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Visible Near-Infrared (Vis-NIR, 400–2500nm) hyperspectral imaging has proven to be a valuable tool for mapping soil properties over bare soils. To date, most predictive models presented in literature, have been built from calibration databases made up of both Vis-NIR imaging spectra (predictor variables) and soil properties (response variables). Nevertheless, the constitution of such calibration databases...
Laboratory Visible-Near Infrared (Vis-NIR) spectroscopy is a good alternative to costly physical and chemical soil analysis to estimate a wide range of soil properties. Various statistical methods relate soil Vis-NIR spectra to soil properties including partial least-squares regression (PLSR), the most common multivariate statistical technique in soil science. Most efforts are generally dedicated...
Visible, near-infrared and short-wave infrared (VNIR/SWIR, 0.4–2.5μm) hyperspectral airborne imaging has been demonstrated to be a potential tool for topsoil property mapping (such as free iron, clay, and organic matter) over bare soils of large areas. Nevertheless, one of the limiting factors of hyperspectral airborne data use for soil property mapping is the need for a set of soil spectra extracted...
The characterization of soil aggregate stability is an important step for assessing the susceptibility of soil to water erosion. A normalized international method has recently been proposed for estimating soil aggregate stability indexes (ISO/FDIS 10930, 2012), but extensive measurements of the soil aggregate stability for mapping erosion risks on the regional scale remain a fastidious enterprise...
Previous studies have demonstrated that Visible Near InfraRed (Vis–NIR) hyperspectral imagery is a cost-efficient way to map soil properties at fine resolutions (~5m) over large areas. However, such mapping is only feasible for the soil surface because the effective penetration depths of optical sensors do not exceed several millimeters. This study aims to determine how Vis–NIR hyperspectral imagery...
Hyperspectral imagery has proven to be a useful technique for mapping soil surface properties. However vegetation cover has a significant influence on spectral reflectance and the applicability of hyperspectral images for soil property estimations decreases when surfaces are partially covered by vegetation. To maximize information extraction from hyperspectral data, we apply a “double-extraction”...
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