Phytoplankton absorption aph(λ) is defined by a set
of pigments that tend to covary with Chla.
Phytoplankton:
Chla : 2 major peaks
at 440 and 665 nm
[e.g., Trees et al. 2000]
definition of specific absorption
aph*(λ)= aph(λ)/Chla
aph(440) for diverse locations
Source: Bidigare et al. 1990
Pigments included in measurements
of recent intercomparison experiments
[Claustre et al 2004]
Chlorophyll a, b, c1, c2, c3
Chlorophyllide a
Divinyl chlorophyll a, b
Alloxanthin
19’-butanoyloxyfucoxanthin
Carotens
Diadinoxanthin
Diatoxanthin
Fucoxanthin
19’-hexanoyloxyfucoxanthin
Lutein
Neoxanthin
Peridin
Prasinoxanthin
Violaxanthin
Zeaxanthin
natural variability
due to
other pigments,
size,
package effect
Source:
Bricaud et al. 1998
Examples of
2 absorption models:
Bricaud et al. 1995
Sathyendranath
et al. 2001
SOLAS summer school, 2005
Optical Remote Sensing: Optically Significant Constituents
Phytoplankton:
Distinction Case1 / Case 2 [Morel and Prieur 1977]
Case 1 : optical properties covary with phytoplankton (Chla)
Case 2 : independent variations of other optically significant constituents
Case 1 mode, with all optical properties=f(Chla)
Reflectance as function of Chla
Band-ratio empirical algorithms
hinge point
Chla, mg.m-3
natural
variability!!
Source: Morel & Maritorena 2001
SOLAS summer school, 2005
Optical Remote Sensing: Optically Significant Constituents
Dissolved and non-pigmented constituents:
Colored Dissolved
Organic Matter
Absorption often represented by exponential functions
S=0.0182±0.0041 nm-1
Non Pigmented
Particulate Matter
S=0.0122±0.0015 nm-1
- large variability of S in nature,
possibly associated with different types of constituents
- part of this variability is due to method of calculations
of the slope (linear/non linear fit, spectral range)
[Twardowski et al. 2004; Sds: 0.010-0.030 nm-1]
Source: D’Alimonte et al. 2004
SOLAS summer school, 2005
Optical Remote Sensing: Optically Significant Constituents
The ‘missing’ backscattering enigma
Source: Stramski et al. 2004
Sources of backscattering:
pure water
turbulence
bubbles (+coating)
microorganisms
non-living organic particles
minerogenic particles
colloids
n(x) ~ x-4
Developments in the field of modeling
and measurements of microbial optics
SOLAS summer school, 2005
Optical Remote Sensing: Optically Significant Constituents
Microbial optical database:
BAC: heterotrophic bacteria
CHLO: chlorophyte (Dunaliella tertiolecta)
CYA: cyanobacteria (Synechococcus)
DIA: diatom (Thalassiosira pseudonana)
and others…
[see Stramski et al 2001]
Source: Stramski & Mobley 1997
SOLAS summer school, 2005
Optical Remote Sensing: Derived Products
Primary products:
Apparent optical properties: Lwn(λ) , Rrs(λ)
Inherent optical properties: a(λ), b(λ), bb(λ), c(λ)=a(λ)+b(λ)
+ decomposition: aph(λ), ads(λ), anp(λ), cp(λ)
photobiology
photochemistry
Derived products:
pigments (Chla)
SPM (suspended particulate matter)
POC (particulate organic carbon)
DOC (particulate organic carbon)
Mass specific optical properties
[ in m-1.(mg.m-3)-1 ]
aph
cp, bb,p
natural variability!!
cp, bb,p
ads
Others:
Photosynthetically Available Radiation (PAR)
Euphotic depth
Secchi disk / depth
Primary productivity
Functional groups
Photochemical sinks & sources
Wave propagation & scales of variability
[Uz et al. 2001, Cipollini et al. 2001, Doney et al. 2003, Uz &Yoder 2004]
SOLAS summer school, 2005
Optical Remote Sensing: Primary Production
The example of one model
(among many),
with 5-year SeaWiFS time series:
PAR(0-)=35.4 E.m-2.d-1
Chla=0.30 mg.m-3
IPP=0.39 gC.m-2.d-1=47.1 PgC.a-1
turn-over: 5.5 days
[see Campbell et al 2002, Carr et al. 2006 for
Primary Production Algorithm Round Robins 2 & 3]
PPARR3: 50.6±8.5 PgC.a-1 (35-68) with 24 models using equal inputs.
SOLAS summer school, 2005
Optical Remote Sensing: Phytoplankton Distribution
Some phytoplankton functional groups, or associated particles,
have a distinctive spectral signature:
Global detection of
Diatoms
Trichodesmium blooms
[Sathyendranath et al 2004]
Detection in coastal
India (IRS-P4 OCM)
Cyanobacteria
[Morel 1997,
Subramaniam et al. 1999,a,b, 2002]
Coccolithophores
Source: Westberry et al. 2005
Source: Sarangi et al. 2005
[Balch et al 1996a,b, Voss et al. 1998]
Bering Sea
17 sep. 2000
English Channel
Black Sea
Source: Cokacar et al. 2004
Source: Gordon et al. 2001
Source: Iida et al. 2002
SOLAS summer school, 2005
Historical OC sensors
Current OC sensors
Source:
http://www.ioccg.org
oceancolor.gsfc.nasa.gov
modis.gsfc.nasa.gov
suzaku.eorc.jaxa.jp/GLI/index.html
parasol-polder.cnes.fr
envisat.esa.int/level3/
www.nrsa.gov.in/engnrsa/products/geninformation.html
….
SOLAS summer school, 2005
Optical Remote Sensing
SeaWiFS
global statistics:
n=1378
mean ratio: 0.975
% diff: 33.3%
r2=0.794
Validation activities:
Validation of Lwn, IOPs, Chla,… using radiometry
(buoys, ships, towers) & lab. samples.
[Hooker & McClain 2000, Eplee et al. 2001,
Carder et al. 2004,Gregg & Casey 2004,
Mélin et al. 2003, 2005, Pinkerton et al. 2003,
Zibordi et al. 2004, 2005, Wang et al. 2005, etc…]
seabass.gsfc.nasa.gov
Northern
Adriatic
Consistent long-term ocean colour record:
Regional comparison: Mediterranean Sea
AQUA
SeaWiFS
Global comparison
SeaWiFS
MERIS
POLDER-2
Source: Djavidnia et al. 2005
MERIS
Conclusions and
Perspectives
Remote sensing: a partial-derivative equation with boundary conditions
and medium characteristics partially or not known
Importance of in situ data for development and validation
More geophysical products available:
Don’t hesitate to use them all together!
Emergence of long-term time series
Corollary: requirement for consistency
Existence of coincident time series
Corollary: activities of merging/blending
New missions
Drive towards operational satellite oceanography
SOLAS summer school, 2005