## 6 Discussion  
The PROSAIL model is sensitive to crop parameters that have been well documented in previous studies [21; 40], and UAV-observed reflectance data can in turn promote LAI and Cab retrieval. Nevertheless, solar radiation and atmospheric environment information from UAV observation in physical models cannot be considered in general. Therefore, the forward model by replacing the built-in surface BRDF model in UNL-VRTM with the PROSAIL model was established in this study, which was calculated based on the optimal estimation inversion framework to obtain crop parameters retrieved. The advantage of the forward model is to achieve atmospheric correction during the radiation transfer process and save the costs in human resources, materials, and time. As a result, compared with the measured data collected by the FieldSpec Handheld, the physical transmission model from solar radiation to the crop canopy, and then to the sensor is used as the UAV observation system. Moreover, crop multiparameters retrieved from current inversion approaches cannot be estimated synchronously. Such as LUTs and machine learning algorithms are not  
Figure 10: Validation of the Cab and LAI with the OE-iteration method: (**a**) validation of the Cab with the OE-iteration method against the true value under different AODs and (**b**) validation of the LAI with the OE-iteration method against the true value under different AODs. The blue solid line, dashed line, and orange solid line are the 1:1 line, error line, and fit line, respectively. The error lines of (**a,b**) are \(E=x\pm 0.04x\pm 0.08\).and \(E=x\pm 0.14x\pm 0.08\). The statistics of the linear fitting result, including slope, intercept, R\({}^{2}\), and RMSE, are listed in the upper left corner of each scatter plot.  
suitable for multiparameter inversion [11; 27]. Therefore, an optimal estimation inversion algorithm from UAV remote sensing data was proposed, and we can obtain a statistical estimate value of the state vector based on optimal estimation theory. The advantage of the proposed inversion algorithm is that it can extract crop LAI and Cab synchronously from UAV observation.  
Based on the optimal estimation theory, we can find the best solution in the continuous state space. Theoretically, the inversion accuracy of crop parameters by the optimal estimation method is higher than LUTs [58]. However, the optimal estimation algorithm requires calculating forward transmission timely and achieves multiple iterations. Thus, it took a lot of time to complete the inversion. After statistics, the average iteration times of test datasets were about 14. We also need to improve the computational efficiency of the inversion algorithm with a larger amount of data.  
From the calculation process of the cost function and its gradient vector, it can be seen that our optimal algorithm is related to a priori constraints and the observation data. The algorithm test was carried out based on simulated data in our inversion framework. Although we added noise to the simulated data, the uncertainty of the actual observation data is more complex than the simulated data in fact. The accuracy of the inversion result not only is affected by the uncertainty of observation data, but also depends on prior constraint values (including nonstate parameters value, the initial value of the state parameters, and their boundary conditions). The prior values are derived from historical data statistics of the same period of crop parameters. Reasonable prior estimates and error assumptions can constrain the inversion results to a relatively optimal range and further speed up the iterative computation. Furthermore, we need to optimize the parameter of the measured data and prior constraints and carry out the inversion verification of the actual observation data.