Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R-CV(2) and RMSECV of 0.59 and 82 kg m(-3), respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe.

Climate warming is causing permafrost thaw and there is an urgent need to understand the spatial distribution of permafrost and its potential changes with climate. This study developed a long-term (1901-2100), 1-km resolution daily meteorological dataset (Met1km) for modeling and mapping permafrost at high spatial resolutions in Canada. Met1km includes eight climate variables (daily minimum, maximum, and mean air temperatures, precipitation, vapor pressure, wind speed, solar radiation, and downward longwave radiation) and is suitable to drive process-based permafrost and other land-surface models. Met1km was developed based on four coarser gridded meteorological datasets for the historical period. Future values were developed using the output of a new Canadian regional climate model under medium-low and high emission scenarios. These datasets were downscaled to 1-km resolution using the re-baselining method based on the WorldClim2 dataset as spatial templates. We assessed Met1km by comparing it to climate station observations across Canada and a gridded monthly anomaly time-series dataset. The accuracy of Met1km is similar to or better than the four coarser gridded datasets. The errors in long-term averages and average seasonal patterns are small. The error occurs mainly in day-to-day fluctuations, thus the error decreases significantly when averaged over 5 to 10 days. Met1km, as a data generating system, is relatively small in data volume, flexible to use, and easy to update when new or improved source datasets are available. The method can also be used to generate similar datasets for other regions, even for the entire global landmass.