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Arid Land Geography >

2022 , Vol. 45 >Issue 2: 488 - 498

DOI: https://doi.org/10.12118/j.issn.1000–6060.2021.236

Earth Information Sciences

Wheat yield estimation with remote sensing in northern Kazakhstan

  • Hanmin YIN ,
  • JIAPAER Guli ,
  • Tao YU ,
  • UMUHOZA Jeanine ,
  • Xu LI
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  • 1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Research Center for Ecology and Environment of Central Asia of Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China

Received date: 2021-05-20

  Revised date: 2021-08-03

  Online published: 2022-04-02

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Abstract

Kazakhstan’s flour export volume ranks first in the world and is called the Central Asian granary. Its northern regions, including North Kazakhstan, Aqmola, and Qostanay, are the world’s important wheat and flour exporters. The proportion of wheat planting structures has reached 86%. Since 2010, its wheat and barley output has ranked 12th in the world, and its export volume has ranked 5th in the world. However, the region is rain-fed and lacks effective irrigation measures. It often suffers from drought stress due to its location in the monsoon climatic zone, resulting in a large-scale reduction in spring wheat production, which severely restricts the economic development of countries that rely on wheat imports. The estimation of wheat production plays a vital crucial role in promoting regional food security and social stability, especially in responding to the food crisis in the post-epidemic era and achieving zero hunger advocated by the United Nations. In this paper, the rain-fed farming area in northern Kazakhstan is used as the research target area. An analysis of the optimal vegetation index for spring wheat yield estimation was conducted based on the statistical spring wheat yield and vegetation index. Various methods, such as regression, random forest, support vector machine, and neural network, are evaluated for the accuracy of wheat yield estimation. In North Kazakhstan, the best vegetation index for estimating spring wheat yield is the greenness chlorophyll index from 2007 to 2016. The forecast time can be advanced to July 12th from 2007 to 2016. In Aqmola, the best vegetation index for estimating spring wheat yield is the greenness dynamic wide-band vegetation index (WDRVIgreen). The forecast time can be advanced to August 5th. In Qostanay, the best vegetation index for estimating spring wheat yield is the WDRVIgreen. The forecast time can be advanced to July 12th. In this paper, MODIS NPP products are selected to verify the estimation results. Through correlation analysis with NPP shows that the neural network has higher accuracy in estimating spring wheat yield in the three northern states of Kazakhstan than other models. Neural networks accounted for 44%, 94%, and 77% of correlations in North Kazakhstan, Aqmola, and Qostanay states, respectively.

Key words： rain-fed wheat farming area; remote sensing yield estimation; vegetation index; regression model; machine learning; northern Kazakhstan

Cite this article

Hanmin YIN , JIAPAER Guli , Tao YU , UMUHOZA Jeanine , Xu LI . Wheat yield estimation with remote sensing in northern Kazakhstan[J]. Arid Land Geography, 2022 , 45(2) : 488 -498 . DOI: 10.12118/j.issn.1000–6060.2021.236

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