A Novel Snow Depth Retrieval Approach for the Northern Hemisphere Based on an Equivalent Volumetric Scattering Index

The spatiotemporal heterogeneity of snowpack properties—particularly snow grain size, density, and liquid water content—combined with the attenuating and radiating effects of forest canopy, continues to pose critical challenges that limit the accuracy of snow depth retrieval from passive microwave remote sensing. To address these issues, this study introduces the Equivalent Volume Scattering Index (EVSI), a physically informed metric designed to isolate the radiative contributions of non-snow-depth factors in microwave signal propagation. The EVSI is defined as the ratio of the differential brightness temperature between high-frequency (e.g., 37 GHz) and low-frequency (e.g., 19 GHz) passive microwave channels to in situ ground-based snow depth observations. Leveraging the joint spatiotemporal patterns of in situ snow depth and EVSI, we first classified Northern Hemisphere snowpack into seven distinct snow types via unsupervised cluster analysis. This typology captures dominant regimes characterized by unique combinations of microphysical and environmental conditions. For each snow type, we then developed a dynamic, regionally adaptive, and partially non-resetting EVSI-based snow depth retrieval model. The “partially non-resetting” design preserves key snow state variables across time steps—such as grain size evolution and liquid water retention—while allowing radiative transfer parameters to adapt dynamically to evolving snow and canopy conditions. In contrast to conventional passive microwave snow depth algorithms, the proposed framework not only ensures physical interpretability through its foundation in microwave radiative transfer theory but also prioritizes operational feasibility by relying exclusively on readily accessible inputs, including daily air temperature, daily precipitation, and daily brightness temperatures from both low- and high-frequency microwave channels. Consequently, the algorithm simultaneously achieves higher retrieval accuracy and enhanced spatiotemporal generalizability, demonstrating robust performance across diverse climatic zones and seasonal cycles—thereby advancing both scientific understanding and practical applicability in global snow monitoring.