Curriculum Vitae

Mohammad Saeedi’s research primarily focused on detecting the best-fitting satellite soil moisture products with in situ measurement data over the Lake Urmia basin. The work then shifted toward rainfall estimation using satellite soil moisture data, leading to the development of a new algorithm based on a bottom-up approach. SM2RAIN-NWF: A new algorithm integrating the SM2RAIN algorithm with the analytical net water flux model. Currently, Mohammad Saeedi is focused on improving the quality of the SM2RAIN-NWF algorithm to enhance rainfall accuracy and expand its application in various studies, including drought, runoff, and irrigation.

Education

2024 - 2028 University of Virginia, VA, USA
Ph.D. in Civil & Environmental Engineering

Awards and Honors

• Selected as a 2024 Vadose Zone Journal Outstanding Reviewer for exceptional peer-review contributions

Invitation to Join Editorial Board/Serve as a Reviewer

Transactions on Geoscience and Remote Sensing
Science of the Total Environment
Journal of Hydrology
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Water Resources Management
International Journal of Climatology
PLOS ONE
American Journal of Remote Sensing
Vadose Zone Journal