James Martin Lab, Room 81C
Department of Soil and Water Systems
University of Idaho
875 Perimeter Drive MS 2060
Moscow, ID 83844-2060
Sanaz research includes the development of sensor-based methodologies for agronomically, economically and environmentally sustainable nutrient and water managements for crops and fruit trees utilizing sensing systems and unmanned aerial vehicles.
Ph.D., Texas Tech University, 2014
M.Sc., Khajeh Nasir Toosi University of Technology, 2008
B.Sc., Khajeh Nasir Toosi University of Technology, 2005
- Precision agriculture
- Remote sensing
- Unmanned Aerial Vehicles (UAVs)
- Machine learning
- Big data management
- Internet-of-things (IoT)-based data-driven decision support tools
- Sustainable crop production and management
- Agricultural water and nutrient managements
- Walsh, O. S., Shafian, S., Marshall, J. M., Jackson, C., McClintick-Chess, J. R., Blanscet, S. M., ... & Walsh, W. L. (2018). Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat. Advances in Remote Sensing, 7(02), 71.
- Shafian, S., Rajan, N., Schnell, R., Bagavathiannan, M., Valasek, J., Shi, Y., & Olsenholler, J. (2018). Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PloS one, 13(5), e0196605.
- Walsh, O. S., Shafian, S., McClintick-Chess, J. R., Belmont, K. M., & Blanscet, S. M. (2018). Potential of Silicon Amendment for Improved Wheat Production. Plants, 7(2), 26.
- Walsh, O. S., Shafian, S., & Christiaens, R. J. (2018). Nitrogen Fertilizer Management in Dryland Wheat Cropping Systems. Plants, 7(1), 9.
- Walsh, O. S., Shafian, S., & Christiaens, R. J. (2018). Evaluation of Sensor-Based Nitrogen Rates and Sources in Wheat. International Journal of Agronomy, 2018.
- Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., ... & Rana, A. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PloS one, 11(7), e0159781.
- Shafian, S., & Maas, S. J. (2015). Index of soil moisture using raw Landsat image digital count data in Texas high plains. Remote Sensing, 7(3), 2352-2372.
- Shafian, S., & Maas, S. J. (2015). Improvement of the Trapezoid method using raw Landsat image digital count data for soil moisture estimation in the Texas (USA) High Plains. Sensors, 15(1), 1925-1944.
- Outstanding Dissertation Award, Texas Tech University, 2015
- A.W. Young Grad Student Endowed Scholarship, Texas Tech University, 2014
- Harold Mary Dregne Scholarship, Texas Tech University, 2013
- Texas Tech Pres Doc scholarship, Texas Tech University, 2012