Evaluating the consistency of remote sensing based snow depth products in arid zone of Western China

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.