Snow Depth Measurement using GNSS-R Techniques: A Review

Snow is a widespread atmospheric constituent on Earth, as well as one of the cryosphere's most important seasonal and inter-seasonal fluctuations. Estimating the amount of snow in hilly areas is essential for a variety of socioeconomic endeavours and environmental research. Traditional methods for monitoring snow depth include accessibility, expense, and coverage limits, especially in isolated and difficult terrain. Global Navigation Satellite System-Reflectometry (GNSS-R) technology has emerged as a promising tool for remote sensing applications that include snow depth estimation. This review study synthesises and evaluates current literature on the use of GNSS-R technology for snow depth retrieval, concentrating on its potential and constraints in various mountainous places around the world. The paper includes a detailed explanation of GNSS-R working principles and receiver's advancement, snow depth retrieval methods using both traditional and remote sensing methods like active microwave, passive microwave, GNSS-R integrating with machine learning and deep learning models to develop a snow depth assessment in diverse geographical contexts. GNSS-R technology aids in snow depth retrieval through Signal-to-Noise Ratio (SNR) and carrier phase pseudorange methods, with optimal choice based on application requirements, accuracy, environmental conditions, resources, and complexity-precision trade-offs. The review study aims to provide a comprehensive understanding of the advances in GNSS-R-based snow depth estimation, as well as insights and guidance for future advancements in this field, particularly in addressing the complexities of snow depth estimation in diverse terrains such as those found in India..