Over the past 40 years, airborne geophysics and geomatics has become an effective and accepted technology for mapping various signatures on the Earth’s surface and sub-surface. But so far, its airborne application in agriculture is perceived as sub-practical and/or its real value unknown to most stakeholders. In this paper, we are reviewing major technical and commercial achievements and latest developments to date, but also potentials for new developments and applications, of airborne geophysics (and geomatics) for soil variability mapping. The contribution of airborne agri-geophysics/geomatics is based on the efficient and versatile integration of four main components: carriers; sensors; data/information; and knowledge. The use of a small and stable airborne multi-sensor data measurement platform, resulting in versatile and highly flexible surveys flown at low/reasonable cost of operation, is the recurring wish of many stakeholders in the agriculture industry. The past years GyroLAG brought to reality the next generation of advanced and innovative light airborne remote sensing platforms with specially designed gyrocopters and also light fixed-wing aircraft. Sensors allow the recording of meaningful information for the critical end product (i.e.knowledge). New technologies and combinations of those technologies must be enhanced from the traditional off-the-shelves offers by manufacturers or traditional airborne geophysics service providers which are expensive, heavy, not necessarily fit for purpose and definitely not integrated, or capable of being so, for multi-sensor application to agricultural problems. Those sensors include notably: (a) fluxgate magnetic; (b) CsI gamma spectrometer; (c) thermal cameras; (d) NIR hyper-spectral camera; (e) portable SWIR camera; (f) use of reflected GNSS data; and (g) electromagnetic. The above airborne geophysics/geomatics tool box allows collection of soil data such as: potassium, thorium and uranium concentrations; conductivity and/or resistivity values; total magnetic intensity; temperature; and moisture. Through further value-adding on their own or in combination with other data, that information can then be converted into knowledge such as: soil types; soil zoning; soil variability; soil physical parameters (porosity, density, magnetic susceptibility, etc); soil depth; soils clay content; ground faults location; soil moisture; soil erosion risks; nematodes preferential habitat imaging; and soil chemistry.