Introduction: As an important type of precipitation, snow is especially important in the hydrological cycle. This importance can be examined and analyzed from several aspects such as water supply in other seasons. The most important aspect is the possibility of creating hazards for human beings and human infrastructure (snow avalanches, floods during seasonsof snowmelt). Therefore, it is necessary to study the snow phenomenon and its covered surfaces in winter. Monitoring the changes in this important climatic phenomenon has always been considered important by researchers and planners. Remote sensing methods have revolutionized the field of natural environment monitoring since their inception. Snow depth is an example of what can be monitored and evaluated by remotely sensed data and techniques. Materials & Methods: The present study seeks to evaluate the efficiency of several important remote sensing indices in monitoring snow depth, andalso to introduce and evaluate a proposed spectral index. To reach this aim, satellite images of Landsat 8 and Sentinel 2 have been used. These images were received from the relevant portal and used to calculate snow indicesafterinitial corrections. Four spectral indices were usedto extract snow covered surfaces. These indices include: NDSI-S3-NDSII-SWI. These indices are based on reflection from snow covered surfaces in light reflection and absorption spectra of snow covered surfaces. Light reflection from snow covered surfaces in the visible spectra and absorption in the short infrared spectrum allow automatic detection and extraction of snow covered surfacesin remote sensing multispectral images. The above mentioned indices have the ability to extract snow, but they fail to differentiatebetween snow and other related phenomena such as water (in the absorption band) and light-color salt marshes (in the reflection band) and thus, similarity of the spectra occurs. This spectral mixing which occurs due to the similarity of the reflections, cannot be eliminated even when threshold limits are defined. Thus, the extracted snow cover includes not only snow, but also other similar zones. To solve this problem and extract snow covered surfaces correctly, a new index is presented in this paper based on principal component analysis (PCA) and the first component of the set, and short wave infrared (SWIR) spectrum reflection. Using the first component of the set with the highest variance makes the difference between reflectance of snow and similar phenomena visible and thus, solves the issue of spectral mixing to a very large extent. The proposed new index called PCSWIRI is also evaluated and validated along with 4 other indices in the present paper. Results: & Discussion: Spectral indices introduced in the previous section were examined and evaluatedusing 7 sets of images (4 Landsat images and 3 sentinel 2images) captured in different days of winter from the main study area (Lake Urmia in the northwest) and two other study areas. The Results: indicate efficiency of the proposed index in the extractionof snow covered surfaces. The proposed index has improved the accuracy of snow cover extractionin the whole collection of images. This increased accuracy has been confirmed withstatistical evaluation criteria, such as kappa coefficient, overall accuracy and in the visual review of indices(comparing to the composition of the original image). The main study area includes Lake Urmia, an important geographic feature containing water and salt and a mixture of the two, which makes its spectrum similar to snow. This lake is incorrectly identified by other indices as a snow covered surface. Like the main study area, the first study and assessment area contains salt covered zones (salt lake). Despite the spectral similarity between snow and salt, the proposed index has been able to distinguish between this phenomena (in both regions) and snow and to extract only realsnow covered surfaces. In addition, visual review of existing water bodies (Dam Lake) and 5 evaluated indicesindicates higher accuracy of the proposed index. In order to automate the process of calculation in the proposed spectral indices, a software was also providedbased on MatLAB. Conclusion: The findings of the present study indicates higher accuracy and efficiency of the proposed index (PCSWIRI) for snow cover extraction. Snow cover maps are very useful in various hydrological, climatic, precipitation-runoff modeling studies, and etc. Therefore, increasing the accuracy of snow cover maps is of great importance and Results: inimprovedaccuracy and reliability of modeling processes.

The spatial resolution of satellite data in determining the area covered with snow was examined in this research. For this purpose, the Advanced Very High Resolution Radiometer (AVHRR) of NOAA satellite, with a nominal resolution of 1,100 m and the TM radiometer of Landsat satellite, with the nominal resolution of 28.5 m, were chosen and the data provided by them were compared. According to this research, which focused on snowy areas of Karun river basin in Iranian Zagros mountain range, the approximate areas derived from images of snow-covered regions produced by NOAA and Landsat satellites in two different dates, one at the beginning of the snow melt season and another at the end of this season, show a discrepancy by 15% and 17%, respectively. Furthermore, the research shows the spatial overlap of polygons by the two satellites is considerably less than the overlap of the images. However, the overlap area in various polygons is significantly correlated with the total area of the snow-covered region. Additionally, as the spatial resolution of satellite data reduces the risk of overestimation of snow-covered area increases. Another issue that must be considered is that only if the size of snow fields must at least is equal to some pixels as viewed by the radiometer distinguishing the fields will be possible.

Variation of snow cover area (SCA) in small to large scale catchment can bestudied using MODIS snow products on daily to monthly time step since theyear 2000. However, one of the major problems in applying the MODIS snowproducts is cloud obscuration which limits the utilization of these products. Inthe current study, variation of SCA was investigated in Karoun basin, westernpart of Iran, using MODIS 8-day snow cover product (MOD10A2). More overin order to overcome the cloud barrier in application of snow cover products, asimultaneous employment of the images from both MODIS optical sensor andAMSR-E microwave sensor was recommended. Meeting our target, thecombination of MODIS and AMSR-E daily images was exercised to accomplishsnow cover area in daily interval and afterwards, a comparison was madebetween the result and those which had been obtained by the sole utilization ofeither of them while the weather had been either cloudy and not been overcast. Validation of snow cover gained by combined images was additionallycompared with the discharge of one of the catchments existing in Karoun basin. The results demonstrate that regardless of the fact that microwave data, featuringa coarse spatial resolution, can penetrate the cloud cover, on average, AMSR-Eimages approximately show 16% more snow cover in comparison to MODISimages. The results also illustrate that the correlation existing between SNOWCOVER rate of AMSR-E and MODIS images during cloudless days, the differenceof average snow cover area decreases from 16% to 5%. Moreover, the upshot ofvalidation by the exercise of daily discharge data indicates that by possessing acorrelation coefficient of 0. 66, the correlation of snow cover and discharge incombined images features a higher accuracy in comparison to MODIS imageswith a correlation coefficient of 0. 55.