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Estimating snow density, depth, volume, and snow water equivalent with InSAR data in the Erciyes mountain/Turkey

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Abstract

In this study, it is aimed to calculate snow density, depth, and snow water equivalent with the help of single polarization TerraSAR-X data. In this context, Mount Erciyes/Turkey was chosen as the pilot study area and TerraSAR-X data with HH polarization were used. In addition, in situ measurements were performed simultaneously with the satellite pass to be used as input and validation data for the model to be used. Also, snow densities were obtained by inverse approach, Kriging, and ISO-4355 methods. Snow densities, in situ measurements, and SAR data were integrated into the produced D-InSAR snow depth model; snow depth, snow volume, and snow water equivalent were estimated. Consequently, it has been revealed that the snow depth, snow volume, and snow water equivalent parameters vary according to the snow density-calculated methods. Also, in this study performed with a single polarization, it is revealed that snow parameters can be accessed without multiple polarization. Snow densities were evaluated separately for 0.31g/cm3 and 0.36 g/cm3, and snow depth, snow volume, and snow water equivalent maps were produced. Our study, which is supported by in situ measurements, has been shown to be consistent with the snow depth results in the region. Besides, the results of the model produced in the study were found to be compatible with in situ measurements.

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Funding

This study was supported by Aksaray University Scientific Research Projects Coordination Unit in 2017-010 and European Space Agency under the project number 39890 entitled “Calculation of Snow Depth and Volume from Radar Images; Erciyes Mountain Example.”

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Correspondence to Ahmet Tarık TORUN.

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The authors declare that they have no competing interest.

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Responsible Editor: Biswajeet Pradhan

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TORUN, A.T., EKERCİN, S. Estimating snow density, depth, volume, and snow water equivalent with InSAR data in the Erciyes mountain/Turkey. Arab J Geosci 14, 1456 (2021). https://doi.org/10.1007/s12517-021-07873-y

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  • DOI: https://doi.org/10.1007/s12517-021-07873-y

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