Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms

This study proposes a low-cost and “proof-of-concept” methodology to obtain high spatial resolution soil moisture (SM) via processing reflected Global Positioning System (GPS) and a multispectral camera data acquired by small Unmanned Aircraft System (UAS) platforms. An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm by combining features obtained from reflected GPS signals (collected by smartphones and commercial off the shelf receivers) in conjunction with ancillary vegetation indices from the multispectral camera data. The proposed ML algorithm usesin-situSM measurements acquired via SM probes as labels. A preliminary field experiment was conducted on 210 m by 110 m (2.31 ha) crop fields (corn and cotton) in 2020 (from January to November, including crop planting through senescence time period) at Mississippi State University (MSU)'s the heavily instrumented North Farm to acquire data needed for the ML model to train and test. Our results showed that both fields could be covered by GPS reflectometry measurements with about 13 minutes of flight time at a 15-m altitude, and SM can be mapped with 5m × 5m spatial resolution (corresponding to the elongated first Fresnel zone). The model is trained with and validated against eightin-situSM station datasets via 10-fold and leave-one-probe-out cross-validation techniques. Overall root-mean-square errors (RMSE) of 0.013 m³m⁻³volumetric SM and R-value of 0.95 [-] are obtained for 10-fold cross-validation. The proposed model reached an RMSE of 0.033 m³m⁻³and an R-value of 0.5 [-] in leave-one-probe-out cross-validation. While having limited data, the results indicate that high resolution SM measurement can be achieved with a low-cost GPS reflectometry system onboard a small UAS platform for use in precision agriculture (PA) applications.