Evaluating a low‐cost portable <scp>NIR</scp> spectrometer for the prediction of soil organic and total carbon using different calibration models

Abstract

Abstract This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon ( OC ) and total carbon ( TC ) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW , Australia, and from which a subset of 24 soil profiles were measured for OC . Two commercial spectrometers including the AgriSpec TM ( ASD ) and NeoSpectra TM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate ( SNV ) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression ( PLSR ) and support vector machine ( SVM ), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM . For OC prediction, Cubist showed R 2 = 0.89 ( RMSE = 0.12%) and R 2 = 0.78 ( RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R 2 = 0.75 ( RMSE = 0.45%) and R 2 = 0.70 ( RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD . These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.