Scientia Agricultura Sinica ›› 2013, Vol. 46 ›› Issue (14): 2880-2893.doi: 10.3864/j.issn.0578-1752.2013.14.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Study on Maize Yield Estimation and Accuracy Assessment Based on PyWOFOST Crop Model in Northeast China

 CHEN  Si-Ning-123, ZHAO  Yan-Xia-2, SHEN  Shuang-He-3, LI  Zhen-Fa-1   

  1. 1.Tianjin Climate Center, Tianjin 300074
    2.Chinese Academy of Meteorological Sciences, Beijing 100081
    3.College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044
  • Received:2012-12-06 Online:2013-07-15 Published:2013-04-18

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Abstract: 【Objective】 The objective of this study is to construct a reasonable crop yield estimation scheme and improve the accuracy of crop yield estimation. 【Method】Based on the PyWOFOST crop model developed according to Ensemble Kalman Filter(EnKF) which combined remote sensing information with crop model, the PyWOFOST model was modified and improved to take LAI as the joint point of the crop model(WOFOST) and remote sensing information and be applicable to the maize-growing area in Northeast China. MODIS LAI data were used as external assimilation data to simulate maize LAI, yield and development stage with the PyWOFOST model at agro-meteorological stations. The impact of uncertainty(i.e. random error) of remote sensing observations(MODIS LAI) and model parameter(thermal time from emergence to anthesis, TSUM1) on the assimilation simulation results was analyzed deeply. Finally, the PyWOFOST model was used to estimate maize yield on a regional scale. 【Result】The result shows that, the modeled maize yield after assimilating MODIS LAI was closer to the observed values by comparing with the results modeled by WOFOST, the errors of maize production before and after assimilation at different uncertainty levels of TSUM1(0, 10, 20, 30℃) of 20 agro-meteorological stations without the impact of meteorological disasters were14.04%, 12.71%, 11.91%, 10.44% and 10.48%, respectively. LAI modeled by PyWOFOST after assimilation which more in line with the change trend of maize LAI was generally closer to the observed LAI than LAI modeled by WOFOST. The mean error of development stage between modeled value of WOFOST and observed value was 3.4 d; the mean errors of development stage between modeled value of PyWOFOST at different uncertainty levels of TSUM1（0, 10, 20, 30℃） and observed value were 3.5, 4.3, 5.0 and 5.5 d, respectively. The model results on the regional scale showed that, the errors of maize yield with assimilation were less than 15% in 58.82% of the study area; the coefficient of determination between modeled yield with assimilation and statistical yield was 0.806. 【Conclusion】Overall, the simulation results on the stations and regional scales both revealed the advantages of the crop yield estimation based on EnKF assimilated remote sensing information into crop models.

Key words: Ensemble Kalman Filter , pywofost crop model , remote sensing , yield estimation , accuracy assessment

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