Title: CIOSS/COAST GOES-R Risk Reduction Activities for HES-CW
1CIOSS/COAST GOES-R Risk Reduction Activities for
HES-CW
- CIOSS Cooperative Institute for Oceanographic
Satellite Studies, College of Oceanic and
Atmospheric Sciences, Oregon State University,
Corvallis, Oregon - COAST Coastal Ocean Applications and Science
Team, Mark Abbott Team Leader, Curtiss Davis
Executive Director
2Risk Reduction ActivitiesPrincipal Roles of
Co-Investigators
- Curtiss Davis, program management, calibration,
atmospheric correction - Mark Abbott, COAST Team Leader, phytoplankton
productivity, chlorophyll and chlorophyll
fluorescence - 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 IOOS - COAST 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, optics - Other COAST members, as needed, in future years
3COAST 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. - 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.
- Here we present an overview of our planned Risk
Reduction Activities.
4Presentation Outline
- Approach to Algorithm Development
- Experience with Hyperion and airborne
hyperspectral sensors - 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
- Summary
5HES-CW Measurements
- Calibrated at sensor radiances for all channels
- For the threshold 14 channels and possibly the
additional goal channels - Measurements are geo-located to approximately 1
Ground Sample Distance (GSD) - Methods for on-orbit calibration and validation
of products are not clearly defined at this time. - Methods for atmospheric correction are not
clearly defined at this time.
6HES-CW Products
- Water-leaving radiance (the product of
atmospheric correction, all other products are
calculated from this one) - Optical properties
- Turbidity (water clarity)
- Particulate absorption (phytoplankton, detritus,
sediments) - Dissolved absorption (CDOM)
- Particulate backscatter (phytoplankton, detritus,
sediments) - Diffuse attenuation (light availability for
seagrasses, corals) - Chlorophyll (phytoplankton biomass)
- Chlorophyll fluorescence (phytoplankton health
and productivity) - Total Suspended Matter (TSM, material transport)
- Colored Dissolved Organic Matter (CDOM, organic
matter transport, track river plumes) - These products tie directly into NOS requirements
for coastal ocean remote sensing.
7NOAA HES-CW Applications
- Water quality monitoring (e.g. Harmful Algal
Blooms, suspended sediments, CDOM) - Coastal hazard assessment
- Navigation safety
- Human and ecosystem health awareness (HABs)
- Natural resource management in coastal and
estuarine areas - Climate variability prediction (sea level rise,
carbon cycle) - Landscape changes
- Coral reef detection and health appraisal
8HES-CW Data flow and Risk Reduction Activities
9Approach 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 - SeaWiFS procedures and algorithms documented in
series of NASA Tech memos and numerous
publications - MODIS algorithms documented in 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.
10Example of Existing Data Sets that are Available
to Develop Algorithms and Demonstrate Products
SeaWiFS 1 km data
PHILLS-2 9 m data mosaic
Near-simultaneous data from 5 ships, two
moorings, three Aircraft and two satellites
collected to address issues of scaling in the
coastal zone. (HyCODE LEO-15 Experiment July 31,
2001.)
Sand waves in PHILLS-1 1.8 m data
Fronts in AVIRIS 20 m data
11Extensive In-situ data for product validation at
LEO-15 site
Profiling Optics and Water Return (POWR) Package
- Comparison at the X. (C. O. Davis, et al.,
(2002), Optics Express 104, 210--221.)
12Example Hyperion data sets for Coastal
Environments
Bahrain 26 Aug 02
Chesapeake Bay, 19 Feb 02
Apalachicola, FL 15 Aug 02
Chesapeake Bay, 6 Sep 02
13Proposed Experiments to Collect Simulated HES-CW
data (1 of 2)
- There are no existing data sets that include all
the key attributes of HES-CW data - Spectral coverage (.4 2.4 mm)
- High signal-to-noise ratio (gt3001 prefer 9001,
for ocean radiances) - High spatial resolution (lt150 m, bin to 300 m)
- Hourly or better revisit
- Propose field experiments in FY2006-2008 to
develop the required data sets for HES-CW
algorithm and model development. - Airborne system
- Hyperspectral imager that can be binned to the
HES-CW bands - Flown at high altitude for minimum of 10 km swath
- Endurance to collect repeat flight lines every
half hour for up to 6 hours - Baseline AVIRIS on ER-2 which can meet all of
these requirements. - Propose three experimental sites
- 2006 Monterey Bay (coastal upwelling, HABs)
- 2007 New York/Mid Atlantic Bight (river input,
urban aerosols) - 2008 Mississippi River Plume (Sediment input,
HABs)
14Proposed 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 three 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.
15Risk 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 (MOBY follow-on) at one or 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 (IOOS sites?) - 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.
16Atmospheric 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, including addressing BRDF issues. - 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).
17Current Atmospheric Correction Algorithms
SeaWiFS and MODIS algorithm (Gordon and Wang 1994)
- rw is the desired quantity in ocean color remote
sensing. - Trg is the sun glint contributionavoided/masked
and residual contamination is corrected. - trwc is the whitecap reflectancecomputed from
wind speed. - rr is the scattering from moleculescomputed
using the Rayleigh lookup tables. - ?A ra rra is the aerosol and Rayleigh-aerosol
contributions estimated using aerosol models. - For Case-1 waters in the open ocean, rw is
usually negligible at 765 865 nm. ?A can be
estimated using these two NIR bands.
Menghua Wang, NOAA/NESDIS/ORA
18HES-CW Channels and Atmospheric Transmission
Windows
UV channels can be used for detecting the
absorbing aerosol cases Two long NIR channels
(1000 1240 nm) are useful for of the Case-2
waters
Menghua Wang, NOAA/NESDIS/ORA
19Risk 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.
20Risk 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(?) fa(?), 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(?) fbb(?)/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.
21Risk Reduction Plans In-water Optical Properties
2
- 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.
22Bathymetry, Bottom Type and Optical Properties
Example Approach Semi-Analytical Models
- Semi-analytical model developed to resolve the
complex optical signature from shallow waters. - Simultaneously produces bathymetry, bottom type,
water optical properties.
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.)
23Bathymetry, 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, 2005.
24Risk Reduction Plans In-water Optical Properties
3
- 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.
25Risk 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
26MODIS FLH bands avoid oxygen absorbance at 687
nm
27MODIS Terra FLH vs Oregon optical drifters
derived FLH
28Testing the MODIS FLH Algorithm
FLH vs. chlorophyll
FLH vs. CDOM
From Hoge et al.
29Frequent measurements in morning can elucidate
quantum yield of fluorescence
30Risk Reduction Plans Phytoplankton chlorophyll,
chlorophyll fluorescence and productivity
- 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 carbonchlorophyll 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.
31Risk Reduction Plans Benthic Productivity
- Benthic habitats are degrading and seagrass beds
are decreasing at an alarming rate. To better
understand and monitor that process we propose to
develop procedures and algorithms for using
GOES-R HES-CW data to quantify benthic
productivity. - Develop algorithms for estimating benthic
productivity from seagrass and sediment across
the large optically shallow carbonate sediment
basins (Florida Bay, Bahamas, etc.). - Conduct sensitivity analysis identifying U.S.
coastal regions (e.g., Chesapeake Bay, Monterey
Bay) with optically shallow water ecosystems that
can be resolved by the GOES-R HES-CW. Potential
benthic constituents for analysis include
seagrasses, kelp, and benthic algal mats. - Use seagrass canopy model (Zimmerman 2003),
bathymetry and remotely derived estimates of diel
and seasonal water column optical properties to
develop predictive maps of the optically shallow
regions that could support seagrass habitats
based on light availability. - Use the field data collected during the process
studies to extrapolate benthic productivity
algorithms from the carbonate systems to other
coastal ecosystems identified in the sensitivity
analysis.
32Risk 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
33Frequent sampling can assist in detection and
classification of HABs
Some properties have a diel cycle associated with
it. Documenting the diel dynamics can thus
potentially assist in documenting and identifying
material in the ocean
Case example
Detection of K. brevis
34When K. brevis Blooms, conditions tend to be
calm. Under these Conditions the cells exhibit
a dramatic diel migration. The net result is 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.
35HABSOS can immediately utilize improved spatial
resolution and frequency of coverage from HES-CW
36Risk 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.
37Coastal 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.)
38Coastal Oregon Study Site
39Undersaturation 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.
Cascade Head time series
40Coastal CO2 Relationship to physics and biology
41Risk Reduction Plans Coastal CO2 Fluxes
- The coastal ocean plays a large and poorly
measured role in the global carbon cycle. - Addresses NOAAs 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 NASAs 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.
42Risk 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.
43EcoSim 2 Model Output for July 31, 2001 HyCODE
experiment at (LEO-15)
Satellite Measured
Bissett, et al., Submitted J. Geophys. Res.
44Risk 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.
45Example CI-CORE Data on GIS Web Server.
Airborne hyperspectral data for the Big Sur Coast
46Risk Reduction Plans Education and Public
Outreach
- For education and outreach CIOSS will support
three activities - Demonstrating and training users on the
algorithms and products developed during the risk
reduction activities. - Informing the general public as to the value and
utility of HES-CW data. - Educating state and local users to the value and
utility of HES-CW products. - For the general public and state and local users
we will work through the Coastal Services Center. - Currently developing a brochure on HES-CW with
CSC. - Initially a very low level effort for the first
three years. - Increase activity according to need and requests
from NOAA.
47Summary
- HES-CW will provide an excellent new tool for the
characterization and management of the coastal
ocean. - We will build on extensive experience in
calibration, atmospheric correction, algorithm
development from SeaWiFS and MODIS and continuing
with VIIRS to provide the necessary algorithms
for HES-CW. - Planned Activities focus on calibration and
algorithm development - Initially utilize existing data sets including
SeaWiFS and MODIS, - 2007-2008 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.
- Initially provide SeaWiFS and MODIS heritage
calibration and algorithms - Calibration approach includes vicarious
calibration, - Heritage band-ratio algorithms.
- 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.