
Research themes
GCER lab offers an interdisciplinary space for agricultural and biosystems engineers, computer scientists, software developers, and geographers to engage in Earth science, deep learning, and new remote sensing technologies.

Digital Agriculture
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Smart sampling and field Zoning
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Crop phenology monitoring
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AI crop cameras
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Spectroscopy for vegetation health

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Coastal and inland water analysis
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Bio-optical modeling for water quality
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Deep learning for flood mapping
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Soil & water conservation Engineering
Water Resources
Deep learning &
High Performance Computing
Satellite Remote Sensing

Building digital agriculture
with satellite sensors
Digital agriculture involves the integration of critical information from various sources, including satellite imagery, soil sensors, weather forecasts, and crop data, to optimize agricultural practices. In particular, satellite remote sensing enables large area coverage and time series analysis of crop growth and health to provide insights into agriculture management. Through the use of satellite imagery and advanced deep learning algorithms, digital farming helps improve resource management, detect early signs of crop stress, and enhance overall farm efficiency, paving the way for a more sustainable and resilient agricultural future.












Access to MSU experimental areas with different crop types and management
Equipment and experience team for fieldwork data collection

















Our agricultural research focus on satellite-based crop monitoring using HPC resources, AI algorithms, and field observations.




Remote sensing of Water Resources
Remote sensing of water resources involves using satellite or aerial imagery to monitor and assess the availability, quality, and distribution of water bodies. By offering a comprehensive view of water systems over large areas, remote sensing supports efficient and sustainable water resource management. Our research includes the satellite image processing (i.e., atmospheric, glint, adjacent corrections), quality assessment, bio-optical modeling with field data collection, and water quantity and quality monitoring.












Research support with fieldwork data collection concomitant to satellite observations
Lab equipment and protocols for water quality analysis















Satellite imaging systems are widely used to monitor water quality and quantity in coastal and inland waters.

