Abstract: WheatSM, a wheat growth model developed for different types of winter wheat in China, is applied in scientific research and public service. The coupling of remote sensing information with crop growth model has important application value in crop growth monitoring and yield estimation in large area. Hebi City in Henan Province is a main winter wheat producing area of China. With Hebi as the study area, the optimized reconstructed time series MODIS LAI data from 2013 to 2017 was coupled with WheatSM model with both SCE-UA optimal assimilation and EnKF assimilation methods to estimate the yield of winter wheat at both site and regional scales. The results showed that the introduction of remote sensing data with uncertainties did not improve the simulation accuracy of the crop model on the premise of strictly calibrating the parameters of WheatSM at site scale. The quality of remote sensing observation data had greater effects on the results of EnKF assimilation method than that of SCE-UA optimal assimilation method. At regional scale, the accuracy of data assimilation results with both SCE-UA and EnKF methods were higher than that without data assimilation. RMSE between simulated yield and statistical yield decreased from 2036.0 kg·hm^-2 to 1641 kg·hm^-2  with SCE-UA method and to 1587.7 kg·hm^-2  with EnKF method, with a reduction of 19.4% and 22.0%, respectively. The efficiency of EnKF assimilation method was higher than that of SCE-UA optimal assimilation method. Our results could provide a basis for the selection of data assimilation strategies coupling WheatSM with remote sensing data.

Key words: mineralization, microbial biomass carbon, dehydrogenase activity, phospholipid fatty acids.