GNSS-MR技术用于雪深探测的初步研究

积雪是全球水循环中的重要组成部分，积雪深度与雪水当量的精确监测对全球气候变化研究极其重要。随着GNSS研究与应用的不断深入，基于多路径效应的GNSS-MR（GNSS multipath reflectometry）技术用于地表环境监测（植被、土壤湿度、雪深、海平面等）已成为一种新兴的遥感手段。分析了SNR（signal-to-noise ratio）信噪比值的变化特性，详细给出了基于SNR观测值的GNSS-MR技术探测雪深的基本原理及其计算流程图。为了验证算法的有效性，利用科罗拉多州17 d连续跟踪站NWOT的GPS数据反演了降雪厚度，其结果与实测的雪深记录数据吻合较好，误差均值为0.07 m。初步研究结果验证了GNSS-MR技术用于积雪深度探测的可行性，并为后续充分利用现有的全球密集GNSS跟踪站数据开展地表环境监测提供重要参考。

Preliminary Research on GNSS-MR for Snow Depth

Snow is a critical storage component in the hydrologiccycle; accurate measurements of snow depth and snow water storage are very important for global climate research. As research and application of GNSS deepens, GNSS-MR based multipath has emerged as a new technology of remote sensing for environmental monitoring of vegetation, soil moisture, snow depth, sea levels, ,and so on. The characteristics of change in SNR data are analyzed and the fundamental principles of GNSS-MR based SNR and calculation flow chart are introduced. Experiments with GPS data collected for 17 days at the NWOT station, Colorado, verify the validity of GNSS-MR based SNR. The retrieval result was consistent with the snow depth recorder; the standard deviation was 0.07m. Results also show that the GNSS-MR for snow depth is very effective and useful for environmental monitoring with abundantglobalGNSS stations.