Microwave and VIR remote sensing for soil moisture and crop phenology using AI
Name of the Speaker: Dr. Jasmeet Judge
Title of the Seminar: Microwave and VIR remote sensing for soil moisture and crop phenology using AI
Date and Time: 08 June 2023 (Thursday), 4:00pm
Online Platform: MS Teams (link to the video of the seminar)
About the Speaker: Jasmeet Judge is a Professor in the Agricultural and Biological Engineering Department at the University of Florida, where she is also the Director of the Center for Remote Sensing. She received the Ph.D. degree in electrical engineering and atmospheric, oceanic, and space sciences from the University of Michigan. Her expertise includes microwave remote-sensing applications to terrestrial hydrology for agricultural regions; machine learning methods for spatio-temporal scaling and data-model fusion. She has led many field experiments with active and passive microwave sensors to develop/improve remote sensing, crop growth, hydrology, and AI algorithms. She has been awarded NASA Group Achievement Awards for interdisciplinary field campaigns. She has been active in advocating for the protection of the EM spectrum for passive scientific use from radiofrequency interference as the past Chair of the National Academe’s Committee on Radio Frequency. She is a Senior Member of the IEEE Geoscience and Remote Sensing Society, where for the past 2.5 decades she has served in many roles.
Abstract: Space-borne sensors provide global information for many valuable applications including agriculture and hydrology. New sensors and technological advances continue to improve spatial and temporal resolutions, further enhancing its value. This seminar will include two research projects conducted at the Center for Remote Sensing: the first, spatio-temporal scaling of microwave observations for soil moisture studies using AI. In this project, data-driven machine learning methods are used to merge data at microwave and other wavelengths to obtain high resolution soil moisture in agricultural regions. In the second project, remotely sensed VIR observations are integrated with physics-guided machine learning for estimating in-season crop phenology.
Date/Time
Date(s) - 08/06/2023
4:00 pm - 5:00 pm
