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Improved estimation of soil clay content by the fusion of remote hyperspectral and proximal geophysical sensing

Abstract : Planning sustainable soil exploitation and land resource evaluation require up-to-date and accurate maps of soil properties. In that respect, geophysical techniques present particular interests given their non-invasiveness and their fast data acquisition capacity, which permit to characterize large areas with fine spatial and/or temporal resolutions. We investigated the relevancy of combining data from airborne hyperspectral (Hs), electromagnetic induction (EMI) and far-field ground-penetrating radar (GPR) for mapping soil properties, in particular soil clay content, at the field scale. Data from the three techniques were acquired at a test site in Mugello (Italy) characterized by relatively strong spatial variations of soil texture. Soil samples were collected for determining ground truth clay content. For the frequencies used in this study (200–650 MHz), the GPR surface reflection is mainly determined by soil dielectric permittivity, itself primarily influenced by soil moisture. In contrast, EMI is mostly sensitive to soil electrical conductivity, which integrates several soil properties including in particular soil moisture and clay content. Taking advantage of the complementary information provided by the two instruments, the GPR and EMI data were combined and correlated to local ground-truth clay content data to provide high-resolution clay content maps over the entire field area. Besides, a relationship was also observed between Hs data and clay content measurements, which permitted to produce a Hs-derived clay content map. EMI–GPR and Hs maps showed close spatial patterns and a relatively high correlation was observed between both clay content estimates, as well as between clay content estimates and ground-truth clay content measurements. Moreover, data fusion allowed constraining the EMI–GPR and Hs information and reduced the uncertainty of mapped clay content estimates. These results demonstrated great promise for integrated, digital soil mapping applications.
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Soumis le : jeudi 17 novembre 2016 - 17:58:08
Dernière modification le : dimanche 26 juin 2022 - 10:11:22




Andrea Ciampalini, Frédéric André, Francesca Garfagnoli, Gilles Grandjean, Sébastien Lambot, et al.. Improved estimation of soil clay content by the fusion of remote hyperspectral and proximal geophysical sensing. Journal of Applied Geophysics, Elsevier, 2015, 116, pp.135 - 145. ⟨10.1016/j.jappgeo.2015.03.009⟩. ⟨hal-01398816⟩



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