عنوان مقاله [English]
Background and objective: Considering the important role of snow in the groundwater cycle, the study of snow characteristics, especially in mountainous regions, seems necessary. Remote sensing technology can be used to study large areas with high spatial and temporal resolution. Synthetic aperture radar sensors with large frequency bands, small wavelengths, and high permeability are preferred in this type of study. Differential Radar interferometry technique Although the volume of information derived from interferometric analysis is high is a powerful tool in calculating the depth of snow, and the Sentinel data set is preferred due to easy access in interferometric studies. On the other hand, the relationship between LST with snow characteristics is considered to be a lot of researchers. In this study, the radar interferometry technique for estimating the depth of snow, as well as the Google Earth Engine, cloud system, has been used to estimate the snow characteristics, including the depth and surface of the snow cover. Also, the relationship between the component of temperature and snow surface and depth is examined.
Materials and methods: The Liqvan watershed with an area of 185 kilometers is located in the northwest of the country and East Azerbaijan province. In this study, for extraction of the depth of snows from 4 radar images of Sentinel 1 related to the time interval of December until March 1398 and a radar image associated with September 1398 in SLC format to implement radar interferometry in SARSCAPE software Used. To increase accuracy part of the work was used from the Google Earth Engine system. For this purpose, to extract the surface of the snow cover and its area of the NDSI daily product of the Modis sensor and the monthly NDSI- DEPTH product was used for extraction of the average depth of snow of each snow month in the Google Earth Engine Cloud System. Also, the Daily Product of Mod11A1 Modis Sensor was used to prepare a temperature map to examine the relationship between temperature and snow characteristics.
Results: Investigating the map of snow surfaces in the area of all months of the study period in the region showed the highest concentration of snow surfaces in high regions. Due to the outputs of the Google Earth Engine system, the highest and lowest snow cover level is calculated by January with 180 kilometers and December with a value of 83 km. The average and the lowest amount of the depth of snow is related to the February and December months, which utilizes the radar interferometry technique of 32 and 9 centimeters and uses the Snow depth- Inst product in the Google Earth Engine system 24 and 4 centimeters Has shown. The values for regression analysis were obtained between the time series of the surface temperature and the surface of the snow cover, respectively, 0/003 and -3/020 for the parameters of Sig and Z. The R2 variable was also obtained 0/47 about the correlation of the depth of snow and lst.
Conclusion: The results of this study indicate the ability of both radar interferometry technique and coding in the Google Earth Engine in calculating the depth of snow.
Maps and measures of the depth of snow can be an appropriate tool for managing water resources in the region for various uses. Also, the results of regression coefficients showed a significant relationship between the LST variable and the depth of snow and snow cover. So that the inverse relationship between the two components of LST and the snow cover (SC) and LST, and the depth of snow, as well as the direct relationship between reduced temperature and LST, showed.