DRI / DIRECTORY / SAYANTAN (MONTY) MAJUMDAR
Sayantan (Monty) Majumdar Profile Photo
Dr. Sayantan (Monty) Majumdar
Assistant Research Professor, Hydrologic Sciences and Remote Sensing
Phone 
775-673-7663
E-Mail 
Sayantan.Majumdar@dri.edu
Campus 
Reno Campus
Full Resume / CV 
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Division 
Hydrologic Sciences

About

Dr. Sayantan (Monty) Majumdar is an Assistant Research Professor of Hydrologic Sciences and Remote Sensing at the Desert Research Institute, Reno, Nevada. He is also serving as an Adjunct Faculty as part of the University of Nevada Reno (UNR) and DRI Graduate Program of Hydrologic Sciences.

Earlier, he was a Postdoctoral Fellow at the Department of Civil and Environmental Engineering, Colorado State University.Dr. Majumdar 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.

Dr. Majumdar 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 and machine learning. He is currently working on multiple projects funded by NASA, USGS, U.S. Bureau of Reclamation (USBR), National Park Service (NPS), State of Nevada/U.S. Department of the Treasury, and National Institutes of Health (NIH).

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

Linkedin:https://www.linkedin.com/in/sayantanmajumdar/

Twitter:https://twitter.com/hydromaj

GitHub:https://github.com/montimaj

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

Keywords

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

 

Publications
2024
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

Ott, T., Majumdar, S., Huntington, J. L., Pearson, C., Bromley, M., Minor, B. A., ReVelle, P., Morton, C. G., Sueki, S., Beamer, J. P., Jasoni, R. L. (2024). Toward field-scale groundwater pumping and improved groundwater management using remote sensing and climate data, Agricultural Water Management, 302, Article No. 109000, 10.1016/j.agwat.2024.109000

Majumdar, S., Smith, R. G., Hasan, M. F., Wilson, J. L., White, V. E., Bristow, E., Rigby, J. R., Kress, W., Painter, J. A. (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, 52, Article No. 101674, 10.1016/j.ejrh.2024.101674

2023
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
2025
Majumdar, S., Smith, R. G., Huntington, J. L. (2025). Integrating High-resolution Satellite Remote Sensing and Climate Data to Quantify Irrigation Groundwater Withdrawals and Consumptive use in the Western U.S.. ASCE EWRI World Environmental and Water Resources Congress: Anchorage, AK, May 18, 2025-May 21, 2025, Oral presentation.

2024
Majumdar, S., Smith, R. G., Conway, B. D., Wogenstahl, C. (2024). A Multi-Decadal Analysis of Groundwater Withdrawals in Arizona Using Remote Sensing and Machine Learning. AGU Fall Meeting: Washington, DC, December 9, 2024-December 13, 2024, Oral presentation.

Hasan, M. F., Smith, R. G., Majumdar, S., Huntington, J. L. (2024). Satellite Data and Machine Learning-Based Effective Precipitation Modeling for the Western United States. AGU Fall Meeting, December 9, 2024-December 13, 2024, Oral presentation.

Doane, T. H., Majumdar, S., Yu, G. (2024). Testing the Utility of SAR for Mapping Surface Flow Events in Post-Fire Settings. AGU Fall Meeting: Washington, DC, December 9, 2024-December 13, 2024

Majumdar, S., Ott, T., Huntington, J. L., Smith, R. G., Pearson, C., Bromley, M., Minor, B. A., Morton, C. G., ReVelle, P., Hasan, M. F., Beamer, J. P., Conway, B. D. (2024). Tracking groundwater pumping, consumptive use, and irrigation efficiencies in the Western U.S. through OpenET. AGU WaterSciCon24: St. Paul, MN, Oral presentation.

ReVelle, P., Minor, B. A., Bromley, M., Pearson, C., Majumdar, S., Morton, C. G., Huntington, J. L. (2024). Enhancing Hydrographic Basin Water Resource Management with OpenET: Quantifying Consumptive Use Volumes and Evaluating Variability across Models and Irrigation Status Datasets. AGU WaterSciCon24: St. Paul, MN

Majumdar, S. (2024). Regional and field scale estimates of groundwater withdrawals using remote sensing and climate data. OpenET Applications Conference: Albuquerque, NM, Oral presentation.

Majumdar, S., Ott, T., Huntington, J. L., Pearson, C., Bromley, M., Minor, B. A., Morton, C. G., Sueki, S., Beamer, J. P., Jasoni, R. L. (2024). Assessing statistical and machine learning approaches to estimate field-scale groundwater pumping using Landsat-based evapotranspiration, irrigation, and climate data. AGU Chapman Conference: Remote Sensing of the Water Cycle: Honolulu, HI

Ott, T., Huntington, J. L., Bromley, M., Morton, C. G., Sueki, S., Majumdar, S. (2024). Estimating field-scale groundwater pumping using Landsat evapotranspiration and climate data: Insights into Diamond Valley, Nevada. NWRA Annual Conference: Las Vegas, NV

2023
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
https://doi.org/10.22541/essoar.170688858.81127989/v1

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

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. AGU Fall Meeting: San Francisco, CA, 10.22541/essoar.170688858.81127989/v1

Other
2024
Majumdar, S. (2024). Integrating Satellite Remote Sensing, Climate Data, and Machine Learning to Quantify Irrigation Water Use. Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO): Dehradun, India, Guest lecture.

Majumdar, S. (2024). Integrating Satellite Remote Sensing, Climate Data, and Machine Learning to Quantify Irrigation Water Use. Birla Instiute of Technology,: Mesra, India, Guest lecture.

Majumdar, S., Smith, R. G., Hasan, M. F., Wilson, J. L., White, V. E., Bristow, E. L., Rigby, J. R., Kress, W. H., Painter, J. A. (2024). Aquaculture and Irrigation Water Use Model (AIWUM) 2.0 input and output datasets, 10.5066/P9CET25K

Majumdar, S., Smith, R. G., Hasan, M. F., Wilson, J. L., White, V. E., Bristow, E. L., Rigby, J. R., Kress, W. H., Painter, J. A. (2024). Aquaculture and Irrigation Water Use Model 2.0 software, 10.5066/P137FIUZ