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]

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]

Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., Boyd, D., Moorhead, R. J., & Crow, W. T. (2022). Quasi-global Machine Learning-based Soil Moisture Estimates at High Spatio-temporal Scales Using CYGNSS and SMAP Observations. Remote Sensing of Environment. Elsevier. 276, 113041. DOI:10.1016/j.rse.2022.113041. [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., Lei, F., Boyd, D., Kurum, M., Gurbuz, A., & Moorhead, R. J. (2020). Machine Learning-based CYGNSS Soil Moisture Estimates over ISMN Sites in CONUS. Remote Sensing. 12(7), 1168. DOI:10.3390/rs12071168. [Abstract] [Document Site]

Kurum, M., Farhad, M., Senyurek, V., & Gurbuz, A. (2023). Enabling Subfield Scale Soil Moisture Mapping in near Real-time by Recycling L-band GNSS Signals from Drones. EGU General Assembly 2023. EGU23-10991. DOI:10.5194/egusphere-egu23-10991. [Document Site]

Senyurek, V., Farhad, M., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). SoilMoistureMapper: a GNSS-R Approach for Soil Moisture Retrieval on UAV. UAAAI-22 AI for Agriculture and Food Systems (AIAFS) Workshop. Vancouver, BC (Canada). [Abstract] [Document Site]

Lei, F., Senyurek, V., Kurum, M., Gurbuz, A., & Moorhead, R. J. (2021). Quasi-Global GNSS-R Soil Moisture Retrievals at High Spatio-Temporal Resolution from Cygnss and Smap Data. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels, Belgium: IEEE. 6303-6306. DOI:10.1109/IGARSS47720.2021.9554005. [Abstract] [Document Site]

Senyurek, V., Gurbuz, A., Kurum, M., Lei, F., Boyd, D., & Moorhead, R. J. (2021). Spatial and Temporal Interpolation of CYGNSS Soil Moisture Estimations. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels, Belgium: IEEE. 6307-6310. DOI:10.1109/IGARSS47720.2021.9553900. [Abstract] [Document Site]

Kurum, M., Gurbuz, A., Barnes, S., Boyd, D. R., Farhad, M., & Senyurek, V. (2021). A UAS-based RF Testbed for Water Utilization in Agroecosystems. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI. Proc. SPIE 11747: International Society for Optics and Photonics. 11747, 117470J. DOI:10.1117/12.2591895. [Abstract] [Document Site]

Lei, F., Senyurek, V., Boyd, D., Kurum, M., Gurbuz, A., & Moorhead, R. J. (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. International Geoscience and Remote Sensing Symposium: IEEE. 4470-4473. DOI:10.1109/IGARSS39084.2020.9323106. [Document Site]

Senyurek, V., Lei, F., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). Machine Learning-based Global Soil Moisture Estimation Using GNSS-R. SoutheastCon 2022. Mobile, AL, USA: IEEE. 434-435. DOI:10.1109/SoutheastCon48659.2022.9764039. [Abstract] [Document Site]