GEOG Seminar 3/16: Sheng Wang, "AI-Empowered Hyperspectral Sensing Advances Agricultural Monitoring across Scales"

Join us for our weekly seminar this Thursday, March 16th from 3:45-5pm via Zoom! Sheng Wang from the University of Illinois Urbana-Champaign will discuss the development of an artificial intelligence-empowered cross-scale hyperspectral sensing framework that provides a scalable solution to upscale agriculture ground truth data.

Abstract:

Sustainable intensification of the full agricultural production pipeline is highly needed to feed and power the growing population. However, conventional agricultural ground-truthing using field sampling, laboratory analysis, and/or grower surveys is time-consuming and costly. To address this challenge, we developed an artificial intelligence-empowered cross-scale hyperspectral sensing framework to integrate proximal, airborne, and spaceborne data to provide a scalable solution to upscale agriculture ground truth data to every field in Illinois, USA. This cross-scaling sensing framework shows high accuracy in detecting large-scale crop nitrogen content, tillage practices, cover crops, and soil organic carbon in Illinois, the heartland of the US Corn Belt. We highlight that hyperspectral data from proximal, airborne, and new/forthcoming spaceborne missions provide great potential to empower agricultural monitoring across scales to support food security and environmental sustainability.

Speaker Bio:

Dr. Sheng Wang is a Research Assistant Professor at Department of Natural Resources and Environmental Sciences (NRES) and a Research Scientist at Agroecosystem Sustainability Center (ASC), University of Illinois Urbana-Champaign (UIUC). Before he joined UIUC, he got his PhD degree in environmental engineering from Technical University of Denmark in Feb 2019. He leads the airborne sensing research team at ASC. He has published 30+ papers in top scientific journals, and serves as PI or Co-PI for federal, state, and university grants. Dr. Wang’s research develops novel ground-airborne-satellite cross-scale sensing technology with process-based models and data-driven algorithms to quantify crop traits, soil properties, and farming practices. His research aims to advance the understanding of key energy-water-carbon-nutrient processes across spatial and temporal scales in agroecosystems. He also provides stakeholders accurate, timely, and actionable data to improve agroecosystem management for food security and environmental sustainability.

Zoom Info: Please reach out to haijunli@umd.edu for Zoom meeting information.