## 4 Model Analysis for UAV Multispectral Measurements  
### The Sensitivity of the Model to Crop Parameters  
The multispectral sensor is widely used in the agricultural field because of its price advantage. In this paper, the band settings of UAV (P4 Multispectral, SZ DJI Technology Co., Ltd., Shenzhen, China) were adopted, as a case study, for sensitivity analysis of the model  
Figure 4: The optimization inversion flow chart based on iteration.  
to crop parameters. The UAV is equipped with a multispectral camera with the central wavelength of 450, 560, 650, 730, and 840 nm. The band settings of UAV multispectral sensors are given in Table 3.  
Obviously, it is difficult to obtain all the crop parameters from the limited channels of the multispectral sensor. Thus, we must figure out exactly which parameter can be retrieved. To solve this problem, firstly, we calculate the influence of each parameter change on the model output reflectance. The simulation results are shown in Figure 5, in which we assumed that the input parameters were independent in the coupling model. For each of the parameters, the reflectance is calculated by fixing other parameter values listed in Tables 1 and 2.  
Figure 5: Spectral response under different input parameter values. (**a**–**d**) are the different reflectance changes of physicochemical parameters Cab, Cw, Cm and LAI, respectively. (**e**,**f**) are the difference reflectance changes of structure parameter N and geometry parameter ALA, respectively. (**g**–**i**) are the difference reflectance changes of parameters Car, Hspot and Psoil, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Band** & **Center Wavelength/nm** & **Bandwidth** \\ \hline Band1-Blue & 450 & 16 \\ Band2-Green & 560 & 16 \\ Band3-Red & 650 & 16 \\ Band4-RedEdge & 730 & 16 \\ Band5-NIR & 840 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The multispectral bands of UAV.  
From Figure 5, we can see that the Cab yields a strong contribution to green band, and a weak influence to red-edge band, and the reflectance gradually decreases with the increase in Cab. It is due to the weak absorption of chlorophyll on the green band. The Cw and Cm reflect the accumulation of water and dry matter during wheat growth, respectively. However, the simulation shows that the sensor is insensitive to Cw in all the channels, and the same is true for the Car parameter on the visible band. The changes of Cm and LAI are well reflected in the reflectance of NIR band, while their contributions are opposite. Parameters related to the structure or geometry, such as the (ALA), leaf structural parameter (N), and Hspot, affect the measurement of all bands. Usually, these parameters are considered as nonstate quantities, using an empirical value with a given error during the inversion. The Soil represents the proportion of bare soil and vegetation cover; therefore, the increase in Soil means the enhancement of contribution from the bare soil surface. From Figure 5i, the reflectance of each band increases with the increase in Psoil, since the reflectance of bare soil has little difference at each wavelength. These results were consistent with the previous research [53; 54; 55].