Advances in image-based estimation of snow variable: A systematic literature review on recent studies

Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are essential for predicting water availability and managing water resources in snow-covered areas. Traditional methods for estimating these parameters often rely on manual measurements or simplistic models, which can be inadequate for capturing the complexity of snow-related processes. In recent years, there has been a growing interest in leveraging deep learning techniques for snow hydrology parameter estimation, offering the potential to overcome these limitations. This review paper comprehensively analyzes the current state, challenges, and future directions of image-based approaches in snow hydrology parameter estimation. By harnessing the power of automated methods, particularly deep learning, these approaches demonstrate the ability to capture intricate spatial and temporal relationships present in image data. A comparative analysis on image-based methods highlights the strengths of automated approaches, including scalability and accuracy. Integration of image sensors, such as satellite imagery and crowd-sourced data, is explored as a crucial component of snow hydrology parameter estimation. Various satellite image sources, including Sentinel 1-2, Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS), are discussed in terms of their suitability for snow hydrological applications. Despite the promise of image-based approaches, challenges remain, including data availability, model interpretability, and transferability. The paper identifies future research directions, emphasizing the exploration of novel deep learning architectures and uncertainty quantification techniques to address these challenges. In conclusion, this review underscores the importance of image-based approaches for advancing snow hydrology parameter estimation. By addressing challenges and maximizing potential impact, these approaches have the potential to revolutionize snow hydrological modeling and environmental management.