## 1 Introduction  
Wireless Sensor Network (WSN) technology is based on collecting data from sensor nodes, which communicate between each other and between them and the gateway, which transmit the data through internet for storage, analysis and processing [1, 2]. It has attracting applications in a variety of fields such as health care [3, 4], means of land transport [5, 6], machine surveillance, military uses and others [7]. In the past few years, the WSN has also emerged into the research field of agriculture, and it is a candidate application for the use cases proposed for Internet of Things in the fifth generation (5G) of wireless mobile communications [8]. This short-coming future of precision agriculture received the name of the fourth revolution [9] and Internet of Agriculture [10]. Among other benefits, the WSN technology provides a feasible way for low-cost, high-efficiency, and high-productivity agriculture farming [11, 12]. Some agriculture related parameters, such as soil temperature, soil moisture, CO\({}_{2}\), PH value and soil nutrients, could be monitored in real-time and then controlled by wireless sensor networks [13] even in an automated manner. The new scope of the smart agriculture will undoubtedly bring a wide variety of business opportunities. However, the use of WSN technology in agriculture is not totally free of challenges. The first issue to overcome might be the fact that the nodes are usually quite close to the ground and sometimes even just on the ground, producing that the height of the antennas above ground is usually low and it may approach zero. This setup leads the radio link that the first Fresnel zone is obstructed in a different way to one introduced by an obstacle. The obstruction due to obstacles can turn the radio path into an obstructed line of sight (OLOS) or a non-line of sight (NLOS) radio link, and it has been largely modelled in literature for different scenarios, including vegetation [14]. Even that a formal definition has not been formulated, if the first Fresnel zone (FFZ) is obstructed along a distance larger than a critical value to not be considered a simple obstacle, it is assumed that the radio propagation operates under a close to ground or near-ground case. The specific distance along which the FFZ is obstructed was modelled in [15; 16] for millimeter frequency bands and depends on the antenna height, the frequency and the ground dielectric parameters. This critical distance \(d_{C}\) is larger than the break point distance \(d_{B}\) given by the two-ray propagation model, and whilst the path loss rate is 20 dB/decade before the break point it increases to 40 dB per decade after \(d_{B}\). Both break and critical points are plausible to be present in a WSN even that the network configuration was not originally planned as a near-ground design. The break point is a function of the antenna height and frequency, and for a 0.5 m of antenna altitude operating at 2.4 GHz \(d_{B}\) is about 20 m. Thus, if the two-ray model is assumed as valid to model the near-ground case, an underestimation of the path loss occurs at larger transmitting distances. In [17] the first model discussing the inverse relation between antenna altitude and the received signal strength was introduced, even that not fulfilled the near-ground definition. There are few models proposed in literature for the near-ground channel: many only based on measurement campaigns, others are based on simulations not corroborated by experimental measurements and others were derived as mathematical models. Most of the few existing models proposed in literature for the near-ground channel are incomplete and become specific to the considered scenario, frequency bands, antenna radiation pattern and polarization, terrain roughness and profile, ground composition and dielectric properties [15; 16] or the fixed antenna height. Recently, near-ground wireless sensor networks are attracting increasing attention, especially for military applications [18; 19; 20; 21; 22].