Mohammad Saeedi is pursuing a doctoral degree in the Department of Civil and Environmental Engineering at the University of Virginia, and his studies primarily revolve around applying hydrological variables and remote sensing to address and solve the major challenges related to Earth system science that we will face in the coming decades.
Mohammad Saeedi received a Master’s degree in Water Resources Engineering and Management (2021), and a Bachelor’s degree in Civil Engineering. During his graduate course/thesis, he concentrated on employing soil moisture data in hydrological simulation and developing algorithms.
His applied research focuses on the following:
(1) enhance the accuracy of the estimated variables in hydrological simulation, (2) investigate data processing approaches to improving the quality of satellite- and model-based data, (3) predict natural disasters by utilizing remotely-sensed satellite data, and (4) The exploitation of the above to use in applied research to solve the significant challenges related to Earth science that we will face in the coming decades.

Research interests:

• Soil moisture remote sensing
• Hydrological modeling
• Rainfall-Runoff modeling using satellite soil moisture data
• Drought monitoring using the SM2RAIN-NWF algorithm, satellite data and in situ measurements
• Spatial downscaling of soil moisture remote sensing products
• Data-assimilation approach to ensure maximum convergence between model predictions and observations
• Applications of machine learning to remote sensing and hydrology

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