Current global snow retrieval algorithms based on spaceborne microwave measurements inherit noticeable biases and uncertainties regarding spatial distribution and temporal variations. In this article, we present an improved spatiotemporally dynamic global snow depth retrieval algorithm to account for the heterogeneity of snowpacks in different seasons worldwide. The proposed model adopts nonlinear machine learning to retrieve snow depths from passive microwave measurements and other auxiliary information. We indirectly characterized the variation in snow grain size using the daily profiles of the temperature gradient within the snowpack. In addition, a zoning and multitemporal modeling strategy was employed to reduce the bias and uncertainty caused by snow heterogeneity across different ecoregions and seasons. The proposed model was implemented to retrieve the global daily snow depth from 2001 to 2010. The results were validated by in situ observations and compared with the NASA Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow water equivalent product (AE_DySno). Satisfactory accuracy was achieved for different ecoregions with regard to daily, monthly, and yearly validations (the root-mean-square error (RMSE) varied from ~7.5 to ~12 cm; the Pearson correlation coefficient $R$ ranged from 0.75 to 0.85). The results of ten trials indicated the promising stability of the proposed model in different ecoregions with small variations in RMSE and $R$ values. Compared with the AE_DySno products, the estimation results did not exhibit the overestimation problem and provided snow depth patterns with greater spatial heterogeneity, showing RMSEs ~5 cm lower and $R$ values ~0.3 higher than those of the AE_DySno products.