2026 ASEE Annual Conference & Exposition

Preliminary Data Analytics with Satellite Data from Passive and Active Sensors for UMES Agricultural Fields

Presented at Smart Agriculture and Emerging Technologies in Biological and Agricultural Engineering Division (BAE)

This exploratory study was conducted for the Stewart Neck research field at the University of Maryland Eastern Shore (UMES), an 1890 land-grant campus. The study investigates the potential use of satellite imagery to support ongoing ‘Smart Farming and Precision Agriculture’ initiatives on the campus. The work reported here was largely performed by a graduate student in the engineering program in the summer of 2025. An undergraduate student in the computer science program is following up on the effort in the fall semester. A postdoctoral researcher is overseeing the efforts in coordination with the lead faculty. The student extracted Sentinel-1 and Sentinel-2 satellite data for the field from January to December of 2023 and analyzed the soybean vegetation dynamics using Sentinel-2 optical indices (NDVI (normalized difference vegetation index), NDMI (normalized difference moisture index) combined with the Sentinel-1 Radar Vegetation Index (RVI). The study utilizes data from passive optical sensors to construct the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). NDVI serves as a proxy for crop health and density, while NDMI provides crucial information regarding vegetation water content and plant stress. To overcome the challenge of high cloud coverage, which frequently obscures passive optical data, the study integrates data from active radar sensors to derive the RVI using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data.

Monthly data from January to December revealed clear seasonal trends: NDVI peaked in July (~0.8) before declining; NDMI showed a sharper post-peak drop, indicating early moisture stress; RVI increased from ~0.7 to ~1.8 with later variability linked to canopy and soil changes. Moderate correlation between NDVI and RVI underscored their complementary insights. Spatial analyses showed consistent crop vigor but varied moisture and structure during peak growth.

The follow up work in the fall of 2025 has utilized 2022 corn data for the same field. The production agriculture practices on campus uses corn and soybean rotations during the growing season with wheat as the cover crop during the winter.

This study presents a methodology for cost-effective and comprehensive crop monitoring using publicly available satellite remote sensing data. This preliminary work lays a foundation for incorporating satellite remote sensing data into UMES ‘smart farming’ programs, providing avenues for learning, discovery, and engagement for students, faculty, and staff in a land grant setting. A key objective is to develop an inexpensive strategy for farmers to gain valuable insights into their fields, crop growth stages, and overall health, thereby reducing reliance on costly ground-based surveys.

Authors

Tassnim Wagiealla Abdalla Mohamed University of Maryland Eastern Shore [biography]

Tassnim Mohamed is a graduate research assistant in the Department of Engineering at the University of Maryland Eastern Shore. She specializes in wireless communication and networks. Her research interests include satellite data analytics for agricultural monitoring.
Dr. Abhijit Nagchaudhuri University of Maryland Eastern Shore [biography]

Dr. Abhijit Nagchaudhuri is currently a tenured professor in the Department of Engineering and Aviation Sciences at the University of Maryland Eastern Shore(UMES). Dr. Nagchaudhuri received his baccalaureate degree from Jadavpur University (India) with honors in mechanical engineering in 1983. Thereafter, he worked in a multinational industry for a little over three years before joining Tulane University as a graduate student in the fall of 1987. He received a master’s degree from Tulane University in 1989 and a doctoral degree from Duke University in 1992. He is a member of the American Society for Mechanical Engineers (ASME), the American Society for Engineering Education (ASEE), and, has also been involved with the American Society for Agricultural and Biological Engineers (ASABE). He is actively involved in teaching and research in the fields of (i) robotics and mechatronics, (ii) remote sensing and precision agriculture, and,(iii) biofuels and renewable energy. He has been involved with several extramurally funded projects as PI or Co-PI to support outreach, education, and research activities from various state, federal, and private agencies; served in leadership roles in professional societies; and has received several awards and certificates from his home institution as well as professional societies. He has published more than 90 refereed articles in journals, book chapters, and conference proceedings. He has been involved with several divisions of ASEE including the Minorities in Engineering Division (MIND), and served as the division chair of the Energy Conversion, Conservation, and Nuclear Engineering Division (ECCNED) in 2016. He is a member of the technical committee of IEEE/ASME Mechatronics and Embedded Systems Application(MESA) and has served as the division chair in 2022. He serves as a program committee member of the Maryland Space Grant Consortium.

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Mr. Jesu Raj Pandya University of Maryland Eastern Shore [biography]

Currently a post doctoral associate researcher in Food and Agricultural Sciences, University of Maryland Eastern Shore (UMES),
MSc - Applied Computer Sciences at UMES,
BSc - Electrical and Electronical Engineering ( JNTU).

Interest in robotics and automation in food prod
Ms. Jossie Gates University of Maryland Eastern Shore [biography]

Jossie Gates is an undergraduate student pursuing a Bachelor of Science in Computer Science at the University of Maryland Eastern Shore.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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