Snow is an important water resource that plays a critical role in the global climate and hydrological cycle. Thus, Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as a new remote sensing technology for monitoring snow depth. We developed the snow parameter processing software GiRsnow, based on GNSS-IR tools and a MATLAB environment, to obtain robust and effective retrievals. That tool allows users to check the data quality, draw reflection point trajectory and Fresnel zone, retrieve snow depth using signal-to-noise ratio (SNR) observations or geometry-free linear carrier phase combination (termed L4) observations, and display the results based on the time and space domain. We conducted two experiments at the Plate Boundary Observation site RN86 and GPS Earth Observation Network (GEONET) site 020877 to validate the performance of the software. Our results demonstrate that GiRsnow can process multi-constellation and multi-frequency GNSS data and obtain robust and effective results through quality control and a grid model to account for topography effects.

雪是重要的水资源，在全球气候和水文循环中起着至关重要的作用。因此，全球导航卫星系统干涉反射法（GNSS-IR）已经成为一种监测雪深的新遥感技术。我们基于GNSS-IR工具和MATLAB环境开发了降雪参数处理软件GiRsnow，以获得强大而有效的检索结果。该工具允许用户检查数据质量，绘制反射点轨迹和菲涅耳带，使用信噪比（SNR）观测值或无几何线性载波相位组合（称为L4）观测值来检索积雪深度，并显示结果基于时空域。我们在板块边界观测站点RN86和GPS地球观测网络（GEONET）站点020877进行了两个实验，以验证软件的性能。我们的结果表明，GiRsnow可以处理多星座和多频率GNSS数据，并通过质量控制和网格模型来解决地形影响，从而获得鲁棒而有效的结果。