Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid‐infrared diffuse reflectance spectroscopy

Abstract

This study investigated the suitability of mid-infrared diffuse reflectance Fourier transform (MIR-DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off-shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross-validation of soil property predictions was assessed by the coefficient of determination (R 2 ), the root mean square error of cross-validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross-validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross-validation appeared to be the most accurate (R 2 = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to 'true' predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross-validation for OC and IC (R 2 = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R 2 = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials.

Recent applications of vibrational mid-Infrared (IR) spectroscopy for studying soil components: a review

“…Strong correlations were found between FTIR spectra and soil parameters: C/N ratio, C and N content, lignin, HA, soil organic carbon (SOC), inorganic carbon, metals, pH, and others. In the last few years, many papers have dealt with the applications of PCA and PLS to the quantitative determination of the above-mentioned parameters on different soil layers and litter, as well as on particulate OM and on OM different fractions (Bornemann, et al, 2010;Calderon, et al, 2011a;D'Acqui, et al, 2010;Fernandez-Getino, et al, 2013;Ludwig, et al, 2008;Tatzber, et al, 2010). MIR spectroscopy predicted the C/N ratio well; the contents of C, N, and lignin, the production of dissolved OC, and the contents of carbonyl-C, aromatic C, O-alkyl -C, and alkyl-C were satisfactorily assessed.…”

“…PLS and PCA are the most popular procedures for quantitative determination or to predict one or several soil components (D'Acqui, et al, 2010;Reeves, et al, 2009;Viscarra Rossel, et al, 2006)…”

“…MIR spectroscopy has frequently been applied to investigate soil properties and soil organic matter (SOM) characteristics (Aguiar, et al, 2013;Bellon-Maurel and McBratney, 2011;Calderon, et al, 2011aCalderon, et al, , 2011bDemyan, et al, 2012;Du and Zhou, 2009;Francioso, et al, 2009;Haberhauer and Gerzabek, 1999;Kaiser, et al, 2011). Currently, the combination of multivariate statistical methods used for the Fourier Transform IR (FTIR) spectra analysis has provided a powerful method for the discrimination and identification and/or quantification of single components in soils (Aguiar, et al, 2013;Bellon-Maurel and McBratney, 2011;Bornemann, et al, 2010;Calderon, et al, 2011aCalderon, et al, , 2011bD'Acqui, et al, 2010;Fernandez-Getino, et al, 2013;Janik, et al, 2007aJanik, et al, , 2007bLudwig, et al, 2008;Minasny, et al, 2008;Pirie, et al, 2005;Reeves III, 2010;Stumpe, et al, 2011;Tatzber, et al, 2010;Viscarra Rossel, et al, 2006;Yang, et al, 2012). The representation of MIR spectra is not uniform in the literature.…”

Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan

“…In addition, the residual predictive deviation (RPD) was used to evaluate the predictive ability of the models, which was defined as the ratio of standard deviation (SD) of reference data in prediction to RMSECV [38]. For determining the performance ability of the calibration models, the goal RPD was at least 3 for agriculture applications; RPD values between 2 and 3 indicate a model with good prediction ability, 1.5 < RPD < 2 is an intermediate model needing some improvement, and an RPD < 1.5 indicates that the model has poor prediction ability [39].…”

Carbon Stocks in Peri-Urban Areas: A Case Study of Remote Sensing Capabilities

“…It is made of multitemporal soil surveys, sampling, and chemical analysis in the laboratory. Once validated locally, RS methodologies coupled with techniques of reflectance spectroscopy [40] and [41], might provide (nondestructive) rapid prediction of CSs in the short-term, with acceptable level of error compared to costs.…”