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  

Water level extraction algorithm based on adaptive weighting and deviation matching of multi-source satellite altimetry data

作　　者：颉旭康 李伟 XIE Xukang;LI Wei(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;National and Local Joint Engineering Research Center for Geographic Monitoring Technology Application,Lanzhou 730070,China;Key Laboratory of Science and Technology in Surveying&Mapping,Gansu Province,Lanzhou 730070,China)

机构地区：[1]兰州交通大学测绘与地理信息学院,甘肃兰州730070 [2]地理国情监测技术应用国家地方联合工程研究中心,甘肃兰州730070 [3]甘肃省测绘科学与技术重点实验室,甘肃兰州730070

出　　处：《测绘学报》2024年第11期2111-2124,共14页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(41930101);中国博士后科学基金(2019M660091XB);甘肃省生态文明建设重点研发专项(24YFFA054);甘肃省自然科学基金(23JRRA857,23JRRG0015,21JR7RA317);甘肃省高等学校青年博士支持项目(2024QB-046);武汉引力与固体潮国家野外科学观测研究站开放基金(WHYWZ202403);国家冰川冻土沙漠科学数据中心开放基金(E01Z790201/2021kf07);兰州市人才创新创业项目(2022-RC-73);兰州交通大学实验教学改革项目(2024002);兰州交通大学本科教学改革项目(JGY202416);甘肃省“青年科技人才托举工程”项目(李伟)。

摘　　要：利用卫星测高技术提取湖库水位信息时,融合多种卫星测高数据构建长时序和高精度的水位尤为重要。本文以青海湖为例,选取Envisat、SARAL、Sentinel-3A和Sentinel-3B这4颗测高卫星数据,基于不同数据源结果及其特征构建了20a时长的数据集,提出了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,其中自适应定权能根据不同场景选择适当的改正算法模型,并为多源测高参数确定不同的权重参数,从而统一数据。偏差匹配方法则最大程度将定性数据定量化,使水位提取更准确。同时建立了人工智能框架实现了水位提取的自动化和一体化。试验显示,经过自适应定权的多源测高特征值可以被合理分类且具有强相关性,可为构建长时序水位信息提供整体高精度的基础数据;结合偏差匹配方法,以天为尺度提取的水位和实测水位相关系数R2在0.9以上,若将相关系数R2阈值设为0.8,可单次提取5个月时长的水位。结合单天提取和多天提取提出长期提取方法,构建了12a的长时序水位,其相关系数R2在0.9以上,平均绝对误差(MAE)值在1.5~2.0cm之间,均方根误差(RMSE)值在2.0~2.5cm之间,成功构建了长时序和高精度的水位。鉴于此,该数据处理算法和构建的模型在水位信息提取及预测方面体现出一定的实用价值,其研究成果也印证了人工智能与卫星测高相结合在小尺度水域构建长时序高精度水位的可行性。The extraction of precise water level information from satellite altimetry data is crucial for long-term monitoring of lake and reservoir levels.Using Qinghai Lake as a case study,a 20-year dataset is compiled by integrating altimetry data from four different satellites:Envisat,SARAL,Sentinel-3A,and Sentinel-3B.In this study,an innovative algorithm is proposed for the extraction of water levels from multi-source satellite altimetry data.This algorithm integrates adaptive weighting and deviation matching techniques to enhance the accuracy and reliability of water level extraction.Adaptive weighting involves the selection of suitable correction algorithm models based on various environmental conditions and the determination of unique weight parameters for each altimetry data source,thus standardizing the data.The deviation matching method quantifies qualitative data to maximize the precision of water level extraction.Additionally,an artificial intelligence framework is established to automate and integrate the water level extraction process,streamlining the workflow.Experimental results demonstrate that applying adaptive weighting to multi-source altimetry data characteristic values enables reasonable classification and exhibits strong correlations.This approach provides a robust foundation for generating high-precision,long-term water level records.When combined with the deviation matching method,the correlation between daily extracted water levels and actual measurements exceeds 0.9.By setting a correlation coefficient threshold of 0.8,reliable water level extraction for up to a 5-month duration in a single extraction is achievable.To address long-term water level extraction requirements,a methodology is introduced that combines single-day and multi-day extraction,resulting in the construction of 12 years of continuous high-precision water level records.The obtained results exhibit correlation coefficients exceeding 0.9,mean absolute error(MAE)values within the range of 1.5cm to 2.0cm,and root mean square error(R

关 键 词：多源卫星测高 自适应定权 偏差匹配 数据集构建 青海湖水位 

分 类 号：P228[天文地球—大地测量学与测量工程]