## 4 Results
### MOD10A1 Dataset Modification and Evaluation  
We used the optimal threshold described above to modify the MOD10A1 snow cover data for the Tarim River Basin as follows. Firstly, we resampled the SZA images onto a 500-m-resolution grid for consistency, and then overlaid the MOD10A1 and SZA data and marked all pixels in MOD10A1 with SZA \(\geq\)22.37\({}^{\circ}\) as the missing data. Finally, a multiday refilling method [32] was used to replace the missing data, so as to produce a modified snow cover dataset free of cloud and the SZA effect. Figure 7 shows a comparison of maps produced before and after the modification of the snow cover data in the Tarim River Basin on 18 November 2003. Figure 7a shows the original map derived from the MOD10A1 data, Figure 7b shows the map from Figure 7a after the removal of pixels with an SZA value \(\geq\)22.37\({}^{\circ}\) threshold (missing data are colored gray), and Figure 7c shows the modified snow cover map after applying the cloud-removal method to the map shown in Figure 7b. Figure 7a shows that there was an extensive cloud cover (blue) across the entire research area and that visible snow-covered areas were indistinguishable. Additionally, Figure 7b shows that almost half the area of study of the Tarim River Basin would have been influenced detrimentally by the effect of the large SZA (gray-colored areas). The SCA MOD10A1 data that fall within the SZA limits of confidence are therefore quite limited. The modified map (Figure 7c) illustrates that the snow-covered areas can be identified clearly, once the missing data have been effectively filled in, and that issues related to cloud cover have been almost eliminated. Figure 6: (**a**) Sensor zenith angle (SZA) changes over the analyzed 17-day period; and (**b**) a histogram of possible SZA thresholds obtained for one example test period. We used this multiday refilling method to modify the MOD10A1 data for 2003 and examined the changes in daily SCP (Figure 8). The SCP values for de-clouded MOD10A1 data were consistently higher than the calculated SCP values obtained from the original MOD10A1 data, whereas the SCP values for the modified snow cover data were lower than those for the de-clouded SCP. Annual SCP average values for the MOD10A1 data, de-clouded MOD10A1 data, and modified MOD10A1 data were 3.42%, 8.69%, and 6.24%, respectively. We can therefore reasonably assume that the differences between the de-clouded and modified data were due to overestimation caused by a larger SZA, which clearly had a significant influence. The results also revealed that the modified data would considerably improve the accuracy of MOD10A1-based interpretations. The SCF data for the 10 years between 2002 and 2011 were also used to evaluate the SZA effect. Figure 9a shows the annual average SCF values in the Tarim River Basin (2002-2011) before and after removing pixels with SZA values larger than the threshold value. The SCF is defined as the number of times that a MODIS pixel is classified as snow covered relative to the total number of valid observations obtained during a certain period [33]; thus, it is a spatiotemporal measure of snow cover distribution. These data show that the average SCF of the modified snow cover data decreased relative to that for the average de-clouded MOD10A1 SCF data over this 10-year period. The maximum difference between the average SCF values was a decrease from 0.087 to 0.076 in 2003, which was a relative change of 12.6%, whereas even the minimum change of SCF in 2004 had a relative change of 1.5%. The average decrease was 5.4%. The average SCF values for the same month in each of the 10 years are shown in Figure 9b, which shows that the monthly SCF difference has a clear seasonal pattern. The SZA effect during periods without snow melting was larger than that during snow-melting periods, with a maximum relative difference of 7.6% in January. Even in April--the snow-melting period--there was a non-negligible difference of 3.03%.