Geosystems Research Institute
Established in 2000, the Geosystems Research Institute is a collaborative of academic scientists, engineers, and government and industry stakeholders active in conducting research to advance knowledge and practice in earth and its systems to improve policy and public awareness.
Research
Outreach & Service
Outreach & Service
News
GRI's Turnage Joins National Aquatic Policy Discussions in Washington
July 1, 2026 - Dr. Gray Turnage of Mississippi State University's Geosystems Research Institute traveled to Washington, D.C. recently as part ...MSU Adds Climavision to University Energy Initiative
March 11, 2026 - STARKVILLE, Miss.—Mississippi State University is adding weather enterprise partners to the university's growing energy-related ...MSU Recognizes University Researchers, Innovators with Annual Awards
October 8, 2025 - STARKVILLE, Miss.—Mississippi State University is celebrating excellence in research and innovation with its annual research ...MSU's 2025 Graduate Summer Research Experience Cohort Grows Research Skills with Advanced Tools, Collaboration
August 12, 2025 - STARKVILLE, Miss.—This summer, seven graduate students from across the country gathered at Mississippi State to address ...MSU-Led GIS Trainings Help Tribal Populations Better Manage Homelands
June 20, 2025 - Mississippi State's Alumnus magazine highlights the Geosystems Research Institute's GIS Training for Tribal Populations Project, ...
Recent Publications
Chaudhary, P., Foley, C., Samiappan, S., Kohler, L., Senyurek, V., Boes, D., & Chakrabarti, P. (2026). Estimating Colony Strength and Pollination Efficiency in Honey Bees Using a Novel Dataset and Deep Learning-based Models. Smart Agricultural Technology. Elsevier. 14, 102005. DOI:https://doi.org/10.1016/j.atech.2026.102005. [Document Site]Rafi, M., Senyurek, V., Adeli, A., Yanbo, H., Ball, J. E., & Gurbuz, A. (2026). Investigation of Machine Learning Based Techniques for Crop Yield Estimation of Corn and Cotton Using Multi-sensor Data Fusion. Journal of Agriculture and Food Research. Elsevier. 26(2026), 102676. DOI:10.1016/j.jafr.2026.102676. [Document Site]
Manjur, S. M., Senyurek, V., Skarke, A., & Gurbuz, A. (2025). Deep Learning Based Sequential Processing of Multibeam Echosounder Images for Automated Detection of Seafloor Gas Seep Occurrence. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 18, 25549-25561. DOI:10.1109/JSTARS.2025.3612936. [Document Site]
Hodges, E., Chew, C., Small, E. E., Bai, D., Al-khaldi, M., Ouellette, J., Johnson, J., Kurum, M., Gurbuz, A., Senyurek, V., & Nabi, M. (2025). A Merged CYGNSS Soil Moisture Product Using a Minimum Variance Estimator. IEEE Transactions on Geoscience and Remote Sensing. IEEE. 1, 1-1. DOI:10.1109/TGRS.2025.3597532. [Abstract] [Document Site]