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遥感技术与应用  2020, Vol. 35 Issue (6): 1320-1328    DOI: 10.11873/j.issn.1004-0323.2020.6.1320
冰雪遥感专栏     
融合FY-3C号和FY-4A号卫星数据的积雪面积变化研究—以祁连山区为例
乔海伟1,2,3(),张彦丽1()
1.西北师范大学地理与环境科学学院,甘肃 兰州 730070
2.中国科学院空天信息创新研究院,北京 100094
3.中国科学院大学,北京 100049
FY-3C and FY-4A Satellite Data were Combined to Study the Variation of Snow Cover Area: A Case Study of Qilian Mountains
Haiwei Qiao1,2,3(),Yanli Zhang1()
1.College of Geography and Environment Sciences,Northwest Normal University,Lanzhou 730070,China
2.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3.University of Chinese Academy of Sciences, Beijing 100094, China
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摘要:

祁连山区积雪类型丰富、判识复杂,是中国积雪研究的典型区域。因此,精确地监测祁连山区积雪面积变化及其时空演变,对祁连山区生态环境和社会经济发展等具有重要意义。FY-3C MULSS利用多阈值积雪指数模型提供全球日积雪覆盖产品,FY-4A AGRI传感器每15~60 min提供一景覆盖全球的多光谱影像。基于FY-4A AGRI高时间分辨率的特征,构建适合于FY-4A号数据的动态多阈值多时相云隙间积雪识别方法,很大程度上减小了云对光学数据识别积雪造成的影响,并结合FY-3C MULSS积雪覆盖日产品较高空间分辨率的优势,融合得到去除云后的FY3C4积雪覆盖数据。利用Landsat 8 OLI卫星数据对融合后的积雪数据进行对比验证,结果表明融合FY-3C和FY-4A后的数据能更好地判识祁连山区的积雪覆盖情况。以MODIS MOD10A2积雪产品为真实值,随机检验了2018年3月~2019年3月融合后数据的积雪判识精度,发现无云情况下方法的总体精度可达到85.25%。进一步研究发现祁连山区积雪面积在海拔、气候和坡向等因素的影响下时空分布极不均匀,总体呈现出冬春季节大于夏秋季节,以及东部积雪面积大于西部积雪面积的特征。

关键词: 祁连山区风云系列影像影像融合积雪识别积雪面积时空变化    
Abstract:

Qilian Mountains is a typical area of snow cover research in China because of its rich snow types and complex identification. Therefore, it is of great significance for the regional ecological environment and socio-economic development to accurately monitor the change of snow area and its spatiotemporal evolution in the Qilian Mountains. FY-3C MULSS uses the multi-threshold snow index model to provide global daily snow cover products, and the FY-4A AGRI sensor provides a global multispectral image every 15~60 min on average. By using the characteristics of FY-4A AGRI with the high temporal resolution, a dynamic multi-threshold and multi-temporal snow detection method suitable for FY-4A data was constructed, which greatly reduced clouds impact on snow detection in optical images. Then combining with the advantages of the higher spatial resolution of snow-covered daily products FY-3C MULSS, the FY3C4 snow-covered data after cloud removal is obtained. Using Landsat 8 OLI high-resolution satellite data to verify the accuracy of the fused snow cover, the results show that the fused FY-3C and FY-4A can better identify the snow cover in the Qilian Mountains. Taking MODIS MOD10A2 snow cover product as the real value, the recognition accuracy of the fused daily snow cover product from March 2018 to March 2019 is tested randomly, and the overall accuracy is as high as 85.25% without the clouds. Further research shows that the snow area in the Qilian Mountains is extremely uneven in time and space under the influence of altitude, climate and slope direction. In general, the snow cover area in winter and spring is larger than that in summer and autumn, and the snow cover area in the east is larger than that in the west.

Key words: Qilian Mountains    FengYun series images    Image fusion    Snow cover identification    Snow cover area    Change of time and space
收稿日期: 2019-12-25 出版日期: 2021-01-26
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(41871277);中国博士后科学基金项目(2016M602893)
通讯作者: 张彦丽     E-mail: qhwgis@gmail.com;zyl0322@nwnu.edu.cn
作者简介: 乔海伟(1997-),男,甘肃张掖人,主要从事积雪遥感、微波遥感、层析SAR研究。E?mail:qhwgis@gmail.com
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引用本文:

乔海伟,张彦丽. 融合FY-3C号和FY-4A号卫星数据的积雪面积变化研究—以祁连山区为例[J]. 遥感技术与应用, 2020, 35(6): 1320-1328.

Haiwei Qiao,Yanli Zhang. FY-3C and FY-4A Satellite Data were Combined to Study the Variation of Snow Cover Area: A Case Study of Qilian Mountains. Remote Sensing Technology and Application, 2020, 35(6): 1320-1328.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.6.1320        http://www.rsta.ac.cn/CN/Y2020/V35/I6/1320

图1  研究区概况图
数据名称空间分辨率 /m时间分辨率格式数量来源

FY-3C MULSS积雪覆盖日

产品数据

1 0001 dHDF730景国家卫星气象中心数据服务网(http:∥satellite.nsmc.org.cn)
FY-4A AGRI成像仪全圆盘2KML1数据2 00015 minHDF6 205景国家卫星气象中心数据服务网(http:∥satellite.nsmc.org.cn)
数字高程模型数据(SRTM)90tif地理空间数据云网站(http:∥www.gscloud.cn/)
MOD10A2积雪覆盖产品数据5008 dHDF92景美国国家冰雪产品数据中心
Landsat 8 OLI卫星遥感数据3016 dtif24景中国科学院遥感与数字地球研究所对地观测数据共享网 (http:∥ids.ceode.ac.cn/)
表1  数据来源
波段/μm星下点空间分辨率/kmS/N或NE△T主要用途
0.45~0.491≥150(ρ=100%)植被
0.55~0.750.5≥150(ρ=100%)植被、雾、云
0.75~0.901≥200(ρ=100%)植被、水面上气溶胶
1.36~1.392≥50(ρ=100%)卷云
1.58~1.642≥150(ρ=100%)低云、雪、水云、冰云
2.10~2.352~4≥200(ρ=100%)卷云、气溶胶
3.5~4.02~4≤0.2 K(315 K)火情
表2  FY-4A号气象卫星辐射成像仪主要性能(2 km)
图2  积雪综合判识流程图
地物类型NDSI
积雪0.33
土壤-0.23
0.22
植被-0.34
0.28
表3  NDSI无法区分雪、卷云和水体
图3  FY-4A动态多阈值积雪识别方法
成像时间段abc
10:00~11:000.050.1730.1
11:15~13:000.060.180.12
13:45~17:450.0780.1770.12
表4  10月~3月积雪动态阈值设置
成像时间段abc
10:00~11:000.110.150.1
11:15~13:000.120.1550.12
13:45~17:450.120.160.12
表5  4月~9月积雪动态阈值设置
图4  FY-4A多时相数据云隙间积雪识别方法
图5  FY-3C MULSS积雪覆盖日产品和FY-4A积雪识别结果融合方法
图6  Landsat 8 OLI数据检验融合方法精度(a) 2018年3月12日FY?3C积雪判识结果 (b) 2018年3月12日FY3C4积雪判识结果
数据名称OAEUEO
FY3C485.25%7.54%7.21%
表6  无云情况下平均精度评价表
图7  2019年1月3种积雪判识结果对比
图8  FY3C4祁连山区积雪面积值
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