Deep learning of seasonal peak snow water content of global boreal forest and arctic using spaceborne L-band radiometry

Abstract

Estimating peak snow water equivalent (SWE) across the Northern Hemisphere is critical for assessing seasonal water availability for both ecosystems and human needs. This study is the first to demonstrate a direct link between peak SWE and the temporal variability of L-band surface emission under a moderately dense vegetation canopy. We introduce SWEFormer, a novel deep transformer neural network that retrieves peak SWE primarily using time series of L-band brightness temperatures from NASA’s Soil Moisture Active and Passive (SMAP) satellite. The model is trained using an incremental learning approach that transfers low-level information from reanalysis data for spatially coherent high-level learning from sparse in situ observations. SWEFormer outperforms leading global products, including ERA5, GlobSnow, and AMSR-based estimates, particularly in complex boreal watersheds, where previous global SWE estimates suffer from significant uncertainties, as vegetation canopy often markedly attenuates high-frequency microwave signatures of snowpack.