Abstract Snow cover plays an important role in the fields of climatology and cryospheric science. Remotely-sensed data have been proven to be effective in monitoring snow covers. Improved methods to process the 8-day snow-cover products derived from MODIS Terra/Aqua data can dramatically increase the data quality and reduce noise. A five-step algorithm for removing cloud effects was designed to improve the quality of MODIS snow products, and the overall accuracy of the MODIS snow data without cloud (defined as cloud-free snow-cover dataset) was enhanced by more than 90% based on direct and indirect validation methods. The snow-cover frequency (SCF) and snow-cover rate (SCR) of Central Asia were analyzed from 2000 to 2015 using trend analysis and empirical orthogonal functions (EOFs). Over the plain regions, the SCF displayed a significant north-south declining trend with a rate of 0.03 per degree of latitude, and the SCR showed a similar north-south gradient. In the mountainous areas, the SCF significantly increased with altitude by 0.12 per kilometer. Within the study area, the SCF in 65% of the study area experienced an increasing trend, but only 4.3% of the SCF-increasing pixels passed a significance test. The remaining 35% of the area underwent a decreasing trend of SCF, but only 5.2% of the SCF-decreasing pixels passed a significance test. For the entire Central Asia, the inter-annual variations of snow-cover presented a slight and insignificant increase trend from 2000 to 2015. However, the change trends of snow cover are different between the plain and mountainous regions. That is, the annual mean SCR in the plain areas displayed an increasing trend, but a decreasing trend was found in the mountainous areas.
2017, Vol. 9 
