Increasing Remotely Sensed Snow Cover through Composite AMSRE/MODIS Product
By
Charley Follett
An Undergraduate Thesis
Submitted in Partial Fulfillment for the Requirements of
Bachelor of Arts
in
Geography and Earth Science
Carthage College
Kenosha, WI
April, 2010
‘
Increasing MODIS Snow Cover through Composite
AMSR-E/MODIS Product
Charley Follett
Abstract
Accurate mapping of snow cover in mountainous terrain is extremely important, as snowmelt is a major source of water storage and runoff. Therefore, remote sensing of snow cover has
wide applications, especially in mountainous terrain where recording ground station data is
difficult. Both MODIS and AMSR-E satellite instruments can be used to study snow cover, with
the possibility of a composite AMSR-E and MODIS snow cover product increasing accuracy of
remote sensing based snow cover. As each instrument complements the other, as AMSR-E can
detect through clouds whereas MODIS cannot, and the resolution of MODIS is superior to
AMSR-E. The focus is in the study area of the Colorado Headwaters region during the month of
January, 2006.
2
Table of Contents
List of Figures……………………………………………………………………………..4
Lists of Tables……………………………………………………………………………...4
Literature Review………………………………………………………………….............5
Introduction.....................................................................................................….......5
Background……………………..……………………………………………….….5
Hypothesis…………………………………………………………………………..……...13
Methods.………….…………………………………………………………………….…..14
Study Period...................…………………………………………………………...14
Study Area………………………….. ……….………………………………….....14
Acquisition…………………………………………………………………………15
Processing……………………………...………………………………...………….17
Results………………………………………………………………………………….........20
Discussion…………………………………………………………………………….........25
Future Research…………………………………………………………………....25
Acknowledgements..............................................................................................................27
Works Cited..........................................................................................................................28
3
List of Figures
Figure 1: Map of study area, shown by red outline
14
Figure 2: AMSR-E and MODIS overlay
18
Figure 3: 2006 January 1st composite AMSR-E and MODIS map.
19
Figure 4: January 8th MODIS snow cover
20
Figure 5: January 8th composite snow cover
20
Figure 6: Chart of accuracy between MODIS and Composite product
21
Figure 7: January 5th snow cover.
22
List of Tables
Table 1: MODIS integer values, from National Snow and Ice Data Center
16
Table 2: Percentage of remotely sensed snow and land cover
21
Table 3: Percentages of snow, land, and cloud cover
22
Table 4: Paired T test of means.
23
4
Introduction
Remote sensing of snow cover has wide applications, especially in mountainous terrain
when ground station data is sparse. Remote sensing of snow cover allows for reliable, continuous
study without the need for coverage to be interpolated through techniques such as isohyetal
mapping. Both the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced
Microwave Scanning Radiometer - Earth Observing System (AMSR-E) satellite instruments can
be used to remotely sense snow cover. Composite snow cover products that combine the AMSRE and MODIS instruments have been developed by researchers and shown to increase accuracy
over using either instrument alone. The purpose of this thesis was to compare a composite
AMSR-E and MODIS product with the cloud removal algorithm against MODIS snow cover
product to see if accuracy is improved as a result.
Background
Accurate mapping of snow cover in mountainous terrain is extremely important, as it
contributes to modeling of snow-melt, which is a major source of water storage and runoff in
many watersheds such as the Colorado River. Data relating to water storage change is relevant to
agricultural interests, reservoir operators, and other agencies and corporations that manage water
resources. The high albedo reflectance of snow also makes it an important factor in climate
change studies and energy budget equations (Foster et al. 2005). Streamflow is statistically
linked to snow cover (Yang et al. 2007), thus snow cover is a vital factor to be investigated when
5
researching areas where snow cover is extensive and hydrologically significant. The Colorado
Headwaters meets this criterion, as snowmelt in the sub-basin is the leading contributor to the
Colorado River (Short, 2010). Remote sensing is an excellent source that has been used to map
snow cover, and the introduction of new satellite instruments and techniques increase our ability
to map snow cover with each passing year. Through developing a composite snow cover
mapping method, which uses both MODIS and AMSR-E snow cover products, this proposal
aimed to increase accuracy of mapped snow cover and provide valuable information to water
managers and other users of the data.
Remote sensing provides valuable tools for looking at snow-covered area (SCA) and
other hydrological factors, such as snow water equivalent (SWE). SCA refers to the geographical
extent of snow cover, while SWE is an estimate of how much water would be obtained from a
melting snowpack. Remote sensing offers continual, uniform, and reliable data over large areas,
and can be used to complement other sources of data such as ground observation networks.
Ground based stations can provide continuous and reliable information about snow cover and
other factors for a specific location, but in mountainous terrain, these values (ex. SWE) may vary
widely due to the complex terrain. In addition, other difficulties arise from inconsistencies
between ground stations, and accessibility issues in maintaining a network of ground stations in
mountainous areas. Remote sensing fills the gaps left by ground station data and can provide
among other things percent snow cover over a mountainous region, which is often not possible
using ground station data. Snowpack Telemetry (SNOTEL) stations record long-term data (i.e.
SWE, temperature, precipitation) which can be statistically analyzed against remote sensing
measurements for validation. The SNOTEL data network has been in operation from the 1970s,
6
and provides data at high elevations and in rugged terrain, where validation of remote sensing
data is most useful.
Besides academia, SCA estimates obtained from remote sensing are used extensively in
the private and government sectors. Organizations such as the National Oceanic and
Atmospheric Administration provide real-time snow cover and snow water equivalency maps
produced using remotely sensed data to the public. These maps are interactive, and allow casual
users to explore regions with only a few clicks. In the private sector, news corporations use these
maps for weather forecasts and visualizations. Communities can use remote sensing snow
products to make decisions regarding water resources and agricultural activity, such as irrigation.
Remote sensing instruments collect data about the earth by measuring electromagnetic
radiation emitted or reflected by the earth’s surface. Remote sensing instruments must collect
data from “windows” in the electromagnetic spectrum where the Earth’s atmosphere does not
interfere (Campbell, 1996). This limits remote sensing to using the visible, infrared, and
microwave radiation portions of the electromagnetic spectrum. Remote sensing is either passive,
or active. Passive remote sensing refers to detect natural radiation emitted or reflected by the
earth’s surface, whereas active remote sensing refers to instruments sending their own energy to
the earth and detecting how it changes when it is reflected back to the remote sensing instrument.
Passive remote sensing in the visible and infrared spectrums measures the reflectance of solar
radiation by the earth’s surface. The wavelength spectrums have different properties and
applications; visible radiation can penetrate water, infrared radiation is extremely useful for landuse and vegetation differentiation, and microwave radiation is not attenuated by the passage
through the atmosphere and can be used at night because there is no dependence on reflected
solar radiation (Campbell, 1996). A multi-spectral scanner uses both the visible and infrared
7
spectrum to measure several bands, or ranges of radiation wavelength. Everything has its own
spectral signature; a field of corn has different reflectivity when it is planted, when it is growing,
and when it is harvested. Multi-spectral scanners are sensitive enough to detect such differences
in reflectivity, which adds to the type of analysis that can be done. For instance, bands can be
combined into a single image, or selected to emphasize differences in reflectivity due to
wavelength.
A multi-spectral instrument, the Moderate Resolution Imaging Spectroradiometer
(MODIS) sensor on the Terra satellite provides global snow and ice products at a 500 meter
resolution on a daily basis, with each swath measuring 2330 kilometers. With a high radiometric
sensitivity across 36 spectral bands, MODIS also has a high temporal cycle of 1-2 days globally.
The MODIS instrument uses both visible and infrared spectral bands. This resolution is excellent
at both regional and more local scales. Applications of MODIS data range from measuring
surface temperature, to measuring global vegetation and snow cover. Snow cover has high
reflectance in the visible spectrum and low reflectance in the short-wave infrared spectrum. High
reflection in the visible spectrum is due to albedo, or the measure of how strongly something
reflects light from light sources. If an object or surface is very white, albedo is high, if it is
darker, albedo will be low, thus snow’s high reflectance in the visible spectrum. Reflectance in
the infrared spectrum is a function of energy emitted.
The algorithm used to create the MODIS snow cover products contains several
enhancements designed to increase the accuracy of the SCA estimates. To prevent snow cover in
forests from being misread, the Normalized Difference Vegetation Index (NDVI) (insert citation)
is calculated to help determine differences between snow-covered areas and snow-free forests
(Yang et al. 2007). Other criteria, related to the reflection of snow in the different spectral bands
8
are used to allow for the discrimination of snow from water and clouds (Yang et al. 2007).
Comparisons of MODIS data and SNOTEL station observations of SWE show high statistical
agreement when cloud-cover does not interfere with the viewing of snow cover from the MODIS
instrument (Klein, 2003). MODIS has a nominal resolution of 500 meters, so sometimes a
MODIS pixel will show that there is snow cover when a SNOTEL station disagrees, because the
pixel is averaged across 500 meters whereas the station detects snow in its immediate area.
Because the MODIS instrument cannot detect snow cover under clouds it is ineffective for the
mapping of SCA under these conditions. Other sensors exist that can detect SCA through cloud
cover, for example those that utilize the microwave portion of the spectrum (i.e. AMSR-E).
The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)
instrument on the Aqua satellite provides passive microwave measurements of snow cover at a
global scale. The sensitivity of the sensor allows it to penetrate cloud-cover and differentiate
between snow, water, ice, and other type of land cover. AMSR-E goes a step further than other
SCA products and can be used to estimate SWE as well. However, the spatial resolution of
AMSR-E products is 25 kilometers, which is very coarse when compared to other. Even so, the
AMSR-E spatial resolution is a major improvement over previous sensors, such as the Special
Sensor Microwave/Imager (SSM/I) instrument. The SSM/I instrument used four frequencies;
AMSR-E uses six, and therefore has a wider range of the microwave spectrum to use for
analysis. Microwave instruments have coarser resolution compared to visible and infrared
spectrum instruments due to physical limitations. Microwave radiation has a longer wavelength
than visible and infrared radiation. An unrealistically sized antenna would be required to obtain
better spatial resolution, and is not possible with current technology. For vast areas such as the
ocean a low resolution is not a serious issue, but in heterogeneous terrain such as mountainous
9
areas, it may present problems. Therefore, AMSR-E snow cover would be well suited for a
supplementary role, but the resolution is too poor and insurmountable to be the primary
instrument used for studying snow cover.
Sanjay et al. 2008 found that that there was good agreement between MODIS derived
SCA data and ground station data in mountainous terrain and concluded that the MODIS data is
can be used as an effective estimator of SCA in the Himalayas region (Sanjay et al. 2008)
.However, the rugged terrain in the area contributed to a mountain shadowing effect (caused by
the low angle of solar illumination in complex terrain, not to be confused with a mountain rain
shadow effect) leading to a less accurate reading of snow-cover. Visible spectrum cannot be
used during nighttime hours, so shadowing will decrease their effectiveness as well. The study
also compared SCA estimates derived from other sensors as well and found that MODIS
outperformed the other sensors at detecting snow in areas where shadowing was a problem
(2008). In a study using MODIS SCA data to examine the relationship between snow cover in
the Tibetan plateau and the East Asian Summer Monsoon, Che Tao and Li Xi found that the
MODIS snow cover product was useful and accurate when compared against ground station data
(Che et al. 2004).
In the paper titled, “Snow depth estimation over north-western Indian Himalayas using
AMSR-E”, Indrani Das and R. N. Sarwade found that AMSR-E was suitable for use in the
Himalayas, although snow depth was off by an average error of 20.34 cm due to the coarse
spatial resolution of AMSR-E (Sarwade et al. 2003). However, the errors were not related to the
question of whether snow was detected by the sensor, but were instead related to the snow depth
estimates derived by the sensor. A few studies have used AMSR-E solely to derive estimates of
SCA, but found that the coarse spatial resolution of the data hindered its use even though it had
10
the ability to penetrate cloud-cover (Comiso, 2003). As a result, there have been numerous
studies that have used combined products of AMSR-E and other high resolution sensors such as
MODIS to map snow cover.
MODIS and AMSR-E composite products have been developed to take into account the
advantages of each sensor to provide a more complete SCA product. This concept is not new and
has been applied in the past using MODIS and SSM/I data. This combination yielded more
accurate estimates of SCA than the use of each product alone. AMSR-E, however, is a much
more powerful microwave sensor than SSM/I and has produced even stronger results (Tait et al.
2000). AMSR-E has the ability to penetrate cloud-cover, and also can penetrate the atmosphere
without losing accuracy. As a supplement to the high resolution MODIS snow product, AMSRE has been found to significantly increase the accuracy of SCA estimate (Wang et al. 2008).
Using a combined MODIS and AMSR-E product suppresses cloud cover effects while still
providing the user with a relatively fine spatial resolution (2008). When validated against
ground stations, the combined MODIS and ASMR-E product shows high agreement with surface
observations, but the accuracy drops when the snow cover is shallow or the snow distribution is
scattered spatially (2008). The mountain-shadow effect encountered in previous studies that used
MODIS to map SCA might also be decreased by using AMSR-E data, which operates the same
regardless of the brightness.
A combined MODIS and ASMR-E SCA product has been found to be more accurate than
when either product is used alone, but the integration of cloud cover removal may increase the
accuracy even further. Relatively few studies have utilized cloud removal algorithms in relation
to MODIS data, but those that have report excellent results (Zhengming 2008). Cloud removal
techniques are new and relatively unexplored, and a delicate balance between predicting the
11
presence of snow under clouds and maintaining accuracy must be preserved (Bardossy 2009).
Using a combined MODIS and AMSR-E product enhanced with an additional cloud cover
removal technique could make for an extremely powerful and accurate snow cover product in
mountainous regions.
The goal of this proposal is to see if accuracy is increased and use tools and techniques
that are replicable without esoteric features. Future research may include refinement of
techniques used and porting of scripts to other GIS and remote sensing programs, such as
IDRISI, ERDAS, or GRASS.
12
Hypothesis
The composite AMSR-E and MODIS snow cover product was designed to test the
following hypothesis:
Null Hypothesis: The snow and land cover detected with the composite snow cover product and
the standard MODIS snow cover product is not significantly different.
Alternate Hypothesis 1: The composite AMSR-E and MODIS product has significantly higher
snow and land cover detected than the standard MODIS snow cover product.
13
Methods:
Study Period:
MODIS and AMSR-E snow cover products are both available as far back as 2003. This
proposal will focus on the winter regime, specifically, January 2006. MODIS snow cover
products are available daily, 8-day composite, or monthly, and AMSR-E is available daily, 5-day
composite, or monthly. Therefore, the composite products will be built using daily snow cover
products of both instruments. Because the concern of this proposal is a proof of concept, and not
determining long term trends in the region, the study period is January 1st through January 8th,
2006.
Study Area:
The Colorado River provides water resources for seven states and Mexico, and
understanding changes in the water availability is essential to people and interested within the
region. 75 percent of the total water within the Colorado River watershed originates as snow in
the Rocky Mountains (Short 2010).
14
Figure 1: Map of study area, shown by red outline
Acquisition
Data from both the AMSR-E and MODIS instruments will be obtained from the Earth
Observing System Data and Information System (EOSDIS), using python scripting to
automatically download data and subset it to the study area. Although the MODIS instrument is
located on the Terra satellite, and the AMSR-E instrument is located on the Aqua satellite, snow
products from both instruments can be subset to the study area extent while being acquired. This
is done using a vector outline of the study area, and then processing the imported HDF files to
have pixels completely within the vector selected and subset.
15
Both AMSR-E and MODIS data utilized are level 3 in terms of processing, which
ensures that necessary adjustments have already been made to the datasets. MODIS derived
snow coverage contains cells with a range of values that indicate either snow, clouds, land,
water, or no data(indicating an error). In comparison, AMSR-E SWE cell values from 1 to 240
indicate millimeters of snow water equivalent. Therefore, any value between 1 and 240 indicates
presence of snow and is considered an indicator of snow cover, and 0 indicates a lack of snow
cover. Values above 240mm are used to indicate parameters besides SWE, such as detection of a
lake or other large body of water, and should be disregarded. Figure 2 shows what each value in
a MODIS HDF snow cover file corresponds to.
16
Daily MODIS Snow
Cover Coded
Integer Values
Sample Explanation
Value
0
Data
missing
1
No decision
11
Night
25
No snow
37
Lake
39
Ocean
50
Cloud
100
Lake ice
200
Snow
254
Detector
saturated
255
Fill
Table 1: MODIS integer values, from National Snow and Ice Data Center
Processing
Using python scripting within ArcGIS, the following steps will be automated. The
ArcGIS product suite allows for extensive scripting capabilities and includes a “Model-Builder”
tool that makes it easy for users without programming experience to automate tasks within
ArcGIS. One script was devoted to automatically creating the composite product. After
processing an appropriate amount of combined AMSR-E and MODIS snow cover products, the
resulting product was compared to MODIS snow cover product without AMSR-E.
17
The script involved several steps of processing. First, both MODIS and AMSR-E data
must be projected in the same coordinate system. To interpolate MODIS images, the HDF file
was converted into GRID format at 500m resolution. Then, the cell values were classified, using
the metadata and documentation, so that the MODIS image is classified according to snow,
cloud, and no snow.
Next, the AMSR-E data was converted into grid format. Using the Mask tool, AMSR-E
pixels (which are much larger than MODIS pixels due to the coarser resolution) that are
completely within MODIS detected cloud cover are selected. If an AMSR-E pixel is not
completely within MODIS detected cloud cover, it will not be selected or used in the composite
product.
Originally during the design of scripting and the project, analysis was intended to include
the use of a cloud removal algorithm, and so the replacement of MODIS cloud cover with
AMSR-E was designed to allow for that, so that the tolerance for non-cloud cover pixels was
very low. Any MODIS pixels that are not cloud cover stop AMSR-E from replacing nearby
cloud cover pixels.
If, however, an AMSR-E pixel is over an area that is completely cloud cover, AMSR-E
replaces that pixel. Because AMSR-E reads through clouds, the encapsulated pixel will represent
either snow or land cover. It is possible for AMSR-E swaths to have errors, but no AMSR-E
swaths in the study had errors within the study area. When a pixel of AMSR-E is within MODIS
cloud cover, the AMSR-E pixel is resampled using Nearest Neighbor analysis to match the 500m
resolution of MODIS. Raster operations use the values of selected AMSR-E to replace the
18
MODIS cloud cover detected. Afterwards, using raster operations the values are combined into a
single composite image. The result is a composite MODIS and AMSR-E product.
Following the creation of composite MODIS and AMSR-E product, statistical testing was
done to see if there is a significant replacement of cloud cover with AMSR-E sensed land or
snow cover.
Figure 2: AMSR-E and MODIS overlay. For the pixels in red, cloud cover was detected. The outline of AMSR-E
pixels can be seen as well. The bottom pixels are mostly completely within the cloud cover and would be replaced
with AMSR-E.
19
Results
Visualization
Below are shown several maps of the composite imagery produced, as well as maps
showing the non-composite imagery.
Figure 3: 2006 January 1st composite AMSR-E and MODIS map.
The next two images compare MODIS snow cover product and a composite snow cover
product over the study area. Visually, it appears as if a dramatic increase in snow cover has
occurred, but the table that follows shows that statistically, the gains are negligible.
20
Figure 4: January 8th MODIS snow cover
Figure 5: January 8th composite snow cover
21
Below in figure 6 is a chart measuring the snow and land cover remotely sensed by MODIS, as
well as the snow and land cover found with the composite product. The most noticeable increase is with
January 1st, with a 10% aggregate increase in snow and land cover sensed, as shown by Table 2.
Percentage of remotely sensed snow and land
120%
100%
80%
60%
MODIS
Composite
40%
20%
0%
1
2
3
4
5
6
7
8
January 1st - January 8th
Figure 6: Chart of accuracy between MODIS and Composite product
1-Jan
2-Jan
3-Jan
4-Jan
5-Jan
6-Jan
7-Jan
8-Jan
MODIS
Composite
29.08% 39.39%
63.66% 63.66%
97.22% 97.88%
53.12% 53.12%
95.24% 95.24%
83.22% 83.32%
77.67% 78.08%
65.23% 66.16%
Table 2: Percentage of remotely sensed snow and land cover
22
MODIS
Snow
Land
Cloud
Composite
Snow
Land
Cloud
Replacement?
1-Jan
2-Jan
3-Jan
4-Jan
5-Jan
6-Jan
7-Jan
8-Jan
19.61%
9.47%
70.92%
1.20%
62.46%
36.34%
4.40%
92.82%
2.78%
2.80%
50.33%
46.88%
8.68%
86.56%
4.76%
3.68%
79.54%
16.78%
5.39%
72.28%
22.33%
2.68%
62.55%
34.77%
22.36%
1.20%
4.61%
2.80%
8.68%
3.74%
5.59%
3.51%
17.02% 62.46% 93.27% 50.33% 86.56% 79.58% 72.49% 62.65%
60.61% 36.34%
2.12% 46.88%
4.76% 16.68% 21.92% 33.84%
Yes
No
Yes
No
No
Yes
Yes
Yes
Table 3: Percentages of snow, land, and cloud cover between MODIS and composite imagery.
The above table measures the day to day changes in snow cover and land cover by the MODIS
sensor as well as the composite product. There were several days (January 2nd, 4th, and 5th) that AMSR-E
could not be used to replace cloud cover. On January 2nd, for instance, AMSR-E coverage did not include
the study area. On January 5th, shown in Figure 7, no pixels were replaced due to lack of cloud cover.
Figure 7: January 5th snow cover. No pixels could be replaced.
23
Paired T for Composite versus MODIS
N Mean StDev SE Mean
Composite 8 0.7210 0.2035 0.0719
MODIS
8 0.7056 0.2278 0.0805
Difference 8 0.0155 0.0355 0.0126
95% lower bound for mean difference: -0.0083
T-Test of mean difference = 0 (vs > 0):
T-Value = 1.23 P-Value = 0.129
Table 4: Paired T test of means.
Table 4 shows the statistical testing that was done at a significance level of .05 using a paired t
test in MiniTab, suggesting that there was not a significant increase in accuracy through the creation of
the composite product.
24
Discussion
Originally this research was intended to answer two independent hypotheses, with the first
regarding the efficacy of a composite product compared to a standard MODIS snow cover product, and
the second intended to be a comparison of said composite product against a snow cover product using a
cloud removal algorithm. The second hypothesis proved too difficult to achieve with the tools available
with multiple errors prohibiting a statistical analysis of results, leading to a shift towards testing the
composite product alone and maintaining the experimental design and hypothesis setup at the beginning
of the experiment.
The results showed that the gains of using the composite product were not significant and suggest
a flaw in methodology and approach, which reflect the challenges of this project. Originally, the
processing of the composite product was meant to make it easier for a cloud removal algorithm to be
applied, which was outside the scope of this undergraduate thesis. This might have affected the efficacy
of the composite product, as a composite product with a lower tolerance for replacing MODIS cloud
cover could have led to a significant level of increased accuracy.
Accepting the null hypothesis does not mean that increasing the accuracy of MODIS with
AMSR-E cannot be done, only that the approach and methodology of this study would need to be reexamined. For instance, the focus was on first 8 days in January of 2006, and AMSR-E could not be used
during several of those days to replace MODIS pixels, because of the coverage of AMSR-E missing the
study area, and for the other days, the lack of cloud cover for AMSR-E to replace. In table 3, it is evident
that January 3rd and 5th have negligible cloud cover. However, January 1st had an almost 10% decrease in
cloud cover due to AMSR-E, and would likely have been even higher if the methodology had been altered
to be more tolerant of non-cloud cover pixels. If the experiment had been set up to test the efficacy of a
composite product when there is a certain amount of cloud cover with an alternate hypothesis suggesting
25
that a composite product significantly increases snow cover of MODIS during periods of high cloud
cover, the design might have shown such a composite product to be significantly effective.
The results of this study do support the conclusion that as a daily product, a composite AMSR-E
and MODIS snow cover product would likely not be a significant improvement.
Future Research
More research with a more robust methodology using the lessons learned from this project would
be needed in order to test a composite AMSR-E and MODIS product with a different approach. Flaws
with the processing steps were realized early on, and alternate methods and tools could be explored. In
addition, the results of January 8th did not seem accurate during verification, suggesting that a bug in the
scripting process may have skewed results.
A follow up project could be an experimental design that does not attempt to implement a cloud
removal algorithm and instead attempts to maximize the potential of a AMSR-E and MODIS composite
product.
26
Acknowledgements
Brian Harshburger, for giving me professional and personal direction and guidance
throughout the last two semesters.
Joy Mast, for being my Senior Seminar advisor, and reminding me of deadlines in her
forceful but extremely polite way.
Wenjie Sun, for being my academic advisor and helping me with remote sensing as well
as helping me attend the AAG conference.
27
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