水科学进展2018，Vol.29Issue(1)：11-19,9.DOI:10.14042/j.cnki.32.1309.2018.01.002
亚洲中部干旱区积雪时空变异遥感分析
Spatial-temproal variability of snow cover in arid regions of Central Asia
摘要
Abstract
Remote sensing of snow information in arid regions of Central Asia can provide data support for the allocation and utilization of water resources in transboundary rivers and play an important role in the ecological security of major national strategies.In this paper,data fusion method was used to merge MOD10A2 and MYD10A2 data for cloud removal and extraction of snow cover.Snow cover data from meteorological stations were used to evaluate the snow recognition accuracy after cloud removal.Information of snow cover percentage (SCP) and snow day (SCD) was extracted and analyzed.Temporal and spatial variation of SCP under different elevation zones was analyzed by using digital elevation model (DEM).The results showed that:① The fusion of MOD10A2 and MYD10A2 data can effectively remove cloud and improves the accuracy of snow information extraction.② During a year,the maximum SCP ranged from 55.7％ to 77.4％ and the minimum ranged from 1.6％ to 2.9％.There was a clear regional difference in the rate of the decline of SCP during the snowmeh period,and the overall SCP showed a slowly increasing trend.③ The overall SCD showed a slight downward trend,32.2％ region showed a downward trend,30.7％ region showed an upward trend,36.9％ of the region remained stable.④ Under the altitude of 1 000 m,the annual variation of SCP during the year is U-shaped and the annual variation is significant.In the regions of 1 000-4 000 m,the variation of seasons is V-shaped during the year of SCP,and the annual variation shows a steady fluctuation;permanent snow,temporal and spatial variation of SCP are not obvious.关键词
亚洲中部干旱区/大尺度遥感/MOD10A2/MYD10A2/时空变异Key words
arid regions of Central Asia/large-scale in remote sensing/MOD10A2/MYD10A2/temporal and spatial variation分类
天文与地球科学引用本文复制引用
陈文倩,丁建丽,马勇刚,张喆,周杰..亚洲中部干旱区积雪时空变异遥感分析[J].水科学进展,2018,29(1):11-19,9.基金项目
国家自然科学基金资助项目(41771470) （41771470）
新疆自治区重点实验室专项基金资助项目(2016D03001)The study is financially supported by the National Natural Science Foundation of China(No.41771470). （2016D03001）
