Gabriele Buttafuoco

TL;DR: In this article, the authors compared the capability of multispectral S2 and airborne hyperspectral remote sensing data for soil organic carbon (SOC) prediction, and investigated the importance of spectral and spatial resolution through the signal-to-noise ratio (SNR), the variable importance in the prediction (VIP) models and the spatial variability of the SOC maps at field and regional scales.

TL;DR: In this paper, the spectral reflectance of the soil organic matter (SOM) was used to predict the soil content in the study area, combining with geostatistics for mapping SOM content, and mapping zones affected by water erosion processes.

TL;DR: In this paper, the authors investigated the capability of geostatistics to incorporate auxiliary geoelectrical information for the prediction of soil properties, such as ground Penetrating Radar (GPR) and electromagnetic induction (EMI) sensors.

TL;DR: In this paper, a probabilistic approach was used to assess the level of topsoil water depletion, based on the use of the geostatistical technique of conditional simulation to yield a series of stochastic images of equally probable spatial distributions of the soil water contents across the site.

TL;DR: In this paper, the authors used partial least square regression (PLSR) with correlated errors for estimating spatially varying SOM content from laboratory-based soil Vis-NIR spectra and producing a continuous map using a geostatistical method.