2.2 Sample site and applied data 
The sample site is selected at the place where Tianshan Station 
for Snowcover & Avalanche Research sits. It is located at the 
western section of Tianshan Mountains at 43?16'N and 84?24'E 
and at an altitude of 1,776 meters above sea level. Annual 
average precipitation for this area is 830.2 mm (XIEG, 2012). 
Snow depth information in the first quarter of a year is focused 
on so as to cover the period from snow falling to snow melting. 
Data from 2008 to 2010 are collected. Apart from some missing 
data, 265 samples (days) are involved in the following analysis. 
2.3 Data preprocessing 
As for long-term snow depth dataset of China, daily snow depth 
information can be directly extracted. While GlobSnow SWE 
product only provides snow water equivalent rather than snow 
depth information. A conversion algorithm between these two 
parameters should be adopted to make the two datasets 
comparable. The following formula can be used to determine 
snow water equivalent from snow depth and density: 
SWE = SD * Psnow {Pater (1) 
Where SWE = snow water equivalent; 
SD = snow depth; 
Psnow = Snow density; 
Pwater = Water density. 
Since snow density might be different at different places, 
conversion of snow depth to snow water equivalent will bring 
data uncertainty. Thus, we convert snow water equivalent to 
snow depth in our experiment, according to the assumption of a 
constant snow density of 0.23 g/cm’ for Eurasia in the retrieval 
algorithm of GlobSnow SWE product (Pulliainen, 2006). 
2.4 Examination of data consistency 
2.4.1 Regression model: Scatter plot is usually adopted to 
visualize the relationship between two variables. Dots in scatter 
plot should be concentrated on the line of y =x if two 
variables measure the same value. For this study, suppose snow 
depth measurements from two different methods are nearly the 
same, a simple linear regression can be used to express their 
relationship: 
Y = bX (2) 
Where Y = one measurement by method A; 
X = another measurement by method B; 
b = the slope of regression line. 
In this regression model, snow depth from long-term snow 
depth dataset of China is set as independent variable (X); and 
snow depth retrieved from GlobSnow SWE product is set as 
dependent variable (Y). The intercept of regression line is 
supposed to be zero when snow depth from /ong-term snow 
depth dataset of China shows a zero value. Therefore, the 
constant in linear regression model is not included. 
If two datasets have a good consistency, the slope of b should 
be close to 1. Larger than 1 means the estimation of GlobSnow 
SWE product tends to be larger than that of long-term snow 
depth dataset of China, and vice versa. 
    
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
2.4.2  Intra-class Correlation Coefficient: Intra-class 
correlation coefficient (ICC) is commonly used in 
psychological studies to measure the reliability of 
measurements from two or more judges (McGraw & Wong, 
1996). It reflects the extent to which ratings of the same group 
given by different judges tend to be alike. People tried to use it 
for answering the question whether two methods can be used 
interchangeably (Bland & Altman, 1990). In this study, this 
index is used for evaluating the consistency of snow depth 
measurements from two different snow depth retrieval methods. 
When using ICC to measure the agreement of ratings, one 
should choose an appropriate ICC model through making three 
decisions: (1) which variance model should be adopted: one- 
way, two-way random or two-way fixed; (2) are you interested 
in absolute agreement or just the consistent ratings without the 
same actual scores? (3) whether you plan to rely on a single 
judge or a combination of several judges? (Shrout & Fleiss, 
1979; Norusis, 2012). As for this study, two-way random model 
with ICC type of absolute agreement is chosen. Average 
measurement for multiple judges is employed. The value of ICC 
should be between 0 and 1. The larger the ICC value is, the 
better the data consistency shows. 
3. RESULTS AND ANALYSIS 
Figure 1 gives an overall perspective of the consistency of snow 
depth measurements from different snow products. Although a 
big part of samples show a good data consistency which are 
concentrated on or crossed by the reference line y = x, many 
dots are still far away from the line. Dots near the bottom (the 
horizontal axis) mean that some samples detected with snow 
cover in EESDCWestChina product are reported as snow-free 
samples in GlobSnow product. Besides, according to the slope 
of regression line in Table 1, 0.76 is not close to 1, which 
means these two datasets do not correlate well. 
  
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EESDCWestChina snow depth product unit mm 
Figure 1. Scatter plot of EESDCWestChina snow depth 
measurement against snow depth retrieved from 
GlobSnow SWE product at sample site for the first 
quarter of the year of 2008 to 2010 
When examining data consistency by different years, results 
show that snow depth measurement has a low level consistency 
in 2008 with the slope of 0.63 and a higher level consistency in 
2010 with the slope of 0.93 separately (see Table 1).