## 1 Introduction  
The International Soil Reference and Information Centre (ISRIC) and the United Nations Environment Programme (UNEP), in their unique global estimate of soil pollution for the 1990's, approximated the extension of soil pollution at 22 million hectares [1]. In the last few decades, 80,000 sites in Australia were estimated to suffer from soil contamination [2], while in China, the Chinese Environmental Protection Ministry reported that 16% of all soil has been categorized as polluted; 19% percent was agricultural soil [3]. In 2013, the US EPA reported that 1300 contaminated sites appeared on the Superfund National Priorities List of the USA [4]. In the European Economic Area and the West Balkans, 2.8 million potentially polluted sites were estimated [5]. The point-source of soil pollution for two-thirds of those were due to industrial and commercial activities, such as waste disposal and treatment [6]. However, the Global Assessment of Soil Pollution Report [7] the most recent update of global status in matters of soil contamination, included additional data for Eastern Europe, Africa, Latin America, the Caribbean and Asia; it was very limited, and then underestimated, in the previous census [8]. Hence, in this environmental scenario, there is an urgent need to achieve fast identification of spatial variability of environmental contaminants to apply targeted prevention and remediation strategies. Remote sensing techniques are alternative and efficient noncontacted detection methods for mapping and monitoring various soil and sediment contaminants [9; 10]. They are essential tools, well-suited for surveying large areas, and monitoring soil contamination at a high temporal and spatial interval. As such, they can serve as a crucial tool in pollution detection [11; 12] and ecological risk monitoring [13]. Despite the progress made on remote sensing techniques for soil studies, several crucial problems related to data acquisition--in particular environmental situations, i.e., the presence of vegetation on soil, clouds and shadows, etc.--have not been solved, and the interpretation of different surface variables from satellite data is compromised at temporal and spatial data scales [6; 14; 15; 16; 17]. Proximal soil sensors (PSS) are efficient and accurate techniques for measuring in-field variations of soil properties at a very fine spatial scale. They are more and more frequently required in environmental studies (i.e., soil pollution, land degradation processes, etc.) and precision farming (i.e., viticulture zoning). PSS are noninvasive, time- and cost-efficient, and offer many advantages over traditional techniques (e.g., no use of environmental polluting acids, little need for sample preparation, simplicity of use and easy portability, wide dynamic range of elemental quantification). PSS help to overcome the limitation of spatially scarce data to achieve fast identification of the spatial variability of contaminants and to apply targeted prevention and remediation strategies. Recently, the number of PSS techniques has increased and a variety of sensors measuring different soil properties (including electromagnetic induction (EMI), electrical resistivity (ER), ground-penetrating radar (GPR), \(\gamma\)-rays emissions, radiometric and fluorometric analyses, portable XRF spectrometer (pXRF), etc.) have become available on international markets. These sensors, generally coupled with GNNS receivers, acquire georeferenced parameters to process and map easily. They are considered effective, rapid and nondestructive techniques for the measurement of physical and chemical properties of soil and sediments. The reliability of geophysical proximal sensors (EMI and GPR) has been exploited to design maps of signal anomalies in agricultural areas [18; 19] and contaminated sites [20]. Additionally, pXRF spectrometer was widely used to predict levels of potentially toxic elements (PTEs) in several environmental matrices [21; 22; 23; 24; 25; 26; 27; 28].