## 3 Current Measurement Techniques
### Other Techniques Applicable to Both Soil Moisture and Snow Property Determination
#### 3.3.3 Satellite Products  
Satellite products theoretically have a significant advantage over point-based measurements in the measurement of soil moisture/snow related parameters in Alpine regions, due to the high spatial variability of the measurements. A spatially distributed measurement, covering the entire region, could provide the detailed information required to predict natural hazards on local and regional scales, e.g., for slope stability, water availability, or flood warning scenarios. Various satellite-based measurements can be used to infer soil moisture. These include measurements from the ERS-scatterometer of microwave backscatter with a spatial resolution of 50 by 50 km [59], and inference from the surface energy balance with algorithms that combine various satellite products, e.g., SEBAL [60]. Some other large scale measurement missions have recently started, with the SMOS (Soil Moisture and Ocean Salinity) satellite of the ESA, in orbit since 2009, that measures soil moisture with a spatial resolution of 35-50 km, with an accuracy of 4%, and a revisit time of one to three days. The satellite uses a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) working in the L band (1.4 GHz) [61, 62]. In the USA, NASA is preparing the SMAP (Soil Moisture Active Passive) mission, with a satellite launch for 2014, carrying a radiometer and synthetic aperture radar (SAR) in the L band as well (1.20-1.41 GHz), with the same accuracy and revisit time as SMOS but an improved resolution of 10 km [63]. In recent times, also measurements of spatiotemporal variability in the gravity field by the GRACE satellite mission have been used to detect changes in mass storage, from which soil moisture or snow signals could be extracted through data assimilation in combination with other measurements. The GRACE data contains information on the large scale storage variations over thousands of kilometers, with an accuracy in the order of centimeters water column, depending on the knowledge of other gravity signals [64]. Comprehensive reviews of satellite-based snow sensing methods and products are provided in [65, 66, 67]. There are four types of products, using: visible, infrared, active microwave, and passive microwave sensing techniques. Visible and infrared products have been used to provide snow/no snow information globally since 1966. A list of the satellites available can be found in [67]. Products usingthe Advanced Very High Resolution Radiometer (AVHRR) are used operationally for snow/no snow information in avalanche forecasting in the Alps (fractional snow coverage information no longer produced operationally). No-snow data points are combined into an interpolation of snow heights from _in situ_ stations to provide a more accurate operational product for snow height [68]. The resolution of these products is 1 km, which, along with possible errors caused by steep terrain, means that it may only be used for the assessment of snow accumulation patterns on a regional-national scale. A similar process has been attempted using the MODerate-resolution Imaging Spectroradiometer (MODIS) instrument, in combination with other data such as _in situ_ measurements or modeled SWE [69, 70, 71] to provide SWE and HS. These calculations are limited to similar resolution. Such a combination of products is a popular method within the satellite community for addressing the limitations of a single dataset. A full comparison of snow/no snow data sets is available in [72]. Although snow/no snow information is interesting to a wider community and more widely used as reliable validation data sets, HS and SWE are more relevant for the applications of interest. It is possible to calculate these parameters using active or passive microwave sensor data. Although these two methods use the same spectrum, the measurement methods are entirely different. Both methods can provide spatial coverage of HS and SWE on a global scale, something which no other measurement method can provide.