## 5 Discussion
In this study, the uncertain effects of the LAI in different temporal scales (phenological stages and temporal frequencies) and spatial scales were given greater emphasis. The optimal yield estimates may be highly dependent on the reasonable spatio-temporal resolution of assimilating LAI. Observations between jointing and anti-s stages (particularly for the elongation, booting and heading stage) play important roles in improving the accuracies of the crop yield estimation when observing scarcity. This finding is similar to the observations of [PERSON] et al. [6], in which the MODIS-LAI model was assimilated into the WOFOST (World Food Studies) model. In addition to this, LAI with a high frequency and spatial resolution significantly improved the accuracy of crop yield estimations, although the increased computing time may be problematic for regional applications. This finding is similar to the observations of [PERSON] et al. [22], in which the HJ-LAI model was assimilated into the CERES-Wheat model. Thus, the use of the POD4 DVar-based strategy to assimilate LAI with moderate spatial resolution (e.g., 1 km), phenological stages between jointing and anti-s stages and an assimilation temporal frequency of eight days appears to be a promising assimilation approach with reasonable spatio-temporal scale to achieve a tradeoff between accuracy and effectiveness in regional wheat estimate applications. When remotely-sensed LAIs are used to replace the value of a model-simulated state variable in a data assimilation system, one assumes that the remotely-sensed LAIs are free of error or assumes that the level of data error is acceptable to be propagated within the simulated system [53]. To reduce the errors, the remotely-sensed LAIs were retrieved using the PROSAIL model from multiple sensors' optical data (i.e., Landsat OLI, HJ-1 and GF-1) and accurately obtained the results. However, the uncertainty effects of the remotely-sensed observations' errors on assimilated yield estimates based on the method of error perturbation are not shown in this study and will be addressed in our future work. In this study, the independent assimilation of LAI was a preferred method for optimally estimating the crop yields from plots with relatively wet soils that were extensively irrigated during the crop growth season. This finding is similar to the observations of [PERSON] et al. and [PERSON] et al. [53; 68], in which the LAI and soil moisture were assimilated into the decision support system for the agro-technology transfer-cropping system (DSSAT-CSM) model. This finding is also similar to the observations of [PERSON] et al. [5], in which the MODIS LAI and ET products were assimilated into the WOFOST model. Thus, assimilating LAI, evapotranspiration (ET) and/or soil moisture represents another important area of research that will be investigated further, especially in arid and semi-arid areas. Although the POD4 DVar-based assimilation method achieved good results in 2014, the capabilities about the method presented in prediction mode with other years were not shown in this study. An extension of multiple years to validate the assimilation method and determine how well it accounts for the inter-annual variability in crop yield prediction is needed and will be addressed in our future work.