Dr. Hosseini is an Associate Research Professor of the NASA Harvest Hub at the Department of Geographical Sciences, University of Maryland, College Park. His research related to optical and SAR applications to crop monitoring and production forecasting for both large scale agricultural systems and smallholder systems at the field to national scales. This is involved developing and refining models for yield forecasting, cropland and crop type mapping, and crop condition assessments.
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 Ph.D. degrees in Remote Sensing from University of Tehran, Tehran, Iran in 2005 and 2011, respectively. From 2012, Dr. Hosseini was working at multiple universities and research centers in Canada. During 2017-2019, Dr. Hosseini was a co-lead of an international project involving 18 countries for 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. He was leading the LAI and biomass part of this project. He developed global models for monitoring crop conditions over five globally important crop types (corn, wheat, soybeans, canola, and rice). A huge amount of multi-polarization SAR data including RADARSAT-2, Sentinel-1 and airborne data were processed in that project. The project had 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 integrating SAR derived biomass and LAI with those derived from optical data and produce high temporal resolution maps. Part of our new findings was recently published at the International Journal of Applied Earth Observation and Geoinformation.
Areas of Interest
- Polarimetric SAR
- Machine Learning
Degree TypePh.D.Degree DetailsRemote Sensing
Degree TypeMS.c.Degree DetailsRemote Sensing
Degree TypeBachelorDegree DetailsGeomatics Engineering