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Analysis of MODIS Snow-Cover Map Changes During Missing Data Period
B. Rayegani, S. J. Khajeddin, S. Soltani , S. Barati,
Volume 12, Issue 44 (7-2008)
Abstract
‏Snow is a huge water resource in most parts of the world. Snow water equivalent supplies 1/3 of the water requirement for farming and irrigation throughout the world. Water content estimation of a snow-cover or estimation of snowmelt runoff is necessary for Hydrologists. Several snowmelt-forecasting models have been suggested, most of which require continuous monitoring of snow-cover. Today monitoring snow-cover patches is done through satellites imagery and remote sensing methods. MODIS have smaller Spatial Resolution and more bands in comparison with Meteorology Satellite like NOAA. Therefore, in this research we used MODIS data for creating snow cover imagery. Existence of cloud in the study area is a major problem for snow cover monitoring. Therefore, in this research snow cover area changes were estimated without MODIS data period, but with DEM imagery and regressions between temperature, height and aspect. For this purpose, on 10 Esfand when the image was suitable we estimated the snow cover area. In comparison with real image, precision of the method was confirmed.
Estimation of Snow Cover Level in Ardebil and Sarein Cities Using Sensor Data Sentinel2 MSI Satellite
S. Asghari Saraskanrood, R. Modirzadeh,
Volume 25, Issue 3 (12-2021)
Abstract
Snow cover is one of the important climatic elements based on which climate change may have a special effect. In general, climate change may be reflected in different climatic elements. Therefore, it is very important to study and measure changes in snow level as one of the important sources of water supply. Ardebil and Sarein cities are located at 48° 18׳ east longitude and 38° 15׳ north latitude. In this study, Sentinel-2 optical satellite was used to monitor the snow cover surface in 2018, and NDVI, S3, NWDI, NDSI, Cloud mask indices were applied to detect snow-covered surfaces using ArcGIS and Snap software. Next, to validate the snow maps extracted from the images, it was compared with the snow data in terrestrial stations using linear regression in MATLAB software and to evaluate the accuracy of the model statistical indices including RMSE, MSE, BIAS, CORR were used. The present study showed that according to Ardabil city climatic conditions, maximum-snow covered area in January with an area of 356.52 km2 and minimum snow-covered area in March with an area of 96.10 km2. The highest snow cover is observed in the high slope areas in the western slopes (Sabalan Mountain Heights) and the lowest snow cover is observed in the lower eastern slopes. The results of linear regression with generalization coefficient are 85% and the results of statistical indices of error are equal to MSE: 0.086, BASAS: 0.165, CORR: 0.924, and RMSE: 0.03. Correlation relationships between terrestrial data and estimated snow maps showed a high degree of correlation. This result is statistically significant at the 99% level. The use of optical images in estimating snow levels is very cost-effective due to the size of the areas and the high cost of installing snowmobiles. The results obtained in the present study indicated that traditional radar images with high spatial resolution and good correlation with terrestrial data can be a good alternative to snowmobiling ground stations at high altitudes or in passable areas.

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