Abstract:
WOFOST (world food studies) model can be used to simulate time-series LAI (leaf area index), the organs' biomass, and the yields of winter wheat. Therefore, it is meaningful for the growth monitoring and production prediction of winter wheat. So far, the calibration of WOFOST usually relies on researches' experience, which brings many problems while using the model in a specific area. As a result, we focus on the calibration problem and try to improve the accuracy of the simulated results in this paper. The potential production WOFOST was analyzed and LAI was chosen as the measure index because it was easy to obtain. In this study, we selected Hengshui as the study area, and two field experiments were carried out in this area during two different periods. One period was from 2017-03-29 to 2017-04-01 and the other was from 2017-05-04 to 2017-05-06. It was divided into 11 sampling areas and 5 sampling points in every area were obtained to measure the LAI, so we got approximately 110 measured data totally. A method called 'Calibrating in area by optimization and correcting at pixel by assimilation' was presented in this paper. Firstly, calibrating WOFOST model in local area: The weather data including sunshine duration data and the maximum and minimum air temperature data every day were used to run the WOFOST model. The data were from Nangong National Weather Station and can be downloaded in National Meteorological Information Center. Then the sensitivity of model parameters can be analyzed with EFAST (extend fourier amplitude sensitivity test) and the 5 most sensitive parameters were selected to optimize the model. It was worthwhile to note that there were different indices to evaluate the sensitivity of every parameter, such as main effect, interaction, and total effect, and the total effect was considered as the most important index in this study. As for the optimization, the SCE (shuffled complex evolution) algorithm was used which could find the global optimal solution fastly. It can solve the initial value dependence problem and local convergence problem which might exist in other optimization algorithms such as MCMC (Markov Chain Monte Carlo). In order to proof that the optimization was valid, the time-series LAI curves simulated were compared by WOFOST before and after optimization with SCE with measured values. It turned out the model after optimization was much more appropriate to simulate the growth of winter wheat in study area. Secondly, assimilating the model in every pixel in the study area: We interpolated weather data from 21 National Weather Stations in Hebei Province in order to run WOFOST in every pixel. Based on this, EnKF (Ensemble Kalman Filter) was used to assimilate LAI in every pixel with the remote sensing data from Sentinel-2. As a result, we could get the time-series LAI curve at every pixel. The LAI curve at point HS01 was illustrated and it was obvious that assimilation made a difference in the simulation. Additionally, the simulated LAI distribution maps were illustrated in Hengshui at date of 2017-03-30 and 2017-05-05. And the simulated LAI values of the pixels according to the sampling points were extracted. By comparing the simulated LAI with measured LAI, we found that R2 was increased from 0.70-0.83 to 0.87 and RMSE was decreased from 0.89-1.36 to 0.62. Therefore, the method proposed in this study solved the calibration problem and improved the accuracy of time-series LAI simulated compared with other studies. In addition, we provided specific theories and methods in every stage from calibration to application. It contributed to the application of WOFOST in our country.