Ponraj Arumugam

Doctoral Researcher

Ponraj Arumugam is a Doctoral Researcher in the Adaptation in Agricultural Systems Working Group of the Climate Resilience Department. His research mainly focuses on Climate Risk Transfer (crop insurance) and identifying climate change impacts and adaptation strategies. He applies process-based models, machine learning, and remote sensing data to estimate real-time yield to support crop insurance payouts in India. He also supports identifying climate change impacts and suitable adaptation strategies using a process-based modeling approach in West Africa. He has professional work experience in crop modeling and remote sensing-based crop/soil inventories. He has a background in Remote sensing and GIS, specializing in sustainable agriculture from the Indian Institute of Remote Sensing, India. Before joining PIK, he was a research associate in the Consultative Group on International Agricultural Research (CGIAR)’s program on Climate Change, Agriculture, and Food Security (CCAFS), involved in projects crop insurance and climate-smart agriculture.


Potsdam Institute for Climate Impact Research (PIK)
T +49 (0)331 288 20766
P.O. Box 60 12 03
14412 Potsdam


Ponraj Arumugam has over six years of professional experience in crop modeling, applications of remote sensing in crop inventories, machine learning, and climate change impacts on agricultural productivity in India. He has a Bachelor’s in Agricultural Information Technology from Tamil Nadu Agricultural University, India, and Master’s in Remote Sensing and GIS from the Indian Institute of Remote Sensing, India. Currently, he is pursuing his Ph.D. from Humboldt University, Berlin.

  • Geo-spatial bio-physical crop modeling
  • Remote sensing and machine learning
  • Climate change impacts in agricultural productivity and adaptation strategies
  • Data assimilation in bio-physical crop modeling

  • Arumugam, Ponraj; Chemura, Abel; Schauberger, Bernhard; Gornott, Christoph. 2021. "Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India" Remote Sens. 13, no. 12: 2379. https://doi.org/10.3390/rs13122379
  • Arumugam, P.; Chemura, A.; Schauberger, B.; Gornott, C. Near Real-Time Biophysical Rice (Oryza sativa L.) Yield Estimation to Support Crop Insurance Implementation in India. Agronomy 202010, 1674.
  • Aggarwal, P., Shirsath, P., Vyas, S., Arumugam, P., Goroshi, S., Aravind, S., Nagpal, M., & Chanana, M. (2020). Application note: Crop-loss assessment monitor – A multi-model and multi-stage decision support system. Computers and Electronics in Agriculture, 175. https://doi.org/10.1016/j.compag.2020.10561
  • Rajasivaranjan, T., Patel, N. R., Ponraj, A., Kumar, V., & Surendran, U. (2019). Development and application of SPI generator using open source for analyzing drought at a varying time scale. Journal of Agrometeorology, 21(4), 420–426.
  • Patel, N. R., Akarsh, A., Ponraj, A., & Singh, J. (2019). Geospatial Technology for Climate Change Impact Assessment of Mountain Agriculture. In Remote Sensing of Northwest Himalayan Ecosystems (pp. 381–400). Springer.
  • Sapkota, T. B., Aryal, J. P., Khatri-Chhetri, A., Shirsath, P. B., Arumugam, P., & Stirling, C. M. (2018). Identifying high-yield low-emission pathways for cereal production in South Asia. Mitigation and Adaptation Strategies for Global Change, 23(4). https://doi.org/10.1007/s11027-017-9752-1
  • Aggarwal, P. K., Shirsath, P. B., Vyas, S., Arumugam, P., & Goroshi, S. (2017). CCAFS Agriculture Monitor (CAM).

  • EPICC (2018-2021): Climate services for Peru, India and Tanzania, funded by IKI and BMUB