Mehmet Kurum, Ph.D.

Affiliations: 
2009 Electrical Engineering The George Washington University, Washington, DC, United States 
Area:
Electronics and Electrical Engineering, Environmental Sciences, Hydrology
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"Mehmet Kurum"

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Roger H. Lang grad student 2009 The George Washington University
 (L-band estimation of forest canopy attenuation by a time-domain analysis of radar backscatter response.)
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Publications

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Kurum M, Kim S, Akbar R, et al. (2021) Surface Soil Moisture Retrievals Under Forest Canopy for $L$-Band SAR Observations Across a Wide Range of Incidence Angles by Inverting a Physical Scattering Model Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14: 1741-1753
Boyd DR, Gurbuz AC, Kurum M, et al. (2020) Cramer-Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 6101-6114
Senyurek V, Lei F, Boyd D, et al. (2020) Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations Remote Sensing. 12: 3503
Boyd D, Kurum M, Eroglu O, et al. (2020) SCoBi Multilayer: A Signals of Opportunity Reflectometry Model for Multilayer Dielectric Reflections Remote Sensing. 12: 3480
Senyurek VY, Lei F, Boyd D, et al. (2020) Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS Remote Sensing. 12: 1168
Sharma A, Lang RH, Kurum M, et al. (2020) L-Band Radar Experiment and Modeling of a Corn Canopy Over a Full Growing Season Ieee Transactions On Geoscience and Remote Sensing. 58: 5821-5835
Eroglu O, Boyd DR, Kurum M. (2020) The Signals of Opportunity Coherent Bistatic Scattering Simulator: A Free, Open Source Simulator Framework Ieee Geoscience and Remote Sensing Magazine. 1-1
Zhou Y, Sharma A, Kurum M, et al. (2020) The backscattering contribution of soybean pods at L-band Remote Sensing of Environment. 248: 111977
Eroglu O, Kurum M, Ball J. (2019) Response of GNSS-R on Dynamic Vegetated Terrain Conditions. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12: 1599-1611
Eroglu O, Kurum M, Boyd D, et al. (2019) High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks Remote Sensing. 11: 2272
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