## 5 Discussion  
We used reference SCA data obtained from the HJ-1 A/B satellite over several consecutive days to demonstrate a relationship between the expanded snow cover within the MOD10A1 dataset and high SZA values. Ten years' continuous hydrological data from MOD10A1 were used to develop SZA sensitivity relationships for both individual points in time and for all suitable days throughout a melting season. This enabled comparisons between SCA calculations made using different sensors. Scatter plots for five subwatersheds of the Tarm River Basin showed that a large SZA directly correlated with an overestimation of snow cover, and that a linear relationship existed between the increased SZA and SCA. Previous investigations into the effect of an increased SZA have focused mainly on the sensitivity of SZA to vegetation indices. For example, a significant SZA effect was reported for a normalized difference vegetation index at a forest site (NDVI), which indicated that the former affected the latter by up to 15% [35]. In the case of the NDVI, the red band has been reported much more sensitive to SZA than the near-infrared band [36]. In our study, the green band was found significantly more sensitive to SZA than the near-infrared band, which caused the NDSI to show acute SZA sensitivity. The different responses that snow or vegetation show to SZA must be investigated further and it is important to note that the SZA effect is not limited to MOD10A1 data. Together, these findings indicate that SZA effects would not cancel each other out and that the NDSI/NDVI would show sensitivity to SZA. This is \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dates**} & \multicolumn{3}{c}{**SCP (\%)**} & \multicolumn{3}{c}{**Difference of SCF (\%)**} & \multicolumn{3}{c}{**OA (\%)**} & \multicolumn{3}{c}{**Increase of OA (\%)**} \\ \cline{2-10}  & **MOD10A1** & **Modified** & **HJ-1A/B** & **MOD10A1-** & **MOD10A1** & **MOD10A1** & **Modified** & **From MOD10A1 to** \\  & **MOD10A1** & **MOD10A1** & **HJ-1A/B** & **HJ-1A/B** & **HD-1A/B** & **MOD10A1** & **MOD10A1** & **Modified** \\ \hline
14 May & 63.79\% & 63.50\% & 61.24\% & 2.55\% & 2.26\% & 67.75\% & 68.14\% & 0.39\% \\
15 May & 66.85\% & 63.64\% & 60.93\% & 5.92\% & 2.71\% & 63.28\% & 67.96\% & 4.68\% \\
16 May & 62.64\% & 62.18\% & 60.83\% & 1.81\% & 1.35\% & 66.68\% & 67.20\% & 0.52\% \\
17 May & 65.32\% & 62.86\% & 60.63\% & 4.69\% & 2.23\% & 62.86\% & 66.63\% & 3.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SCPs, SCP differences, and overall accuracies (OAs) of snow cover data extracted from MOD10A1, modified MOD10A1, and HJ-1A/B datasets; OAs increase from 14 to 17 May 2011. Figure 10: Comparison of snow cover determined from MOD10A1 data, modified MOD10A1 data, and HJ-1A/B data on 14–17 May 2011.  
because a normalized difference index will not eliminate SZA sensitivity if both spectral bands in the index have different SZA sensitivities. In any case, when dealing with an index that is an arithmetic combination of reflectance in several bands, the magnitude of the SZA effect is also dependent on both the arithmetic form of the index and the changes in the actual index value for different canopy components that might be viewed at different angles. A nadir SZA area was used as a reference in our work to establish the minimum sensitive angle that caused overestimation of snow-covered pixels. In the Tarim River Basin, this angular threshold was determined to be 22.37\({}^{\circ}\), which is close to that of MODIS Persistent Ice (MODICE). MODICE determines the annual minimum amount of exposed snow and ice cover by searching each pixel's time series of MODSCAG retrievals for a given snow-free date, and it only includes MODSCAG retrievals for pixels that have an SZA of less than or equal to 25\({}^{\circ}\). The existence of a regional difference of this angular threshold is interesting and worthy of further study.