osu_08/1/2005_davis.1 coast risk reduction activities curtiss o. davis college of oceanic and...

OSU_08/1/2005_Davis.1

COAST Risk Reduction ActivitiesCOAST Risk Reduction Activities

Curtiss O. DavisCollege of Oceanic and Atmospheric Sciences

Oregon State University, Corvallis, Oregon 97331

[email protected]

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HES-CW Products and ApplicationsHES-CW Products and Applications

Products: • Spectral water leaving radiances• Chlorophyll• Chlorophyll fluorescence• Turbidity• Spectral absorption and scattering

Applications: • Water quality monitoring• Coastal hazard assessment• Navigation safety• Human and ecosystem health awareness (Harmful Algal Blooms)• Natural resource management in coastal and estuarine areas• Climate variability prediction (e.g., role of the coastal ocean in the

carbon cycle)• Nowcast and Forecast models of the coastal ocean

OSU_08/1/2005_Davis.3

COAST and Risk Reduction ActivitiesCOAST and Risk Reduction Activities

• The Coastal Ocean Applications and Science Team (COAST) was created in August 2004 to support NOAA to develop coastal ocean applications for HES-CW:– Mark Abbott, Dean of the College of Oceanic and Atmospheric Sciences

(COAS) at Oregon State University is the COAST team leader,– COAST activities are managed through the Cooperative Institute for

Oceanographic Satellite Studies (CIOSS) a part of COAS, Ted Strub, Director

– Curtiss Davis, Senior Research Professor at COAS, is the Executive Director of COAST.

• Initial activity to evaluate HES-CW requirements and suggest improvements• Paul Menzel Presented GOES-R Risk Reduction Program at the first COAST

meeting in September 2004 and invited COAST to participate.– Curt Davis and Mark Abbott presented proposed activities in Feb. 2005. – CIOSS/COAST invited to become part of GOES-R Risk Reduction Activity

beginning in FY 2006.– Proposal Submitted to NOAA Sept 6, 2005.– Activities to begin in May, 2006.

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Risk Reduction Activities:Principal Roles of Co-Investigators

Risk Reduction Activities:Principal Roles of Co-Investigators

• Curtiss Davis, program management, calibration, atmospheric correction• Mark Abbott, COAST Team Leader• Ricardo Letelier, phytoplankton productivity and chlorophyll fluorescence,

data management • Peter Strutton, coastal carbon cycle, Harmful Algal Blooms (HABs)• Ted Strub, CIOSS Director, coastal dynamics, links to IOOSCOAST Participants:• Bob Arnone, NRL, optical products, calibration, atmospheric correction,

data management• Paul Bissett, FERI, optical products, data management• Heidi Dierssen, U. Conn., benthic productivity• Raphael Kudela, UCSC, HABs, IOOS• Steve Lohrenz, USM, suspended sediments, HABs• Oscar Schofield, Rutgers U., product validation, IOOS, coastal models• Heidi Sosik, WHOI, productivity and optics• Ken Voss, U. Miami, calibration, atmospheric correction, opticsNOAA/STAR • Menghua Wang, atmospheric correction• Mike Ondrusek, calibration, MOBY

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HES-CW Data flow and Risk Reduction Activities

HES-CW Data flow and Risk Reduction Activities

Raw sensor data

Calibrated radiances

at the sensor

Water Leaving

Radiances

In-Water Optical

Properties

Applications and products

Users

CalibrationCalibration Atmospheric Correction

Atmospheric Correction

Optical properties Algorithms

Optical properties Algorithms

Product models and algorithms

Product models and algorithms

now-cast and forecast models Data

assimilation into models

Data assimilation into models

Education and outreach

OSU_08/1/2005_Davis.6

Approach to Algorithm DevelopmentApproach to Algorithm Development

• Directly involve the ocean color community which has extensive algorithm development experience with SeaWiFS and MODIS– NASA funded science teams developed, tested and validated calibration,

atmospheric correction and product algorithms– Additional product development and testing funded by U. S. Navy– Documented in Technical reports, publications and Algorithm

Theoretical Basis Documents (ATBDs)– Algorithms are continuously evaluated and updated; SeaWiFS and

MODIS data routinely reprocessed to provide Climate Data Records with latest algorithms.

• Design program to assure compatibility of HES-CW products with VIIRS– VIIRS algorithms based on MODIS ATBDs– Similar calibration and atmospheric correction approaches– Use the same ocean calibration sites for vicarious calibration

• Initial plans and algorithms based on SeaWiFS and MODIS experience modified to fit HES-CW in geostationary orbit.

• Advanced algorithms tested and implemented when available.• Early tests planned using airborne hyperspectral data.

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Risk Reduction ActivitiesRisk Reduction Activities

• Approach to Algorithm Development– Experience with SeaWiFS and MODIS– Field Experiments to collect prototype HES-CW data

• Planned Risk Reduction activities:– Calibration and vicarious calibration– Atmospheric correction– Optical properties– Phytoplankton chlorophyll, chlorophyll fluorescence and productivity– Benthic productivity– Coastal carbon budget– Harmful algal blooms– Data access and visualization– Education and public outreach

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Risk Reduction Plans: CalibrationRisk Reduction Plans: Calibration

• Develop plan for on-orbit calibration:– At sensor radiance calibration must be +/- 0.3% to meet proposed

Chlorophyll product accuracy requirement of +/- 30%– Follow SeaWiFS and MODIS approach using moon imaging, solar

diffuser and vicarious calibration to achieve this accuracy– Risk reduction activity includes planning for highly accurate water

leaving radiance measurements at two clear water ocean sites (NOAA ORA led effort)

– Additional coastal sites for validation of atmospheric correction in coastal waters and validation of coastal products

– Coordinated effort between NOAA ORA, CICS, CIOSS • Good on-orbit calibration is only possible if the instrument is properly

designed to provide stable accurate radiances over its lifetime. This must be demonstrated with good pre-launch calibration and characterization for MTF, stray light, etc.– Support NOAA/NASA to provide feed back to instrument builders. – Pre-launch calibration requirement is +/- 5% absolute, +/- 0.5% channel to

channel. The needed higher accuracies on-orbit can only be achieved with vicarious calibration.

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Atmospheric Correction ChallengesAtmospheric Correction Challenges

We anticipate three major challenges in developing the atmospheric correction algorithms for HES-CW.

1. Adaptation of the current algorithms for SeaWiFS and MODIS to the geostationary viewing geometry.

2. Dealing with Absorbing Aerosols which are common downwind from urban and industrial areas.

3. In coastal waters with high levels of suspended sediments, or large phytoplankton blooms the contributions at the NIR bands are not negligible. This can lead to significant underestimation of the satellite-derived water-leaving radiance spectrum (SeaWiFS, MODIS).

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Current Atmospheric Correction AlgorithmsCurrent Atmospheric Correction Algorithms

w is the desired quantity in ocean color remote sensing.

Tg is the sun glint contribution—avoided/masked and residual

contamination is corrected.

twc is the whitecap reflectance—computed from wind speed.

r is the scattering from molecules—computed using the Rayleigh

lookup tables.

A = a + ra is the aerosol and Rayleigh-aerosol contributions —

estimated using aerosol models.For Case-1 waters in the open ocean, w is usually negligible at

765 & 865 nm. A can be estimated using these two NIR bands.

w is the desired quantity in ocean color remote sensing.

Tg is the sun glint contribution—avoided/masked and residual

contamination is corrected.

twc is the whitecap reflectance—computed from wind speed.

r is the scattering from molecules—computed using the Rayleigh

lookup tables.

A = a + ra is the aerosol and Rayleigh-aerosol contributions —

estimated using aerosol models.For Case-1 waters in the open ocean, w is usually negligible at

765 & 865 nm. A can be estimated using these two NIR bands.Menghua Wang, NOAA/NESDIS/ORAMenghua Wang, NOAA/NESDIS/ORA

SeaWiFS and MODIS algorithm (Gordon and Wang 1994)SeaWiFS and MODIS algorithm (Gordon and Wang 1994)

t r A twc Tg tw, L 0 F0

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HES-CW Channels and Atmospheric Transmission Windows

0

0.2

0.4

0.6

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

Tra

nsm

ittan

ce

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

Menghua Wang, NOAA/NESDIS/ORAMenghua Wang, NOAA/NESDIS/ORA

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Risk Reduction Plans: Atmospheric correctionRisk Reduction Plans: Atmospheric correction

• Atmospheric correction needed to produce water-leaving radiance.• Approach:

– Evolution of algorithms from the current SeaWiFS, MODIS algorithms.– Adjustments for Geostationary orbit geometry– Adaptation to different spectral channels– Development of coastal atmospheric correction algorithm:

- Address absorbing aerosols,- Address high reflectance in coastal waters where NIR channels cannot

be used for aerosol calculations. – Current effort between NOAA ORA, CICS, CIOSS

- Providing feedback to NOAA/NASA and instrument and spacecraft vendors to assure spectral channel characteristics, etc.

– Would like to expand effort to include collaborative efforts with CIMSS, CIRA, others?

– Explore advantages of using HES sounder and ABI data to improve atmospheric correction.

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Risk Reduction Plans: In-water Optical Properties 1Risk Reduction Plans: In-water Optical Properties 1

Remote-sensing reflectance (Rrs, water-leaving radiance normalized by

the downwelling irradiance) 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. In optically deep waters (when the bottom is not imaged),

Rrs() = f[bb()/a() + bb()] (2)

Where f is a proportionality constant that varies slightly as a function of the shape of the volume scattering function and the angular distribution of the light field. The backscattering coefficient bb() is the sum of the

backscattering from the phytoplankton, detritus, suspended sediments and the water itself. The absorption coefficient a() is the sum of the absorption by CDOM, phytoplankton, detritus, suspended sediments and the water itself.

OSU_08/1/2005_Davis.14


• Algorithms for SeaWiFS and MODIS use spectral channel ratios to calculate specific products, such as suspended sediments, chlorophyll and CDOM.

– This approach does not work if the bottom is imaged (e.g. West Florida Shelf), or in the presence of high levels of suspended sediments (e.g. Mississippi River Plume)

• Excellent Radiative Transfer Models (e.g. HYDROLIGHT) are available to model the light field – the challenge for remote sensing is to invert those models to go from remote sensing reflectance to estimates of the in-water constituents.

• Two approaches are demonstrated that solve the full problem and produce values for water column optical properties, bathymetry and bottom type.

– A predictor-corrector approach is used to invert a semi-analytical model

– A look-up table approach has been used to invert HYDROLIGHT.

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Bathymetry, Bottom Type and Optical Properties Example Approach: Semi-Analytical Models

Bathymetry, Bottom Type and Optical Properties Example Approach: Semi-Analytical Models

a) Bottom type and b) bathymetry derived from an AVIRIS image of Tampa Bay, FL using automated processing of the hyperspectral data. Accurate values were retrieved in spite of the fact that water clarity varies greatly over the scene.

(Lee, et al., J. Geophys. Research, 106(C6), 11,639-11,651, 2001.)

Seagrass beds

Sand barsNavigation channel

• Semi-analytical model developed to resolve the complex optical signature from shallow waters.

• Simultaneously produces bathymetry, bottom type, water optical properties.

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Bathymetry, Bottom Type and Optical Properties Example Approach: Look-up Tables

Bathymetry, Bottom Type and Optical Properties Example Approach: Look-up Tables

Interpretation of hyperspectral remote-sensing imagery via spectrum matching and look-up tables. Mobley, C. D., et al., Applied Optics, In Press.Interpretation of hyperspectral remote-sensing imagery via spectrum matching and look-up tables. Mobley, C. D., et al., Applied Optics, In Press.

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Planned Risk Reduction Activities:

• NASA and the Navy have a set of band ratio type algorithms to produce in-water optical properties from SeaWiFS and MODIS data.

• Initial approach will be to adapt those algorithms for use with HES-CW. • Main Risk Reduction effort will be to develop comprehensive methods along

the lines of the Lee et al. and Mobley, et al. approaches that have been demonstrate for airborne hyperspectral data.– Will work in all conditions even when the bottom is imaged

• Algorithm work can be initiated immediately with existing data sets but the HES-CW demonstration data set will be essential for the full demonstration of the algorithms.

• Initiate effort in 2006 to use existing data sets and to participate in the planning of the HES-CW demonstration experiment to assure that all of the essential data is collected.

• Expanded effort in 2009 utilizing the demonstration data set and Web based data system.

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Risk Reduction Plans: Phytoplankton chlorophyll, chlorophyll fluorescence and productivity


• Chlorophyll and Chlorophyll fluorescence – Fluorescence unambiguously associated with chlorophyll– Signal is small, but use of baseline approach greatly reduces impact of

atmosphere on retrievals– Amount of fluorescence per unit chlorophyll varies as function of light,

phytoplankton physiology, and species composition• Validation relies on long time series of high quality measurements to ensure

consistency– IOOS, MOBY sites– Analysis of MODIS Aqua and Terra data sets– AVIRIS or other overflights

• Estimates of chlorophyll and productivity– Continued field and satellite data analysis– Modeling of quantum yield of fluorescence based on laboratory

analyses, comparison with field measurements– Incorporate quantum yield into productivity models– Compare with recent chlorophyll/backscatter models using SeaWiFS

OSU_08/1/2005_Davis.19

Weighting factor used to compensate for off-center FLH

MODIS FLH bands: avoid oxygen absorbance at 687 nmMODIS FLH bands: avoid oxygen absorbance at 687 nm

letelier

MODIS Fluorescence wavebands (!#, 14, and 15) superimposed to the upwelling light spectrum of the ocean for surface waters containing 0.01 and 10 mg chl/m3Note that band 14 is not centered around 683 nm. This is due to the fact that at 683 nm it would be affected by the absorption of oxygen at 685 nm in the atmosphere.

OSU_08/1/2005_Davis.20

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 0.05 0.1 0.15 0.2 0.25

Oregon Drifters FLH, W m-2 m-1 sr-1

MO

DIS

Terr

a F

LH,

W m

-2

m-1 s

r-1

MODIS Terra FLH vs Oregon optical drifters derived FLH

MODIS Terra FLH vs Oregon optical drifters derived FLH

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From Hoge et al.

FLH vs. chlorophyll

FLH vs. CDOM

Testing the MODIS FLH AlgorithmTesting the MODIS FLH Algorithm

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Frequent measurements in morning can elucidate quantum yield of fluorescenceFrequent measurements in morning can elucidate quantum yield of fluorescence

I nitial slope proportional to FI nitial slope proportional to F

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• Proposed activities:

- Development of chlorophyll and fluorescence algorithms based on SeaWiFS and MODIS legacy and modified to fit HES-CW in geostationary orbit.

- Characterization of chlorophyll and chlorophyll fluorescence algorithm sensitivity based on HES-CW (waveband position and SNR) characteristics (i.e. Letelier and Abbott 1996)

- Generation of HES-CW synthetic chlorophyll and fluorescence products in coastal (case II) waters using Hyperion and PHILLS data, and data from field experiments in 2007-2008.

- These field experiments will serve to:

1) Validate a chlorophyll algorithm for case II waters based on chlorophyll fluorescence.

2) Assess diurnal changes in algal physiology affecting carbon:chlorophyll ratio and the chlorophyll fluorescence efficiency.

3) Evaluate how water column stability and CDOM concentrations affect the apparent relationship between chlorophyll concentration and the chlorophyll fluorescence in algorithms inherited from SeaWiFS and MODIS.

4) Develop improved productivity models incorporating laboratory estimates of quantum yield.

OSU_08/1/2005_Davis.24

Risk Reduction Plans: Harmful Algal Blooms Background

Risk Reduction Plans: Harmful Algal Blooms Background

• In the Gulf of Mexico, blooms of the toxic algae Karenia brevis result in shellfish bed closures and lost tourism that cost the state of Florida millions of dollars each year.

• Similar problems in other parts of the country with other toxic species.

• Ship based monitoring very expensive and time consuming

• Inadequate data frequently leads to unnecessary closings.

• HABSOS system being developed to provide early warnings using SeaWiFS data and models

• HES-CW will greatly improve warning systems like HABSOS

– More frequent data for cloud clearing

– Higher spatial resolution to assess conditions closer to the shell fish beds and beaches

• In the Gulf of Mexico, blooms of the toxic algae Karenia brevis result in shellfish bed closures and lost tourism that cost the state of Florida millions of dollars each year.

• Similar problems in other parts of the country with other toxic species.

• Ship based monitoring very expensive and time consuming

• Inadequate data frequently leads to unnecessary closings.

• HABSOS system being developed to provide early warnings using SeaWiFS data and models

• HES-CW will greatly improve warning systems like HABSOS

– More frequent data for cloud clearing

– Higher spatial resolution to assess conditions closer to the shell fish beds and beaches

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Some properties have a diel cycle associated with it.

Documenting the diel dynamics can thus potentiallyassist in documenting and identifying material in the ocean

-81º-83º-85º-87º

-82.5º-83º-83.5º-84º

27º

27.5º

28º

26.5ºOctober 2001 EcoHAB StationOctober 2001 EcoHAB Diel Station

29º

27º

25º

31ºCase example:

Detection of K. brevis

Frequent sampling can assist in detection and classification of HABs

Frequent sampling can assist in detection and classification of HABs

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Cel

ls L

-1

(x10

5 )

0.0

0.4

0.8

1.2

1.6

0.20

0.25

0.30

dissolved a(440) (m-1)

A) Karenia brevis cell abundance

08:00 14:00 20:00 02:00Time of Day

10a

total 676 nm

5

0

Dep

th (

m)

A)

B)

0.20

0.10

When K. brevisBlooms, conditions

tendto be calm. Under

these Conditions the cells exhibit a dramatic

dielmigration. The net

resultis a 10X increase in

cells at the air-sea

interface over a several

hour period. This unique feature will be readily detected

in HES-CW data.

OSU_08/1/2005_Davis.27

HABSOS can immediately utilize improved spatial resolution and frequency of coverage from HES-CWHABSOS can immediately utilize improved spatial

resolution and frequency of coverage from HES-CW

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Risk Reduction Plans: Harmful Algal BloomsRisk Reduction Plans: Harmful Algal Blooms

Proposed Risk Reduction Activities:• Improve methods for early detection of HABs from optical remote sensing

data– Not all HABs have a unique optical signature – use additional

information, e.g. vertical migration to identify blooms.– Specific methods needed for each region of the country to identify local

species, etc.• Continue development of models of HAB dynamics

– Higher frequency of HES-CW data critical for cloud clearing and to include vertical migration in the models

• Prepare to use HES-CW data in warning systems, such as, HABSOS– Increased frequency of sampling for cloud clearing will provide faster

updates allowing more precise system for warnings - Avoid unnecessary costly beach and shellfish bed closures

• Strong education component to educate the state and local managers and the public as to the benefits of HES-CW data and improved models and forecasts.

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Coastal Carbon CycleCoastal Carbon Cycle

• Detailed studies of the Oregon coastal upwelling system to determine its role as a CO2 source or sink.

• pCO2 in coastal (and other) environments is associated with

characteristic chlorophyll and SST signatures.

• Using multiple satellite products and techniques, such as multiple linear regression, we have developed an approach to determine sea surface pCO2 from space.

• Combine this with winds from either scatterometer(s) or coastal/buoy meteorological stations to facilitate flux calculations.

(Hales et al., 2004. Atmospheric CO2 uptake by a coastal upwelling system. Global Biogeochemical Cycles, 19, GB1009, 10.1029/2004GB002295.)

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Cascade Head: Repeat

sections

Cape Perpetua: Extended sections

Coastal Oregon Study SiteCoastal Oregon Study Site

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Undersaturation of CO2 in coastal watersUndersaturation of CO2 in coastal waters

Freshly upwelled water near the Oregon coast is a CO2 source to the atmosphere. As the water moves offshore the phytoplankton bloom making the same waters a CO2 sink.

Freshly upwelled water near the Oregon coast is a CO2 source to the atmosphere. As the water moves offshore the phytoplankton bloom making the same waters a CO2 sink.

Cascade Head time series

Cascade Head time series

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Coastal CO2: Relationship to physics and biologyCoastal CO2: Relationship to physics and biology

Productivity & CO2 uptake

N limitation offshore

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Risk Reduction Plans: Coastal CO2 FluxesRisk Reduction Plans: Coastal CO2 Fluxes

• The coastal ocean plays a large and poorly measured role in the global carbon cycle.

– Addresses NOAA’s climate change goals • HES-WC will provide valuable data to study this process;

– Temporal sampling of 3 hours will enable basic budgets to be calculated and the tracking of processes such as productivity and subduction.

– This is a dynamic environment – any ability to ‘clear’ or alias clouds will enhance badly-needed coverage.

• Coupling with NASA’s Orbiting Carbon Observatory (2008) will add significant coupling to atmospheric data.

• Proposed risk reduction activities:– Continue to develop and refine current models and algorithms using

SeaWiFS, MODIS and shipboard data.– Update algorithms to take advantage of HES-CW data.– Adapt approach to take advantage of IOOS and associated modeling

efforts.

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Risk Reduction Plans: Now-cast and forecast models

Risk Reduction Plans: Now-cast and forecast models

• Now-cast and forecast models are currently under development for the coastal ocean;– Model development will be closely coupled with IOOS,– Current emphasis is on getting the physics right and on assimilating

surface currents, wind data and other physical parameters,– Some bio-optical models that could make excellent use of HES-CW data

have been demonstrated,– Work in this area will require the HES-CW demonstration data set to be

collected in 2007-2008,– Plan to initiate modeling efforts in 2009.

• A second class of prognostic models for HABs are being developed for several coastal regions– Begin limited effort in 2006 to support those models specifically

emphasizing the utility of HES-CW data to improve skill of those models– Utilize the HES-CW demonstration data set beginning in 2009.

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EcoSim 2 Model Output for July 31, 2001 HyCODE experiment at (LEO-15)

EcoSim 2 Model Output for July 31, 2001 HyCODE experiment at (LEO-15)

July 31 SeaWiFS Chlor-a (mg/m3)

.5

2

3

4

5

39:30N

39:00N

Node A

UCSB

Small diatoms

Large diatoms

Satellite Measured

Bissett, et al., Submitted J. Geophys. Res.

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Risk Reduction Plans: Data ManagementRisk Reduction Plans: Data Management

• Data processing, distribution and archiving issues.– Need more processing capacity for atmospheric correction and product

algorithms (3-5 X the calibration processing)– Need for reprocessing with updated calibrations and new algorithms to

make Climate Data Records and the need to archive CDRs- Planned data system not sized for reprocessing.

• Next generation product generation and delivery services will build on the notion of “web services,” which are industry standard tools for building complex services from building block components and multiple data streams. Web services can provide new capabilities that are not anticipated in the original systems design. By designing these services as linked components rather than monolithic systems, GOES-R can provide a much greater degree of flexibility and evolution within a cost-constrained environment.

• We propose monitoring and providing advice on current plans for the HES-CW data system, with specific risk reduction activities beginning in 2008. – Web based server with the the Simulated HES-CW data from the

proposed experiments. Include all ancillary data and access to models for testing.

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BACKUPBACKUP

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SummarySummary

• HES-CW will provide an excellent new tool for the characterization and management of the coastal ocean.

• Risk Reduction activities focus on calibration and algorithm development;– Initially provide SeaWiFS and MODIS heritage calibration and algorithms;– 2006-2008 field experiments to develop example HES-CW data for

- algorithm development and testing,- Coordination with IOOS for in-situ data and coastal ocean models,- Demonstrate terabyte web-based data system.

– Major focus on developing advanced algorithms that take advantage of HES-CW unique characteristics.

• Efforts coordinated with NOAA ORA, NMFS and NOS with a focus on meeting their operational needs.

Special thanks to Ted Strub, Amy Vandehey and the COAST for their hard work getting this program started.

Thanks to NOAA for funding and particularly to Stan Wilson, John Pereira, Eric Bayler and Paul Menzel for their support and guidance.

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COAST proposed new HES-CW Channel Specifications


Nominal Threshold

Channel Center

Nominal Threshold Resolution

Nominal Threshold

Signal to Noise

Nominal GOAL Channel Center

Wavelength (um)

Nominal GOAL

Resolution

Nominal Goal Signal to Noise

0.412 0.02 0.345 0.02

0.443 0.02 0.38 0.020.49 0.02 0.407 through 0.987 0.010.51 0.02 0.57 0.010.53 0.02 0.72 0.020.55 0.02 1.24 0.040.58 0.02 1.38 0.03

0.61 0.02 1.61 0.06

0.645 0.01 2.26 0.05

0.667 0.01 11.2 (2 km) 0.8

0.678 0.01 12.3 (2 km) 1

0.709 0.02

Nominal Threshold

Horiz. Resolution

Nominal Goal Horiz.

Resolution

0.75

COAST Proposed HES-CW Channel Specifications

375-meters (at Equator)


except for LW IR channels.865 0.02

300 to 1 all channels


0.02

OSU_08/1/2005_Davis.40



Nominal Threshold

Channel Center

Nominal Threshold Resolution

Nominal Threshold

Signal to Noise

Nominal GOAL Channel Center

Wavelength (um)

Nominal GOAL

Resolution

Nominal Goal Signal to Noise

0.412 0.02 0.345 0.02

0.443 0.02 0.38 0.020.49 0.02 0.407 through 0.987 0.010.51 0.02 0.57 0.010.53 0.02 0.72 0.020.55 0.02 1.24 0.040.58 0.02 1.38 0.03

0.61 0.02 1.61 0.06

0.645 0.01 2.26 0.05

0.667 0.01 11.2 (2 km) 0.8

0.678 0.01 12.3 (2 km) 1

0.75 0.02

Nominal Threshold

Horiz. Resolution

Nominal Goal Horiz.

Resolution

0.865

COAST Proposed HES-CW Channel Specifications



except for LW IR channels

1.000 (0.72 backup)

0.04 (0.02)



0.02

OSU_08/1/2005_Davis.41

Frequency of Sampling and Prioritizing Goal Requirements

Frequency of Sampling and Prioritizing Goal Requirements

• COAST top priority goals are:– Higher frequency of sampling– Goal channels for atmospheric correction– Hyperspectral instead of multispectral

• Threshold requirement is to sample all Hawaii and Continental U. S. coastal waters once every three hours during daylight

– Plus additional hourly sampling of selected areas

• Goal requirement is hourly sampling of all U.S. coastal waters is strongly recommended, for cloud clearing and to better resolve coastal ocean dynamics.

• Goal requirements compete with each other, e.g. higher spatial resolution makes it harder to increase sampling frequency or SNR.

• Threshold requirement is to sample all Hawaii and Continental U. S. coastal waters once every three hours during daylight

– Plus additional hourly sampling of selected areas

• Goal requirement is hourly sampling of all U.S. coastal waters is strongly recommended, for cloud clearing and to better resolve coastal ocean dynamics.

• Goal requirements compete with each other, e.g. higher spatial resolution makes it harder to increase sampling frequency or SNR.

HES-CW built to the threshold requirements will be a dramatic improvement over present capabilities for coastal imaging.

OSU_08/1/2005_Davis.42

Proposed Experiments to collect simulated HES-CW data (2 of 2)

Proposed Experiments to collect simulated HES-CW data (2 of 2)

• Experimental Design– Choose sites with IOOS or other long term monitoring and modeling

activities– Intensive effort for 2 weeks to assure that all essential parameters are

measured:- Supplement standard measurements at the site with shipboard or

mooring measurements of water-leaving radiance, optical properties and products expected from HES-CW algorithms,

- Additional atmospheric measurements as needed to validate atmospheric correction parameters,

- As needed, enhance modeling efforts to include bio-optical models that will utilize HES-CW data.

– Aircraft overflights for at least four clear days and one partially cloudy day (to evaluate cloud clearing) during the two week period. - High altitude to include 90% or more of the atmosphere- 30 min repeat flight lines for up to 6 hours to provide a time series for

models and to evaluate changes with time of day (illumination, phytoplankton physiology, etc.)

• All data to be processed and then distributed over the Web for all users to test and evaluate algorithms and models.

OSU_08/1/2005_Davis.43

Chlorophyll and chlorophyll fluorescence of optically-complex coastal waters

Chlorophyll and chlorophyll fluorescence of optically-complex coastal waters

MODIS Terra scene from 3 October 2001.

• The ratio of Fluorescence Line

Height (FLH) to chlorophyll is a good indicator of the health of the

phytoplankton population.

• FLH separates chlorophyll from

suspended sediments in the

Columbia River Plume.

Fluorescence line height not available from VIIRS.

MODIS Terra scene from 3 October 2001.

• The ratio of Fluorescence Line

Height (FLH) to chlorophyll is a good indicator of the health of the

phytoplankton population.

• FLH separates chlorophyll from

suspended sediments in the

Columbia River Plume.

Fluorescence line height not available from VIIRS.

OSU_08/1/2005_Davis.44

Monterey Bay Harmful Algal BloomMonterey Bay Harmful Algal Bloom

Harmful Algal Bloom in Monterey Bay threatens

beach areas.

• Bloom near coast and on the order of 2 x 5 km would not be

resolved in 1 km VIIRS data.

• Additional channels on HES-CW aid bloom identification.

Harmful Algal Bloom in Monterey Bay threatens

beach areas.

• Bloom near coast and on the order of 2 x 5 km would not be

resolved in 1 km VIIRS data.

• Additional channels on HES-CW aid bloom identification.

PHILLS-2 airborne hyperspectral data from

Paul Bissett, Florida Environmental Research Institute. (October 2002 Ceratium spp. bloom)

OSU_08/1/2005_Davis.45

HES-Will support the higher temporal and spatial resolution required for coastal models

HES-Will support the higher temporal and spatial resolution required for coastal models

July 31 SeaWiFS Chlor-a (mg/m3)

.5

2

3

4

5

39:30N

39:00N

Node A

UCSB

Small diatoms

Large diatoms

Satellite Measured

ECOSIM run for July 31, 2001 with ROMS Physical model 15 minute time step and

300 m spatial resolution(Paul Bissett, Florida Environmental Research

Institute)