极地研究

极地研究 ›› 2023, Vol. 35 ›› Issue (2): 197-211.DOI: 10.13679/j.jdyj.20220204

• 研究论文 • 上一篇    下一篇

基于GNSS-R技术的阿拉斯加州积雪深度反演及其应用    

陈芳霖1  常亮1,2  冯贵平1   

  1. 1上海海洋大学海洋科学学院, 上海 201306; 
    2自然资源部第二海洋研究所, 卫星海洋环境动力学国家重点实验室, 浙江 杭州 310012
  • 出版日期:2023-06-30 发布日期:2023-06-20
  • 通讯作者: 常亮
  • 作者简介:陈芳霖, 女, 1997年生。硕士研究生, 主要从事GNSS-R技术研究。E-mail: chenfanglin123@163.com
  • 基金资助:
    国家自然科学基金(42174016, 42076240)、自然资源部卫星海洋环境动力学国家重点实验室开放研究基金(QNHX2324)资助

Inversion of snow depth in Alaska based on GNSS-R technology and its application

Chen Fanglin1, Chang Liang1,2, Feng Guiping1   

  1. 1College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
    2State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

  • Online:2023-06-30 Published:2023-06-20

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摘要： 利用GNSS-R(全球导航卫星系统反射测量)技术进行准确的雪深监测已成为传统雪深测量的重要补充手段。本文使用GNSS-R技术反演了2012—2018年美国阿拉斯加州4个GPS观测站附近的雪深结果, 结合加拿大气象中心(Canadian Meteorological Centre, CMC)提供的雪深模型数据产品, 以PBO(Plate Boundary Observatory)H2O项目组提供的雪深资料为参考值, 分析了不同手段获取的雪深值在不同时间尺度上的变化特征, 同时评估了GNSS-R反演雪深结果作为独立数据集验证CMC模型数据的能力。结果表明: GNSS-R、CMC和PBO得到的长时间序列雪深结果均具有较为一致的明显周期性变化, 整体上GNSS-R反演结果比CMC数据精度更高, 更能反映雪深的年际变化情况。GNSS-R反演值和CMC模拟值均能够反映各测站PBO雪深值的逐月变化规律, 但GNSS-R反演值的精度和稳定性总体上优于CMC模拟值。GNSS-R反演结果比CMC模拟值与PBO雪深值的季节性变化更具一致性, 且对于本文研究的4个测站, GNSS-R反演雪深的精度和稳定性在雪深值较大的春季和冬季较高, 雪深值较小的秋季略差。此外, 本文还证实了GNSS-R反演的雪深结果可用于评估CMC模拟雪深值的精度, 且评估效果在冬春季优于秋季。

关键词: 全球导航卫星系统反射测量(GNSS-R), 加拿大气象中心(CMC), 雪深, 阿拉斯加

Abstract: Global navigation satellite system reflectometry (GNSS-R) technology has become an important supplement to traditional snow depth measurement. In this study, the GNSS-R technique was used to obtain snow depths near four GPS stations in Alaska during 2012–2018. In combination with snow depths from the Canadian Meteorological Centre (CMC) model, and taking snow depth data from the Plate Boundary Observatory (PBO) H2O project team as reference, variations in snow depth obtained using different methods on different time scales were analyzed. The capability of GNSS-R-derived snow depth was taken as an independent dataset to evaluate the performance of the CMC model data. Results showed that long-term snow depths from GNSS-R, CMC, and PBO all exhibit obvious and consistent periodic variation. Typically, the GNSS-R-derived results are more accurate than the CMC data in detecting interannual variation in snow depth. Both GNSS-R and CMC can capture the monthly variation seen in the PBO data for each station, although the accuracy and stability of the GNSS-R results are generally better than those of the CMC. In comparison with the CMC results, seasonal variation in GNSS-R-derived snow depth is more consistent with the PBO data. For the four studied stations, GNSS-R-derived snow depth accuracy is higher in spring and winter when snow depth is larger, and slightly worse in autumn when snow depth is smaller. Overall, GNSS-R is proven effective for evaluation of the accuracy of CMC-simulated snow depths, and the evaluation effect is generally better in winter and spring than in autumn.

Key words: GNSS-R, CMC, snow depth, Alaska