## Materials and Methods
### Assessment of LAI inversions for a Heterogeneous Surface  
The performances of the LAI inversions from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD data were evaluated using the LAI field measurements. Three indices, R\({}^{2}\), RMSE, and bias (BIAS), were used to evaluate the absolute discrepancies between the LAI inversions and LAI field measurements. R\({}^{2}\) describes the entire correlation between the LAI inversions and LAI field measurements. RMSE  
Figure 3: Flowchart of the LAI inversion method based on ZY-3 MUX, GF-1 WFV, and HJ-1 CCD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**SVIs**} & \multicolumn{2}{c}{**NDVI-LAI Relationship**} & \multicolumn{2}{c}{**NIRv-LAI Relationship**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Expression**} & \(\mathbf{R^{2}}\) & **Expression** & \(\mathbf{R^{2}}\) \\ \hline ZY-3 MUX & LAI = 0.0484 exp(5.2397 \(\,\)* NDVI) & 0.91 & LAI = 0.1725 exp(6.4087 \(\,\)* NIRv) & 0.98 \\ GF-1 WFV & LAI = 0.0385 exp(5.4728 \(\,\)* NDVI) & 0.92 & LAI = 0.1578 exp(5.6711 \(\,\)* NIRv) & 0.98 \\ HJ-1 CCD & LAI = 0.0380 exp(5.4241 \(\,\)* NDVI) & 0.91 & LAI = 0.1543 exp(5.6960 \(\,\)* NIRv) & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The SVI-LAI exponential relationships for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD.  
represents the standard deviation between the LAI inversions and LAI field measurements. Bias is a systematic variation that results from a random sampling or estimation process that does not give accurate results on average.  
The heterogeneity of the surface vegetation has a strong effect on the LAI inversions at different spatial resolutions. Therefore, the spatial representativeness of the LAI field measurements was assessed based on the LAI inversions from the NDVI-LAI relationships for ZY-3 MUX data within a 30 \(\times\) 30 m subpixel region. The spatial representativeness was evaluated using the relative absolute error (RAE) and the coefficient of sill (CS), according to [PERSON] et al. (2019). In this study, the thresholds of RAE and CS were 10% for the representativeness evaluation based on LAI. Values of RAE and CS that were higher than 10% represented significant heterogeneity of the surface and lower spatial representativeness of the LAI field measurements. Then, the LAI inversions for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD were evaluated based on the high and low spatial representativeness of the LAI field measurements.  
Finally, a 3 \(\times\) 3 km\({}^{2}\) range of LAI inversions was extracted from the NDVI-LAI relationships to further compare the LAI inversions from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD at a consistent spatial resolution. After inversion of LAI from the remote sensing data, the LAI result from ZY-3 MUX was resampled to 16 and 30 m spatial resolutions, and the LAI result from GF-1 WFV was resampled to a 30 m spatial resolution using an upscaling method that considered the surface heterogeneity that described the variance of NDVI. Then, we compared the LAI inversions at the corresponding spatial resolutions. The LAI upscaling function is expressed in Equation (4) [80].  
\[\text{LAI}_{upscaling}=\text{LAI}_{mean}-\frac{1}{2}\sigma_{\text{NDVI}}\times g ^{\prime\prime}\left(m\text{NDVI}\right) \tag{4}\]  
where \(\text{LAI}_{upscaling}\) is the LAI value after upscaling from the high spatial resolution to the low spatial resolution, \(\text{LAI}_{mean}\) is the mean LAI value at the high spatial resolution within a pixel of low spatial resolution, and \(\sigma_{\text{NDVI}}\) and \(g^{\prime\prime}\left(m\text{NDVI}\right)\) are the variance and the second order differential of the mean NDVI value at the high spatial resolution within a pixel with low spatial resolution that was calculated from the NDVI-LAI exponential function, respectively.