Extended Abstract



Introduction

The advent of high-resolution remote sensing satellites has motivated various researchers to use multiple techniques and digital data sets to accurately map the snow-covered area on a regional and global scale. Indices are the most common automated methods used for snow and ice cover mapping. S3, NDSI and NDSII-1 indices are examples of indices that have the maximum power of discrimination between snow, ice, clouds and vegetation and have proven their role in snow mapping through satellite data. These indices use the reflective and absorptive properties of snow in the visible, NIR and SWIR bands. Although these indices are often used for snow cover mapping and their importance in extracting snow cover in different conditions has been proven, but water and cloud are the two main impurities that challenge the extraction of snow pixels from multispectral images.



Methodology

The method of this research includes choosing the right area, collecting data and satellite images of Landsat 8 and using spectral indices. OLI and TIRS images of Landsat satellite were used to extract snow cover in the studied area. For this purpose, Landsat 5 and 8 satellite images for the month of April of 1994, 2001, 2013 and 2023 were downloaded. All images were downloaded from the US Geological Survey website. Then, different bands of NDSI, NDSII-1, S3 and SWI indexes were used to estimate the snow cover, and then LST index was used to check the changes in ground surface temperature.

In this research, the performance of snow spectral indices was tested using Pearson's correlation and coefficient of determination. Pearson's correlation is a statistical indicator for establishing linear relationships between two variables.



Results and Discussion

In 2023, all indicators obtained values close to each other. The lowest amount belongs to the S3 index and the highest amount of snow cover in these indices belongs to the NDSII-1 index. The difference between the lowest and the highest amount is equal to 3.27 square kilometers, which covers 1.3% of the surface of the studied area and shows that all the indicators have reached values close to each other. In 2013, the difference between the lowest and highest results in the indices was equal to 3.27 and it shows that the difference between this year and 2023 was at the same level.

In the discussion of the use of indicators, the main goal is to accurately separate the complications that have interfered with the detection of snow cover. These phenomena in the studied area include water sources and cloud cover, which can be clearly seen from the produced images. In order to avoid this occurrence, several bands have been used in these indices so that this problem can be overcome. Therefore, in this section, Pearson's correlation coefficient and coefficient of determination have been used between each of the indices and bands used. These bands include green, red, NIR and SWIR bands. But these correlations were established between these bands and the SWI index. The reason for this is to find out the relationship between this index and the used bands. Among these bands, it can be seen that the SWI index has a strong negative relationship with the short-wave infrared or SWIR band. The correlation between these two indicators in the years 1994, 2001, 2013 and 2023 was equal to -0.93, -0.73, -0.89 and 0.87, respectively, in which the highest rate belonged to 1994. It means the same year with the highest amount of snow cover. The most positive correlation between this index was with the green band. Only in 1994, the red band had the highest correlation. In the rest of the years, the highest correlation was 0.86, 0.93, and 0.94 for the years 2001, 2013, and 2023, respectively. The lowest positive correlation belongs to the near infrared band.

In the last part of the research, changes in ground surface temperature in this area were investigated to determine the effect of changes in ground surface temperature or LST on changes in snow cover. The lowest temperature in the region was -4.6 degrees Celsius on April 10, 1994. The expansion of snow covers this year has been a sign of heavy rainfall in this area during that time period. The highest recorded temperature for this region in the desired period of time is 34.46 degrees Celsius and it happened on April 29, 2001.



Conclusion

In this research, NDSI, NDSII-1, S3 and SWI indices were used, among which the SWI index is one of the newest indices in this field. In order to investigate the changes in snow cover, images from 1994 to 2023 were considered in a 29-year period. In 2023 and based on SWI indicators, the area of snow cover was equal to 28.41 square kilometers. The correlation coefficient between this index with the red, green, NIR and SWIR bands showed that the highest negative correlation between the SWI index and the SWIR band was established at -0.93 in 1994, and the highest positive correlation between this index and the green band was established in 1994. It was 0.94 in 2023. In the discussion of changes in the temperature of the earth's surface, it was also observed that the lowest temperature was -4.6 degrees Celsius in 1994 and the highest temperature was 34.46 degrees Celsius in 2001.