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Comparison of Snow Indices in Assessing Snow Cover Depth in Northern Kazakhstan

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  • Zhanassyl Teleubay

    (Center for Technological Competence in the Field of Digitalization of the Agro-Industrial Complex, S.Seifullin Kazakh Agrotechnical University, Nur-Sultan 010000, Kazakhstan)

  • Farabi Yermekov

    (Center for Technological Competence in the Field of Digitalization of the Agro-Industrial Complex, S.Seifullin Kazakh Agrotechnical University, Nur-Sultan 010000, Kazakhstan)

  • Ismail Tokbergenov

    (Center for Technological Competence in the Field of Digitalization of the Agro-Industrial Complex, S.Seifullin Kazakh Agrotechnical University, Nur-Sultan 010000, Kazakhstan)

  • Zhanat Toleubekova

    (Faculty of Land Administration and Architecture, S.Seifullin Kazakh Agrotechnical University, Nur-Sultan 010000, Kazakhstan)

  • Amangeldy Igilmanov

    (Faculty of Land Administration and Architecture, S.Seifullin Kazakh Agrotechnical University, Nur-Sultan 010000, Kazakhstan)

  • Zhadyra Yermekova

    (Faculty of Physics and Technical Sciences, L.N.Gumilyov Eurasian National University, Nur-Sultan 010000, Kazakhstan)

  • Aigerim Assylkhanova

    (Faculty of Science and Informatics, University of Szeged, 6720 Szeged, Hungary)

Abstract

This study compares the performances of four existing snow indices (Normalized-Difference Snow Index, Normalized-Difference Snow and Ice Index, Difference Snow Index, and Ratio Snow Index) in estimating snow cover depth at three agricultural enterprises in different soil zones, namely, the “North Kazakhstan Agricultural Experimental Station”, A.I. Barayev “Research and Production Center for Grain Farming”, and “Naidorovskoe”. From 30 January to 9 February 2022, the snow cover thickness and density were measured at 410 and 285 points, respectively, throughout the agricultural enterprise territories. It was found that: (1) snow-covered territories were effectively classified using all spectral indices except both combinations of RSI; (2) the snow cover fraction maps generated from DSI most accurately classified the non-snow areas as forest plantations, settlements, and strongly blown uplands; (3) the maps generated from DSI-2 presented a clear pattern of objects in all three study areas; (4) the liquid water in snowpacks is available in excess for possible reservation and rational use in agriculture during the dry season. At the “North Kazakhstan AES”, A.I. Barayev “Research and Production Center for Grain Farming”, and “Naidorovskoe”, the RMSE varied from 5.62 (DSI-2) to 6.85 (NDSII-2), from 3.46 (DSI-2) to 4.86 (RSI-1), and from 2.86 (DSI-2) to 3.53 (NDSII-1), respectively. The DSI-2-based snow depths best matched the ground truth, with correlations of 0.78, 0.69, and 0.80, respectively.

Suggested Citation

  • Zhanassyl Teleubay & Farabi Yermekov & Ismail Tokbergenov & Zhanat Toleubekova & Amangeldy Igilmanov & Zhadyra Yermekova & Aigerim Assylkhanova, 2022. "Comparison of Snow Indices in Assessing Snow Cover Depth in Northern Kazakhstan," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9643-:d:881139
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    References listed on IDEAS

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