Visible and near infrared reflectance spectroscopy for field-scale assessment of Stagnosols properties

Ivana Šestak, Milan Mesić, Željka Zgorelec, Aleksandra Perčin and Ivan Stupnišek
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Milan Mesić: Department of General Agronomy, Faculty of Agriculture, University of Zagreb, Zagreb, Croatia
Željka Zgorelec: Department of General Agronomy, Faculty of Agriculture, University of Zagreb, Zagreb, Croatia
Aleksandra Perčin: Department of General Agronomy, Faculty of Agriculture, University of Zagreb, Zagreb, Croatia
Ivan Stupnišek: Department of General Agronomy, Faculty of Agriculture, University of Zagreb, Zagreb, Croatia

Plant, Soil and Environment, 2018, vol. 64, issue 6, 276-282

Abstract: Spectral data contain information on soil organic and mineral composition, which can be useful for soil quality monitoring. The objective of research was to evaluate hyperspectral visible and near infrared reflectance (VNIR) spectroscopy for field-scale prediction of soil properties and assessment of factors affecting soil spectra. Two hundred soil samples taken from the experiment field (soil depth: 30 cm; sampling grid: 15 × 15 m) were scanned using portable spectroradiometer (350-1050 nm) to identify spectral differences of soil treated with ten different rates of mineral nitrogen (N) fertilizer (0-300 kg N/ha). Principal component analysis revealed distinction between higher- and lower-N level treatments conditioned by differences in soil pH, texture and soil organic matter (SOM) composition. Partial least square regression resulted in very strong correlation and low root mean square error (RMSE) between predicted and measured values for the calibration (C) and validation (V) dataset, respectively (SOM, %: RC2 = 0.75 and RV2 = 0.74; RMSEC = 0.334 and RMSEV = 0.346; soil pH: RC2 = 0.78 and RV2 = 0.62; RMSEC = 0.448 and RMSEV = 0.591). Results indicated that hyperspectral VNIR spectroscopy is an efficient method for measurement of soil functional attributes within precision farming framework.

Keywords: nitrogen fertilization; remote sensing; non-destructive method; climatic condition; soil texture; linear calibration (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlpse:v:64:y:2018:i:6:id:220-2018-pse

DOI: 10.17221/220/2018-PSE

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