Prospective participants are both scientists and practitioners in the field of hydrological extremes (floods and droughts) management and remote sensing. Water Authorities interested in improving water resources management by means of multiple remote sensing sources and Google Earth Engine are also welcome.
The use of remote sensing data for hydrological extremes management allows for a more distributed representation of the catchment than the standard in-situ network of sensors. However, the processing of remote sensing information over a vast area may require considerable computational time in case of complex spatiotemporal analysis at fine resolution. For this reason, the open-access Google Earth Engine Platform can be used for this type of analyses.
This course will initially provide an overview of remote sensing products and then present their multiple uses in flood and droughts management. After that, the Google Earth Engine tool will be introduced in a detailed and comprehensive way. Theory and practical exercises correspond to 60% and 40% of the course time respectively. Participants are invited to bring their own case study to apply the Google Earth Engine tool on existing water-related issues.
The structure of the course will be as follows:
1. Theoretical background on remote sensing
2. Introduction to Google Earth Engine and Java scripting
3. Data collection, visualization and classification with Google Earth Engine
4. Time series analysis
5. Hydraulic modelling for flood management
6. Application of Google Earth Engine in flood management
7. Droughts analysis
Upon completion, the participant should be able to:
– To acquire a theoretical understanding of the remote sensing products available for water resources management purposes
– To master the Google Earth Engine platform for data collection, visualization and classification.
– To be able to perform complex spatial-temporal analysis with Google Earth Engine using the latest remote sensing products.
– To apply Google Earth Engine for large-scale hydrological extremes management