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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 221-229     DOI: 10.6046/zrzyyg.2022239
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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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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.
Keywords Sentinel-3A      waveform classification      waveform retracking      Yangtze River      water level change     
ZTFLH:  TP79  
Issue Date: 19 September 2023
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Yanhan LOU
Jingjuan LIAO
Jiaming CHEN
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Yanhan LOU,Jingjuan LIAO,Jiaming CHEN. Monitoring water level changes in the middle and lower reaches of the Yangtze River using Sentinel-3A satellite altimetry data[J]. Remote Sensing for Natural Resources, 2023, 35(3): 221-229.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022239     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/221
Fig.1  Overview of the study area and data coverage of Radar altimeter
Fig.2  Flow chart of the study
Fig.3  Waveform of Radar altimeter
Fig.4  Comparison of retracked gates obtained by five retracking algorithms
Fig.5  Correlation between Sentinel-3A/SRAL altimeter water level retracked by OCOG and in-situ water level
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  Comparison of Sentinel-3A/SRAL altimeter water levels obtained by different re-tracking algorithms and in- situ water level
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  Locations of virtual water level stations of each track and the highest and lowest water levels from 2016 to 2021
Fig.6  Time series of water level changes in the middle and lower reaches of the Yangtze River from 2016 to 2021
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