Use of Vis-Nirs for Land Management Classification With a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties

Abstract

G. Debaene, D. Pikuła, and J. Niedźwiecki. 2014. Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties. Cien. Inv. Agr. 41(1): 21-32. The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P 2 O 5 , K 2 O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To this aim, chemometric techniques, such as partial least square (PLS) regression and support vector machine (SVM) classification, were used. Our results show that visible and near infrared spectroscopy (VIS-NIRS) is suitable for the prediction of properties of texturally homogeneous samples. The effects of fertilizer applications were sufficient to modify the soil chemical composition to a range suitable for using VIS-NIRS for calibration and modeling purposes. The best results were obtained for SOC and N content prediction using the full dataset with cross-validation (r 2 = 0.76, RMSECV = 0.04, RPD = 2.02 and r 2 = 0.81, RMSECV = 0.01, RPD = 2.20, respectively) and with an independent validation dataset (r 2 = 0.70, RMSEP = 0.04, RPD = 1.80 and r 2 = 0.73, RMSEP = 0.03, RPD = 1.22, for SOC and N content, respectively). The use of fertilizers and the type of crop rotation appear to have a significant impact on soil spectral properties; the SVM methodology with a linear kernel function was able to classify soil samples as functions of the applied doses of organic and inorganic fertilizers with 75% accuracy with cross-validation and the type of crop rotation with more than 90% accuracy with full validation of separate datasets.

Screening Risk Assessment of Agricultural Areas under a High Level of Anthropopressure Based on Chemical Indexes and VIS-NIR Spectroscopy

“…It was successfully used for the quantitative analysis of a variety of physical and chemical soil properties such as moisture, total C, N and P content, quality of organic matter, etc. [ 20 , 23 ], and soil biological parameters such as respiration, microbial biomass, potential mineralization of N and ratio of microbial C to organic C [ 18 ]. VIS-NIR spectroscopy is gradually becoming more popular for the rapid screening of contaminants in soils as more recent studies have demonstrated its efficacy in the detection of heavy metals (Cd, Pb, Cu, Zn) [ 24 , 25 ], total and bioavailable PAHs [ 12 , 26 , 27 , 28 ], individual compounds, e.g., benzo[a]pyrene [ 12 ] or phenanthrene [ 29 ], and total petroleum hydrocarbons [ 30 , 31 ].…”

Dissipation and sorption processes of polycyclic aromatic hydrocarbons (PAHs) to organic matter in soils amended by exogenous rich-carbon material

“…Usually, a higher SOC content means a lower reflectance but this is not the case here. This is probably due to differences in soil texture having a significant impact on spectra (Debaene et al 2014;Hermansen et al 2017). The small peak observed in natural soils at 390 nm can be attributed to humic acids (Ouatmane et al 2000).…”

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

“…there is more variability in medium soc content spectra probably due to the mixing of the top soil with deeper layers. the spectra are clearly related to the soc content in that example since the soil textures are similar (table 1) and, therefore, have no effect on the spectral characteristics (Debaene et al 2014b). the reflectance for the low soc content samples in the 400-1,000 nm range has a different shape since that region of the spectra is influenced by the soc content.…”

“…Visible and near-infrared (Vis-nir) spectroscopy is regarded as an efficient technology to investigate soils for various purposes like soil monitoring, soil mapping, and precision agriculture (Debaene et al 2014b(Debaene et al , stenberg et al 2010). the method is based on the absorption by the soil constituents of the near-infrared radiation by different chemical bonds such as o-H, c-H, n-H, s-H and c=o.…”