Considering different water supplies can improve the accuracyof the WOFOST crop model and remote sensing assimilation in predicting wheat yield
Xin Xu 1,2
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College of Information and Management Science, Henan Agricultural University, China
College of Agronomy, Henan Agricultural University, China
Final revision date: 2022-07-10
Acceptance date: 2022-09-23
Publication date: 2022-11-17
Corresponding author
Xinming Ma   

College of Information and Management Science, Henan Agricultural University, China
Int. Agrophys. 2022, 36(4): 337-349
  • Different water supplies affect remote sensing and crop model assimilation.
  • Calibration of the WOFOST model should consider different water conditions.
  • Crop model and remote sensing data assimilation should be regional assimilation.
To clarify the effects of different water and irrigation conditions on crop models and remote sensing assimilation results. This study took winter wheat from 17 test sites in Henan Province as the research object and calibrated the WOFOST model. The ensemble Kalman filter algorithm was used to calibrate the two modes and MODIS-LAI of the calibrated WOFOST model. The study found that the average error of the WOFOST model for simulating the flowering and maturity periods is within 2 days, the R2 of the leaf area index calibration results is between 0.87-0.98, and the R2 and RMSE of the verification results are 0.77 respectively and 1.06; Under the latent model, the R2 of the WOFOST model considering the water supply situation and the assimilation results without considering the water supply situation are 0.50 and 0.48, respectively. In the water restriction mode, the R2 increased from 0.79 to 0.86 compared with the assimilation results without considering the water supply. The results show that: according to the water supply of different regions, selecting the corresponding assimilation parameters can effectively improve the prediction accuracy of crop models and remote sensing assimilation for wheat yield under different water and irrigation conditions.
The authors would like to thank the graduate students at the College of Information and Management Science and the College of Agronomy at Henan Agricultural University for their continued support of this research.
This work was funded by the 13th Five-year National Key Research and Development Plan of China (Grant No. 2016YFD0300609; 2016-2020), the Outstanding Science and Technology Innovation Talents Programme of Henan province (Grant No. 184200510008; 2018-2019), the Modern Agricultural Technology System Project of Henan Province (Grant No. S2010-01-G04; 2010-2022).
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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