Novel Framework for Diagnosing Spatial Representativeness Ground Snow Stations in Variable Terrain

Ground-based snow monitoring stations have long supported water management, forecasting, and planning. Recent advancements in remote sensing technology have improved the ability to monitor snow cover at broader scales. Satellite imagery, such as that from the Moderate Resolutiion Imaging Spectroradiometer (MODIS), have been used to track snow cover across large areas. However, the moderate spatial resolution (500 m) of these sensors limits their ability to capture fine scale variability in snow cover, especially in areas with intermittent snowpacks. Airborne lidar surveys provide higher spatial resolution (1 m) but are limited in temporal frequency, making it challenging to track rapid changes in snow cover dynamics.

 

Researchers at Arizona State University have developed a novel diagnostic framework that combines CubeSat imagery and deep learning to evaluate the spatial representativeness of ground snow observation stations in complex terrains. This innovative technology leverages near-daily, 3-meter resolution snow cover maps from CubeSat data processed via a U-Net deep learning model to assess how well point-based ground snow stations represent the surrounding area. It addresses limitations of traditional ground stations and coarse-resolution remote sensing by providing detailed spatial analysis of snow persistence variability, improving station network design and hydrological model accuracy in heterogeneous landscapes.