Mahya Hashemi
(SCIENTIST)
| Email: | mahyasadat.ghazizadehhashemi@nasa.gov |
| Org Code: | |
| Address: |
NASA/GSFC Mail Code 617 Greenbelt, MD 20771 |
| Employer: | SCIENCE SYSTEMS AND APPLICATIONS INC |
Brief Bio
Mahya is a Research Scientist at NASA Goddard Space Flight Center's Hydrological Sciences Laboratory, working with SAIC. Her research focuses on leveraging satellite observations, particularly Synthetic Aperture Radar (SAR) from Sentinel-1 and NISAR, combined with advanced AI techniques, including geospatial foundation models, for applications in agriculture, vegetation water content estimation, and vegetation optical depth retrieval.
Mahya developed a global dataset to assess restoration potential for freshwater quality in support of the UN Freshwater Challenge. She is currently investigating the impact of wetland and riparian zone restoration on freshwater quality using optical imagery and geospatial foundation models for broad-scale generalization.
She is collaborating with Microsoft to develop a Hydrology Copilot for hydrological and drought monitoring analysis. This work leverages NASA's cutting-edge high-resolution (1 km) NLDAS-3 dataset and employs multi-agent systems and retrieval-augmented generation (RAG) architectures built with Azure AI Foundry.
Mahya also collaborates with the snow hydrology team at Goddard to estimate high-resolution snow water equivalent (SWE) by integrating optical, climate, and microwave data with geospatial foundation models, contributing toward a national-scale SWE product.
Research Interests
Global Water and Energy Cycles
Ecohydrology
Crop Growth Monitoring
Microwave and Optical Remote Sensing
Current Projects
Map of Riparian zone globally and assess their impact on fresh water quality
Remote Sensing
Estimating high-resolution vegetation optical depth using GEDI and PACE observations
Remote Sensing
Hydrology Copilot: A Cloud Native AI designed for Hydrological Data Analysis
Hydrology / Water Cycle
High resolution SWE and snow depth estimation using Prithvi foundation model
Snow
Positions/Employment
Research Scientist
Science Applications International Corporation - NASA Goddard Space Flight Center
October 2024 - Present
Research Assistant
Michigan State University - East Lansing, Michigan
August 2021 - October 2024
Education
Ph.D., 2024, Michigan State University, Civil and Environmental Engineering (Advisor: Narendra Das)
M.S., 2014, Sharif University of Technology, Civil and Environmental Engineering
B.S., 2012, Sharif University of Technology, Civil Engineering
Grants
Prithvi-EO-Enabled High Resolution Snow Load Mapping in Support of ASCE Structural Design Standards in Colorado and Beyond
NASA ROSES 2025 A.9: User-Centered Applications with Large Earth Foundation Models - NASA (NNH25ZDA001N-EAFM) - Awarded: 2026-05-06
Dates: -
Coverage: PY1: 0.50 FTE (6 months / 12) PY2: 0.45 FTE (5.4 months / 12)
Amount $499,428
Selected Publications
Refereed
2026. "Mapping global freshwater ecosystems to guide national restoration targets and nature-based solutions.", Nature Water, [10.1038/s44221-025-00573-x] [Journal Article/Letter]
2025. "Estimating crop biophysical parameters from satellite-based SAR and optical observations using self-supervised learning with geospatial foundation models.", Remote Sensing of Environment, 327 114825 [10.1016/j.rse.2025.114825] [Journal Article/Letter]
2024. "Review of synthetic aperture radar with deep learning in agricultural applications.", ISPRS Journal of Photogrammetry and Remote Sensing, 218 20-49 [10.1016/j.isprsjprs.2024.08.018] [Journal Article/Letter]
2024. "Yield estimation from SAR data using patch-based deep learning and machine learning techniques.", Computers and Electronics in Agriculture, 226 109340 [10.1016/j.compag.2024.109340] [Journal Article/Letter]
2023. "Dryspells and Minimum Air Temperatures Influence Rice Yields and their Forecast Uncertainties in Rainfed Systems.", Agricultural and Forest Meteorology, 341 109683 [10.1016/j.agrformet.2023.109683] [Journal Article/Letter]
2022. "Assessing the impact of Sentinel-1 derived planting dates on rice crop yield modeling.", International Journal of Applied Earth Observation and Geoinformation, 114 103047 [10.1016/j.jag.2022.103047] [Journal Article/Letter]
2020. "The Impact of Pavement Permeability on Time of Concentration in a Small Urban Watershed with a Semi-Arid Climate.", Water Resources Management, 34 (9): 2969-2988 [10.1007/s11269-020-02596-3] [Journal Article/Letter]
2019. "Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region.", Remote Sensing of Environment, 231 111226 [10.1016/j.rse.2019.111226] [Journal Article/Letter]
2018. "Estimating the drainage rate from surface soil moisture drydowns: Application of DfD model to in situ soil moisture data.", Journal of Hydrology, 565 489-501 [10.1016/j.jhydrol.2018.08.035] [Journal Article/Letter]
Talks, Presentations and Posters
Invited
Smarter Waters: AI, Satellites, and the Future of Water Management
April 10, 2026
delivered the opening keynote address at the 14th Annual American Water Resources Association National Capital Region Section (AWRA-NCRS) Water Resources Symposium on April 10, 2026, at the University of the District of Columbia. Her talk, titled 'Smarter Waters: AI, Satellites, and the Future of Water Management,' demonstrated how geospatial foundation models trained on NASA satellite observations can estimate vegetation water content for crop monitoring and vegetation optical depth for wildfire fuel assessment at 30-meter resolution — generalizing across continents with minimal labeled data. She also showcased the NASA Hydrology Copilot, a multi-agent AI system that enables users to perform complex drought and hydrology analysis using NLDAS-3 data through plain English queries. Following the keynote, Hashemi participated in a panel discussion on AI in water resources with Robert Bornhofen (DC Water, Director of Innovation), John C. Hammond (USGS MD-DE-DC Water Science Center, Research Hydrologist), Lawrence Band (University of Virginia, Professor of Environmental Science), Sydney Samples (Water Research Foundation, Research Principal), and Clayton Wise (Hampton Roads Sanitation District, Digital Water Engineer), addressing topics including AI ethics, water affordability, and the future of AI-driven water management. David Mocko (617/SSAI), Kim Locke (617/SSAI), and Eunsaem Cho (617/UMD) also attended to engage with regional, federal and private sector experts in water management.
From Petabytes to Conversations: Foundation Models and the NASA Hydrology Copilot
April 8, 2026
presented 'Petabytes to Conversation: Foundation Models and the NASA Hydrology Copilot' at the NASA Goddard AI Center of Excellence (AI CoE) monthly seminar on April 8, 2026. The talk covered the development of geospatial foundation models for vegetation water content and vegetation optical depth estimation using NASA satellite observations, and demonstrated the NASA–Microsoft Hydrology Copilot — a multi-agent AI system that enables plain-English drought and hydrology analysis using NLDAS-3 data.
December 15, 2024
Assessing Relative Disturbance in Riparian Areas and Implications for Conservation
December 12, 2024