微波亮温与雪深之间的关系十分复杂，地形和积雪状态的多变性对其影响十分显著，传统方法难以精确反演。为有效解决微波亮温与雪深之间的复杂非线性关系，提出了一种融合小波变换与残差卷积神经网络的被动微波雪深反演模型，利用小波变换提取多频微波亮温的多尺度特征，以表征雪深的整体变化与局地扰动，并通过残差结构提升深层特征传递与非线性拟合能力。在此基础上，选取北半球不同积雪气候类型下的代表性站点评估了该模型在不同积雪气候类型中的适用性。结果表明，融合小波变换与残差卷积神经网络模型在站点尺度实验中，雪深反演的均方根误差为0.23 m，相关系数为0.93，平均偏差为0.01 m，纳什效率系数为0.86，各项误差统计指标相较于传统卷积神经网络和残差卷积神经网络模型均有较大改善。不同积雪深度下的性能均优于传统深度学习模型，且该模型在积雪较厚和变化较快时能够表现出更准确的估算能力。此外，该模型在不同积雪气候类型下的时序响应能力和空间迁移性能均优于雪深反演产品，可以为不同积雪气候类型下的雪深监测提供参考依据。

Abstract： Accurate retrieval of snow depth from passive microwave observations remains a major challenge because of the pronounced nonlinearity between microwave brightness temperature and snow physical properties, which is further modulated by complex terrain and the temporal evolution of snowpacks. Conventional retrieval approaches, including physically based radiative transfer models, are often constrained by high computational costs and strong sensitivity to uncertain input parameters, resulting in degraded performance under heterogeneous snow conditions and across different snow climate regimes. To overcome these limitations, this study develops a passive microwave snow depth retrieval model that integrates wavelet transform with a residual convolutional neural network (Wavedec-ResNet-CNN) to better represent multi-scale features and complex nonlinear relationships. Specifically, the wavelet transform is applied to multi-frequency brightness temperature observations to extract scale-dependent information, allowing the model to capture both large-scale snow depth variability and localized perturbations associated with rapid snow accumulation and melt processes. The resulting multi-scale features are then ingested into a residual convolutional neural network, in which residual connections facilitate deep feature propagation and enhance nonlinear fitting capability while mitigating performance degradation in deep architectures. The proposed model is evaluated using representative groundbased stations spanning different snow climate classes in the Northern Hemisphere to assess its applicability under diverse snow conditions at the site scale. Model performance is quantified using multiple statistical metrics, including root mean square error (RMSE), correlation coefficient (R), mean bias, and Nash-Sutcliffe efficiency (NSE), and is benchmarked against conventional convolutional neural network and residual convolutional neural network models. The results show that the Wavedec-ResNet-CNN achieves an RMSE of 0.23 m, an R of 0.93, a mean bias of 0.01 m, and an NSE of 0.86, indicating consistent improvements over the reference deep learning models across all evaluation metrics. Additional analyses demonstrate that the proposed model outperforms traditional deep learning approaches across a wide range of snow depths, with particularly notable gains during periods of deep snow accumulation and rapid snow depth changes. Furthermore, compared with existing snow depth retrieval products, the model exhibits enhanced temporal responsiveness and improved spatial transferability across different snow climate types, highlighting its potential for robust passive microwave snow depth monitoring in regions with diverse snow regimes.