## Materials and Methods
### LAI Inversion Procedures  
Before implementing the LAI inversion, the NDVI was used to separate the vegetation and non-vegetation pixels, and the non-vegetation pixels were removed. Generally, the NDVI values were less than 0.05 for bare soil [66; 67]. Therefore, the pixels with NDVI values of less than 0.05 were identified as non-vegetation and were set to a filled value (marked as 0).  
The LAI inversion method was chosen from the SVI-LAI empirical relationship based on the PROSAIL physical model to reduce the influences from the differences between the various sensors and spectral response curves and the residual errors from data preprocessing. The selected SVIs of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Abbreviations** & **Units** & **Value Range** & **Interval** \\ \hline Leaf mesophyll structure & N & - & 1.518 & - \\ Leaf chlorophyll-\(a\) and -\(b\) content & C\({}_{\text{ab}}\) & \(\mu\)g/cm & 40-60 & 10 \\ Leaf dry matter content & C\({}_{\text{m}}\) & g/cm & 0.003662 & - \\ Leaf water content & C\({}_{\text{w}}\) & cm & 0.0131 & - \\ Carotenoid content & C\({}_{\text{ar}}\) & \(\mu\)g/cm & 10 & - \\ Brown pigment content & C\({}_{\text{brown}}\) & - & 0.05 & - \\ Average leaf inclination angle & ALA & \(\circ\) & 40–70 & 10 \\ Hot-spot & Hot-spot & - & 0.1 & - \\ Leaf area index & LAI & m\({}^{2}\)/m\({}^{2}\) & 0–8 & 0.1 \\ Solar zenith angle & SZA & \(\circ\) & 0–85 & 1 \\ View zenith angle & VZA & \(\circ\) & 0–35 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The input variables for the PROSAIL model used to generate the forward simulation.  
Figure 2: Spectral response curves for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD from CRESDA, and for Landsat-8 OLI from USGS.  
this study include the NDVI and an NDVI combined with the NIR reflectance of vegetation (NIRv). The NDVI is a key parameter that is used to improve the accuracy of yield prediction for sugar beets, spring wheat, corn, and sunflower based on the NDVI relationships with optical signals under different nitrogen (N) and sulfur (S) contents [68; 69; 70; 71; 72; 73; 74; 75; 76]. Notably, the NDVI has been widely used for LAI extraction from high spatial resolution imagery [21; 22; 24; 25; 29; 77]. NIRv is a new index that directly reflects the proportion of photons intercepted by chlorophyll, and it has a stronger linear relationship with LAI than does the NDVI [78].  
Based on the simulation input parameters in Table 4, there are 2,972,160 records in the LUTs for each sensor. During the process of LAI inversion, the specific relationship between SVI and LAI was established according to the values of VZA and SZA for each sensor. Under the illumination and observation conditions of each sensor in the study area, the specific NDVI-LAI and NIRv-LAI exponential relationships for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD were established from the PROSAIL LUT simulations for each sensor (details shown in Table 5). The NDVI and NIRv both had strong relationships with the LAI, and the R\({}^{2}\) values for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD were all higher than 0.91. Moreover, all of the R\({}^{2}\) values for the NIRv-LAI exponential relationship were higher than those of the NDVI-LAI exponential relationship. LAI estimated from the specific NDVI-LAI and NIRv-LAI exponential relationships based on the image NDVI and NIRv calculations for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD. The flowchart of the LAI inversion method based on ZY-3 MUX, GF-1 WFV, and HJ-1 CCD imagery is shown in Figure 3.