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自然资源遥感  2023, Vol. 35 Issue (3): 221-229    DOI: 10.6046/zrzyyg.2022239
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
Sentinel-3A卫星测高数据监测长江中下游河流水位变化
娄燕寒1,2,3(), 廖静娟1,2(), 陈嘉明1,4
1.中国科学院空天信息创新研究院数字地球重点实验室,北京 100094
2.可持续发展大数据国际研究中心,北京 100094
3.中国科学院大学,北京 100049
4.波恩大学大地测量学和地理信息研究所,波恩 53115,德国
Monitoring water level changes in the middle and lower reaches of the Yangtze River using Sentinel-3A satellite altimetry data
LOU Yanhan1,2,3(), LIAO Jingjuan1,2(), CHEN Jiaming1,4
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Institute of Geodesy and Geoinformation, University of Bonn, Bonn 53115, Germany
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摘要 

河流水位是了解水循环和水资源变化状况的重要参数。新型雷达高度计技术是提取河流水位变化的有利工具。为了验证新型雷达高度计Sentinel-3A/SRAL数据监测河流水位的能力,提高其提取河流水位变化的精度,以长江中下游干流为研究对象,利用重心偏移法、阈值主波峰重跟踪算法(阈值取50%和80%)、重心主波形重跟踪算法和多回波波峰一致重跟踪算法对Sentinel-3A/SRAL L2级数据进行波形重跟踪,提取了长江中下游干流各区域2016—2021年间河流水位,并对比不同算法获取水位的精度,得到最优重跟踪算法,从而提取了12条轨道过境区域的水位变化信息,分析了水位变化规律。结果表明,重心偏移法算法是提取河流水位精度最好的重跟踪算法,各区域虚拟水位与实测水位相比具有最大相关系数(达0.968)、最小均方根误差(达0.680 m); 2016—2021年间长江中下游干流水位总体呈上升趋势,年内水位变化呈现明显的季节性。

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娄燕寒
廖静娟
陈嘉明
关键词 Sentinel-3A波形分类波形重跟踪长江水位变化    
Abstract

River levels serve as a critical parameter for understanding the changes in water cycles and water resources. An advanced Radar altimeter is a favorable tool for extracting the changes in river levels. This study aims to verify the ability of the Sentinel-3A/SRAL Radar altimeter to monitor river levels and improve the extraction accuracy of this Radar altimeter. With the main streams in the middle and lower reaches of the Yangtze River as the study area, this study conducted waveform retracking for the Sentinel-3A/SRAL L2 data using the center-of-gravity offset method, the primary peak threshold retracking algorithm (thresholds: 50% and 80%), the primary waveform centroid retracking algorithm, and the multiple-echo peak consistency retracking algorithm. Then, this study extracted the river levels during 2016—2021 in the study area and obtained the optimal retracking algorithm by comparing the accuracy of different algorithms. Based on the optimal retracking algorithm, this study extracted the water level changes in transit areas of 12 satellite orbits to analyze the water level change patterns. The results show that the center-of-gravity offset method is the optimal retracking algorithm for extracting river levels with the highest accuracy. Compared with the measured water levels, the water levels simulated using the center-of-gravity offset method exhibited the highest correlation coefficient (up to 0.968) and the smallest root mean square error (up to 0.680 m). During 2016—2021, the water levels in the study area generally showed an upward trend, with significant intra-annual seasonal changes.

Key wordsSentinel-3A    waveform classification    waveform retracking    Yangtze River    water level change
收稿日期: 2022-06-10      出版日期: 2023-09-19
ZTFLH:  TP79  
基金资助:国家自然科学基金项目“合成孔径干涉雷达高度计数据湖泊水位高精度反演模型研究”(41871256)
通讯作者: 廖静娟(1966-),女,研究员,研究方向为微波遥感。Email: liaojj@radi.ac.cn
作者简介: 娄燕寒(1999-),女,硕士研究生,研究方向为微波遥感(资源与环境)。Email: louyanhan20@mails.ucas.ac.cn
引用本文:   
娄燕寒, 廖静娟, 陈嘉明. Sentinel-3A卫星测高数据监测长江中下游河流水位变化[J]. 自然资源遥感, 2023, 35(3): 221-229.
LOU Yanhan, LIAO Jingjuan, CHEN Jiaming. Monitoring water level changes in the middle and lower reaches of the Yangtze River using Sentinel-3A satellite altimetry data. Remote Sensing for Natural Resources, 2023, 35(3): 221-229.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022239      或      https://www.gtzyyg.com/CN/Y2023/V35/I3/221
Fig.1  研究区概况及雷达高度计数据覆盖示意图
Fig.2  研究方法流程
Fig.3  雷达高度计回波波形
Fig.4  5种重跟踪算法获得的重定点位置对比
Fig.5  OCOG重跟踪的Sentinel-3A/SRAL 高度计水位与实测水位的相关性
算法 指标 038 089 095 146 152 203 260 266 309 323 360 366
MWaPP R M S E/m 1.545 1.524 1.708 1.695 1.256 1.338 0.872 1.601 1.246 0.553 1.074 1.681
r 0.897 0.915 0.631 0.818 0.937 0.627 0.965 0.686 0.930 0.953 0.901 0.864
d 54 51 42 29 70 45 51 52 60 57 58 53
NPPOR R M S E/m 1.442 1.439 1.630 1.638 1.205 1.389 0.859 1.427 1.261 0.719 1.105 1.475
r 0.924 0.902 0.707 0.882 0.944 0.662 0.965 0.769 0.929 0.937 0.886 0.916
d 60 43 35 34 70 45 47 57 58 58 60 51
NPPTR05 R M S E/m 1.462 1.459 1.730 1.616 1.223 1.605 0.867 1.407 1.284 0.610 1.028 1.630
r 0.921 0.892 0.642 0.892 0.944 0.561 0.963 0.771 0.930 0.950 0.892 0.873
d 60 46 38 35 73 49 49 57 59 58 59 58
NPPTR08 R M S E/m 1.333 1.667 1.714 1.844 1.202 1.572 0.864 1.476 1.255 0.676 1.048 1.579
r 0.934 0.916 0.686 0.868 0.944 0.578 0.964 0.734 0.932 0.930 0.890 0.880
d 58 50 37 38 65 45 48 55 59 57 60 54
OCOG R M S E/m 1.145 1.409 1.469 1.469 1.201 1.197 0.841 1.158 1.126 0.881 0.680 1.565
r 0.952 0.866 0.667 0.891 0.941 0.840 0.968 0.824 0.940 0.893 0.941 0.891
d 61 45 43 47 63 45 45 57 58 58 60 47
Tab.1  不同重跟踪算法得到的Sentinel-3A/SRAL 高度计水位与实测水位比较结果
轨道编号 虚拟水位站点位置 最高水位/m 最高水位日期 最低水位/m 最低水位日期
038 E114°058',N30°253' 28.360 2020-06-28 13.530 2019-11-25
089 E113°333',N29°664' 33.350 2020-06-05 18.811 2017-11-30
095 E114°947',N30°416' 26.175 2020-06-05 19.210 2019-08-13
146 E114°547',N30°666' 28.190 2016-07-09 13.600 2019-12-03
152 E116°063',N29°789' 21.659 2016-07-09 8.420 2019-12-03
203 E115°323',N30°078' 24.328 2020-06-13 18.401 2016-03-27
260 E116°188',N29°827' 22.495 2020-07-14 8.358 2019-12-11
266 E117°654',N30°779' 16.190 2020-07-14 4.620 2019-12-11
309 E112°216',N30°178' 42.111 2020-07-17 30.198 2019-11-25
323 E118°393',N31°556' 12.140 2020-07-18 3.460 2019-12-15
360 E111°652',N30°353' 48.289 2020-08-17 37.929 2018-12-05
366 E113°294',N29°626' 33.408 2020-07-21 18.970 2019-11-21
Tab.2  各轨道虚拟水位站点位置及2016—2021年最高最低水位情况
Fig.6  长江中下游干流2016—2021年水位变化时间序列
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