|NASA EOS Aqua satellite AMSR-E data were used for snow
variation study in Northern Hemisphere (NH) from 2007 to 2011 for
January, April, July and October months with 500 m elevation difference.
Monitoring of the seasonal snow cover with different elevation is
important for several purposes such as climatology, hydrometeorology,
water use and control and hydrology, including flood forecasting and
|The objective of this study was to analyze the seasonal snow type and
snow cover changes on the NH and its relations with different elevation.
Such information is urgently need for the satellite precipitation community to better delineate snow covered regions to minimize the
impact of falsely classifying raining areas from snow on the ground [1,2].
This type of research work is also useful to improve the quality of future
NASA satellite data . This paper describes an approach to assemble
a consistent 5-year record of seasonal snow covered area of NH. There
are, however, very limited data that can be used to corroborate our
findings (satellite data, secondary data or otherwise), making extensive
quantitative validation of the snow estimates extremely challenging .
|The methodology involves conversion of NASA EOS Aqua
satellite AMSR-E SWE data into 6 snow classes, computation of NDSI,
determination of the boundary between snow classes from spectral
response data and threshold slicing of the image data . Accuracy
assessment of AMSR-E snow products was accomplished using
Geographic Information System (GIS) techniques. There are many
techniques available for detecting and recording differences, such as
image differencing, ratios and correlation [6,7]. However, the simple
detection of change is rarely sufficient in itself: information is generally
required about the initial and final snow cover analysis as described by
|Furthermore, detection of image differences may be confused
with problems in penology and cropping and such problems may be
exacerbated by limited image availability, poor quality in temperate
zones and difficulties in calibrating poor images . Post-classification
comparisons of derived, thematic maps go beyond simple change
detection because they attempt to quantify the different types of change
. Their degree of success depends upon the reliability of the maps
that have been made by image classification. Broadly speaking, both
large scale changes such as very low snow class, and small scale changes
like extreme snow, might be mapped reasonably easily [11,12].
|Data and Image Classification
|The Advanced Microwave Scanning Radiometer - Earth Observing
System (AMSR-E) is a twelve-channel, six-frequency, passivemicrowave
radiometer system. It measures horizontally and vertically
polarized brightness temperatures at 6.9, 10.7, 18.7, 23.8, 36.5 and 89.0
GHz. Spatial resolutions of the individual measurements varies from
5.4 km at 89 GHz to 56 km at 6.9 GHz. AMSR-E improves upon past
microwave radiometers. The monthly level-3 AMSR-E snow water
equivalent (SWE) data AE MoSno (AMSR-E/Aqua monthly L3 Global
Snow Water Equivalent EASE-Grids) in Northern Hemisphere were
obtained from the NSIDC NASA website [13-15]. These data are stored
in Hierarchical Data Format–Earth Observing System (HDF–EOS)
format and contain SWE data and quality assurance flags mapped to
25 km Equal-Area Scalable Earth Grids (EASE-Grids). Actual SWE
values are scaled down by a factor of 2 for storing in the HDF-EOS file,
resulting in a stored data range of 0-240. Users must multiply the SWE
values in the file by a factor of 2 to scale the snow depth data up to the
correct range of 0-480 mm. Finally Shuttle Radar Topography Mission
(SRTM) data of approximately 90 m resolution were downloaded from
the website and used to prepare the digital elevation map (DEM) .
|Unsupervised classification was performed here using 0 to 255 gray
levels and digital topographic maps. All AMSR-E monthly SWE images
were transformed into ESRI grid format files with Lambert Azimuthal
equal area projection and the grid was re-sampled by binary approach
[17,18]. The end gray levels from 240 to 255 of AMSR-E data indicates a
snow free surface (or land surface), off-earth, land or snow impossible,
ice sheet, water and data missing, respectively. In terms of snow depth
each gray level need to multiply by factor 2 so this data show snow
depth from 0 to 480 mm [19,20].
|Spatial-temporal Variability of Snow Covers with
|Snow cover classification was computed from 2007 to 2011 for the
months of January, April, July and October (Figure 1). Separate analyses
were done for 500 m elevation ranges. The snow was classified into six
main classes based on SWE values: Very low snow, low snow, medium
snow, high snow, very high snow and extreme snow and land which was
covered by snow in winter but not in other seasons was classified as “No
Snow” class (Figure 2).
|The coldest month has all six snow type classes due to snow pack
growth whereas the summer months only contain residual snow at the
highest elevations. Sharp season-to-season differences were noted. The
final results show the greatest snow cover extent in January whereas
total snow in April is 60%, July 3% and in October near to 25% (Table
1). In terms of inter-seasonal variations during the study period, the
minimum (1.53 million km2) snow cover extent was observed in July
2008 and the maximum (60.0 km2) in January 2010 (Figure 2). In terms
of elevation, in January snow covered area represent more than 70% of
surfaces with altitudes in between 0 to 2000m, and in summer more
than 70% for altitudes higher than 5000m and it`s totally constant at
altitude from 7000m and above (Figure 3 and Table 2).
|The seasonal snow cover extent changes from 2007 to 2011 were
successfully monitored by NASA EOS Aqua satellite AMSR-E data.
Finally, this study shows how NASA EOS Aqua satellite AMSR-E data
can be useful for the long-term observation of the intra and inter-annual
variability of snow packs in rather inaccessible regions and providing
useful information on a critical component of the hydrological cycle,
where the network of meteorological stations is deficient.
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