Hamed Alemohammad

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Associate Professor

Clark University

United States

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About

Hamed Alemohammad is an Associate Professor at Clark University in the United States. His research areas encompass remote sensing, agricultural applications of satellite data, soil moisture estimation, and food security enhancements. He has recently contributed to studies on precipitation products related to the Indian summer monsoon and advanced techniques in SAR signal analysis.

Recent Grants

Grant: Close

Evaluating Performance of a Foundation Model for Optical Earth Observation on Downstream Tasks

Open Date: 2023-03-01

Close Date:

Grant: Close

Advancing an Open-Access Repository for Earth Observation Training Data and Machine Learning Models

Open Date: 2020-10-01

Close Date:

Grant: Close

Synthetic Agricultural Training Data for Satellite Observations

Open Date: 2020-01-01

Close Date:

Grant: Close

An Expert Workshop on Advancing Application of Machine Learning Tools for NASA’s Earth Observation Data

Open Date: 2019-01-01

Close Date: 2020-01-01

Articles (11)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2022

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2022

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2020

Collaborators (2)

Jessica McCarty

Assistant Professor

University of Miami

UNITED STATES

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Pang-Ning Tan

Professor

Michigan State University

UNITED STATES

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Hamed Alemohammad

Research Interests

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Contact this professor

About

Recent Grants

Articles (11)

Examining the performance of precipitation products in characterizing the Indian summer monsoon rainfall (ISMR) using triple collocation

Analyzing the dihedral scattering component of P-band SAR signals for trunk permittivity estimation – a concept study

Enhancing Food Security With High-Quality Land-Use and Land-Cover Maps: A Local Model Approach

Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples

Review of synthetic aperture radar with deep learning in agricultural applications

Yield estimation from SAR data using patch-based deep learning and machine learning techniques

Soil moisture profile estimation by combining P-band SAR polarimetry with hydrological and multi-layer scattering models

Tropical Cyclone Wind Speed Estimation: A Large Scale Training Data Set and Community Benchmarking

Remote Sensing of Complex Permittivity and Penetration Depth of Soils Using P-Band SAR Polarimetry

Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Collaborators (2)

Jessica McCarty

Pang-Ning Tan

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