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Estimation of Winter Wheat Yield Using the Principal Component Analysis Based on the Integration of Satellite and Ground Information

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Abstract

The results of the principal component analysis application for estimating the average regional winter wheat yield based on the integration of satellite and ground meteorological information for the southern regions of the Russian Federation are presented. The satellite indices such as NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and satellite product LAI (Leaf Area Index) were used. Meteorological information was represented by temperature, humidity deficit, total precipitation, and the Selyaninov hydrothermal coefficient. The parameters that have the greatest impact on the yield were selected. The components with the largest eigenvalues were extracted from this set of parameters using the principal component analysis. Equations for the dependence of the winter wheat yield on the extracted components were calculated. To check the equations, the expected yield for the period from 2012 to 2017 was calculated. The relative error varied within 5–12%. In all cases, the yield calculation error when using the principal component analysis is smaller than when using correlation and regression dependences.

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Authors and Affiliations

  1. All-Russian Research Institute of Agricultural Meteorology, 249020, Obninsk, Kaluga oblast, Russia

    A. D. Kleshchenko & O. V. Savitskaya

Authors
  1. A. D. Kleshchenko
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  2. O. V. Savitskaya
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Correspondence to A. D. Kleshchenko.

Additional information

Translated from Meteorologiya i Gidrologiya, 2021, No. 12, pp. 127-136. https://doi.org/10.52002/0130-2906-2021-12-127-136.

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Kleshchenko, A.D., Savitskaya, O.V. Estimation of Winter Wheat Yield Using the Principal Component Analysis Based on the Integration of Satellite and Ground Information. Russ. Meteorol. Hydrol. 46, 881–887 (2021). https://doi.org/10.3103/S1068373921120104

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  • DOI: https://doi.org/10.3103/S1068373921120104

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