IEEE GRSS Germany Chapter Webinar

#artificial-intelligence #data-analysis #earth #deep-learning #geoscience #global-navigation-satellite-system

Please join the next meeting of the German GRSS Chapter (but open to all who are interested) on Wednesday, Dec 10, 2pm Berlin time, featuring a keynote talk of Dr. Milad Asgarimehr, GFZ Helmholtz Centre for Geosciences, followed by general updates.

Title of the presentation:

Innovative remote sensing with GNSS Reflectometry: From hurricanes to forests’ water stress

Abstract:

GNSS Reflectometry (GNSS-R) is an passive radar technique that repurposes signals from navigation satellites to monitor the Earth’s surface at low cost and with high temporal frequency. This study presents recent advances showing how GNSS-R, combined with modern AI, can deliver improved environmental monitoring across oceans, land, and forests. Over oceans, AI-enhanced GNSS-R provides more accurate wind-speed retrievals, including during heavy rain and extreme events such as Hurricane Laura (2020). On land, it yields high-quality soil moisture estimates. We also show how GNSS-R enables forest water stress monitoring, capturing sub-daily moisture dynamics that conventional sensors miss. We further introduce a self-supervised, generalist GNSS-R framework capable of retrieving multiple geophysical variables from a single model with reduced training data requirements. These developments demonstrate the growing potential of AI-driven GNSS-R as a next-generation Earth observation capability for environmental monitoring and early-warning systems.

Bio:

Dr. Milad Asgarimehr's research is dedicated to the remote sensing of the Earth's surface and atmosphere and studying climate trends using Remote Sensing data especially from Global Navigation Satellite System (GNSS) signals. He studies the Earth exploiting GNSS signals after reflection off the Earth's surface, the technique known as GNSS Reflectometry.  This includes the physics associated with bistatic radar and GNSS concepts, geophysics, data analysis, and geoinformation retrieval algorithms and Earth system modeling including those based on Artificial Intelligence.

Dr. Milad Asgarimehr is the PI of the Helmholtz AI project "AI4GNSS-R": The project AI for GNSS-R (AI4GNSSR) aims at implementing deep learning for novel remote sensing data products based on spaceborne GNSS-R measurements. These include high-quality ocean surface wind speed data, especially at extreme conditions and hurricanes, and potentially precipitation over calm oceans for the first time using GNSS signals.