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**Report Prepared for NASA Earth Science Applications**

**By the Naval Research Laboratory**

**Applications of Satellite Ocean Products**

**For Naval Operations**

**"The Evolution of Navy Diver Visibility Products for Coastal Waters
Using the NASA MODIS Satellites in Support of Operation IRAQI FREEDOM"**

**Robert Arnone Alan Weidemann**

**A. Rost Parsons Sherwin Ladner**

**Richard Gould Paul Martinolich**

**Naval Research Laboratory ,**

**Ocean Science Branch Code 7330**

**Oceanography Division**

**Stennis Space Center, MS 39529-5004**

[**arnone@nrlssc.navy.mil**](mailto:arnone@nrlssc.navy.mil)

Table of Contents

[Executive Summary 3](#executive-summary)

[Introduction 5](#introduction)

[1.0 Identifying the customer's problem, existing capabilities, and
product needs
8](#identifying-the-customers-problem-existing-capabilities-and-product-needs)

[1.1 The Problem during OIF 8](#the-problem-during-oif)

[1.2 Operational Area and Special Requirements for the Persian Gulf
Area-
9](#operational-area-and-special-requirements-for-the-persian-gulf-area-)

[1.3 Previous Operational Capabilities for Diver Visibility
12](#__RefHeading___Toc60540138)

[2.0 Value added using satellite ocean color and evaluating its
contributions to diver visibility
17](#value-added-using-satellite-ocean-color-and-evaluating-its-contributions-to-diver-visibility)

[2.1 Baseline capabilities \-- CZCS Satellite
17](#baseline-capabilities----czcs-satellite)

[2.2 SeaWiFS and APS 19](#seawifs-and-aps)

[2.3 MODIS 25](#modis)

[3.0 Calibration and Validation Requirements
29](#calibration-and-validation-requirements)

[3.1 Issues in calibration and validation for coastal waters and OIF-
36](#issues-in-calibration-and-validation-for-coastal-waters-and-oif-)

[3.2 Product calibration and validation
50](#product-calibration-and-validation)

[3.3 Special validation for a customer
53](#special-validation-for-a-customer)

[4.0 Making the Decision Support Tool for OIF- Expanding the science and
setting up the product
55](#making-the-decision-support-tool-for-oif--expanding-the-science-and-setting-up-the-product)

[5.0 Product Dissemination (Capturing the customer's interest,
responding to their needs, and implementing new specialized products \--
The pro-active approach)
59](#product-dissemination-capturing-the-customers-interest-responding-to-their-needs-and-implementing-new-specialized-products----the-pro-active-approach)

[5.1 Chronology of rapid development and delivery of a 250 m resolution
diver visibility product for OIF
60](#chronology-of-rapid-development-and-delivery-of-a-250-m-resolution-diver-visibility-product-for-oif)

[5.2 Implementation 63](#implementation)

[6.0 Post-evaluation-Benchmarking 69](#post-evaluation-benchmarking)

[7.0 Long term cost of developing MODIS Ocean Products in support of
Operational Iraqi Freedom.
71](#long-term-cost-of-developing-modis-ocean-products-in-support-of-operational-iraqi-freedom.)

[References 75](#references)

# Executive Summary

This document describes the progression of Earth Observing Satellite
ocean optical properties algorithm formation from research and
development (R&D) to a Navy operational product. The document traces the
"transition path" of remote sensing and optical oceanographic research,
generation of science properties, exploratory development of potential
Navy relevant products, to the distribution of specialized operational
products which are used by Navy fleet operations. We describe how
existing capabilities and infrastructure in place at both National
Aeronautics and Space Administration (NASA) and Department of Defense
(DOD) facilitated long-term and rapid product development and enabled
near real-time distribution of the operational product to support a
customer, in this case the Navy Warfighter. The process presented can be
used as a guide and a model for any agency (such as NASA) that requires
vertical integration of basic research efforts, the ingestion of large
data with seamless data flow and processing, and the formation of
decision support tools. These decision support tools are tailored to
meet customer needs based on data availability/ distribution, algorithm
development with calibration and validation, and then product
dissemination in a usable fashion. This document describes how this
process was accomplished for Diver Visibility in support of Operation
IRAQI FREEDOM (OIF).

We describe the mechanism and support structure that is required to
create ocean satellite products and the level of effort both in R&D, the
system architecture development, and customer response and reach-back
required to create a customer-tailored product line. Included in this is
process is: 1) understanding the customer's problem, requirements, and
knowing what is currently available, 2) determining what contribution,
decision support tool, and "value added" a satellite product can offer,
3) addressing the sensor calibration and validation required to meet the
product's needs, 4) movement of science based properties and satellite
derived information to a product and decision support application, and
5) efficient dissemination of the product with customer participation,
feedback, and product adjustment. In order to address the contribution
that satellites can make to an application also requires the ability to
handle the data and implement algorithms in an efficient, timely, and
effective manner. This document addresses the issues associated with
this process.

Our specific purpose is to describe the use of ocean parameters and
their associated decision support tool development using Moderate
Resolution Imaging Spectroradiometer (MODIS) satellite imagery.
Specifically we illustrate the steps required in the above process that
were undertaken to produce and distribute a underwater diver visibility
and vulnerability product using near real-time access to the MODIS data
stream. This effort was design to create a decision support tool for use
by Explosive Ordnance Disposal (EOD) teams and Navy Special Warfare
(NSW) operations in the Persian Gulf during OIF. As is demonstrated, the
development of ocean operational products is not a simple process. While
it can take place rapidly it requires inherent coordinated efforts of
years of prior research, the infrastructure to allow creation and
validation of the product and decision support tool, and coupled
developer and customer (Navy Warfighter) interaction.

This document describes how a process was created at NRL-SSC Code 7333
Ocean Optics Section that includes retrieval of the data stream, image
processing, evolution of the final products, and then distribution via
the Internet to provide access to Naval operations in-theater within
about 3-6 hours of the satellite overpass. Portions that will be
highlighted are initial capabilities of the customer and of the
satellite data flow, required calibration and validation of the sensor
and the retrieved properties, product evolution, the coupling of the R&D
community with feedback from the operational warfighter, and the
development and distribution of the satellite products that were used to
enhance the diving operations. We limit the details of the product
evolution to the unclassified aspects and do not describe classified
aspects that are related to specific customer requests. In the end,
however, it will be shown how the MODIS satellite provided near real
time diver visibility and vulnerability products at 250 m resolution in
theater in support of OIF. The delivery of these products is a template
for future transition of satellite products to a variety of customers.

# Introduction

The utility of space borne satellite sensors for monitoring and
understanding the ocean cycles is maturing. Our capability to detect
ocean movements and understand coastal processes has been addressed by
the R&D community for years, starting with the launch of the first ocean
color satellite, the Coastal Zone Color Scanner (CZCS) in 1978.
Follow-on ocean color satellite sensors include the multispectral
Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS), the MODIS, the Medium
Resolution Imaging Spectrometer (MERIS), and the hyperspectral Hyperion
sensor. These satellites provide the ability to detect subtle changes in
the ocean color and have permitted retrieval of quantitative geophysical
values of the ocean optical properties. We have matured in our ability
to reliably extract ocean properties with these sensors. With higher
temporal coverage, provided by new satellites, it is possible to monitor
changes in the coastal environment. The need for high temporal coverage
has been strongly requested by customers with coastal zone missions
where the changing ocean properties require frequent or continuous
updates.

For coastal applications there is a need for frequent updating of
information. This is being addressed in planned next generations of
ocean color sensors, which include satellites in geostationary orbit.
These sensors will provide coastal monitoring on time scales under an
hour. Therefore, future ocean color satellites will be capable of
monitoring coastal processes such as river plume dispersion, tidal
fluctuations, coastal jets and filaments, coastal advection, storm
events, sediment resuspension, phytoplankton growth, and others. Coastal
monitoring using these satellites is based on the optical signatures of
ocean waters and the optical properties and techniques learned from past
sensors. However, from a customer perspective the utility of these data,
outside of research, is often not explored thoroughly nor is the
customer's problem addressed. Consequently, the application of the
satellite information is not exploited. One use of satellite data that
until recently has been largely overlooked is in the development of
decision support tools. But issues of identification of customer needs,
satellite calibration and validation, data flow, product development,
and customer acceptance have not been addressed. A substantial
commitment of time and resources from both the researcher and the
end-user of the application are required to produce useful products from
these data.

Our ability to derive and deliver routine customer based ocean products
from satellite ocean color has resulted from four advancements.

In the last two decades there have been major breakthroughs in:

a)  Calibration and validation (including radiometric calibration,
    measurement protocols of optical properties of the water and remote
    sensing reflectance, data match-ups, and sensor monitoring),

b)  Management of data flow and standardization of formats for data
    transfer,

c)  Increased computational capabilities that allow for immediate
    testing and implementation of new algorithms and product
    implementation, and

d)  Development of new rapid methods for information dissemination.

The later has been the most important to the customer; if they cannot
get the product or do not know it is available then they have little to
benefit from the product development. Furthermore, in most cases, the
advances in science have exceeded the ability of the research and
applications communities to couple the research products with applied
products. In other words, there is a "disconnect" between the science
and the technology with the customer base. For example, from the
research perspective, new algorithms to estimate the optical absorption
and backscattering coefficients from space are extremely valuable tools.
On the other hand, the end-user (the warfighter in this case for the
Navy) has no direct use for this scientific information. From the
customer's perspective, they require a simple derived product that
focuses on the operational parameter of interest. The customer has
specific applied problems that sometimes are distantly related to the
basic research. Therefore, to reach our customer base, we must
demonstrate how resolving the fundamental spectral optical properties of
the water impacts Navy operations.

This document presents an example of how an operational product was
generated from R&D satellite properties to provide unique customer
support and a decision support tool. Specifically, we show how the MODIS
(Terra and Aqua) and SeaWiFS satellites were used to generate diver
visibility and vulnerability products to support Navy divers during OIF.

By combining NASA satellite ocean color imagery, NRL-SSC research
capabilities in ocean optics, and customer interaction, we successfully
developed a new satellite product tailored to the needs of our customer.

Here we demonstrate the progressive steps that occurred in advancing
from a R&D project to the implementation and dissemination of an
operational product. The process is described in the following steps:

1)  Understanding the customer's problem, his requirements, his
    knowledge base, and the fundamental research that can help with his
    problem,

2)  Determining what contribution, decision support tool, and "value
    added" a satellite product can offer including what has been
    previously used from other satellites, and what data flow steps need
    to occur,

3)  Addressing the sensor calibration and validation required to meet
    the product's needs,

4)  Movement of science based properties and satellite derived
    information to a product and decision support application, and

5)  Efficient dissemination of the product with customer participation,
    feedback, and rapid changes to meet the customer's needs.

An important component in addressing "the value added" that satellites
can make to an application is assessing the efficiency and "speed" of
product formation when a product change is required. This includes data
handling, quality control (QC), algorithm updates, continuous
calibration and validation, and "getting the information to the user."
This document addresses the issues associated with this process. We use
the chronology of events associated with delivery of a diver
visibility/vulnerability product in OIF to demonstrate how customer
request, algorithm development, product generation and adjustments, and
finally distribution can take place on short time scales.

# 1.0 Identifying the customer's problem, existing capabilities, and product needs

In the progression of "R&D" to an application, the first step is always
identifying the problem that is being addressed and the present
operational solution. Assuming a problem is identified, a thorough
understanding of what limitations and contributions can be made toward
the problem from satellite derived information is required. The problem
that is addressed in this document is *[diver visibility for Navy
applications.]{.underline}*

## 1.1 The Problem during OIF 

Divers and camera systems are limited by the visibility of the water.
Poor visibility restricts the capability of divers to detect underwater
objects and restricts operations. Navy divers are required to conduct
underwater identification of objects i.e. mines or navigational hazards
located by acoustic systems. In addition, search and rescue operations
and covert operations, where acoustics detection is not allowed or
feasible, requires divers to locate objects on hulls of vessels, bottom
obstructions, and visually clear ingress routes for follow-on
operations. For their mission in OIF EOD divers were required to
identify underwater objects which were detected by acoustic systems.
Since their operations are time and risk sensitive, and because there
are specific stand-off requirements that a ship must use as part of
their Standard Operating Procedures, poor visibility results in higher
risk to the ship and the diver (i.e. both need to get closer to a
potential threat that is being examined). Additionally, divers were
required to inspect ship hulls exiting the Tigris-Euphrates River as
they entered the Persian Gulf. The hull inspection for potential threats
to in-theater forces required divers know the visibility in order to
optimize "where to do the inspection." Consequently, the greater the
visibility the more vulnerable a diver is to visual detection from above
the water's surface by an observer. In the above operations diver
visibility and vulnerability (due to daylight operational requirements)
are critical operational parameters that impacted operations in the
Northern Persian Gulf. The task for remote sensing is to determine how
to use satellite ocean color properties to obtain the key parameters in
horizontal (identification) and vertical (vulnerability) visibility
estimations. This includes determining how to get such information to
the warfighter in a timely and operational manner.

## 1.2 Operational Area and Special Requirements for the Persian Gulf Area-

The majority of maritime operations for OIF were focused in the northern
portion of the Arabian Gulf and the Khawr Abd Allah (KAA) waterway.
Deriving tactical environmental products from the optical properties of
the Arabian Gulf to support OIF was challenging due to the complex and
varied oceanographic conditions. Extended aircraft carrier flight
operations during OIF and Maritime Intercept Operations (MIO) did
require NRL-SSC ocean environmental support over most of the Middle East
region (Red Sea, Eastern Mediterranean Sea, Gulf of Oman, and Arabian
Sea) but the Arabian Gulf support was viewed as the most crucial to
operations.

The Arabian Gulf is very shallow with a mean depth of only 35 meters.
Even with these shallow depths, the arid climate and unimpeded
connection to the Arabian Sea through the Strait of Hormuz and Gulf of
Oman establishes unique oceanographic conditions. Fresher surface waters
flows northward into the Gulf through the Strait while oxygen rich
saline water, formed due to high evaporation rates, flows southward out
of the Gulf at depth. The surface circulation in the southern Gulf is
generally characterized by a cyclonic gyre due to this inflow.
Circulation in the Northern Gulf is primarily dominated by along-axis
wind forcing that generates southeastward flowing coastal currents along
both the Arabian and Persian coasts. Along the northeastern shore of the
Gulf, these coastal currents induce upwelling favorable conditions that
lead to enhanced chlorophyll plumes which can be observed in remotely
sensed data (Johns et al., 2000) (Fig. 1).

Several other features contribute to the observed variability of ocean
properties. The primary fresh water flux into the northern Gulf comes
from the Tigris-Euphrates river system that discharges into the Northern
Gulf. These discharges are laden with fine sediments as well as
carbonate sands and generally are carried along the western coasts of
the Gulf southwards (Basham and El-Sayed, 1998). The discharges can be
clearly seen in the ocean color imagery (MODIS true color) and are
observed in imagery loops to be oscillating with the tides (Fig. 2).
Additionally, periods of strong northwesterly winds predominate in
summer months, called *shamals*, can carry dust and terrigenous material
far out over the Arabian Gulf both contributing to the sediment loading
as well as limiting retrieval of ocean optical properties. In the winter
months, northwesterly winds following frontal systems can also generate
significant dust events in the northern Gulf. It is estimated that
one-third of all bottom sediments in the Arabian Gulf are of aeolian
origin (Sugden, 1963),

Tides in the Arabian Gulf are also complex as the forcing is derived
from co-oscillation with the Gulf of Oman through the Strait of Hormuz.
Two semi-diurnal amphidromes (one north and one south) and a single
diurnal amphidrome are observed. As such, some locations with the Gulf
are primarily diurnal while others are mixed or semi-diurnal. The
semi-diurnal tidal amplitude has a local maximum in the northern Gulf
creating significant tidal currents affecting both optical variability
and diving conditions on short time and spatial scales.

![](media/image4.wmf){width="6.379861111111111in"
height="4.753472222222222in"}

**Figure 1. - General Oceanography of the Persian Gulf**

![](media/image5.jpeg){width="5.959722222222222in"
height="6.127083333333333in"}

Figure 2. - Persian Gulf imaged by MODIS AQUA - True Color (Channel 7,
8, 10) -- 1 km

[]{#__RefHeading___Toc60540138 .anchor}***1.3 Previous Operational
Capabilities for Diver Visibility*** -

Diver visibility has long been an issue as a product for the operational
Navy. It has been a "requirement" without an official product line
associated with it. Part of this has been due to the unpopularity of
previous visibility products that the Navy used, and the historically
"blue water" Navy for which diver visibility was a secondary problem. As
Mine Warfare, Special Forces operations, and shallow water Antisubmarine
Warfare increased in importance in the coastal environment, the
end-user's interest in the diver visibility product increased sharply.

### 1.3.1 Theory of Diver Underwater Visibility.

Historically very detailed nomographs were developed in the 1950's and
1960's by S.Q. Duntley (1952, 1960) that used contrast transmittance
theory to derive underwater visibility based on extrapolation of the
atmospheric concepts to give a "hydrologic range." Fundamentally, the
contrast transmittance theory states that the diver visibility range for
an object is reached when the contrast threshold of the human eye is
reached and there is no "discernable difference" in radiance between the
object and its surrounding medium. The inherent contrast (range at zero
distance) and the contrast at range 'r' for the target (i.e. the
apparent contrast) are described by:

$C(0) = \frac{L_{T}(0) - L_{B}(0)}{L_{B}(0)}$ and
$C(r) = \frac{L_{T}(r) - L_{B}(r)}{L_{B}(r)}$ ,

where C(0) and C(r) are the inherent contrast and contrast at range 'r';
and L~T~ and L~B~ are the radiances of the target and background
respectively. Underwater, solar light is attenuated approximately
exponentially with depth. The extinction rate of this light is described
by the "diffuse attenuation K~d.~" In early formulations for diver
visibility it was assumed that the underwater light field had reached a
condition in which the light field distribution no longer changed,
except in intensity (i.e. reached the asymptotic condition and K~d~ was
constant). It was also assumed that the target was "small enough" and/or
"at sufficient distance" that the radiance from the target propagates as
L(r) = L(0) e^-cr^ where 'c' is the total attenuation coefficient. The
total attenuation coefficient 'c' represents the loss rate in
transmission along a path due to the sum of absorption 'a' and
scattering 'b'.

Through radiative transfer theory (see Bowers 2003 for further details)
it can be shown that the contrast at range r and at look angle θ can be
described by:

$$C(r,\theta) = C(0,\theta)e^{- \lbrack c + K_{d}\text{cos}(\theta)\rbrack r}$$

When the sight path distance (range, r) is such that the apparent
contrast C(r) is equal to the eye's threshold limit (i.e. the contrast
threshold C~Threshold~), then this distance represents the visibility
range. By setting C(r) equal to C~Threshold~ and then solving for the
range we arrive at:

$$r = \frac{- \text{ln}(\frac{C_{\text{Threshold}}}{C(0,\theta)})}{c + K_{d}\text{cos}(\theta)}$$

which, in the special case of horizontal viewing (K~d~ cos(θ) = 0),
reduces to

$r = \frac{- \text{ln}(\frac{C_{\text{Threshold}}}{C(0,\text{90})})}{c}$.

The nomographs for diver visibility were developed on sound fundamental
physics. However, these charts required target information and optical
property measurements that were not available at the time (specifically
'c' and K~d~). But they did give visibility as a function of light
level, solar angle, target reflectivity, look angle, etc. all which are
important to accurate visibilities estimates. The assumptions for these
nomographs were: a) the presence of a vertically and horizontally
homogenous ocean, b) use of a black target, c) divers are deep enough so
that an asymptotic light field is present, and d) the contrast threshold
for the human eye is a fixed value of about 0.02. In the final analysis,
however, the customer rejected the nomographs as "impractical" and
"valueless" since insufficient information regarding the inputs was
available. In addition, the charts could not be used on-scene and the
divers could not decipher the scientific quantities required. There was
confusion on "where to get the inputs", and even how to use the
nomographs.

In an effort to keep the basic information provided in the nomograph,
diver visibility prediction was reduced by simplifying the problem.
Using the 0.02 for C~Threshold~, the above equation can be reduced to a
single horizontal visibility of 3.9/c (rounded off to the 4/c in common
use). But as shown by numerous investigators the "background
illumination", eye adaptation level (ranging from \<0.006 to \> 0.03;
depending on exposure time), water color, etc can cause this
relationship to vary from 3.5/c to about 6.3/c. This range is
problematic for diver visibility prediction particularly when values for
"c" were not available on-scene with instrumentation available to the
warfighter.

### 1.3.2 Secchi Depth - Solution

The utility of the nomographs required that specific optical properties
of the waters are available. These measurements were difficult to
obtain, especially in foreign areas. Without a viable alternative the
Navy opted to use the Secchi Disk as a measure of visibility. The Secchi
disk is an 18 cm white disk that is lowered into the water until it
"just disappears" from the human eye. It is a measure of "water
clarity." The Navy developed a global climatology atlas of the seasonal
"Secchi depth" to determine the optical properties of strategic regions
for operational planning (Arnone, 1985). This atlas was developed from
data gathered by the National Oceanographic Data Center and from open
literature for water depths less than 500 meters, but still the spatial
and temporal coverage was severely limited (Fig. 3). Note that little
information is available for the Area of Interest (only a few "large
areas"). However, even with limited measures of actual visibility
available, the Secchi Disk visibility developed into the present water
clarity and visibility product provided by Naval Oceanographic Office
(NAVOCEANO) for the Special Tactical Oceanographic Information Charts
(STOIC). When data is available for total attenuation, 4/c is used to
give a "seasonal or regional diver visibility." With the wide range in
Secchi Disk values, and few at-sea measurements of "c", the product is
not utilized by normal fleet operations.

![](media/image6.png){width="5.375in" height="3.1909722222222223in"}

Figure 3. - Secchi Disk Visibility in Persian Gulf

\-

It is in the context of the deficiencies in existing operational diver
visibility products and the complexity of the operations in the Persian
Gulf that motivated development of a diver visibility decision support
tool for Navy operations. Several key points about the customer's
requirements can be made.

-   **First**, to this point there is no on-scene capability. With the
    oceanographic complexity for the area and the time-critical nature
    of operations, this was a foremost requirement from the customer.
    Even the digitally available STOIC was insufficient for tactical use
    in the area.

-   **Second,** only regional and seasonal "best statistical"
    information is available.

-   **Third,** temporal and spatial information is lost even if it is
    available due to the method of dissemination. As requests arise for
    higher and higher spatial resolution for riverine and coastal
    operations none of the existing capabilities meet this need.

-   **And lastly**, the climatologically based product did not meet the
    needs of the warfighter to rapidly assess areas of high and low
    diver visibility present during OIF.

**The utilization of satellite ocean color to produce an operational
product addressed these deficiencies.**

# 2.0 Value added using satellite ocean color and evaluating its contributions to diver visibility

To evaluate the contribution and value-added that satellite ocean color
can make to diver visibility and product generation requires knowledge
of the evolution of ocean color satellite capabilities and what was, and
is, done with the data provided. To act as a guide in product formation,
previous work and use of satellites such as the CZCS and SeaWiFS for
providing oceanographic information is examined. This previous work is
the template for product development at NRL-SSC. Work from these sensors
define the infrastructure and foundation required for data flow, sensor
and property calibration and validation, algorithm development, product
formation, and product dissemination used in the diver visibility
product created using the MODIS 250 meter channels. When modifications
in the process are required they can be quickly implemented to provide a
more valued product.

## 2.1 Baseline capabilities \-- CZCS Satellite 

With the launch of the CZCS (1978-1986 operational lifespan) the Navy
environmental optical research programs matured. This was the first
multi-spectral ocean color sensor capable of quantitative ocean
radiometry. This satellite pioneered space borne retrieval of ocean
bio-optical properties and provided the first global understanding of
the Earth's bio-optical climate. The satellite was specifically designed
for bio-optical environmental applications and had four visible channels
(443 nm, 520 nm, 550 nm, 670 nm, all with 20 nm bandwidths). The two
most important aspects of the satellite to Navy applications were the
demonstration of increased spatial coverage, particularly in denied
access areas, and the capability to retrieve optical information from
space borne platforms. Optical maps were developed with this sensor
which demonstrated the ability of a satellite to derive optical
properties, such as the diffuse attenuation coefficient K~d~(490), from
space (Fig. 4). For CZCS this property was obtained for the bands of
443, 520, and 550 nm by using the 670 nm band for atmospheric
correction. The property represented a measure of water clarity beyond
the Secchi disk.

![](media/image7.png){width="3.7527777777777778in"
height="3.7527777777777778in"}

Figure 4. - CZCS K~d~(490)

Because satellites penetrate the surface, and radiance from all points
below the surface contribute to upwelled light, the value of K~d~(490)
represents an integrated value. But as presented above, the K~d~(490)
can not be directly applied to diver visibility in a straight-forward
fashion. Since CZCS was designed for monitoring of changes in
chlorophyll, it was not optimized for extraction of optical properties.
Data was distributed to users as tapes weeks after collection.
Limitations arose with continuous reprocessing of the data when changes
in calibration or modifications in the algorithms occurred. The limited
extent to which this data could be processed, or more appropriately
reprocessed, difficulties in distributing data to the users, and
problems with sensor calibration left this sensor as a "first" step
toward a satellite used for ocean optical property retrieval. Attempts
to improve the issues of sensor calibration, improved atmospheric
correction, and inadequate validation programs were developed for the
follow-on SeaWiFS program. Even so, the capability offered by satellite
retrieval of bio-optical properties showed a potential contribution to
the problem of diver visibility. The extensive coverage area and large
data volume could vastly improve the "Secchi Disk climatology". Thus the
Navy identified optical data derived from ocean color satellites as a
present and future solution to many Navy optical requirements.

## 2.2 SeaWiFS and APS

As a CZCS follow-on, the SeaWiFS sensor developed jointly by Orbital
Sciences Corporation and NASA was launched August 01, 1997. This has
been the research sensor for algorithm development to uncouple optical
properties from remote sensing reflectances, the baseline for long-term
calibration and validation efforts for oceanic studies, and the standard
for development of data flow. It was past work with this sensor that
resulted in the rapid product formation during OIF with the MODIS
imagery.

The SeaWiFS project office is based at Goddard Space Flight Center
(GSFC); its mission is to make the ocean color sensor "science ready"
through an extensive calibration and validation effort. The Goddard Data
Active Archive Center (DAAC) provides data to NASA-authorized users.
Because this is a joint NASA/industry venture, researchers experience a
2-week data delay and commercial users must purchase the data from
Orbital Sciences. For operational use, however, such a delay is
unacceptable.

An advantage for the customer from SeaWiFS is that it can provide
locally available high resolution data transmitted during the overpass
and then global data from the Goddard data archive. SeaWiFS provides
Local Area Coverage (LAC)/High Resolution Picture Transmission (HRPT)
coverage at a swath width of 2801 km and Global Area Coverage (GAC)
coverage at a swath width of 1502 km. SeaWiFS has a ground pixel
resolution of 1.1 km for LAC and 4.5 km for GAC coverage. Another sensor
attribute for the Navy is that SeaWiFS has overlapping orbits so that it
covers any point on the globe at least one time per day. NRL-SCC is one
of 128 authorized NASA research stations globally for receiving SeaWiFS
high resolution LAC/HRPT data and one of five authorized by NASA for
covering the Continental US in real-time. NRL-SSC captures every SeaWiFS
pass covering the Gulf of Mexico and transmits the data to the SeaWiFS
Goddard DAAC for archiving and dissemination. The importance of being a
receiving site is that this allows NRL-SSC to develop and test
algorithms for various applications.

The SeaWiFS sensor was specifically developed for ocean color
applications. It has 8 channels, six visible channels for ocean color
and two channels in the near-infrared for atmospheric correction (Table
1). The two infrared channels for atmospheric correction channels are a
specific result of the problems associated with CZCS. Channels in the
blue end of the spectrum are used to partition colored dissolved organic
material (CDOM) and phytoplankton absorption. The baseline SeaWiFS
bio-optical capability for the Navy includes all the standard algorithms
approved by the SeaWiFS science team and developed by NASA in
collaboration with university investigators and government agencies.
NRL-SSC played an important role in providing software and algorithm
capabilities specifically designed for coastal properties (see Section
3).

Table 1. - SeaWiFS Bands and primary algorithm contribution.

  ------------- ------------ ---------------------------------------------
  Waveband      Spatial      Normal Algorithm Use
                Resolution   

  402-422 nm    1 kilometer  Dissolved Organic Matter and Detrital
                             Absorption

  433-453 nm    1 kilometer  Chlorophyll concentration, Particulate
                             Absorption

  480-500 nm    1 kilometer  Chlorophyll concentration

  500-520 nm    1 kilometer  Total Absorption, Separation into components

  545-565 nm    1 kilometer  Phytoplankton separation, Sediment
                             concentration

  660-680 nm    1 kilometer  Chlorophyll concentration, Particulate
                             Absorption, Phytoplankton Fluorescence,
                             Bottom classification

  745-785 nm    1 kilometer  Aerosol type, Atmospheric Correction

  845-885 nm    1 kilometer  Aerosol type, Atmospheric Correction
  ------------- ------------ ---------------------------------------------

As a research satellite and project, SeaWiFS resulted in the development
of many required algorithms to uncouple remote sensing and optical
properties of the water. Most of these algorithms are semi-analytical
and therefore have physical basis that can be exploited in creating
customer specific products. The properties that are relevant to the
diver visibility product are the spectral absorption coefficient which
is separated into the particulate and dissolved components, the spectral
backscattering components, the diffuse attenuation coefficient, and
atmospheric correction techniques. These algorithms are investigated
through joint NASA/Navy efforts and are under constant validation,
re-examination, and modification. Each derived property, calibrated
radiances, and data manipulation undergoes calibration and validation
processes outlined Section 3. These algorithms form the basis of those
being developed for MODIS and consequently were used in the development
of a diver visibility product for OIF.

With 1 km daily global coverage the data volume from SeaWiFS was much
greater than previous imagery. In order to provide data in a timely
manner, to meet research and application needs, to meet requirements of
calibration and validation, and to handle the continuous and growing
stream of satellite data, NRL-SSC developed an Automated Processing
System (APS). Originally, the system was designed to produce sea surface
temperature maps from data collected by the Advanced Very High
Resolution Radiometer (AVHRR). The APS provides for near real time
processing with the option of reprocessing historical data. The
advantage of this system is that the processing method can be
continuously modified which allows for easy implementation of new
algorithms, test data sets for validation, and customized user specific
products.

APS consists of a collection of UNIX programs and shell scripts designed
to generate map-projected image databases of satellite derived products
from a large flow of raw satellite input data in an automated fashion.
The APS uses a simple monitoring technique. The main driver regularly
polls a specified input directory for incoming data. For each data set
found the driver executes what are known as areas scripts on the file in
a working directory. Area scripts allow modification and customization
of outputs for selected geographical regions. After each area script has
been run on the file, it is moved to an output directory. This method
uses the directory as the queuing system for data to be processed. The
areas scripts do the actual construction of the desired results (i.e.
the databases). The system executes on Red Hat Linux 7.1 and SGI IRIX
6.5 (Martinolich, 2001).

The processing steps are as follows. APS first applies sensor
calibrations to convert from digital counts to calibrated radiances
(Level 0 to Level 1a). Next, conversion of the level 1a through 1b and
then to the level 2 occurs by applying a radiance calibration/correction
and implementation of the atmospheric correction procedures. Following
atmospheric correction (level 1b to level 2) the derived remote sensing
reflectances are input into bio-optical algorithms to yield the final
bio-optical properties (Fig. 5). The optical properties and the derived
products are processed to four different temporal (daily, 8-day, 8-day
latest valid pixel, monthly, and yearly) composites or averages (level 3
to level 4) to remove the effects of clouds and to show trends in
seasonal features. The resulting imagery is stored in the Hierarchical
Data Format (HDF) used by NASA in its satellite product archive. The APS
also automatically generates quick-look "browse" images in JPEG format
that are used to populate an SQL database for product dissemination and
queries by potential customers. At each step quality control (QC) checks
are routinely done and imagery examined for continuity and consistency.

![](media/image8.png){width="3.4715277777777778in"
height="4.629166666666666in"}

Figure 5 - Automated Processing System steps

Navy work under the SeaWiFS program included environmental R&D through
transition of the APS and automated production of optical properties.
SeaWiFS has been used as a "test bed" for developing ocean optical
products. This effort established a transition mechanism for optical
products for the Navy. The advancements included the daily production of
1 km optical properties in selected regions using APS, enhancements in
data flow and format structure, improved stability of the sensor and
calibration/validation protocols, and the development of optical
property retrievals. Increased coverage and the use of APS to process
data in a timely manner (a matter of hours instead of days and weeks)
and then dissemination of a product to the user resulted in a merging of
the R&D and a customer. The processing speed and the 1 km resolution
demonstrated to the Navy the application potential for this sensor.
Consequently, the Navy purchased three licenses from Orbital Sciences
and established three regional S-band Terascan antennas to capture
SeaWiFS imagery over strategic regions. These ground stations are
located at Naval Meteorology and Oceanography (METOC) centers at Rota,
Spain, Yokuska, Japan, and Manama, Bahrain.

With the multiple ground stations, the Navy has local satellite coverage
in several strategic areas of the globe. NRL-SSC plays a critical role
in the exploitation of this imagery. Imagery from these sites is
transferred to the Naval Oceanographic Office and then to NRL-SSC for
real-time processing (Fig. 6.) using APS. As shown in Figure 6, imagery
from the regional center is transferred to NAVOCEANO to an ftp site
where it can be processed by NAVOCEANO. NRL-SSC can access the data from
the NAVOCEANO site and Level 0 data is moved over to NRL-SSC and
ingested into the APS system. In order to meet regional needs, quick
look images are posted on a password protected web site (see Section 5).
The processed optical properties and resulting customer products (Level
3 and 4 data) are put in HDF format for achieving and distribution to
the customer.

![](media/image9.png){width="5.997916666666667in"
height="2.6486111111111112in"}

Figure 6 - Data Flow from Regional Center and NAVOCEANO to NRL-SSC

The SeaWiFS imagery provided evidence of the impact that remote sensing
could have on situational awareness for Navy operations. The 1 kilometer
biological and optical properties provided quantitative information on
the changing environment. However as an operational product it did not
meet the customer's needs because details were required at higher
spatial resolution.

Table 2. - MODIS -- First 18 Channels

## 2.3 MODIS

The first MODIS instrument was launched December 18, 1999 aboard the
Terra (Earth Observing System (EOS) AM-1) spacecraft. MODIS Terra is in
a descending orbit and passes over the equator at 10:30 AM. It is in a
sun synchronous polar orbit. The MODIS Terra has a total of 36 bands. Of
these bands there are 18 that are used extensively in oceanographic
research. Two bands (center band at 645 nm, and 858 nm) are imaged at a
nominal resolution of 250 m at nadir, and 5 bands (469 nm, 555 nm,
1240nm, 1640 nm, and 2130 nm) at 500 meters. The remaining bands have a
spatial resolution of 1 km (Table 2). It was the combining of channels
with differing spatial resolutions that was an important step in the
product formation for OIF.

MODIS Terra provides LAC at a swath width of 2330 km (cross track) by 10
km (along track at nadir). Similar to SeaWiFS, MODIS- Terra covers any
point on the globe at least once every one to two days. In order to meet
needs specific to Navy applications, the Navy has established a regional
X-band Terascan antenna to capture MODIS imagery over the strategic
Persian Gulf region. This ground station exists at the Bahrain METOC
center. While MODIS imagery from this site was not used by NRL-SSC for
the OIF customized product, the goal of the OIF effort is for future
formation of the product at a METOC Center.

The second MODIS flight instrument was integrated on the Aqua (EOS PM-1)
spacecraft. It was successfully launched on May 04, 2002. MODIS Aqua is
similar to the MODIS Terra sensor except Aqua collects daytime data
during an ascending orbit, which crosses the equator at 1:30 PM.
Together with MODIS Terra and SeaWiFS, this trio of sensors provides a
critical "three looks per day" at an operational region. The
"three-looks" per day for strategic areas permits assessment of rapidly
changing optical conditions and reassures the customer that
time-critical information that impacts diver visibility missions is
available (Fig. 7).

The NASA MODIS science team developed MODIS bio-optical algorithms and
processing software. These algorithms were extended by the NRL-SSC into
coastal areas through improvements to atmospheric correction and
in-water optical algorithms. These algorithms are currently being tested
and evaluated. The MODIS Terra is currently being integrated into
operations at NAVOCEANO with a follow-on of MODIS-Aqua in scheduled in
2004. For the most part the algorithms and optical properties are
extensions of those developed under the SeaWiFS program. However
contributions in the form of coastal specific atmospheric corrections
were added and will be discussed in the calibration and validation
section. Also, because of the additional wavebands available in MODIS,
new algorithms were developed for fluorescence, phycoerythrin and
phycobiliproteins, chlorophyll *a* concentration, and CDOM absorption.
The development of APS serves as a link between this algorithm research
and Navy operations. As such any new algorithms (such as for bio-optical
properties) that arise in research or improved or new calibration
coefficients that must be applied can be implemented immediately. For
MODIS processing, the inclusion of biweekly updates in the calibration
coefficients are critical. The MODIS Calibration and Science Team (MCST)
maintains a close biweekly calibration update of Look-Up Tables which is
used to generate Level 1a from the Level 0 data.

![](media/image10.wmf){width="5.520833333333333in"
height="7.624305555555556in"}

Figure 7. - MODIS AM, SeaWiFS, MODIS PM True Color and Diffuse
Attenuation Coefficient K~d~ (532) on 7 Feb'03 showing 3 looks per day
(K~d~ is m^-1^).

Currently, NRL-SSC uses the level 1a data in APS and in the past relied
on the receiving sites to apply the correct calibration in generation of
Level 1a. As will be shown later, receive sites and MODIS data
distribution centers do not always update this software in a timely
fashion. As a test bed for MODIS software and algorithms NRL-SSC can
quickly examine state-of-the-art updates, go through calibration and
validation, and then transfer these from the research community into
potential operational products.

To achieve the "three looks a day" on a timely basis for the Persian
Gulf required several modifications to data flow (Figs. 5 and 6). MODIS
data is transmitted from multiple sites to NRL-SSC. Persian Gulf and
Adriatic imagery was initially acquired from the Goddard DAAC which
resulted in a 2 day delay (unacceptable for real-time missions by the
customer). To solve this problem an agreement between the NOAA /NASA and
NRL-SSC was reached by streamlining data transfer using a "bent pipe".
The "bent pipe" for MODIS data is located in Building 32 at Goddard,
where the global MODIS data stream is being sent to the DACC for
processing on MODAPS.

As the global data set is being sent, subsets of these data (critical
areas) is duplicated and stripped out to meet immediate needs. While all
the global data set is sent to the DACC, the "bent pipe" provides a real
time data flow that otherwise would take several days to obtain. A
subscription to theses critical areas was used by the Navy, Air Force,
and NOAA and provided to several users through the DREN (Defense
Research Educational Network). These data were obtained at NRL-SSC
through the DREN from the bent pipe within 2-3 hours after the imagery
collects and was used to support the war effort for real time needs. A
direct file transfer protocol (ftp) was developed that allowed NRL-SSC
to receive all MODIS (MOD02) Level 1b data as soon as NOAA completed
processing. This improved the data flow for rapid-response and delivery
of Navy optical products from MODIS imagery. NRL-SSC retrieved MODIS
granule from the Persian Gulf, Arabian Sea and the Red Sea. A granule
represents an area of approximately 1 minute of satellite data which is
approximately 500 square km. For each MODIS satellite there are three
granules for each area at different spatial resolutions 1) 250m
(channels 1-2), 2) 500m (channels 3-4) and 3) 1000m (channels 6-32).
Each granule is approximately 1 gigabyte. NRL-SSC obtains approximately
5 -10 gigabytes per day of MODIS data. This large data volume limits the
number of ocean products that can be produced since both Terra and Aqua
data are obtained. The limitations of the DREN network impact the
processing capabilities at the present time.

MODIS imagery is also collected for the Gulf of Mexico and U.S. East
Coast via Goddard's Direct Broadcast System, while imagery from the U.S.
West Coast is pulled from Oregon State University. NRL-SSC receives and
processes approximately five gigabytes of SeaWiFS and MODIS imagery on a
daily basis for many different areas of the world. When a customer
requests a product and imagery for an area, special scripts can be
written to develop a "custom" tailored region with specific processing
and product formation using APS. The results are assessed by the
customer via web pages (secure and unrestricted/password protected).

# 3.0 Calibration and Validation Requirements

Before supplying the customer with either standard optical properties or
a customized product, extensive calibration and validation efforts are
necessary. Both SeaWiFS and MODIS satellites have calibration and
validation efforts that continuously are evaluated and for which NRL-SSC
processing and field efforts are an integral part. Calibration and
validation is much more than just algorithm formation and testing,
particularly for customized products such as those required by the Navy.
Calibration and validation efforts are required at several levels that
include:

1)  Pre and post launch sensor calibration,

2)  Long-term sensor stability and application of accurate calibration
    coefficients,

3)  Bio-optical property and water leaving radiance match-ups,

4)  Vicarious calibration at monitoring areas and local data collects
    for inter-sensor comparison, and

5)  Customer product validation, evaluation, and calibration.

Each type of effort occurs at various stages between sensor radiance
collection/down-link and delivery of the final product. In order for
NRL-SSC to produce dependable operational products Quality Control (QC)
checks are conducted on sensor output, the atmospherically corrected
radiances, the calculated normalized water leaving radiances, retrieved
optical properties, and derived products. Consistency amongst various
optical properties and oceanographic features is used to recognize
subtle changes in algorithm performance. These procedures are nominally
applied to the 1 km SeaWiFS, MODIS Terra, and MODIS Aqua sensors output.
This provides a sensor to end-product/user calibration and validation
(including on-scene feedback).

![](media/image11.png){width="4.338888888888889in"
height="4.574305555555555in"}

**Figure 8. - Calibration/Validation process for data collection and
product dissemination**

Figure 8 is a generalized representation of the cross calibration and
validation efforts that take place in developing and delivering a useful
product. The first calibration and validation step is in the image
reception with identification of a bad data stream. These can occur when
improper decryption coefficients have been applied. During processing of
the raw level 0 imagery to level 1, the sensor calibration and
atmospheric corrections are applied. As will be demonstrated below, lack
of careful monitoring and application of calibration coefficients can
result in a poor quality product. Long-term temporal monitoring programs
and in situ field programs provide calibration and validation points for
the remote sensing reflectances derived from the satellite. Sensor
calibration changes can also be evaluated in this fashion. For multiple
sensors that pass over the same area in a close temporal period, the
comparison (and perhaps calibration) of one sensor with the other is
valuable. For OIF this type of calibration was important since the
SeaWiFS satellite has been more extensively characterized than MODIS and
is used as the "radiometrically correct standard" for areas without
field-truth. The process of vicariously calibrating sensors (i.e. using
ground-truth and other sensors to monitor performance) is important to
make a seamless transition from one sensor and its algorithms to another
sensor. The next step in Figure 8 is the validation of retrieved optical
properties with those measured in situ. For coastal areas there are
issues of spatial variability (addressed below) questioning how to best
accomplish this, particularly for denied access areas. Lastly is the
product validation. While being the most important phase, it is also the
most difficult since data is seldom available and therefore the
validation requires constant customer feedback for the customer to the
developer. In each part of the process error analysis is conducted to
determine where future efforts should be concentrated.

Figure 9 illustrates the specific process which is used to generate Navy
ocean products (Arnone et al., 2003). This process is constructed and
based on radiometric accuracy of the sensors and standardized procedures
so that universal algorithms can be applied to develop a Navy product
(this is discussed in more detail in the next section on "match-ups").
The process ensures a procedure for developing the most accurate Navy
product for global applications in areas where no *in situ* observations
are available. To accomplish this requires strict radiometric control
which is maintained through the entire product generation process. The
process is being applied to SeaWiFS and MODIS to successfully generate
products. This process will be applied to follow-on sensors to insure
consistency in Navy products and permit new algorithms to be defined.
The process is very closely aligned with NASA- and NOAA product
development. However, there are some differences. Differences arise in
that the Navy's requirement for a real time capability that can be
developed within the calibration-validation procedures, whereas
NASA-NOAA looking for long-term changes and capabilities.

![](media/image12.png){width="6.011111111111111in"
height="4.502083333333333in"}

**Figure 9. - Generation of Navy products, outlining the steps of the
calibration and validation processes used to develop products. A series
of algorithms and procedures are used to generate Navy products. Each
step contributes to the product error and accuracy.**

The steps in the Navy product development process are outlined as
follows.

> *Step 1: Sensor Calibration-*

The initial step involves the senor calibration which is typically the
most difficult. This step includes sensor drift and spectral (channel)
accuracy and involves the long term stability of the sensor. This
includes the performance and characterization of the spectral channels
based on time (or orbit number). The initial sensor calibration is from
*level 1*, which will initially be determined through on-board internal
radiometric control and sensor stability, such as a solar diffuser or
periodic views of the moon. The stability and spectral drift are
critical inputs to follow-on steps. But the results from the initial
sensor calibration step can be modified via an optimization process to
improve the product. For NRL-SSC the first step in the process is to use
the sensor calibration provided by the SeaWiFS/MODIS project office at
*level 2*. However, these calibrations will be adjusted by follow-on
steps to insure product quality as will be shown. If discrepancies are
still evident after proceeding through subsequent calibration-validation
procedures, then sensor calibration is refined to optimize the final
Navy product.

This procedure of vicarious calibration works well and minimizes the
error of the final product. NASA and Navy have used this procedure based
on the radiometric measurements from the in-water Marine Optical Buoy
(MOBY) buoy to optimize the retrieved radiance for this open ocean,
relatively stable site. For many spectral channels this adjustment to
calibration is important for ocean open waters. However, for Navy
applications, additional vicarious calibration is required to enable
improved calibration of the channels critical to coastal algorithms.
This requires that a coastal calibration site be established to augment
the open ocean calibration.

> *Step2: Atmospheric Correction*

Because the atmosphere can contribute to as much as 90% of the total
signal measured in space, the atmospheric correction step is extremely
important for ocean products. To remove the atmosphere "noise" from the
total radiance measured, the radiometric contribution of the Rayleigh
(molecular) and aerosol scattering of the atmosphere is modeled from
spectral information. The atmospheric correction for water leaving
radiance uses the near-IR spectral channels and then extrapolates this
to other spectral regions. Errors in radiometric calibration and modeled
loss assigned to the atmosphere result in major errors in the resulting
ocean signal. Ocean products require that ocean reflectivity is known to
within 2-5% for Navy products. Therefore high precision calibration is
required between the channels to limit errors. However, there are
problems in the near-IR atmospheric correction when applied in coastal
waters.

The Navy has extended the atmospheric correction used in blue waters to
coastal waters by applying an iterative solution between the atmospheric
correction step and the next step in calculating ocean water
reflectivity. This coupled ocean- atmospheric iterative model solution
determines an optimum solution for separation of water and atmosphere
radiance contributions to the total radiance at the sensor. Additionally
by this methodology we account for the reflectivity of the sea surface
(sky reflectance) and include influences from white foam on the surface.

> *Step 3: Water reflectivity -*

The spectral water reflectivity is the amount light coming out of the
water measured just below the sea surface relative to that entering the
water and is termed the remote sensing reflectance. The satellite sensor
measures the radiance (dependence on angular distribution). This
radiance is converted to the normalized radiance (directly overheard)
and is then related to the remote sensing reflectance (RRS) by
normalizing with the incoming spectral solar radiance. The RRS are the
values which can be measured at the sea surface and are the first stage
of validation. However, RRS is not a Navy product. These spectral values
are used by algorithms to derive the bio-optical properties of the near
surface waters. Measurement of RRS is difficult and requires exact
protocols. Changes of 10 -- 15 % between instruments and protocols can
occur.

> *Step 4: In-water optical properties -*

Bio-optical properties are determined by using the spectral remote
sensing reflectance (RRS(λ)) and a suite of bio-optical algorithms. For
example, NASA uses a simple empirical algorithm applying the lowest
ratio of 3 channels of the RRS to estimate the chlorophyll (called OC3).
Other semi--analytical algorithms use the spectral RRS to estimate the
in-water optical properties of spectral absorption, backscattering,
colored dissolved organic absorption and beam attenuation coefficient.
Currently there are many Navy algorithms that are available in research
that are undergoing testing and validation. The Navy currently uses
approximately 10 algorithms which use RRS to determine in-water optical
properties. These bio-optical ocean properties are validated by *in
situ* observations and are used to generate specific Navy ocean
products.

Navy research has an advanced set of optical instruments and has
established procedures for measuring these in-water optical properties.
These measurements are used to determine the accuracy of the satellite
products. The measurements are an area of active ocean research in ocean
algorithm development.

Navy products are tightly linked to the fundamental optical properties
of the waters. This is slightly different from NASA or NOAA who are
interested in biological properties such as chlorophyll. It is usually
easier to quantify the relationship of the RRS with optical properties
than it is to link RRS with the biological properties. This is due to
the inherent difficulties associated with biological processes such as
"cell structure and physiology".

> *Step 5: Navy Ocean Products --*

The last step in the Navy product generation is to use the optical
properties, such as absorption and scattering, to derive a Navy product
such as diver visibility. Other examples include using optical
properties to determine the depth that an underwater laser will
penetrate. This last step requires a suite of algorithms based on
specific Navy systems and how their performance is linked to ocean
optics. This step is difficult to validate since these products deal
with the 1) classification of systems and 2) non -- quantitative
metrics - diver eyesight etc.

As demonstrated above the calibration and validation of each step going
from "sensor to product" is required. It is not just a simple match-up
of the end product with observations. Each step in the development
contributes errors to the final product. In addition there is no one
algorithm but a number of algorithms that contribute to uncertainty and
error and therefore impact product accuracy. Understanding the accuracy
and limitations of each step from sensor calibration, atmospheric
correction, in water optical algorithms, and the final product
development is critical to the sensor calibration and validation. These
include the errors associated with:

-   Sensor calibration

-   Atmospheric correction algorithms

-   Remote sensing reflectance algorithms

-   In water bio-optical algorithms

-   Navy product algorithms

Initially, the vicarious procedure is used to minimize the error in
satellite RRS based on the *in situ* observations by adjusting the
sensor calibration. However the error in optical property (e.g,
absorption, backscattering, etc.) retrieval is *also* minimized based on
the *in situ* observations by adjusting the calibration (Step 1) for
subsequent evaluations.

## 3.1 Issues in calibration and validation for coastal waters and OIF-

The standard for calibration and validation of bio-optical properties
being derived from satellites over blue water has been set by NASA (5%
accuracy for retrieval of water radiances, ± 30% for retrieval of
chlorophyll). The importance of such calibration and validation has been
recognized through the reprocessing of data and the emergence of a
variety of algorithms and atmospheric correction schemes. It is the
SeaWiFS and the follow-on MODIS development cycles that have set the
infrastructure, architecture, *in situ* data measurement protocols,
standardization and inter-comparison of measurement sensors (through
SIMBIOS), and data accuracies that are required to retrieve accurate
bio-optical properties. This process is the template and goal for the
operational products, although some latitude may be available since the
Navy's (customer) requirements are often not as stringent as those for
monitoring global climate change. NRL-SSC requirements for ocean color
sensor suite accuracies, however, are aligned with those of NASA since
both basic and applied algorithm development occur simultaneously to
nurture and promote rapid product development and distribution. As will
be demonstrated, however, the spatial and temporal resolution of the
product required by the Navy during OIF surpassed that needed for
monitoring environmental change.

### 3.1.1 Updating coefficients for imagery

Without accurate imagery that has geo-location and sensor calibration
coefficients applied then results of algorithms and product generation
can be futile. During the SeaWiFS program and continuing into the MODIS
program NRL-SSC has and continues to interact with scientists at
University of Miami and NASA's GSFC to ensure that we receive the latest
MODIS processing code updates. The University of Miami (RSMAS) is the
software provider for Ocean algorithms (level 1 to 2) to NASA Goddard
and the MODAPS processing. MODAPS is the software that is used by the
Goddard DACC for global MODIS ocean products that has been approved by
the Science team.

The need for the most recent calibration tables was evident in the MODIS
products during OIF. The calibration tables are updated on bi-weekly
basis by the MODIS Characterization Support Team (MCST). These
calibration look up tables (LUT) are used in processing MODIS from level
0 (raw) to level 1a. These LUT are used to calibrate the 10 individual
sensors which strip across the scene as the image is created. When these
calibration updates are not implemented, then "striping" can occur on an
image (Fig. 10) as miss-calibration of these sensors. Note that this
occurs at level 0 to 1 processing which is where NRL-SSC normally
receives MODIS data and uses APS for processing. (Level 1b MODIS data is
the lowest data level that can be ordered from the DACC. When there are
major changes in MODIS calibration then the entire reprocessing must
occur to generate the level 1b data set. This has been a problematic
issue of Terra during the first two years.) The striping is the easiest
and most visual way to detect errors in the calibration coefficients.
This striping is not evident in the true color image. It is following
removal of the atmospheric correction (which constitutes \~80-90% of the
total signal) for ocean applications that errors and striping are
observed. Inaccuracy in the calibrations is immediately noted in the
ocean products because ocean signal is low in comparison to the total.
When this occurred NRL-SSC contacted the NOAA/ NASA bent pipe data
provider to update the calibration tables. During the OIF, these
calibration updates were not being updated quickly and poor MODIS
products were initially generated. By interacting directly with the
source of the coefficients NRL-SSC was able to improve the accuracy of
the product. This was in part made easier by the flexibility of APS
which allowed for the rapid and efficient upgrade. Once the appropriate
coefficients are applied the striping is reduced or eliminated thus
improving the product.

![](media/image13.wmf){width="3.7847222222222223in"
height="2.7930555555555556in"}

**Figure 10. - MODIS image demonstrating "striping" when biweekly
updates in calibration coefficients and corrections are not applied.
Optical property shown is K~d~(532).**

Communication between NRL-SSC and the University of Miami has also
helped to quickly test and implement new software changes. As a beta
test site for the Miami Level 1 to Level 2 MODIS software, NRL-SSC has
been able to examine calibration and algorithm updates and routinely
carries out anomaly detection. This is used to monitor what changes in
the product and radiances occur after modifications of calibration
coefficients or changes in bio-optical algorithms are implemented. The
Miami software for ocean processing has been improved by NRL-SSC for
coastal waters using the near-IR atmospheric correction. Although not
influencing the offshore waters, the coastal Navy products are improved
with this upgrade to the Miami software. Presently these changes are not
formally approved by the NASA MODIS science team and therefore have not
been implemented into the DACC processing.

### 3.1.2 Long-term Radiometric Monitoring

SeaWiFS and MODIS are environmental monitoring satellites; consequently,
their calibration and validation requirements concentrate on the
"blue-water" condition. Because of the sensor drift in the CZCS sensor,
NASA recognized the need for a long-term monitoring capability for which
the radiometric accuracies measured by the satellite could be compared
with those measured collected at the ocean surface. In addition to these
long-term at-sea measurements, the sensors are required to have onboard
sensor calibration practices. This includes having the sensor
periodically view the moon as a source of stable light to track the long
term stability of the sensors. Additionally, on-board sensors routinely
view the solar spectrum using a solar diffuser to determine sensor drift
and long-term stability of the spectral characteristics and dynamic
range of the sensors. However, the long-term stability of the solar
diffusers also must be monitored as they degrade in space as was
determined by CZCS.

To help resolve these issues, the SeaWiFS/MODIS team deployed a Marine
Optical Buoy (MOBY) off the coast of Lanai, Hawaii on February 21, 1994.
This effort was funded jointly by NASA and NOAA and is headed by Dr.
Dennis Clark, a member of both the SeaWiFS and MODIS science teams. The
primary purpose of MOBY is to measure, with high accuracy and high
precision, the visible and near-infrared radiation entering and
emanating from the ocean. This ocean region is basically radiometrically
stable and has small variations of reflected radiation that is critical
to the biological and optical properties derived from ocean color. MOBY
is located in an area that is intentionally spatially and temporally
homogeneous so that issues of bio-optical variability in and around the
MOBY site are greatly reduced.

Measurements at MOBY cover the complete set of parameters set forth by
the SeaWiFS and MODIS science teams. This includes spectral upwelling
radiance, spectral downwelling irradiance, all the inherent optical
properties (absorption, attenuation, and backscattering), biological
properties (such as chlorophyll), and above water leaving radiances and
sky/solar radiance spectra. Consequently, MOBY serves as a calibration
reference station for satellite instruments such as SeaWiFS and MODIS.
The site ensures that the satellite instruments remain accurate and
stable and that sensor calibration changes can be monitored and
accounted for via modifications in calibration coefficients. MOBY also
assists in supporting atmospheric correction programs. Hence, the
long-term monitoring using the MOBY measurements combined with
coincident SeaWiFS and MODIS imagery is used to vicariously calibrate
most ocean color sensors.

However, while deployment in clear blue waters helps with long-term
sensor calibration issues, performance and sensor calibrations issues in
coastal and turbid waters are not addressed by MOBY. To help overcome
this NRL-SSC uses point measurements in a variety of coastal
environments, interaction with numerous investigators, and often must
rely on vicarious calibration using *in situ* remote sensing reflectance
estimates in coastal waters. This results in algorithm/calibration
coefficient adjustments specific to coastal environments.

### 3.1.3 Match-ups of Inherent Optical Properties and Remote Sensing Reflectance

NRL-SSC plays a leading role using *in situ* measurements to help
calibrate and validate a suite of ocean color satellites and their
derived ocean water properties. NRL-SSC maintains an *in situ*
validation database of over 800 stations collected in coastal and
offshore waters (Ladner et al., 2002). Measurements of remote sensing
reflectance and bio-optical properties are available at these stations.
The field measurements have been used to develop algorithms, calibrate
sensor performance, and validate satellite-derived properties for
coastal waters. This database is made up of *in situ* measurements of
spectral absorption (total, phytoplankton component, detritus, colored
dissolved organic matter, and water), spectral particulate
backscattering, spectral total attenuation, remote sensing reflectance,
chlorophyll concentration, sun and sky contributions to incident light,
particle size concentrations, and the spectral diffuse attenuation
coefficient. The database includes bio-optical properties collected on
more than 60 cruises in the Gulf of Mexico, Monterey Bay, CA, New York
Bight, Chesapeake Bay, Fort Lauderdale, FL, Arabian Sea, Adriatic Sea,
Japan/East Sea and Yellow Sea. This field program leverages off of
programs supported by NASA, ONR, and NRL-SSC.

To provide the best bio-optical properties and calibration/validation,
NRL-SSC requirements for a radiometric satellite are strongly linked
with the stringent radiometric control of the NASA science team. The
most critical field measurement is the spectral water leaving radiance
(remote sensing reflectance). Redundant measurements of above surface
and subsurface optical properties are required to validate satellite
products and provide for accurate calibration and validation of spectral
remote sensing reflectance. Remote sensing reflectances are collected in
the field by using a handheld radiometer. The above water RSR at 512
channels are measured using Analytical Spectral Devices (ASD) and Remote
Optical Systems Incorporated (Spectrix) radiometers (Fig. 11). A
Microtops sensor, that measures solar light levels is used to evaluate
atmospheric properties.

A complete set of in-water measurements of optical properties is also
collected. A Satlantic Incorporated Hyperspectral Tethered Radiometric
Buoy (HSTRB) is used to measure remote sensing reflectance just below
the surface at 123 channels and provides a consistency check for surface
reflectance losses. A Satlantic Inc., K-chain Buoy measures upwelled
radiance, L~u~, at 7 channels just below the surface and downwelling
irradiance, E~d~, at 3 channels just above the surface. Throughout the
water column this instrument measures E~d~ at 5 depths at 532nm, and
therefore provides a measure of the diffuse attenuation coefficient K~d~
for comparison with the satellite derived property. NRL-SSC uses a
WetLabs' AC9 (9 channels) and Hystar (100 channels) instrument to
measure spectral total attenuation, *c*, and spectral total absorption,
*a*. Spectral total scattering, *b* is derived from the difference (*b =
c-a*). Backscattering properties are measured at 6 channels using a
HOBI-LABS Hydroscat-6. In order to examine spatial variability in
coastal waters, which is a fundamental issue in calibration and
validation, NRL-SSC maintains an optical underway measurement system
containing a WetLabs AC9, Seabird CTD, fluorometer, depth, and GPS. This
provides measurements of optical properties near the surface over
significant distances. With this data statistical variability in optical
properties can be examined relative to the pixel size of the satellite.

![](media/image14.png){width="5.999305555555556in"
height="3.3715277777777777in"}

**Figure 11. - Instrumentation used for field calibration and validation
program.**

The *in situ* field data (remote sensing reflectance and water optical
properties) is used to validate bio-optical property retrievals, monitor
performance of the sensor, and evaluate algorithms in coastal
environments. Data points are selected in which *in situ* data is
compared with satellite properties as "match-ups." These match-ups
compare the satellite derived optical properties with those from the
field measurements. These match-ups are invaluable in determining
stability of the satellite, algorithm performance, defining new optical
properties that can be retrieved from the satellite signal, and finding
data anomalies. The match-ups are used to calibrate and validate the
remote sensing reflectance retrievals. This has been particularly
important in the coastal waters where difficulties due to high
backscattering and local variability have been factors. The data
match-ups provide a means of evaluating the accuracy of derived
properties and the reliability one assigns to the end-product.

Figure 12 shows match-ups of remote sensing reflectance from the SeaWiFS
satellite and that measured *in situ*. The variance is a measure of
satellite uncertainty, the ability to accurately "measure" remote
sensing reflectance *in situ*, and atmospheric correction and spatial
measurement difficulties present in coastal waters (see next section).
While there is a high degree of spread in the data, the measured remote
sensing reflectance from above the water surface agree favorably with
those derived from SeaWiFS after NRL-SSC applied gain settings and
calibration coefficients that had been determined vicariously for the
coastal areas. ![](media/image15.png){width="5.990972222222222in"
height="2.3680555555555554in"}

**Figure 12. - Match-ups of Remote Sensing Reflectance from in situ
measurements and that derived from SeaWiFS.**

In the examination of the match-ups, we look for systematic divergences
between the *in situ* and satellite remote sensing reflectances. If the
*in situ* remote sensing reflectances from the handheld radiometer do
not match those derived from the satellite, then a vicarious calibration
is performed. This adjusts the satellite calibration to optimize a match
between the field and satellite data. Slight adjustments to the sensor
calibration coefficients are performed, imagery is reprocessed and the
comparison is repeated until the *in situ* and satellite remote sensing
reflectances converge. After convergence is obtained, the retrieval and
calibration/validation of the derived properties is carried out.

Using optical *in situ* data collected in the field, validation of the
satellite optical algorithms is performed. Figures 13-A and B show the
match-ups between absorption and scattering at 510 and 555 nm. These are
wavebands appropriate to the diver visibility algorithm. Note that while
the remote sensing reflectance variability were randomly distributed
around a near 1:1 line (Fig. 12), the discrepancies between the measured
optical property values and those derived from the satellite are
substantial. Previous validations of the optical properties have yielded
errors of 10-40% for absorption and scattering. The smaller errors occur
most often in clearer waters and higher errors occur in coastal waters
(Table 3) due to errors in sensor calibration, atmospheric correction,
and optical algorithms (Ladner et al., 2002). The difference between the
*in situ* optical property and that derived from satellite remote
sensing reflectances is adjusted only after other collaborative
information such as optical property inter-relationships and forward
modeling of remote sensing reflectances has been completed. The mismatch
between the optical properties is a challenge for providing accurate
products to the customer. Once an algorithm is found to require
correction it is adjusted for global or regional application and then
the imagery is reprocessed and a re-validation is performed.

![](media/image16.png){width="5.990972222222222in"
height="4.219444444444444in"}

**Figure 13. - (A&B) Comparison of SeaWiFS derived absorption at 412 and
510 nm and in situ measured properties (Top Panels); Comparison of
SeaWiFS derived scattering (with assumed backscattering/scattering
ratio) and in situ measured scattering at 412 and 510 nm**

The retrieval of optical properties is often uncertain, particularly in
coastal waters (Table 3). For the most part there are systematic
underestimates of both absorption and total scattering (derived assuming
b~b~/b of 0.02). The errors in both the absorption and scattering are
highest in the most variable environments (Gulf of Mexico and LEO).
These results suggest that measurement of the optical properties in
these environments is also non-trivial.

Table 3. - Error Analysis in retrieval of Absorption and Scattering
(with assumed b~b~/b of 0.02).

+--------------+------+-------+-----+-----+------+-------+-----+-----+
| **           | **Ab |       |     |     | **Sc |       |     |     |
| Wavelength** | sorp |       |     |     | atte |       |     |     |
|              | tion |       |     |     | ring |       |     |     |
|              | E    |       |     |     | E    |       |     |     |
|              | rror |       |     |     | rror |       |     |     |
|              | as   |       |     |     | as   |       |     |     |
|              | p    |       |     |     | P    |       |     |     |
|              | erce |       |     |     | erce |       |     |     |
|              | nt** |       |     |     | nt** |       |     |     |
|              |      |       |     |     |      |       |     |     |
|              | **Ar |       |     |     | **Ar |       |     |     |
|              | none |       |     |     | none |       |     |     |
|              | (C   |       |     |     | (C   |       |     |     |
|              | arde |       |     |     | arde |       |     |     |
|              | r)** |       |     |     | r)** |       |     |     |
+--------------+------+-------+-----+-----+------+-------+-----+-----+
| **           | **G  | **T   | *   | *   | **G  | **T   | *   | *   |
| Nanometers** | OM** | BAY** | *LE | *EA | OM** | BAY** | *LE | *EA |
|              |      |       | O** | S** |      |       | O** | S** |
+--------------+------+-------+-----+-----+------+-------+-----+-----+
| **412**      | **47 | **37  | *   | *   | **58 | **56  | *   | *   |
|              | (6   | (     | *48 | *67 | (6   | (     | *71 | *65 |
|              | 0)** | 21)** | (35 | (69 | 4)** | 63)** | (74 | (74 |
|              |      |       | )** | )** |      |       | )** | )** |
+--------------+------+-------+-----+-----+------+-------+-----+-----+
| **443**      | **45 | **37  | *   | *   | **56 | **31  | *   | *   |
|              | (5   | (     | *42 | *64 | (6   | (     | *70 | *65 |
|              | 8)** | 21)** | (42 | (71 | 3)** | 39)** | (74 | (74 |
|              |      |       | )** | )** |      |       | )** | )** |
+--------------+------+-------+-----+-----+------+-------+-----+-----+
| **490**      | **38 | **41  | *   | *   | **54 | **34  | *   | *   |
|              | (5   | (     | *42 | *56 | (6   | (     | *71 | *65 |
|              | 9)** | 36)** | (44 | (75 | 2)** | 56)** | (74 | (74 |
|              |      |       | )** | )** |      |       | )** | )** |
+--------------+------+-------+-----+-----+------+-------+-----+-----+
| ***510***    | *    | ***42 | **  | **  | *    | ***31 | **  | **  |
|              | **42 | (4    | *42 | *57 | **54 | (4    | *71 | *65 |
|              | (65  | 0)*** | (   | (   | (62  | 7)*** | (   | (   |
|              | )*** |       | 47) | 72) | )*** |       | 74) | 74) |
|              |      |       | *** | *** |      |       | *** | *** |
+--------------+------+-------+-----+-----+------+-------+-----+-----+
| ***555***    | *    | ***36 | **  | **  | *    | ***29 | **  | **  |
|              | **40 | (3    | *63 | *66 | **55 | (4    | *73 | *61 |
|              | (60  | 2)*** | (   | (   | (62  | 6)*** | (   | (   |
|              | )*** |       | 64) | 74) | )*** |       | 73) | 73) |
|              |      |       | *** | *** |      |       | *** | *** |
+--------------+------+-------+-----+-----+------+-------+-----+-----+
| **670**      | *    | **-** | **  | **  | **55 | **33  | *   | *   |
|              | *-** |       | -** | -** | (6   | (     | *66 | *64 |
|              |      |       |     |     | 4)** | 52)** | (72 | (74 |
|              |      |       |     |     |      |       | )** | )** |
+--------------+------+-------+-----+-----+------+-------+-----+-----+

The total attenuation coefficient '*c*' is not a standard SeaWiFS or
MODIS bio-optical property derived from remote sensing reflectance. The
normal remote sensing algorithms derive total absorption and total
backscattering. But diver visibility is based on total attenuation, the
sum of absorption and total scattering, and only backscattering and
absorption are available. The issue for diver visibility in calibration
and validation is two fold. The first is deriving an optical property
not normally retrieved from the satellite (which we will address here).
The second is the calibration and validation of the diver visibility
product (discussed in section 3.2).

Part of the error in '*b'* (scattering) that is apparent in Figure 13
and Table 3 comes from the extreme difficulty in retrieving an optical
property for which limited information is available and when assumptions
are being made. This underlines the accurate calibration and validation
work that must be maintained to derive a useful product since error bars
are critical. For diver visibility, NRL-SSC's normal algorithm assumes a
shape for the volume scattering function (a quantitative measure of the
angular dependence for light scattering) described by Petzold (1972) for
San Diego Harbor. This common assumption yields a value of 0.02 for the
ratio of backscattering to scattering. Using this assumption, the
scattering coefficient can be directly obtained from the backscattering
coefficient derived from the ocean color. However, it is useful to note
that the range in the backscattering to scattering ratio extends from
0.006 to 0.03 for coastal waters (phytoplankton to sediment dominated).
Research is being conducted to constrain this range and improve the
retrieval of the scattering coefficient. Another source of uncertainty
is in the "assumed" coefficients that are used to relate remote sensing
reflectance to the ratio of backscattering and the sum of backscattering
and absorption (i.e. b~b~/(a+b~b~)). Since the remote sensing
reflectances agree well in Figure 12 one can speculate that the
coefficient used to define relationships between light distribution in
the nadir direction and the scattering function may be non-linear and/or
a function of total scattering. While such relationships have been
suggested by Lee (2002) they have not been incorporated into retrievals
of the scattering coefficient.

A match-up comparison of retrieved values for total attenuation at 670
nm and that measured by the Wetlab's Inc attenuation meter is presented
in Figure 14. It highlights the improvements that are being made for
retrieval of beam attenuation. Note that measured values and satellite
derived attenuation from the Arnone algorithm are close even at high
attenuation (i.e. high scattering) near the mouth of the Mississippi
River (Stations 9 and 10 in red). It also shows the significance in
comparing various algorithms for backscattering (assuming a constant
backscattering:scattering ratio) and provides a consistency check with
total attenuation. This helps show where refinements are needed in an
algorithm.

![](media/image17.wmf){width="5.999305555555556in"
height="2.6680555555555556in"}

Figure 14. - Comparison of measured and SeaWiFS derived "total
attenuation" c at 670 nm using algorithms of Carder, Arnone, and Gould.

### 3.1.4 Coastal issues of water leaving radiance 

There are numerous problems that NRL-SSC had to overcome to calibrate
and validate ocean color properties in coastal areas. The three most
important to the diver visibility product are the atmospheric
correction, the non-uniform atmospheric conditions common in coastal
areas, and spatial uncertainty in measurements of bio-optical properties
and the averaging over satellite "pixel" areas. For the OIF effort all
three of these were issues in optical property validation, algorithm
formation, and product validation.

As previously indicated 90% of the signal measured at the satellite
originates from the atmosphere: with Rayleigh scattering accounting for
\~60% and aerosol scattering \~30%. Therefore, in order to retrieve
estimates of water color, and subsequently optical properties it is
extremely critical to accurately calculate and then remove this portion
of the signal. Standard NASA algorithms were developed for open oceanic
conditions and as such do not address issues specific to coastal waters.
Initial results from processing SeaWiFS and MODIS imagery using NASA's
standard software programs, calibration tables, and atmospheric
correction algorithms have commonly resulted in negative radiances
appearing in the coastal waters (Fig. 15). Obviously this can be
problematic for coastal product generation and required identification
of the source of the negative radiances and then development and
implementation of a solution.

The problem was isolated to the treatment used in determining
atmospheric correction by NASA. With the standard NASA algorithms, the
water is assumed to be "black", or totally absorbing at red and near-IR
wavelengths. The requirement for a "black" wavelength is necessary since
it is these near-infrared bands that are used to determine the
atmospheric contribution. While valid in open-ocean conditions, this
assumption is most often erroneous in turbid coastal areas where
suspended sediments are in high concentration and reflect at these
wavelengths. This erroneous assumption in coastal waters leads to an
over-correction by the atmospheric correction algorithms resulting in
negative water leaving radiances (i.e. radiance in the near infrared
bands that was assumed to be zero and was added to the "atmospheric
loss" term were in reality a property of the water).

![](media/image18.wmf){width="5.230555555555555in"
height="3.442361111111111in"}

Figure 15. - Negative Radiances caused by Atmospheric Correction in
coastal waters and then the application of the Near-Infrared iteration
to improved water leaving radiance (Persian Gulf on 18 April 2003,
SeaWiFS image). (Arnone et al., 1998).

To remedy this problem NRL-SSC developed a technique to improve the
separation of the atmosphere radiance contribution from the turbid water
radiance contribution for coastal waters. This is an iterative algorithm
that uses a NIR radiance correction to determine the water reflectance
in the 765 and 865 nm channels (assumed to be zero by the standard NASA
algorithm). This correction is then applied to the entire spectrum
increasing reflectances in the blue channels, mainly 412 and 443 nm.
This algorithm resulted in significantly improved performance and
retrieval of optical properties in the coastal areas (Figure 15). NASA
SeaWiFS program has adopted the NRL-SSC improvements to the baseline
atmospheric correction routines used by their standard processing. Its
implementation permitted extension of ocean color algorithms into
estuaries and bays critical to the customer.

Another issue for calibration and validation in the coastal waters is
variability of the environment itself. Figure 16 shows how extremely
variable a coastal environment can be using the MODIS 250 m product. As
demonstrated there are several features in the derived optical property
MODIS 250 m image that do not appear in the 1 km image. Other sensors
have shown that this variability is pronounced to scales less than 10's
of meters. This pronounced variability cascades into calibration and
validation since a measurement point may in truth be correct, but the
variability and statistical techniques used to "validate" the product
may cause uncertainty in the product. For most environmental monitoring
applications the 1 km satellite resolution is sufficient for customer
use. However, as demonstrated during OIF, this can also be a limitation
that requires attention.

![](media/image19.png){width="5.999305555555556in" height="3.85625in"}

Figure 16. - Coastal Variability comparison between SeaWiFS (1 km) and
MODIS (250 m) diver visibility product. Note the details present in the
250 m imagery that are not apparent in the 1 km data. (See Arnone et
al., 2002).

The error caused by local variability is particularly problematic in the
"match-ups" used for algorithm formation and calibration in the coastal
waters. The field collection measurement is at a "point" which when
compared to a satellite may or may not be correct depending on the
spatial variability of the environment, the positional accuracies, and
"temporal variability" (such as tidal fluctuations or local physical
forcing). NRL-SSC addressed this with "along-track" measurements that
are made to determine spatial variability in and around a measurement
point.

## 3.2 Product calibration and validation

Diver visibility is a product which has undergone limited calibration
and validation due to in part to the product itself. As described,
visibility is not a property that can be derived directly from ocean
color. However, as presented above there have been R&D efforts
attempting to provide a "total attenuation" property at 532 nm
(wavelength applicable to diver visibility and underwater laser
propagation). For diver visibility, calibration efforts have centered
around 1) determining the best algorithm to use , 2) providing a
straightforward definition of visibility given all of the variables, and
3) how can we best validate a product. The later is a very important
component and any discrepancy between the satellite derived product, the
measured visibility, and a theoretically determined visibility limit is
where the interaction with the customer is absolutely critical in the
calibration and validation step. This step of the calibration and
validation process can sometimes result in the entire procedure being
re-evaluated and can point to new required algorithms or new research
areas.

For diver visibility NRL-SSC relied on work conducted under an ONR
sponsored Gauging Littoral Optics for the Warfighter (GLOW) project, and
an ONR sponsored effort in validating a scientific visibility algorithm
called the "Distance Visibility Algorithm" (DiVA). There were also two
masters theses that where conducted in collaboration with NRL-SSC that
provided diver visibility calibration and validation information (Thesis
of LT Erica Museler, USN, Naval Postgraduate School; and Todd Bowers,
University of Southern Mississippi). The GLOW project used Navy reserve
divers to make measurements underwater of objects at specific ranges to
determine how a Navy diver would interpret "diver visibility" and at
what range various objects would disappear. This was invaluable since it
was the background necessary to move the science into the fleet
application. Measurements of the optical properties were collected
concurrently with diver observations. The various efforts provided four
distinct aspects related to the validation of the Navy's diver
visibility product. These were:

1.  GLOW- Used the reserve divers to come up with a "standard"
    definition for diver visibility that could be directly related to
    the fleet as a product, and collected data for comparison of the
    historical 4/c product and Secchi disk product with other models.
    Both diver visibility and diver vulnerability (being seen from above
    the surface) were addressed.

2.  Bower Thesis- Expanded the theoretical treatment originally part of
    the contrast transmittance theory used in formation of the
    nomographs (recall these nomographs were not used by the fleet). The
    approach was to develop a computer aided model of diver visibility
    that could take into account target size, solar angle, background
    illumination, eye adaptation, and optical properties of the water.
    The thesis also addressed the visibility of object of given size
    from above the surface.

3.  DiVA- Compared "new" exploratory models of diver and camera
    visibility which had potential for transition and application using
    remote sensing properties. The primary contribution of this project
    was forcing a comparison between all of the diver visibility models
    using coastal water data.

4.  Museler Thesis- This work is the most relevant project related to
    product validation. LT. Museler, as a Navy Postgraduate School
    thesis student, helped make measurements of in-water optical
    properties; collect diver visibility data; and applied SeaWiFS
    algorithms to compare "derived visibility from SeaWiFS" versus
    "Measured Visibility" in a turbid environment (Visibility less than
    15 feet).

All of the efforts above concentrate on the 1 km SeaWiFS product and not
the MODIS 250 m product which was the final product requested and
produced for the Navy Warfighter in OIF. As such, there is a calibration
and validation step that is missing (i.e. 250 m vs. 1 km optical
property retrieval). Hence this was conducted during OIF using on-scene
information and feedback to the developer.

In the above work, U.S. Navy Reserve divers measured the distance at
which a 20" black, 20" gray, and 20" black/white sphere disappeared at
depths of 10, 20, and 30 feet (Figure 17). Measurements of the
corresponding optical properties (absorption, attenuation,
backscattering, and diffuse attenuation) were collected as a function of
depth. This was conducted at several locations (clean to turbid water)
and at directions corresponding to "into the sun, 90˚ from the sun, and
away from the sun (i.e. sun behind look direction of the diver).

![](media/image20.png){width="5.990972222222222in"
height="3.747916666666667in"}

Figure 17. - - Diver Visibility Validation: Divers measured visibility
of black, black and white, and gray targets at 10, 20, and 30 foot water
depths. Figure panels A and B show 20" black and white disk and a 20"
sphere disappearing with increasing distance.

The results of the field validation for diver visibility can be
summarized as follows. The work with GLOW and DiVA showed that the 4/c
was normally inappropriate for most turbid water applications and that a
value of 4.8/c had a lower error for the waters tested. This value was
also obtained by Davies-Colley (1988) and more recently by Zaneveld and
Pegau (2003). The work in the Gulf of Mexico showed values varied from
3.9/c to 5.5/c but most values were within 20% of 4.8/c. Hence this
value (4.8/c) has been adopted as the "newest" diver visibility
algorithm and was implemented into the MODIS and SeaWiFS processing
using APS. Work by Bowers demonstrated the importance of solar zenith
angle and provided a method by which both diver visibility and
vulnerability could be estimated. However, the inputs for the Bowers
model required that the absorption and scattering be known. Hence the
model, while being more accurate and precise, required the same
information as previous models. But this model became a critical
component for the OIF product because of the ability to calculate
visibility and vulnerability as a function of target since, solar
direction, and above and below surface capabilities.

The research work by LT. Museler demonstrated the capability to retrieve
the required information for diver visibility from SeaWiFS, and then its
application using APS. This was henceforth moved to the Naval
Postgraduate School for demonstration of diver visibility (part of the
'customer preparation' below). This movement closer to the operational
user is an important calibration and validation step since performance
is best determined by those that use the product and their response. One
other important point raised by the work of Museler was the mismatch
between the predicted and measured diver visibility. Underestimation of
"c" using SeaWiFS (the Carder algorithm) lead to overestimations of
diver visibility. This calibration and validation component of the
product has thus spawned a renewed interest in the derivation of the
coefficients needed by the product and how to best determine these (i.e.
what algorithm is best for beam attenuation from ocean color).

## 3.3 Special validation for a customer

The creation of a specific product (diver visibility and vulnerability)
from SeaWiFS and then from MODIS for a specific customer required some
special validation protocols. It required both a knowledge base from the
customer about the satellite and then the developer understanding the
customer's intended product use. The first point was that the customer
"preferred" a visibility product for a depth of 10 feet. This impacted
how the product was derived and set the protocols for the calibration
and validation above (i.e. how the measurements were collected and how
models and algorithms were implemented). The customer also requested
that a "black" target be the standard instead of a black and white
target as a previous study had used. Other factors that the customer
asked to be considered were the "size" of the target, and its shape.
During calibration and validation the algorithm was based on specific
size, color, and shape in order to allow the customer to determine if
additional information was required. The 20" target was selected so that
effects of a small target on visibility could be minimized. A round
target provided a means of standardizing the product. In the calibration
and validation process however, targets of different colors were also
used so the customer would understand the uncertainties associated with
the final products validation as far as color was concerned (Fig. 18).

![](media/image21.png){width="5.990972222222222in"
height="1.4256944444444444in"}

Figure 18. - Visibility of three color panel (olive gray, gray, and
light gray) representing "typical underwater targets" that a Navy
customer would deal with for comparison with the "black" target.

So that the customer understood the "situation" being described by the
product, a "graphical illustration" representing the visibility and
vulnerability accompanied the product (Fig. 19). This illustration was
designed so that the customer was aware of the conditions represented by
the product. This helped the customer easily translate the product into
their application. As shown in Figure 19 there is a product description.
This showed the customer that the product is for 1) visibility at 10
foot water depth, 2) the sun is directly overhead, 3) the target is
black and is 20 inches in diameter, and 4) there are no clouds in the
sky. In a similar fashion the illustration for the "vulnerability"
product showed that: 1) the "observer" would be looking straight down,
2) the target was about the size of an underwater diver, 3) the water
depth would be 10 feet, and 4) the sun was setting. A similar
vulnerability was created for a "larger" object and also for daytime
operations.

![](media/image22.png){width="5.990972222222222in"
height="2.092361111111111in"}

Figure 19. - Graphical Illustration of the conditions represented by the
product (Visibility on the left and Vulnerability on the right).

A key point above is that the transition of the science to an
operational product initially takes place in calibration and validation,
but all operational products are continually refined and reassessed to
determine if they meet the customer's needs. In the case of the diver
visibility product from the 250 m MODIS imagery for OIF a comparison of
two products and their errors was required over a short time period.
Under these circumstances calibration and validation was difficult if
not impossible. Procedures for calibration and validation of two
properties were compared and then one method was selected over the
other. Until a more extensive calibration and validation can be
conducted this was the "selected algorithm" for the operational product.
For this product feedback from customer evaluation and on-scene
information was the primary method for validation until the operational
product can be more fully validated at a science level. For the diver
visibility product, this feedback allowed a customized and more accurate
representation to be developed. This was especially important during the
time-critical product generation and dissemination associated with OIF.

# 4.0 Making the Decision Support Tool for OIF- Expanding the science and setting up the product 

The initial effort during OIF was an extension of the routine work in
the retrieval of bio-optical properties, algorithm development, and
product generation (such as the 1 km diver visibility) conducted at
NRL-SSC to meet customer needs (METOC Navy community). Part of this
routine work includes interaction with academia, NASA, NOAA, and Navy
investigators on remote sensing. In order to exploit the technical
expertise in retrieval of bio-optical properties from remote sensing a
password protected public web site has been available for researchers to
examine work by NRL-SSC (www7333.nrlssc.navy.mil). The web page was
designed for interaction at the scientific level but also to show
potential customers what capabilities remote sensing may have to meeting
their product needs (Fig. 20). These products may include applications
that have not been envisioned by NRL-SSC or others.

![](media/image23.wmf){width="5.999305555555556in"
height="5.1402777777777775in"}

**Figure 20. - NRL-SSC Home webpage developed for interaction with
scientists and potential customers.**

In preparing the customer (i.e. the Navy) for various products, such as
diver visibility, NRL-SSC collected daily imagery over several key
regions throughout the world that a user could select (i.e. Persian
Gulf, Mediterranean, Sea of Japan, and coastal United States). The user
could browse on imagery that had been collected for the region with the
MODIS and SeaWiFS (if research oriented) 1 km bio-optical properties
(absorption, diffuse attenuation coefficient, backscattering, and
chlorophyll), a true color image, and the diver visibility product. It
was hoped that once the operational Navy (via the METOC regional
centers) knew that products such as diver visibility were available they
would go to the web page for further information. The web page also
contained information defining what was meant by diver visibility or the
diffuse attenuation coefficient. The imagery contained the latest
available images as well as weekly and monthly averages, and "best
pixel" composites designed to give the customer and researcher a "best
representation" of the properties and products by minimizing
interpolation necessary due to cloud cover.

In the past a potential customer was required to "search" the image
database and find a product that met their needs. While bio-optical
properties and diver visibility were always available on the web page,
there was little effort to make it as "real time" as possible,
customized the product for a specific requirement, or go the customer
directly. The unclassified web page was updated as part of the routine
APS operation.

Early in OIF, a specific request was received from the Bahrain METOC
Center to subdivide the Persian Gulf area into five operational
sub-regions. These areas represented specific Navy operational areas.
The sub-regions provided a template to develop customized and simplified
products to meet specific needs. Images where routinely put on the web
to get feedback from the customer as to their utility. The regions were
(Fig. 21):

1)  Entire Persian Gulf,

2)  North Persian Gulf (i.e. Kuwait (KAA) area),

3)  Central Persian Gulf,

4)  Qatar region, and

5)  Strait of Hormuz

Similarly a change in the color tables for diver visibility followed
interaction with the operational fleet and NAVOCEANO to standardize the
visibility product that was sent to the fleet. This included more
information in the 0 to 15 foot visibility range and less gradient
information at visibilities greater than 15 feet. It was apparent,
however, that the fleet needed more time-critical products that were
tailored for their application and area of interest (see the chronology
of diver visibility product below and Table 4).

Getting the information to Navy operations in a time critical fashion
and in a customized manner required special attention with product
dissemination a critical issue. The customer wanted the data faster,
special products, and focus on their immediate needs. The specific
products were diver visibility and vulnerability, and true color. The
true color offered an overview of land operations and a visual
representation of the environment in-theater.

![](media/image24.png){width="5.253472222222222in"
height="5.251388888888889in"}

**Figure 21. - Diver Visibility shown for areas of the Persian Gulf
available via the web-site: A) Persian Gulf, B) Kuwait, C) Central
Persian Gulf, D) Straits of Hormuz, E) Oman Gulf.**

To get the customer the desired products in-theater and in a timely
manner a secure web site was formed that was much reduced from the
original research web page. An operational diver visibility product was
produce for SeaWiFS, and MODIS Aqua and Terra sensors for each day.
Initially the major images were the 1 km diver visibility product and a
true color image produced daily for each of the five regions. Each image
was geo-rectified into a standard projection. The images of diver
visibility were quality controlled by a "man-in-the-loop" to ensure a
"best image" was available. This reduced the interpretation required by
the customer. The time delay in the imagery product was too great for
the customer, therefore, NRL-SSC worked with NOAA and NASA to receive
the Persian Gulf imagery as quickly as possible. Using the APS system
the quality controlled imagery could be placed on a secure web site
within about three to six hours of data reception. The flexibility of
APS allowed tailored products requested by the customer to be created
and distributed. These products were adapted from products initially
available over the public website. In addition, the web page for OIF was
"streamlined" so that the customer could quickly find his tailored
product. The products changed from the 1 km diver visibility and true
color imagery to the higher resolution 250 m MODIS diver visibility and
vulnerability products (see below). The key was capturing the customer's
interest with a product that could potentially be useful by showing him
the application directly. This was accomplished by a proactive approach
of going to the customer and getting initial interest in the products.

# 5.0 Product Dissemination (Capturing the customer's interest, responding to their needs, and implementing new specialized products \-- The pro-active approach)

During the OIF effort NRL-SSC the first issue to be addressed was to
provide new products and imagery to the Fleet as quickly as possible
(less than 3 hours). This request came from in-theater METOC personnel
since information was not forthcoming from NAVOCEANO and operations were
changing daily. In order to meet the time-critical operational nature of
the customer the products were produced each day and on weekends. This
is more "operational" support than a R&D laboratory is accustomed to
providing. The operational Navy could access the streamlined web-site
over the SIPRNET at the METOC regional centers, or aboard major command
vessels. The customer was informed that the product was available via
e-mail messages and asked to comment on product utility, desired
information, and presentation. When the 1 km diver visibility product
was available to the customer, comments were actively solicited about
the product and how it could be improved. The dissemination of the
product changed from a "come and get it" approach to a "here it is for
you, what else do you need." This proactive interaction with the
customer led to several additional requests involving what products were
available. The chronology of the events and the development sequence of
these new products are outlined in the following section and summarized
in Table 4. The interaction with the customer resulted in the
development of a 250 m MODIS diver visibility and a vulnerability
product for the EOD and Special Forces communities. Note that in this
case calibration and validation was abbreviated, but still was partially
carried out using in-field observations and consistency checks among
several sensors.

## 5.1 Chronology of rapid development and delivery of a 250 m resolution diver visibility product for OIF

After a week of using the 1 km products, response directly from the
fleet was positive, but they particularly wanted information
concentrating in the northern Persian Gulf (Kuwait area) at higher
resolution if possible and with more "gradient" information available.
It was indicated that while the 1 km products were helpful, the true
color pictures of the 250 MODIS showed other features and more details.
The customer wanted to wanted to know if products could be developed
from this imagery. The high resolution products could improve planning
and operations in the riverine and coastal areas. The product requests
called for identification of areas were visibility was improved by 20%
or more and at the resolution of the 250 m MODIS true color image. The
customer (here the Explosive Ordinance Disposal teams) wanted to know
where they could "see" farther and when they could be "seen" from a
further distance.

There was no algorithm or product line for the 250 m MODIS imagery at
that time. Since the SeaWiFS and MODIS 1 km standard products could not
meet the immediate mission needs of the customer, a new product was
required using the 250 m MODIS imagery. Specifically a diver visibility
product was requested that concentrated on the KAA region to help with
visibility in the highly variable confined areas close to the coast and
in bays and estuaries. The customer wanted to extend the product up the
estuary as far as possible. This was well beyond previous efforts with
MODIS.

There were several difficulties using the 250 m MODIS imagery for the
diver visibility product. Foremost is that the channels available do not
normally lend themselves to such a product, since there are only two
MODIS channels available at 250 m resolution and they are in the red
(620-670 nm) and the near infrared (841-876 nm). The issues with these
wavebands are 1) the normal channels used for atmospheric correction are
no longer available, 2) any optical property will need to be converted
to the proper wavelength for diver visibility (\~532 nm), and 3) water
absorption that is high in the red would cause most clear waters to
appear "black" and therefore have reduced sensitivity in the open ocean.
For the customer the last issue was not as important as the improved
resolution. This is an example were the customer determined the level of
scientific accuracy required.

The NRL-SSC Ocean Optics Section put together a joint effort to provide
the customer with the products requested, and time was a critical
factor. There were several steps involved in producing the 250 m diver
visibility product using the MODIS channel 1 (620-670 nm) and channel 2
(841-876 nm). As stated above two principle factors needed to be
overcome in using the 250 m MODIS imagery. The first was developing an
algorithm from which optical properties related to diver visibility
could be derived. And the second was producing atmospherically corrected
imagery that was accurate enough to provide a useful product using that
optical algorithm.

A solution was agreed upon using previous work of Gould and Arnone
(1997). They developed an algorithm for deriving the total attenuation
coefficient 'c' at 660 nm using AVHRR bands. Because the MODIS 250 m
channels and the bands in AVHRR were similar it was determined that this
algorithm would be used as an initial starting point. The AVHRR
algorithm of Gould and Arnone relates the remote sensing reflectance at
660 nm to '*c*' in the form:

$$c = \text{1028}\text{.}7\text{Rsr}(\text{660}\text{nm})^{1\text{.}\text{282}}$$

where 1028.7 is a scaling constant and Rsr(660 nm) is the remote sensing
reflectance at 660 nm. The c(660 nm) was assumed to be approximately
equal to the c(532 nm) used for diver visibility following the work of
Barnard (1998), Zaneveld and Pegau (2003) and measurements made during
GLOW. The diver visibility would then be obtained using the
Davies-Colley and GLOW derived 4.8/c(532 nm) algorithm.

The next step was deriving a remote sensing reflectance at 660 nm which
required implementation of an atmospheric correction using only the two
250 m MODIS channels The final atmospheric corrected radiances would be
compared to SeaWiFS and MODIS 1 km data. It was determined that channel
2 would be used for a "coarse" correction for the atmosphere and
radiance in this band offshore would be treated as the "dark pixel
correction." The method used was a simple subtraction. Channel 2 was
scanned for the smallest positive reflectance in the scene (dark pixel
subtraction), which was then subtracted from channel 1 giving a coarse
atmospheric correction. This approximate atmospheric correction does not
include the water reflectivity differences as a function of wavelength
and all aerosol and Rayleigh components. The results also showed an
offshore to onshore trend as expected but this could not be fully
accounted for in the methodology.

The Gould and Arnone algorithm was then applied to the "atmospherically
corrected" Channel 1 of MODIS and diver visibility calculated using the
4.8/c algorithm. This preliminary product was compared to the standard 1
km SeaWiFS diver visibility product in "transition" zones or in areas
where only small changes in diver visibility were apparent in the
imagery. Areas were selected based on criteria of low "local" spatial
variance, but included high and low "c" values. In match-ups for the
area it was found that a "linear scaling" factor (0.35) was required to
provide consistent "c" values (and therefore diver visibility) between
the 1 km SeaWiFS and the 250 MODIS. Therefore, the MODIS derived
visibility was multiplied by 0.35 to give the final visibility for the
250 m MODIS imagery.

There are several reasons for the required scaling factor. One was the
differences in the "definitions" used for remote sensing reflectance in
algorithm formation. Another reason was undoubtedly due to the coarse
atmospheric correction applied (lack of complete aerosol correction).
The incomplete aerosol correction was also evident in comparison of the
visibility product and the true color imagery. In addition, the initial
Gould and Arnone algorithm produced a "total" reflectance and this
required conversion to remote sensing reflectance. The assumption that
the attenuation coefficient (c) at 660 nm was the same as that at 532 nm
was also likely a source of error in waters high in estuarine discharge
such as the KAA area. The above uncertainties together with previous
confidence in the "c" SeaWiFS work lead to the opinion that it would be
more accurate to use the SeaWiFS product with the scaling factor.
Therefore, the scaling factor was derived using the random points
selected across the imagery that met the transition criteria.

## 5.2 Implementation

To make the final product for fleet operational use, several other steps
were followed. To automate the process for the 250 m MODIS as much as
possible, a new program was written and shell scripts were developed to
make these products quickly and efficiently. Software was modified to
warp and project the image by working on a pixel-by-pixel basis to
correct for the "bowtie effect." This occurs when overlapping scenes are
found and some differences occur in the imagery. This new method of
projecting images also produced a more accurate geographic
representation for the riverine and near-coastal operations. Adjustments
in the product were also made for land and clouds. Land was detected
using a Normalized Difference Vegetation Index (NDVI) threshold so that
this would minimize the coastal effect on the 'c' calculations. Clouds
were also identified using thresholds in channels 1 and 2. When the
process was completed a 250 m MODIS diver visibility product was
visually inspected for obvious uncertainties and then transferred to the
secure web site. The procedure put only the "best available product" out
to the customer. This was to make the "on-scene" interpretation by the
customer as "straight-forward" as possible since a "science" team at the
customer's end was not available.

Within a week of the request for the higher resolution product, a diver
visibility product using the 250 m MODIS data was available for
inspection by the customer. The initial feedback was very positive and
included requests for small graphical modifications (i.e. annotated text
regarding critical features, geographic areas, true color overlays,
etc). To meet these requests, scripts were generated and enhancements
were made to an image browse routine that gave the user control of
annotations / notes and color tables. Later another software change was
made that permitted a 250 m MODIS true color image to be underlayed as
background to derived optical and Navy support products. This enhanced
the Navy's use of the product by not only yielding an operational
product but also giving them a snapshot of the land activity at the same
time. The browse imagery was once again quality checked daily and the
latest-best imagery was placed on the secure web site (SIPRNET, Fig.
22).

The interaction with the customer was quick and valuable in the product
generation and response cycle. E-mail activity between NRL-SSC and the
customer indicated that the diver visibility product was accurate and
useful. From the developer side, requests for "confirmation" of the
diver visibility product were made. The customer stated that "changes in
visibility were consistent with observations and observed data was sent
to NAVOCEANO for post calibration and validation of the image product.

Occurring concurrently another Navy customer saw the 250 m MODIS
visibility product and asked if a "vulnerability product" be made. This
vulnerability product was for Special Forces diver operations and
swimmer delivery vehicle vulnerability. To meet this challenge NRL-SSC
met with PSI to determine if the visibility model developed by Todd
Bowers could be used. This model had been tested during GLOW for
potential use in diver vulnerability scenarios. Since the model of
Bowers was complex and only a "go, no-go" decision tool was requested,
the model was run with customer specific object sizes, observation
conditions, and various optical properties to provide a "look-up table
(LUT). This matrix was then used with the retrieved optical properties
to produce a vulnerability map (Fig. 23) that indicated where potential
problems due to enemy observers could occur (no-go) versus areas where
underwater operations could not be detected (go). The "go, no-go"
customer tailored product required implementation of the LUT into APS
and then annotation made by visual inspection. The customer could then
select between the visibility and vulnerability products.

![](media/image25.png){width="5.999305555555556in" height="4.33125in"}

**Figure 22. - Overview of the "secure" web site with diver visibility
demonstrated.**

![](media/image26.png){width="5.990972222222222in"
height="2.379861111111111in"}

**Figure 23 - Diver Vulnerability (left) and Visibility (right) showing
the go/no go areas for operations and where "improved" visibility up
river can be found.**

Table 4 - . Chronological summary of fleet supported events as they
evolved.

  ---------- ------------------------------------------------------------
  Date       Event

  February   NRL 7000 directs latest and best research efforts in the
  2003       Persian Gulf to be made available to the fleet. NRL-SSC Code
             7333 initiates support by streamlining a website for ocean
             color imagery to support a potential war effort in Iraq. The
             goal of NRL was to use the web site to provide only the
             latest and best cloudless imagery available. Fleet users
             were not required to search a database to find best imagery
             to support their missions. This support required NRL-SSC
             personnel to manually QC each day's imagery and make the
             scientific judgment on which was the best optical product
             for the fleet to use. A developmental version of NRL-SSC
             Code 7333's APS was implemented in a classified space to
             produce MODIS (1Km & 250m) and SeaWiFS (1Km) optical and
             Navy products for the region of interest. This developmental
             version of APS was implemented due to the volume of imagery
             received and processed daily at NRL-SSC. This allowed us to
             produce and validate the Navy products in a timely manner
             and gave the flexibility of customizing products to meet
             fleet requirements.

  28         Revised Ocean Color Website is put online and advertised to
  February   Fleet users in Persian Gulf Region. This was enhanced by the
  2003       addition of 250-meter true color plus 1 kilometer derived
             horizontal visibility derived from SeaWiFS and MODIS.

  1-3 March  Considerable / positive feedback from the fleet was
  2003       received.

  5 March    Feedback from EOD team through Commander Cruiser Destroyer
  2003       Group 1 (CCDG1) on the aircraft carrier USS Abraham Lincoln
             indicating they would like a product that could indicate
             regions, which would show relative, increases of 20%
             visibility in KAA (overall poor visibility) for underwater
             searches. CCDG1 noted that the 1-kilometer resolution
             imagery products did not reach far enough into the KAA.

  5-11 March Developed an algorithm to produce a 250-meter resolution
  2003       horizontal visibility product.

  11 March   First Horizontal Diver Visibility product derived using
  2003       MODIS 250m channels sent to fleet for comment.

  12 March   Feedback from CCDG1 indicates spatial area of scene should
  2003       be smaller covering the northern coastal area and the entire
             KAA. Revisions made. Feedback from mission support Center
             and NAVPACMETOCCEN San Diego indicates requirement for
             product for diver and diver delivery vehicle vertical
             vulnerability per SOCOM 525-6.

  12-14      Optics team at NRL-SSC Code 7333 develops new vertical
  March 2003 vulnerability algorithms derived from the MODIS 250 meter
             vertical diver visibility product and optical model of
             Bowers with some calibration and validation using human
             observed field measurements from the GLOW exercises
             conducted by Alan Weidemann. Unix scripts were developed to
             produce these products.

  13 March   Feedback from EOD teams indicated that MODIS 250m horizontal
  2003       visibility products were consistent with observations from
             dive teams.

  14 March   New vertical vulnerability product using 250m MODIS released
  2003       to fleet for SDV and Divers. Received positive feedback.

  17 March   Vertical Vulnerability product put on SIPRNET web site.
  2003       

  18 March   Positive feedback from Naval Special Warfare Mission Support
  2003       Center on vulnerability products briefed to SPECWARGRU 2.

  20 March   Brief to Commander Naval Meteorology and Oceanography
  2003       Command (CNMOC), RADM Donaldson, on new optical products to
             support war effort. Admiral commenced continuous support (7
             day / week).

  21 March   OIF commenced.
  2003       

  23 March   Period of bad weather caused a 5 delay on ocean color
  2003       updates. Visibility products requested by UK to support
             downed Helicopter operations.

  28 March   Hybrid visibility products with PC Tides overlaid on 250 m
  2003       MODIS products to give tidal affects on visibility were
             released to fleet. Sporadic coverage due to weather and time
             laden tidal currents due to product delivery made this
             product less useful to fleet users.

  4 April    Returned to Monday thru Friday updates.
  2003       

  12 May     Last update to SIPRNET website of special support.
  2003       
  ---------- ------------------------------------------------------------

The effort with the MODIS 250 m product also included a "combined"
product for the warfighter. A PC-Tides model developed at NRL-SSC was
used to provide tidal currents that could be overlayed on the
operational products. This effort was to demonstrate to the customer any
relationships between physical forcing of certain water masses and the
optical properties (Fig. 24. PC-Tides Overlay). This was important due
to the "time-critical" nature of many of the operations. It also was
"value-added" to the MODIS 250 m product that was another customer
requirement. By overlaying the information this reduced the analysis
time needed for pre-operation planning.

![](media/image27.png){width="4.997222222222222in"
height="4.909722222222222in"}

Figure 24 - Combined PC Tides and Optical properties (diver visibility)
from the MODIS 250 m Product; note the information available at a
considerable distance upriver.

#  6.0 Post-evaluation-Benchmarking

The diver visibility/vulnerability produced for the warfighter during
OIF was disseminated on the SPIRNET until May 2003. The post-analysis of
the effort demonstrates four important results. The first is that
proactive interaction with the customer is critical in the development
and transition of an operational product. This was especially important
during OIF when a timely custom tailored product was required. The
second is that customer "request" to product "delivery" time can be
extremely short when a strong infrastructure and calibration/validation
capability is already in place. Third, there is no silver bullet in many
operational products; some products must be derived from a combination
of information or sensors. And lastly, that the MODIS satellite system
could be used in near-real-time to produce a product for the warfighter
on-scene use.

Validation of the diver visibility product was very difficult and
required feedback from the customer (the vulnerability product is even
more difficult to validate). The customer's use of the product and their
"independent" analysis of the product helped create a more
straightforward transition of the visibility and vulnerability products.
Trust in the products reliability is established as the customer
"validates" the product against his own experience. The MODIS 250 m
horizontal diver visibility products produced from the AVHRR C660
algorithm looked extremely well and showed a lot of detail in the
coastal and riverine waters. For the customer, the MODIS 250 m product
was much better than the 1 km visibility product since his primary
interest is in estuarine and coastal areas where variability is highest,
and the details of the MODIS 250 m product allowed the customer to look
for the spatial areas that are tactically significant. More importantly,
it was much better than the Secchi Disk range normally provided as
visibility.

The product to delivery time for the diver visibility using the 250 m
MODIS sensor took only one week. This was accomplished by the utility of
the APS and the pre-existing calibration and validation work at NRL-SSC,
which had been developed over years of R&D. Because of this prior
experience and development, NRL-SSC was poised to respond rapidly to the
fleet request. The processing capabilities expedited changes in the
product and the incorporation of beta-test versions of the new algorithm
using the 250 m MODIS imagery. The output could then easily be compared
with the standard 1 km SeaWiFS product for an evaluation.

The work with OIF also demonstrated the importance of multi-sensor work.
The final product used information provided by SeaWiFS, MODIS, and the
AVHRR satellites for its development and then "quick" calibration and
validation. The "three looks a day" permitted monitoring of the product
for quality and consistency. The processing speed and dissemination made
the product available to the customer quickly. The customer wanted the
product in minutes. Through use of automated processing the time was
reduced from over eight hours to approximately 2-3 hours. In this time
period, we are approaching the current computational limits for
processing the satellite imagery (reception, atmospheric correction,
application of optical algorithms, georectification). However, there is
room for improvement so that the "human-in-the-loop" can be eliminated.
Some of this will come about from further refinements in the data flow
into secure areas. It was clear from the customer feedback that the
products were far superior to what they had available. There are issues
with "in-stride" adjustments based on on-scene information that must be
addressed. Likewise the integration of "other valued information", such
as the tidal cycle and current fields, creates a product that is much
more valued to the customer. It is the interaction with the customer
that will define and prioritize the level of effort that each of these
improvements should receive. The process of going from the baseline
effort to formation of a new product should even be "smoother" and more
efficient in the future. The OIF effort provides a template of the
process needed to transition science to an immediate operational
requirement and ocean color application.

# 7.0 Long term cost of developing MODIS Ocean Products in support of Operational Iraqi Freedom. 

We evaluated what is required to develop and produce these decision
making products used in the Operation Iraqi Freedom. We assessed our:

1.  Prior commitment which was made in research and development at
    NRL-SSC and other agencies,

2.  Prior Navy programs which were used to develop transition of
    research to operations and establish direct communication with the
    customer (warfighter)

The real cost of the swimmer delivery product is assessed as a
combination of 1) man-years of efforts, 2) the experience of the
personnel and 3) the establishment of competent team which is composed
of research scientists, programmers, and operations specialists.

## Prior and ongoing research

The Navy has a dedicated satellite ocean research program which is
conducting basic and applied research. The Ocean Optics Section at
NRL-SSC is a lead ocean optics laboratory for the Navy and has been
developing new algorithms and conducting optical radiometric research.
The mission of the section is to develop new understanding of how ocean
optical properties can be used for improved Navy applications. This
group specifically has been working with NASA research programs for the
past 10 years and has exchanged research algorithms, calibration and
validation methods and protocols. This research group is well respected
within the national and international ocean color community for state of
the art ocean color research. The section is experienced with NASA
algorithms and software applications. NRL-SSC works closely with NASA in
SeaWiFS and MODIS and Hyperion satellites. They are an approved NASA
SeaWIFS Receive Site for the Gulf of Mexico.

This prior research provides the basis which enabled understanding of
the science issues in satellite oceanography. This research also
included *in situ* calibration and validation instrumentation,
radiometric stability and long term vicarious calibration.

We attribute a team of **four scientists per year for the past 5 years
was required to develop this diver visibility product (20 man-years)**.
Note that this research team in composed personnel experienced in
satellite oceanography, optical oceanography and bio-optical
oceanography.

## Prior and ongoing programs in transition of Navy research to operations

Navy R&D programs include funding support (6.4 funding) where research
programs can be transitioned to Navy operations. These programs are
coordinated by the Commander, Naval Meteorological Oceanography Command
(CNMOC) and the Oceanographer of the Navy for ocean research. Their job
is to examine new Navy capabilities and requirements and match them to
evolving research to meet those requirements -- in this cases new
satellites and ocean products. As part of this process, CNMOC and the
Oceanographer of the Navy identified mature elements of NRL-SSC research
in satellite oceanography that should be transitioned to the NAVOCEANO.
NAVOCEANO is the environmental center where the operational Navy
requests and obtains all ocean products. NAVOCEANO provides a continuous
(24/7) suite of ocean products for locations around the globe to Navy
users. NAVOCEANO is responsible for developing a structured roadmap of
getting new products and ocean monitoring and measurements into their
operational product line. NRL-SSC has assisted in this effort and has
been supported to develop a pathway where ocean color satellites can be
automatically processed into Navy products. This software is now
utilized at NAVOCEANO and gets updated periodically as the new products
are deemed "operational".

Note that the process for developing new ocean products at NAVOCEANO
undergoes a transition period to ensure quality control is being applied
to the products, proper implementation of the products into Navy
dissemination systems, as well as sufficient time for software and
system training. NRL-SSC's commitment to making operational software and
products for Navy ocean color products is based on a long-term
structured architecture of how product transitions are made and how they
are integrated into operations at NAVOCEANO. This transition process is
extremely important and includes:

a)  Identifying a roadmap of what and when products are coming on-line
    for NAVOCEANO to prepare for future products.

b)  Identifying a transition team from Research and operations

c)  Identifying a transition plan

d)  Locating where the product line will be produced at NAVOCEANO and
    with associated personnel, hardware, and software commitments.

e)  Determining the validation requirements of the products based on
    NAVOCEANO and Navy operational requirements

f)  Software and hardware configuration management

g)  Identification of a continued data sources (e.g. different
    satellites, etc.)

This is a team effort with NRL-SSC and NAVOCEANO teams. We have
developed these methods for the last 5 years utilizing transition team
of three persons. This team is composed of programmers, and operations
specialists. The transition team works as the link between the research
scientists and the operations at the NAVOCEANO. This team is essential
if research is ever to go operational. Thus, this element **represents a
15 man-year effort** which allowed these new MODIS products to be
available.

## OIF MODIS Product Transition 

These new products developed for OIF are scheduled to be transitioned to
NAVOCEANO for operational use in the near future. As we developed these
new products with direct input from with the warfighter, NAVOCEANO was
very interested at the response that we received. They are enthusiastic
to begin producing these new products for many other ocean regions and
to develop additional Navy warfighter customers.

Note that the NAVOCEANO is not the actual customer that we identified in
the OIF effort. NAVOCEANO is not the decision maker, but a supplier of
environmental data to the warfighter. In Navy terms, the warfighter is
assisted in making decisions by integrating information involved in
operations and employment of force by use of a Tactical Decisions Aid
(TDA). Typically, environmental data are integrated into TDA where
criteria are evaluated to assist in a tactical decision. In the OIF
effort, the warfighter used the MODIS visibility products to make
decisions in managing his diving assets. This was the first time this
capability was available and we expect that a TDA will be developed to
include these diver visibility products.

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