my good fortune today too, introduced to dr Martha
Henderson who started her Yes her scientific life with the
doctrine and Astrophysics University of Minnesota in 1993. You
can tell your tiny bit more about what happened next
then something's happened. Uh crossed paths have been remote
sensing and she after a long stand research scientists moving
into US agricultural research service and she is currently at
the hydrology remote sensing lab in Beltsville Maryland. Her
research interests focus on mapping, water, energy and
carbon laid surface flux is feel the continental scales using
and or remote sensing with applications throughout monitoring soil moisture
estimation. We have been on lands that science team
together since 2006, known each other a very long
time before that. And so we're very very pleased
today to Dr now they came with the lights the
way they are. Okay, well thank you thanks
so much for having me here. It's a pleasure
to speak with you in the seminar. So as
randy mentioned, I work for the Agricultural Research Service
which is the research arm of the U. S
. D. A. And I'm in this hydrology
remote sensing lab which is located in Beltsville Maryland.
It's right outside the Washington D. C. Area
. And I work with a group of people primarily
trying to develop techniques for routine monitoring of evaporate transpiration
E. T. For short crop water use if
we're talking about agricultural lands and drought using multi scale
satellite remote sensing data. And in particular what we've
been working on most recently as part of the Lance
at science team is trying to find ways to fuse
together information from multiple different satellite platforms to get towards
the ultimate goal of mapping water use at daily time
steps and at very fine spatial scales at sub field
scales to support agricultural applications. So I'll be talking
to you about these data. Fusion techniques were working
on today. First of all, why do we
care about evaporate transpiration? This may be obvious to
everybody and some groups, it's not so clear.
There are just a plethora of applications and demands for
this kind of information, both within the United States
and globally. We can use these satellite derived heat
maps to assess our current spatial patterns in water use
at different spatial scales. And in terms of agriculture
, we want to know how effectively our agricultural systems
are using the water that's available to them. This
can be used globally to pinpoint areas of potential food
and water insecurity. We can also build up time
series maps of of water use and study how water
use is changing spatially and temporally in different areas due
to different kind of drivers. Great changing climate drivers
, changing precipitation patterns, changing air temperature patterns,
changes in land use is we're converting forests into agricultural
lands in some areas and then agricultural lands into no
no oh come on. Make changes to your system
um into urban settings. How is this impacting the
regional landscape, water use and as populations grow in
some areas and there's competing demands on water resources in
areas. We need a way to account that the
changes in water use due to these factors. E
. T. Is an important input to a lot
of other types of modeling systems. It's very important
to accurately quantify the exchange of water vapor between the
land and the atmosphere in our new numerical weather prediction
systems to improve the weather forecast. So satellite remote
sensing could be an input to improve forecasting systems and
hydrologic modeling systems to predict early early detection of flooding
, drought, high runoff events. And finally we're
also using these et maps as an indicator of vegetation
health. As the crops become stressed, they shut
down their stomach to their transpiration flux is our lower
we can detect those those changes and transpiration from a
satellite platform, early potential signal of crop stress and
maybe impacts on yield. The bottom line is we
really can't hope to uh manage our freshwater resources either
in the United States or globally if we cannot accurately
measure how these resources are being used. So this
is just kind of a schematic outline of the talk
I'm gonna be giving today. I'll talk about some
of the modeling tools that we use in this work
. There's a multi scale E. T. Modeling
system. We call Alexey Dis Alexey primarily uses remotely
sensed maps of land surface temperature acquired in the thermal
infrared wave bands. We also have a thermal image
sharpener tool, the D. M. S.
Data mining sharpener tool that we use to improve the
resolution of our input thermal imagery. And I'll be
talking about the star FM data fusion algorithm that fuses
together individual data streams from different satellite sensors. We
apply these tools to a number of different satellite assets
. We use the geo stationary satellites that are usually
used just for for the meteorological Services. For weather
forecasting provides excellent land surface information as well. Really
, really good temporal information. We get imagery every
15 minutes but at pretty coarse spatial resolution. So
we also want to combine information from other satellite systems
with finer details. Some of the polar orbiting systems
like modus and land set. And I'll talk about
how we're applying these tools in the satellite assets to
some applications in agriculture, in mapping daily crop water
use at field scale, also monitoring crop technology at
the same field scale, combining those two data streams
to try to predict impact of stress on yields and
as a drought early warning tools. So this is
what I'll be covering today now. I'm just gonna
keep talking randy when I hit my limit. Just
kind of make some wrap it up signs and it
will stop wherever I am. So first of all
multi scale E. T. Retrieval algorithm that we
use in our work is based on principles of service
energy balance. There are a lot of different ways
to map evaporative flux is over landscapes and I'm just
going to kind of categories them in two general categories
. The first category is a water balance approach used
in a lot of land surface modeling systems. A
lot of hydrologic modeling systems. And in these types
of models, the key input is precipitation. You
have to know the precipitation rainfall very accurately over your
modeling domain and then that precipitation is partitioned into all
these different flux is and stores using typically a system
of prognostic physically based equations. Really useful approach for
a lot of applications because you get a lot of
information about the hydrologic status of your land surface system
. But the challenges, it just requires a whole
lot of surface parameters to get this to run with
correctly. We need to know a lot about the
subsurface soil properties, the soil moisture holding capacity in
the soil hydraulic conductivity to get the infiltration and the
apple soil evaporation component of VT right. And we
also have to know a lot about the vegetation.
We need to know about its roots, dense routing
distribution in the soil sensitivities of different plant types to
moisture deficiencies in the root zone. To get the
transpiration component right and very importantly, you need to
know your precipitation very accurately. And that's not such
a challenge here in the United States. We've got
a lot of re engages in the US, we
got a lot of Doppler uh radar stations to correct
our satellite based estimates of precipitation more of a challenge
in some other parts of the world where that the
media logical infrastructure isn't so dense. Also you need
to know ancillary sources of moisture into the system that
aren't related to precept and I'll talk about that in
a moment. So in contrast in this energy balance
approach two et modeling that I'll be talking about,
we don't need to know quite as much about the
subsurface properties and we don't need to know as much
about the plant response. And very importantly, we're
not using precipitation as an input to our model,
in this case the primary input our maps of land
surface temperature. Again, we can derive, we
can retrieve using satellite platforms equipped with thermal infrared imaging
systems. We know there's a strong physical connection between
evaporative cooling and the temperature of an object. So
in this kind of modeling system, if we know
how much energy is being put into the land surface
system into a pixel on the land surface system down
, welling radiation from the sun from the atmosphere minus
reflected and emitted radiation, the net radiation at the
land surface, we can estimate how much evaporative cooling
must be occurring under that kind of radiation load to
keep the soil and the temperature at the soil in
the canopy at the temperatures we observe from the satellite
platform. And we can answer that question using a
model based on surface energy balance. So again,
advantages here, we don't need to know as much
about the system and were independent of precipitation information.
We can map surface temperature much more accurately than we
can map precipitation. But we're really getting only a
handle on the evaporative component of the hydrologic budget.
You're getting a lot more information with water balance models
. So there's really good synergy here. We found
that there's a lot of value in comparing estimates from
these two types of modeling systems. We learn a
lot about the models, we learn about the systems
that were trying to model. And this is just
an example of this over the Continental United States were
comparing E. T. Estimates from a land surface
model with our satellite based thermal retrievals of ET.
And what we're finding is we get good agreement over
a lot of the United States and areas that are
these are kind of indicating some differences. The white
regions here models agree fairly well, but there are
specific areas where the satellite retrieval is consistently predicting higher
evaporative flexes than the water balance model. And these
areas can be kind of related to three different factors
in general areas of high irrigation, we're getting more
eating out of the satellite retrieval in the central valley
of California and the Snake River Plain in Idaho along
the Mississippi River basin. A lot of irrigation going
on there, growing a lot of rice in the
Mississippi basin. Um These are areas where there's kind
of additional water added to the system not necessarily correlated
with the local precipitation rates. We also find enhanced
flux is in areas of relatively shallow water table.
Again, along the Mississippi Basin, in the florida
, Everglades and some of the coastal areas. These
are areas where there may be direct extraction of additional
moisture from the groundwater table by free apathetic vegetation.
Maybe some direct evaporation. Again difficult to capture in
a water balance model unless you know a priority what
the groundwater table distribution is and another source is you
can see up here all these little peaks, high
density of subpixel water bodies is another area where we're
seeing these enhancements. So we've got the prairie pothole
regions in the Dakotas, the prairie kyoto, the
Missouri Coteau, a lot of little potholes just holding
up the moisture, but they're very small pixel skills
. You've got the land of 10,000 lakes, Minnesota
, a lot of little lakes, little wetlands contributing
to the remote sensing et flux. But not captured
by these water balance models. That doesn't know where
all these little potholes are are lying on the ground
. These are all hydrologic features that are of great
interest to water resource managers. They need to know
about how irrigation water is being used. They need
to know about the hydrology of the the florida,
Everglades. So this is one of the reasons why
water resource managers have really kind of pinpointed thermal based
E. T. Retrieval as an important tool for
their water management decisions. So I'm not going to
get into a lot of details about the modeling system
. There's a lot of even thermal based ET models
. The model we used is based on a two
source representation of the land surface system. We partition
surface temperature and flux is between the soil component of
the scene and the vegetation component of the scene based
on the local fraction of vegetation cover. And that's
what's going on here partitioning up the bulk surface temperature
. We also solve the energy budget for the combined
system and the soil and the canopy independently. And
we do this because soil and canopy components, the
scene are differently coupled with the atmosphere and their their
contribution to the flux. This is not necessarily in
proportion to their their component temperatures. So we found
this kind of system to be much more robust over
a broad range of vegetation cover conditions more so than
a very simple single source model. So surface temperature
is very important in constraining the sensible heat flux off
the land surface. Has anybody heard of sensible heat
flux before you've heard of this term before? It's
just the convection of heat off the um the ground
that goes into heating the atmosphere. Uh So driven
by temperature gradients temperature is also important for partitioning the
net radiation between the soil and the canopy and for
estimating that component of the energy balance that's conducted into
the soil that goes into heating the soil profile.
We have some different techniques for estimating the canopy component
, evaporation component, basically the canopy transpiration rate.
One method is a canopy resistance methodology that also allows
us to estimate couple of carbon assimilation flux is along
with the water flexes, and then the soil evaporation
component is computed as a residual to the overall energy
balanced model. So we've tested this model regionally in
the form of it's kind of the land surface representation
in this atmosphere land exchange inverse model, or Alexey
. If we want to robust remote sensing retrieval,
we have to make the model as insensitive to expected
errors in the remote sensing data as possible. And
we know we can never map land surface temperature perfectly
. In an absolute sense, there's always atmospheric corrections
emissivity corrections that we may not get exactly right.
So this Alexey model actually uses time changes in surface
temperature rather than absolute temperature. We can always measure
time changes in any quantity much more accurately than we
can measure absolutely quantities. So we run this,
we run a time change model over the morning hours
as the boundary layer is developing to do this,
we need a satellite that gives us surface temperature at
multiple times during the morning. So we have to
run this model using geo stationary satellites That are sampling
the land surface temperature every 15 minutes. But we
may want to map flux is at higher spatial resolution
than is supported by the geo stationary satellites. So
that's why we have this flux desegregation algorithm which we
just call this Alexey that uses higher temperature or higher
resolution temperature information from satellites like land set and motives
to get these finer scale maps and the net.
I think it is a kind of a multi scale
water use monitoring or mapping system going from global scales
all the way down to sub field scales. So
at the continental scale of the United States, again
, we're using the geo stationary satellite data. We
can get hourly flux is 5-10 km resolution. For
higher resolution applications, we can disaggregate down to the
modus scale. That's a kilometer resolution in the thermal
wave bands. And you can see you can start
to see some hydrologic patterns in the landscape at this
scale, resuming into a region in central Iowa here
. But most of our demand for information is at
this highest spatial resolution resolution that's supported by the land
set satellite. The field the field level. This
is the level at which water is being actively managed
over much of our global land surface. And this
is where people want the information. Unfortunately the temporal
support at that highest spatial resolution is not great.
So with a single active land set satellite we might
get an image every 16 days. If it's a
perfectly clear area all the time. With two land
sets, we'll get an image every eight days.
If you factor in cloud cover, you might get
an image every month, every couple months, maybe
once per year if you're lucky, Very difficult to
monitor seasonal water use based on one satellite image.
So this is why we're working towards these data fusion
algorithms. We're trying to combine information from all these
different satellite platforms to get at the ultimate goal of
mapping the daily water use at this finest spatial resolution
. Yes, this is just a schematic diagram of
how we apply our data. Fusion too. The
problem of E. T. Mapping. And we're
using an algorithm developed by Single, who's now a
scientist in our lab called the spatial temporal adaptive reflect
its fusion model. Originally developed this refusing reflect inst
data but we're applying it now to ET data as
well and it seems to work fairly well. So
we can map daily flux is at the continental scale
. Using the geo stationary satellites using Alexi, we
can disaggregate down to a kilometer resolution more or less
once per day with the motor satellites kilometer scale.
We have to do a little gap filling here due
to cloud cover but it's not too bad. And
then periodically whenever we have a clear land SAT scene
, We further disaggregate down to the 30 m scale
of land set. So this was back in 2007
. We had a fully functional lance at five and
7. So we had an eight day gap in
this case. And we're trying to fill in the
flux maps for these days. Star FM compares modus
and land sat image pairs on days when they're both
available and develop some kind of desegregation statistics based on
the spatial and spectral similarity between those maps. And
then applies those statistics to all the most scenes in
the intervening time period to reconstruct the high resolution flux
distribution at that land set scale. So we're getting
temporal information from goes and modus. We're getting the
spatial information from the land sat satellite. We want
to know how well it looks okay, but we
want to know quantitatively how well this This fusion works
. One way to test it is just to use
one pair of images as input to starve them and
let it predict the flux field at the second date
. Lancet date compared to a direct retrieval. Using
landside imagery. We've done this in a limited sense
, we need to do this more thoroughly at more
validation sites, but it seems to retrieve the the
spatial distribution fairly well. But the more direct way
to test these algorithms is in direct comparison with ground
based measurements of surface evaporative flux is and energy balance
flux is and this is some work that was done
by post doc, was in our lab for about
a year and a half from Italy carmelo Camilleri.
And he really looked at three major field experiments sites
in the United States conducted over the past decade.
We picked these sites because we like to have a
lot of flux towers within a single lands that scene
, because we could get the most points for uh
the amount of effort we put into this uh this
enterprise. So we looked at the soil moisture experiment
of 2002, this is ames Iowa bunch of flux
towers during this growing season, in rain fat,
corn and soybean fields. We also looked at the
Bear XO eight experiment which was conducted in bushland texas
. It's in the texas panhandle, right outside of
Amarillo texas doing a lot of irrigation. They're here
, we're looking at irrigated and rain fed cotton fields
. These are sitting on the Ogallala aquifer. They
are depleting the Ogallala aquifer currently at an unsustainable rate
, mostly in support of irrigated agriculture. So there's
a lot of interest in, in water use efficiency
in this area. And as a contrasting state.
Looking at the meat amara flux site, outside of
Lincoln Nebraska where they have corn and corn soybean rotations
growing growing under irrigated management and rain fed management.
If we look at the model results at these three
sites on land, sad dates. When we actually
have land set thermal uh imagery, we can see
the model does a pretty good job at partitioning the
surface energy balance at these three different very different kind
of climatic regimes with different crops. So the red
is the net radiation, the blue are the evaporative
flux is the latent heat. If you're talking about
energy energy terms E. T. If you're talking
about mass flux and sensible heat flux. So we're
getting errors in our et flux is on the order
of 10 on a daily time step. Which is
pretty good for any kind of satellite based retrieval.
But the question is how well can we estimate what
the evaporative flux is? Our between these labs land
set dates using our data fusion algorithm. And this
is just an example over one rain fed soybean field
. From this Mexico to experiment in Central Iowa to
demonstrate how we do this. In this case the
red squares are E. T. Retrievals on land
set dates. The from the dis Alexey model,
the blue circles are observed. Daily flux is the
gray line. Is the reference ET. That we
can compute on a daily basis using local just meteorological
data. So the potential the maximum ET we would
expect on a given date if we were going to
try to estimate daily et on the basis of land
sad alone. And we do try to do this
even with the Spar city of sampling, what we
would do is compute the ratio of actual to reference
ET On each of these lands that dates. So
it's a normalized et like 0-1. Yeah. One
being maximally T. And assume that that's kind of
an indicator of the surface moisture status, something that's
relatively conserved in time. We would interpolate those ratios
between the land set dates and retrieve the actually t
from the reference et on non land set dates.
And if we do that, this is what we
get an estimate of daily ET just based on land
sad. And it's not it's not too bad.
It does pretty well. You know, at the
beginning of the season, doesn't do too poorly at
the end of the season. But you can see
we're really underestimating the water use in the mid part
of the season right here. So what happened There
was a rainfall event that occurred close to the 4th
of July during sex. So to so it kind
of makes sense this land said image didn't know about
the moisture enhancements associated with this rainfall event and its
effects that kind of worn off by the time of
this second lands at date. we really can't hope
to recover that using Lance at alone and this is
the time period where the data fusion has added the
most value in this particular case. This is the
fuse time series. And you can see it's done
a much better job at capturing those enhanced flux is
due to that rainfall event. So what's going on
here, motus? It's kilometer resolution, it's pretty
coast of course, but it is giving us some
temporal information about this kind of larger scale rainfall system
that moved through the the system, the area,
giving us some indication that the moisture was a little
higher during this time period we saw. Excuse me
, this is the cumulative water use curve that we
retrieve. The dotted line is a land set only
retrieval the red line is the lancet motus Fused results
and it agrees pretty well with the observed water use
curve much better than land said. Only. We
saw that kind of value added at most of the
soybean sites in this soil moisture experiment of 2002 and
that kind of a sickly cornfield site. We saw
improvements due to the fusion. We saw less improvement
due to fusion at the the healthy corn sites because
these sites had already kind of Fully emerged by four
July um and they weren't quite as responsive as a
system to this rainfall event as the soybean fields.
But overall, the improvement in the seasonal cumulative water
use Went from about seven relative error using land said
only to about four relative error with very little bias
at the seasonal time steps. So that's that's useful
information. And just a summary of the results of
the other two sites barracks mead. So here we're
looking at a new irrigated cotton water use irrigated cotton
water use the green are observed. The black line
is the fused model estimates and then here are the
cumulative water use curves for the rain fed and irrigated
cotton, both modelled and measured and at the more
humid need site, we're looking at an irrigated irrigated
corn and the water cumulative water use curves at those
two sites. The model is doing a pretty good
job. It's maybe not capturing every little irrigation event
in these cotton fields, which is kind of reasonable
. The fields are fairly small scale, but we're
recovering the seasonal water use fairly well. It's interesting
to note the difference in the water use in these
two climatic systems. So, Barracks, that's in
the texas panhandle, very hot, dry site,
lot of dryer infection can really drive some strong evaporative
losses on these very windy hot days. So you
can see the water use between these two management is
peeling peeling off pretty early in the system. Whereas
in this more humid Nebraska site, the water use
between irrigated and non irrigated corn is pretty similar until
the very end of the season. This kind of
water use information, spatially detailed, temporal detailed is
just gold for water resource managers. If we can
do this accurately over large areas, they need to
know how to allocate water in advance between different competing
uses between irrigation demands, ecosystem services, urban uses
, if they can do this reliably using satellite data
at this spatial and temporal scale, it's gonna be
it's gonna be a a great asset. So this
is just a picture of one of our experimental sites
. Because give you an idea of how we run
these experiments. This is at the barracks site.
So here we have the irrigated cotton field in the
background and this is the in irrigated cotton field.
You can really see why they have to irrigate in
this area. Because this this cotton field did nothing
produce nothing during this growing season. This is an
eddy co variant system that's measuring the turbulent flux is
off the land surface, The sensible heat, the
latent heat Off a patch that's maybe 100 m in
dimension up wind of that sensor. We have a
rain gauge. We have some radiation sensors. We
also collected some high resolution aircraft data to help bridge
the gap between the observation scale and the satellite pixel
scale. And then this is a fused map of
ET over the whole experimental area. We can make
those maps for every day during the growing season and
kind of make an animation or a movie of changing
water. Use using this this fusion technique. And
let's see if this movie plays here. Okay,
so early in the season you can see some pulsing
between some more muted tones. Those are cloudy days
where there's not a lot of ev apple transpiration.
The dark green indicates high eV apple transpiration on clear
sky days. You can see some changes in the
water management going on. So the southern part of
this big pivot is turning on. We're going to
see the southern part of this pivot turning on a
little later in the season. This is the irrigated
cotton field next to the un irrigated cotton field.
This is our reference et site. Little grass plot
. Well watered. This is a concentrated animal feeding
operation. Big manure lagoons in the center of the
operation evaporating a lot of water. And over here
is the city of bushland texas. A lot of
residential land, probably irrigating their lawns using a lot
of water. You can see some strange experimental plots
here. This this is why I love land stats
so much. I can look at this map and
figure out what's going on. I can make connections
to what humans are doing on the landscape to cause
these moisture patterns. I can't do that with motive
scale data at a kilometer resolution. So this is
why why Lance has become such a, such a
big deal for water resource management. Now I'm coming
to the Forestry department so I thought I'd better put
a forest slide in here as well as some corn
and soybean sides. What's it? It is also
Okay. Okay. Forrest. Okay. But I
wanted to have at least something over a forest system
and this is some early work I did with dr
win and trying to validate some of these models over
forested. Manager's managed forested plots. And this is
using our carbon enabled version of the model. So
we're uh huh. Kind of evaluating carbon assimilation patterns
, latent heat flux in correlation with L. A
. And we were just talking about how to reinvigorate
this this particular course of research and create some dense
time series over some sites in north Carolina. This
is an example of this data mining sharpener that I
mentioned. It's a very nice tool, thermal imagery
is typically collected at course or resolution than shortwave bands
on the same satellite platform just because of the wavelength
effect on the sensors. So we thermal people were
always stuck with kind of crappy looking images like this
. Whereas the shortwave guys uh like these nice crisp
images and we get jealous. So we want to
find a way to improve the resolution of this to
match that of the shortwave bands. Doctor go in
. Our lab has has come up with this regression
tree approach where you throw all the band data into
a regression tree uses the short wave data to sharpen
up the The thermal image. So we're doing everything
at 30 m resolution. And we've tested over a
number of sites. We talked earlier about testing this
over some urban sites today but it does a good
job at recovering shadows and field boundaries. So that's
that's just kind of a data enhancement tool. So
what I've talked about so far is applying these different
modeling tools, the data fusion, the sharpening tool
, the et modeling to a number of different satellite
as assets To make these maps of water use at
30 m resolution. We're also doing work and this
is more work done by Fungal in my lab on
mapping crop technology at this same spatial resolution crowd phrenology
is a really useful way to interpret some of the
water use curves. We're seeing, we want to
see the water use turn on as the crops uh
emerge. And we want to know when the most
sensitive stage of crop growth is to moisture deficiencies.
If we want to do good yield estimates. So
I'm just gonna go through this quickly. We use
the same star FM algorithm but applied to the shortwave
reflect INce's and get time series of some kind of
vegetation index to which we can kind of fit uh
a curve. And in our case we're using a
time set algorithm. So here's star FM. Applied
to reflect ince's same thing, we use modus lands
that image pairs then apply star FM two Modis imagery
. When we don't have Lance at data to predict
the full time series Of the reflect ince's at 30
m resolution. So this is just a time series
of tv. Using landscape modus fusion over this area
. In central Iowa, we can pick out some
different land use classes and look at the difference in
the phonological evolution in these different classes, comparing.
So these little these are land set and these little
stars are lancet modest fusion to really kind of fill
out the the time series, fit our curve for
the different classes and pick out these Fiona logical metrics
that will be useful for other analyses. If we
do that spatially over central Iowa, we can see
the forests are emerging first. They're usually occurring in
right period areas along the river systems in this landscape
, corn is planted, first emerges first, soybean
emerges a little bit later and we can map out
for example, start of season. The next,
the next part of this talk is how do we
combine this water use information with this phrenology information two
for agricultural applications including yield estimation. Oh that's what
this slide is showing. We're combining these two to
eat data streams. Um to look at the stress
on yield. Now this is a drought index that
we've created in our lab. It's called the evaporative
stress index. Or see and there are drought in
this. I don't know how many different drought indices
. There are hundreds of different drought indices there for
you to choose from associated with every component of the
hydrologic budget you can think of. There are precipitation
anomalies, the standardized precipitation in disease. Those are
the most commonly used. There are anomalies and soil
moisture anomalies in stream flow anomalies and groundwater table.
This is based on anomalies in E. T.
And we think that this is a pretty good indicator
of vegetation health because it's really directly sampling changes in
the transpiration flux uh from this year to prior years
, a really good indicator of physiological functioning of the
vegetative canopy, even more directly related than anomalies in
the precipitation inputs itself. So here we're looking at
anomalies in this normalized et ratio et over pt the
red indicates areas where it was anomalous li lo in
this case for a three month period ending in july
of uh huh 2012. And this was in fact
the heart Of the area in the corn belt that
was affected by a flash drought in 2012. So
significant depletion of the soil moisture reserves in this area
. And this drought came on very very rapidly.
So they call it a flash drought event. And
this is happening more and more frequently in different parts
of the world. So people really want advanced notice
of these flash drought events as early as possible.
So you can adjust your decision making process. So
this is how it played out in 2012. This
is the U. S. Drought monitor. I'm
sure you've all seen the U. S. Drought
monitor maps. Its well publicized their updated every seven
days. It's kind of a subjective uh combination of
a number of different drought indicators indicating drought severity.
This is our C. E. Based anomaly map
and this is veg dry. This is a drought
index that's widely used. It's based mostly on anomalies
and ndvi I so it's tracking anomalies in the green
vegetation cover amount. So we're going through 2012.
We saw something going on in in the Central US
back in May and R. E. S.
I. That wasn't really being reported in other drought
and disease. As little concern that we had an
algorithm problem in our our model but as time evolved
we saw that same area just getting stronger and stronger
and finally in july and in august the other indicators
kind of caught up. This is a drought that
was driven not only by lower than normal precept but
also a lingering heat waves, very hot temperatures baking
the moisture out of the soil more rapidly than usual
. And some kind of windy periods also exacerbating the
evaporative losses. So we were sensitive that sensitive to
that fairly early on in this E. T.
Based index. Um So the theory here is you
may see a signal of impending crop stress first in
one of these thermal based retrievals. The canopy temperatures
are elevating. You're seeing the stress and thermal ban
before you see physical degradation of the green canopy that
might be conveyed by NdVI I So we're thinking there
may be some early warning potential associated with its index
. These are observations that were collected by observers on
the ground in every county, qualitatively assessing what the
moisture status was in every county. And and those
observers were seeing something going on in May, but
it wasn't being reflected in the precept or the vegetation
index. And that's exactly where the hit. The
biggest hits in the corn yields occurred that year.
So we've been working with Dave johnson, he's another
member of the science team, Lance, that science
team works with the National Egg Statistics Service. He
has to put out estimates of corn yield starting in
august every year. And then he updates that every
couple uh weeks as as more information comes in,
we're thinking we may have some early warning potential of
yield hits in this thermal based ET index. So
we're looking at correlations for different corn growing states in
this case with c distributions in day 105 for example
. And how that correlated with at harvest yield anomalies
and for all the different corn growing states. And
we find that the maximum correlations are occurring about when
he needs to put his first yield estimates out.
So that's a good thing. There are some states
that don't behave nicely North Dakota Minnesota. Well those
were some of those states where we have all these
little wetlands sitting around corrupting our course resolution stress signal
. So this was all done with a 10 km
map. We're hoping once we can get this data
fusion algorithm running more operationally, we can use it
to spatially desegregate these core stress signals down to the
field scale where we can really isolate corn and soybeans
from, from these water bodies are forest uh sites
. And make these okay, make these maps at
much finer spatial resolution. So this is where we're
going with this right now. I'll just skip over
this. So this map is being put out operationally
over the Continental United States at our website right now
. I just turned it on for this season last
week but we're trying to add additional tabs onto this
website covering some different geographic locations. And I'll just
briefly talk about some of the areas other areas we're
working on. So this is the coverage of goes
east and goes west, our noah satellites, weather
satellites. So we're stitching together east and west over
Coronas. We're also expanding to north America. We
hope to turn on that tab this season. And
also we got a very, very nice view of
south America with goes east. So these were some
of our first areas. So that's an example of
the product will be putting out over north America because
droughts don't end at the border, they continue into
Canada into Mexico. And this is an example of
a first E. T. Map. This is
clear sky et over south America generated at 10 kilometers
resolution. I had a scientist visiting our lab for
a year from Embrapa, the Brazilian agricultural research System
and he's been helping me kind of evaluate these maps
over brazil and South America. We can make the
same time series of maps here and look and see
if we see some of the there was a major
drought in the amazon In the western Amazon in 2005
That's showing up here even bigger one in 2010 and
a very lingering drought in northeast brazil that was really
impacting the local communities. They had to bring in
some water trucks to supply the water needs. It
lasted for a long time. There's another one turning
on down here in Southeast brazil right now. We
also see some flooding impacts, big floods in the
Amazon in 2009 And one in 2012. So we're
writing some papers an evaluation of this Brazilian, uh
huh drought map. We've done some yield estimation,
correlation studies and some of the the crop growing states
in brazil, we have to be very careful of
trying to mask out forested lands. I'm sorry,
but we got to get rid of those forest pixels
in this particular application because especially in the amazon,
those trees are really deep rooted and they're very resilient
to drought impacts compared to the shorter rooted agricultural crops
. So it's going to just ruin our signal.
If we have all those forested pixels in there,
we've done the best we can. We need to
do better job at doing this mask and we need
to do this at high spatial resolution as well because
our initial results, are there okay. Work in
some states, not so well in other states were
tracking down what the causes of this. This problem
is, but I think land use masking and going
to higher spatial resolution is going to be the key
here. So soybean, clearly a big export from
brazil. We looked at some other crops as well
, other parts of the world. So these are
geo stationary satellites operated by the european Union called the
Media's satellites. And with the operational satellite, we
get a really good view of the full African continent
and we get a more oblique view of europe.
We've been looking at both these areas. This is
a three kilometer resolution maps supported by mediaset over North
africa. Lots of nice spatial detail in this map
. We've done a lot of research over the Nile
River basin. I don't know if this is going
to work. So this is kind of a promo
video that Nasa put together for this project comparing a
lot of different data sources, trim precipitation distribution over
the Nile basin. So satellite dr precipitation maps over
the course of a year and you can see the
pulsing of the the rains during the dry season.
So this is Alexey. Drived evaporate transpiration over the
same time period. You can see the greening moving
through the Ethiopian Highlands and then receding southward. This
is a retrieve soil moisture. This is from a
hydrologic model. You can see some of the problems
in their inputs. This is a different soil type
down here which is putting this weird discontinuity and V
. C. The green up in this map.
And there may be one other thing. So this
is the grace. This is groundwater storage retrieved from
a satellite that measures gravitational anomalies. We're trying to
piece all these different satellite based indicators together to see
if we can do what water balance just using satellite
data. So this is the focus area of of
the Nile study. This is the comparison. That's
another place where we've compared energy balance versus water balance
estimates. So on the left is an energy balance
model on the right as the water balance model.
And if you look from a distance, the broad
patterns are very similar. But there are some very
important differences were capturing the intense irrigation in the Nile
river delta that is not captured in this water balance
. That doesn't really know where the irrigation is occurring
. And this is another irrigation scheme in Sudan.
The Gezira scheme that we're seeing enhanced flux is missed
here. This is the sued wetland in which is
a really major sink of water along the white Nile
before it reaches up with the blue Nile. Really
important to quantify on a basin scale. All these
different extractions and water use, very limited information available
about this on the ground. We can get kind
of an objective use of this water use situation using
the satellite data, but we really have to be
sensitive to some of these non standard moisture sources and
sinks that are difficult to capture a priority. A
map of the famine, drought impacted area in the
Horn of Africa in 2011 shows up in the fetal
anomalies and I just gave this presentation in the Czech
Republic. So I had to throw in some stuff
about europe. I'm not going to show you this
this uh movie. So I was in the Czech
Republic. This is a really interesting example of why
we need land set in some, some kinds of
landscapes. So this is the Czech Austrian border here
and this is a glance at pixel size and this
is the modus pixel size. So you can see
that there's a big kind of change in land use
patterns right at that border. Well this, the
Czech Republic was under a kind of a socialized farming
regime For a number of years, the field sizes
have been consolidated into bigger fields, whereas in Austria
, the fields are just progressively chopped into smaller and
smaller parts through inheritance. So we certainly need land
set down in Austria. We may get away with
motive scale evaluations in in the Czech Republic. And
this is just a demonstration of our first drought maps
that we just produced about a month ago over europe
using the media set satellites. The next step of
course, we've got to got to tackle the whole
globe. We're doing this with a modus satellite.
We're using modus in a geo stationary mode. In
this case modus gives a stay night temperature differences.
Were using kind of a time differential temperature measurement from
a single satellite in this case to map the evaporative
flux is over the whole globe. And we want
to put out these drought indices at the global scale
as well, which we can compare back to geo
stationary satellites. So I just have time sequences of
these two different satellite retrievals and randy. I'm I'm
going to wrap up with these closing thoughts about the
utility of satellite based E. T. Retrievals and
the importance of land surface temperature in in making these
estimations over certain types of landscapes and for certain types
of applications. So thank you for for listening so
well and staying away and I appreciate that. Mhm
. We don't have time for but we'll take a
question yes about story friend who is not working as
a host of Mhm martin. And I remember that
this a family physical methods that fuses now if we're
talking and which is pretty regular for talking about the
fact that we're talking about irrigation, which we don't
you to say cloud doesn't that mean? For example
you'll also be seeing it down the process of reducing
administration because of the house. We can't do a
direct retrieval using under cloud covered areas. So we're
only using estimates of the clear sky evaporative flux is
so we need to do more testing of this under
kind of mixed sky uh conditions. But where we're
getting the actual Lance that data and the actual motive
retrieval is under clear sky conditions. And then we
have kind of a cloudy sky gap filling algorithm that
we have to superimpose on this. So great.
Well, yes, it would it would the gap
filling algorithm. We have to estimate evaporate transpiration under
cloudy conditions. So we would we would we know
that the reduced we know we can quantify the reduced
radiation load in those cases. So we can we
can retrieve that. And the center pivot irrigation here
. You got moisture. Oh, it can it
can affect your potential et estimates. So if you're
trying to estimate the potentially t over center irrigated pivot
with meteorological data, that's not inside the pivot,
it's going to estimate uh drier conditions in a larger
pt than you actually realize in that field because of
this uh self humidifier thing situation. So that is
that is something that we have to take into account
if we have extended irrigation areas irrigated areas. If
we have little isolated pivots here or there, it's
not going to make such a big difference. But
if you have a whole huge irrigation district, then
you have to modify your P. T. Estimates
over those areas. But it won't affect the direct
retrieval of the evaporative flux which is obtained through energy
balance rather than atmospheric kind of moisture exchange principles.
Yes. About water sources. There's never come.
Who is using? Okay. I will comment on
who's using. Not my model estimates, but somebody
else's model estimates who's who's working in Idaho. And
it's uh one big use in the United States is
water rights management. So in the Western U.
S. You have your water rights, you can
pump up into those water rights, you can't exceed
the water rights and you have to pump a certain
amount of water every year. Otherwise you forfeit your
right. So there's just a huge pressure to monitor
water use at scales of individual fields. And they
used to do this based on electrical records, the
pump electricity. And they would empirically relate that to
water pumps. Now they're doing it with satellite E
. T. So you can identify users who are
pumping more than their allocated allotment and go after those
people. And you can also use this information to
negotiate water rights, trades and transfers interstate water compacts
. It's being used. Satellite T retrieval is being
used in the courts right now to negotiate interstate water
compacts and some disputes about water uses. Or if
you need to establish a curtailment of irrigation in Syria
. So if it's in a drought time, you
need to supply the senior water rights, the junior
water rights holders may be curtailed and they use this
information to make those decisions as well. So those
are some kind of legalistic applications. Were using it
more in terms of crop stress identification and yield yield
estimations, drought monitoring notice in in Arkansas, there
was some difference in terms of agreement. You mentioned
Minnesota and some of these areas of career. Yeah
. How does that take positive model? Deal with
the underlying substrate? Say if you have a shallow
parcel quick. Yeah, in Arkansas is a special
case because Arkansas, most the corn is grown under
irrigation. And so we're not just, we're just
not seeing a lot of variability in Arkansas and it's
not really related to the climate. So I think
that's what's going in Arkansas, but hopefully this isn't
, this is another area where these techniques might be
useful if there is some hidden moisture source that we
don't know about a priority. The land surface temperature
should be giving us a clue that that moisture is
available. So I think I think we should be
able to pick this up. Now. My my
husband is a forest hydrologist for the Forest Service.
He's working on groundwater impacts on forestry. He's really
interested in identifying forest patches that are being influenced by
by groundwater systems. So I think we want to
look spatially and see here's a strangely behaving patch of
forest or something sitting over a cursed substrate, it's
wetter than we expect. And, and uh,
and see if we can explain why that is.
And that's why I think doing these comparisons is so
, so useful because these strange areas just pop out
regionally. Thanks for together. Thank you. Thank
you all