Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content

Abstract

Soil organic matter (SOM) has beneficial effects on soil properties for plant growth and production. Moreover, SOM changes carbon dioxide concentrations in the atmosphere and can influence climate warming. Conventional methods for SOM determination based on laboratory analyses are costly and time consuming. Use of soil reflectance spectra is an alternative approach for SOM estimation and has the advantage of being rapid, non-destructive and cost effective. This method assumes that residuals are independent and identically distributed. However, in most cases this assumption does not hold owing to spatial dependence in soil samples.The aim of the paper was to test the potential of laboratory Vis–NIR spectroscopy to develop an approach of partial least square regression (PLSR) with correlated errors for estimating spatially varying SOM content from laboratory-based soil Vis–NIR spectra and producing a continuous map using a geostatistical method.The study area was the Turbolo watershed (Calabria, southern Italy), which is representative of Mediterranean areas being highly susceptible to soil degradation. Topsoil samples were collected at 201 locations. To reduce the lack of linearity that may exist in the spectra, reflectance (R) spectra were transformed in absorbance spectra (log (1/R)). Partial least squared regression (PLSR) analysis was then used to predict SOM from reflectance spectra. To take into account spatial correlation between observations, the significant latent variables from PLSR were used as regressors in a linear mixed effect model with correlated errors of SOM. The spatial approach and traditional PLSR were compared through the calculation of root mean square prediction error (RMSPE). In order to produce a continuous map, the estimated SOM data were interpolated by ordinary kriging. The approach is particularly advantageous when the data exhibit a pronounced spatial autocorrelation and could be used in digital soil mapping.