Séminaire MIDI : Claudia Paris

Title

Timely monitoring and short-term forecasting of crop phenology integrating Satellite Image Time Series (SITS) and meteorological information

Abstract

Increasingly frequent extreme weather conditions, changes in temperature and precipitation strongly affect agriculture and pose a threat to sustainable food production. How crops are affected by adverse weather conditions strongly depends on the crop’s stage of development. In this context, timely monitoring systems of crop phenology is needed to understand and assess the impact of climate change on crop production. To this end, information provided by long and dense Satellite Image Time Series (SITS) acquired at high spatial (e.g., 10 m) and temporal (e.g., 5 days) resolutions, as well as by the meteorological parameters continuously collected by weather stations (e.g., 15 minutes), can be exploited. Despite recent advances in Earth Observation (EO) data and artificial intelligence (AI), little has been done in investigating the potential of combining detailed meteorological information (e.g., temperature and precipitation) and SITS to forecast and monitor crop phenology. Most studies on crop phenology are site-specific, thus hampering their generalization capability in space and time, mainly because the same crop type can have substantially different phenology in different areas due to different climatic conditions and management decisions. This talk will provide an overview of the limitations, challenges and opportunities of combining EO and meteorological data to bring new insights on the relations between crop plant phenology and extreme weather events.

Bio

Claudia Paris received the B.S. and M.S. (summa cum laude) degrees in telecommunication engineering and the Ph.D. degree in information and communication technology from the University of Trento, Trento, Italy, in 2010, 2012, and 2016, respectively. She is currently Assistant Professor with the Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, the Netherlands. Her main research includes image and signal processing, machine learning and deep learning with applications to remote sensing image analysis and, in particular, on designing novel and automatic methods for large-scale environmental monitoring. Moreover, her research interests are also focused on classification and fusion of multisource remote sensing data (LiDAR data, hyperspectral, multispectral and high resolution optical images), multi-temporal image analysis, domain-adaptation methods, land cover map update and remote sensing single date and time series image classification. She has been conducting research on these topics in the framework of national and international projects. She is a member of the scientific and programme committee of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) and the SPIE International Symposium on Remote Conferences, respectively, and is also a referee for several international journals. Dr Paris won the prestigious Symposium Prize Paper Award (SPPA) at the International Symposium on Geoscience and Remote Sensing 2016 (Beijing, China, 2016) and the International Symposium on Geoscience and Remote Sensing 2017 (Fort Worth, Texas, USA, 2017).