## 5 Results
### Retrieval Demonstration and Self-Consistency Tests  
Self-consistency tests following the inversion framework are conducted. Firstly, forward simulation datasets are obtained by the coupling model according to the bands of P4 multispectral UAV. The input parameters varied by 10% themselves. The observation geometries are adopted for the yearly solar altitude variation at mid-latitudes in the northern hemisphere [57], in which the solar zenith angle varies from 10\({}^{\circ}\) to 60\({}^{\circ}\), view zenith angle and relative azimuth are both 0\({}^{\circ}\). Secondly, to simulate the measurement error, we also added the 5% Gaussian noise to the simulation results. Finally, we obtained a total of 671 sets of validation data. Figure 8a,b show the iterative process of apparent reflectance and the convergence of cost function for Cab = 63, LAI = 1.3, and the solar zenith angle of 15\({}^{\circ}\) in the optimized inversion. The red solid line represents the measured results from the forward model simulation. As shown in Figure 8a, the model simulated value gradually approaches the observations with the increase in iteration times, and the best fit has been achieved through the OE algorithm at the 14 th iteration. The fitting residual error is used to describe the difference between the observation and simulated values. In Figure 8a, the residual error of the last iteration is only 0.023%, and the average residual error of all simulated data is 0.3% after statistics. From Figure 8b, the cost function changes rapidly from the second to the fifth iteration and gradually approached a minimum value after the eighth iteration. The minimum value is determined by the observation error and the error in the radiation transfer and coupling process between the vegetation canopy and the atmosphere. After statistics, the average iteration times of test datasets were about 14 when the cost function reaches the minimum value. The details of the inversion process indicated that the optimal estimation inversion algorithm can realize the dynamic adjustment of inversion parameters and had good convergence in obtaining the retrieval results. A comparison is summarized in Figure 9 between the inversion values reconstructed from the optimized inversion framework and the true value used in the dataset. The coefficient of determination (R\({}^{2}\)) and root-mean-square error (RMSE) are used to verify the consistency of the optimized algorithm. A larger R\({}^{2}\) and a smaller RMSE indicate the higher accuracy of the forward model and the better consistency of the inversion framework. Figure 7: Comparison of simulated values and ground truth values. As seen in Figure 9a, the R\({}^{2}\) value for Cab was 0.9999, while the RMSE was 0.1007, which indicated that the Cab had good retrieval results. Similarly, the verification results of LAI were consistent with Cab. Meanwhile, it is worth noting that the slopes of the fit lines were all close to 1. With the variation of solar zenith angle, the error lines of Cab and LAI were within \(E=x\pm 0.02x\pm 0.08\) and \(E=x\pm 0.08x\pm 0.08\), respectively. The error line is  
Figure 8: Retrieval process demonstration: (**a**) the illustration of the iterative process for reflectance based on the coupling model and (**b**) the description of the convergence process for the cost function. Figure 9: 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 observation geometries and (**b**) validation of the LAI with the OE-iteration method against the true value under different observation geometries. 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 \(E=x\pm 0.02x\pm 0.08\) and \(E=x\pm 0.08x\pm 0.08\). 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.