Sadenova M.A., Khrapov S.S., Beysekenov N.A. Mathematical Modeling of Crop Yield Forecast Based on Field Monitoring and Remote Sensing Data

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https://doi.org/10.15688/mpcm.jvolsu.2023.3.5

Marzhan A. Sadenova
Candidate of Sciences (Chemistry), Leading Researcher, Priority Branch of the Veritas Center,
D. Serikbayev East Kazakhstan Technical University
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https://orcid.org/0000-0002-2870-6668
Dzerzhinskogo St, 12, 367025 Makhachkala, Russian Federation

Sergey S. Khrapov
Candidate of Sciences (Physics and Mathematics), Associate Professor, Department of Information Systems and Computing Modeling,
Volgograd State University
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https://orcid.org/0000-0003-2660-2491
Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation

Nail A. Beysekenov
Master’s Student, Junior Researcher, Priority Branch of the Veritas Center
D. Serikbayev East Kazakhstan Technical University
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https://orcid.org/0000-0003-4014-2903
Serikbayeva St, 19, 070000 Ust-Kamenogorsk, Kazakhstan

Abstract. A mathematical model for forecasting crop yields based on field monitoring data and remote sensing of the Earth has been constructed. The model includes the following main values: vegetation indices NDVI, total solar radiation flux at the lower boundary of the atmosphere, efficiency of using photosynthetically active solar radiation, biomass respiration costs. After the parameterization of the mathematical model using observational data, the number of uncertain (calibration) coefficients of the model decreases from 8 to 2. These coefficients are determined by the method of successive approximations when comparing the results of calculations with observational data on the yield of a particular agricultural crop (ACC) in a given field. It is shown that the values of these coefficients strongly depend on the choice of optimal conditions for the growth of ACC. To reduce the yield forecast error, an approach based on the numerical integration of the total energy flux density by methods of the second and fourth orders of accuracy is proposed. When using numerical integration methods of a high order of accuracy, the yield forecast error decreases on average by 20% compared to the widely used model for calculating biomass growth, which has the first order of accuracy.

Key words: mathematical modeling, numerical integration methods, yield forecast models, biomass, NDVI indices, NDWI indices, solar radiation flux, photosynthetically active radiation.

Mathematical Modeling of Crop Yield Forecast Based on Field Monitoring and Remote Sensing Data by Sadenova M.A., Khrapov S.S., Beysekenov N.A. is licensed under a Creative Commons Attribution 4.0 International License.

Citation in English: Mathematical Physics and Computer Simulation. Vol. 26 No. 3 2023, pp. 56-72

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МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ ПРОГНОЗА Саденова Храпов Бейсекенов.pdf URL: https://mp.jvolsu.com/index.php/en/component/attachments/download/1113

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