• 👋 Hi, I’m Sayantan. I also go by Monty.
• 👀 My research interests include hydrology, remote sensing, machine learning, geospatial data analytics, and scientific software development.
• 🌱 I’m currently an Assistant Research Professor of Hydrologic Sciences and Remote Sensing at the Desert Research Institute in Reno, Nevada, USA.

Assistant Research Professor of Hydrologic Sciences and Remote Sensing Desert Research Institute (DRI), Reno, NV, USA

I am an Assistant Research Professor of Hydrologic Sciences and Remote Sensing at the Desert Research Institute in Reno, NV. I also serve as an Adjunct Faculty member in the Graduate Program of Hydrologic Sciences at the University of Nevada, Reno (UNR). My work primarily focuses on the intersection of hydrology, remote sensing, and machine learning.

• Hydrology
• Remote Sensing
• Machine Learning
• Geospatial Data Science
• Scientific Software Development

• Ph.D. in Geological Engineering

  • Missouri University of Science and Technology, USA
  • Dissertation: "Groundwater Withdrawal Estimation using Integrated Remote Sensing Products and Machine Learning"

• M.Sc. in Geoinformatics (Graduated Cum Laude)

  • Faculty ITC, University of Twente, Netherlands
  • Thesis: "Snow depth and SWE estimation using Spaceborne Polarimetric and Interferometric Synthetic Aperture Radar"

• M.Sc. & B.Sc. in Computer Science (Graduated First Class)

  • St. Xavier's College (Autonomous) Kolkata, India

• Postdoctoral Fellow | Colorado State University, Fort Collins, CO, USA (Sep 2022 - Jun 2023)

  • Worked with the U.S. Geological Survey (USGS) on using machine learning and hydrologic remote sensing to estimate agricultural water use.

• Research Scientist Intern | Meta Platforms, Inc., Menlo Park, CA, USA (May 2022 - Aug 2022)

  • Worked with the Physical Modeling Team on sustainability efforts related to nature-based carbon credits.
  • Integrated high-resolution satellite imagery, LiDAR data, and deep learning to develop global reforestation monitoring products.

• Analytics Modeling Intern | Planet Labs, Remote, USA (Jun 2021 - Aug 2021)

  • Developed an automated pipeline on Google Cloud Platform using PlanetScope scenes and deep learning to monitor surface water bodies.

• Co-Investigator | Improving remote sensing and machine learning-driven groundwater withdrawal estimation in Arizona

  • Source of Support: NASA
  • Project Dates: 01/2024 - 12/2025

• Principal Investigator | Machine Learning-driven Assessment of Groundwater Level Changes in the Western U.S. using Remote Sensing and Climate Data (Pending)

  • Source of Support: NASA

• Faculty Role | OpenET Planning

  • This project enhances the OpenET platform, co-led by DRI, to support the National Water Census.
  • Source of Support: DOI - USGS

See my Google Scholar page for a full list of publications.

• Ott, T. J., Majumdar, S., Huntington, J. L., et al. (2024). "Toward field-scale groundwater pumping and improved groundwater management using remote sensing and climate data." Agricultural Water Management. [Equal contribution].
• Tolan, J., Yang, H.-I., ..., Majumdar, S., et al. (2024). "Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar." Remote Sensing of Environment.
• Hasan, M. F., Smith, R., Vajedian, S., Pommerenke, R., & Majumdar, S. (2023). "Global land subsidence mapping reveals widespread loss of aquifer storage capacity." Nature Communications.
• Majumdar, S., Smith, R. G., Hasan, M. F., et al. (2024). "Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions." Journal of Hydrology: Regional Studies.
• Majumdar, S., Smith, R., Conway, B. D., & Lakshmi, V. (2022). "Advancing remote sensing and machine learning-driven frameworks for groundwater withdrawal estimation in Arizona: Linking land subsidence to groundwater withdrawals." Hydrological Processes.

• Majumdar, S., Smith, R. G., Hasan, M. F., et al. (2024). "Aquaculture and Irrigation Water Use Model (AIWUM) 2.0 input and output datasets." U.S. Geological Survey data release.
• Majumdar, S., Smith, R. G., Hasan, M. F., et al. (2024). "Aquaculture and Irrigation Water Use Model 2.0 software." U.S. Geological Survey software release.
• Tolan, J., Yang, H.-I., ..., Majumdar, S., et al. (2023). "High Resolution Canopy Height Maps by WRI and Meta." Meta and World Resources Institute (WRI). Data Link | Code Link.

• Editorial Board Member: Springer Nature Scientific Reports
• Scientific Advisor:
  • Thazhal Geospatial Analytics (Aug 2023-present)
  • Mizu Risk Lab (Mar 2024-present)
  • Oregon Water Resources Department (OWRD) Technical Advisory Group
• Panelist:
  • NSF GEO/RISE 2025
  • NASA Early Career Investigator Program in Earth Science (ECIP-ES) 2023
  • NASA ROSES 2023
• Journal Reviewer: Served as a manuscript reviewer for numerous journals, including Nature Communications, AGU Water Resources Research, Elsevier Remote Sensing of Environment, Journal of Hydrology, Agricultural Water Management, IEEE Transactions on Geoscience and Remote Sensing, and others.