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《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:

第44卷

期数:

2022年第05期

页码:

789-801

栏目:

大地测量、遥感与地学大数据

出版日期:

2022-09-15

文章信息/Info

Title:

Estimation of Snow Water Equivalent Based on GPS Dual-frequency Signal Enhancement

文章编号:

1672-6561(2022)05-0789-13

作者:

王佳彤¹; 2; 胡羽丰¹; 2; 3*; 李振洪¹; 2; 3

(1. 长安大学 地质工程与测绘学院,陕西 西安 710054; 2. 长安大学 地学与卫星大数据研究中心,陕西 西安 710054; 3. 长安大学 西部矿产资源与地质工程教育部重点实验室,陕西 西安 710054)

Author(s):

WANG Jia-tong¹; 2;  HU Yu-feng¹; 2; 3*;  LI Zhen-hong¹; 2; 3

(1. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China; 2. Big Data Center for Geosciences and Satellites, Chang'an University, Xi'an 710054, Shaanxi, China; 3. Key Laboratory of Western China's Mineral Resources and Geological Engineering of Ministry of Education, Chang'an University, Xi'an 710054, Shaanxi, China)

关键词:

积雪深度;  雪水当量;  GPS-IR技术;  L2C信号;  信噪比;  双频增强;  地基反演;  地表水环境

Keywords:

snow depth;  snow water equivalent;  GPS-IR technology;  L2C signal;  signal-to-noise ratio;  dual-frequency enhancement;  ground-based reflectometry;  surface water environment

分类号:

P333; P237

DOI:

10.19814/j.jese.2022.03006

文献标志码:

A

摘要:

雪水当量是重要的积雪参数之一,对气候变化预测与水资源管理等有重要意义。GPS干涉反射技术(GPS-IR)是一种十分有效的积雪深度监测技术,结合积雪密度估计模型可实现雪水当量的估计。基于此,提出了一种GPS-IR双频积雪参数反演增强方法。首先,利用美国板块边界观测(PBO)AB33测站2016年水文年与P019测站2020年水文年的L1和L2C信号信噪比数据,通过GPS-IR技术获取了两个测站的双频反射高度,建立了双频反射高度的线性关系模型,并通过该模型填补了L2C信号反射高度缺测数据,进而获取双频增强积雪深度时间序列,然后通过积雪密度估计模型转换得到雪水当量日估计值,最后采用SNOTEL测站实测积雪参数进行检验。结果表明:基于L2C信号的积雪深度反演精度要优于L1信号,基于增强方法的积雪深度反演精度介于L1信号和L2C信号之间; 基于增强方法的雪水当量反演精度与L2C信号基本相当,且均优于L1信号; 增强方法在AB33测站与P019测站分别有效填补了基于L2C信号的积雪深度/雪水当量时间序列25.8%与13.7%的空缺数据。本文提出的增强方法充分利用了GPS双频信号数据资源,可获取高连续性的GPS积雪深度和雪水当量,为设备缺乏地区的雪水当量估计、水环境监测等研究提供参考。

Abstract:

Snow water equivalent(SWE)is one of the important snow parameters, and is of great significance to climate change prediction and water resource management. GPS interferometric reflectometry(GPS-IR)is an effective technique for snow depth monitoring. Combined with the snow density model, the SWE can be estimated. An efficient framework to enhance the snow parameters estimation with GPS-IR dual-frequency data was presented. Firstly, by applying GPS-IR to the dual-frequency signal to noise ratio(SNR)data recorded by two Plate Boundary Observatory(PBO)GPS stations AB33 and P019 in water years of 2016 and 2020, the corresponding daily measurements of reflector heights were obtained. Secondly, based on the linear relationship between the L1 and L2C signal reflector heights, a simple linear model to calibrate the L1 to L2C signal results was proposed. Finally, the calibrated reflector heights were used to obtain the daily snow depth measurements, which were then converted to SWE by an empirical snow density model. By using in-situ observations from Snow Telemetry(SNOTEL)sites, the results show that the precision of L2C signal snow depth measurement is better than that of L1 signal; the precision of the snow depth measurements from the enhanced method is moderate but closer to the precision of L2C signal; the precision of SWE from the enhanced method is comparable to that of L2C signal, and both are better than that of L1 signal; the dual-frequency enhanced method effectively fills 25.8% and 13.7% data gaps in the L2C signal snow depth/SWE time series at stations AB33 and P019, respectively. The proposed method makes full use of the GPS dual-frequency SNR data resources, and can obtain high-continuity GPS snow depth and snow water equivalent, which provides data for the effective estimation of snow water equivalent and water environment monitoring in areas with poor equipment.

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相似文献/References:

备注/Memo

备注/Memo:

收稿日期:2022-03-06; 修回日期:2022-04-28投稿网址:http:∥jese.chd.edu.cn/
基金项目:国家自然科学基金项目(41941019,41904020); 陕西省自然科学研究计划项目(2020JQ-350); 陕西省科技创新团队项目(2021TD-51); 中欧合作龙计划五期项目(59339); 中央高校基本科研业务费专项资金项目(300102260301,300102261108,300102261404)
作者简介:王佳彤(1996-),男,黑龙江大庆人,工学硕士研究生,E-mail:jiatong_wang417@163.com。
*通讯作者:胡羽丰(1989-),男,湖北应城人,讲师,工学博士,E-mail:yfhu@chd.edu.cn。

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更新日期/Last Update: 2022-10-01