## 2 Study Region and Data Sources
### Satellite Data and Preprocessing  
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 four-band multispectral optical image 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 [45]. The HJ-1A/1B CCD images were downloaded from the China Centre for Resources Satellite Data and Application (CRESDA) ([[http://cresda.spacedina.com](http://cresda.spacedina.com)]([http://cresda.spacedina.com](http://cresda.spacedina.com))). The satellite images used in this study consisted of 195 HJ-1A/1B CCD images of Level 2A for Hetao Irrigation District covering the years from 2009 to 2015 with the cloud cover less than 5%.  
The HJ-1A/1B CCD images preprocessing procedures mainly include radiometric calibration and atmospheric and geometric corrections. Theradiometric calibration was used to convert image digital number (DN) to the sensor radiance value for each band using Equation (1).  
\[L_{\lambda}=\frac{DN}{g}+L_{0} \tag{1}\]  
where \(L_{\lambda}\) is the spectral radiance of each band, \(g\) and \(L_{0}\) are calibration coefficients that can be found in the meta-data file attached with the image.  
Atmospheric correction was based on image data according to Equations (2)-(5) [46] instead of using the FLAASH module embedded in the ENVI software [29; 47]. The main advantage of this method is that the coefficients necessary for the atmospheric correction are obtained from the image itself.  
\[\rho_{s\lambda}=\frac{\pi\cdot(L_{\lambda}-L_{p})\cdot d^{2}}{ESUIN_{\lambda} \cdot\cos\theta_{z}\cdot T_{z}} \tag{2}\]  
\[d=1+0.0167\cdot\sin(\frac{2\pi\cdot(\text{DOY}-93.5)}{365}) \tag{3}\]  
\[L_{p}=L_{\text{min}}-L_{1\%} \tag{4}\]  
\[L_{1\%}=\frac{0.01\cdot ESUIN_{\lambda}\cdot\cos\theta_{z}\cdot T_{z}}{\pi \cdot d^{2}} \tag{5}\]  
where \(\rho_{s\lambda}\) is at-surface reflectivity of each band, \(ESUIN_{\lambda}\) is the mean solar exoatmospheric radiation over each band (Table 2), \(\theta_{z}\) is solar zenith angle, \(d\) is the relative Earth-Sun distance, \(T_{z}\) is the atmospheric transmissivity that can be approximated by cos\(\theta_{z}\), DOY is the day of year, \(L_{p}\) is the atmospheric radiation, \(L_{\text{min}}\) is the radiance computed using the minimum radiance in band \(\lambda\), \(L_{1\%}\) is the blackbody radiation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Maze**} & \multicolumn{2}{c}{**Sunflower**} \\ \hline
**Growing Period** & **Date** & **Growing Period** & **Date** \\ \hline Sowing-Jointing & 5.1–6.19 & Sowing-Seedling & 6.1–7.5 \\ Jointing-Trumpet & 6.20–7.9 & Seedling-Emergence & 7.6–7.24 \\ Trumpet-Heading & 7.10–7.29 & Emergence-Blooming & 7.25–8.6 \\ Heading-Grouting & 7.30–8.19 & Blooming-Grouting & 8.7–8.27 \\ Grouting-Harvest & 9.1–9.20 & Grouting-Harvest & 8.28–9.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Field investigated maize and sunflower phenological metrics in 2012.  
The geometric correction was completed using the TIMES module embedded in the ArcGIS software.