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遥感技术与应用  2022, Vol. 37 Issue (1): 61-72    DOI: 10.11873/j.issn.1004-0323.2022.1.0061
青促会十周年专栏     
卫星雷达测高水位数据产品在中国区河流的监测精度评价
雷逍(),柯灵红(),雍斌,张金山,曹倩怡
河海大学 水文水资源学院,江苏 南京 210098
Evaluation of River Water Level Monitoring from Satellite Radar Altimetry Datasets over Chinese Rivers
Xiao Lei(),Linghong Ke(),Bin Yong,Jinshan Zhang,Qianyi Cao
College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China
 全文: PDF(6104 KB)   HTML
摘要:

河流水位监测对于淡水资源供应、灾害预防至关重要, 同时关系到气候变化及其对水循环影响的理解和应对。随着卫星遥感技术的发展,基于卫星平台的河流水位观测提供了一种新型的自动化、长时序、低成本的河流监测方案。对于卫星雷达河流观测数据的原理、特征和精度的把握是产品应用的首要条件。研究总结了目前国际上3种主要的卫星雷达河流观测数据集Hydroweb、DAHITI、GRRATS及主要卫星传感器的特征和现状,并结合我国境内32个水位站实测数据 (2008~2018)开展了精度验证及分析。验证结果表明:Hydroweb数据集整体精度(RMSE平均0.70 m)高于DAHITI(RMSE平均1.29 m)与GRRATS(RMSE平均3.21 m)。Hydroweb数据集大量使用Sentinle-3卫星观测数据,Sentinel-3卫星水位观测精度(RMSE平均为0.51 m)显著高于Envisat(RMSE平均为3.34 m)与Jason卫星(Hydroweb与GRRATS的Jason卫星RMSE平均分别为1.69 m与2.96 m)。3个数据集均应用了Jason卫星,3个数据集基于Jason卫星均在个别站点有较好结果,其中DAHITI在高村站精度最高(RMSE为0.22 m),但3个数据集精度均不稳定(5/9的站点RMSE大于2 m)。另外河流干湿季水位变化与河流周边的小型湖泊、水塘、季节性水体会影响水位观测的精度。本研究为相关水位测高数据集应用提供指导,同时对明晰现有水位提取算法在我国区域的问题及将来可能算法改进研究提供参考。

关键词: 河流水位雷达高度计测高数据集精度评价    
Abstract:

The water surface level is essential for the assessment of fresh water resources, disaster prevention, and highly related to the understanding and response to climate change and its impact on water cycle. With the development of remote sensing technology, observation of water level based on satellite platforms provides an alternative way of river water level monitoring featured by automated, long-time, and low-cost river monitoring solution. The principle, characteristics and accuracy of satellite-based river observations are the basis for applications. In this paper, the characteristics and accuracy of three major satellite river water level datasets, Hydroweb, DAHITI, GRRATS are summarized verified with in-situ water level measurements from gauge stations in China. Taking water level time series derived from the Jason mission, we evaluated the accuracy of different water level retrieval algorithms employed by the three datasets. The global accuracy of the Hydroweb dataset (average RMSE 0.70 m) is higher than the other two sources (average RMSE 1.29 m and 3.21 m for the DAHITI and GRRATS), and that is owing to the usage of a large number of Sentinel-3 observations which are characterized by smaller footprints and Synthetic Aperture Radar (SAR) and the on-board tracking system in open-loop. The accuracy of river water level derived from the Sentinel-3 mission (with average RMSE of 0.51 m) is significantly higher than that of ENVISAT (with average RMSE 3.34 m) and Jason (with average RMSE 1.69 m for Hydroweb and 2.96 m for GRRATS).Generally, the three datasets can capture reliable river water level changes at some stations (with RMSE < 1.2 m and R2 > 0.8), but their performances vary considerably among different stations (with RMSE > 2 m for majority of the evaluated stations). Among all the stations, the Gaocun virtual station from the DAHITI dataset shows the highest accuracy (RMSE 0.22 m). In addition, the variation of river water level in dry and wet seasons and the small lakes, ponds and seasonal water around rivers pose significant influences on the accuracy of retrieved water level. This study provides guidance for future applications of relevant data sets, and also highlights the challenges of accurate water level retrieval over land surface conditions in China as well as the necessity of algorithm improvement in the future.

Key words: Stage of river    Radar altimetry    Altimetry data set    Accuracy evaluation
收稿日期: 2021-06-15 出版日期: 2022-04-08
ZTFLH:  P332  
基金资助: 国家重点研发计划(2018YFA0605400);中央高校基本科研业务费专项资金(B210202003)
通讯作者: 柯灵红     E-mail: leixiao@hhu.edu.cn;kelinghong@hhu.edu.cn
作者简介: 雷 逍(1997-),男,四川宜宾人,硕士研究生,主要从事河流水文遥感研究。Email:leixiao@hhu.edu.cn
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引用本文:

雷逍,柯灵红,雍斌,张金山,曹倩怡. 卫星雷达测高水位数据产品在中国区河流的监测精度评价[J]. 遥感技术与应用, 2022, 37(1): 61-72.

Xiao Lei,Linghong Ke,Bin Yong,Jinshan Zhang,Qianyi Cao. Evaluation of River Water Level Monitoring from Satellite Radar Altimetry Datasets over Chinese Rivers. Remote Sensing Technology and Application, 2022, 37(1): 61-72.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0061        http://www.rsta.ac.cn/CN/Y2022/V37/I1/61

数据集提出者数据源卫星时间跨度空间跨度(虚拟站)算法
HydrowebLEGOS

Envisat/

Jason/ Sentinel-3

2002~2012/2008~2016/2016至今1 049/907/10 592GDR数据/无波形重跟踪/Ice-1算法/三倍标准差去噪
DAHITIDGFI-TUM

Envisat/

Jason

2002~2012/2008~2016/2016至今52/143SGDR数据/波形重跟踪/10%的改进thre-shold算法/扩展的异常值剔除和卡尔曼滤波
GRRATSStephen Coss et al.

Envisat/

Jason/

2002~2012/2008~2016696/235GDR数据/无波形重跟踪/Ice-1算法/先验DEM去噪
表1  卫星测高数据集信息
图1  全国虚拟站点分布图审图号:GS(2016)2886
卫星应用数据集轨道高度/km运行时间重复周期雷达高度计步幅直径
Envisat

GRRATS

DAHITI

Hydroweb

8002002~201235 dPoseidon-3B2~10 km
Jason2/Jason-3

GRRATS

DAHITI

Hydroweb

1 3152008~今 2016~今10 dRadar Altimeter(RA2)2~4 km
Sentinel-3Hydroweb814.52016~今27 dSAR Radar Altimeter (SRAL)300 m
表2  测高数据集使用卫星主要信息
数据集

国内虚拟站点

(可验证点/总站点)

R2

均值

RMSE均值

NSE

均值

RRMSE均值
EnvisatJasonSentinel-3
Hydroweb0/34/3621/5870.650.70 m0.1418%
DAHITI0/12/10-0.801.29 m-6.5118%
GRRATS6/293/7-0.533.21 m-0.3830%
表3  全国河流可验证站点验证结果
图2  3种数据集精度评价结果箱型图
数据集站点R2RMSENSERRMSE
Hydroweb贵德0.920.25 m0.8310%
沙市0.243.04 m-15.826%
肖家湾0.641.23 m0.2416%
樟树0.462.25 m-15.2419%
DAHITI枝城0.662.36 m-13.929%
高村0.940.22 m0.886%
GRRATS汉口0.44.33 m-0.8329%
枝城0.921.17 m0.8310%
沙市0.423.38 m-1.5228%
表4  不同数据集Jason卫星观测结果验证
图3  Jason水位观测结果与实测结果对比
图4  Hydroweb数据集Sentinel-3水位观测结果与实测结果对比
图5  GRRATS数据集Envisat水位观测结果与实测结果对比
图6  典型站点干湿季卫星水位与实测水位对比图
数据集季节R2RMSE/mNSERRMSE
Hydroweb干季0.540.75-0.5435%
湿季0.700.600.2422%
DAHITI干季0.470.75-1.0919%
湿季0.741.63-5.3525%
GRRATS干季0.323.64-0.4550%
湿季0.502.66-0.0228%
表5  干湿季水位精度统计
图7  Sentinel-3卫星水位精度空间分布审图号:GS(2016)2886
图8  不同地表环境下的水位提取精度
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