**Final Technical Report: December 20, 2005**

**Validation of AMSR-E Snow Products (NAG5-11107**)
**AQUA-0090-0026/NRA-00-OES-03**

**From: Richard L. Armstrong, CIRES/NSIDC, University of Colorado, UCB
449**

**Boulder, CO 80309**

**To: David O'C. Starr, Head, Mesoscale Atmospheric Processes Branch,
Code 912, NASA GSFC, Greenbelt, MD 20771**

**To: Eleanor A. Harvey, Grants Specialist, Office of Naval Research,
Seattle Regional Office, 1107 NE 45th Street, Suite 350, Seattle , WA 
98104-4631**

**I. Background**

The extent of the seasonal snow cover, which may include as much as 50
percent of the Northern Hemisphere land surface, is an extremely
important parameter in global climate and hydrologic systems due to
significant effects on energy and moisture budgets. Realistic simulation
of snow cover in climate models is essential for correct representation
of the surface energy balance, as well as for understanding winter water
storage and predicting year-round runoff. Passive microwave signatures
of seasonal snow cover are clearly characterized by a strong dielectric
contrast between snow-covered and snow-free ground, by decreasing
emissivity (dry snow) with increasing microwave frequency (negative
spectral gradient) and by decreasing emissivity with increasing snow
mass. Because of this clear capability, a microwave snow cover algorithm
is included in the suite of algorithms supported by the AMSR-E Science
Team.

The AMSR-E snow products will contribute to one of the longest time
series of any environmental product derived from satellite remote
sensing. The National Snow and Ice Data Center (NSIDC) has produced a
27-year, consistently processed, time series of gridded satellite
passive microwave data in a common format called the Equal-Area Scalable
Earth Grid (EASE-Grid). This data set was developed using SMMR (Scanning
Multichannel Microwave Radiometer) data for the period 1978 to 1987 and
SSM/I (Special Sensor Microwave Imager) data for 1987 to 2005 and has
been used to derive a series of snow and ice products. Figure 1 shows
this time series for Northern Hemisphere snow cover and includes data
from the NOAA visible satellites for comparison.

![](media/image1.png) Figure 1. Northern Hemisphere snow-covered area (x
10^6^ km^2^) derived from visible (NOAA) and passive microwave (SMMR and
SSM/I) satellite data, 1978-2005.

Previous studies have shown than current passive microwave snow cover
algorithms tend to undermeasure snow extent as indicated in general in
Figure 1. In particular this occurs over large areas in early fall, and
at the edges of the seasonal snow pack, where snow is typically shallow
and/or intermittent (Armstrong and Brodzik, 2001). During shallow snow
conditions of the early winter season microwave data consistently
indicate less snow-covered area than the visible data. This
underestimate of snow extent results from at least two factors; 1)
shallow snow cover (less than about 5.0 cm) often possibly combined with
wet snow, does not provide a scattering signal of sufficient strength to
be detected by the algorithms and 2) even when snow exists at greater
depths across the microwave sensor field of view but is intermittent in
extent, the scattering signature integrated over this mixed pixel is not
adequate to trigger current microwave algorithms. The increased spatial
resolution of AMSR-E provides improved retrievals of intermittent snow
cover as shown in Section 4 of this report. As the snow cover continues
to build during the winter months and on into the melt season, agreement
between the two data types continually improves (see Figure 3). This
occurs because as the snow becomes deeper and more continuous in extent
and the layered structure becomes more complex, the negative spectral
gradient driving the passive microwave algorithm is enhanced. The
increased accuracy during spring melt is fortunate as this is the most
important period of the snow cover season in terms of snow hydrology,
thus allowing confident application of these snow water equivalent data
to hydrologic forecasting and modeling. The only large region where the
passive microwave data consistently indicate snow and the visible data
do not is over the Tibetan Plateau and surrounding mountain areas. In
this case it is not entirely clear which remote sensing data set is
correct due to the fact that there have been only a very limited amount
of surface station data available to support objective validation.
However, recent acquisition of validation data and the increased spatial
resolution of AMSR-E and MODIS data allow improved accuracy of snow
mapping in this region as described in Section 4 of this report.

**II. [Summary]{.underline} of Progress and Accomplishments**

**1. Comparison of AMSR-E and SSM/I Brightness Temperatures over
Snow/Ice Stable Targets**

Savoie, M., E. Njoku, R.L. Armstrong, M.J. Brodzik, 2004  Identification
and Characterization of Potential Ground Targets for Satellite Microwave
Sensor Calibration Monitoring, EOS Transactions AGU, 85(47), fall
Meeting Supplement.

<http://cires.colorado.edu/~savoie/docs/Savoie_Njoku_2004AGU.ppt>

**2. An AMSR-E and MODIS Blended Snow Product**

Armstrong, R.L., M.J. Brodzik, M. Savoie. Multi-Sensor Approach to Snow
Cover Mapping using Data from NASA's EOS Aqua and Terra Spacecraft, Eos,
Transactions, American Geophysical Union, Vol.84, No. 46. Fall AGU, San
Francisco, 2003

[http://cires/\~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf](http://cires/~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf%09)

**3. A Quantitative Method to Compare Snow Covered Area (SCA) from MODIS
and AMSR-E**

Armstrong, R.L., M.J. Brodzik, M. Savoie. Multi-Sensor Approach to Snow
Cover Mapping using Data from NASA's EOS Aqua and Terra Spacecraft, Eos,
Transactions, American Geophysical Union, Vol.84, No. 46. Fall AGU, San
Francisco, 2003

[http://cires/\~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf](http://cires/~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf%09)

**4. AMSR-E Mapping of Snow over the Tibet Plateau: Enhanced spatial
resolution and**

**atmospheric correction**

Armstrong, R.L., M.J. Brodzik, J. Wang, M. Savoie, O. Frauenfeld and T.
Zhang, 2004. Enhanced Snow Cover Mapping on the Tibet Plateau, EOS
Transactions AGU, 85(47), fall Meeting Supplement.

<http://cires/~brodzik/2004agu/2004agu_poster.pdf>

**5. Comparison of AMSR-E and SSM/I-derived Snow Water Equivalent
(SWE)**

Armstrong, R.L., M.J. Brodzik, M. Savoie. Multi-Sensor Approach to Snow
Cover Mapping using Data from NASA's EOS Aqua and Terra Spacecraft, Eos,
Transactions, American Geophysical Union, Vol.84, No. 46. Fall AGU, San
Francisco, 2003

[http://cires/\~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf](http://cires/~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf%09)

**6. Continuous Interaction with the AMSR-E Science Team**

> <http://www.ghcc.msfc.nasa.gov/AMSR/team_meeting_200305.html> (2003)
>
> <http://www.ghcc.msfc.nasa.gov/AMSR/ftcollins/brodzik.pdf> (2004)
>
> (2005 yet to be posted by the AMSR-E Science Team)

**III. [Details]{.underline} of Progress and Accomplishments**

**1. Comparison of AMSR-E and SSM/I Brightness Temperatures over
Snow/Ice Stable Targets**

As a baseline accuracy check, we have compared AMSR-E brightness
temperatures for a snow covered surface with SSM/I brightness
temperatures at Dome C in Antarctica (74.5S, 123.0E) (Figure 2). A site
such as Dome C is selected because it represents a target with microwave
emissivity that is reasonably consistent across an area comparable to a
passive microwave satellite sensor footprint. Therefore, the annual time
series is primarily a response to fluctuations in physical temperature.

In the example shown here we compare vertically polarized 36/37 GHz data
at Dome C, Antarctica. The increased variability in both signals during
the winter (July-September) is caused by the cycling of katabatic winds
that remove the near surface temperature inversion resulting in higher
physical temperatures. The slight positive bias shown by AMSR-E 36 GHz
is possibly due to the lower frequency. Overpass times in this example
are coincident. Although only the 36/37 GHz vertical polarization is
shown here this comparison has been performed for all AMSR-E channels
which correspond to the SSM/I channels. Correlation coefficients
typically exceed 0.99.

![](media/image2.png){width="2.9637795275590553in"
height="2.9637795275590553in"}![](media/image3.png){width="2.9681102362204723in"
height="2.9688976377952754in"}

Figure 2. Comparison of AMSR-E and SSM/I 36/37 GHz brightness
temperatures at Dome C, Antarctica, July 2002 to March 2005.

**2. An AMSR-E and MODIS Blended Snow Product**

Because of the significant differences between snow products derived
from passive microwave and visible data, especially during early winter,
our current efforts to produce climate data records (CDRs) for snow
cover focus on a method which combines the advantages of both the
microwave and the visible data. The historic product, a monthly mean
snow water equivalent (SWE) and snow extent for 1978 to 2005, is
produced through the blending of SMMR and SSM/I passive microwave data
with the NOAA weekly snow extent charts derived primarily from AVHRR.

For the NASA EOS suite of sensors we have developed an optimal procedure
to blend MODIS and AMSR-E snow products. This work has involved building
a turnkey software system, whereby we "blend" global SWE derived from
passive microwave with snow extent derived from the MODIS CMG product.
Given an 8-day MODIS product file, with one command we currently produce
a blended product using AMSR-E data, up to and including the most recent
"near real-time" (within the last 24 hours) data.

This development includes generating gridded AMSR-E T~B~s from the
standard Level 2A T~B~ product, and modifying our current software that
produces SWE from SSM/I or SMMR data to also accept AMSR-E gridded T~B~s
as input. With some operator intervention (i.e. retrieving and staging
Level 2A T~B~ files for the desired time period), we produce Northern
Hemisphere gridded AMSR-E T~B~s. The SSM/I SWE algorithm software has
been modified to accept AMSR-E T~B~s as input.

Because the current AMSR-E snow algorithms do not differ fundamentally
from the earlier SSM/I algorithms, the pattern of early season
underestimation by passive microwave persists. This can been seen in
Figure 3a, which combines AMSR-E-derived snow water equivalent with
MODIS

![](media/image4.png){width="5.5784722222222225in" height="1.4625in"}

![](media/image5.png){width="5.5784722222222225in" height="1.4625in"}

Figure 3a. Blended snow prototypes, SWE (from SSM/I (left side) and
AMSR-E (right side)) with snow cover from MODIS, Fall, 2003. Top panels
in each set of four represent SWE (mm), with additional area indicated
as snow by MODIS in red. Lower panels represent passive microwave snow
extent in grey, with percent area of additional pixels that MODIS
classifies as snow in blues.

![](media/image6.png){width="5.5784722222222225in" height="1.4625in"}

![](media/image7.png){width="5.5784722222222225in" height="1.4625in"}

Figure 3b. Same as 4a, Spring, 2003.

snow cover during fall of 2003. Snow cover for two 8-day periods derived
from SSM/I and from AMSR-E shows good agreement, with some differences
in SWE. (The algorithms are not yet intercalibrated.) Comparison with
MODIS snow cover exhibits the typical patterns seen in the historical
data described above, microwave tends to undermeasure snow cover over
large areas in early fall, and at the edges of the seasonal snow pack,
where snow is typically shallow and/or intermittent. Agreement between
the visible and microwave data improves as the winter progresses as seen
in Figure 3b.

Armstrong, R.L., M.J. Brodzik, M. Savoie. Multi-Sensor Approach to Snow
Cover Mapping using Data from NASA's EOS Aqua and Terra Spacecraft, Eos,
Transactions, American Geophysical Union, Vol.84, No. 46. Fall AGU, San
Francisco, 2003

[http://cires/\~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf](http://cires/~brodzik/2003agu/2003agu_poster_armstrong_brodzik_savoie.pdf%09)

**3. A Quantitative Method to Compare Snow Covered Area (SCA) from MODIS
and AMSR-E**

We have developed a method to quantify the relationship between passive
microwave snow retrievals and MODIS snow extent. Figure 4 shows a
histogram that determines the threshold at which a microwave algorithm
begins to detect snow cover. In this example, for the Northern
Hemisphere on March 11, 2003, each EASE-Grid pixel (625 km^2^) is
assigned a percent MODIS snow covered area by calculating the average of
the 0.05 deg. Climate Modelers Grid (CMG) visible snow product. A second
histogram is generated by counting the number of pixels in each MODIS
bin where the passive microwave algorithm detected a SWE of greater than
zero. The ratio of these histograms is used to generate the monthly data
shown in Figure 5 which presents this threshold throughout the winter
season. Notice the increased threshold as the winter season progresses.
A theoretically perfect SWE algorithm would plot as a horizontal line at
1.0, where every time the MODIS sensor sees snow in a pixel, the
microwave also sees some signal.

This method will be used in our future evaluations of the accuracy of
the standard AMSR-E snow products.

![](media/image8.png){width="2.8402777777777777in"
height="2.4854166666666666in"}![](media/image9.png){width="2.8333333333333335in"
height="2.4791666666666665in"}

Figure 4. Histogram to determine threshold Figure 5. Microwave SWE
threshold representing

at which microwave begins to detect snow. the fraction of all pixels
examined by microwave

that showed a positive SWE as a function of

MODIS percent snow cover.

In general, the more the grid cell is covered by snow, the more likely
the passive microwave algorithm will respond with an indication that
there is snow present. Grids cells that are only 10 to 20% snow covered
will not generate a scattering signal strong enough for the passive
microwave algorithms to detect snow. As the winter season progresses,
less snow cover is needed to trigger a response from the microwave
algorithm. For example, in October, 50% snow cover from MODIS causes a
microwave response of greater than zero in only about 30% of the pixels
while even 100% snow cover only triggers a response in about 40% of the
cases. In contrast, later in the season, during February and March, 50%
MODIS snow cover corresponds to about 75 to 85% response in the
microwave. This occurs because as the season progresses, the snow, even
in cases where cover is not yet 100%, becomes deeper and develops a
layered structure, often with larger grain sizes, all factors that will
increase the scattering and enhance the negative spectral gradient that
will cause the algorithm to generate snow water equivalent values of
greater than zero.

**4. AMSR-E Mapping of Snow over the Tibet Plateau: Enhanced spatial
resolution and**

**atmospheric correction**

Example of enhanced spatial resolution provided by AMSR-E data.

Comparison of snow cover derived from MODIS, SSM/I and AMSR-E over the
Tibet Plateau provides a clear example of the higher spatial resolution
of AMSR-E yielding more accurate snow extent maps. The Tibet Plateau
typically receives only small snowfall amounts, often accompanied by
strong winds. This results in a pattern of spatially intermittent, i.e.
patchy, shallow snow cover, which is apparent in the MODIS image. The
coarse resolution SSM/I data are not capable of resolving this pattern
of intermittent snow, in essence spatially smoothing the pattern and
appearing as continuous thin snow cover. The smaller footprint of the
AMSR-E, however, provides a much improved capability to identify these
individual areas of patchy snow cover.

![](media/image10.png){width="2.15in"
height="1.683464566929134in"}![](media/image11.png){width="1.9082677165354331in"
height="1.6771653543307086in"}![](media/image12.png){width="1.910236220472441in"
height="1.6771653543307086in"}

Figure 6. Tibetan Snow Cover, February 26 - March 5, 2003, left to
right: percent area with MODIS snow, SWE derived from SSM/I and SWE
derived from AMSR-E. Red box outlines area of patchy visible snow
(MODIS) that is smoothed by SSM/I but better resolved by AMSR-E\'s
improved spatial resolution.

Figure 6 clearly shows the improved snow mapping capability resulting
from the increased spatial resolution of AMSR-E. The red box in the
figure outlines the area of patchy visible snow (MODIS) that is smoothed
by SSM/I but better resolved by AMSR-E\'s improved spatial resolution.
This example demonstrates the greater value of AMSR-E snow data over
smaller hydrologic catchment basins with intermittent snow cover. In
future investigations we will explore the possibility to enhance the
spatial resolution of the AMSR-E brightness temperatures using advanced
re-sampling techniques.

Previous studies have shown that the Tibetan Plateau is one the areas of
largest disagreement between optical and microwave satellite snow
products (Armstrong and Brodzik, 2001). Even with the application of the
higher resolution AMSR-E data, the apparent overestimation persists.
This

may be partially explained by the effect on the microwave algorithms of
an enhanced spectral gradient resulting from decreased atmospheric
absorption in this region of extremely high elevation (Wang et al.,
1992). Most all current microwave snow algorithms are based on the
assumption that the atmosphere is essentially transparent at frequencies
around 18 and 36 GHz. However, recent work by Wang and Manning (2003)
demonstrates that the atmosphere should be considered when validating
microwave satellite retrieval algorithms based on surface and
low-elevation aircraft measurements. In the case of the Tibet Plateau,
the reduced atmosphere between the orbiting sensor and the ground
surface at the mean elevation of 4,000 m would significantly affect the
spectral gradient at 18 and 36 GHz. We have applied the corrected
temperature gradient, (Figure 7 )provided by our colleague Dr. James
Wang, to pixels on the Plateau resulting in a microwave-retrieved snow
extent that agrees more closely with that of the optical sensor (Fig.
8). Details of the preliminary results from this study can be found in
the following publication from Fall AGU 2004.

Armstrong, R.L., M.J. Brodzik, J. Wang, M. Savoie, O. Frauenfeld and T.
Zhang, 2004. Enhanced Snow Cover Mapping on the Tibet Plateau, EOS
Transactions AGU, 85(47), fall Meeting Supplement.

<http://cires/~brodzik/2004agu/2004agu_poster.pdf>

![](media/image13.png){width="3.040277777777778in"
height="2.8673611111111112in"}

Figure 7. Atmospheric Correction Applied to Passive Microwave
Frequencies

over the Tibet Plateau. (from J. Wang)

![](media/image14.png){width="2.9805555555555556in"
height="2.1666666666666665in"}![](media/image15.png){width="2.9805555555555556in"
height="2.1666666666666665in"}

## Figure 8. Result of atmospheric correction, November 29, 2003. SWE derived from uncorrected AMSR-E (left) and corrected AMSR-E (right). 

**5. Comparison of AMSR-E and SSM/I-derived Snow Water Equivalent
(SWE)**

As of the date of this report, we have evaluated selected examples of
the AMSR-E Science Team standard snow product (Kelly et al. 20003), but
a stable time series of this product is only now becoming available.
Earlier versions of this product were problematic due to errors
associated with the processing of the algorithm. Therefore, we deemed it
inappropriate to undertake any quantitative validation of this earlier
product. These errors have been corrected and the latest algorithm has,
as of the date of this report, been delivered for processing to the
AMSR-E SCF and SIPS. However, these standard products will not be
available until early 2006 and therefore cannot be evaluated here. We do
intend to undertake an evaluation and rigorous validation of this
product during 2006 under other NASA funding (NNG04GN38G).

In the meantime, we have made use of the B01 version AMSR-E Level 2A
Brightness Temperature data as it has been made available to the
validation team. We have derived SWE from these AMSR-E data, as well as
SSM/I data, using a single algorithm based on earlier work by Chang et
al. (1987) which also includes the later forest correction by Chang et
al. (1996) as well as precipitation filters (Brodzik and Armstrong,
unpublished). This general algorithm, known as NSIDC6, approximates that
utilized by the AMSR-E algorithm team, and we expect results of future
comparisons with the standard snow product to be similar to the results
presented here. In the examples shown in Figure 9 we provide a basic
test of the integrity and consistency of the AMSR-E brightness
temperatures when applied to the retrieval of snow cover data. We look
forward to examining the full time series of the standard snow product
using much of the same methodology and software tools described here.

Figure 9 compares monthly average SWE derived from AMSR-E and SSM/I
brightness temperatures for November 2003 to February 2004. Both the
absolute values and the spatial patterns of the two data sets are
similar. In order to eventually merge the AMSR-E snow products with the
SSM/I data to produce a single climate data record, it will be necessary
to adjust the AMSR-E brightness temperatures to match the SSM/I
(Armstrong, Njoku, Brodzik and Savoie, in preparation). The results of
this preliminary comparison indicate that these intercalibration
adjustments will be small.

  --------------------------------------- ---------------------------------------
  ![](media/image16.png){width="2.75in"   ![](media/image17.png){width="2.75in"
  height="5.5in"}                         height="5.5in"}

  --------------------------------------- ---------------------------------------

Figure 9. Northern Hemisphere monthly average snow water equivalent
derived from SSM/I (left) and AMSR-E (right), November 2003 -- February
2004.

**6. Continuous Interaction with the AMSR-E Science Team**

Interactions with the AMSR-E snow algorithm team include follow up to
the concerns we raised in our reports during year one and two of this
project. The team has addressed an issue we raised with the range of
values represented in the data. We continue to collaborate on various
masks being used. We provided the team with a new version of the
land-ocean-permanent ice mask, derived from the International
Geosphere-Biosphere Programme (IGBP) Global Land Cover Classification
(GLCC) data. At the request of Richard Kelly, we evaluated the use of a
new MODIS land mask to replace the GLCC in a future version of the
processing.

We recommend that the current static snow extent climatology be replaced
by monthly climatologies derived from the NOAA weekly snow charts. We
provided to the algorithm team the historical monthly average SWE
derived from SSM/I (1987-2003) using the NSIDC6 algorithm, which closely
matches the approach taken in the AMSR-E standard snow algorithm.

We evaluated and commented on the standard snow algorithm examples
released in February 2004. The prototype data were visibly affected by
an orbital maneuver, and we suggested that this type of data might be
eliminated by an addition to the algorithm that would check orbital
metadata. The algorithm team is exploring this possibility for a future
release. We have also identified some discrepancies in the snow product
documentation, and brought the corrections to the attention of the NSIDC
documentation team.

**Data Management and Archival**

All pertinent data resulting from this validation study will be archived
at the National Snow and Ice Data Center (NSIDC), University of
Colorado, Boulder. Selected data sets are available to the wider
scientific community through NSIDC's User Services Office
(nsidc@nsidcorg).

**References**

Armstrong, R.L. and M.J. Brodzik, 2002. Hemispheric-scale comparison and
evaluation of passive microwave snow algorithms, Annals of Glaciology
Volume 34:38-44.

Armstrong, R.L. and M.J. Brodzik, 2001. Recent Northern Hemisphere snow
extent: a comparison of data derived from visible and microwave sensors,
Geophysical Research Letters, Vol. 28, No.19.

Chang, A. T. C., J. L. Foster, D. K. Hall. 1987. Nimbus-7 SMMR Derived
Global Snow Cover Parameters. Annals of Glaciology, 9:39-44.

Chang, A. T. C., J. L. Foster, D. K. Hall. 1996. Effects of Forest on
the Snow Parameters Derived from Microwave Measurements during the
BOREAS Winter Field Campaign. Hydrological Processes, 10:1565-1574.

Kelly, R.E., A.T.C. Chang, L. Tsang, and J.L. Foster. 2003. A prototype
AMSR-E global snow area and snow depth algorithm. IEEE Transactions on
Geoscience and Remote Sensing 41(2): 230-242.

Wang, J.R. and W. Manning, 2003. Near concurrent MIR, SSM/T-2, and SSM/I
observations over snow-covered surfaces. Remote Sensing of Environment,
84:457-470