SegFormer model estimates snow water and height from satellite data
Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions
Machine Learning
Summary
Measuring how much water is stored in mountain snow is hard but important for managing water resources. This study checked three computer programs to figure out snow measurements from satellite images taken over the Alps. The SegFormer program did the best job estimating both snow depth and water content. The researchers also found that including every possible input didn’t always improve the results, and that most errors were due to consistent differences in average estimates rather than errors in detailed patterns.
What this means in practice
- •For water resource managers: Estimate snow water content and height variations more accurately from satellite radar data to improve water supply planning in mountainous regions.
- •For environmental monitoring teams: Use machine learning models on Sentinel-1 data to track regional snow changes for climate assessments and ecosystem management.
Authors
Luca Barco, Lorenzo Innocenti, Bianca Bartoli, Claudio Rossi, Edoardo Arnaudo, Paolo Garza
Abstract
Managing water resources in mountainous regions depends heavily on reliable Snow Water Equivalent (SWE) and Snow Height (HS) data, yet these variables remain difficult to track at scale. This study evaluates three machine learning architectures (XGBoost, U-Net and SegFormer) for the joint estimation of SWE and HS variations from Sentinel-1 InSAR data over the Italian Alps, using the IT-SNOW reanalysis as reference. SegFormer achieves the best results on both targets, with an MAE of 10.391 cm for HS and 27.113 mm w.e. for SWE and the lowest variability across initializations. A feature sensitivity analysis shows that including all available features does not guarantee the lowest error, with model- and task-specific sensitivities. Spatial metrics (R2, Pearson's r) separate the three architectures far more clearly than mean error (MAE, RMSE) does, and decomposing the error per window attributes most of it to a systematic offset in the estimated mean variation rather than to the spatial pattern.