The study of soil agrophysical and agrochemical properties is based on ground-based point measurements and measurements conducted using radiometric remote sensing systems (satellite or airborne). A disadvantage of the existing remote sensing systems using normal surface irradiation is the insignificant depth of penetration of the probing radiation into the soil layer. It is proposed to use a radar system for remote sensing agricultural lands to eliminate this drawback. The system uses a method for assessing the soil’s physical and chemical properties based on the interference measurements of direct and reflected electromagnetic waves at incidence angles that provide a total refraction effect, i.e., close to Brewster’s angle. The possibility of using this method for remote assessment of soil’s physical and chemical properties, including the subsurface layer moisture, was established. A feature of the bistatic system is that it is necessary to coordinate the mutual arrangement of the transmitting and receiving positions, which imposes special requirements on the UAVs’ flight algorithm. The UAVs’ relative position makes it possible to form the conditions for the manifestation of the total refraction effect, to determine the current value of Brewster’s angle, and to fix these conditions for the subsequent flight, making it possible to measure the soil’s physical and chemical parameters. The research results can be used to implement precision farming technology in hard-to-reach places, large agricultural areas, and digital agriculture.

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• Ludeno, G., Catapano, I., Renga, A., Vetrella, A.R., Fasano, G., & Soldovieri, F. (2018). Assessment of a micro-UAV system for microwave tomography radar imaging. Remote Sensing of Environment, 212, 90-102. https://doi.org/10.1016/j.rse.2018.04.040

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• Nguyen, T. T., Ngo, H. H., Guo, W., Chang, S. W., Nguyen, D. D., Nguyen, C. T., Zhang, J., Liang, S., Bui, X. T., & Hoang, N. B. (2022). A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm. Science of the Total Environment, 833, Article 155066. https://doi.org/10.1016/j.scitotenv.2022.155066

• Pandey, A., & Jain, K. (2022) An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network. Computers and Electronics in Agriculture, 192, Article 106543.https://doi.org/10.1016/j.compag.2021.106543

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• Zhu, L., Walker, J. P., Tsang, L., Huang, H., Ye, N., & Rüdiger, C. (2019). Soil moisture retrieval from time series multi-angular radar data using a dry down constraint. Remote Sensing of Environment, 231, Article 111237. https://doi.org/10.1016/j.rse.2019.111237