Document Type : Research Paper
Department of Water Science Engineering, Faculty of Agriculture, Bu Ali Sina University, Hamedan, Iran.
Professor, Department of Water Engineering, Bu Ali Sina University in Hamedan
Department of Civil Engineering, Faculty of Engineering, Bu Ali Sina University, Hamedan, Iran.
Remote sensing is a fast and cost-effective solution in preparing and presenting this data to climate models due to the difficulty of preparing snow meteorological data in the field. The presence of clouds is one of the problems of satellite images that cause temporal-spatial fragmentation of snow data and increase its resolution efficiency. This study aims to provide a suitable framework for removing the effect of clouds on satellite images and generating the satellite snow metering data without disturbing cloud effects. For this purpose, first, daily MOD10A1 images of the MODIS sensor products were refined (temporally and spatially) using local snow depth data, average temperature, and daily rainfall of the Lake Urmia catchment and western part of the Caspian Sea basin. A multivariate linear regression model application showed that the normalized snow differential index (NDSI) values correlate with existing snow metering station data (r= 0.85 and RMSE= 0.047). Accordingly, the cloud-covered areas in the MODIS images were replaced with the values obtained from the model. Then, new NDSI values were calculated using geostatistical methods and the location of each pixel's location. By examining the relationship between the NDSI and the cumulative snowfall from January to March 2016, it was found that replacing the primary satellite images with corrected images can increase the R2 from 0.63 to 0.81. Therefore the proposed methodology could improve the accuracy of satellite snow metering.