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Proceedings Paper

Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece
Author(s): Nikolaos L. Tsakiridis; Nikolaos Tziolas; Agathoklis Dimitrakos; Georgios Galanis; Eleftheria Ntonou; Anastasia Tsirika; Evangelia Terzopoulou; Eleni Kalopesa; George C. Zalidis
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Paper Abstract

Soil Spectral Libraries facilitate agricultural production taking into account the principles of a low-input sustainable agriculture and provide more valuable knowledge to environmental policy makers, enabling improved decision making and effective management of natural resources in the region. In this paper, a comparison in the predictive performance of two state of the art algorithms, one linear (Partial Least Squares Regression) and one non-linear (Cubist), employed in soil spectroscopy is conducted. The comparison was carried out in a regional Soil Spectral Library developed in the Eastern Macedonia and Thrace region of Northern Greece, comprised of roughly 450 Entisol soil samples from soil horizons A (0-30 cm) and B (30-60 cm). The soil spectra were acquired in the visible – Near Infrared Red region (vis- NIR, 350nm-2500nm) using a standard protocol in the laboratory. Three soil properties, which are essential for agriculture, were analyzed and taken into account for the comparison. These were the Organic Matter, the Clay content and the concentration of nitrate-N. Additionally, three different spectral pre-processing techniques were utilized, namely the continuum removal, the absorbance transformation, and the first derivative. Following the removal of outliers using the Mahalanobis distance in the first 5 principal components of the spectra (accounting for ~99.8% of the variance), a five-fold cross-validation experiment was considered for all 12 datasets. Statistical comparisons were conducted on the results, which indicate that the Cubist algorithm outperforms PLSR, while the most informative transformation is the first derivative.

Paper Details

Date Published: 6 September 2017
PDF: 8 pages
Proc. SPIE 10444, Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 104441F (6 September 2017); doi: 10.1117/12.2277905
Show Author Affiliations
Nikolaos L. Tsakiridis, Aristotle Univ. of Thessaloniki (Greece)
Nikolaos Tziolas, Aristotle Univ. of Thessaloniki (Greece)
Agathoklis Dimitrakos, Interbalkan Environment Ctr. (Greece)
Georgios Galanis, Interbalkan Environment Ctr. (Greece)
Eleftheria Ntonou, Aristotle Univ. of Thessaloniki (Greece)
Anastasia Tsirika, Aristotle Univ. of Thessaloniki (Greece)
Evangelia Terzopoulou, Interbalkan Environment Ctr. (Greece)
Eleni Kalopesa, Interbalkan Environment Ctr. (Greece)
George C. Zalidis, Aristotle Univ. of Thessaloniki (Greece)
Interbalkan Environment Ctr. (Greece)

Published in SPIE Proceedings Vol. 10444:
Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017)
Kyriacos Themistocleous; Silas Michaelides; Giorgos Papadavid; Vincent Ambrosia; Gunter Schreier; Diofantos G. Hadjimitsis, Editor(s)

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