Dr. Dipankar Mandal

About

Dr. Dipankar Mandal received the M.Tech + Ph.D. dual degree in geoinformatics and natural resources engineering in the Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India in 2020. He has completed the B.Tech. degree in agricultural engineering from Bidhan Chandra Krishi Viswavidyalaya, India in 2015. His research interest includes applications of SAR polarimetry for crop classification, vegetation biophysical parameter estimation and yield forecasting. Mr. Mandal received the Shastri Research Student Fellowship 2018 Award by the Shastri Indo-Canadian Institute, India and serving currently as a visiting researcher to Carleton University, Ottawa and Agriculture and Agri-Food, Ottawa, Canada.

Employment

• Indian Institute of Technology Guwahati Assistant Professor
  2024 - Present
• Kansas State University Postdoctoral Fellow
  2021 - 2024
• Agriculture and Agri-Food Canada Visiting Researcher
  2018 - 2019
• Carleton University Visiting Researcher
  2018 - 2019
• Indian Institute of Technology Bombay Teaching Assistant
  2015 - 2020

Education

• Indian Institute of Technology Bombay M. Tech + Ph. D. Dual Degree
  2015 - 2020
• Bidhan Chandra Krishi Viswa Vidyalaya B.Tech (Agricultural Engineering)
  2011 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (48)

• Crop Classification Using Dual-Pol Scattering Powers From Simulated NISAR L-Band SAR Data
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2026 0 citations DOI: 10.1109/JSTARS.2026.3672479
  +Save
• Assessing Multi-mode Temporal PolSAR Data for Winter Wheat and Barley Discrimination Using Convolutional Neural Networks
  2026 0 citations DOI: 10.1007/978-3-032-17083-5_1
  +Save
• Enhancing Vegetation Monitoring: A Proposal for a Sentinel-2 Based Vegetation Health Index
  2025 0 citations DOI: 10.20944/preprints202501.1235.v1
  +Save
• Soil moisture sensor location-allocation using spatial association of surface moisture data
  Smart Agricultural Technology 2025 6 citations DOI: 10.1016/j.atech.2025.100929
  +Save
• An Evidence Modified Gaussian Process Classifier (EM-GPC) for Crop Classification Using Dual-Polarimetric C- and L- Band SAR Data
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024 5 citations DOI: 10.1109/JSTARS.2024.3470956
  +Save
• Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany
  Geocarto International 2022 7 citations DOI: 10.1080/10106049.2022.2039306
  +Save
• Assessing Nitrogen Variability at Early Stages of Maize Using Mobile Fluorescence Sensing
  Remote Sensing 2022 12 citations DOI: 10.3390/rs14205077
  +Save
• Development of User-Friendly Software to Design Dairy Heat Exchanger and Performance Evaluation
  Open Science Framework 2022 DOI: 10.17605/osf.io/m8q3q
  +Save
• Development of a Multipurpose Cold Storage Design Software MCSBCKV
  Open Science Framework 2022 DOI: 10.17605/osf.io/gqhh3
  +Save
• Experimental Field Campaigns at Vijayawada Test Site
  Open Science Framework 2022 13 citations DOI: 10.17605/osf.io/dn3e8
  +Save