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

doi:10.11947/j.AGCS.2024.20230360

测绘学报 ›› 2024, Vol. 53 ›› Issue (11): 2111-2124.doi: 10.11947/j.AGCS.2024.20230360

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

颉旭康¹^,²^,³(), 李伟¹^,²^,³()

^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

收稿日期:2023-09-08 出版日期:2024-12-13 发布日期:2024-12-13
通讯作者: 李伟 E-mail:11210877@stu.lzjtu.edu.cn;geosci.wli@lzjtu.edu.cn
作者简介:颉旭康（1999—），男，硕士，研究方向为卫星大地测量与水文学。　E-mail：11210877@stu.lzjtu.edu.cn
基金资助:
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);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）

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

Xukang XIE¹^,²^,³(), Wei LI¹^,²^,³()

^1.Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
^2.National and Local Joint Engineering Research Center for Geographic Monitoring Technology Application, Lanzhou 730070, China
^3.Key Laboratory of Science and Technology in Surveying & Mapping, Gansu Province, Lanzhou 730070, China

Received:2023-09-08 Online:2024-12-13 Published:2024-12-13
Contact: Wei LI E-mail:11210877@stu.lzjtu.edu.cn;geosci.wli@lzjtu.edu.cn
About author:XIE Xukang (1999—), male, master, majors in satellite geodesy and hydrology.　E-mail: 11210877@stu.lzjtu.edu.cn
Supported by:
The National Natural Science Foundation of China(41930101);China Postdoctoral Science Foundation(2019M660091XB);The Key Research and Development Project of Ecological Civilization Construction in Gansu Province(24YFFA054);The Natural Science Foundation of Gansu Province(23JRRA857);The Gansu Province Higher Education Institutions Young Doctor(2024QB-046);Wuhan Gravitational Field and Solid Tides National Field Observation and Research Station Open Fund(WHYWZ202403);The National Cryosphere Desert Data Center(E01Z790201/2021kf07);Lanzhou Talent Innovation and Entrepreneurship(2022-RC-73);The Experimental Teaching Reform Project of Lanzhou Jiaotong University(2024002);Undergraduate Teaching Reform Project of Lanzhou Jiaotong University(JGY202416);“Young Scientific and Technological Talents Supporting Project” Project of Gansu Province (LI Wei)

摘要/Abstract

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20 a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12 a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1.5~2.0 cm之间，均方根误差（RMSE）值在2.0~2.5 cm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。

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

Abstract:

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 1.5 cm to 2.0 cm, and root mean square error (RMSE) values ranging from 2.0 cm to 2.5 cm. This success underscores the practical value of the data processing algorithm and model in the context of water level extraction and prediction. In conclusion, this research demonstrates the feasibility and utility of combining artificial intelligence with satellite altimetry in constructing long-term, high-precision water level records for small-scale water bodies.

Key words: multi-source satellite altimetry, adaptive weighting, deviation matching, dataset construction, Qinghai Lake water level

中图分类号:

P228

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.

Xukang XIE, Wei LI. Water level extraction algorithm based on adaptive weighting and deviation matching of multi-source satellite altimetry data[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(11): 2111-2124.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2024.20230360

http://xb.chinasmp.com/CN/Y2024/V53/I11/2111

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数据来源	数据频率/Hz	时间尺度	模型修正
Sentinel-3A	20	2016-03-09—2021-12-22	多种重跟踪算法
Sentinel-3B	20	2018-11-28—2021-12-09	多种重跟踪算法
Envisat	20	2010-01-28—2010-10-22	多种重跟踪算法
Envisat	20	2010-11-05—2012-03-29	多种重跟踪算法
Envisat	20	2002-07-04—2009-12-27	模拟实测数据
SARAL	40	2013-03-14—2016-07-02	多种重跟踪算法
模拟数据	无	2012-04-10—2013-03-10	模拟测高数据

主成分	初始特征值			提取载荷平方和			旋转载荷平方和总计
主成分	总计	方差/（%）	累积/（%）	总计	方差/（%）	累积/（%）	旋转载荷平方和总计
1	8.948	35.791	35.791	8.948	35.791	35.791	8.486
2	4.557	18.228	54.019	4.557	18.228	54.019	4.856
3	3.382	13.526	67.545	3.382	13.526	67.545	3.104
4	1.532	6.127	73.673	1.532	6.127	73.673	1.690
5	1.352	5.407	79.080	1.352	5.407	79.080	1.531
6	1.169	4.674	83.754	1.169	4.674	83.754	1.272

主成分	参数	归属	特征	载荷	选取
	peakiness_1_plrm_ku	波峰	限制类因素	0.959	是
	peakiness_2_c	波峰	限制类因素	0.953	是
	peakiness_1_c	波峰	限制类因素	0.953	是
	tb_365	表面亮温	外界因素	0.928	是
	tb_238	表面亮温	外界因素	0.925	是
1	peakiness_1_plrm_ku	波峰	限制类因素	0.922	是
	sig0_ocog_c	后向散射系数	限制类因素	0.921	是
	sig0_ice_plrm_ku	后向散射系数	限制类因素	0.827	否
	sig0_ice_c	后向散射系数	限制类因素	0.718	否
	mod_dry	干对流层	传播修正	0.625	是
	sig0_ocog_ku	后向散射系数	限制类因素	0.606	否
	data	日期	时间特征	0.952	是
	cycle	周期	识别特征	0.951	是
2	in_situ	实测水位值	标签值	0.920	是
2	num	时序	识别特征	0.789	是
	sig0_ocog_ku	后向散射系数	限制类因素	-0.694	否
	sig0_ice_sheet_ku	后向散射系数	限制类因素	-0.743	否
	geoid	大地水准面	限制类因素	0.988	是
3	lon	经度	限制类因素	0.986	是
	lat	纬度	限制类因素	-0.993	否
4	iono_cor_gim	电离层	传播修正	0.892	是
5	ASOE	独热编码	识别特征	0.970	是
6	mod_wet	湿对流层	传播修正	0.653	是

单天算法	时间间隔/d	MAE/cm	RMSE/cm	R²
Ice1	Pass间隔天数	14.38	15.29	0.99
Ocean	Pass间隔天数	34.21	37.29	0.95
Sea-ice	Pass间隔天数	67.89	44.54	0.95
本文算法	1	1.95	2.19	0.91

测高卫星	卫星周期/d	Pass轨迹号	Pass间隔天数/d
Envisat	35	94、479、552	3、13、16、19、22、32
SARAL	35	21、94、479、552	3、13、16、19
S3A	27	135、664	8、19、27
S3B	27	249、664	12、15、27

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

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参考文献 31

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多天算法	时间间隔/d	MAE/cm	RMSE/cm	R²
Ice1	Pass间隔天数	13.19	14.11	0.84
Ocean	Pass间隔天数	42.16	42.54	0.41
Sea-ice	Pass间隔天数	33.71	34.10	0.47
本文算法	Pass间隔天数	1.85	2.58	0.85

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