Project Description

While airborne and space-based microwave remote sensing has advanced in recent years, significant challenges remain in the collection and utilization of data at longer wavelengths and higher spatial and temporal resolution. The upcoming launch of the NiSAR S/L-band synthetic aperture radars will provide new capability to measure important environmental variables. Advances in reflectometry have also recently demonstrated a great potential for longer wavelength sensing.

The Garrison/Crawford labs have initiated a collaborative research initiative focused on active and passive microwave remote sensing techniques to address critical science and applications in agriculture and forestry. S-band SAR data acquired from a UAV over different environmental conditions will provide data at high spatial resolution. Integration of these local measurements for downscaling satellite data (e.g., SMAP, CYGNSS and NISAR) will be studied as well as the opportunities in innovative new observables, such as P-band signals of opportunity to be demonstrated on the SNoOPI mission. Developing signal processing algorithms, forward models and retrieval methods will be a significant part of this research. The College of Agriculture: Hardiman (Forestry & Natural Resources) and Bowling (Agronomy) will be partners to develop and implement applications to estimate forest biomass and retrieve soil moisture for field-scale irrigation management.

Start Date

June 1, 2024

Postdoc Qualifications

Doctorate in Electrical Engineering, geophysics, or related field. Strong background in signal processing and electromagnetics. Thesis research related to microwave remote sensing is desirable. Field experience in forestry, agriculture, earth, soil and/or plant science is desirable. 

Co-Advisors

James Garrison, jgarriso@purdue.edu, School of Aeronautics and Astronautics https://engineering.purdue.edu/RNL/

Short Bibliography

1. Kim, S., Garrison, J. L., and Kurum, M., "Retrieval of Subsurface Soil Moisture and Vegetation Water Content: Sensitivity to SoOp-Reflectometry Parameters,” IEEE Transactions on Geoscience and Remote Sensing, June 2023, DOI: 10.1109/TGRS.2023.3284800
2. Garrison, J.L., Nold, B., Masters, D., Mansell, J.,Vega, M, Bindlish, R., Piepmeier, J.R., Brown, C., and Bridgeman, J., “A Spaceborne Demonstration of P-Band Signals-of-Opportunity (SoOp) Reflectometry,” IEEE Geoscience and Remote Sensing Letters, in review. Preprint at: https://doi.org/10.36227/techrxiv.22573075.v1
3. Boyd, D., Kurum, M., Eroglu, O., Gurbuz, A.C., Garrison, J.L., Nold, B.R., Vega, M.A., Piepmeier, J.R., and Bindlish, R., “SCoBi Multilayer: A Signals of Opportunity Reflectometry Model for Multilayer Dielectric Reflections,” Remote Sens. 2020, 12, 3480, doi:10.3390/rs12213480
4. Boyd, D. R., Gurbuz , A. C., Kurum, M., Garrison, J. L., Nold, B.R., Piepmeier, J.R., Vega, M., and Bindlish, R., "Cramer–Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6101-6114, 2020, doi: 10.1109/JSTARS.2020.3029158
5. Yueh S., Xu, X., Shah, R., Kim, Y., Garrison, J., Komanduru, A., and Elder, K., “Remote Sensing of Snow Water Equivalent Using Coherent Reflection from Satellite Signals of Opportunity: Theoretical Modeling,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, in press, Vol. 10, No. 12, Dec. 2017, DOI: 10.1109/JSTARS.2017.2743172