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

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。

Evaluation of River Water Level Monitoring from Satellite Radar Altimetry Datasets over Chinese Rivers

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.