## 3 Materials
### Satellite Image Pre-Processing
#### 3.3.4 Surface Reflectance Normalization  
Within the study area, the primary maize-growing season ranges from Julian days 137 to 257; during this period, the zenith observation angle for the HJ-1, GF-1 and Landsat satellite data usually varies between 0\({}^{\circ}\) and 30\({}^{\circ}\), and the satellite passes Huailai between 10:00 AM and 11:00 AM local time every day. The variation in viewing angle will cause apparent change in observed surface reflectance. In order to eliminate the directional effect, we used the method developed by [PERSON] et al. [52] to normalize the high-spatial-resolution surface reflectance to nadir BRDF adjusted reflectance (HR NBAR). So that we can use the MODIS BRDF model parameters (MCD43A1) in the HR NBAR derivation. The data processing technique uses fixed BRDF kernel coefficients for each spectral band derived from a large number of pixels in the BRDF product that are globally and temporally distributed. The technique is very stable, reversible, easy to implement for operational processing [52].  
Due to the differences between the spectral response functions of MODIS and the HR sensors, the variation in the surface reflectance is simulated with the 6S radiative transfer model [44] under different geometric observations (SZA, VZA, and RA) and atmospheric conditions. The ranges of parameters used to simulate 6S model are shown in Table 2. The different sensor characters are corrected by comparing the surface reflectance simulated under the same parameters. Figure 5 shows the comparison of HR and MODIS surface reflectance of red and NIR bands.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Description** & **Parameter Name** & **Range** \\ \hline \multirow{3}{*}{geometrical conditions} & Solar zenith angle & 20–60 (\({}^{\circ}\)) \\  & Satellite zenith angle & 20–60 (\({}^{\circ}\)) \\  & Relative azimuth angle & 0–180 (\({}^{\circ}\)) \\ \hline \multirow{3}{*}{atmospheric model} & Midlatitude summer & - \\  & Midlatitude winter & - \\ \hline \multirow{3}{*}{aerosol model} & continental model & - \\  & urban model & - \\ \hline \multirow{3}{*}{aerosol optical depth} & aerosol optical depth at 550 & 0–1 \\ \hline \multirow{2}{*}{reflectance} & TOA & 0–0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of variables required to 6S model and their ranges.  
Figure 5: The relationships between Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and high resolution (HR) surface reflectance in the red and near-infrared (NIR) bands. **(a–e)** Surface reflectance in the red band; **(f–j)** surface reflectance in the NIR band.  
The NIR band needs to be considered and can be corrected as follows  
\[r_{MODIS}=a\cdot r_{hr}+b \tag{8}\]  
where \(a\) and \(b\) are two linear fitting coefficients, \(r_{hr}\) is the surface reflectance of HR data, and \(r_{MODIS}\) is the surface reflectance of MODIS.  
The composite method of the HR dataset is based on the generation algorithm of the MOD09 products. For each pixel, a value is selected from all acquisitions within the 8-day composite period. The criterion for the pixel choice is that the HR surface reflectance data are the minimum-blue [53; 54]. The surface reflectance values of each of the different bands for the same location and day are copied to the composite image files using minimum blue criteria. The composite HR data not only reduces the inconsistency between the two data due to cloud contamination, but also reduces the difference in reflectance between the data, which facilitates the training of the neural network to retrieve a smooth LAI.