Ocean Satellite Data:

Requests from the Fishery and

Aquaculture Community

A report summarizing the outcome of the

Dream Ocean Satellite Image Workshop

June 4-5, 2013

Newport, Oregon

Workshop Hosted by

Oregon State University

Workshop Funded by

NASA

Report Compiled in Coordination with the

International Direct Readout Ocean Steering Committee

Jasmine Nahorniak Oregon State University, USA

Ricardo Letelier Oregon State University, USA

Ichio Asanuma The Tokyo University of Information Sciences, Japan

Frank Muller-Karger University of South Florida, USA

Edward King CSIRO, Australia

Marcelo Colazo CONAE, Argentina

July 31, 2013

2

Contents Workshop Overview ..................................................................................................................................... 4

Workshop Outcome ...................................................................................................................................... 5

Goals ............................................................................................................................................................. 6

Fish & shellfish stocks ............................................................................................................................... 6

Site surveys ............................................................................................................................................... 7

Maps of commercial activity ..................................................................................................................... 7

Whale habitat use patterns ...................................................................................................................... 9

3-D water column structure ...................................................................................................................... 9

Water mass tracking ................................................................................................................................. 9

Inland water masses ................................................................................................................................. 9

Improvement of models ......................................................................................................................... 10

Harmful Algal Bloom and Vibrio warnings .............................................................................................. 10

Shellfish conditions warning ................................................................................................................... 10

Ocean Products ........................................................................................................................................... 11

Main Issues ................................................................................................................................................. 14

Clouds/fog ............................................................................................................................................... 14

Data sources ............................................................................................................................................ 14

Atmospheric correction .......................................................................................................................... 14

Long term time series ............................................................................................................................. 14

Coastal data ............................................................................................................................................ 15

Spatial resolution .................................................................................................................................... 16

Temporal resolution................................................................................................................................ 16

Spectral resolution .................................................................................................................................. 17

Flags ........................................................................................................................................................ 17

In situ data from volunteers.................................................................................................................... 17

Lines of communication .......................................................................................................................... 17

Data accuracy .......................................................................................................................................... 17

Data formats ........................................................................................................................................... 18

Internet access ........................................................................................................................................ 18

Legal issues.............................................................................................................................................. 18

3

Data latency ............................................................................................................................................ 18

Real-time downlink size limits ................................................................................................................ 19

LIDAR limitations ..................................................................................................................................... 19

Sensor Needs .............................................................................................................................................. 20

Conclusion ................................................................................................................................................... 22

Acknowledgements ..................................................................................................................................... 23

Appendix A: List of attendees ..................................................................................................................... 24

Appendix B: Ocean satellite data tools ....................................................................................................... 25

Appendix C: Forecast models ...................................................................................................................... 26

Appendix D: Geostationary specs ............................................................................................................... 27

Appendix E: In situ data types collected ..................................................................................................... 28

Appendix F: Ocean data providers .............................................................................................................. 29

Appendix G: Ocean satellite sensors........................................................................................................... 30

Appendix H: Potential funding sources ....................................................................................................... 31

Appendix I: Dream satellite image descriptions ......................................................................................... 32

David Landkamer .................................................................................................................................... 32

Mitchell Roffer ........................................................................................................................................ 33

4 Workshop Overview

Workshop Overview

The workshop was designed to brainstorm possible future ocean satellite sensors and data products that

will benefit the fisheries and aquaculture communities and associated research. Twenty-one U.S. and

international participants from various fields associated with fisheries and aquaculture attended

(Appendix A).

To begin the workshop, each participant briefly described their field of expertise and use of satellite

data. This was followed by the presentation “Introduction to Ocean Color Satellite Data” by Morgaine

McKibben (Oregon State University) to lay the groundwork for discussions later in the day. During the

afternoon, the discussions focused on ocean satellite products and requirements, such as possible new

products and desired spatial resolutions.

The second day began with a summary of “Sources of Ocean Satellite Data” by Ricardo Letelier (Oregon

State University). This presentation was immediately followed by a discussion on existing data sources.

Data formats were also addressed. Later in the day, the concept of “Direct Broadcast” was introduced by

Jasmine Nahorniak (Oregon State University).

The workshop concluded with a discussion on the desired attributes of a “dream” ocean satellite sensor.

Results of a survey distributed to the attendees following the workshop were very positive – in

particular, all participants noted that they would recommend the workshop to a colleague.

Further information about the workshop, including the agenda, list of attendees, and presentations,

may be viewed at:

http://omel_test.coas.oregonstate.edu/home/events/2013_DOSI/2013_DOSI_Workshop_Industry_NASA.shtml

.

Many of the recommendations summarized in this document are currently not

(and may never be) feasible. The workshop was designed to solicit visions of

“dream” ocean satellite data from participants. The resulting suggestions are

intended only as a guide to the wants and needs of the ocean direct readout

community. We hope that they will be taken into consideration when planning

future improvements to satellite oceanography.

5 Workshop Outcome

Workshop Outcome

The key areas of particular interest to the workshop attendees were: (1) ocean products, (2) data

formats, and (3) sensor characteristics for the future.

Numerous new products were suggested to aid in stock assessment, location of fisheries, and mapping

harmful algal blooms. In addition, the weaknesses of existing products were identified and are listed in

the “Ocean Products” section.

The “Main Issues” section lists the most pressing issues for the ocean satellite data community. These

issues include: loss of data due to cloud cover and fog, poor coastal products, insufficient spatial

resolution, overly conservative flags, poor or unknown data accuracy, and agency-specific data formats.

Suggestions for addressing these issues are located throughout the document.

Many of the issues could be addressed with an appropriate geostationary sensor. The “Sensor Needs”

section lists the desired attributes for future sensors. GEO-CAPE, planned for 2022, satisfies many of

the criteria.

6 Goals

Goals To set the stage, a sampling of goals from the oceanographic community is listed below. These goals

were contributed by the twenty-one workshop attendees, and represent only a tiny subset of oceanic

research and activities currently underway. They are included here to illustrate the wide variety of topics

that the community hopes to address with satellite data.

Fish & shellfish stocks There is a high potential for accurate stock assessment and management with satellite data. Currently

stock assessments are derived from fishery catch data only. For some operators collecting catch data

costs $50,000 per year; the fish caught as part of this effort are sold to cover the cost, but this reduces

the net profit of fisheries. It is hoped that satellite data can be used to improve the accuracy of stock

assessments and decrease the amount of catch data needed.

The variety of stock abundance, distribution, and tracking needs are listed below.

Predictive models of the fine-scale distribution of stock abundance (e.g. salmon)

Species tracking (sardines, anchovies, albacore, sharks, copepods, juvenile salmon)

Demersal species tracking (rockfish, blackfish, etc.)

Relate fish distribution and patterns to the local environment

Track eddies to identify potential locations of sardine eggs

Dispersal of crustacean larvae (these are found only in the first km offshore and hence this

requires high spatial resolution)

Sardine school tracking (this was shown to be possible with IKONOS data; Figure 1)

Figure 1: 2009 Ikonos satellite image. The dark areas indicate sardine schools (validated with in situ observations). Presented by Jerry Thon.

7 Goals

Site surveys Site surveys are necessary to determine current and potential usage of different regions. Examples of

usage include whale migration paths, recreational uses, commercial fisheries, military uses, wave energy

structures, transportation and shipping lanes, and offshore aquaculture (mollusks and crustaceans).

The Southern Oregon Resource Coalition did a spatial usage study for a wave energy business (EcoTrust)

to find a location where a wind turbine would have minimal impact. As part of this effort, fishermen

were asked to mark productive fishing locations on a map using pennies. The size of the pennies relative

to the map provided only a rough estimate of fishing sites. This method was likely used due to

confidentiality issues. Improved accuracy is desirable.

Existing site survey databases include:

Coastal and Marine Spatial Planning (CMSP) - takes anything and everything as input

Essential Fish Habitat (EFH) database – fishery data and marine habitat data

Chris Goldfinger – topography and benthic habitat database (Oregon State University)

The survey maps should be able to resolve features such as aquaculture farm structures, commercial

fishing fleet moorings, recreational fishing fleet moorages, and aquaculture/seafood processing

facilities. The recommended spatial resolution is 10 meters.

In addition, the maps should identify suitable water masses for the proposed use based on biology,

physics and chemistry (SST, salinity, dissolved oxygen, chlorophyll, wave energy, wind …). These water

mass and environmental characteristics need to be monitored over an extended period before a site is

selected for a designed commercial activity.

A combination of all of the above factors is used to determine the optimum location(s) for the desired

use. See the note from David Landkamer in Appendix I for more information.

Maps of commercial activity Maps of persistence in effort (the

regions where it is most cost

effective to catch fish) would be

highly beneficial to the commercial

fishing industry. In addition, it should

be possible to superimpose locations

of catch data on satellite data to find

correlations (Figures 2 & 3).

However, the main issue is

confidentiality; fishermen are

reluctant to advertise their prime

fishing locations.

Figure 2: Fish catch data in 2006 superimposed on SST off the Oregon coast.

Presented by Pete Lawson. Image by Bobby Ireland, Project CROOS.

8 Goals

Some volunteer ships provide their

GPS locations every 30 minutes or so

in addition to catch information.

However, catch data can be

unreliable and it is difficult to judge

where fishing occurs based on

location alone (Figure 4).

Figure 4: NASA/NOAA composite nighttime image of fishing vessel lights in the Yellow Sea. The sharp

lines indicate maritime borders. Without catch data, it isn’t possible to determine where fishing is

productive from vessel locations alone.

Figure 3: Relationship between SST fronts (red/yellow) and

Albacore catch data (black dots). Presented by Karen Nieto.

9 Goals

Whale habitat use patterns Numerous species of endangered large whales have been

tagged with location devices to monitor their migration and

movement patterns (Figure 5). By combining satellite data

with these whale tracks predictive models of whale

abundance can be developed to better define important

habitat for the whales, and to help reduce anthropogenic

impacts on the whales (ship strikes, excessive noise).

One of the main issues with developing habitat models for

marine mammals is that the whales are foraging well below

the surface (in some cases as much as 600 – 800 m down),

and often on prey that are three or four trophic levels above

the phytoplankton currently measured by satellites.

Therefore, satellite products that could penetrate into the

water column, and derived products that could infer the

presence and vertical distribution of higher trophic level

organisms would aid the research of whale habitat analysis

enormously.

3-D water column structure It may be possible to derive profiles of water column

parameters (to the first optical depth) by combining in situ

glider and mooring data with satellite data and by

assimilating these near real time data into high resolution

oceanographic forecasting models.

Water mass tracking Water masses can be distinguished by their temperature/salinity characteristics as well as spectral

characteristics. By distinguishing different water masses, it is possible to discern and track mesoscale

features such as filaments, low salinity plumes, oil spills, fronts, and the coherence / persistence /

perturbation of fronts. In situ drifters, such as tagged tsunami debris, can also be used to track the

location of water masses.

The intersection of fronts and their persistence is an important indicator of fishing “hot spots” –

potential locations of high congregations of fish stock.

Low salinity plumes are important for crabbing and juvenile salmon populations. Knowledge of the

location of these plumes is also important for glider piloting; gliders frequently become trapped in these

low salinity waters due to the change in buoyancy.

Inland water masses High spatial resolution is required to study lakes, rivers, estuaries, etc.

Figure 5: Blue whale track overlaid on a

GOES 8d SST composite. Presented by

Ladd Irvine.

10 Goals

Improvement of models Satellite data can be used to validate models and provide estimates of the uncertainties. See Appendix

C for a partial list of ocean models.

Harmful Algal Bloom and

Vibrio warnings Harmful Algal Blooms (HABs) and

Vibrio spp. can have a significant

impact on the health of the aquatic

environment (including fish, shellfish,

birds, and humans), and hence on

commercial fishing and recreation.

Two of the main drivers for the

creation of HABs appear to be

temperature and salinity.

In Chesapeake Bay, Vibrio is harmful to

humans (both by consumption of

shellfish and through skin exposure).

Higher spatial resolution of satellite

data is needed to monitor the narrow

branches of Chesapeake Bay near

regions of high population in order to

produce warnings for the public (Figure

6).

Some of the major species of concern

include Alexandrium sp.,

Pseudonitzschia sp., Dinophysis,

Prorocentrum, Karenia sp., Vibrio

parahaemolyticus and Vibrio vulnificus.

Detection of these species (and others)

or their toxins would help prevent food

borne and contact illnesses.

One website providing updated HABs information off the Oregon coast is MOCHA (Monitoring Oregon

Coastal Harmful Algae). CoastWatch will also be providing HABs data soon.

Shellfish conditions warning Poor water quality conditions can harm shellfish larvae. A system is needed to alert shellfish hatchery

operators in real time when conditions deteriorate. Products to monitor include temperature, pH, CO2

concentration and aragonite saturation state.

Figure 6: Probability of presence of Vibrio vulnificus in

Chesapeake Bay. Urquhart et al., RSE 135 (2013).

11 Ocean Products

Ocean Products

Existing and desired ocean color products are listed in the table below.

Issues are highlighted in red.

Current or potential uses are in blue.

General information about the product is in black.

SST Not useful when water temperature is above 30oC. If the air temperature is too close to water temperature, inaccurate cloud flagging occurs. SST is the skin temperature only; this is different than in situ temperature which is measured below the surface. Ideal accuracy of 0.1oC requested.

Chl Nominal accuracy only 30% - really only good for features, not absolute values. Better accuracy and precision of 0.5 mg/m3 is desired (not 30% error).

Sea ice The onset of sea ice may trigger whale migration.

Upwelling/ Downwelling

Can be derived from SST and chl. Low SST with high chl indicates strong upwelling (upwelling index). There is a clear relationship between Chinook salmon and upwelling locations. Models exist that provide feature tracking (stretching -> upwelling, compression -> downwelling) (Nick Tufillaro).

Fronts SST fronts provide an indicator of albacore locations. Types of front products:

o chl and SST maps o chl or SST gradients (aka temperature breaks) o coherence and persistence of fronts

Need high temporal resolution because the location of the front is not accurate a week later. A week or more of persistence is required for fish to accumulate there. Several persistence front products exist (e.g. Karen Nieto (Figure 7)).

Turbidity

Salinity Need a better salinity product to improve the detection of river plumes. Currently product is only 1 degree resolution (it is used for global models); this is not useful near the coast. Currently chl or CDOM are used to derive 1 km salinity, but it requires a lot of in situ data for validation. Error of 0.5 PSU or better is needed to see upwelling. Available from Aquarius. Erin Urquhart (2012 paper) has created her own salinity product for Chesapeake Bay, accurate to 2.5 PSU. Request 1 – 4 km hourly salinity.

Water mass identification

Generally base on salinity and SST. Precipitation/salinity/runoff combination. Locates plumes and nutrients in estuarine systems.

12 Ocean Products

Chl max and profiles

Chl max = depth of maximum chlorophyll concentration. Would be difficult to estimate since chl/biomass relationship changes with light (and hence depth). Could perhaps derive from combined analysis of wavelengths with diffuse attenuation coefficient.

Fluorescence Not included on VIIRS.

K490 Max depth about 25 m for clear waters. Max depth only a few meters for coastal waters.

Mixed layer depth Currently derived from models and in situ data. Accurate mixed layer depth is valuable for fisheries – higher concentrations of catchable fish occur when the mixed layer depth comes within a few meters of the surface. Needs to be accurate and timely. Could derive probability of shallow mixed layer depth from the difference between day/night SST (for calm waters, the mixed layer depth is shallow and the day/night SST difference is high).

SSH Can be used to estimate the relative displacement of the thermocline. Based on altimetry. Not good close to shore (i.e. within shelf break, approx. 200 m). Surface gradient could be used to estimate upwelling, but not in real-time. Doesn’t care about clouds. Could use to predict where fronts are going. Could combine with tide data, chl, SST, … Produces the most complete maps without modeling. 2 cm resolution.

Depth of thermocline

Currently modeled.

Eddies Eddy tracking software (Dudley Chelton). Eddy models (Karen Nieto).

Persistence Snapshot doesn’t tell you everything. Chl and SST front persistence needed.

Convergence zones

Derived from currents.

Filaments

Currents Need accurate values.

Anomaly Difference between today’s image and the preceding 8-day composite or the climatology.

Retention Areas Mainly topographic. Locations of nurseries and/or seeding stocks.

Hypoxia Modeled from upwelling and persistence of upwelling (Francis Chan). From oxygen concentrations at the beginning of the season along the shelf break, can calculate oxygen on the shelf (paper by Kate Adams and Jack Barth). A function of wind, chl and SST. Maps of probability of hypoxia.

Biologically Effective Upwelling

In progress (Chavez, Dave Foley).

13 Ocean Products

Streamlines Attempts have been made to match streamlines of current with stock distributions.

Jet streams Coastal water moving offshore in plumes.

8-day composite Updated every 8 days Would be beneficial to update daily instead.

Night visible Like VIIRS has.

Habitat classification

pH

CO2 concentration (Hales et al. 2012)

Aragonite (CaCO3) saturation state

(Hales et al. 2012)

Figure 7: Average frontal frequency, frontal index, and filament index for Morocco (July 2002 -

December 2007). Nieto et. al, RSE 123 (2012).

14 Main Issues

Main Issues

Clouds/fog

some users resort to monthly images

fog tends to lift in the afternoon; multiple images per day would help

interpolation in space and time is inaccurate

inaccurate cloud masking

ideal sensor would see through clouds (e.g. passive microwave)

Data sources There are many source of satellite data and information – it is difficult to get started.

The NOAA website that tracks tsunami debris isn’t updated.

NOAA’s data files are too large for use at sea.

NOAA doesn’t yet have the capability to routinely reprocess data quickly, unlike NASA and ESA

who can do the entire archives in a day. This may be an issue for the next VIIRS.

Commercial satellites require a month’s advance notice for data collection, and cost

approximately $1000 per clear image. However it is hard to work meaningfully with them.

Difficult to get actual data; many online sources just provide images. Need access to the data

behind the images.

Poor accessibility to data (different sources, different formats, …)

It is difficult to locate the desired data from so many websites; currently the user must discover

the data themselves; software should do this for you.

There is a lack of tutorials and sample scripts.

Atmospheric correction Near-shore (within the first 50 km) is affected by aerosols.

Regional atmospheric correction may provide better product, but then creates a step between

regions. This becomes a problem if fish move from one region to another.

Not so important if looking at trends rather than absolute values

NOAA and NASA focus on global algorithms; this approach doesn’t do anything well for anybody,

but it does get around region issues.

Long term time series Time series are essential for monitoring and understanding system changes. For example, off the coast

of Japan the sardine catch numbers have changed dramatically over the past few decades, from a low in

1970 to a peak in 1988 then back to a low in 2005 (Figure 8). Satellite data time series can help

determine the cause of the regime shift.

15 Main Issues

Time series data must be consistent between sensors to be useful. Inter-calibration between sensors is

essential. Improvements to technology result in sensor design changes; careful calibration must be

made to ensure that the new sensors are comparable to the old. One example is Pathfinder, which has

19 different sensors overlapping in time. Although it was challenging, inter-calibration of all of the

Pathfinder sensors paid off with a 30-year time series.

Another challenge is reprocessing of the entire time series or portions of it. Often maintenance of

several versions is required.

Coastal data Many key oceanographic events occur in coastal areas, such as commercial fishing, whale migration,

river outflows, nutrient runoff, and pollution discharge. Physical and biological properties in coastal

regions are often transient, with small spatial and temporal resolutions. In addition, clouds and fog

frequently occlude the satellite view.

Currently, the accuracy of ocean satellite products in coastal areas is poor. To increase the spatial

resolution of existing data, attempts have been made to derive ocean products from the 250 m and 500

m resolution MODIS land bands. Ideally, future satellite sensors would contain ocean bands at higher

spatial resolution (such as the planned European OLCI on Sentinels 3A & 3B, scheduled for launch in

2014/2017).

Figure 8: Regime shift of sardine catch in Japan. Presented by Ichio Asanuma.

16 Main Issues

Spatial resolution For many aqueous applications (such as coastal regions, rivers and lakes), 1 km is not high enough

spatial resolution. A common complaint is that “the military can read words off a bottle from space, yet

we can’t get good SST”.

Two approaches are frequently made to combat this issue:

Aerial measurements

High spatial resolution

Small regions only

Expensive

Weather dependent

Spatial interpolation of satellite data

Can be highly inaccurate for products like chlorophyll. SST interpolation is not so bad

since the physics is well understood.

For coastal zones, estuaries and wetlands, near-daily 250 m multispectral or hyperspectral data are

needed.

For regions such as aquaculture sites, a spatial resolution of 10 m is needed.

Temporal resolution Poor temporal resolution is another common issue. This can be a result of orbit repeat times and/or

clouds. For example, even though multiple altimeters are active in space, the combined repeat time is

still only 7 days. Note that the decorrelation scale is 5-7 days between biology and physics along the

Oregon coast. Satellite data at higher temporal resolution would help to improve real-time research,

calibration/validation, and model output.

Hourly data are required in coastal areas to resolve tidal issues. In other regions, multiple passes a day

are needed to interpret diurnal variability.

This issue could be resolved with a geostationary satellite carrying an ocean color sensor. These

satellites can collect measurements at frequent intervals (such as hourly). A geostationary satellite

(GEO-CAPE) with an ocean color sensor is planned for launch in 2022. This satellite will provide coverage

of North and South America and adjacent waters. Similar satellites are desired around the globe.

A set of several polar-orbiting global coverage satellites can also provide multiple shots of a given

location per day (at higher latitudes). An example is the existing TERRA/AQUA set.

A combination of polar-orbiting and geostationary satellites would be ideal.

17 Main Issues

Spectral resolution Hyperspectral satellite data provide a wealth of information about the earth’s surface. When not

limited to a small set of wavebands, researchers are able to tease out valuable data such as the spectral

characteristics of Harmful Algal Blooms.

One recent disappointment to the community was the lack of a fluorescence band on VIIRS.

Fluorescence provides information about phytoplankton health, and can often provide estimates of

chlorophyll patterns in regions where the chlorophyll algorithms fail (such as in coastal areas). If

hyperspectral data are not possible, at the very least this valuable waveband should be included in the

standard set of wavebands on any future sensors.

Flags The flags provided for MODIS are very conservative, often masking data in productive coastal regions.

There is a reluctance to ignore the flags, but it is currently the only way to get better data coverage.

Researchers frequently either choose just a subset of the existing flags or derive their own.

In situ data from volunteers There is no known existing mechanism for volunteers (such as fishing boats) to provide their in situ data

to researchers to improve local oceanographic models. The models would benefit from the data, and the

volunteers would benefit from improved output products that the fleet can rely on.

The World Meteorological Organization (WMO) has a system in place called VOS (Voluntary Observing

Ships). Meteorological data are collected from ships at sea and are used as ground truth and input to

numerical weather prediction models. There is no cost to the ship. A similar mechanism could be used

to improve oceanographic models.

Many fishing boats already collect some in situ data such as temperature and salinity. Volunteer vessels

could also be equipped with XBTs and fluorometers. Data could be transferred in real time via cell

phone or satellite phone. This project would require funding (perhaps partly from the commercial

fishing industry) and willing participants.

Confidentiality would be required.

Lines of communication It was readily apparent that the lines of communication between the fisheries/aquaculture community

and the remote sensing community could be improved. While some data providers do act as conduits

(e.g. Roffer’s Ocean Fishing), the communication is generally limited to their clients. The establishment

of a formal line of communication and closer interactions between representatives from the fisheries

and aquaculture communities and the remote sensing community would be a major benefit to all

parties involved.

Data accuracy It is important to be able to understand and reduce the uncertainties of products. For example, older

AVHRR sensors provide inaccurate values, although their feature recognition is still good. The accurate

18 Main Issues

AVHRR sensors include NOAA 18, 19, METOP-A and METOP B. In addition, products are never really

considered “validated”, although many are useful from day one because of the patterns they show.

It is strongly recommended that the uncertainty of each pixel be included in the data files for each

product.

Data formats Every ocean sensor has its own agency-specific data format. These files are often difficult (if not

impossible) to open in commonly used software packages (see Appendix B for a list of software

commonly used by the ocean community). This is compounded by a lack of script examples and

tutorials.

One possible solution is to provide the data through the Environmental Data Connector (EDC; ERDDAP

on top of THREDDS). This would provide the data in a user-specified format.

Another possible solution is to provide the Level 2 and Level 3 products in a standard, interoperable

data format. Whatever format is chosen should include metadata and limitations of the data. The

format requested by the ocean community is: NetCDF v4 (CF compliant).

Internet access Internet and cell phone access is frequently limited at sea. For example, albacore fleets fish far offshore;

they have no radar and no cell, only satellite phones. In most cases, the file sizes of images relayed to

end-users must be small and easily downloadable at sea.

Legal issues Federal entities (like CoastWatch)

Unable to produce advanced products as they are legally not allowed to compete with

commercial companies.

Are required to obtain permission from commercial companies before outputting new products.

Rick Goche suggested keeping the fleet informed of any objections from commercial

companies; he may be able to help resolve any potential problems.

CoastWatch includes the disclaimer “not to be used for navigation” on images.

Nonprofits (like NANOOS)

Do not have the same restrictions as federal entities on providing new products.

NANOOS has a charter to protect them from lawsuits.

Data latency While direct broadcast data are collected in real-time, the ocean products produced are generally not

available for another 2 – 6 hours, depending on the software used and processing speed at the station.

The longer the delay, the less valuable the data becomes. Ideally, the latency should be no longer than

40 minutes.

19 Main Issues

Real-time downlink size limits Downlink channels are not always large enough to downlink all data in real-time. Sacrifices often need

to be made to account for the downlink size limits. For example, the S-NPP direct readout team is

currently discussing dropping a waveband from VIIRS direct broadcast to make room for data from a

different sensor.

Downlinking hyperspectral data at 250 m resolution all over the earth is (currently) unrealistic. Instead,

real-time data may have to be limited to some combination of:

- A minimal or decreased set of wavebands

- Lower spatial resolution

- High spatial resolution data from small targeted areas only

- Hyperspectral data from small targeted areas only

- Delayed full spatial and spectral resolution data from the orbital downlink via the internet

Note that the last option (delayed data via the internet) is not feasible for many international stations

due to poor network connections. For this reason it is critical to provide the best possible data in real-

time via satellite downlink.

LIDAR limitations LIDARs utilize lasers to penetrate the water column. One candidate for a satellite laser source is the

green solid state Nd:YAG laser (532 nm) (e.g. GLAS and ATLAS onboard the ICESat satellites). However,

to excite chlorophyll-a fluorescence, a laser in the red or blue is needed. A powerful solid state blue or

red laser has yet to be developed. A sensor to detect the resulting chlorophyll-a fluorescence emission

would also be necessary.

20 Sensor Needs

Sensor Needs A summary of desirable characteristics of future ocean sensors follows below. Note that GEO-CAPE,

already planned for 2022, satisfies many of these requirements (Appendix D).

CHARACTERISTICS DETAILS

Satellite type

Geostationary Needed to increase spatial and temporal resolution

Polar orbiting Multiple satellites required to provide high repeat (multiple passes per day)

Spatial resolution

250 m resolution Specifically needed for coastal zones, estuaries and wetlands

10 m resolution For aquaculture sites and important coastal areas

Temporal resolution

Hourly For coastal zones to resolve tidal issues

Multiple times per day To examine diurnal patterns

Spectral resolution

Hyperspectral Specifically needed for coastal zones, estuaries and wetlands to monitor algal species

Multispectral Standard wavebands and products needed to monitor the oceans: UV, chlorophyll, CDOM, fluorescence, IR (SST)

Sensor types

Synthetic aperture radar Provides higher spatial resolution for specific targets

21 Sensor Needs

1 km or 4 km microwave thermal Sees through clouds

LIDAR from satellites (blue/red) Accurate maps of chlorophyll

Altimeters Provide sea surface height within a few centimeters

22 Conclusion

Conclusion

The workshop attendees represented a wide variety of fields related to the fishing and aquaculture

industries. In spite of their diverse goals and needs, consensus was readily reached on many issues,

suggesting that the issues raised in this report are representative of the wider ocean community.

Many of the new ocean products requested are already under investigation (and even in use) by

researchers. Flexibility to include new products and improve old ones in the operational and/or direct

broadcast code is highly desired.

The inclusion of uncertainty values for each pixel is strongly recommended for each product.

Unanimous consensus was reached on the preferred data format for Level 2 and Level 3 data: NetCDF v4

(CF compliant). Data in this format can be readily imported into all of the main software packages used

by the ocean community.

Decreased latency of real-time ocean products (to within 40 minutes) is also requested.

It was also recommended that ERDDAP/THREDDS be investigated as a possible method of serving the

data. This package could be installed at all data provider locations (such as direct readout stations) to

provide easier data access.

Geostationary ocean satellite data is desired unanimously for the high spatial and temporal resolution it

can offer. It is hoped that GEO-CAPE will satisfy many of the ocean community needs. Wishes were also

expressed for satellite LIDAR and high resolution microwave data.

This workshop provided an invaluable opportunity for the ocean community to express their wants and

needs in the realm of satellite data. Our hope is that the suggestions and wishes presented in this

report will be taken into consideration during the planning and development of future satellite software

and sensors.

23 Acknowledgements

Acknowledgements

We are grateful to each of the workshop attendees for their time and invaluable contributions to the

workshop and this report. The attendees in alphabetical order were: Ichio Asanuma, Renee Bellinger,

Patrick Coronado, Jennifer Fisher, Dave Foley, Rick Goche, Ladd Irvine, Dave Landkamer, Pete Lawson,

Morgaine McKibben, Karen Nieto, Jay Peterson, Craig Risien, Mitchell Roffer, Brandon Sackmann, Jerry

Thon, Erin Urquhart, Curt Whitmire, and Geoffrey Wilkie. Special thanks go to Morgaine McKibben for

her introductory presentation on ocean satellite data. We would also like to particularly acknowledge

Rick Goche for representing the views of the commercial fishing industry and providing valuable

opinions and insight. We sincerely thank Patrick Coronado and Kelvin Brentzel for their continued

support and dedication to the ocean direct readout community. This project was funded under NASA

grant NNX10AM70G (Ricardo Letelier).

24 Appendix A: List of attendees

Appendix A: List of attendees

Ichio Asanuma The Tokyo University of Information Sciences

Japanese fisheries and rivers

Renee Bellinger HMSC, Oregon State University Migratory distribution of salmon Patrick Coronado NASA DRL Direct Readout Lab Jennifer Fisher NOAA Fisheries Salmon forecasting Dave Foley UC Santa Cruz / NOAA Fisheries CoastWatch data management

Rick Goche Oregon Albacore Commission Commercial tuna fisherman Ladd Irvine Marine Mammal Institute, OSU Whale tracking and habitat

assessment Dave Landkamer Oregon Sea Grant, Aquaculture Shellfish aquaculture site surveys Pete Lawson NOAA/NMFS Ecology of coastal salmon, Pacific Fish

Trax Ricardo Letelier CEOAS, Oregon State University Fluorescence, dynamics of coastal

ocean, fronts, marine microbes Morgaine McKibben CEOAS, Oregon State University HABs, bloom products Jasmine Nahorniak CEOAS, Oregon State University Direct broadcast station at OSU Karen Nieto SW Fisheries Science Center, NOAA Fronts and eddies, habitats Jay Peterson CIMRS, Oregon State University Zooplankton and climate variability Craig Risien CEOAS, Oregon State University NANOOS data management and OOI Mitchell Roffer Roffer’s Ocean Fishing Forecasting

Service, Inc. Commercial satellite image provider for fishermen

Brandon Sackmann Integral Consulting, Inc. Water quality and ferry-based monitoring program

Jerry Thon Astoria Holdings / NW Sardine Survey LLC

Sardine abundance

Erin Urquhart Johns Hopkins University Harmful bacteria in Chesapeake Bay Curt Whitmire NW Fisheries Science Center, NOAA Surveys and stock assessments of

groundfish species Geoffrey Wilkie NOAA OMAO Terrestrial data and physical

oceanography GIS

25 Appendix B: Ocean satellite data tools

Appendix B: Ocean satellite data tools

The various tools used by the workshop attendees for ocean satellite data manipulation and processing

are listed below. This is only a partial list of all tools used by the ocean community (inclusion or

exclusion does not imply endorsement).

EDC – connects ArcGIS to THREDDS/OPeNDAP

Worldwide Telescope – 3-d animation tool (e.g. to show fish catch)

ERDDAP

o Data translation layer

o Provide a URL, outputs data in desired format

o Very flexible

THREDDS - provides easy access to products

SeaDAS

o difficult to overlay other data

o CoastWatch is unable to use SeaDAS (uses BEAM instead)

ArcGIS

o can’t display chl on a log scale

o is used to overlay lines and points before sharing images

Matlab

R – statistical computing and graphics

Simulcast

o Would like to see chl and SST included in real-time

BEAM

ENVI

IDL

26 Appendix C: Forecast models

Appendix C: Forecast models

For reference, the forecast models mentioned during the workshop follow below. This is just a small

sampling of the forecast models available.

Waves (Tuba Ozkan-Haller)

ROMS (Regional Ocean Modeling System) – daily forecasts and nowcasts

HYCOM (Hybrid Coordinate Ocean Model) – global ocean model, needs improvement

27 Appendix D: Geostationary specs

Appendix D: Geostationary specs

TEMPO 2018

Cone over North America (from Mexico City to the tar sands of Canada)

Hourly

2 km x 4.5 km (over Kansas)

UV – 700 nm

S/N 1000

Intended for terrestrial use but should also be good for oceans

Will be on a commercial communications satellite

GEO-CAPE 2022

Centered over the Galapagos, covering south and north America (including Hawaii but not

Alaska)

Designed for oceans

Will have SST

28 Appendix E: In situ data types collected

Appendix E: In situ data types collected

Following is a partial list of the variety of in situ data that are collected by fishermen and researchers.

Temperature profiles (to locate the thermocline)

Fish catch data (depth, location, species, and photos)

Locations where tried to fish in addition to where fish were caught (effort)

Gliders (can go close to coast)

Temperature and salinity collected during trawls

Local winds

Buoys

CTD sampling

Optical sensors on passenger ferries in Puget Sound (temperature, salinity, turbidity,

chlorophyll)

Moorings

Salinity

Floats (tend to be far offshore)

XBT (expendable thermosalinograph) – very cheap, shoot over side, provides instant plot

Fishing vessels – some have SST loggers and loggers on cannonballs that do profiles

Bucket samples

Whale sightings

Wave video camera (shows distinct features as the ship crosses the front)

Electronic logs from fishing vessels

Aircraft (e.g. flights over Chesapeake Bay)

aerial unmanned vehicles (AUVs) to fly under clouds – hyperspectral – costs about $1 million

per day

LIDAR – laser driven sensor, penetrates water column. Used from ships and aircraft. Uses

the 535 nm waveband. Can see salmon and herring. Uses a fairly large spot for safety.

SAR drones – NASA has one – spatial resolution of cm to m, perhaps could use for marine

debris

29 Appendix F: Ocean data providers

Appendix F: Ocean data providers

The list below includes commercial and non-commercial providers of ocean satellite data used regularly

by the twenty-one workshop attendees.

Coastwatch – used for access to data from a wide variety of sources, not really used for imagery or

interpreted products – could provide the data to more appealing pages (eg. NANOOS)

NANOOS - develops products for various communities – includes forecasts and customized views – uses

in situ data – has daily SSH (normally only weekly) – needs feedback that daily is desired

Roffer’s Ocean Fishing – commercial, forecasting and interpreted analyses, ship routing, environmental

monitoring, oil spills, species tracking (squid, bluefin tuna, marlin). Uses ocean color, microwave, radar,

space station imagery, etc.)

Eyes over Puget Sound – monthly PDF report (aerial photos, satellite data, mooring data …)

Ocean Imaging – commercial, vast majority of albacore fleet use this

Pacific Fish Trax - online utility for recording fish catch data

Ocean Color website - very challenging to access data; non-intuitive

GlobColour - large scale ocean color dataset

Higher end commercial products – on west coast, the main clients are tuna boats

Vessel Monitoring System (VMS) – collects ship and catch location info, but can’t release data for

research due to confidentiality issues - may be possible to use the data to inform a model. Outputs

distributions of fish and catch probability distributions.

AVISO - altimetry (SSH) – 2 day latency

30 Appendix G: Ocean satellite sensors

Appendix G: Ocean satellite sensors

A list of the numerous satellite sensors used by the ocean community (researchers and end-users) is

given below.

MODIS – ocean color products and SST, 1 km resolution for ocean bands

VIIRS – chlorophyll and SST

Altimetry - SSH

Scatterometers (ASCAT and QuikSCAT) - wind climatologies

Surface currents

MERIS 300m (this is to be replaced by OLCI)

IKONOS – commercial – spatial resolution “barely good enough” for sardine tracking

AVHRR Pathfinder SST – 2 hour latency – direct broadcast only

Surface winds

Commercial satellites - used by NOAA during emergencies

HF radar data

Landsat 8 - repeat time 35 days; usually 1 pass per week - free

Sentinel 2A & 2B – European equivalent to Landsat but twice the swath

Military satellites o very high resolution o if data are requested for research, resolution would likely be decreased before receipt o personnel often do not have the needed expertise to provide accurate end product(s)

Aquarius - salinity

Direct broadcast – 2 hour latency

METOP – 2 hour latency

SAR (synthetic aperture radar) – can read through clouds, 10 – 100 m resolution, NASA has a big

team working on getting one. Can derive surface roughness, fronts, ship wakes, ice, and high

resolution winds. There are existing European and Canadian satellites.

ASAR – NOAA/NASA owned 15% of this commercial satellite – it recently died. Images were

sent to helicopters to rescue turtles. Global, but narrow swath. Sporadic data.

SWOT – wide swath altimeter, 5 km resolution, provides accurate currents along the swath

31 Appendix H: Potential funding sources

Appendix H: Potential funding sources

Some possible sources of funding for research, in situ data collection, and satellite sensors are:

1. The commercial fishery industry

Rick Goche would be willing to make a case for it.

This may require an assessment report from supporters that describes the main issues

and how they could be addressed.

2. Sea Grant

A recent proposal was submitted by Hal Batchelder suggesting bucket sampling from

ships of opportunity. Status unknown. Could add a satellite data component.

3. NASA

Money for researchers to work with new, improved data

ROSES calls (3 in the last month were relevant to oceans)

4. NOAA

32 Appendix I: Dream satellite image descriptions

Appendix I: Dream satellite image descriptions

David Landkamer Here are a few of my thoughts on my dream satellite data images. I have three separate data ideas. Some of this data exists already, and some is far out, but here goes. 1. Current Aquaculture Site images at several magnifications to see both scale and scope; high resolution snapshots to see farm structures (10 meters), entire west coast coverage, both extreme low and high tide views (for tidal farms like oysters and clams). Other features such as commercial fishing fleet moorings, recreational fishing fleet moorages, aquaculture and seafood processing facilities identified. 2. Potential Aquaculture Siting images, showing water characteristics where aquaculture might be feasible biologically, physically, and per ocean chemistry, in the territorial sea, the EEZ, and in estuaries. 50-100 meter resolution for farm scale detail. Collected weekly and continuously for years, using multiple sensors to show SST, salinity, DO, phytoplankton biomass/density, and wave energy present. The derived images would be composites showing areas combining various parameters that provided ideal conditions for specific species (e.g, oysters, mussels, scallops, kelp greenling). Additional data layers from separate sensors would show existing use patterns such as commercial and recreational fishing activity, transportation and shipping lanes, military use, wave energy structures, recreational zones (surfing, sailing, etc.). Images could be merged to indicate best locations for species culture in terms of target species survival and growth and minimized competing uses. 3. Real Time Shellfish Warning Data. These images would show water pH, CO2 concentration, and aragonite saturation state in order to predict poor water quality conditions for shellfish larval rearing. This would be especially useful in waters near and offshore from hatchery areas including Netarts Bay, Dabob Bay (upper Hood Canal), and Humboldt Bay, to alert shellfish hatchery operators. Other sensors would also detect naturally occurring harmful algal blooms (and/or their toxins) of human health concern, including Alexandrium sp. (produce saxitoxin), Pseudo-nitzschia sp. (produce domoic acid), Dinophysis and Prorocentrum (produce okadaic acid), Karenia sp. (produce brevetoxins), and specific vibrio species such as V. parahaemolyticus and V. vulnificus. Detection of these species, and others, would help prevent food born and contact illnesses.

33 Appendix I: Dream satellite image descriptions

Mitchell Roffer

Geostationary with 250 m resolution in the infrared, hyperspectral bands including those for chlorophyll,

colored dissolved organic matter (CDOM), UV, synthetic aperture radar and microwave thermal at 1KM.

Having microwave thermal is not an engineering impossibility. The size of the antennae can be

accomplished. Even if not 1km microwave, then 4km. Images every hour. Salinity at 1-4km hourly is

needed. This would include a night visible like VIIRS has. Data availability within one hour of pass for all

data. Within 30-45 minutes for IR and ocean color.

For us the most useful satellite data now is infrared due to their repeated coverage and frequency of

data. Limits clouds, but clouds can be removed if moving. Ocean color is problematic now with MODIS

Aqua providing the best data, but it is aging. Terra is not usable. The data from VIIRS is not as good as

MODIS, too green. The data are not available in real-time. We MUST have this VIIRS data within 90

minutes of pass at the latest. The present 4-6 hour delay of data is intolerable and reduces the value of

the data.

Hourly passes are needed for data to resolve tidal issues. Spatial resolution of 1km is adequate, but 250

m is much better.

New images: hyperspectral, SAR, microwave at higher resolution and greater temporal frequency. LIDAR

from satellites, more altimeters are needed, but not at the expense of infrared - sea surface

temperature or ocean color.

Values: sea surface temperature at 0.1°C; chlorophyll at 0.5 with better accuracy and precision without

30-50% error.

Satellite images are used for habitat classification, ocean current detection, water mass boundary

identification, sea surface temperature, water color, turbidity, chlorophyll estimates.