测绘学报 ›› 2020, Vol. 49 ›› Issue (9): 1179-1188.doi: 10.11947/j.AGCS.2020.20200268

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小波变换与滑动窗口相结合的GNSS-IR雪深估测模型

边少锋1, 周威1, 刘立龙2,3, 李厚朴1, 刘备1   

  1. 1. 海军工程大学导航工程系, 湖北 武汉 430079;
    2. 桂林理工大学测绘地理信息学院, 广西 桂林 541004;
    3. 广西空间信息与测绘重点实验室, 广西 桂林 541004
  • 收稿日期:2020-06-24 修回日期:2020-08-23 发布日期:2020-09-19
  • 通讯作者: 周威 E-mail:boy123455@126.com
  • 作者简介:边少锋(1961-),男,博士,教授,研究方向为大地测量学。E-mail:sfbian@sina.com
  • 基金资助:
    国家自然科学基金(41631072; 41971416); 海军工程大学自主立项项目(2019055);广西空间信息与测绘重点实验室开放基金(19-050-11-02); 湖北省杰出青年科学基金(2019CFA086); 广西自然科学基金(2018GXNSFAA294045)

GNSS-IR model of snow depth estimation combining wavelet transform with sliding window

BIAN Shaofeng1, ZHOU Wei1, LIU Lilong2,3, LI Houpu1, LIU Bei1   

  1. 1. Department of Navigation Engineering, Naval University of Engineering, Wuhan 430079, China;
    2. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China;
    3. Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China
  • Received:2020-06-24 Revised:2020-08-23 Published:2020-09-19
  • Supported by:
    The National Natural Science Foundation of China (Nos. 41631072;41971416);The Independent Project of Naval University of Engineering (No. 2019055);The Open Fund of Guangxi Key Laboratory of Spatial Information and Geomatics (No. 19-050-11-02);The Natural Science Foundation for Distinguished Young Scholars of Hubei Province of China (No. 2019CFA086);The Guangxi Natural Science Foundation of China (No. 2018GXNSFAA294045)

摘要: GNSS干涉反射技术(GNSS interferometric reflectometry)是一种新型的地表雪深监测方式。针对当前信号分离不佳和随机估测偏差的问题,提出联合小波变换和滑动窗口构建一种多卫星融合的GNSS-IR雪深估测精化模型。该模型采用离散小波变换代替常用的多项式方法,获取高质量的信噪比序列。通过利用阈值约束下的滑动窗口筛选多卫星有效反射高度,并进行等权平均。以PBO H2O和SNOTEL的雪深数据为参考值,利用2016—2017年雪季的GNSS观测数据建立模型并验证精度。结果表明:①GNSS-IR精化模型估测结果与实测数据在整体趋势上保持高一致性;②与单颗卫星结果相比,多卫星融合估测结果在精度和稳定性方面明显改善,其均方根误差(RMSE)为10 cm,相较于PBO H2O减少了近50%。此外,考虑到地表粗糙度作为一种误差影响因素,采用新的反射高度基准修正的雪深估测相对RMSE误差约4 cm,同时估测值与实际值的相关系数达到0.98。

关键词: 全球卫星导航系统干涉反射技术, 小波变换, 滑动窗口, 雪深估测, 地表粗糙度

Abstract: Currently, GNSS interferometric reflectometry technology has become a high-precision method for monitoring land surface snow depth. Aiming at the problems of signal separation and random estimation biases, we developed a GNSS-IR refined model with multi-satellite fusion for snow depth estimation combining wavelet transform with sliding window. The common polynomial method was replaced by discrete wavelet transform to obtain the high-quality SNR sequences of the reflected signals which can calculate the reflected height of GPS antenna. Then, these reflected heights from SNR observations of multi-satellite were effectively selected and averaged using the sliding window under a constrained threshold. The refined model was established using GNSS observations for snow season from 2016 to 2017, and then the snow depth datasets of both PBO H2O and SNOTEL were regarded as reference to verify the performance of the refined model. The results show that there is a high agreement between snow depths derived from the refined model and in situ measurements, and the RMSE is 10 cm. Compared with the results of a single satellite, the accuracy and the stability of the refined model with multi-satellite fusion are obviously better. In terms of RMSE, the accuracy of the refined model has been improved by 50% when compared with PBO H2O dataset. In addition, taking into consideration that land surface roughness is an error factor, a relative RMSE value of snow depth estimations corrected by a new datum of the reflection height is approximately 4 cm, and the correlation coefficient between snow depth estimations and in situ measurements reaches 0.98.

Key words: global navigation satellite system interferometric reflectometry, wavelet transform, sliding window, snow depth estimation, land surface roughness

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