The snow cover is a key component of land surface hydrology, especially in mountain areas
where it governs the amount and timing of water availability in downstream areas. It is …

Monitoring snowpack depth is essential in many applications at regional and global scales.
Space-borne passive microwave (PMW) remote sensing observations have been widely …

In this study, a machine learning algorithm was introduced to fuse gridded snow depth
datasets. The input variables of the machine learning method included geolocation (latitude …

Observation‐based long‐term gridded snow water equivalent (SWE) products are important
assets for hydrological and climate research. However, an evaluation of the currently …

Modelling the impact of climate change on streamflow for remote or data sparse regions is a
challenge for hydrologists, as large datasets are often needed to adequately characterise …

As a future satellite mission concept, active microwave sensors have the potential to
measure snow water equivalent (SWE) with advantages including finer spatial resolution …

Arctic snow and ice cover are vital indicators of climate variability and change, yet while the
Arctic shows overall warming and dramatic changes in snow and ice cover, the response of …

Snow cover plays an important role in climate, hydrology, and ecosystem. At present,
passive microwave remote sensing is the most effective method for monitoring global and …

The optimal use of hemispheric‐scale snow depth (SD) products for various
hydrometeorological applications requires a comprehensive assessment of their quality …

Snow geophysical parameters such as wetness, density and permittivity are a significant
input in hydrological models and water resource management. In this paper, we utilize the …