Bio
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 agricultural and 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. 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).
Areas of Interest
Polarimetric SAR
InSAR
Machine Learning
Publications
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1 => "Kerner, H. R., Sahajpal, R., Pai, D. B., Skakun, S., Puricelli, E., Hosseini, M., ... & Becker-Reshef, I. (2022). Phenological normalization can improve in-season classification of maize and soybean: A case study in the central US Corn Belt.</li> "
2 => "Ranjbar S., Akhoondzadeh M., Brisco B., Amani M., Hosseini M., 2021, Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI: 10.110</li> "
3 => "Hosseini, M., McNairn, H., Mitchell, S., Robertson, L. D., Davidson, A., Ahmadian, N., Bhattacharya, A., et al. (2021). A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing, 13(7), 1348. MDP</li> "
4 => "Hosseini M., Kerner H., Sahajpal R., Estefania I. P., Lu Y.H., Lawal A., Humber M., Mitkish M., Meyer S., Becker-Reshef I., 2020, Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar, Remote Sensing, 12,</li> "
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Kerner, H. R., Sahajpal, R., Pai, D. B., Skakun, S., Puricelli, E., Hosseini, M., ... & Becker-Reshef, I. (2022). Phenological normalization can improve in-season classification of maize and soybean: A case study in the central US Corn Belt. Ranjbar S., Akhoondzadeh M., Brisco B., Amani M., Hosseini M., 2021, Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI: 10.110 Hosseini, M., McNairn, H., Mitchell, S., Robertson, L. D., Davidson, A., Ahmadian, N., Bhattacharya, A., et al. (2021). A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing, 13(7), 1348. MDP
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