Mehdi Hosseini

Dr. Hosseini has expertise in model development for environmental applications using radar and optical remote sensing data. His interests include using physical, statistical and machine learning approaches for modeling of agricultural and environmental parameters. Dr. Hosseini obtained his M.Sc. and PhD degrees in Remote Sensing from the University of Tehran, Iran in 2005 and 2011, respectively. Since 2012, Dr. Hosseini has worked at multiple universities and research centers in Canada. From 2017-2019, Dr. Hosseini was a co-lead of an international project involving 18 countries, studying the best practices for crop biomass and leaf area index (LAI) estimations and crop type classification using Synthetic Aperture Radar (SAR). LAI and biomass are two important biophysical parameters that are linked directly to crop yield, and Dr. Hosseini was leading these major parts of the project. He also developed global models for monitoring crop conditions over five globally important crop types (corn, wheat, soybeans, canola and rice). Huge amounts of multi-polarization SAR data including RADARSAT-2, Sentinel-1 and airborne data were processed during the duration of this project. The research process incurred multiple challenges, from data processing to lack of sufficient data over some of the international sites. But one interesting and challenging part of the project was the integration of SAR-derived biomass and LAI with those derived from optical data in addition to the production of high temporal resolution maps. Part of the team's new findings were recently published in the International Journal of Applied Earth Observation and Geoinformation.