Remotely Sensed Snow Cover through
An Undergraduate Thesis
Submitted in Partial Fulfillment for the Requirements of
Bachelor of Arts
Geography and Earth Science
Increasing MODIS Snow Cover
Accurate mapping of snow cover in mountainous terrain is extremely important, as snow
melt is a major source of water storage and runoff. Therefore, remote sensing of snow cover has
wide applications, especially in mountainous terrain where recording groun
d station data is
difficult. Both MODIS and AMSR
E satellite instruments can be used to study snow cover,
the possibility of a composite AMSR
E and MODIS snow cover product increasing accuracy of
remote sensing based snow cover
. As each instrument com
plements the other, as AMSR
detect through clouds whereas MODIS cannot, and the resolution of MODIS is superior to
The focus is
in the study area of the Colorado Headwaters region
during the month of
List of Figures
Lists of Tables
List of Figures
Figure 1: Map of study area, shown by red outline
Figure 2: AMSR
E and MODIS overlay
Figure 3: 2006 January 1st composite AMSR
E and MODIS map.
Figure 4: January 8th MODIS snow cover
Figure 5: January 8th composite snow cover
Figure 6: Chart of accuracy between MODIS and Composite product
Figure 7: January 5th snow cover.
List of Tables
Table 1: MODIS integer values, from National Snow and Ice Data
Table 2: Percentage of remotely sensed snow and land cover
Table 3: Percentages of snow, land, and cloud cover
Table 4: Paired T test of means.
of snow cover has wide application
, especially in mountainous terrain
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 isohye
Moderate Resolution Imaging Spectroradiomete
Microwave Scanning Radiometer
satellite instruments can
be used to
omposite snow cover products that comb
ine the AMSR
E and MODIS instruments have been
shown to increase accuracy
over using either instrument alone
ose of this thesis was
to compare a
E and MODIS product with
see if accuracy is improved
as a result
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
The high albedo reflectance of snow also makes it an important factor in climate
change studies and energy budget e
(Foster et al. 2005)
linked to snow cover
(Yang et al. 2007)
snow cover is a vital factor to be investigated when
researching areas where s
now cover is extensive and hydrologically significant
, as snowmelt in the sub
basin is the leading contributor to the
Remote sensing is an excellent
source that has been used
snow cover, and the introduction of new satellite instruments and techn
iques increase our ability
to map sno
w cover with each passing year.
developing a composite
, which uses both
MODIS and AMSR
E snow cover
snow cover and provide valuable
nd other users of the data
Remote sensing provides valuable tools
for looking at snow
factors, such as snow water
refers to t
extent of snow
is an estimate of how
much water would be obtained from a
Remote sensing offers continual, uniform, and reliable data over large areas,
be used to
other sources of data
uch as ground
Ground based stations can provide continuous and reliable information about snow cover and
for a specific location
, but in mountainous
these values (ex. SWE) may vary
dely due to the complex
In addition, other difficulties arise from
und stations, and accessibility issues in maintaining a network of ground stations
. Remote sensing fills the gaps left by ground station data and can prov
among other things percent snow cover over a mountainous region, which is
using ground station data.
(SNOTEL) stations record long
SWE, temperature, precipitation)
which can be statistically analyze
d against remote sensi
measurements for validation. The SNOTEL
has been in operation from the 1970s,
at high elevations and in rugged terrain, where validation of remote sensing
data is most useful.
stimates obtained from
used extensively in
private and government sectors. Organizations such as the National Oceanic and
Atmospheric Administration provide real
time snow cover and snow water equivalen
to the public. These maps are interactive, and allow casual
with only a few clicks. In the private sector, news corporations use these
maps for weather forecasts and visualizations.
Communities can use remote sensing
products to make decisions regarding water resources and agricultural activity
, such as irrigation.
Remote sensing instruments collect data about the earth by measuring electromagnetic
mitted or reflected by the earth’s surface
sing instruments must collect
data from “windows” in the electromagnetic spectrum where the Earth’
s atmosphere does not
This limits remote sensing to using the visible, infrared, and
of the electroma
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 a
nd 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
t properties and
applications; visible radiation can penetrate water, infrared radiation is extremely useful for land
use and vegetation differentiation, and microwave radiation is not attenuated by
through the atmosphere
and can be used at nig
ht because there is no dependence on reflected
both the visible and infrared
spectrum to measure several
f radiation wavelength.
spectral signature; a field of corn has different reflectivity when it is planted, when it is growing,
and when it is harvested.
spectral scanners are sensitive enough to detect such differences
in reflectivity, which adds to the type of analysi
s that can be done.
combined into a single image, or selected to emphasize differences in reflectivity due to
spectral instrument, t
he Moderate Resolution Imaging Spectroradiometer
on the Terra satellite provides
ow and ice products at a 500 meter
resolution on a daily basis, with
each swath measuring
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 has high
reflectance in the visible spectrum and low reflectance in the short
wave infrared spectrum.
reflection in the vi
sible 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. R
the infrared spectrum is a function of energy emitted.
algorithm used to creat
MODIS snow cover products
enhancements designed to increase the accuracy of the SCA
. To prevent snow cover in
forests from bei
Normalized Difference Vegetation Index
is calculated to help determine differences between snow
(Yang et al. 2007)
. Other criteria, related to
in the different
e used to allow for the discrimina
tion of snow from water
(Yang et al. 2007)
Comparisons of MODIS data
observations of SWE
show high statistical
agreement when cloud
does not interfere
with the viewing of snow cover from the MODIS
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
pixel is averaged across 500
meters whereas the station detects snow in
ineffective for the
mapping of SCA
conditions. Other sensors exist
for example those that utilize the
portion of the spectrum (i.e. AMSR
dvanced Microwave Scanning Radiometer
on the Aqua satellite provides passive microwave measurements
f snow cover
global scale. The sensitivity of the sensor allows it to
between snow, water, ice, and
other type of
land cover. AMSR
E goes a step further
be used to estimate
well. However, the spatial resolution of
products is 25 kilometers, which is very coarse when compared to other
Even so, the
E spatial resolution is a major improvement over previous sensors,
I) instrument. The SSM/I instrument used four frequencies;
E uses six
, and therefore has a wider range of the microwave spectrum to use for
Microwave instruments have
resolution compared to visible and infrared
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
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
re was good agreement between
ground station data in mountainous terrain and
that the MODIS data is
can be used as
an effective estimator of
in the Himalayas region
(Sanjay et al. 2008)
the rugged terrain
in the area contributed to a
shadowing effect (caused by
the low angle of solar illumination in complex terrain
, not to be confused with a mountain rain
) leading to
a less accurate reading of snow
decrease their effectiveness as well.
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
In a study using MODIS
SCA data to examine the relationship between s
now cover in
the Tibetan plateau and the East Asian Summer Monsoon,
MODIS snow cover product
useful and accurate when compar
ed against ground station data
(Che et al. 2004)
the paper titled,
“Snow depth estimation over north
western Indian Himalayas using
E”, Indrani Das and R. N. Sarwade found that AMSR
E was suitable for use in the
Himalayas, although snow depth was
an average error of
20.34 cm due to the coarse
resolution of AMSR
(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
derived by the sensor
A few studies have used
to derive estimates of
SCA, but found that the coarse spatial resolution of the data hindered its use even though it had
the ability to penetrate cloud
As a result
combined products of AMSR
high resolution sensors such as
MODIS and AMSR
omposite products have been developed
to take into account
advantages of each sensor to provide a more complete
This concept is not new and
has been applied
in the past
MODIS and SSM/I data
. This combination yielded more
accurate estimates of SCA
than the use of each product alone.
more powerful microwave sensor than SSM/I and has produced even stronger results
(Tait et al.
has the ability to penetrate
can penetrate the atmosphere
without losing accuracy.
As a supplement to
the high resolution MODIS snow product, AMSR
E has been found to
(Wang et al. 2008)
sing a combined MODIS and AMSR
E product suppresses cloud cover
providing the user with a
relatively fine spatial resolution
combined MODIS and ASMR
but the accuracy drops when the snow
is shallow or the snow distribution is
shadow effect encountered
in previous studies that used
MODIS to map
also be decreased
, which operates the same
A combined MODIS and ASMR
has been found to be
more accurate than
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
techniques are new and relatively unexplored, and a delicate balance between
presence of snow under clouds and
maintaining accuracy must be
a combined MODIS and AMSR
E product enhanced with an additional cloud cover
could make for an extremely powerful and accurate snow
The goal of this proposal is to see if accuracy is increased and us
e 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.
E and MODIS snow cover product was designed t
o test the
The snow and land cover detected with the composite snow cover product and
the standard MODIS snow cover product is not significantly different.
The composite AMSR
E and MODIS product has significantly higher
snow and land cover detected than the standard MODIS snow cover product.
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 dail
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 p
through January 8
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
75 percent o
f the total water within the Colorado River watershed originates as snow in
the Rocky Mountains
Figure 1: Map of study area, shown by red outline
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.
he MODIS instrument is
ocated on the Terra satellite
and the AMSR
E instrument is located on the Aqua satellite
products from both instruments
can be subset to the study area
while being acquired.
is done using a vector outline of the study area, and then proc
essing the imported HDF files to
have pixels completely within the vector selected and subset.
E and MODIS data utilized are level 3 in terms of processing, which
ensures that necessary adjustments have already been made to the datasets. MODI
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
file corresponds to.
Daily MODIS Snow
: MODIS integer values
, from National Snow and Ice Data Center
Using python scripting within ArcGIS, the following steps will be automated.
ArcGIS product suite allows for extensive scripting capabilities and includes a “Model
tool that makes it easy for users without programming experience to automate tasks within
devoted to automatically creating the compo
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
several steps of processing. First, b
oth MODIS and AMS
must be projected in the same coordinate system. To interpolate MODIS images, the HDF file
converted into GRID format at 500m resol
ution. Then, the cell values were
the metadata and documentation, so that the MODIS image is c
ing to snow,
cloud, and no snow.
Next, the AMSR
E data was
converted into grid format. Using the Mask tool, AMSR
much larger t
han MODIS pixels due to the coarser
resolution) that are
completely within MODIS detected clo
selected. If an AMSR
E pixel is not
completely within MODIS detected cloud cover, it will not be selected
or used in the composite
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
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
loud cover pixels.
If, however, an AMSR
E pixel is over an area that is completely cloud cover, AMSR
replaces that pixel.
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
swaths in the study had errors within the study area. When a
, the AMSR
E pixel is
using Nearest Neighbor analysis to match the 500m
ution of MODIS
. Raster operations use
the values of
E to replace the
MODIS cloud cover
using raster operations the values are
combined into a
single composite image. The result is a composite MODIS and AMSR
llowing the creation of composite MODIS and AMSR
product, statistical testing was
to see if there is a significant replacement of cloud cover with AMSR
E sensed land or
E and MODIS overlay. For the pixels
in red, cloud cover was detected. The outline of AMSR
pixels can be seen as well. The bottom pixels are mostly completely within the cloud cover and would be replaced
Below are shown several maps of the
composite imagery produced, as well as maps
showing the non
: 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. Visua
lly, it appears as if a dramatic increase in snow cover has
occurred, but the table that follows shows that statistically, the gains are negligible.
: January 8th MODIS snow cover
: January 8th composite snow cover
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
10% aggregate increase in snow and land cover s
ensed, as shown by Table 2.
: Chart of accuracy between MODIS and Composite product
: Percentage of remotely sensed snow and land cover
Percentage of remotely sensed snow and land
: 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 2
, and 5
) that AMSR
could not be used to replace cloud cover. On January 2
E coverage did not include
the study area. On January 5
shown in Figure
, no pixels were replaced due to lack of cloud cover.
: January 5th snow cover. No pixels could be replaced.
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:
Test of mean difference = 0 (vs > 0):
Value = 1.23 P
Value = 0.129
: Paired T test of means.
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.
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
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 s
tatistical 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.
showed that the gains of using the composite produc
were not significant and suggest
a flaw in methodology
, which reflect the challenges of this project
of the composite product was meant to make it easier for a cloud removal algorithm to be
applied, which was outs
ide 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.
ing the null hypothesis does not mean that increasing the accuracy of MODIS with
E cannot be done, only that the approach and methodology of this study would need to be re
examined. For instance, the focus was on first 8 days in January of 2006, and A
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 5
have negligible cloud cover. However, January 1
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 bee
n set up to test the efficacy of a
composite product when there is a certain amount of cloud cover with an alternate hypothesis suggesting
that a composite product significantly increases snow cover of MODIS during periods of high cloud
cover, the design m
ight have shown such a compos
ite product to be significantly effective.
The results of this study do support the conclusion that as a daily product, a composite AMSR
and MODIS snow cover product would likely not be a significant improvement.
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 re
alized early on, and al
ternate methods and tools could be explored. In
addition, the results of January 8
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 tha
t does not attempt to implement a cloud
removal algorithm and instead attempts to maximize the potential of a AMSR
E and MODIS composite
Brian Harshburger, for giving me
professional and personal
direction and guidance
out 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
g me attend the AAG conference.
Armstrong, E, Chin, M, LaFontaine, F, et al.
An Enhanced MODIS/AMSR
E SST composite
” Last accessed December 6.
NASA Earth Science Office.
Balk, B, Elder, K.
Combining Binary Decision Tree and
Geostatistical Methods to Estimate
Snow Distribution in a Mountain Watershed.
. 2000; 36: 13
Campbell, James B.
Introduction to Remote Sensing
. New York: Guilford.
Che, T, Xin, L, Feng, G.
Estimation of Snow Water Equivalent in t
he Tibetan Plateau Using
passive Microwave Remote Sensing Data(SSM/I).
Journal of Glaciology and Geocryology
Comiso, J.C; Cavalieri, D,J, Markust, T.
ea ice concentration, ice temperature, and snow depth
Geoscience and Remote
; 41(2): 243
Das, I, Sarwade, R. N.
Snow Depth Estimation of north
western Indian Himalaya using AMSR
Journal of Remote Sensing.
2003; 29: 4237
Dressler, K, Leavesley, G.
Evaluation of gridded snow water equiva
lent and satellite snow
cover products for mountain basins in a hydrologic model.
Foster, J, Sun, Chaojiao, Walker, J, Kelly, R, Chang, A, Dong, Jiarui, Powell, H.
the uncertainty in passive
microwave snow water equivalent observations.
Remote Sensing of
2005; 94: 187
snow cover product.
Hydrology and Earth System Sciences.
Hall, D, Riggs, G.
Accuracy assessment of the MODIS snow products.
2007; 21: 1534
Harshburger, B, Humes, K, Walden, V, Blandford, T, Moore, B, Dezzani, R.
Interpolation of Snow Water Equivalency Using Surface Obser
vations and Remotely Sensed
Images of Snow
2010; 24: 1285
Klein, A. G, Barnett, Ann, C.
Validation of daily MODIS snow cover maps of the Upper Rio
Grande River Basin for the 2000
2001 snow year.
ing of Environment.
Lee, S., Klein, A. G., Over, T. M.
A comparison of MODIS and NOHRSC snowcoverproducts
for simulating streamflow using the Snowmelt Runoff Model.
Maurer, E, Rhoads, J, Dubayah, R, Lettenmaier, D.
Evaluation of the snow
covered area data
product from MODIS.
2003; 17(1): 59
National Snow and Ice Data Center. MOD10A2 and MYD10A2 Local Snow Cover Attributes,
net]. Available from:
Sanjay, J, Ajanta, G, Saraf, A.
racy assessment of MODIS, NOAA and IRS data in snow
mapping under Himalayan conditions.
International Journal of Remote Sensing.
Effect of TRMM Orbit Boost on Ra
dar Reflectivity Distributions.
Utilizing multiple datasets for snow
New refinements and validation of the MODIS Land
Remote sensing of Environment.
2008; 112: 59
Wang, X, Xie, Hongjie, Liang, T.
ion of MODIS snow cover and clo
mask and its
application in Northern Xinjiang, China.
Remote Sensing of Environment.
2008; 112(4): 1497
Xiaobing Z, Hongjie X, Jan H.
Statistical evaluation of remotely sen
with constraints from streamflow.
Remote Sensing of Environment.
2005; 94: 214
Yang, D., Robinson, D., Zhao
, Y, Armstrong, R, Brodzik, M.J.
Streamflow response to seasonal
snow cover extent chang
es in large Siberian
Journal of Geophysical Research.