Directory at the HPC²
Biography
Volkan Senyurek received his B.S., M.S., and Ph.D. degrees in electronics and communication engineering from Marmara University in 2003, 2007, 2013, respectively. After he got his Ph.D., he worked as an assistant professor at Marmara University until 2015. Between 2015 and 2017, he worked as a Post-doctoral researcher in the Department of Mechanical and Materials Engineering at Florida International University. Between 2017 and 2019, he joined the Department of Electric and Computer Engineering at the University of Alabama as a Post-doctoral researcher.
He is an assistant research professor at Geosystems Research Institute at Mississippi State University.
His research interests are remote sensing, precision agriculture, biomedical signal processing, wearable sensors, pattern recognition, fiber optic sensors, and structural health monitoring.
He is an assistant research professor at Geosystems Research Institute at Mississippi State University.
His research interests are remote sensing, precision agriculture, biomedical signal processing, wearable sensors, pattern recognition, fiber optic sensors, and structural health monitoring.
Research Interest
Remote sensing
Machine learning, pattern recognition
Precision agriculture
Biomedical Signal Processing
Wearable sensors
Structural Health Monitoring,
Fiber Optic Sensors
Machine learning, pattern recognition
Precision agriculture
Biomedical Signal Processing
Wearable sensors
Structural Health Monitoring,
Fiber Optic Sensors
Hobbies
Martial arts
Selected Publications
Total Publications: 62
Nabi, M., Senyurek, V., Lei, F., Kurum, M., & Gurbuz, A. (2023). Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 16, 5629-5644. DOI:10.1109/JSTARS.2023.3287591. [Abstract] [Document Site]
Senyurek, V., Farhad, M. M., Gurbuz, A., Kurum, M., & Adeli, A. (2022). Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6843-6855. DOI:10.1109/JSTARS.2022.3197794. [Abstract] [Document Site]
Nabi, M., Senyurek, V., Gurbuz, A., & Kurum, M. (2022). Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6876-6881. DOI:10.1109/JSTARS.2022.3196658. [Abstract] [Document Site]
Senyurek, V., Gurbuz, A., & Kurum, M. (2021). Assessment of Interpolation Errors of CYGNSS Soil Moisture Estimations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14, 1. DOI:10.1109/JSTARS.2021.3113565. [Document] [Document Site]
Senyurek, V., Lei, F., Boyd, D., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2020). Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sensing. 12(21), 3503. DOI:10.3390/rs12213503. [Abstract] [Document Site]
Senyurek, V., Farhad, M. M., Gurbuz, A., Kurum, M., & Adeli, A. (2022). Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6843-6855. DOI:10.1109/JSTARS.2022.3197794. [Abstract] [Document Site]
Nabi, M., Senyurek, V., Gurbuz, A., & Kurum, M. (2022). Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps. Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE. 15, 6876-6881. DOI:10.1109/JSTARS.2022.3196658. [Abstract] [Document Site]
Senyurek, V., Gurbuz, A., & Kurum, M. (2021). Assessment of Interpolation Errors of CYGNSS Soil Moisture Estimations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14, 1. DOI:10.1109/JSTARS.2021.3113565. [Document] [Document Site]
Senyurek, V., Lei, F., Boyd, D., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2020). Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sensing. 12(21), 3503. DOI:10.3390/rs12213503. [Abstract] [Document Site]
