Abstract
Snow cover dynamics impact a whole range of systems in mountain regions, from society to economy to ecology; and they also affect downstream regions. Monitoring and analyzing snow cover dynamics has been facilitated with remote sensing products. Here, we present two high-resolution daily snow cover data sets for the entire European Alps covering the years 2002 to 2019, and with automatic updates. The first is based on moderate resolution imaging spectroradiometer (MODIS) and its implementation is specifically tailored to the complex terrain, exploiting the highest possible resolution available of 250 m. The second is a nearly cloud-free product derived from the first using temporal and spatial filters, which reduce average cloud cover from 41.9% to less than 0.1%. Validation has been performed using an extensive network of 312 ground stations, and for the cloud filtering also with cross-validation. Average overall accuracies were 93% for the initial and 91.5% for the cloud-filtered product using the ground stations; and 95.3% for the cross-validation of the cloud-filter. The data can be accessed online and via the R and python programming languages. Possible applications of the data include but are not limited to hydrology, cryosphere and climate.
Highlights
Of the currently available remote sensing snow cover datasets, moderate resolution imaging spectroradiometer (MODIS) offers the best trade-off between spatial resolution, temporal resolution, and temporal extent, since it is available at 250m horizontal resolution, with daily acquisitions, and since the years 2000 (Terra satellite) and 2002 (Terra and Aqua satellites)
The first is a MODIS based snow cover product for the European Alps, whose algorithms have been developed until 2013 [1], and which is publicly available since 2018; before the data was made available upon request
EURAC_SNOW is a snow cover product based on MODIS imagery and has been developed by Notarnicola et al [1]
Summary
Snow is a key environmental parameter in mountain regions. measurements using in-situ data are sparse and heterogenous because of the complex terrain, and the altitudinal gradient is not appropriately represented since there are only few stations above 2000m altitude. The. Data 2020, 5, 1 presented paper is a data descriptor, and its intent is to summarize the methods and post-2013 changes for EURAC_SNOW, to describe the methods used to create EURAC_SNOW_CLOUDREMOVAL, to provide some validation results, and to show how to access both data sets as a whole or in parts. Data 2020, 5, 1 presented paper is a data descriptor, and its intent is to summarize the methods and post-2013 changes for EURAC_SNOW, to describe the methods used to create EURAC_SNOW_CLOUDREMOVAL, to provide some validation results, and to show how to access both data sets as a whole or in parts This is explicitly not a research paper, since nothing new has been developed. It has already been used in internal projects such as SAO (Sentinel Alpine Observatory, http://sao.eurac.edu/), in regionally funded projects such as MONALISA (http: //www.monalisa-project.eu/) or CRYOMON-SciPro (http://www.eurac.edu/en/research/projects/Pages/ projectdetail4240.aspx), in EU-funded projects such as ECOPOTENTIAL (https://www.ecopotentialproject.eu/), openEO (https://openeo.org/), CliRSnow (http://www.eurac.eu/en/research/projects/Pages/ projectdetail4488.aspx), and in various publications [2,3,4,5,6,7,8,9]
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