Rahul Raj

About

Rahul Raj is a Principal Research Scientist at Pixxel. His work focuses on remote sensing systems, spanning drone-based and satellite-based Earth observation, with particular emphasis on hyperspectral imaging.

He has extensive experience in sensor calibration and validation, radiometric and spectral data processing, and the development of end-to-end pipelines for generating analysis-ready remote sensing data. At Pixxel, he contributes to hyperspectral satellite missions, including the design, calibration, and operational data processing of spaceborne sensors.

Rahul completed his PhD through the IITB–Monash Research Academy, where his research focused on drone-based vegetation monitoring and the retrieval of biophysical and biochemical parameters using optical remote sensing. His work integrates physics-based modeling with data-driven approaches to extract meaningful information from spectral data.

His broader research vision is to develop integrated sensing systems that connect instrument design, calibration, and data analytics, enabling physically consistent and scalable applications across Earth observation and planetary sensing domains. His interests include environmental monitoring, agriculture, and the study of surface–atmosphere interactions using remote sensing technologies.

Employment

• Monash University Affiliate Researcher
  2021 - Present
• Pixxel Research Scientist
  2021 - Present
• Monash University Researcher
  2020 - 2020
• Monash University Researcher
  2019 - 2020
• Monash University Field Research Volunteer
  2019 - 2019
• Indian Institute of Technology Bombay Teaching Assistant
  2017 - 2021

Education

• IITB-Monash Research Academy PhD
  2016 - 2021

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• Post-launch vicarious calibration-validation of Pixxel’s technology demonstrator vnir hyperspectral satellite using RadCalNet data
  IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium 2025 DOI: 10.1109/igarss55030.2025.11242462
  +Save
• Distinguishing Nitrogen and Water Stress in Maize Crop Using Hyperspectral Data
  Advances in Agri-Food Systems 2025 DOI: 10.1007/978-981-96-0759-4_1
  +Save
• Canopy height estimation using drone-based RGB images
  Smart Agricultural Technology 2023 DOI: 10.1016/j.atech.2022.100145
  +Save
• Maize On-Farm Stressed Area Identification Using Airborne RGB Images Derived Leaf Area Index and Canopy Height
  Agriculture 2023 DOI: 10.3390/agriculture13071292
  +Save
• Super-Resolution for Cross-Sensor Optical Remote Sensing Images
  IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium 2022 DOI: 10.1109/igarss46834.2022.9883182
  +Save
• Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing
  Agronomy 2022 DOI: 10.3390/agronomy12020425
  +Save
• Leaf nitrogen content estimation using top-of-canopy airborne hyperspectral data
  International Journal of Applied Earth Observation and Geoinformation 2021 DOI: 10.1016/j.jag.2021.102584
  +Save
• Leaf water content estimation using top-of-canopy airborne hyperspectral data
  International Journal of Applied Earth Observation and Geoinformation 2021 DOI: 10.1016/j.jag.2021.102393
  +Save
• Leaf area index estimation using top-of-canopy airborne RGB images
  International Journal of Applied Earth Observation and Geoinformation 2021 DOI: 10.1016/j.jag.2020.102282
  +Save
• Estimating Leaf Water Content using Remotely Sensed Hyperspectral Data
  2021 DOI: 10.48550/ARXIV.2109.02250
  +Save