measuring the ocean from space, 1 - florida state university€¦ · measuring the ocean from...
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![Page 1: Measuring the Ocean from Space, 1 - Florida State University€¦ · Measuring the Ocean from Space, 1 Principles of measurement using altimetry and radiometry Professor of Oceanography](https://reader030.vdocument.in/reader030/viewer/2022041117/5f2d2392ab940720e74b0706/html5/thumbnails/1.jpg)
Ian RobinsonIan Robinson
21st Sep, 200421st Sep, 2004
GODAE Summer School, ToulonGODAE Summer School, Toulon
SouthamptonOceanography Centre
Measuring the Ocean from Space, 1Measuring the Ocean from Space, 1Measuring the Ocean from Space, 1
Principles of measurement using altimetry and radiometry
Professor of Oceanography from Space, University of Southampton.Head of the SOC Laboratory for Satellite Oceanography.
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221st Sep, 2004GODAE Summer School, Toulon
Purpose of these Lectures Purpose of these Lectures Purpose of these Lectures
The Aims: To learn about the basic methods of Space Oceanography so that you can critically appraise global ocean datasets from satellites. To understand how to confront models effectively with satellite data in the context of operational applications.
Objective of Lecture 1 (Today): To understand what sensors in Space actually measure, and how to derive useful ocean parameters from the primary measurements.
Objective of Lecture 2 (Tomorrow): To recognise the measurement and sampling limitations of satellite sensors, and learn how best to exploit the benefits of satellite data.
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321st Sep, 2004GODAE Summer School, Toulon
Outline of lectureOutline of lectureOutline of lecture
Methods of measuring ocean properties from satellites : A generic outline
Observing ocean currents using satellite altimetry
Estimating chlorophyll concentrations from ocean colour
Measuring sea surface temperature by radiometry
In tomorrow’s lecture, wherethe GODAE high resolution SST Project
provides a case study
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421st Sep, 2004GODAE Summer School, Toulon
Methods of measuring ocean properties from satellites :
A generic outline
Methods of measuring ocean Methods of measuring ocean properties from satellites : properties from satellites :
A generic outlineA generic outline
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521st Sep, 2004GODAE Summer School, Toulon
Using the Electromagnetic SpectrumUsing the Electromagnetic SpectrumUsing the Electromagnetic Spectrum
Ocean colourradiometry
Microwave Radiometry Radars
IR radiometry
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621st Sep, 2004GODAE Summer School, Toulon
Sensor Types: 1Sensor Types: 1Passive Sensors Passive Sensors -- solar radiationsolar radiation
Use visible and near infra-red wavelengthsMultispectral (detects colour)Scanning (generates images)
Obstructed by cloudsCorrupted by the atmosphere
Measures water properties which colour the seaAlso measures light reflected at the surfaceNear infra-red light does not penetrate the sea
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721st Sep, 2004GODAE Summer School, Toulon
Satellite ocean datasets, 1: from ocean colourSatellite ocean datasets, 1: from ocean colourSatellite ocean datasets, 1: from ocean colour
Global distribution of surface chlorophyll concentration derived from the SeaWiFS sensor during 28th August to 4th September 2004
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821st Sep, 2004GODAE Summer School, Toulon
Sensor Types: 2Passive sensors - emitted radiation
Sensor Types: 2Sensor Types: 2Passive sensors Passive sensors -- emitted radiationemitted radiation
Thermal Infra-red radiometersMultiple wavebands Obstructed by cloudsRequires atmospheric correctionMeasure sea surface temperature
Infra-red
Microwave radiometersMultiple frequency bandsSee through cloudsAlmost independent of atmosphereMeasure sea surface temperatureMeasure surface roughnessMeasure salinity ?
Microwave
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921st Sep, 2004GODAE Summer School, Toulon
Satellite ocean datasets, 2: from infrared sensorsSatellite ocean datasets, 2: from infrared sensorsSatellite ocean datasets, 2: from infrared sensors
Global Sea Surface Temperature (SST) distribution derived from the NOAA AVHRR sensors during 14th to 18th September 2004
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1021st Sep, 2004GODAE Summer School, Toulon
Satellite ocean datasets, 3: from microwave radiometers
Satellite ocean datasets, 3: from microwave Satellite ocean datasets, 3: from microwave radiometersradiometers
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1121st Sep, 2004GODAE Summer School, Toulon
Sensor Types: 3. Active SensorsSensor Types: 3. Active Sensors
Emit radar pulse obliquelyScatterometer (course resolution)Measure wind speed and directionImaging radar – SAR (fine resolution)Detect surface roughness patterns
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1221st Sep, 2004GODAE Summer School, Toulon
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1321st Sep, 2004GODAE Summer School, Toulon
Synthetic Aperture Radar DataSynthetic Aperture Radar DataSynthetic Aperture Radar Data
SAR images: a unique view of the Ocean
Measure short scale (5-50 cm) roughness of the seaBright = roughDark = smooth
Capable of observing a variety of ocean phenomena
Anything can be imaged that modulates surface roughnessEven subsurface phenomenaWind conditions must be right
GibraltarGibraltar
rough
calm
InternalInternalwaveswaves
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1421st Sep, 2004GODAE Summer School, Toulon
Synthetic aperture radar image – Andaman SeaSynthetic aperture radar Synthetic aperture radar image image –– Andaman SeaAndaman Sea
100 km100 km
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1521st Sep, 2004GODAE Summer School, Toulon
~40 m
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1621st Sep, 2004GODAE Summer School, Toulon
Sensor Types: 3. Active SensorsSensor Types: 3. Active Sensors
Emit radar pulse obliquelyScatterometer (course resolution)Measure wind speed and directionImaging radar – SAR (fine resolution)Detect surface roughness patterns
Emit radar pulse verticallyAltimeterMeasure timing of returnDetect distance to surfaceMeasure shape and strength of return pulseDetect wave height & wind
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1721st Sep, 2004GODAE Summer School, Toulon
Satellite ocean datasets, 4: from altimetrySatellite ocean datasets, 4: from altimetrySatellite ocean datasets, 4: from altimetry
SS Height Anomaly (from Altimeter)SS Height Anomaly (from Altimeter)Height 16-26 Sep, 2003 – mean height over several years
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1821st Sep, 2004GODAE Summer School, Toulon
Information Flow in a RInformation Flow in a R--S SystemS System
Ocean phenomenon
Remotely sensed ocean property
Water-leaving signal
Atmospherictransmission
Noise
DigitizerTransducer
Spectral filterData transmissionto ground station
Upper ocean processes
Ocean / e-minteractions
Atmosphericphysics
Sensor physicsand technology
Signal processingcharacteristics
IFOV
Knowledge needed to
understand each stage of the
remote sensing process
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1921st Sep, 2004GODAE Summer School, Toulon
Sensor types and what they measureSensor types and what they measure
Geostrophiccurrents
Ocean geoidSea-floorbathymetry
Surface windsWave heightWave spectraInternal wavessurface slicks
Mixed-layertemperatureSkintemperature
ChlorophyllSuspendedparticles
Bathymetry
Oceancolour
Surfaceroughness
Multi-spectralscanners
Imagingspectrometers
Sea surfacetemperature
Infra-redimagingradiometers
VisibleWavebandsensors
Infra-red sensors
Microwavesensors
Surfaceslope
Radar instruments
ScatterometerImaging radarAltimeter
ACTIVEPASSIVE SENSORS
SensorClass
SensorType
Primarymeasure
Derivedparameters
Scanningmicrowaveradiometers
Salinity
Assimilation of SSHA in OCMs
Wind forcing of OCM. Assimilation of wave height orwave spectra in wave models
Assimilation of SST and salinity in OCMs
Re-initialisation or assimilation of C in biogeo-chemical models
Applications to models
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2021st Sep, 2004GODAE Summer School, Toulon
Stages of Processing : Levels of processed dataStages of Processing : Levels of processed dataStages of Processing : Levels of processed data
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2121st Sep, 2004GODAE Summer School, Toulon
How important are satellite ocean data ?How important are satellite ocean data ?How important are satellite ocean data ?Satellite remote sensing has opened up the study of global ocean phenomena
We can now ask questions about large scale processes that previously could not be addressedWe can now make observations of ocean scale phenomena which test and stretch theoretical models
21st Century Oceanography has become dependent on satellite observations
All branches of ocean science now expect to use satellite image dataInterest is no longer limited only to specialist “satellite oceanographers” There is a growing use of satellites for operational ocean monitoring and forecasting
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2221st Sep, 2004GODAE Summer School, Toulon
Fundamental limitations of satellite ocean remote sensing
Fundamental limitations of satellite Fundamental limitations of satellite ocean remote sensingocean remote sensing
Can observe only some of the ocean’s properties and variablesMeasures the ocean only at or near the surface
Although the surface is the most critical place to measure
Ocean measurements may be corrupted by the atmosphereSome methods cannot see through clouds at allMeasurements can be made only when the satellite is in the right placeAll measurements require calibration and validation using in situ data
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2321st Sep, 2004GODAE Summer School, Toulon
Observing ocean currents using satellite altimetry
Observing ocean currents Observing ocean currents using satellite altimetryusing satellite altimetry
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2421st Sep, 2004GODAE Summer School, Toulon
Principles of altimetryPrinciples of altimetry
Measure distance between satellite and seaDetermine position of satelliteHence determine height of sea surfaceOceanographers require height relative to geoid
Geoid Sea Surface
Referenceellipsoid
Orbitheight
Satelliteorbit
Altimetermeasurement (A)
Geoid undulation
Ocean dynamic surface topography (SSH)
SSH = Orbit - A - Geoid
Sea surface height
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2521st Sep, 2004GODAE Summer School, Toulon
Measuring distance with radarMeasuring distance with radar
Measure pulse travel time, 2T, from emit to returnh = T/c (c = 3 x 108 m/s)Resolution to 1cm needs a timing precision of 3 x 10-11s (30 picoseconds)Requires using chirp pulses and compressionAverage ~1000 pulsesApply corrections for reduced c in atmosphere / ionosphere, and surface reflection delays
Nadir view
Specular reflection
h
orbit
sea surface
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2621st Sep, 2004GODAE Summer School, Toulon
Typical radar altimeter pulse reflectionsTypical radar altimeter pulse reflectionsTypical radar altimeter pulse reflections
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2721st Sep, 2004GODAE Summer School, Toulon
Determining the orbitDetermining the orbit
Orbit affected by:Earth’s gravitational fieldAir drag (at 800 km loses 35cm height per orbit)Solar radiation pressure
Predict the orbitTracking the satellite
Laser ranging from the groundOnboard range and range-rate measurement to ground station (DORIS, PRARE)GPS
Model integrates the equations of motionRequires knowledge of gravity field
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2821st Sep, 2004GODAE Summer School, Toulon
Interpreting the Ocean Surface Interpreting the Ocean Surface TopographyTopography
Geoid (~100 m)Time invariantNeeds to be independently measured (gravity survey)GRACE (~200 km resn.)GOCE (~80 km resolution)
Tides (~1-2 m)Apply a tidal predictionNew tidal models derived from altimetrySpecial orbits needed to avoid tidal aliasing
Atmospheric pressure (~0.5 m)Apply inverse barometer correction (1mbar ~ 1 cm)
Dynamic topography (~1 m)The intended measurement
reference level
GeoidLow tide
High tideDynamic topography
Pressure
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2921st Sep, 2004GODAE Summer School, Toulon
Ocean dynamic topographyOcean dynamic topographyOcean dynamic topography
Estimated from Topex/Poseidon altimeter height measurements gridded and averaged over the 10 days of cycle 123, (20th Jan, 1996),
minus the best estimated geoid.This type of product is of limited value until the Geoid is known more precisely. Image generated with data produced by JPL on March 8th, 1996 and obtained from the JPL website.
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3021st Sep, 2004GODAE Summer School, Toulon
Using altimetry when the Geoid is not known
Using altimetry when the Geoid is not Using altimetry when the Geoid is not knownknown
Repeat-cycle analysis, when orbit errors are smallStep 1: Remove tide and atmospheric pressure contributions.Step 2: At each location, average over all overpassesStep 3: Subtract time average from individual overpasses to generate the sea-surface height anomalies (SSHA)
The result:Contains only the time-varying SSH signal at time scales shorter than the record length used to determine the mean SSHCannot provide any information about the mean flow
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3121st Sep, 2004GODAE Summer School, Toulon
Empirical orbit removalEmpirical orbit removal
If orbit errors dominateEither: Use repeat tracks
Subtract average of all tracksFit linear or quadratic function to each pass to remove trendResiduals give the time varying signal within the region
Or: Use cross-over pointsCompute height difference between ascending and descending tracksFit smooth function to each pass to minimise cross-over differencesSubtract this function to give SSH residual
A
B
Individual pass
Mean of all passesA B
A BResidual
Trend
De-trended residualA B
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3221st Sep, 2004GODAE Summer School, Toulon
SSH residual, or SSH anomalySSH residual, or SSH anomalySSH residual, or SSH anomaly
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:Altimetry.3321st Sep, 2004 GODAE Summer School, Toulon
Geostrophic currents from Geostrophic currents from AltimetryAltimetry
Assume geostrophic balance
Unavoidable limitationsMeasures only cross-track component of currentCannot recover currents near the equatorOnly variable (non-steady) currents are detectable
∂∂Hx
fvg
= f latitude= 2Ωsin( )
v gf
Hx
=∂∂
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3421st Sep, 2004GODAE Summer School, Toulon
The Gulf StreamThe Gulf StreamThe Gulf StreamAs detected by Altimetry: sees the variable currents only
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3521st Sep, 2004GODAE Summer School, Toulon
Limitations of Satellite AltimetryLimitations of Satellite AltimetryMeasures SSHA to an accuracy of 2-3 cm
Measures only along the precise orbit repeat trackDetects only the variable signal
Long term mean SSH lost in the geoidMean ocean currents cannot be detectedNeeds longer time series to measure lower frequenciesAn independent measure of gravity is needed
SSH unreliable in shelf seasThe tidal signal cannot be accurately predictedOther ageostrophic motions are likely
Currents cannot be recovered in equatorial watersGeostrophy not valid
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3621st Sep, 2004GODAE Summer School, Toulon
Measuring Significant Wave Height from Measuring Significant Wave Height from AltimetersAltimeters
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3721st Sep, 2004GODAE Summer School, Toulon
Theoretical waveform equationTheoretical waveform equationTheoretical waveform equation
( ) ⎟⎟⎠
⎞⎜⎜⎝
⎛−
⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟⎠
⎞⎜⎜⎝
⎛+=
sp tt
tt
HsKctP 2experf132τ
point"track " at the defined is 0constant
hpower widt-half antennaradiusearth
/12ln8
/2
height ocean wave rms
22ln16
2
slope-ocean wave totalrms pulse ed transmittofh power widt half
level seamean above satellite ofheight
2
22
2
212
22
===Ψ=
⎥⎦
⎤⎢⎣
⎡ ++
Ψ=Ψ
Ψ=
=
⎥⎥⎦
⎤
⎢⎢⎣
⎡+=
==
−
tK
a
saH
cHt
h
hcc
t
s=
H
R
e
e
Re
es
pτ
τ
where:
Indicates the echo magnitude
The steepnessof the waveform
The decay as thereflecting area moves away from nadir
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3821st Sep, 2004GODAE Summer School, Toulon
Typical altimeter echoesTypical altimeter echoesTypical altimeter echoes
1 second averages (1000 echoes)
(a) SWH = 9.29 m(b) SWH = 5.21 m(c) SWH = 2.75 m
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3921st Sep, 2004GODAE Summer School, Toulon
Examples of SWH products from TOPEXExamples of SWH products from TOPEX
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4021st Sep, 2004GODAE Summer School, Toulon
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4121st Sep, 2004GODAE Summer School, Toulon
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4221st Sep, 2004GODAE Summer School, Toulon
Estimating chlorophyll concentrations from
ocean colour
Estimating chlorophyll Estimating chlorophyll concentrations from concentrations from
ocean colourocean colour
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4321st Sep, 2004 GODAE Summer School, Toulon
Western Europe on 16Western Europe on 16thth October, 2003 October, 2003 --SeaWiFSSeaWiFS
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4421st Sep, 2004 GODAE Summer School, Toulon
Upwelling off Oregon, 6 Oct 2002Upwelling off Oregon, 6 Oct 2002
Chlorophyllmap derived
from SeawiFS
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4521st Sep, 2004 GODAE Summer School, Toulon
Principles of Ocean Colour Remote Principles of Ocean Colour Remote Sensing (OCRS): what can be detected?Sensing (OCRS): what can be detected?
Note that 1, 2 and 3 are all wavelength dependent. Measure the coloursignal in sufficient spectral detail to distinguish the oceanand atmospheric contents. Ideally the roughness should be known.
Atmosphericscattering
3. Aerosol and gaseous contentof atmosphere
Surfacereflection
2. Surface roughness
Absorbed
Scattered
Ocean coloursignal
1. Inherent Optical Propertiesof the sea water and its contents
1.1 km
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4621st Sep, 2004 GODAE Summer School, Toulon
Using colour to carry informationUsing colour to carry information
What is colour?The spectral distribution of visible lightThe spectrum is typically a continuum
How is it measured?A detector samples discrete wavebandsThe eye detects the response in threebands defined by three spectral functionsThe “colour” is simply the set of valuesmeasured in the different wavebands or “channels” .May be a few broad bands or many narrow bands
400 600 800Wavelength, nm
RadianceSignal to be
measured
400 600 800nm
400 600 800nm
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4721st Sep, 2004 GODAE Summer School, Toulon
Colour capability of different sensorsColour capability of different sensors
Sensor Number of channels Character of channels
Human eye 3 Interleaved
Landsat 4 Wide bands, contiguous
CZCS 4 Narrow, visible
SeaWiFS 8 Narrow, separated Vis and near IR
MERIS 13 Narrow, Vis and near-IR
MODIS 9 Narrow, Vis and near-IR
CASI (airborne) 64 Spectrometer
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48October 2003 SOES3016 Ocean colour lectures
Information from ocean colourInformation from ocean colourSunlight reflected from below the sea surface
402-422 nm
433-453 nm
480-500 nm
500-520 nm
545-565 nm
Colour composite
Derived Chlorophyll-a
FunctionData from SeaWiFS
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4921st Sep, 2004 GODAE Summer School, Toulon
Ocean colour image : English ChannelOcean colour image : English Channel
555 nm → red gun510 nm → green gun443 nm → blue gun
Enhanced near-real colour composite of English Channel from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS); 19th May, 1998.
Enhanced near-real colour composite of English Channel from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS); 19th May, 1998.
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5021st Sep, 2004 GODAE Summer School, Toulon
What affects light in the sea ?What affects light in the sea ?Light entering the sea:
Comes from the sunoften scattered by the atmosphere or clouds (75%-95% of TOA radiance)
Light in the seais absorbed (1)is scattered (2)may be emitted by fluorescence (3)may be frequency shifted (Raman scattering) (4)
Light leaving the seaconsists of photons which have been scattered into a direction which brings them back to the surfacewavelength distribution (colour) is altered by the sea compared with those that enter the sea.
1
4 32
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5121st Sep, 2004 GODAE Summer School, Toulon
What affects the colour of the sea? What affects the colour of the sea? Spectral makeup determined by absorption (a) and scattering (b)Reflectance is roughly 0.33 (b/a).Backscattering (b) is caused by:
Phytoplankton (bc)Suspended particulate matter (bs)The water molecules themselves (bw)
Absorption (a) is caused by:Phytoplankton (ac)The water molecules themselves (aw)Dissolved organic material (DOM, Gelbstoff or yellow substance) (ay)
Each ax and bx has its particular spectral formTherefore the colour depends on the concentrations of those
water constituents which interact with the light.
W
C
S
WC
Y
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5221st Sep, 2004 GODAE Summer School, Toulon
Spectral variation of absorption and Spectral variation of absorption and scattering of light in the seascattering of light in the sea
Absolute values for sea water absorption, a and backscattering, bb
Relative values of absorption, a and backscattering, b for Chlorophyll in Phytoplankton
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5321st Sep, 2004 GODAE Summer School, Toulon
Reflectance spectra associated with Reflectance spectra associated with phytoplanktonphytoplankton
The dashed line is the reflectance associated with pure sea water
The arrow indicates the direction of increasing chlorophyll concentration for the different lines drawn.
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5421st Sep, 2004 GODAE Summer School, Toulon
What can be measured from ocean What can be measured from ocean colour?colour?
In principleColour can tell us about relative and absolute concentrations of those water constituents which interact with the light.Hence we measure chlorophyll, yellow substance and sediment load
In practiceDifficult to distinguish independently varying water constituentsCASE 1 waters are where the phytoplankton population dominates the optical properties (typically open sea)CASE 2 waters are where other factors (terrigenous DOM, suspended or river borne sediments) are also present.Most success with CASE 1 waters so far, using green/blue ratio algorithms for chlorophyll, of the form: C = A(R550/R490)B
Accuracy for C of ~ ±30% is achievable in open oceanData from CASE 2 waters are harder to analyse.
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5521st Sep, 2004 GODAE Summer School, Toulon
Wavebands for important ocean colour sensors
Note the bands common to most sensors:
440 nm
490 nm
550-565 nm
670 nm
750 nm
870 nm
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5621st Sep, 2004 GODAE Summer School, Toulon
Applications of Ocean ColourApplications of Ocean Colour
Measurement of ChlorophyllGlobal distribution of chlorophyllEstimates of primary productionDetection of plankton blooms
Measure optical diffuse attenuation coefficientMeasurement of suspended sedimentMeasurement of dissolved organic materialAs a tracer of dynamical processesMonitoring pollutionWater depth
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5721st Sep, 2004 GODAE Summer School, Toulon
Global 8Global 8--day composite of Chlorophyllday composite of Chlorophyll--aadistribution from SeaWiFSdistribution from SeaWiFS
12 July - 19 July 2001. Scale below in mg / m3 Chl-a
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5821st Sep, 2004 GODAE Summer School, Toulon
Global monthly composite of Chlorophyll-adistribution from SeaWiFS
Global monthly composite of ChlorophyllGlobal monthly composite of Chlorophyll--aadistribution from SeaWiFSdistribution from SeaWiFS
July 2001
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5921st Sep, 2004 GODAE Summer School, Toulon
Tracking bloom development with SeaWiFSTracking bloom development with SeaWiFSTracking bloom development with SeaWiFS
Provided by the SeaWiFS Project, NASA/Goddard Space Flight
Center and ORBIMAGE
6-13 Mar
200114-21 Mar22-29 Mar
30 Mar - 6 Apr7-14 Apr
15-22 Apr23-30 Apr
1- 8 May
9-16 May
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6021st Sep, 2004 GODAE Summer School, Toulon
Coccolithophore bloom in the Celtic SeaCoccolithophoreCoccolithophore bloom in the Celtic Seabloom in the Celtic Sea
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6121st Sep, 2004 GODAE Summer School, Toulon
Coccolithophore bloomsCoccolithophoreCoccolithophore bloomsblooms
http://www.soes.soton.ac.uk/staff/tt/eh/satbloompics.htmlDr Toby Tyrrell
Emiliania huxleyi (Ehux)
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6221st Sep, 2004 GODAE Summer School, Toulon
Global map of K490 (diffuse attenuation coeff)Global map of KGlobal map of K490490 (diffuse attenuation (diffuse attenuation coeffcoeff))
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6321st Sep, 2004 GODAE Summer School, Toulon
PAR derived from SeaWiFSPAR derived from SeaWiFSPAR derived from SeaWiFS
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6421st Sep, 2004 GODAE Summer School, Toulon
A conclusion on ocean colourA conclusion on ocean colourA conclusion on ocean colourDevelopment lags behind other remote sensing methodsInherently difficult to retrieve ocean variables accurately and confidentlyBut the rich information content has considerable potential = a scientific and technical challenge Just beginning to use ocean colour products in ocean modelsA challenging subject for future generations of young researchers !
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6521st Sep, 2004GODAE Summer School, Toulon
Measuring sea surface temperature by radiometers
on satellites
Measuring sea surface Measuring sea surface temperature by radiometers temperature by radiometers
on satelliteson satellites
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6621st Sep, 2004GODAE Summer School, Toulon
A mesoscale eddy in the South AtlanticA mesoscale eddy in the South AtlanticA mesoscale eddy in the South Atlantic
Apparent sea surface temperature in the South Atlantic from an infra-red sensor
Cooler Warmer
The image size is 500 x 500 km.
The data set consists of 1/4 million precise measurements of temperature .
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6721st Sep, 2004GODAE Summer School, Toulon