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NASA EOS Aqua Satellite AMSR-E Data for Snow Variation
Journal of Geology & Geophysics

Journal of Geology & Geophysics
Open Access

ISSN: 2381-8719

+44 20 3868 9735

Editorial - (2014) Volume 3, Issue 4

NASA EOS Aqua Satellite AMSR-E Data for Snow Variation

Boori MS1,2,3*, Ferraro RR2 and Voženílek V3
1National Research Council (NRC), USA
2NOAA/NESDIS/STAR/ Satellite Climate Studies Branch and Cooperative Institute for Climate and Satellites (CICS), ESSIC, University of Maryland, College Park, Maryland, USA
3Palacky University Olomouc, 17.listopadu 50, 771 46 Olomouc, Czech Republic
*Corresponding Author: Boori MS, Palacky University Olomouc, 17 Listopadu 50, 771 46 Olomouc, Czech Republic, Tel: 420585631111 Email:

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 food production.

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 [3]. 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 [4].

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 [5]. 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 [8]

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 [9]. Post-classification comparisons of derived, thematic maps go beyond simple change detection because they attempt to quantify the different types of change [10]. 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) [16].

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 Elevation

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).

geology-geosciences-Seasonal-snow

Figure 1: Seasonal snow cover area in km2 for 2007 to 2011.

geology-geosciences-Snow-cover

Figure 2: Snow cover and snow classes from 2007 to 2011 for January, April, July, and October months.

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 7000 m and above (Figure 3 and Table 2).

  2011_01 2010_01 2009_01 2008_01 2007_01
Class Area % Area % Area % Area % Area %
Very low snow 21.9 36.4 21.4 35.7 22.2 37.0 21.7 36.2 24.3 40.6
Low snow 13.4 22.3 13.2 21.9 15.1 25.1 14.8 24.7 13.2 22.1
Medium snow 11.5 19.1 11.2 18.7 11.2 18.7 11.9 19.9 11.4 19.0
High snow 7.5 12.5 8.6 14.4 6.7 11.1 6.7 11.2 6.3 10.5
Very high snow 4.3 7.2 4.4 7.3 3.7 6.1 3.5 5.8 3.6 5.9
Extreme snow 1.5 2.5 1.2 2.0 1.2 1.9 1.3 2.2 1.2 1.9
Total snow 60.0 100.0 60.0 100.0 60.0 100.0 60.0 100.0 60.0 100.0
RPI 264.9   264.9   264.9   264.9   264.9  
Total 324.8   324.8   324.8   324.8   324.8  
  2011_04   2010_04 2009_04 2008_04 2007_04
Class Area % % Area % % Area % % Area % % Area % %
Very low snow 10.7 27.8 17.8 8.6 24.2 14.3 8.8 24.3 14.7 9.5 26.6 15.9 9.6 26.7 16.0
Low snow 9.8 25.6 16.4 8.9 25.1 14.8 8.9 24.5 14.8 8.8 24.6 14.6 9.3 25.8 15.5
Medium snow 7.7 20.0 12.8 8.3 23.5 13.9 8.2 22.6 13.7 7.4 20.6 12.3 7.5 20.9 12.6
High snow 5.5 14.4 9.2 6.0 16.8 9.9 5.9 16.3 9.9 5.9 16.5 9.8 5.3 14.6 8.8
Very high snow 3.5 9.1 5.8 2.9 8.1 4.8 3.3 9.2 5.6 3.2 9.0 5.4 3.3 9.1 5.5
Extreme snow 1.1 3.0 1.9 0.8 2.2 1.3 1.1 3.1 1.9 0.9 2.6 1.6 1.1 2.9 1.8
Total snow 38.4 100.0 64.0 35.4 100.0 59.0 36.2 100.0 60.5 35.8 100.0 59.6 36.1 100.0 60.1
No snow 21.6   36.0 24.6   41.0 23.7   39.5 24.2   40.4 23.9   39.9
Total classes 60.0   100.0 60.0   100.0 60.0   100.0 60.0   100.0 60.0   100.0
RPI 264.9     264.9     264.9     264.9     264.9    
Total 324.8     324.8     324.8     324.8     324.8    
  2011_07 2010_07 2009_07 2008_07 2007_07
Class Area % % Area % % Area % % Area % % Area % %
Low snow 1.5 73.4 2.5 1.1 66.2 1.8 1.3 69.9 2.2 1.1 72.4 1.8 1.1 70.9 1.9
Medium snow 0.4 18.8 0.7 0.3 20.3 0.5 0.3 18.3 0.6 0.3 19.7 0.5 0.3 21.5 0.6
High snow 0.1 5.8 0.2 0.2 9.2 0.2 0.2 8.1 0.2 0.1 5.9 0.1 0.1 5.1 0.1
Very high snow 0.0 1.9 0.1 0.1 4.3 0.1 0.1 3.8 0.1 0.0 2.0 0.0 0.0 2.5 0.1
Total snow 2.1 100.0 3.5 1.6 100.0 2.7 1.9 100.0 3.1 1.5 100.0 2.5 1.6 100.0 2.6
No snow 57.9   96.6 58.4   97.3 58.2   96.9 58.5   97.5 58.4   97.4
Total classes 60.0   100.0 60.0   100.0 60.0   100.0 60.0   100.0 60.0   100.0
RPI 264.8     264.8     264.8     264.8     264.8    
Total 324.8     324.8     324.8     324.8     324.8    
  2011_09 2010_10 2009_10 2008_10 2007_10
Class Area % % Area % % Area % % Area % % Area % %
Low snow 2.6 59.6 4.3 7.4 54.0 12.4 11.0 62.0 18.4 7.3 54.1 12.2 7.4 52.2 12.3
Medium snow 1.2 28.4 2.1 4.4 31.7 7.3 4.4 24.8 7.4 3.5 26.2 5.9 4.4 31.1 7.3
High snow 0.4 8.1 0.6 1.7 12.5 2.9 1.9 10.9 3.2 2.0 15.0 3.4 1.8 12.6 3.0
Very high snow 0.1 2.8 0.2 0.3 1.8 0.4 0.4 2.0 0.6 0.6 4.1 0.9 0.5 3.5 0.8
Extreme snow 0.1 1.2 0.1 0.0 0.0 0.0 0.1 0.3 0.1 0.1 0.5 0.1 0.1 0.6 0.2
Total snow 4.3 100.0 7.2 13.7 100.0 22.9 17.8 100.0 29.7 13.5 100.0 22.5 14.2 100.0 23.6
No snow 55.7   92.8 46.2   77.1 42.2   70.3 46.5   77.5 45.8   76.4
Total classes 60.0   100.0 60.0   100.0 60.0   100.0 60.0   100.0 59.9   100.0
RPI 264.9     264.9     264.9     264.9     264.9    
Total 324.8     324.8     324.8     324.8     324.8    

Table 1: Snow classes and snow cover area in million km2 for January, April, July and October months from 2007 to 2011.

geology-geosciences-elevation-intervals

Figure 3: Snow cover area on 500m elevation intervals for January, April, July, and October months from 2007 to 2011.

  2011_07 2010_07 2009_07 2008_07 2007_07
Contour Area % Area % Area % Area % Area %
0 24977.1 5.1 16251.0 3.2 22070.7 3.6 17640.8 3.7 19238.2 3.7
500 9376.2 1.9 5903.8 1.2 4172.7 0.7 4828.9 1.0 6057.8 1.2
1000 3766.8 0.8 0.0 0.0 0.0 0.0 1486.4 0.3 0.0 0.0
1500 2717.8 0.6 1885.1 0.4 1885.1 0.3 3164.3 0.7 2513.5 0.5
2000 4927.9 1.0 3494.8 0.7 3494.8 0.6 2640.4 0.5 2238.0 0.4
2500 3374.0 0.7 3374.0 0.7 6714.0 1.1 1256.8 0.3 628.4 0.1
3000 17821.4 3.7 4172.7 0.8 20510.6 3.4 6686.2 1.4 6686.2 1.3
3500 21783.6 4.5 10230.5 2.0 21139.0 3.5 16740.3 3.5 16111.9 3.1
4000 25537.6 5.2 10230.5 2.0 24683.3 4.1 16111.9 3.4 14855.2 2.8
4500 36174.2 7.4 29109.8 5.8 34737.4 5.7 26568.4 5.5 29533.9 5.7
5000 159247.9 32.7 207542.1 41.2 230191.7 37.9 191164.9 39.8 223048.0 42.7
5500 119869.5 24.6 149102.7 29.6 181150.1 29.8 140140.4 29.2 152697.2 29.3
6000 49246.4 10.1 53054.7 10.5 46500.2 7.6 42377.8 8.8 40100.9 7.7
6500 6686.2 1.4 6283.8 1.2 6912.2 1.1 7314.6 1.5 6283.8 1.2
7000 628.4 0.1 628.4 0.1 1885.1 0.3 1256.8 0.3 628.4 0.1
7500 628.4 0.1 1285.1 0.3 1256.8 0.2 628.4 0.1 628.4 0.1
8000 628.4 0.1 628.4 0.1 628.4 0.1 628.4 0.1 628.4 0.1
Total 487391.7 100.0 503177.3 100.0 607931.9 100.0 480635.6 100.0 521878.2 100.0
  2011_09 2010_10 2009_10 2008_10 2007_10
Contour Area % Area % Area % Area % Area %
0 111976.6 6.8 188062.3 6.4 533296.6 10.9 185548.8 5.9 218392.8 6.7
500 41247.1 2.5 47128.6 1.6 952328.4 19.4 47531.0 1.5 109753.1 3.4
1000 126556.5 7.7 197488.0 6.7 390727.4 8.0 197714.0 6.3 236673.6 7.3
1500 184694.4 11.2 417068.5 14.1 573907.7 11.7 388305.3 12.3 454947.7 14.0
2000 145332.4 8.8 304891.6 10.3 359504.6 7.3 302405.9 9.6 321080.9 9.9
2500 112882.6 6.8 205480.5 6.9 218952.8 4.5 223351.5 7.1 249665.3 7.7
3000 100544.6 6.1 149656.5 5.1 185774.7 3.8 169803.2 5.4 176804.6 5.4
3500 50474.8 3.1 105568.0 3.6 121933.7 2.5 115423.9 3.7 117507.2 3.6
4000 50877.1 3.1 86920.8 2.9 120108.8 2.5 103484.7 3.3 117683.6 3.6
4500 87929.9 5.3 214935.6 7.3 300326.2 6.1 275859.2 8.8 225362.6 6.9
5000 352463.8 21.3 610560.2 20.6 694321.3 14.2 678827.7 21.5 592111.2 18.2
5500 214404.7 13.0 342467.6 11.6 362801.7 7.4 364863.3 11.6 341210.8 10.5
6000 59696.2 3.6 78547.6 2.7 73520.6 1.5 87570.9 2.8 80432.7 2.5
6500 8169.0 0.5 8169.0 0.3 9425.7 0.2 8169.0 0.3 8797.3 0.3
7000 1885.1 0.1 1885.1 0.1 1885.1 0.0 1885.1 0.1 1885.1 0.1
7500 1256.8 0.1 1256.8 0.0 1256.8 0.0 1256.8 0.0 1256.8 0.0
8000 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0
Total 1651019.9 100.0 2960714.9 100.0 4900700.4 100.0 3152628.5 100.0 3254193.8 100.0
  2011_01 2010_01 2009_01 2008_01 2007_01
Contour Area % Area % Area % Area % Area %
0 17362649.6 32.6 17959009.5 33.0 16539754.7 30.7 17177288.2 32.7 17481910.7 32.1
500 9197864.9 17.3 10935393.3 20.1 10494707.4 19.5 9463614.5 18.0 11692291.3 21.5
1000 10294087.4 19.3 8425619.9 15.5 10313948.1 19.1 10085143.7 19.2 8252253.1 15.1
1500 4284155.9 8.0 4197795.3 7.7 4046441.8 7.5 6478086.7 12.3 4001756.6 7.3
2000 4800833.2 9.0 8012046.2 14.7 7669374.4 14.2 4443398.8 8.5 8167279.0 15.0
2500 3665846.9 6.9 1174627.0 2.2 1126771.6 2.1 1123920.2 2.1 1188233.3 2.2
3000 637913.4 1.2 628591.2 1.2 627988.2 1.2 645518.8 1.2 641266.3 1.2
3500 426450.2 0.8 400986.6 0.7 411614.3 0.8 430249.4 0.8 422342.9 0.8
4000 400835.7 0.8 405413.7 0.7 406438.4 0.8 393439.4 0.7 389942.0 0.7
4500 604524.6 1.1 580856.0 1.1 595727.4 1.1 581812.2 1.1 609286.1 1.1
5000 955138.9 1.8 951997.0 1.8 937544.2 1.7 971476.8 1.9 962679.4 1.8
5500 516529.0 1.0 524896.1 1.0 542921.1 1.0 525954.8 1.0 513200.8 0.9
6000 136987.0 0.3 138872.2 0.3 128189.7 0.2 131331.6 0.3 134473.5 0.2
6500 19479.8 0.0 17594.7 0.0 17594.7 0.0 19479.8 0.0 16966.3 0.0
7000 3141.9 0.0 3141.9 0.0 3141.9 0.0 2513.5 0.0 3141.9 0.0
7500 1256.8 0.0 1256.8 0.0 1256.8 0.0 1256.8 0.0 1256.8 0.0
8000 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0
Total 53308323.6 100.0 54358725.6 100.0 53864042.9 100.0 52475113.4 100.0 54478908.3 100.0
  2011_04 2010_04 2009_04 2008_04 2007_04
Contour Area % Area % Area % Area % Area %
0 10999024.2 30.4 7878629.6 23.5 9080069.2 26.9 7997883.2 24.8 8436200.9 26.5
500 6764994.4 18.7 13234929.7 39.4 4685703.7 13.9 7064639.2 21.9 5465795.2 17.2
1000 3436179.6 9.5 3521242.5 10.5 5145792.3 15.3 2824878.9 8.8 3415148.5 10.7
1500 8661662.3 24.0 2653021.0 7.9 7965703.4 23.6 7551817.1 23.4 2296278.4 7.2
2000 1919586.5 5.3 1469056.9 4.4 2054501.5 6.1 2015961.6 6.2 1909271.0 6.0
2500 869231.9 2.4 1361802.1 4.1 886655.9 2.6 1335973.3 4.1 6460291.5 20.3
3000 548500.3 1.5 552940.8 1.6 1021159.8 3.0 536248.4 1.7 1026730.1 3.2
3500 368967.9 1.0 355960.5 1.1 349032.5 1.0 363113.1 1.1 348273.2 1.1
4000 342367.1 0.9 319343.4 1.0 335367.3 1.0 354689.3 1.1 337351.4 1.1
4500 594704.9 1.6 573096.7 1.7 559153.8 1.7 580175.6 1.8 531475.6 1.7
5000 956298.9 2.6 993456.0 3.0 935009.0 2.8 947195.9 2.9 957132.0 3.0
5500 521782.1 1.4 508988.5 1.5 543753.1 1.6 528694.3 1.6 522184.5 1.6
6000 135730.3 0.4 136987.0 0.4 128818.1 0.4 135730.3 0.4 136358.6 0.4
6500 17594.7 0.0 18851.4 0.1 16966.3 0.1 18223.0 0.1 17594.7 0.1
7000 3141.9 0.0 3141.9 0.0 3141.9 0.0 3141.9 0.0 3770.3 0.0
7500 1256.8 0.0 1256.8 0.0 1256.8 0.0 628.4 0.0 1256.8 0.0
8000 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0
Total 36141652.0 100.0 33583333.0 100.0 33712712.8 100.0 32259621.6 100.0 31865740.9 100.0
  2011_07 2010_07 2009_07 2008_07 2007_07
Contour Area % Area % Area % Area % Area %
0 24977.1 5.1 16251.0 3.2 22070.7 3.6 17640.8 3.7 19238.2 3.7
500 9376.2 1.9 5903.8 1.2 4172.7 0.7 4828.9 1.0 6057.8 1.2
1000 3766.8 0.8 0.0 0.0 0.0 0.0 1486.4 0.3 0.0 0.0
1500 2717.8 0.6 1885.1 0.4 1885.1 0.3 3164.3 0.7 2513.5 0.5
2000 4927.9 1.0 3494.8 0.7 3494.8 0.6 2640.4 0.5 2238.0 0.4
2500 3374.0 0.7 3374.0 0.7 6714.0 1.1 1256.8 0.3 628.4 0.1
3000 17821.4 3.7 4172.7 0.8 20510.6 3.4 6686.2 1.4 6686.2 1.3
3500 21783.6 4.5 10230.5 2.0 21139.0 3.5 16740.3 3.5 16111.9 3.1
4000 25537.6 5.2 10230.5 2.0 24683.3 4.1 16111.9 3.4 14855.2 2.8
4500 36174.2 7.4 29109.8 5.8 34737.4 5.7 26568.4 5.5 29533.9 5.7
5000 159247.9 32.7 207542.1 41.2 230191.7 37.9 191164.9 39.8 223048.0 42.7
5500 119869.5 24.6 149102.7 29.6 181150.1 29.8 140140.4 29.2 152697.2 29.3
6000 49246.4 10.1 53054.7 10.5 46500.2 7.6 42377.8 8.8 40100.9 7.7
6500 6686.2 1.4 6283.8 1.2 6912.2 1.1 7314.6 1.5 6283.8 1.2
7000 628.4 0.1 628.4 0.1 1885.1 0.3 1256.8 0.3 628.4 0.1
7500 628.4 0.1 1285.1 0.3 1256.8 0.2 628.4 0.1 628.4 0.1
8000 628.4 0.1 628.4 0.1 628.4 0.1 628.4 0.1 628.4 0.1
Total 487391.7 100.0 503177.3 100.0 607931.9 100.0 480635.6 100.0 521878.2 100.0
  2011_09 2010_10 2009_10 2008_10 2007_10
Contour Area % Area % Area % Area % Area %
0 111976.6 6.8 188062.3 6.4 533296.6 10.9 185548.8 5.9 218392.8 6.7
500 41247.1 2.5 47128.6 1.6 952328.4 19.4 47531.0 1.5 109753.1 3.4
1000 126556.5 7.7 197488.0 6.7 390727.4 8.0 197714.0 6.3 236673.6 7.3
1500 184694.4 11.2 417068.5 14.1 573907.7 11.7 388305.3 12.3 454947.7 14.0
2000 145332.4 8.8 304891.6 10.3 359504.6 7.3 302405.9 9.6 321080.9 9.9
2500 112882.6 6.8 205480.5 6.9 218952.8 4.5 223351.5 7.1 249665.3 7.7
3000 100544.6 6.1 149656.5 5.1 185774.7 3.8 169803.2 5.4 176804.6 5.4
3500 50474.8 3.1 105568.0 3.6 121933.7 2.5 115423.9 3.7 117507.2 3.6
4000 50877.1 3.1 86920.8 2.9 120108.8 2.5 103484.7 3.3 117683.6 3.6
4500 87929.9 5.3 214935.6 7.3 300326.2 6.1 275859.2 8.8 225362.6 6.9
5000 352463.8 21.3 610560.2 20.6 694321.3 14.2 678827.7 21.5 592111.2 18.2
5500 214404.7 13.0 342467.6 11.6 362801.7 7.4 364863.3 11.6 341210.8 10.5
6000 59696.2 3.6 78547.6 2.7 73520.6 1.5 87570.9 2.8 80432.7 2.5
6500 8169.0 0.5 8169.0 0.3 9425.7 0.2 8169.0 0.3 8797.3 0.3
7000 1885.1 0.1 1885.1 0.1 1885.1 0.0 1885.1 0.1 1885.1 0.1
7500 1256.8 0.1 1256.8 0.0 1256.8 0.0 1256.8 0.0 1256.8 0.0
8000 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0 628.4 0.0
Total 1651019.9 100.0 2960714.9 100.0 4900700.4 100.0 3152628.5 100.0 3254193.8 100.0

Table 2: Snow cover area in km2 on 500m elevation intervals from 0 to 8500m for January, April, July and October months from 2007 to 2011.

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.

References

Citation: Boori MS, Ferraro RR, Voženílek V (2014) NASA EOS Aqua Satellite AMSR-E Data for Snow Variation. J Geol Geosci 3:e116.

Copyright: © 2014 Boori MS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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