Arctic Snow Density Retrieval from AMSR-2 Passive Microwave Brightness Temperatures: A Comparative Evaluation of Machine-Learning and Deep-Learning Models

Abstract

Snow density influences Arctic climate, ecosystems, and surface energy exchange, yet spatially continuous observations remain limited. This study constructed an ERA5-supervised snow-density dataset for 60–90° N by collocating Advanced Microwave Scanning Radiometer 2 (AMSR-2) Level-1R brightness temperatures with ECMWF Reanalysis v5 (ERA5) snow density, Soil Moisture Active Passive (SMAP) surface roughness, and auxiliary variables. Ten models were evaluated using 29 observation days spanning September 2022–February 2023 under a chronological training–validation–test split. Extra Trees achieved the best overall performance, with a root mean square error of 18.54 kg m−3 and an R2 of 0.87, while the bidirectional gated recurrent unit (BiGRU) was the strongest deep-learning model. Feature-attribution and ablation analyses showed that microwave brightness temperatures contained predictive information, although geographic and auxiliary variables also contributed substantially. The evaluated models could reproduce ERA5-referenced Arctic snow-density patterns, but their performance partly reflected regional information. Moreover, ERA5 showed limited consistency with station-based Northern Hemisphere Snow Water Equivalent estimates. Consequently, the reported metrics quantify agreement with ERA5 rather than accuracy against independently observed snow density. Temporally coincident and spatially independent field validation remains necessary in the future.