Earth Observation and physical models to monitor snow variables
The eo4alps snow project is based on a hybrid technology that merges the advantages of the physical model with high-resolution high-frequency Earth Observation snow products.Let’s get in touch
The maximum potential from the latest techniques
By combining the latest technology in snow monitoring, we want to improve the temporal and spatial aggregation of Snow Water Equivalent (SWE) monitoring techniques and provide a service of high-resolution SWE monitoring in quasi-real time at Alpine scale.
Copernicus Sentinel missions
To improve the revisit frequency of the snow cover product, the project is taking advantage of high-resolution binary snow cover maps from Sentinel-2, SAR data from Sentinel-1 and coarser resolution daily optical images (e.g. Sentinel-3).
Public and private institutions are interested in snow for civil protection, hydrological balances and energy production.
The quantification of the snow water equivalent (SWE) accumulated upstream a production plant is a recurrent activity that is performed by hydropower companies to monitor the energy potential stored in the snow.
Estimating in real-time the snow evolution at regional scale is becoming ever more important to manage the avalanche risk and to monitor the hydrological balance of water resources.
Find new practical insights into the use of satellite data for the study of snow and obtain snow monitoring data for research purposes.
Learning about the snow conditions and evolution for the practice of winter sports. Ski resorts may benefit from information about snow conditions around the pistes for safety reasons and off-piste explorers on snow availability.
During wintertime and Spring it is important to monitor snow evolution, not only for outdoor activities or civil protection, but also for hydrological balances of water resources. In eo4alsp snow project we aim to use the available in-situ observations and to assimilate high-resolution satellite data for a better snow covered area detection.
Frequently Asked Questions
What is the eo4alps snow project?
eo4alps snow is one of the four Application Development projects under 'Alpine Regional Initiative (eo4alps) – application developments' initiated by the European Space Agency alongside two projects under eo4alps 'Alpine Regional Initiative (eo4alps) – Science'.
Who is participating in the project?
The eo4alps snow consortium is composed of three partners: MobyGIS (IT), lead partner, with experience in physically-based snow monitoring through the project Mysnowmaps®, EURAC (IT), experienced in remote-sensed snow monitoring and Sinergise (SI), experts in providing Earth Observation data through the Sentinel Hub offered in the Euro Data Cube.
What is the purpose of the project?
The project focuses on implementing a high-resolution quasi real-time snow monitoring to improve water resource management. The core service is a snow water equivalent (SWE) product generated using a cloud based processing environment to be delivered over the entire Alpine arc region.
What technology is being used?
Taking advantage of recent developments in physically-based snow modelling, the project uses high-resolution optical and radar Earth Observation missions such as Sentinel-1, Sentinel-2 and Sentinel-3 from the EU Copernicus program.
Who will be the beneficiaries of the service?
The results of eo4alps snow will be made available to different groups of users. The eo4alps team is planning to engage users from public and private sectors, such as public agencies, research centers, associations and hydropower companies, who are interested in snow for civil protection, hydrological balances and energy production.
How will the data be delivered?
The end-products will be made available on a dedicated web-based application in the Euro Data Cube. The data will also be accessible via OGC compliant APIs that allow users to connect directly to the Euro Data Cube. This flexibility is intended to comply with different user requirements in terms of easy data access, the ability to generate areas of interest and request several snow variable datasets in a systematic manner.