留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

陈晓东,  郭金运,  孙明智,  朱广彬,  常晓涛

downloadPDF
文章导航 > 武汉大学学报 ● 信息科学版  > 优先出版
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. doi: 10.13203/j.whugis20200688
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. doi: 10.13203/j.whugis20200688
shu
CHEN Xiaodong, GUO Jinyun, SUN Mingzhi, ZHU Guangbin, CHANG Xiaotao. Time-varying analysis of backscatter coefficient corresponding to different surface types in the Tibetan Plateau[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200688
Citation: CHEN Xiaodong, GUO Jinyun, SUN Mingzhi, ZHU Guangbin, CHANG Xiaotao. Time-varying analysis of backscatter coefficient corresponding to different surface types in the Tibetan Plateau[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200688
shu

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

doi: 10.13203/j.whugis20200688

  • 1 山东科技大学测绘科学与工程学院, 山东 青岛, 266590;

  • 2 自然资源部国土卫星遥感应用中心, 北京 100048

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）。

详细信息
    作者简介:

    陈晓东,硕士生,主要从事卫星测高相关研究。ralking@163.com

  • 中图分类号: P237

Time-varying analysis of backscatter coefficient corresponding to different surface types in the Tibetan Plateau

  • 1 College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;

  • 2 Land Satellite Remote Sensing Application Center of MNR, Beijing 100048, China

Funds: 

The National Natural Science Foundation of China(41774001)

  • 摘要: 后向散射系数（sigma0）是卫星雷达高度计的观测量之一，在地表状态监测、积雪冰层厚度反演、卫星测高定标与验证等过程中广泛应用。根据Jason-2测高卫星Geophysical Data Record（GDR）数据分离出青藏高原Ku波段的sigma0数据，以GlobeLand30 2020版本地表数据作为分类基础，通过经纬度数据对sigma0赋予地表属性，获取不同种类地表特征对应sigma0数据在2008年12月到2016年9月期间的时变序列，利用奇异谱分析原理提取识别出不同地表属性中sigma0的趋势和周期项信息，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sigma0数值较高，冰川和永久积雪区域对应的sigma0数值较低。整个区域sigma0存在多种周期信号，人造地表、裸地、灌木地区域类型的地表性质稳定，区域对应的sigma0周期不显著。其余区域sigma0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sigma0趋势变化有所差异。
    Abstract: Backscatter coefficient (sigma0) is one of observations of satellite radar altimetry. It is related to the physical and geometric characteristics of land surface under the influence of global/regional climate change. Sigma0 can be used for monitoring surface features under climate change, data calibration and verification of satellite altimeters, inversion of surface features (e.g. soil moisture, snow thickness, etc.) and other fields. GlobeLand30, as the global land cover information with the resolution of 30 m, was produced in China. The Geophysical Data Record (GDR) data of Jason-2 was used to extract and isolate the Ku-band sigma0 data of the Tibetan Plateau (TP). By using the GlobeLand30 2020 version as the basis for surface classification, the latitude and longitude paired sigma0 data give surface attributes, and then we obtain the time-varying sequence of sigma0 under different types of surface features. The singular spectrum analysis (SSA) interpolation is used to fill in missing data. The sigma0 time change trend and period information of the entire TP and different surface attributes are extracted and identified with SSA, and the period results are analyzed by FFT. From the analysis of sigma0 under different surface attributes, its time-varying sequence has different characteristic results:(1) The sigma0 is higher in waters and wetland areas, and the sigma0 is lower in permanent snow and ice areas. (2) There are stable annual, semi-annual and quarterly signals for sigma0 in the TP. The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual sigma0 change is not significant. The changes of sigma0 in other regions have significant annual and semi-annual periods. The amplitude of the semi-annual and quarterly signal is varies with the nature of the surface. (3) The sigma0 changes in the TP show an increasing trend. It is caused by climate change on the TP and wet surface. The sigma0 data of forest, grassland, and shrubland have the increasing trend, the sigma0 of wetland has a trend of decreasing. Besides geophysics and ocean dynamics research, satellite radar altimeter is also feasible for monitoring land environment. The sigma0 obtained by altimeter is closely related to ground properties and climate change. The effects of different geographical attributes on sigma0 in the TP show different time-varying status:(1) the change cycle of sigma0 is mainly annual cycle, and different land surface states have different periodic attributes, which is related to the response state of different land surface to climate change; (2) The sigma0 value in water and wetland is significantly higher than other areas, which may be caused by the difference of surface complex dielectric constant.
  • [1] Fu L L, Cazenave A. Satellite Altimetry and Earth Sciences[M]. San Diego:Academic Press, 2001
    [2] Freilich M H, Vanhoff B A. The relationship between winds, surface roughness, and radar backscatter at low incidence angles from TRMM precipitation radar measurements[J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(4):549-562
    [3] Brown G S, Stanley H R, Roy N A. The wind-speed measurement capability of spaceborne radar altimeters[J]. in IEEE Journal of Oceanic Engineering, 1981, 6(2):59-63
    [4] Chelton D B, McCabe P J. A review of satellite altimeter measurement of sea surface wind speed:With a proposed new algorithm[J]. Journal of Geophysical Research:Oceans, 1985, 90(C3):4707-4720.
    [5] Frison P L, Mougin E. Use of ERS-1 wind scatterometer data over land surfaces[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2):550-560
    [6] Remy F, Legresy B, Bleuzen S, et al. Dual-frequency Topex altimeter observations of Greenland[J]. Journal of Electromagnetic Waves and Applications, 1996,10(11):1507-1525
    [7] Ridley J, Strawbridge F, Card R, et al. Radar backscatter characteristics of a desert surface[J]. Remote Sensing of Environment, 1996, 57(2):63-78
    [8] Wang J R, Engman E T, Mo T, et al. The effects of soil moisture, surface roughness, and vegetation on L-band emission and backscatter[J]. IEEE Transactions on Geoscience and Remote Sensing, 1987, 25(6):825-833
    [9] Méndez-Barroso L A, Vivoni E R, Watts C J, et al. Seasonal and interannual relations between precipitation, surface soil moisture and vegetation dynamics in the North American monsoon region[J]. Journal of Hydrology, 2009, 377(2):59-70
    [10] Jun C, Ban Y F, Li S N. China:Open access to Earth land-cover map[J]. Nature, 2014, 514(7523):434-434
    [11] Liu Xiaodong, Chen Baode. Climatic warming in the Tibetan Plateau during recent decades[J]. International Journal of Climatology, 2000, 20(14):1729-1742
    [12] Dewey J F, Shackleton R M, Chengfa C, et al. The tectonic evolution of the Tibetan Plateau[J]. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 1988, 327(1594):379-413
    [13] Kang Shichang, Xu Yanwei, You Qinglong, et al. Review of climate and cryospheric change in the Tibetan Plateau[J]. Environmental Research Letters, 2010, 5(1):015101
    [14] Guo Jinyun, Yang Lei, Liu Xin, et al. On temporal-spatial distribution of backscatter coefficients over China determined by TOPEX/Poseidon mission[J]. Science China Earth Sciences, 2012, 55(12):2068-2083
    [15] Papa F, Legrésy B, Rémy F. Use of the Topex-Poseidon dual-frequency radar altimeter over land surfaces[J]. Remote Sensing of Environment, 2003, 87(2-3):136-147
    [16] Lambin J, Morrow R, Fu L L, et al. The ostm/jason-2 mission[J]. Marine Geodesy, 2010, 33(S1):4-25
    [17] Picot N, Case K, Desai S, et al. AVISO and PODAAC user handbook. IGDR and GDR Jason products[R]. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 2003
    [18] Martin T V, Zwally H J, Brenner A C, et al. Analysis and retracking of continental ice sheet radar altimeter waveforms[J]. Journal of Geophysical Research:Oceans, 1983, 88(C3):1608-1616
    [19] Kondrashov D, Ghil M. Spatio-temporal filling of missing points in geophysical data sets. Nonlinear Process Geophys, 2006, 13(2):151-159
    [20] Hassani H. Singular spectrum analysis:methodology and comparison[J]. Journal of Data Science, 2007, 5(2):239-257
    [21] Shen Yi, Guo Jinyun, Liu Xin, et al. Long-term prediction of polar motion using a combined SSA and ARMA model[J]. Journal of Geodesy, 2018, 92(3):333-343
    [22] Guo Jinyun, Yang Lei, Liu Xin, et al. Decadal variation in surface characteristics over Xinjiang, Western China, from TAP altimetry backscatter coefficients:evidence of climate change[J]. Terrestrial, Atmospheric & Oceanic Sciences, 2013, 24(4):565
    [23] Adodo F I, Remy F, Picard G. Seasonal variations of the backscattering coefficient measured by radar altimeters over the Antarctic Ice Sheet[J]. The Cryosphere, 2018,12(5):1767-1778
    [24] Chen Jin, Zhu Xiaolin, Vogelmann J E, et al. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images[J]. Remote sensing of environment, 2011, 115(4):1053-1064
    [25] Papa F, Legresy B, Mognard N M, et al. Estimating terrestrial snow depth with the TOPEX-Poseidon altimeter and radiometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(10):2162-2169
    [26] Haas C, Beckers J, King J, et al. Ice and snow thickness variability and change in the high Arctic Ocean observed by in situ measurements[J]. Geophysical Research Letters, 2017, 44(20):10462-10469
    [27] Yang Le, Du Hejuan, Ma Hongzhang, et al. Use of the merged dual-frequency radar altimeter backscatter data over China land surface[C]. 2010 IEEE International Geoscience and Remote Sensing Symposium, 2010:1454-1457
    [28] Yang Le, Liu Qinhuo, Zhao Jing, et al. Global land surface backscatter at Ku-band using merged Jason1, Envisat, and Jason2 data sets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 53(2):784-794
    [29] Yuan Jiajia, Guo Jinyun, Liu Xin, et al. Mean sea surface model over China seas and its adjacent ocean established with the 19-year moving average method from multi-satellite altimeter data[J]. Continental Shelf Research, 2020, 192:104009
    [30] Hwang Cheinway, Cheng Yungsheng, Yang Wanhsin, et al. Lake level changes in the Tibetan Plateau from Cryosat-2, SARAL, ICESat, and Jason-2 altimeters[J]. Terrestrial Atmospheric and Oceanic Sciences, 2019, 30(1):1-18
    [31] Chen Fei, Chen Jun, Wu Hao, et al. A landscape shape index-based sampling approach for land cover accuracy assessment[J]. Science China Earth Sciences, 2016, 59(12):2263-2274
    [32] Gong Peng, Wang Jie, Yu Le, et al. Finer resolution observation and monitoring of global land cover:First mapping results with Landsat TM and ETM+ data[J]. International Journal of Remote Sensing, 2013, 34(7):2607-2654
    [33] Young I R, Sanina E, Babanin A V. Calibration and cross validation of a global wind and wave database of altimeter, radiometer, and scatterometer measurements[J]. Journal of Atmospheric and Oceanic Technology, 2017, 34(6):1285-1306.
  • [1] 戴海亮, 孙付平, 姜卫平, 肖凯, 朱新慧, 刘婧.  小波多尺度分解和奇异谱分析在GNSS站坐标时间序列分析中的应用 . 武汉大学学报 ● 信息科学版, doi: 10.13203/j.whugis20190107
    [2] 郎芹, 牛振国, 洪孝琪, 杨鑫莹.  青藏高原湿地遥感监测与变化分析 . 武汉大学学报 ● 信息科学版, doi: 10.13203/j.whugis20180277
    [3] 李婉秋, 王伟, 章传银, 文汉江, 钟玉龙.  利用Forward-Modeling方法反演青藏高原水储量变化 . 武汉大学学报 ● 信息科学版, doi: 10.13203/j.whugis20180263
    [4] 朱骥, 施建成, 张祥信, 张素梅.  基于AVHRR/2 10 d合成数据的青藏高原亚像元雪填图 . 武汉大学学报 ● 信息科学版, doi: 10.13203/j.whugis20150394
    [5] 张胜军, 李建成, 褚永海, 孔祥雪.  基于Cryosat和Jason1GM数据的垂线偏差计算与分析 . 武汉大学学报 ● 信息科学版, doi: 10.13203/j.whugis20130796
    [6] 李冲, 李建成, 黄瑞金, 谭理.  青藏高原东部地壳物质流变模型及汶川地震机理探讨 . 武汉大学学报 ● 信息科学版, doi: 10.13203/j.whugis20130655
    [7] 李文文, 李敏, 施闯, 赵齐乐.  利用DORIS相位数据的Jason-2卫星精密定轨 . 武汉大学学报 ● 信息科学版,
    [8] 张双喜, 张晨, 李孟奎, 高冰玉.  青藏高原形变对中国西部边界的影响 . 武汉大学学报 ● 信息科学版,
    [9] 金涛勇, 胡敏章, 蒋涛, 张守建.  卫星测高资料的电离层延迟改正交叉检验与误差分析 . 武汉大学学报 ● 信息科学版,
    [10] 谢酬, 李震, 李新武.  青藏高原冻土形变监测的永久散射体方法研究 . 武汉大学学报 ● 信息科学版,
    [11] 林珲, 江吉喜, 杨育彬, 方兆宝.  青藏高原中尺度对流系统的时空演变特征及规律 . 武汉大学学报 ● 信息科学版,
    [12] 柯小平, 王勇, 许厚泽.  用变密度模型反演青藏高原的莫霍面深度 . 武汉大学学报 ● 信息科学版,
    [13] 文汉江, 章传银.  由ERS-2和TOPEX卫星测高数据推算的海面高异常的主成分分析 . 武汉大学学报 ● 信息科学版,
    [14] 史红岭, 陆洋, 王勇.  CHAMP重力卫星结果在中国海及邻近海域的初步分析 . 武汉大学学报 ● 信息科学版,
    [15] 熊熊, 许厚泽, 滕吉文, 王继业.  青藏高原及邻区地幔对流应力场及地球动力学含义 . 武汉大学学报 ● 信息科学版,
    [16] 熊熊, 刘孙君, 许厚泽.  青藏高原地壳东-西向拉张及力学机制 . 武汉大学学报 ● 信息科学版,
    [17] 张世强, 卢健, 刘时银.  利用TM高光谱图像提取青藏高原喀喇昆仑山区现代冰川边界 . 武汉大学学报 ● 信息科学版,
    [18] 宋成骅, 许才军, 刘经南, 蔡宏翔.  青藏高原块体相对运动模型的GPS方法确定与分析 . 武汉大学学报 ● 信息科学版,
    [19] 许才军, 陶本藻, 晁定波.  论大地测量研究青藏高原地学问题的方法 . 武汉大学学报 ● 信息科学版,
    [20] 张德涵.  关于青藏高原地壳的隆升与增厚 . 武汉大学学报 ● 信息科学版,
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:  191
  • HTML全文浏览量:  34
  • PDF下载量:  12
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-12-21
  • 网络出版日期:  2021-05-07

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

doi: 10.13203/j.whugis20200688
  • 1 山东科技大学测绘科学与工程学院, 山东 青岛, 266590;
  • 2 自然资源部国土卫星遥感应用中心, 北京 100048
    • 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）。

    作者简介:

    陈晓东,硕士生,主要从事卫星测高相关研究。ralking@163.com

  • 收稿日期: 2020-12-21
  • 网络出版日期: 2021-05-07

  • 中图分类号: P237

摘要: 后向散射系数（sigma0）是卫星雷达高度计的观测量之一，在地表状态监测、积雪冰层厚度反演、卫星测高定标与验证等过程中广泛应用。根据Jason-2测高卫星Geophysical Data Record（GDR）数据分离出青藏高原Ku波段的sigma0数据，以GlobeLand30 2020版本地表数据作为分类基础，通过经纬度数据对sigma0赋予地表属性，获取不同种类地表特征对应sigma0数据在2008年12月到2016年9月期间的时变序列，利用奇异谱分析原理提取识别出不同地表属性中sigma0的趋势和周期项信息，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sigma0数值较高，冰川和永久积雪区域对应的sigma0数值较低。整个区域sigma0存在多种周期信号，人造地表、裸地、灌木地区域类型的地表性质稳定，区域对应的sigma0周期不显著。其余区域sigma0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sigma0趋势变化有所差异。

English Abstract

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. doi: 10.13203/j.whugis20200688
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. doi: 10.13203/j.whugis20200688
shu
CHEN Xiaodong, GUO Jinyun, SUN Mingzhi, ZHU Guangbin, CHANG Xiaotao. Time-varying analysis of backscatter coefficient corresponding to different surface types in the Tibetan Plateau[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200688
Citation: CHEN Xiaodong, GUO Jinyun, SUN Mingzhi, ZHU Guangbin, CHANG Xiaotao. Time-varying analysis of backscatter coefficient corresponding to different surface types in the Tibetan Plateau[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200688
shu

    全文HTML

参考文献 (33)

目录

    /

    返回文章
    返回

    我要投稿

    投稿攻略

    联系我们

    二维码

    版权所有 © 2013 《武汉大学学报·信息科学版》编辑部

    地址：武汉大学文理学部本科生院楼北5楼邮政编码：430072

    电话：027-68778465,68778045E-mail:whuxxb@vip.163.com

    本系统由 北京仁和汇智信息技术有限公司 开发    百度统计