## 2 Materials and Methods
### Data Preprocessing  
#### 2.2.1 HJ-1 Data Preprocessing  
To obtain more accurate data, we used the earth resources data analysis system (ERDAS) software, based on the Landsat 8 data of the same region, to georeference the HJ-1 data again. Based on the size of the target object and filed survey, 20 control points for geometric correction were selected through a visual recognition method. By selecting the ground object points with obvious visual features on the image, such as road intersections and waterway junction lines, we ensured that the control points were evenly distributed on the two images. The results showed that the geometric correction only caused shift correction, and there were few changes before and after image correction. The accuracy of Chinese satellite data was gradually increased. Due to the influence of the atmosphere, product data with geometric corrections were distorted when analyzing the true surface reflectivity. To obtain the real surface information from satellite images, the next step was to accurately remove the atmospheric effect, or atmospheric correction, which eliminates the influence of atmospheric aerosol on clutter reflections and obtains the real surface reflectivity. To improve the efficiency of atmospheric correction, we calculated the lookup table offline based on the 6S model [20]. The table contained different atmospheric aerosol optical thickness, solar zenith angle, observed zenith angle, and relative azimuth angle. It provided the atmospheric correction coefficient to achieve atmospheric correction of HJ-1 A/B data. To omit steps, we used the absolute atmospheric correction methods, while removing the effect of atmosphere in the image, to convert digital number (DN) value into surface reflectivity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Products**} & \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{\
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} } & \multirow{2}{*}{**Time (Year)**} & \multicolumn{3}{c}{
\begin{tabular}{c} **Spectral Region (nm)** \\ **Green** \\ \end{tabular} } & \multirow{2}{*}{**NIR**} & \multirow{2}{*}{**Data Sources**} \\  & & & & & & & \\ \hline HJ-1A/HJ-1B & HJ-1 CCD & 30 m & 2011–2014 & 430–520 & 520–600 & 630–690 & 760–900 & [[http://www.cresda.com](http://www.cresda.com)]([http://www.cresda.com](http://www.cresda.com)) \\ MOD43A4/ MOD9 GA & MODIS & 500 m & 2011–2014 & 459–479 & 545–565 & 620–670 & 841–846 & [[https://glovis.usgsg.gov/](https://glovis.usgsg.gov/)]([https://glovis.usgsg.gov/](https://glovis.usgsg.gov/)) (accessed on 2 April 2019) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental data and band comparison. In the 6S model, the atmospheric correction is based on the following equation:  
\[\left\{ \begin{array}{l}y=x_{a}*L_{i}-x_{b}\\ \rho=\frac{y}{x_{c}*y+1}\end{array} \right. \tag{1}\]  
where \(x_{a}\), \(x_{b}\), and \(x_{c}\) represent the three atmospheric correction coefficients; \(L_{i}\) represents the radiance value measured for the \(i\)-th band of the sensor; \(\rho\) represents the surface reflectance corrected by atmosphere. When entering a parameter, we selected the predefined standard mode in 6S as the input. The two selected atmospheric models were mid-latitude summer and mid-latitude winter. The aerosol mode selected was the continental type, and assumed that the surface has uniform Lambertian reflection characteristics. The aerosol optical thickness at 550 nm was set to 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.0, 1.5, and 2.0. Six sensor zeniths were set up within the viewing angle of the CCD camera. Nineteen relative azimuth angles were set up within the range of 0-85\({}^{\circ}\). Eighteen solar zenith angles were set up within the range of 0-180\({}^{\circ}\)[21]. After atmospheric correction, we obtained more realistic surface information, as indicated in Figure 2a.