Winter Wheat Yield Estimation Remote Sensing Research Based On WOFOST Crop Model And Leaf Area Index Of Assimilation

Accurate crop growth monitoring and yield predictive information is significant to accelerate agricultural sustainable development and guarantee country food security. Remote sensing observation and crop growth simulation models as two new technologies have highly potential application in crop growth monitoring and yield forecasting in recent years. However, both of them has limitations in mechanism or regional application respectively.Remote sensing information can not reveal crop growth and development,inner mechanism of yield formation and the affection of environmental meteorological conditions; Crop growth simulation models have difficulties in obtaining data and parameters regionalization from single-point to regional application. The advantages and disadvantages of the two respective caused great concern of researchers growth model coupling technique of remote sensing information and crop. Screening and optimization of the model parameters is a key problem for yield estimation by remote sensing and crop model based on regional crop assimilation.In this study, we selected the winter wheat of GaoCheng of Hebei province area as the experimental field, and then collected the essential data such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. And to obtain quasi environmental mitigation small satellite HJ-CCD image data synchronization.We took vegetation indices to retrieve winter wheat LAI. The Extend Fourier Amplitude Sensitivity Test(EFAST) was used to analyze the sensitivity of crop growth WOFOST model parameters and screen sensitive parameters to regulate WOFOST model. Finally, we chose Look-up table algorithm to achieve the assimilation of remote sensing information and WOFOST model and the quantitative prediction of winter wheat yield and regional level. In this paper, research work and major conclusions are as follows:(1) Seven vegetation indices of characterization of the strong ability of winter wheat population characteristics were selected to retrieve LAI, each was utilized to build model with measured LAI respectively. The result showed that the accuracy of EVI model was the highest(R2=0.964 at anthesis stage and R2=0.920 at filling stage).In other words, its ability of prediction was the optimal. Thus, we chose EVI as the preferred vegetation remote sensing inversion LAI index.(2) EFAST was used to the construction of sensitivity index evaluation the sensitivity of WOFOST model parameters and the influence of every parameter to the winter wheat yield formation. Finally, we chose six parameters that sensitivity index more than 0.1 as sensitivity factors, namely TSUM1, SLATB1, SLATB2, SPAN, EFFTB3 and TMPF4. To other parameters, we confirmed them via practical measurement and calculation, available literature or WOFOST default. Eventually, we completed the regulation of WOFOST parameters.(3) Look-up table algorithm was used to realize single-point yield estimation through the assimilation of WOFOST model and retrieval LAI. Simulation precision reached the purpose of assimilation(R2=0.941 and RMSE=194.58kg/hm2). We found out the optimum value of sensitivity parameters and finished the estimation of single-point yield. Meanwhile, the result demonstrated that Look-up table algorithm is the feasible method to realize assimilation technology can effectively achieve regional winter wheat yield prediction and mapping.