The CZCS launched
with Nimbus-7.
It collected oceanographic
data from 1978-1986
on a limited duty cycle;
meaning it didn't capture
the globe everyday
but it got a lot of it.
Most importantly, this was
NASA's Proof of Concept Mission
that you could study a
marine biosphere from space.
And while that might sound...
well, we'll get to this, but
it might sound a little silly,
it required some significant
technological advances;
you're all to be commended,
I wouldn't be here
without these advances.
But for the first time
you're looking at a
very, very dark ocean,
and what I mean by dark is
the ocean is really dark
compared to the land for space
and it's really dark
compared to the atmosphere,
and you have the
compounding problem
it's under the atmosphere.
So what the satellite
is actually seeing
is 90% atmosphere
and 10% water,
and so to get to that
water leaving signal,
not only do you need a
really, really good radiometer,
but you need it to be
really, really well calibrated.
From what I hear,
from the stories I've
heard Gene and others tell,
it was not necessarily
an easy sell
to get this bad boy
on the spacecraft;
you would know better than I
but there were
some naysayers
who said it's
technologically difficult,
but also it's kind of
pretty picture science.
And to a first order
it is pretty pictures,
what the Coastal Zone
Color Scanner did
was measure the
intensity of light
at different colors
of the rainbow,
it looked at the
color of the ocean,
but the color of the ocean is
shaped by what's in the ocean;
very... clear water
is very, very blue,
but as you get to more
productive green waters,
where there are a lot of
things growing in them,
the lake in the back here,
they turn green.
Sometimes you have some
nasty phytoplankton in there,
they turn red.
These are the ones that if
they get into your oysters,
you don't want to eat them.
And then of course you
have the brown water,
where it is just recess
bended sediments
or some river
responding to storm.
So first principle, yeah,
you're looking at the color,
the science is based,
if I know what the color is,
then I can tell
you what's in it.
And you know,
for some people
that might have been
hard to believe,
but the mother worked.
And not only did it work,
but images like this,
which Gene produced,
it created a defining
moment in oceanography.
I had professors tell me
that they remember when
they saw their first images
that they couldn't believe
how this changed how they
thought about the ocean.
And in fact, it gets to
my personal story here.
I am not a spring chicken,
but I was in grad
school in the mid-90s,
and at that time CZCS
had already been proved
to be very successful
and I was handed a textbook,
and lo and behold, in my
first oceanographic textbook
there was a picture
of CZCS imagery.
And so I come from a place
where not only was
biological oceanography
from space possible,
I was entitled to it,
and now my kids can
do it on their phone,
as mentioned before.
But it was very successful;
I believe in the
first 15 or 20 years
there were 27 publications
on Science alone,
got the cover a
couple of times;
This is Gene's cover,
really, really
successful mission,
and really changed the way
oceanographers like myself
can study the ocean.
So standing on the
shoulders of this giant
there have been a number
of follow-on missions,
most notably the NASA
Orbital Science at the time,
SeaWiFS mission, the
EOS sensors, Terra and Aqua;
the European Space
Agency got in the game
to with MERIS and
now we have SWAMI
and PPN VEERS plus
many, many others.
CZCS looked at four
different colors of the ocean
and the visible
colors of the rainbow,
but some of these
other satellites
expanded the wavelength suites.
The first principle of this is
the more colors you look at,
the more different things
you can discriminate between.
And the mission
where Goddard
actually is really
fighting for in the future;
PACE, we'll try to
look at all of them.
So just a couple of
notable achievements
in the first year of
the SeaWiFS' mission,
it had the good or bad
fortune to be launched
at the time of a huge El Ni–o,
La Ni–a transition.
And the calibration
team of course is like,
great, the first thing we look
at is something different
than what we would expect,
but then you get to the science
that you can do with this.
I don't think that's me.
SeaWiFS is able...
actually able to
capture the transition
from an El Ni–o condition
in the Pacific Ocean
back to La Ni–a.
You can't do this
from a boat,
you can't do this
from an aircraft,
you can't do this from even
autonomous platforms,
but you can do
it from space
and you can do
it from space,
in the first year of its
launch for the first time.
And so three years later
one of the most interesting
time series was put together,
and I should have said
right away too that,
you know, looking at visible
colors of the rainbow
enable studying
the land as well.
And the study that
came out in 2001
after three years
of SeaWiFS data
was the first paper to
provide a consistent estimate
of how land and ocean
carbon fluxes behaved.
And what we're looking at
down here is on both X axis
are just three years;
1998, 1999 and 2000.
The bottom is a
vegetation index.
The top is an index of...
it's chlorophyl concentration,
which is an index of
phytoplankton biomass.
These black dots are
the annual cycles
and then these little
hollow dots are the anomaly,
you know, the chain...
the month to month variations
compared to the average,
and what you can see is that
land had a flat anomaly,
the ocean did not;
they were decoupled.
So you could study the land
and ocean simultaneously
and start thinking about how
they responded differently
to carbon cycles.
So this is after
three years.
And the same
series of authors
put another paper
out after six years,
what I have hidden here is the
remainder of the time series.
So the first three years,
this is chlorophyl again,
this is productivity,
this is the climb you saw in
the... in the previous slide.
The thing is, is that
when you add in another
bunch of years to it,
it changed directions,
which is all to say that
it's very, very important
to be in the long haul.
And I know I am
preaching to the choir,
but again, continuous missions
really give you a chance
to start thinking about
long-term changes in
response to climate.
What I think our group is
probably the most proud of,
and I say our lightly because
I've been here for 15 years,
but this is a lot bigger
than... lot longer than me,
is that NASA correctly took
an open data policy to this,
and what that enabled
as of two weeks ago when
I created this chart
is that there are over 2,300
unique peer review publications
that use SeaWiFS data,
in 17 years.
MODIS ocean has
another 1,900.
If you add CZCS and you get
rid of the ones that overlap,
you're talking about 4,000
publications in 17 years
from an international community
using OceanColor data.
That's not possible
without the Nimbus program.
So looking towards the
future of what's possible,
things you might see in
headline news or the newspaper
when you're thinking
about the ocean
and now that you're convinced
you left phytoplankton
and you're thinking
about phytoplankton,
the deserts in the
ocean are expanding,
Chesapeake Bay is dying; that
might be a popular one too.
And then there are
ton of other things
that we can study
using OceanColor,
like ocean acidification,
where does all
the carbon go?
I don't think those
budgets are reconciled,
but I'll do two quick case
studies to wrap this up.
Expansion of the
ocean deserts;
now, what I mean by a
desert in this case
is kind of the classic
desert with the cactus.
It means it's an area that's
not particularly productive,
that there is light,
but it's missing something else,
you know, plants aren't growing,
algae aren't growing
because there aren't
a lot of nutrients.
And so if you look at these
black areas of the ocean,
they're the lowest biomass
areas anywhere in the world,
and according to
several papers
this is not just the only one,
the aerial size of these deserts
is getting bigger every year.
So the ocean is somehow
responding to climate change.
What that means
biologically is that
what happens to be successful
growing there is also changing.
So you have these phytoplanktons
that are big diatoms,
huge chain forming things
that fish love to eat.
Well, they're usually
found up here,
you know, the very
productive areas.
The cool colors are
low productivity,
the reds and the oranges
are high productivity.
When you get to
these little guys,
the submicron particles,
the ones that are
evolutionarily adapted
to be in the deserts,
well, if their desert is bigger,
they're moving,
there's more of them.
And this has influences
for food webs,
this has influences
for all different
kinds of trophic levels,
carbon exports and
things like that.
And this is important
enough that we...
well, I should preface
this by saying OceanColor
now has an international
governing office
to make recommendations,
the International Ocean
Colour Coordinating Group,
we're that popular now.
But they actually have a
series of technical reports
and they've assembled
groups of people
to write reports on,
not only how to tell you
how much phytoplankton
biomass is in the ocean,
but what kind it is.
That's the next generation,
what is it exactly, almost at
a species or a function level.
The need to do so
is also written into
the PACE Science
Definition Team report;
and as a reminder PACE is an
upcoming OceanColor mission
that we hope for.
So the science is going not
just towards total abundances,
but what's
actually out there.
It's a difficult problem,
it's a really fun one,
and to me it's a lively one and
I want to go forward with it,
because now we're seeing
not only can we see
microscopic plankton,
but we can tell you
what kind it is.
So the second example would
be watershed management;
how can this stuff be used
for watershed management?
Well, there's a lot of
pressures on coastal ecosystems.
The one I am going to
focus on for my example are,
you know, human
populations and land use,
the fact that,
for better or for worse,
nitrogen or phosphorus
are finding their way
into Chesapeake Bay,
what is the
response to this?
So I am going to show
you three panels of work
by other esteemed colleagues.
All of them on the X axis
are going to be time
from roughly, you
know, 1940-2000.
And what you're
looking at here
are nutrient inputs
in the Chesapeake Bay.
And the punch line is
they're going up over time.
This is Algae Biomass and
you're seeing the trend,
as you add more food for
the algae to feed upon,
well, you're
seeing more algae.
But consequences of
that are things like
the percent cover of
submerged aquatic vegetation
are going down.
You add more to the water,
you make it a lot harder
for sunlight to penetrate,
sunlight can't get all
the way to the bottom,
then seagrasses don't grow.
The seagrasses don't grow,
the chemistry of
the bottom changes,
the amount of oxygen and
the water column changes,
you get erosion,
you lose oysters.
Well, nevertheless, we know
these problems are happening
and there are
wonderful programs
that have been
operating since the 80s
to go out and measure,
to measure this.
So the Chesapeake Bay program,
just as an example,
goes out more than every month,
they visit 49 stations.
They take 19 hydrographic
measurements.
They're a busy, busy group,
and they get to these things
that can give you some indicator
of the health of the Bay.
But these dots are big
relative to the bulk.
So what you think is pretty
good coverage really isn't,
whereas something like
MODIS Aqua sees the whole Bay
in one day.
I mean, I am sure you see
really cloudy days too,
but in practice you get
ten good views here.
So it's a
complementary dataset.
It doesn't replace
going to sea,
but it sure is a nice
complement to this.
And in fact, if you start
looking at time series
of different water quality
parameters in the Bay,
you can really piece
together a nice story.
So the black dots
in the top line
are the field measurements
of Algae Biomass I believe,
and then the blue line is
SeaWiFS over the top of that.
And so, you know, the
OceanColor instrument
is doing a pretty good job
of tracking the changes,
but from the
OceanColor instrument
you also fill a
lot of the gaps
and you can start getting
quantities of dissolved
material, phytoplankton
and other kinds of particles.
If you squint your eyes you
can start to piece together
really great
stories about this.
So the green lines just
indicate the year boundary.
So wintertime, usually wetter,
usually higher stream flow.
Water... stream
flow is higher,
things are entering the Bay,
dissolved material from land,
from plants and terrestrial
degradation flowing in.
And you can see
those peaks there.
It's also bringing
in nutrients,
it's also bringing in food
for the phytoplankton.
Well, then there's a lag
because the water is still cold
and the sun really
isn't out yet,
just like your lawn is still
brown in February and March.
But all of a sudden
in the summer,
after all of this
influx of things to eat,
the phytoplankton
start to graze
and they start to pop up
and their biomass increases.
And then as they
start to die off,
other kinds of
particles come up,
either degradation
particles or things
that are eating
the phytoplankton,
and so the satellite
data provides
a really nice
complementary dataset,
not only to fill the gaps,
but also to create
other products
that are, you know,
complementary
and a nice story
really tends to emerge.
But the time series I showed
you is still just a fraction
of these longer term
time series out there,
so just to drive
the point home.
And this isn't the audience
for it, you all know this,
because of Nimbus we
can do these things,
but we need
to be in this
decades and decades
and decades long haul.
And it's been
so successful,
even our friends in the land
community put a blue band on
so we can start doing
OceanColor from land now.
For that we're
grateful too,
because the spatial
resolution is spectacular,
it's very, very nice.
And so I'll leave you with
probably my favorite thing
that I've seen come out,
my favorite animation
that's ever come out,
this is the Global Biosphere
that was created on the 10th
anniversary of SeaWiFS, so 2007.
It's got the Vegetation
Index on Land,
it's got Phytoplankton
Biomass as different colors,
again, cool low biomass, reds
and greens, higher biomass.
But if you sit
and stare at it,
you'll see something
different than I do,
you'll see something
different than your neighbor,
but this is unbelievable,
and for me, it's like watching
the earth actually breathe.
And it's four minutes long
so I won't make you suffer
through that, it's 5 o'clock.
So with that, I will end.
Thank you for your time!
[Applause]