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
EVALUATING THE CONSISTENCY OF REMOTE SENSING BASED SNOW DEPTH
PRODUCTS IN ARID ZONE OF WESTERN CHINA
Qiming Zhou & Bo Sun*
*corresponding author, E-mail: sun.bo(gmsn.com
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, S.A.R., China
KEY WORDS: Snow ice, depth, global-environmental-databases, evaluation, data reliability, western China
ABSTRACT:
Snow cover is a sensitive indicator of global climate change. Among various snow cover parameters, snow depth which can indicate
snow accumulation is essential for retrieving snow water equivalent. In arid zone of western China, based on different inversion
models, snow depth products retrieved from passive microwave remote sensing sensors have been issued. However, none of them
can promise a high accuracy due to the spatial heterogeneity of snow cover especially in mountain areas with complex terrain. This
study aims to analyse the reliability of existing long-term snow depth products in arid zone of western China. Two datasets are
compared including GlobSnow snow water equivalent (SWE) product and snow depth dataset provided by Environmental and
Ecological Science Data Center for West China. Statistical techniques like regression and intra-class correlation coefficient (ICC)
models are employed to examine the consistency of these two remote sensing based snow depth products in a selected sampling site.
More than 260 samples during three years are tested covering from snow falling to snow melting periods. Result shows that there is a
discrepancy between the two datasets. Accordingly, remote sensing based snow depth measurement is not reliable in mountain areas
in arid zone of western China. This study gives an awareness of the stabilities of current snow depth detection models. A further
study is expected to calibrate snow depth products based on in-situ observation and measurements from ground monitoring stations.
1. INTRODUCTION
Snow cover in mountain areas is increasingly considered as one
of the most sensitive indicators of global climate change due to
the less direct influence of human activities. Under the
background of global warming, the long-term trend of snow
cover change has been focused.
Among snow cover change studies, snow depth which indicates
snow accumulation is an essential parameter for retrieving the
amount of water contained within the snowpack (snow water
equivalent). Passive microwave remote sensing data has shown
the capability of providing a large-extent and successive dataset
for long-term snow cover change studies in terms of snow depth
information. The retrieval of snow depth from passive
microwave data is on the basis of scattering theory. Retrieval
algorithms are based on the difference in emissivity between
two microwave frequencies, e.g., 18/19 and 36/37 GHz (Chang
et al., 1987). Historical passive microwave remote sensing data
can date back to 1978. Commonly used sensors and platforms
include SMMR carried by Nimbus-7, SSM/I carried by DMSP
satellite series and AMSR-E carried by EOS satellite. Based on
the same data source, various snow depth retrieval algorithms
have been developed and several snow products have been
released.
At present, although long-term snow depth and snow water
equivalent products at global scale have been issued, none of
them can promise a high accuracy for everywhere in the world.
One of the major reasons is that physical parameters of snow
cover can be different at different places. Besides, as a known
issue, the error caused by spatial heterogeneity is hard to be
solved. Data accuracy might become lower in mountain areas
and in the place where underlying surface of snow cover is
complex.
In this study, two existing snow products are collected. Depth
information is retrieved from the same long-term passive
microwave remote sensing dataset. We attempt to evaluate the
consistency of snow depth measurements from these two
products and assess the reliability of the products for mountain
areas in arid zone of western China.
2. METHODOLOGY
Pairs of snow depth measurements sampled from two snow
datasets are compared for examining their differences by using
statistical techniques. Given that the data is at ratio scale in
terms of the level of measurement, regression and intra-class
correlation coefficient (ICC) models are employed to test the
consistency of the two datasets.
2.1 Existing snow depth products
Two existing snow depth products are tested in this study
including Long-term Snow Depth Dataset of China and
GlobSnow SWE product.
Long-term Snow Depth Dataset of China is issued by
Environmental and Ecological Science Data Center for West
China (EESDCWestChina). Snow depth retrieval algorithm is
based on a modified Chang's algorithm so that the algorithm
can be suitable for snow depth retrieval in China (Che, et al.,
2008). For the accuracy of snow depth, it is reported that the
standard deviations can reach to 60 mm regarding different
passive microwave sensors (Che, et al., 2008).
GlobSnow SWE product is issued by Finish Meteorological
Institute, which is supported by a European Space Agency
(ESA) project. Snow depth retrieval algorithm is based on a
semi-empirical snow emission model (called HUT model)
(Pulliainen, 2006).
