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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...
Visible, near-infrared and short wave infrared (VNIR/SWIR) hyperspectral imagery has proven to be a useful technique for mapping the soil surface properties over bare soils pixels. Multivariate regression models are usually built linking a set of soil surface properties (response Y-variables) to a set of imaging reflectance spectra over bare soil pixels (predictor X-variables), and then, they are...
The applicability of Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery for soil property mapping decreases when surfaces are partially covered by vegetation. The objective of this research was to develop and evaluate the performance of a “double-extraction” technique for clay content estimation over semi-vegetated surfaces using VNIR/SWIR hyperspectral imagery. The “double-extraction”...
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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