Estimation of changes in snow depth in Ardabil and Sarein city using Sentinel1 satellite data with Radar interferometry method

Document Type : Original Article

Authors

1 Associate Professor of Geomorghology, Department of Geography, Faculty Literature of Humanities, Mohaghegh Ardabili University, Ardabil.

2 M.Sc. Student in RS and GIS, Mohaghegh Ardabili University, Ardabil

Abstract

Snow as an important part of the hydrological cycle, is considered to be a major source of fresh water in many areas above 45 degrees latitude, so it is important to study and measure changes in snow levels as an important source of water supply.In the present study, first using Sentinel2 optical satellite images in 1397 in Ardabil and Sarein, snow cover level was obtained through NDSI index, then in order to monitor snow depth changes in the study area, SentinelA1 macro images and DINSAR technique were used. Finally, in order to validate the snow depth maps extracted through radar images, the snow depth data in land snowfall stations were compared using linear regression in MATLAB software. The results of linear regression with a generalization coefficient of 85% and the results of error statistical indicators are equal to 0.86-MSE, 0.165-BIAS, 0.924-CORR and RMSE equal to 0.043. The correlations between ground data and snow depth estimation maps show a high degree of correlation. This result is statistically significant at 99%. The results of the present study showed that according to the climatic conditions of the study area, the values of snow depth related to January with a maximum amount of 33 cm and the lowest values of snow depth in March with a minimum of 10 cm. The lowest snow depth was in the eastern slopes and the highest in the western slopes.

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References

  • Akerson J, Yang Y, Hara Y, Wu B, Kong J (2000) Automatic phase unwrapping algorithms in Synthetic Aperture Radar (SAR) Interferometry. IEICE Transactions on Electronics, E83C (12):1896-1904., A(1):405-408
  • Almodarresi A, Hatami J, Sarkaregar A (2016) Computational physical properties of snow using differential radar interferometry and TerraSAR-X and MODIS. Remote Sensing and Geographic Information Systems in Natural Resources 7(2):59-76 (In Persian)
  • Chen H (2018) Wet snow mapping in Southern Ontario with Sentinel-1A Observations. Waterloo, Ontario, Canada, 2018. University of Waterloo Library 200 University Avenue West Waterloo, Ontario, Canada N2L 3G1 519 888 4883:1-87
  • Eckerstorfer M, Vickers H, Malnes E, Grahn J (2019) Real-time automatic snow avalanche activity monitoring system using Sentinel-1 SAR data in Norway. Remote Sensing 11, 2863:1-23
  • Elizabeth A, Burakowski E (2008) Trends in wintertime climate in the northestern United States1965-2005. Journal of Geophysical Research; 113. D20114:1-12
  • Hantel M, ehrendorfer M, Haslinger A (2002) Climate sensitivity of snow covers duration in Austria. International Journal of Climatology 20(6):615–640
  • Heilig A, Wendleder A, Schmitt A, Mayer CH (2019) Discrimination of snow and firn for alpine glaciers using Sentinel-1data: A case study in Rufenthal Austria. Geosciences 69:1-22
  • Hori M, Sugiura A,Kobayashi K, Aoki T, Tanikawa T (2017) A 38-year (1978–2015) Northern Hemisphere daily snow cover extent product derived using consistent objective criteria from satellite-borne optical sensors. Remote Sensing of Environment 191:402-418
  • Jesenko D, Mongus D, Liu S, Čekada M (2019) Detection of snow levels in the Slovenian Alps at different seasons using Sentinel-1. International Conference on Engineering, Science, and Industrial Applications (ICESI), Tokyo, Japan:1-4
  • Kaviani M, Alijani B (2011) Fundamentals of climatology. Samat Publications, 16th Edition, (In Persian)
  • Liu Y, Li L, Yang J, Chen X, Hao J (2017) Estimating snow depth using multi-source data fusion based on the D-InSAR method and 3DVAR Fusion Algorithm. Remote Sensing 9, 1195:1-17
  • Maghsoudi Y, Mahdavi S (2013) Fundamentals of radar remote sensing. First Edition, Tehran, Sharif University of Technology, Tehran (In Persian)
  • Majumdar S, Thakur P, Chang L, Kumar SH (2018) X-Band polarimetric SAR copolar phase difference for fresh snow depth estimation in the northwestern Himalayan watershed.IGARSS IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 4102-4105
  • Meyer F (2018) Sentinel-1 InSAR processing using the Sentinel-1 toolbox, Alaska satellite facility. adapted from coursework developed 4(1):1-18
  • Nagajothi V, Priya C, Sharma P (2019) Snow cover estimation of Western Himalayas using Sentinel-2 high spatial resolution data. Indian Journal of Ecology, 88-93
  • Nagler T, Rott H, Ripper E, Bippus G (2016) Advancements for snowmelt monitoring by means of Sentinel-1 SAR. Remote Sensing 8:348:1-17
  • Panahi H (2012) ENVI4.8 application guide with ArcGis 10.1. Yazd Azad University Press, Yazd (In Persian)
  • Patil A, Singh G , Rüdiger CH (2019) A novel approach for the retrieval of snow water equivalent using SAR data. IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 3233-3236
  • Raygani B, Khajehuddin S, Kopai S, Barati S (2008) Calculation of changes in snow cover maps prepared from MODIS satellite images in periods without images. Agricultural and Natural Resources Science and Technology, 12th year, number 44 (In Persian)
  • Seifi H, Feizizadeh B (2019) Application of interferometric method and radar remote sensing images for estimating the depth of snow and water discharge, Case study: (Yamchie Basin). Iran-Water Resources Research 15(1):311-34 (In Persian)
  • Snapir B, Momblanch A, Jain S K, Waine T W, Holman I P (2019) A method for monthly wet and dry snow mapping using Sentinel-1 and MODIS Application in the Himalayan River Basin. International Journal of Applied Earth Observation and Geoinformation, Ðolume 74:222-230
  • Tsai Y, Dietz A, Oppelt N, Kuenzer C (2019) Wet and dry snow detection using Sentinel-1 SAR data for mountainous areas with a machine learning technique. Remote Sensing 11, 895:1-29
  • Tsai Y, Dietz A, Oppelt N, Kuenzer C (2019) Remote sensing of snow cover using spaceborne SAR: A review. Remote Sensing 11, 1456:1-44
  • Varade D, Dikshit O (2018) Estimation of surface snow wetness using Sentinel-2 multispectral data. Remote Sensing and Spatial Information Sciences, IV-5:223-228
Iran-Water Resources Research
Volume 16, Issue 1
May 2020
Pages 394-407

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History

  • Receive Date: 28 February 2020
  • Revise Date: 24 May 2020
  • Accept Date: 29 May 2020

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