Improving the accuracy of gridded snow depth estimation through multi-source data and a machine learning fusion model.

ABSTRACT: Snow depth (SD) provides information on the spatial distribution of snow cover, which is critical to assess water resources and global climate change. Currently, SD can be obtained from passive microwave radiometers, reanalysis models, and in-situ observations. However, the SD data produced from different methods have poor performance in completeness and consistency, and it is difficult to meet the needs of related scientific research. In this study, we developed an SD fusion method based on the random forest algorithm (RF) and used it to generate the SD spatial distribution over China from 2014 to 2018. This method can combine the information from multiple sources of SD data (ground-based, satellite-derived, and reanalysis) to improve the representation of the spatiotemporal distribution

SUBMITTER: Qiao D 

PROVIDER: S-EPMC12634684 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature