Sayantan (Monty) Majumdar Profile Photo
Dr. Sayantan (Monty) Majumdar
Assistant Research Professor, Hydrologic Sciences and Remote Sensing


Dr. Sayantan Majumdar is an Assistant Research Professor of Hydrologic Sciences and Remote Sensing at the Desert Research Institute, Reno, Nevada. Earlier, he was a Postdoctoral Fellow at the Department of Civil and Environmental Engineering, Colorado State University. Sayantan (Monty) is a computational hydrologist with expertise in geospatial data science, machine learning, remote sensing, and scientific computing. He has a strong track record of producing high-impact, cross-disciplinary research at the intersection of remote sensing, machine learning, geoinformatics, and hydrology.

Sayantan has a Ph.D. degree in Geological Engineering from Missouri S&T, USA. His doctoral research was focused on groundwater withdrawal estimation using integrated remote sensing datasets (satellite, airborne, modeled) and machine learning (deep). For his doctoral research, Sayantan was involved in the United States Geological Survey (USGS) Mississippi Alluvial Plain (MAP) project as part of the Water Use team, which dealt with integrating remote sensing and machine learning for estimating crop-specific groundwater use. Essentially, he developed the Aquaculture and Irrigation Water-Use Model (AIWUM) version 2.0. Sayantan also collaborated on a National Geospatial-Intelligence Agency (NGA) funded project for monitoring global land subsidence due to groundwater pumping. In addition, he was partially involved in a NASA-funded project on estimating groundwater withdrawals using remote sensing and machine learning.

In Summer 2022, Sayantan joined the Physical Modeling Team at Meta as a Research Scientist Intern working on their sustainability efforts related to nature-based carbon credits. His work dealt with integrating high-resolution satellite imagery, LiDAR data sets, and deep learning for developing global reforestation monitoring products.

Previously, in Summer 2021, Sayantan worked as an Analytics Modeling Intern at Planet Labs. He collaborated with the Analytics Modeling team to develop a high-resolution global inland surface water monitoring tool deployed on the Google Cloud Platform utilizing PlanetScope data and semi-supervised learning.

Research Areas of Interest

  • Hydrologic Remote Sensing
  • Geospatial Data Science
  • Applied Machine Learning
  • Irrigation Water Use
  • InSAR
  • Land Subsidence
  • Open-source Geospatial Scientific Software Development

Related links




Google Scholar:https://scholar.google.com/citations?user=iYlO-VcAAAAJ&hl=en


hydrology, remote sensing, applied machine learning, irrigation water use, geospatial data science, scientific software development, InSAR, land subsidence


Tolan, J., Yang, H., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., Moutakanni, T., Bojanowski, P., Johns, T., White, B., Tiecke, T., Couprie, C. (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, 300, Article No. 113888, 10.1016/j.rse.2023.113888

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, 14 (1), Article No. 6180. 10.1038/s41467-023-41933-z

Conference Proceedings
Majumdar, S., Ott, T., Huntington, J. L., Smith, R., Fang, B., Lakshmi, V. (2023). Toward Field Scale Groundwater Withdrawals in the Western U.S. using Remote Sensing and Climate Data. American Geophysical Union, AGU Fall Meeting: San Francisco, CA, December 11, 2023-December 15, 2023, 10.13140/RG.2.2.35583.18085

Asfaw, D., Smith, R., Majumdar, S., Lakshmi, V., Fang, B., Grote, K., Butler, J. J., Wilson, B. B. (2023). Capturing the Spatio-Temporal Variability of Groundwater Pumping Using Remote Sensing Products and Machine Learning Techniques: An Assessment of Training Data Quality and Quantity Implications on Model Performance. American Geophysical Union, AGU Fall Meeting: San Francisco, CA, December 11, 2023-December 15, 2023