Assimilation of D-InSAR snow depth data by an ensemble Kalman filter
Assimilation of D-InSAR snow depth data by an ensemble Kalman filter
复制标题
通过集合卡尔曼滤波器同化 D-InSAR 雪深数据
DOI：
10.1007/s12517-021-06699-y
复制
发表时间：
2021-03
影响因子：
--
通讯作者：
Li Chengzhi
中科院分区：
文献类型：
--
作者：
Yang Jinming;Li Chengzhi
雪深是区域气候变化的重要反映，是中长期气候数值预报、陆面水文过程数值模拟和水资源评价的重要参数。然而，现有的积雪深度遥感反演产品质量不可避免地受到云和山影的影响，积雪深度数据的时空分辨率不能满足水文研究和辅助决策的需要。因此，迫切需要一种提高雪深数据准确性的方法。基于集合卡尔曼滤波，对Sentinel-1合成孔径雷达遥感图像的D-InSAR数据、超声波雪深探测器自动测量数据和人工测量数据进行同化。同化的雪深资料具有时空连续性和综合性。在实测资料的约束下，同化后的雪深资料精度较高，满足后续研究的需要。超声波雪深探测仪的研制和D-InSAR技术在雪深反演中的应用，极大地缓解了雪深数据在类型和数量上的不足。同时，集合卡尔曼滤波对多源雪深数据的同化也为遥感水文研究、水资源评价、雪灾防治方案提供了高精度数据支持。
Snow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.
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DOI：
10.5194/tcd-9-4997-2015
发表时间：
2015-09
期刊：
The Cryosphere Discussions
影响因子：
--
作者：
C. Huang;H. Wang;J. Hou
通讯作者：
C. Huang;H. Wang;J. Hou
DOI：
10.5194/isprsarchives-xl-1-w3-253-2013
发表时间：
2013-09
期刊：
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
影响因子：
--
作者：
M. Maghsoudi;A. Hajizadeh;M. A. Nezammahalleh;H. Seyedrezai;A. Jalali;M. Mahzoun
通讯作者：
M. Maghsoudi;A. Hajizadeh;M. A. Nezammahalleh;H. Seyedrezai;A. Jalali;M. Mahzoun
影响因子：
3.2
作者：
Wendy A. Ryan;N. Doesken;S. Fassnacht
通讯作者：
Wendy A. Ryan;N. Doesken;S. Fassnacht
DOI：
10.1201/9781420023749
发表时间：
2005-08
期刊：
--
影响因子：
--
作者：
W. Rees
通讯作者：
W. Rees
影响因子：
4.6
作者：
W. Zhou;Lilong Liu;Liangke Huang;Yibin Yao;Jun Chen;Songqing Li
通讯作者：
W. Zhou;Lilong Liu;Liangke Huang;Yibin Yao;Jun Chen;Songqing Li