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Advanced UAS/UAV Application Systems, Data Management Systems, and Bioinformatics Tools

Publications

Peer-reviewed journal articles

Bheemanahalli R, Samiappan S, Kodadinne N, McCraine D, Czarnecki J, Ardeshir A. Moorhead RJ (November 2020). Evaluation of cotton seeding performance at early growth stage following cover cropping using aerial imagery. ASA-CSSA-SSSA International Annual Meeting, USA. (Abstract).

Bheemanahalli R, Shrestha A, Kodadinne N, Samiappan S, Czarnecki J, McCraine D Ardeshir A. Reddy KR, Moorhead RJ (August 2021). Integrated aerial and destructive methods differentiate plant health of cotton in response to cover cropping. Mississippi Academy of Sciences. (Abstract)

Eroglu, O., Kurum, M., Boyd, D., and Gurbuz, A., 2019. High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote Sensing, vol. 11, no. 19, pp.1-32, 2019. DOI: 10.3390/rs11192272

Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., Moorhead, R., Crow, W., and Eroglu, O., 2021. Quasi-Global Machine Learning-based Soil Moisture Estimates at High Spatio-temporal Scales using CYGNSS and SMAP Observations. Remote Sensing of Environment, under review.

M. Kurum, M. Farhad, and A. C. Gurbuz, “Integration of Smartphones into Small Unmanned Aircraft Systems to Sense Water in Soil by Using Reflected GPS Signals,” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, vol. 14, pp. 1048 – 1059, 2021. DOI: 10.1109/JSTARS.2020.3041162

Munyon JW, Bheemanahalli R, Walne CH, Reddy KR (2021) Developing functional relationships between temperature and cover crop species vegetative growth and development. Agronomy Journal, 113: 1333-1348.

Samiappan S, Bheemanahalli R, Zhou M, Brooks J, Wubben M (2021). Early detection of root-knot nematode (Meloidogyne incognita) infestation in cotton using hyperspectral data. IGARSS. (Peer-reviewed, Proceeding paper).

V. Senyurek, F. Lei, D. R. Boyd, A. Gurbuz, M. Kurum, and R. Moorhead, “Evaluations of Machine Learning-based CYGNSS Soil Moisture Estimates against SMAP Observations,” MDPI Remote Sensing, vol.12, no. 12, pp.3503, 2020. DOI: 10.3390/rs12213503

Senyurek, V., Lei, F., Boyd, D., Kurum, M., Gurbuz, A., and Moorhead, R., 2020. Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sensing, vol. 12, no. 7, pp.1168, 2020. DOI: 10.3390/rs12071168

Senyurek, V., Lei, F., Boyd, D., Gurbuz, A., Kurum, M., and Moorhead, R., 2020. Evaluations of a Machine Learning-based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sensing, vol. 12, no. 21, pp.3503, 2020. DOI: 10.3390/rs12213503

Conference Presentations

Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Moorhead, R., and Boyd, D., 2020. Machine-Learning Based Retrieval of Soil Moisture at High Spatio-Temporal Scales Using CYGNSS and SMAP Observations. IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium.

Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., and Moorhead, R., 2020. A quasi-global machine learning-based CYGNSS soil moisture product at high spatio-temporal resolution. NASA CYGNSS Virtual Meeting 2020.

Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., and Moorhead, R., 2021. Quasi-global GNSS-R Soil Moisture Retrievals at High Spatio-temporal Resolution from CYGNSS and SMAP. IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium.

Senyurek, V., Gurbuz, A., Kurum, M., Lei, F., Boyd, D., and Moorhead, R., 2021. Spatial and Temporal Interpolation of CYGNSS Soil Moisture Estimations. IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium.