measure these variables a variety of techniques are used: surface observations, remote sensing, land surface models and reanalysis products. These techniques are often combined to form the most complete datasets. Satellite data Remote sensing of snowpacks with satellites and other platforms typically includes multi-spectral collection of imagery. Multi-faceted interpretation of the data obtained allows inferences about what is observed. The science behind these remote observations has been verified with ground-truth studies of the actual conditions. Satellite observations record a decrease in snow-covered areas since the 1960s, when satellite observations began. In some regions such as China, a trend of increasing snow cover was observed from 1978 to 2006. These changes are attributed to global climate change, which may lead to earlier melting and less coverage area. In some areas, snow depth increases because of higher temperatures in latitudes north of 40°. For the Northern Hemisphere as a whole the mean monthly snow-cover extent has been decreasing by 1.3% per decade. The most frequently used methods to map and measure snow extent, snow depth and snow water equivalent employ multiple inputs on the visible–infrared spectrum to deduce the presence and properties of snow. The National Snow and Ice Data Center (NSIDC) uses the reflectance of visible and infrared radiation to calculate a normalized difference snow index, which is a ratio of radiation parameters that can distinguish between clouds and snow. Other researchers have developed decision trees, employing the available data to make more accurate assessments. One challenge to this assessment is where snow cover is patchy, for example during periods of accumulation or ablation and also in forested areas. Cloud cover inhibits optical sensing of surface reflectance, which has led to other methods for estimating ground conditions underneath clouds. For hydrological models, it is important to have continuous information about the snow cover. Passive microwave sensors are especially valuable for temporal and spatial continuity because they can map the surface beneath clouds and in darkness. When combined with reflective measurements, passive microwave sensing greatly extends the inferences possible about the snowpack. Satellite measurements show that snow cover has been decreasing in many areas of the world since 1978. Models Snow science often leads to predictive models that include snow deposition, snow melt, and snow hydrology—elements of the Earth's water cycle—which help describe global climate change. Global climate change models (GCMs) incorporate snow as a factor in their calculations. Some important aspects