Evaluation of Winter Wheat Yield Simulation Based on Assimilating LAI Retrieved From Networked Optical and SAR Remotely Sensed Images Into the WOFOST Model

To obtain sufficient observation data and simulate higher-precision crop yields, a crop yield simulation scheme was built based on the WOrld FOod STudies (WOFOST) crop growth model and a 4-D ensemble square root filter (4-DEnSRF) assimilation algorithm, and the time series of the leaf area index (LAI) retrieved by optical and synthetic aperture radar (SAR) networking data was introduced into the crop yield estimation scheme. Taking Shenzhou County, Hebei Province, as the study area, using the field-measured data as verification data, the regional application of winter wheat yield estimation was effectively carried out with a grid size of 500 m. Comparisons were made between the simulated yields based on different networked data of three key phenologies of winter wheat. The regional yield estimation results revealed an R-2 and normalized root mean squared error (NRMSE) between the simulated yield based on optical LAIs and the field-measured yield of 0.517 and 17.60%, respectively, while the R-2 and NRMSE between the simulated yield based on networked optical-SAR LAIs filtered by the Gaussian filtering algorithm (GFA) and the field-measured yield were 0.573 and 12.98%, respectively. From the comparisons between the simulated yields based on networked data of different combinations of key phenologies, the R-2 and NRMSE between the simulated yield based on the introduced SAR LAI at the jointing stage and the field-measured yield were 0.437 and 21.49%, respectively, and were higher correlation among the three modes of networked data of different combinations of key phenologies. The winter wheat yield simulation results showed that the introduction of SAR LAIs at key crop growth stages (especially the jointing and booting stage) as outer observation data had a mild impact on the value of simulated winter wheat yield. Moreover, Gaussian filtering could reduce errors caused by multisource networked data to a certain extent. Thus, it can be concluded that using some radar images instead of optical images to retrieve LAI and assimilating multisource remotely sensed LAI into the crop model to simulate crop yield could enhance the reliability and robustness of the crop yield simulation system to some extent.