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Those facts can be corroborated by ICC index. From Table 2,
there are some variations of ICC scores among different sample
periods. ICC of 2010 is larger than that of the other sample
periods. Although this, low score in general (0.485) means that
snow depth measurements are not in agreement as for the whole
sample space.
Test period Slope (b) R square Sig. level
2008 - 2010 0.76 0.79 0.05
2008 0.63 0.78 0.05
2009 0.81 0.79 0.05
2010 0.93 0.87 0.05
Table 1. Regression results based on pairs of snow depth
measurements from two datasets from January to
March at different periods
Test period ICC score
2008 - 2010 0.485
2008 0.575
2009 0.383
2010 0.668
Table 2. ICC scores in terms of average measures at different
periods
The impact of snow melting on the stability of snow depth
detection is analyzed. To contrast snow depth measurements in
January to March, result shows a better consistency in January
than March (Figure 2). The regression results shown in Table 3
can also indicate the same conclusion. Data from the two
products sampled in January has a high correlation.
Retrieved from GlobSnow SWE product
T T :
x 2553 em «5
EESDCWestChina snow depth product GN mte
(a) January
Retwleved from GlobSnsw SWE product
dibus etie Rt. 12.
E E aie
p
EESDCWestChina snow depth product api. vns
(b) March
Figure 2. Scatter plot of EESDCWestChina snow depth
measurement against snow depth retrieved from
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
GlobSnow SWE product for snow falling (figure (a))
and snow melting (figure (b)) periods
Test period Slope (b) R square Sig. level
January 1.03 0.95 0.05
March 0.38 0.52 0.05
Table 3. Regression results based on the data in snow falling
period (January) and snow melting period (March)
4. DISCUSSION
Statistical techniques provide a quantitative means to assess the
reliability of remote sensing based snow depth products. Scatter
plot and simple linear regression model can give a general
description of data consistency. However, the conclusion could
be unreliable in some cases. Consider a case that samples are
evenly dispersed in the upper and lower parts of the line y — x,
the slope of regression line based on those samples can highly
approach to 1. However, this kind of dataset does not fit the
requirement of data consistency.
ICC index gives another way to measure the consistency of two
datasets. It is conceptualized as the ratio of between groups
variance to the total variance (Shrout & Fleiss, 1979).
Something should be pointed out that ICC also has its limitation.
This kind of coefficient depends on the range of observed
values. For example, as for this study, ICC is not suitable for
evaluating the consistency of snow depth measurements in
January because the values in January are homogeneous.
In this study, snow depth measurement from two snow products
didn't show a good consistency. The reasons may come from
the following aspects:
1. The inaccuracy originated from snow depth retrieval
algorithm;
2. The spatial resolution of snow depth products is too
coarse (around 25km). In mountain areas, spatial
heterogeneity within a pixel will cause measurement
uncertainty in snow depth detection methods;
3. Snow depth retrieval algorithms are normally based
on dry snow. The quality of snow depth products during
snow melting periods will become instable and terrible.
5. CONCLUSION
This study has tested the reliability of two remote sensing based
snow depth products in mountain areas of arid zone of western
China. Statistical techniques include regression and intra-class
correlation coefficient methods are employed to examine the
consistency of snow depth measurements.
Results show that snow depth measurements from the two
selected products do not correlate well. The difference between
them can be very large especially in snow melting period.
Because of the low consistency, it is hard to say that snow depth
measurement based on passive microwave remote sensing is
reliable for mountain areas. Although challenges still exist
when retrieving snow depth from passive microwave data with
a high accuracy, effort should be undertaken to make the remote
sensing based snow data more reliable. In the future, in-situ
observation data from ground monitoring station is expected to
be collected for accuracy assessment and calibration of existing
snow depth products.
