| 青藏高原区域不同地表类型对应后向散射系数的时变分析 |
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| 引用本文： | 陈晓东,郭金运,孙明智,朱广彬,常晓涛.青藏高原区域不同地表类型对应后向散射系数的时变分析[J].武汉大学学报(信息科学版),2023,48(5):730-740. |
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| 作者姓名： | 陈晓东 郭金运 孙明智 朱广彬 常晓涛 |
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| 作者单位： | 1.山东科技大学测绘科学与工程学院, 山东 青岛, 266590 |
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| 基金项目： | 国家自然科学基金41774001国家测绘自主可控专项816-517 |
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| 摘 要： | 后向散射系数（$ {\sigma }_{0} $）是卫星雷达高度计的观测量之一，被广泛应用于地表状态监测、积雪冰层厚度反演、卫星测高定标与验证等过程。根据Jason-2测高卫星的地球物理数据记录分离出青藏高原Ku波段的$ {\sigma }_{0} $数据，以GlobeLand30 2020版本的地表数据为分类基础，通过经纬度数据对$ {\sigma }_{0} $赋予地表属性，获取不同种类地表特征对应的$ {\sigma }_{0} $数据在2008-12—2016-09期间的时变序列，利用奇异谱分析原理提取出的不同地表属性中$ {\sigma }_{0} $的趋势项信息和周期项信息，并对周期项结果进行快速傅里叶变换分析。结果表明:水体、湿地区域对应的$ {\sigma }_{0} $数值较高，冰川和永久积雪区域对应的$ {\sigma }_{0} $数值较低。在整个区域，$ {\sigma }_{0} $存在多种周期信号。人造地表、裸地、灌木地的地表性质稳定，区域对应的$ {\sigma }_{0} $周期不显著。在其余区域，$ {\sigma }_{0} $的变化具有显著的周年和半年周期，且变化振幅不一致，各个区域对应的$ {\sigma }_{0} $趋势变化有所差异。
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| 关 键 词： | 卫星测高 Jason-2 后向散射系数 青藏高原 奇异谱分析 |
| 收稿时间： | 2021-01-19 |
Time-Varying Analysis of Backscatter Coefficient Corresponding to Different Surface Types in the Tibetan Plateau |
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| Affiliation: | 1.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China2.Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China |
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| Abstract: | Objectives Backscatter coefficient ($ {\sigma }_{0} $) is one of observations of satellite radar altimetry, which is widely used in the processes of surface state monitoring, snow thickness inversion, data calibration and verification of satellite altimeters, and other fields. The geophysical data record data of Jason-2 is used to extract and isolate the Ku-band $ {\sigma }_{0} $ data of the Tibetan Plateau (TP). Methods Taking the GlobeLand30 2020 version data as the basis for surface classification, $ {\sigma }_{0} $ is given surface attributes by latitude and longitude data. And we obtain the time-varying sequences of $ {\sigma }_{0} $ under different types of surface features from December 2008 to September 2016. The singular spectrum analysis principle is used to extract the $ {\sigma }_{0} $ time change trend and period information, and the period results are analyzed by fast Fourier transform. Results The results show that the $ {\sigma }_{0} $ is higher in waters and wetland areas, and is lower in permanent snow and ice areas. There are multiple period signals of $ {\sigma }_{0} $ in the TP. Conclusions The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual $ {\sigma }_{0} $ change is not significant. The other regions have significant annual and semi-annual cycles of $ {\sigma }_{0} $ variability, and the amplitude of variability is not consistent across regions, with different regions corresponding to different changes in $ {\sigma }_{0} $ trends. |
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