## 3 Data and Method  
### Data Sources  
Data used in the present study mainly include HJ-1A/1B CCD images, field measured and official statistical crop yield data, crop distribution and land use maps.  
The two-day-repeat CCD sensors of Chinese HJ-1A/1B satellites capture ground features with 30 m pixel resolution, with each satellite carrying a 4-band multispectral optical imagers that captures radiation in the blue (0.43-0.52 \(\upmu\)m), green (0.52-0.60 \(\upmu\)m), red (0.63-0.69 \(\upmu\)m), and near-infrared (0.76-0.90 \(\upmu\)m) bands [48]. Level 2A images for HID covering the crop growth period from April to October in the years from 2010 to 2015 with the cloud cover less than 5% were downloaded from the China Centre for Resources Satellite Data and Application (CRESDA) [55].  
Crop type and yield were surveyed on the ground in 2014 and 2015. Fifty-five sampling points were first determined considering the spatial distribution uniformity and the homogenous of crop type within the area of cropland based on the 1:100,000 land use map of the cropland in 2005 (Figure 1) provided by National Earth System Science Data Sharing Infrastructure [56]. Distributing sampling points uniformly throughout the entire study area ensured the representativeness and diversity of crops with different growth conditions and crop yield. Then we conducted field survey about crop type and yield based on the location of the sampling points on the map. Each sampling point represented an area of over one hectare with the same crop, which could prevent the possible existence of mixed pixels in developing remote sensing-based models for crop yield estimation. Meanwhile, the crop type of each point may be different in these two years due to crop rotation. As a result, thirty-four sampling points of maize, fifty-four points of sunflower, and twenty-two points of other crops were obtained in 2014 and 2015, and the statistics of measured crop yields at the sampling points were shown in Table 1. The spatial resolution of HJ-1A/1B CCD images was 30 m and the area of each sampling point was over one hectare, then we selected eight pixels (30 \(\times\) 30 m\({}^{2}\)) at each sampling point. Consequently, we have 272 and 432 pixels in total with measured yield data for model calibration for maize and sunflower, respectively. For each sampling point, we selected thirty plants with relatively uniform growth to measure the average crop yield. The sampled maize cob and sunflower plate were air-dried, and the maize and sunflower seeds were threshed and weighed to obtain the dry grain weight. Then, the crop yields per unit area of the sampling point were calculated considering the crop density (Table 1).  
For the selected four counties in HID, we obtained the average maize and sunflower yield statistics per county from 2010 to 2015 from the Bayannur Agricultural Information Network [57]. We also used the crop distribution maps with 30 m spatial resolution of maize and sunflower from 2010 to 2015 obtained in our previous study [49].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Maize**} & \multicolumn{3}{c}{**Sunflower**} \\ \cline{2-7}
**Statistics** & \begin{tabular}{c} **Yield** \\ **(2014) t/ha** \\ \end{tabular} & \begin{tabular}{c} **Yield** \\ **(2015) t/ha** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **plants/ha** \\ \end{tabular} & \begin{tabular}{c} **Yield** \\ **(2014) t/ha** \\ \end{tabular} & \
\begin{tabular}{c} **Yield** \\ **(2015) t/ha** \\ \end{tabular} &
\begin{tabular}{c} **Density** \\ **plants/ha** \\ \end{tabular} \\ \hline Minimum & 6.225 & 6.880 & 33,317 & 2.772 & 1.264 & 19,810 \\ Maximum & 14.756 & 15.627 & 85,543 & 5.637 & 4.299 & 56,028 \\ Mean & 11.092 & 11.938 & 61,131 & 4.190 & 3.083 & 33,317 \\ Standard Deviation & 2.449 & 2.057 & 11,906 & 0.937 & 0.970 & 6003 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of crop yields at sampling points in Figure 1 in 2014 and 2015.