Accelerated decline of snow cover in China from 1979 to 2018 observed from space.

Snow cover is an important indicator of climate change. Variations of snow integrate the competing effect of increasing temperature and precipitation. In this study, based on Theil-Sen Median (TSM) and Mann-Kendall (M-K) methods, observational evidence from space was used to investigate the variation of snow parameters in China from 1979 to 2018, and some meaningful conclusions were found. (1) The downward trend of snow depth (SD) with a median of -0.02 cm/year was generally found in the high altitude mountains, and the upward trend of SD with a median of 0.01 cm/year occurs in the plains. A widespread and accelerated decrease of SD was observed in the latest period (2009-2018) in NC and NX. (2) The mean annual areas with snow cover days (SCDs) greater than 150 days (d) accounted for 17.8%, 24.73% and 38.14% in NC, NX and QTP. SCDs in NC and Northern QTP were widely reduced, but the longest snow season with more than eight months is still maintained on QTP. (3) The downward trend of snow storage (SS) was found in all three snow areas. The difference of snow phenology is reflected in the slowest accumulation and melting rate of SS on QTP; the largest peak value of SS and the shortest snow season in NC; the slow accumulation and rapid melting of snow in the NX, and the peak value is achieved at the latest. The trend of maximum temperature was judged as the most important factor affecting the change of SD, while longitude and latitude are closely related to the change of SCD.

[Hyperspectral remote sensing estimation models for snow grain size].

Snow grain size is a key parameter not only to affect the energy budget of the global or local region but also characterizing the status of snow vapor transport and temperature gradient. It is significant to monitor and estimate the snow grain size in large area for global or local climate change and water resource management. Recently, remote sensing technology has become a useful tool for snow grain size monitoring and estimating. In the present paper, the estimate models were built based on simulating the snow surface spectral reflectance curve in visible-infrared region and the sensitive bands and snow indices for snow grain size were selected. These models help estimate snow grain size by hyperspectral remote sensing. Through validating with ground true data, the results show that these models have higher explorative accuracy using 1 030, 1 260 nm and normalized difference snow index (460 and 1 030 nm). In addition, the correlation slopes of estimated and observed valves are 1.37, 0.61 and 0.62, respectively. R2 are 0.82, 0.86 and 0.93 and RMSE are 55.65, 50.83 and 35.91 microm, respectively. The result can provide a scientific basis for snow grain size monitoring and estimating.