Potential Model Overfitting in Predicting Soil Carbon Content by Visible and Near-Infrared Spectroscopy

Abstract

Soil spectroscopy is known as a rapid and cost-effective method for predicting soil properties from spectral data. The objective of this work was to build a statistical model to predict soil carbon content from spectral data by partial least squares regression using a limited number of soil samples. Soil samples were collected from two soil orders (Andisol and Ultisol), where the dominant land cover is native Nothofagus forest. Total carbon was analyzed in the laboratory and samples were scanned using a spectroradiometer. We found evidence that the reflectance was influenced by soil carbon content, which is consistent with the literature. However, the reflectance was not useful for building an appropriate regression model. Thus, we report here intriguing results obtained in the calibration process that can be confusing and misinterpreted. For instance, using the Savitzky-Golay filter for pre-processing spectral data, we obtained R 2 = 0.82 and root-mean-squared error (RMSE) = 0.61% in model calibration. However, despite these values being comparable with those of other similar studies, in the cross-validation procedure, the data showed an unusual behavior that leads to the conclusion that the model overfits the data. This indicates that the model should not be used on unobserved data.

Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression

“…The hyper-parameter optimisation and calibration of the model was done through leave-one-out cross-validation (LOOCV) 39 . For the calibration dataset of n = 74 samples, LOOCV means that n-1 samples are used to calibrate the model and 1 sample is used to assess the accuracy; this is repeated n times for each single sample in the calibration dataset 40 .…”

Estimating soil chemical properties by diffuse reflectance spectroscopy: Promise versus reality

“…Diffuse reflectance spectra are particularly sensitive to soil particle size, shape and aggregation, with the result that prediction accuracy of soil properties is very sensitive to soil particle fineness, sample surface roughness and soil moisture (Chang et al, 2001;Liu et al, 2020;Manage et al, 2018;Nduwamungu et al, 2009;Reeves, 2010;Sun, 2021;Tekin et al, 2012). Not surprisingly then, soil properties such as texture, C and N content, CEC and pH have been estimated by NIR and VNIR with widely varying degrees of success (Gates, 2018;Nduwamungu et al, 2009;Reyna et al, 2017;Viscarra Rossel et al, 2006). Critical variables contributing to large errors in the calibration of the method are the geographic range of soils included in the calibration step, sample preparation (including sieving, grinding and drying) and the spectral preprocessing and calibration procedures used.…”

“…Wadoux et al (2020) further demonstrated this concept by introducing meaningless pseudo-covariates into a model, thereby enabling the accurate prediction of soil organic carbon, and concluded that pattern recognition models based on machine learning are not reliable ways to obtain knowledge about soil properties. In another example, Reyna et al (2017) concluded from their modelling of the VNIR reflectance spectra to estimate the organic carbon content of the soil that the predictive model was not robust and was in fact overfitting and misinterpreting the data. Thus, the spectral data could only provide descriptive, not quantitative, information about soil organic carbon.…”