Multitemporal COSMO-SkyMed (CSK) images are exploited to map wet snow cover in a mountainous area in South Tyrol by using a ratio and a probability of error (POE) approach. Free water in the snowpack attenuates the X-band synthetic aperture radar (SAR) signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. The three steps of the algorithms are: preprocessing of SAR data with particular attention on the potential of speckle filtering to improve the classification, classification of wet snow and postprocessing of the snow cover area (SCA) map. Furthermore, the choice of the snow-free reference and wet snow images on the classification threshold and the SCA is assessed as well as the influence of different landcover classes (blocky scree, grassland, forest). Thresholds to distinguish snow-covered and snow-free pixels are - 2.6 dB for grassland and rocks. To quantify the accuracy of the ratio method, POE maps are calculated. The advantage of the POE method is its independency from auxiliary information on snow cover and the possibility to limit the maximum error. SCA maps derived with a maximum POE of 25% and ratio SCA maps show good overall agreement with total SCA of 66.8% (ratio) and 65.6% (POE) on 26th April 2010. A comparison to SCA derived from Landsat 7 ETM+ reveals that total SCA is similar to SAR SCA when a NDSI threshold of 0.7 is applied, but only 86% of the pixels are detected as snow from both sensors at the same time.

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

The multi-temporal technique for the detection of wet snow, based on the different response of snow and no-snow area was applied to COSMO-SkyMed (CSK) images acquired over South Tyrol (Northern Italy) during the first year of activity in the project “SNOX - snow cover and glacier monitoring in alpine areas with COSMO-SkyMed X-band data” funded by the Italian Space Agency. A standard threshold value of -3 dB and a less restrictive threshold of -2.3 dB are compared for an improved distinction of the snow and no-snow distributions. The application of these two different thresholds determines a variation of snow cover area (SCA) between 2% and 8%. Furthermore, it has been proved that the choice of the reference image for the no-snow areas is critical and can determine a variability in the resulting SCA up to 10%. The depolarization factor, σ ⁰ _VH /σ ⁰ _VV , is also exploited to evaluate its contribution to the identification of snow covered areas. Preliminary results indicate that the contribution of the depolarization factor to the SCA detection is very limited.

This paper is the third of a series that introduces some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform is a brokering infrastructure that brokers more than 190 autonomous information systems and data catalogs; it was created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS). This manuscript focuses on the analysis of Eurac Research datasets and illustrates the data publishing process to enroll the Eurac Research Data Provider to the GEOSS Platform through the administrative and technical registrations. The study provides an analysis of the GEOSS user searches for Eurac Research data in order to understand the main use of datasets of an important Data Provider.