Abstract: PyWOFOST model based on Ensemble Kalman Filter (EnKF) was introduced to assimilate LAI into crop model. Meteorological data, agrometeorological data and MODIS LAI data were used to test the applicability of PyWOFOST model in simulating maize yield in Northeast China. 16 agrometeorological stations with effective MODIS LAI data which distributed evenly and contained all maize varieties in study area were selected to model maize LAI and yield on each stations at different levels of uncertainty of TSUM1(Thermal time from emergence to anthesis）. The result showed that, compared with WOFOST model, the PyWOFOST model greatly improved in simulating LAI and yield of maize. The mean errors of maize yield simulated by PyWOFOST were 10.32%，9.25%，7.31% and 8.49% at the uncertainty of TSUM1 with 0，10，20，30℃ respectively which all were lower than the mean error of maize yield (10.55%) simulated by WOFOST without assimilating LAI. The trajectory of LAI simulated by PyWOFOST which was more in line with maize growth and development trends was closer to observed LAI than LAI simulated by WOFOST. Therefore, the PyWOFOST based on EnKF was applicable for yield simulation of maize in Northeast China.

Key words: Data assimilation, Ensemble Kalman Filter(EnKF), MODIS LAI, PyWOFOST