## 3 Methodology  
Snow-covered areas identified from MOD10A1 data were compared with reference data obtained from the HJ-1A/B satellite in the same area in order to verify the amount of overestimation of the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{3}{c}{**CCD**} & \multicolumn{3}{c}{**IRS**} \\ \cline{2-9}  & **Satellite** & **Sensor** & **Path** & **Row** & **Satellite** & **Sensor** & **Path** & **Row** \\ \hline
14 May 2011 & HJ-1A & CCD1 & 50 & 72 & HJ-1B & IRS & 52 & 70 \\
15 May 2011 & HJ-1A & CCD2 & 47 & 72 & HJ-1B & IRS & 45 & 77 \\
16 May 2011 & HJ-1B & CCD1 & 50 & 72 & HJ-1B & IRS & 45 & 70 \\
17 May 2011 & HJ-1B & CCD2 & 47 & 72 & HJ-1B & IRS & 52 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Path and row numbers of the referenced HJ-1A/B data acquired on 14–17 May 2011.  
former at large SZA values. Snow-melting periods (May-September) were selected for the study period, and data from the meteorological station were used to exclude those dates that registered snowfall events. For comparison purposes, suitable MOD10A1 data were also required for the same area and for dates consistent with the available HJ-1A/B satellite images. Visual inspection of the imagery resulted in the selection of four continuous days (14-17 May 2011) of cloud-free MOD10A1 data and HJ-1A/B satellite data. Precipitation and temperature data from nearby meteorological stations verified that snowfall did not occur during this period. Snow pixels in MOD10A1 were extracted for the sample area to calculate the extent of the snow-covered areas. These same areas were categorized from the HJ-1A/B data using a snow-mapping algorithm, such that a pixel was classified as a snow-covered area when the NDSI value was \(>\)0.4 and the near-infrared reflectance (Band 4 of HJ-CCD) was \(>\)0.11 [9]. The snow-mapping algorithm has been proven capable of extracting the snow-covered area from the HJ satellite data [27; 28]. Thus, the SCA and the valid SCPs of the MOD10A1 and HJ-1A/B data were calculated, and the maximum, minimum, and average values of the corresponding SZA were determined for each pixel. A comparison between these values was used to confirm that the snow-covered areas were overestimated in a typical sample locality. Five typical subwatersheds were also investigated to explore the effect of SZA across the entire study area. These subwatersheds were selected based on their different elevations, which allowed representative coverage of the Tarim River Basin's snow-covered areas. Analysis of the overestimation effect caused by the SZA required selection of suitable precipitation-free paired dates, with an effective index (such as the valid SCP) also calculated for comparison. Specifically, the paired dates used to analyze the SZA overestimation effect in the Tarim River Basin were selected primarily based on the following two factors: whether they occurred during the melting season or within 10 years of each other. The daily SCPs of these dates were calculated by dividing the daily SCA by the study region's overall area, and the values of the daily SCPs and their dates were plotted on 10 curves showing these paired dates over the course of 10 years. The ascending limb of each curve indicates an increase in SCA (i.e., new snowfall events), while the descending limb indicates a period of depletion. To exclude snowfall events, we only considered those dates that lay on the descending limb of the SCP curve, and we used two consistent days from each as a valid date pair. Finally, we used the valid SCP instead of the SCP in order to eliminate the influence of cloud cover. The valid SCP was calculated from the ratio of the mutual SCA to the mutual cloud-free area in the same region on both dates in each pair. Thus, the difference between the valid SCPs on each of these two days more accurately reflected the trend in snow cover variation.