We investigate the feasibility of using built-in GNSS sensors within ubiquitous smartphone devices from a small UAS for the purpose of land remote sensing. We summarize the experimental findings and challenges that need to be resolved in order to perform the GNSS Reflectometry (GNSS-R) technique via smartphones. In late 2018, a series of experiments was conducted and designed by integrating two smartphones into a multi-copter UAS by attaching them to ground plates to isolate and record both direct and reflected GNSS carrier-tonoise density ratio (C/N0) separately. It was demonstrated that (1) fluctuations of moving GNSS specular reflections are correlated with spatial ground features with appreciable dynamic range and (2) radiation pattern of the smartphone's inbuilt antenna has a significant effect on the received signal strength. In 2020, more experiments were conducted to examine the quality of in-built chip and antenna of a smartphone with regard to the GNSS-R approach as well as the consistency of measurements. These follow-up experiments involved (1) placement of the smartphone on a pan-tilt mechanism on a tripod (2) formation flights with smartphone on a gimbal and a high quality custom-built dualchannel GNSS-R receiver, and (3) flying the UAS at different times of the day on two consecutive days. It was demonstrated that (1) the radiation pattern of smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and (2) internal GNSS chip produces observables of sufficient quality, and (3) the fluctuations of the reflected signal are repeatable under the same configuration at different times.

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This study proposes a low-cost and "proof-of-1 concept" methodology to obtain high spatial resolution soil 2 moisture (SM) via processing reflected Global Positioning System 3 (GPS) and a multispectral camera data acquired by small 4 Unmanned Aircraft System (UAS) platforms. An SM estimation 5 model is developed using a random forest (RF) machine-learning 6 (ML) algorithm by combining features obtained from reflected 7 GPS signals (collected by smartphones and commercial off the 8 shelf receivers) in conjunction with ancillary vegetation indices 9 from the multispectral camera data. The proposed ML algorithm 10 uses in-situ SM measurements acquired via SM probes as labels. 11 A preliminary field experiment was conducted on 210 m by 110 m 12 (2.31 ha) crop fields (corn and cotton) in 2020 (from January 13 to November, including crop planting through senescence time 14 period) at Mississippi State University (MSU)'s the heavily 15 instrumented North Farm to acquire data needed for the ML 16 model to train and test. Our results showed that both fields 17 could be covered by GPS reflectometry measurements with about 18 13 minutes of flight time at a 15-m altitude, and SM can be 19 mapped with 5m × 5m spatial resolution (corresponding to 20 the elongated first Fresnel zone). The model is trained with 21 and validated against eight in-situ SM station datasets via 10-22 fold and leave-one-probe-out cross-validation techniques. Overall 23 root-mean-square errors (RMSE) of 0.013 m 3 m −3 volumetric SM 24 and R-value of 0.95 [-] are obtained for 10-fold cross-validation. 25 The proposed model reached an RMSE of 0.033 m 3 m −3 and an 26 R-value of 0.5 [-] in leave-one-probe-out cross-validation. While 27 having limited data, the results indicate that high resolution SM 28 measurement can be achieved with a low-cost GPS reflectometry 29 system onboard a small UAS platform for use in precision 30 agriculture (PA) applications.31

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