## 5 Discussion
### Comparison with MODIS  
Figure 9 shows the phenology for all land cover types extracted from actual daily 500 m MODIS NDVI data. Comparing Figures 6 and 9 their NDVI phenological trajectories were very similar. The OGI and OGD calculated from time series 500 m MODIS NDVI data in Bole and Luntai (Table 10) were also fitted well with the crop calendars of the main crops (Table 3) and the OGI and OGD calculated from time series of the high spatial resolution data (Table 7). However, the largest difference of OGI and OGD between MODIS NDVI and 30 m NDVI detections was 12 days in Luntai and three days in Bole.  
Due to the high spatial resolution, the time series data has two advantages. First, much information can be directly obtained through visual interpretation of the time series high spatial resolution data, such as the land cover types, the OGI and OGD of each land cover type, and data quality information (clouds or snow cover). Second, land cover types in most pixels are pure, allowing more sophisticated classification. For example, roadside trees in Bole and wheat-corn in small plots in Luntai can be well classified. However, the disadvantage of using those data is the large data-processing workload, especially using HJ and GF-1 data, including cloud masking and atmospheric and geometric correction. Due to the high spatial resolution, the data storage space is very large. For example, the data storage space for a 480 \(\times\) 480 30 m area in Bole is more than 300 GB. In addition, both the HJ and GF-1 data are non-standardized data, where the radiation accuracy,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Land Use**} & \multicolumn{2}{c}{**Luntai**} & \multicolumn{2}{c}{**Bole**} \\ \cline{2-5}  & **OGI (DOY)** & **OGD (DOY)** & **OGI (DOY)** & **OGD (DOY)** \\ \hline Tree & 116 & 296 & & \\ Cotton & 158 & 278 & 162 & 277 \\ Summer corn & & & 172 & 227 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The OGI and OGD for each land use type extracted by MODIS NDVI data in Luntai and Bole.  
Figure 9: Phenology for different land cover types extracted from actual MODIS NDVI data in (**a**) Bole and (**b**) Luntai.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor** & **HJ** & **GF** & **MODIS** & **OLI** & **HJ + GF** & **Fusion Data** \\ \hline Useful data number & 55 & 18 & 139 & 10 & 70 & 162 \\ The proportion of days of good data (\%) & 0.15 & 0.10 & 0.38 & 0.04 & 0.19 & 0.44 \\ Max gap (day) & 34 & 36 & 14 & 80 & 34 & 11 \\ Mean gap (day) & 8 & 6.35 & 2.64 & 32 & 4.9 & 2.26 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The proportion of days with good data in each sensor in Luntai.  
clarity, and signal to noise ratio of HJ CCD data are lower than Landsat TM data [53]. Overall, time series high spatial resolution data are more suitable for small area detailed phenology extraction.  
In contrast to time series high spatial resolution data, time series MODIS data are more suitable for phenology extraction in large scale areas. Due to the low spatial resolution, the data storage space for MODIS data is much smaller. However, when the size of land objects are smaller, a MODIS pixel often represents a mixture of different land cover types, making it difficult to classify high spatial resolution land-cover types and to extract phenology. For example, roadside trees in Bole and wheat-corn in small plots in Luntai cannot be classified using time series 500 m MODIS NDVI data. Further, obtaining land cover information from the visual information in the low spatial resolution data is difficult.