PICO
Remote sensing products have a high potential to contribute to monitoring and modelling of water resources. Nevertheless, their use by water managers is still limited due to lack of quality, resolution, trust, accessibility, or experience.
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We are looking for research to increase the quality of remote sensing products, such as higher spatial and/or temporal resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources, drought monitoring/modelling and its impact on water-stressed vegetation, as well as on irrigation volumes monitoring and modelling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (physically based models or artificial intelligence techniques) or a combination of different sources of data (remote sensing and in situ) and different imagery types (satellite, airborne, drone). Finally, we wish to attract presentations on developments of user-friendly platforms (as open as possible), providing smooth access to remote sensing data for water applications.
We are particularly interested in applications of remote sensing to determine the human water interactions and the climate change impacts on the whole water cycle (including the inland and coastal links).
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We are looking for research to increase the quality of remote sensing products, such as higher spatial and/or temporal resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources, drought monitoring/modelling and its impact on water-stressed vegetation, as well as on irrigation volumes monitoring and modelling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (physically based models or artificial intelligence techniques) or a combination of different sources of data (remote sensing and in situ) and different imagery types (satellite, airborne, drone). Finally, we wish to attract presentations on developments of user-friendly platforms (as open as possible), providing smooth access to remote sensing data for water applications.
We are particularly interested in applications of remote sensing to determine the human water interactions and the climate change impacts on the whole water cycle (including the inland and coastal links).