A Ubiquitous GNSS-R Methodology to Estimate Surface Reflectivity Using Spinning Smartphone Onboard a Small UAS

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Abstract

Global Navigation Satellite Systems (GNSS) Reflectometry (GNSS-R) has gained significant attention in retrieving geophysical parameters of the Earth's surface using ground, airborne, and spaceborne systems in the past decade. Such studies have mainly been investigated through custom-built systems or networks of geodetic receivers and antennas. For the broader adaptation of such an approach in precision agriculture or small-scale experiments, we have recently conjectured that a smartphone's built-in GNSS chip and antenna mounted on a small Unmanned Aircraft System (UAS) platform could be used to estimate the reflectivity of the soil surface using reflected GNSS signals. The main barrier to using a smartphone as a ubiquitous GNSS-R receiver is the built-in antenna's irregular radiation pattern that makes the measurement signal highly angular dependent. This study provides a unique and practical solution to lessen the impact of antenna radiation patterns on reflectivity estimation by spinning two smartphones mounted on two separate ground plate and taking the logarithmic difference of such simultaneous measurements. In this proposed configuration, a down-facing spinning smartphone on a UAS platform collects reflected signals. At the same time, another identical spinning smartphone is located on the ground, providing reference data in an open area. We compared the results from measurements with the spinning smartphone on a small UAS and the ground. We also discuss the trade-offs involved in rotation and flight dynamics. Our findings show that a ubiquitous GNSS-R system based on spinning smartphones operating from a small UAS platform can estimate surface reflectivity at the sub-field scale.

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APA

Farhad, M. M., Kurum, M., & Gurbuz, A. C. (2023). A Ubiquitous GNSS-R Methodology to Estimate Surface Reflectivity Using Spinning Smartphone Onboard a Small UAS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 6568–6578. https://doi.org/10.1109/JSTARS.2023.3294833

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