Use of Indices Applied to Remote Sensing for Establishing Winter–Spring Cropping Areas in the Republic of Kazakhstan

Asset Arystanov, Natalya Karabkina, Janay Sagin (), Marat Nurguzhin, Rebecca King and Roza Bekseitova
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Asset Arystanov: Department of Remote Sensing, National Center for Space Research and Technology, 15 Shevchenko, Almaty 050010, Kazakhstan
Natalya Karabkina: Department of Remote Sensing, National Center for Space Research and Technology, 15 Shevchenko, Almaty 050010, Kazakhstan
Janay Sagin: School of Information Technology and Engineering (SITE), Kazakh British Technical University, Almaty 050010, Kazakhstan
Marat Nurguzhin: Department of Remote Sensing, National Center for Space Research and Technology, 15 Shevchenko, Almaty 050010, Kazakhstan
Rebecca King: School of Information Technology and Engineering (SITE), Kazakh British Technical University, Almaty 050010, Kazakhstan
Roza Bekseitova: Department of Cartography and Geoinformatics, Al-Farabi Kazakh National University, 71 al-Farabi, Almaty 050010, Kazakhstan

Sustainability, 2024, vol. 16, issue 17, 1-20

Abstract: Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government to strengthen food security. The proper monitoring of crop production is vital to government agencies, as well as insurance and banking structures. These organizations offer subsidies through different levels support. Some farmers already use farmland soil monitoring combined with adaptive combinations of different crops. These include winter–spring plowing crop programs. Winter wheat crops are generally more adaptive and may survive summer droughts. Kazakhstan is a large country with large plots of farmland, which are complicated to monitor. Therefore, it would be reasonable to adapt more efficient technologies and methodologies, such as remote sensing. This research work presents a method for identifying winter wheat crops in the foothills of South Kazakhstan by employing multi-temporal Sentinel-2 data. Here, the researchers adapted and applied a Plowed Land Index, derived from the Brightness Index. The methodology encompasses satellite data processing, the computation of Plowed Land Index values for the swift recognition of plowed fields and the demarcation of winter wheat crop sowing regions, along with a comparative analysis of the acquired data with ground surveys.

Keywords: plowed land index; winter wheat; spring plowing; South Kazakhstan; sentinel (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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