## 5 Discussion  
This study applied and demonstrated the use of ESTARFM and STDFA in combining HJ CCD, GF-1 WFV and MODIS data to generate high spatial resolution data. Although the data quality of HJ CCD data is lower than Landsat data, both the ESTARFM and STDFA algorithms can generate daily synthetic high spatial resolution data accurately, with \(r\) values higher than 0.8643 and RMSEs lower than 0.0360. As the MODIS sensor can acquire daily images, ESTARFM and STDFA can be used to enhance the proportion of useful HJ CCD and GF-1 WFV data. For example, the proportion of useful MODIS data in Luntai for 2013 is 48.49%, while the proportions of useful HJ CCD data and useful GF-1 WFV data are 15.07% and 10.29%, respectively. Owing to the high proportion of useful data, this method is potentially useful for high spatial resolution environmental process monitoring, and can be applied in natural resource damage assessments and environmental policy and management. However, there are some issues should be addressed in the application of this method:  
(1) Influence of the satellite data quality. The test areas for the HJ-MODIS fusion and the GF-MODIS fusion in Kuche were the same, while the test area for the GF-MODIS fusion was a little larger than the area for the HJ-MODIS fusion in Luntai. Therefore, we can only analyse the influence of the satellite data quality on the data fusion for the Kuche area. As an early Chinese moderate-resolution satellite, the quality of HJ satellite data is lower than the quality of GF-1 satellite data. The position error of HJ CCD data is greater than 1 km, while the position error of GF-1 WFV data is less than 100 m. Furthermore, the SNR of HJ CCD data is much lower than the SNR of GF-1 WFV data. Table 6 lists the differences between Table 5 and Table 4, showing the influence of the sensor differences on the model accuracy. From Table 8, we can see that the influence of the sensor differences for STDFA is less significant than for ESTARFM. This is because STDFA had better noise immunity. Similar results were found for the fusion of ASTER and MODIS land surface temperature products [46]. (2) Influence of bidirectional reflectance distribution function (BRDF) changes. For different data acquisition dates, the solar and satellite azimuth and zenith angles are different (Table 9). This leads to changes in the BRDF and hence in the information received by the sensor. More seriously, this could lead to differences in the shadow direction and length for the same object on different dates. Figure 4 shows an example of the difference in the shadow direction and length for trees on 3 October 2013, 7 October 2013 and 15 October 2013 recorded by the GF-1 WFV sensor. In the shaded area, the differences of reflectance in the NIR band between actual and synthetic GF-1 WFV data can reach \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**STDFA**} & \multicolumn{4}{c}{**ESTARFM**} \\ \cline{2-10}  & Blue & Green & Red & NIR & Blue & Green & Red & NIR \\ \hline \(r\) & 0.0165 & 0.0045 & 0.0018 & 0.0283 & 0.0206 & 0.0104 & 0.0077 & 0.0574 \\ Var & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ MAD & \(-\)0.0012 & \(-\)0.0015 & \(-\)0.0003 & \(-\)0.0005 & \(-\)0.0017 & \(-\)0.0025 & \(-\)0.0018 & \(-\)0.0022 \\ RMSE & \(-\)0.0230 & \(-\)0.0143 & 0.0088 & \(-\)0.0142 & \(-\)0.0205 & \(-\)0.0096 & \(-\)0.0038 & \(-\)0.0009 \\ bias & \(-\)0.0238 & \(-\)0.0147 & 0.0113 & \(-\)0.0156 & \(-\)0.0213 & \(-\)0.0096 & \(-\)0.0034 & 0.0009 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Influence of sensor differences on model accuracy. 0.0676 for STDFA and 0.0638 for ESTARFM. However, in the unshaded area, the differences of reflectance in the NIR band between actual and synthetic GF-1 WFV data were only 0.0078 for STDFA and 0.0153 for ESTARFM. Therefore, we conclude that further study is required regarding this problem.