基于SWOT卫星的中国地区河流观测能力分析与精度评估

doi:10.11947/j.AGCS.2026.20250484

测绘学报 ›› 2026, Vol. 55 ›› Issue (7): 1240-1253.doi: 10.11947/j.AGCS.2026.20250484

• 大地测量学与导航 • 上一篇

基于SWOT卫星的中国地区河流观测能力分析与精度评估

路浩¹(), 冯伟¹(), 王晓兵², 陈威³, 钟敏¹

^1.中山大学遥感科学与技术学院，广东　珠海　519082
^2.青岛市勘察测绘研究院，山东　青岛　266033
^3.湖北文理学院资源环境与旅游学院，湖北　襄阳　441053

收稿日期:2025-11-19 修回日期:2026-07-07 发布日期:2026-08-18
通讯作者: 冯伟 E-mail:luhao23@mail2.sysu.edu.cn;fengwei@mail.sysu.edu.cn
作者简介:路浩（2000—），男，硕士，研究方向为水文大地测量学。 E-mail：luhao23@mail2.sysu.edu.cn
基金资助:
国家自然科学基金(42574068; 42504054)

Assessing the capability and accuracy of SWOT satellite river observations in China

Hao Lu¹(), Wei Feng¹(), Xiaobing Wang², Wei Chen³, Min Zhong¹

^1.School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
^2.Qingdao Research Institute of Surveying and Mapping, Qingdao 266033, China
^3.College of Resource Environment and Tourism, Hubei University of Arts and Science, Xiangyang 441053, China

Received:2025-11-19 Revised:2026-07-07 Published:2026-08-18
Contact: Wei Feng E-mail:luhao23@mail2.sysu.edu.cn;fengwei@mail.sysu.edu.cn
About author:Lu Hao (2000—), male, master, majors in hydrogeodesy. E-mail: luhao23@mail2.sysu.edu.cn
Supported by:
The National Natural Science Foundation of China(42574068; 42504054)

摘要/Abstract

摘要：

本文评估了地表水与海洋地形（SWOT）宽刈幅测高卫星对中国地区河流的观测能力及其在河流水位和水位变化监测中的精度。在21天重访周期内，SWOT可对97.39%的中国河流河段进行至少一次的观测。基于301个水文站点的水位实测数据验证表明，SWOT监测中国河流水位变化的平均绝对误差（MAE）为0.35 m，水位变化的相关系数均值大于0.9。不同流域的验证结果显示，SWOT在海河、淮河、松辽流域和黄河流域的水位变化监测精度较高（MAE<0.3 m），而在珠江和长江流域误差相对较大。针对大坝附近因水位空间不连续导致的相位解缠错误，本文采用基于低相干水体识别与最优整周模糊度确定的校正方法，通过空间分割上下游水体并遍历不同整周模糊度以恢复正确高程。针对窄河流噪声非对称分布的特点，本文提出了基于空间连续性检验的多级自适应滤波算法（MAF），通过k近邻空间连续性检验识别异常点，并结合偏度特征自适应剔除离群值。在10个测试站点的验证中，经相位解缠校正与MAF算法处理后，水位监测的平均MAE从1.73 m降低至0.32 m。本文研究验证了SWOT卫星在中国地区河流水位变化监测的可靠性，可为大范围高精度水文参数获取提供了一种技术手段，对水资源管理和洪涝灾害监测具有重要应用价值。

关键词: SWOT卫星, 卫星测高, 河流水位, 相位解缠, 去噪

Abstract:

This study evaluates the observation capability and accuracy of the SWOT wide-swath altimetry satellite for monitoring river water levels and water level changes in China. Within a 21-day revisit cycle, SWOT can observe 97.39% of river reaches in China at least once. Validation against in situ water level data from 301 hydrological gauge stations demonstrates that SWOT achieves a mean absolute error (MAE) of 0.35 m for monitoring river water level variations in China, with an overall correlation coefficient greater than 0.9. Results across different basins indicate that SWOT exhibits higher monitoring accuracy in the Songhua-Liaohe, Haihe, Huaihe, and Yellow River basins (MAE<0.3 m), while relatively larger errors are observed in the Pearl River and Yangtze River basins. To address phase unwrapping errors near dams caused by spatial discontinuity of water levels, this study employs a correction method based on low-coherence water body identification and optimal integer ambiguity determination, which spatially separates upstream and downstream water bodies and iterates through different integer ambiguities to recover correct elevations. To address the asymmetric noise distribution characteristic of narrow rivers, this study proposes a multi-level adaptive filtering (MAF) algorithm based on spatial continuity testing, which identifies anomalous points through k-nearest neighbor spatial continuity testing and adaptively removes outliers based on skewness characteristics. Validation at 10 test stations shows that after phase unwrapping correction and MAF algorithm processing, the average MAE decreased from 1.73 m to 0.32 m. This research validates the reliability of SWOT satellite for river water level monitoring variations in China, providing a technical approach for large-scale, high-precision hydrological parameter acquisition, with important reference value for water resource management and flood disaster monitoring.

Key words: SWOT satellite, satellite altimetry, river water level, phase unwrapping, denoising

中图分类号:

P228

路浩, 冯伟, 王晓兵, 陈威, 钟敏. 基于SWOT卫星的中国地区河流观测能力分析与精度评估[J]. 测绘学报, 2026, 55(7): 1240-1253.

Hao Lu, Wei Feng, Xiaobing Wang, Wei Chen, Min Zhong. Assessing the capability and accuracy of SWOT satellite river observations in China[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(7): 1240-1253.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250484

http://xb.chinasmp.com/CN/Y2026/V55/I7/1240

图/表 17

图1

表1

图2

图3

表2

图4

图5

图6

表3

图7

图8

图9

图10

图11

图12

图13

图14

参考文献 32

[1]	夏军, 谈戈. 全球变化与水文科学新的进展与挑战[J]. 资源科学, 2002, 24(3): 1-7.
	Xia Jun, Tan Ge. Hydrological science towards global change: progress and challenge[J]. Resources Science, 2002, 24(3): 1-7.
[2]	Fekete B M, Vörösmarty C J. The current status of global river discharge monitoring and potential new technologies complementing traditional discharge measurements[C]//Proceedings of 2003 PUB Kick-off Meeting, 2003: 129-140.
[3]	Hannah D M, Demuth S, Van Lanen H A J, et al. Large-scale river flow archives: importance, current status and future needs[J]. Hydrological Processes, 2011, 25(7): 1191-1200.
[4]	Crochemore L, Isberg K, Pimentel R, et al. Lessons learnt from checking the quality of openly accessible river flow data worldwide[J]. Hydrological Sciences Journal, 2020, 65(5): 699-711.
[5]	Coleman R. Satellite altimetry and earth sciences: a handbook of techniques and applications[J]. Eos, Transactions American Geophysical Union, 2001, 82(34): 376.
[6]	Stammer D, Cazenave A. Satellite altimetry over oceans and land surfaces[M]. Boca Raton: Taylor & Francis, 2017.
[7]	Abdalla S, Abdeh Kolahchi A, Ablain M, et al. Altimetry for the future: building on 25 years of progress[J]. Advances in Space Research, 2021, 68(2): 319-363.
[8]	单杰, 田祥希, 李爽, 等. 星载激光测高技术进展[J]. 测绘学报, 2022, 51(6): 964-982. DOI: . doi: 10.11947/j.AGCS.2022.20220174
	Shan Jie, Tian Xiangxi, Li Shuang, et al. Advances of spaceborne laser altimetry technology[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 964-982. DOI: . doi: 10.11947/j.AGCS.2022.20220174
[9]	许可, 蒋茂飞. 海洋卫星雷达测高技术进展[J]. 空间科学学报, 2023, 43(6): 1036-1057.
	Xu Ke, Jiang Maofei. Advance in ocean satellite radar altimetry technology[J]. Chinese Journal of Space Science, 2023, 43(6): 1036-1057.
[10]	史卓琳, 黄昌. 河流水情要素遥感研究进展[J]. 地理科学进展, 2020, 39(4): 670-684.
	Shi Zhuolin, Huang Chang. Recent advances in remote sensing of river characteristics[J]. Progress in Geography, 2020, 39(4): 670-684.
[11]	Pham H T, Marshall L, Johnson F, et al. Deriving daily water levels from satellite altimetry and land surface temperature for sparsely gauged catchments: a case study for the Mekong river[J]. Remote Sensing of Environment, 2018, 212: 31-46.
[12]	Biancamaria S, Lettenmaier D P, Pavelsky T M. The SWOT mission and its capabilities for land hydrology[J]. Surveys in Geophysics, 2016, 37(2): 307-337.
[13]	俞昊天, 李国元. “地表水和海洋地形”卫星进展[J]. 国际太空, 2023(1): 32-37.
	Yu Haotian, Li Guoyuan. Progress of “surface water and ocean topography” satellite[J]. Space International, 2023(1): 32-37.
[14]	Domeneghetti A, Schumann G J P, Frasson R P M, et al. Characterizing water surface elevation under different flow conditions for the upcoming SWOT mission[J]. Journal of Hydrology, 2018, 561: 848-861.
[15]	Yu Linpeng, Zhang Haowei, Gong Wei, et al. Validation of mainland water level elevation products from SWOT satellite[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 13494-13505.
[16]	Yao Jiaqi, Xu Nan, Wang Mengran, et al. SWOT satellite for global hydrological applications: accuracy assessment and insights into surface water dynamics[J]. International Journal of Digital Earth, 2025, 18(1): 2472924.
[17]	Hamoudzadeh A, Ravanelli R, Crespi M. SWOT level 2 lake single-pass product: the L2_HR_LakeSP data preliminary analysis for water level monitoring[J]. Remote Sensing, 2024, 16(7): 1244.
[18]	Maubant L, Dodd L, Tregoning P. Assessing the accuracy of SWOT measurements of water bodies in Australia[J]. Geophysical Research Letters, 2025, 52(6): 11.
[19]	Normandin C, Frappart F, Baghdadi N, et al. First results of the surface water ocean topography (SWOT) observations to rivers elevation profiles in the Cuvette Centrale of the Congo Basin[J]. Frontiers in Remote Sensing, 2024, 5: 1466695.
[20]	He Zhiyue, Cai Yu, Wu Suhui, et al. Evaluation of SWOT's performance for river water level retrieval in the Yangtze River Basin[J]. International Journal of Remote Sensing, 2025, 46(12): 4736-4763.
[21]	Zhao Yao, Fu Jun'e, Pang Zhiguo, et al. Validation of inland water surface elevation from SWOT satellite products: a case study in the middle and lower reaches of the Yangtze River[J]. Remote Sensing, 2025, 17(8): 1330.
[22]	Bazzi H, Baghdadi N, Ngo Y N, et al. Assessing SWOT interferometric SAR altimetry for inland water monitoring: insights from Lake Léman[J]. Frontiers in Remote Sensing, 2025, 6: 1572114.
[23]	长江水利委员会. 长江流域及西南诸河水资源公报(1998—2016)[R]. 武汉: 长江水利委员会, 1999-2017.
	Changjiang Water Resources Commission. Water resources bulletin of Yangtze River Basin and southwest rivers (1998—2016)[R]. Wuhan: Changjiang Water Resources Commission, 1999-2017.
[24]	Surface water ocean topography. SWOT level 2 water mask pixel cloud data product, version C[DS/OL]. Jet Propulsion Laboratory: JPL NASA, (2024-02-01) [2025-03-15]. https://doi.org/10.5067/SWOT-PIXC-2.0.
[25]	Surface water ocean topography. SWOT level 2 river single-pass vector data product, version C[DS/OL]. Jet Propulsion Laboratory: JPL NASA, (2024-02-01) [2025-03-15]. https://doi.org/10.5067/SWOT-RIVERSP-2.0.
[26]	Jet Propulsion Laboratory. SWOT product description: level 2 KaRIn high rate water mask pixel cloud product, JPL D-56411, Revision C[R]. Pasadena: Jet Propulsion Laboratory, 2025.
[27]	Chen C, Desai S, Picot N. SWOT science data products user handbook: JPL D-109532[R]. Pasadena: Jet Propulsion Laboratory, 2024.
[28]	Jet Propulsion Laboratory. SWOT product description document: level 2 KaRIn high rate river single pass vector (L2_HR_RiverSP) data product, JPL D-56413, Revision C[R]. Pasadena: Jet Propulsion Laboratory, 2025.
[29]	Jet Propulsion Laboratory. SWOT algorithm theoretical basis document: level 2 KaRIn high rate river single pass (L2_HR_RiverSP) science algorithm software, JPL D-105505[R]. Pasadena: Jet Propulsion Laboratory, 2023.
[30]	Fjø rtoft R, Gaudin J M, Pourthié N, et al. KaRIn on SWOT: characteristics of near-nadir ka-band interferometric SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4): 2172-2185.
[31]	Jet Propulsion Laboratory. SWOT project mission performance and error budget, JPL D-79084, Revision A[R]. Pasadena: Jet Propulsion Laboratory, 2017.
[32]	PO. DAAC. SWOT PIXC dataset phase unwrapping on a local machine[EB/OL]. NASA Physical Oceanography Distributed Active Archive Center, 2024 [2025-03-15]. https://podaac.github.io/tutorials/notebooks/datasets/SWOT_PIXC_PhaseUnwrap_localmachine.html.

误差来源	高程误差/cm
电离层信号	0.8
干对流层信号	0.7
湿对流层信号	4.0
径向轨道误差	1.6
KaRIn随机+系统误差	8.9
运动误差	0.8

流域	河段数	单个周期观测次数占比					河宽<50 m占比次
流域	河段数	0次	1次	2次	3次	4次	河宽<50 m占比次
长江流域	9737	2.63	22.73	66.53	7.89	0.23	18.61
珠江流域	3960	3.13	25.91	70.96	0	0	24.19
松辽流域	2518	2.46	15.85	40.63	30.10	10.96	23.11
西南诸河	2046	1.76	21.26	68.04	8.75	0.20	25.56
内陆河	1881	4.73	19.88	48.33	23.23	3.83	25.78
东南诸河	1530	1.18	23.14	72.88	2.81	0	16.27
淮河流域	1502	1.6	20.84	62.85	13.12	1.60	11.72
黄河流域	1489	1.68	25.12	57.56	13.57	2.08	16.59
海河流域	460	3.26	22.83	43.91	23.26	6.74	27.83

流域	水位变化的平均MAE/m	MAE标准差/m	最大MAE/m	最小MAE/m	站点数量
东南诸河	0.43	0.30	1.13	0.07	20
内陆河	0.46	0.40	1.32	0.07	8
松辽流域	0.20	0.14	0.50	0.05	17
海河流域	0.21	0.12	0.34	0.10	3
淮河流域	0.14	0.15	0.71	0.02	25
珠江流域	0.42	0.64	4.54	0.02	58
长江流域	0.41	0.52	3.05	0.02	129
黄河流域	0.29	0.27	1.02	0.04	41

基于SWOT卫星的中国地区河流观测能力分析与精度评估

Assessing the capability and accuracy of SWOT satellite river observations in China

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 32

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张文渊, 彭劲松, 韦纳都, 高雨, 张书毕. 基于SSA去噪的级联LSTM网络地球极移短期预报方法[J]. 测绘学报, 2026, 55(1): 46-58.
[2]	陈晓东, 杨萌, 袁园, 冯伟, 黄金维, 钟敏. 中国近海SWOT_02重力场模型精度与空间分辨率评估[J]. 测绘学报, 2025, 54(6): 1031-1041.
[3]	翟振和, 孙中苗, 马健, 管斌, 黄河, 欧阳明达, 黄令勇, 黄志勇, 潘星辰, 袁仕耿, 刘胜利, 刘森. 中国编队海洋测绘卫星数据反演海域重力扰动矢量及性能分析[J]. 测绘学报, 2025, 54(4): 714-724.
[4]	韩潇然, 柯樱海, 于金媛, 赵孟杰, 张国庆. 基于SWOT卫星模拟数据的青藏高原湖泊水位与面积测量精度评估[J]. 测绘学报, 2024, 53(5): 823-834.
[5]	高延东, 贾义琨, 李世金, 陈宇, 李怀展, 郑南山, 张书毕. InSAR相位解缠最大流/最小割权值改进算法[J]. 测绘学报, 2024, 53(4): 644-652.
[6]	何毅, 杨旺, 朱庆. 基于R2AU-Net的InSAR相位解缠方法[J]. 测绘学报, 2024, 53(3): 435-449.
[7]	岳佳伟, 黄其欢, 刘辉, 马张烽. 离散优化框架下的多基线InSAR相位解缠方法[J]. 测绘学报, 2024, 53(3): 473-481.
[8]	孙中苗, 翟振和, 管斌, 阮仁桂, 黄令勇. 双星跟飞测高卫星在轨初步验证[J]. 测绘学报, 2024, 53(2): 207-216.
[9]	高贤文, 金涛勇, 李建成. 兼顾内陆水体水位时空变化特性的合成孔径雷达波形重跟踪算法[J]. 测绘学报, 2024, 53(2): 217-230.
[10]	李倩倩, 鲍李峰, 王勇. 2022汤加海底火山活动的测高海面观测异常分析[J]. 测绘学报, 2024, 53(2): 263-273.
[11]	刘焕玲, 杨蔚然, 张放, 文汉江, 胡敏章, 蒋涛, 蔺文奇, 黎晨曦. 海域重力异常模型的多尺度分析[J]. 测绘学报, 2024, 53(2): 274-285.
[12]	颉旭康, 李伟. 融合自适应定权和偏差匹配的多源卫星测高数据水位提取算法[J]. 测绘学报, 2024, 53(11): 2111-2124.
[13]	高延东, 郑南山, 张艳锁, 李世金, 杨化超, 卞和方, 张秋昭, 张书毕, 田雨. 基于相位质量融合估计与信息滤波的相位解缠方法[J]. 测绘学报, 2024, 53(10): 1910-1919.
[14]	赵闯, 金涛勇, 秦朋波, 杨连俊. 顾及残差约束的多面函数法融合卫星测高和船载重力[J]. 测绘学报, 2023, 52(4): 605-613.
[15]	范雕, 李姗姗, 冯进凯, 黄炎, 范昊鹏, 张金辉, 李新星. 基于最小二乘配置的陌生海域海底地形反演方法[J]. 测绘学报, 2023, 52(12): 2039-2053.