Snow depth retrieval over Pan-Arctic sea ice (2012–2021) using multi-source data and machine learning models

Abstract. Snow depth is a critical climate indicator and a key parameter for Arctic sea ice retrieval. In this study, we retrieve pan-Arctic snow depth from 2012 to 2021 by integrating satellite altimetry, passive microwave brightness temperatures, and multi-source ground/airborne data. We employ four machine learning models—Light Gradient Boosting Machine (LightGBM), Multiple Linear Regression (MLR), Random Forest (RF), and Long Short-Term Memory (LSTM)—to leverage the complementary strengths of altimetry and microwave datasets while evaluating the performance of different machine learning (ML) architectures. Through permutation feature importance analysis, we identified that the 89 GHz polarization ratio has a significantly greater influence on snow depth retrieval over multi-year ice compared to that over first-year ice. Validation against Operation IceBridge and MOSAiC measurements reveals complementary strengths of snow retrieval among the models. The MLR model achieves the highest overall snow depth accuracy (root-means-square-error = 7.19 cm, correlation = 0.67 against OIB), while the LSTM demonstrates minimal mean bias of snow depth between satellite-based and in situ observations (1.98 cm against OIB; 0.30 cm against MOSAiC). All ML models exhibit robust generalization capabilities. Our retrieved snow depth products improved sea ice thickness estimation significantly, reducing bias between satellite-based and a standard climatology-based ice thickness product by nearly an order of magnitude. Our long-term snow products offer users a reliable, high-accuracy dataset for advancing Arctic energy budget modeling and sea ice studies.