| 亚洲中部干旱区积雪时空变异遥感分析 |
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| 引用本文： | 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. |
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| 作者姓名： | 陈文倩 丁建丽 马勇刚 张喆 周杰 |
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| 作者单位： | 1.新疆大学资源与环境科学学院智慧城市与环境建模自治区普通高校重点实验室, 新疆 乌鲁木齐 830046; |
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| 基金项目： | 国家自然科学基金资助项目（41771470）；新疆自治区重点实验室专项基金资助项目（2016D03001） |
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| 摘 要： | 亚洲中部干旱区的大尺度遥感积雪信息研究，可在跨界河流水资源分配利用方面提供数据支持，对国家重大战略的生态安全保障有重要作用。采用数据融合方法，将MOD10A2和MYD10A2数据进行融合去云处理，结合气象站点积雪数据评估去云后的积雪识别精度；提取积雪覆盖率（SCP）与积雪日数（SCD）信息，分析SCP与SCD年际、年内变化差异；结合数字高程模型，分析不同高程带下SCP的时空变化规律。结果表明:①MOD10A2与MYD10A2融合去云处理，可有效去除云的干扰，准确提取亚洲中部干旱区积雪变化信息。②年内SCP最大值范围为55.7%~77.4%，最小值范围为1.6%~2.9%，融雪期SCP下降速率具有明显地域差异，总体SCP呈缓慢增加趋势。③总体SCD呈略微下降趋势，32.2%的区域呈下降趋势，30.9%的区域呈增加趋势，36.9%的区域保持稳定不变。④海拔1 000 m以下，SCP年内随季节变化呈U型，年际变化显著；1 000~4 000 m区域，SCP年内均随季节的变化呈现出V型，年际变化呈现出稳定性波动；6 000 m以上为永久性积雪，季节、时空变化差异性均不明显。
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| 关 键 词： | 亚洲中部干旱区 大尺度遥感 MOD10A2 MYD10A2 时空变异 |
| 收稿时间： | 2017-05-15 |
Spatial-temproal variability of snow cover in arid regions of Central Asia |
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| CHEN Wenqian,DING Jianli,MA Yonggang,ZHANG Zhe,ZHOU Jie.Spatial-temproal variability of snow cover in arid regions of Central Asia[J].Advances in Water Science,2018,29(1):11-19. |
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| Authors: | CHEN Wenqian DING Jianli MA Yonggang ZHANG Zhe ZHOU Jie |
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| Affiliation: | 1.Xinjiang Common University Key Laboratory of Smart City and Environmental Stimulation, Xinjiang University, Urumqi 830046, China;2.Key Laboratory of Oasis Ecology Ministry of Education, Urumqi 830046, China;3.Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China |
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| 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 snowmelt 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. |
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| Keywords: | arid regions of Central Asia large-scale in remote sensing MOD10A2 MYD10A2 temporal and spatial variation |
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