SoilMoistureMapper: a GNSS-R Approach for Soil Moisture Retrieval on UAV

Senyurek, V., Farhad, M., Gurbuz, A., Kurum, M., & Moorhead, R. J. (2022). SoilMoistureMapper: a GNSS-R Approach for Soil Moisture Retrieval on UAV. UAAAI-22 AI for Agriculture and Food Systems (AIAFS) Workshop. Vancouver, BC (Canada).

Abstract

Measuring of distribution of the soil moisture (SM) content is an essential requirement in precision agriculture. This paper demonstrates practical and low-cost soil moisture mapping techniques based on Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) observations via a small-size unmanned-aerial vehicle (UAV). An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm combining GNSS-R signals with ancillary vegetation indices from a multispectral camera. The ML model is trained and tested using in-situ data from eight SM probes located in a 2.48ha farm. The study results showed that SM maps of the field can be obtained with about 13 mins flight with 5m 5m spatial resolution. The developed ML model reached RMSE of 0.032 m^3/m^3 and R-value of 0.93 in 10-fold cross-validation.