Document Type

Journal Article


Department of Geography


Reliability of long-term snow depth data sets from remote sensing over the western arid zone of China




Due to the coarse spatial resolution of imagery and heterogeneity of snow physical parameters, the currently available snow depth data sets derived from passive microwave sensors have shown significant accuracy variations in different parts of the world. This study aims to analyse the reliability of existing remote sensing snow depth products for the arid zone of China. Two long-term products were compared and evaluated including the GlobSnow Snow Water Equivalent product (the GlobSnow product) and the Long-term Snow Depth Dataset of China (the WestDC product). Nine-year ground measurements from 35 sampling sites with diverse topographical conditions were used as ground references for the accuracy assessment. Statistical analysis methods such as intra-class correlation coefficient and root mean square error (RMSE) were adopted for the consistency test and accuracy assessment. Analysis of variance (ANOVA) was employed to examine whether the data reliability varied by seasonal or locational factors. The results show that the two products in general do not agree well in the study region. However, the generalized data with different temporal resolutions tend to yield better results in terms of data consistency and accuracy. Compared to the GlobSnow product, the WestDC product has shown a better accuracy with the RMSE of 8.23, 7.43, and 6.56 cm for the original daily, and generalized weekly, and monthly data, respectively. According to the ANOVA test, season and latitude show statistically significant impacts on data reliability, while altitude and terrain complexity do not. Data reliability declines with higher latitude and it rapidly falls down to an unacceptable level in snow melting periods. This study provides a quantitative assessment on the quality of the snow depth products and raises the awareness of data consistency issues of these products for regional applications. © 2013 Taylor and Francis.

Publication Date


Source Publication Title

Remote Sensing Letters





Start Page


End Page



Taylor & Francis

ISSN (print)


ISSN (electronic)


This document is currently not available here.