Comparison of NDSI and LSU Methods in Estimation of Snow Cover by MODIS (Case Study: Saghez Watershed Basin)

Document Type : Full length article


1 MA in RS and GIS, Faculty of Geography, University of Tehran, Iran

2 Professor of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran

3 Assistant Professor of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran

4 Assistant Professor of Agriculture, University of Lorestan, Iran

5 Assistant Professor of Natural Resources, University of Kurdistan, Iran


More than 30 percent of the Earth is covered by seasonal snow and about 10 percent by permanent glaciers. Approximately about 5 percent of global precipitation is snow. About 50 to 95 percent of the value is in polar areas. Spatial-temporal distribution of snow is important. The estimation of snow cover area provides valuable information on snow-melting water in terms of runoff and water supply of the watershed in mountainous regions. Snow is an important geophysical factor for climate through its role in the Earth’s albedo and hydrology. It is important role in water storage, agriculture, hydropower generation, and flooding in local scales. Snow creates an insulation to keep plants from the cold weather in winter. Therefore, it is necessary to study the snow cover area, snow depth, and snow water. The snow cover area is affected by environmental factors leading to different melting patterns which are important for a deterministic model.
The importance of snow has been recently understood by scientists and watershed managers in different snow studies. Applying remotely sensed data for such studies is cheaper, faster and easier than traditional approaches. One can also study larger areas using these data which is more beneficial. Snow cover area is the most accurate factor of snow which can be estimated using remotely sensed data. Different sensors have been applied to study snow areas with their own advantages and disadvantages.  Earth Observing System (EOS)-Terra was launched with 5 mounted sensors on December 18, 1999. One of the 5 sensors in EOS is MODIS. The sensor was embedded on Aqua satellite launched on May 3, 2002. Terra is a sun-synchronous satellite, elevated at 705 Km, with polar orbit. Terra passes occur at roughly 11:00 – 12:00 AM and 10:00 – 11:00 PM local standard time each day. MODIS is the biggest sensor in EOS. Its mission is to measure temperature, ocean color, vegetation and deforestation, clouds, aerosols, and snow covers. Different ground resolution, the capability of distinguishing cloud from snow provides complete coverage of the Earth. Therefore, this sensor has very high potentials in snow cover studies. The sensor has a radiometric resolution of 12 bites, spectral resolution of 36 bands from 0.4 till 14.4 µ. It also has a high temporal resolution (Repeating cycle of 1 to 2 days) and moderate spatial resolution (250, 500, and 1000 m).
Materials and Methods
MODIS satellite images are used for estimating snow cover area in this study. In this research, two common techniques including Normalized Difference Snow Index (NDSI) and Linear Spectral Unmixing (LSU) were used. In order to determine the accuracy of NDSI and LSU approaches (MODIS images), the IRS images were selected since their spatial resolution is very high (24 m). The MODIS pixels were interpreted as snow using Snow map algorithm. A number of 11 similar sites on MODIS and IRS were selected to compare the results. The snow area of MODIS images (NDIS and LSU) were compared with the corresponding value on IRS images using t-student test and regression coefficients. A scatter plot of non-snow against snow was used. A regression model was established for the same purpose.
Results and discussions
The scatter plots of the snow areas produced by crossing IRS versus that estimated by NDSI and LSU approaches were separately investigated. The regression model of each scatter plot was then calculated. The results show that both NDSI and LSU methods have high efficiency to compute snow cover areas; however, the LSU method shows a little more efficiency than the NDSI method. Another comparative investigation over the NDSI and LSU methods was performed by t-student test with significant level of 5%. The t-student test indicated that the LSU method has a higher potential in estimating snow cover area in the study area than the NDSI method.
The use of remote sensing techniques, satellite images, GIS, and statistical methods for studying and monitoring ground features such as snow is very beneficial due to their lower expenses and ease of use. Among them, high temporal and spatial resolution images are preferred. Due to the importance of snow in the study area, the snow cover area was computed using MODIS and IRS satellite images to determine the best approach. The results showed that to use the methods we apply subpixels to calculate snow cover area. The study reveals that remote sensing techniques can provide reliable information on snow and can overcome the problems stemming from traditional approaches


Main Subjects

داداشی خانقاه، س. (1387). بررسی پوشش برف در حوضة کرج و لتیان با به‌کارگیری الگوریتم‏های پردازش تصویر، پایان‌نامة کارشناسی ارشد، دانشگاه شهید بهشتی.
رضایی، ع. (1391). مفاهیم آمار و احتمالات، نشر مشهد.
شرکت سهامی آب منطقه‌ای کردستان (1387). آمار سالانة استان کردستان.
شفیع‌زاده مقدم، ح. (1387). امکان‌سنجی ارتقای توان سنجندة MODIS با به‌کارگیری هم‌زمان سنجندة ASTER جهت برآورد مساحت سطح برف، پایان‌نامة کارشناسی ارشد، دانشگاه تربیت مدرس.
Barnett, T.P.; Menil, L.D.; Schlese, U.; Roeckner, E. and Latif, M. (1989). The Effect of Eurasian Snow Covers on Regional and Global Climatic Variations, Journal of Atmospheric Science, 46(5): 661-685.
Dadashi Kh, S. )2008(. Detection Snow Cover using Image Processing Algorithms in Karaj and Latyan Watershed, Master Thesis, Shahid Beheshti University.
Dozier, J. )2000(. Remote Sensing of Alpine Snow Cover Invisible and Near-Infrared Wavelengths, NASA Goddard Space Flight Center, USA.
Flueraru, C.; Stancalie, G.; Savin, E. and Craciuescu, V. (2006). Validation of MODIS Snow Cover Products in Romania, Methodology and conclusions, Paper Presented at the 9th AGILE Geographic information Science, Visegrad Hungary, 21-28.
Foppa, N.; Wunderle, S. and Hauser, A. (2002). Spectral Unmixing of NOAA-AVHRR Data for Snow Cover Estimation, Proceedings of EASEL-LISSIG-Workshop Observing our Cryosphere from Space, Bern, pp.155-162.
Hoinkes, H. (1967). Glaciology in the International Hydrological Decade, IAHS Commission on Snow and Ice: Reports and Discussions, 79: 7-16.
Klein, A.G.; Hall, D.K. and Riggs, G.A. (1998). Global Snow Cover Monitoring Using MODIS, 27th International Symposium on Remote Sensing of Environment, 8-12 June 1998, Tromsø, 363-36.
Kurdistan Regional Water Authority (2008). Annual Statistics Kurdistan.
Nolin, A.W. and Stroeve, J. (1997). The Changing Albedo of the Greenland Ice Sheet: Implications for Climate Modeling. Annals of Glaciology, 25:51-57.
Rango, A. (1993). Snow Hydrology Processes and Remote Sensing, Hydrological Processes, 7(2):121-138.
Rango, A. and Shalaby, A. (1998). Operational Applications of Remote Sensing in Hydrology: Success, Prospects and Problems, Hydrological Sciences Journal (Journal Des Sciences Hydrologiques), 43(6): 947-968.
Rees, G.W. (1959). Remote Sensing of Snow and Ice, Cambridge University England.
Rezaei, A. (2012). Probability and Statistics, Mashhad Publishing Co.
Ross, B. and Walsh, J. (1986). Synoptic-Scale Influences of Snow Cover and Sea Ice, Monthly Weather Review, 114(10):1795-1810.
Sabol, D.E.; Adams, J.B. and Smith, M.O. (1992). Quantitive Subpixel Spectral Detection of Targets in Multispectral Images, Journal of Geophysical Research, 97: 2659-2672.
Salomonson, V.V. and Appel, I. (2003). Estimating Fractional Snow Cover from MODIS using the Normalized Difference Snow Index, Elsevier Journal, 89: 351-360.
Salomonson, V.V. and Appel, I. (2006). Development of the Aqua MODIS NDSI Fractional Snow Cover Algorithm and Validation Results, IEEE Transactions on Geoscience and Remote Sensing, 44(7).
Samantha, K.M. (2004). Hydrological Modeling Using MODIS Data for Snow Covered Area in the Northern Boreal Forest of Manitoba, Thesis for the Degree of Masters of Science, University of Calgary.
Shafizade, M.H. (2008). Feasibility Promote the Power of MODIS Sensor use Simultaneous ASTER Images to Estimate the Surface Area of Snow, Master Thesis, Tarbiat Modares University.
Singh, P. and Singh, V.P. (2001). Snow and Glacier Hydrology, Kluwer Academic Publishers, Boston.
Winther, J.G and Hall, D.K. )1999(. Satellite-Derived Snow Coverage Related to Hydropower Production in Norway: Present and Future, International Journal of Remote Sensing, 20(15): 2991-3008.