Evaluating three calibration transfer methods for predictions of soil properties using mid‐infrared spectroscopy

Abstract

Mid-infrared (MIR) spectroscopy models have been developed for rapid assessment of soils but are often soil and instrument specific because of differences in laboratory conditions and sensor setup. Calibration transfer is required to apply a spectral model such as partial least squares (PLS) regression developed from a primary instrument to a spectral dataset measured by a secondary instrument with statistically retained accuracy and precision. The study aimed to compare the performance of three transfer methods (i.e., direct standardization [DS], piecewise direct standardization [PDS], and spectral space transfer [SST]) and investigate the effects of transfer sample size and sample selection methods. The transfer methods were developed for predicting total C, clay, silt, and sand contents, cation exchange capacity (CEC), pH in water (pH W ) and CaCl 2 , CaCO 3 equivalent, and −1,500-kPa water retention using spectral measurements of a secondary instrument. Calibration transfer methods of three PLS models for estimating soil properties with a high (total C), intermediate (clay content), and low (pH W ) predictability were discussed. The effect of sample size required for the development of the calibration transfer and the selection method of the transferred samples were investigated. It was found that SST was most favorable for a relatively small sample size used in calibration transfer (≤12 samples). The performance of transfer methods was optimal when the transfer samples accounted for the variability of MIR spectra from the secondary instrument. We conclude that SST and PDS have the potential to be applied in spectroscopy for predicting soil properties using secondary instruments.

Transferability of a large mid‐infrared soil spectral library between two Fourier‐transform infrared spectrometers

“…A more detailed description of the calibration transfer techniques can be found in Pittaki‐Chrysodonta et al. (2021).…”

“…Spectral preprocessing techniques can minimize the differences between different scanning environments (Kramer et al., 2008) and have been shown to be effective in a study exploring the use of a large MIR spectral library on a different instrument (Seybold et al., 2019). However, calibration transfer, where a set of common samples are scanned on both instruments and a transfer function is developed, is often necessary for improving predictions (Dangal & Sanderman, 2020; Ge et al., 2011; Pittaki‐Chrysodonta et al., 2021). The main drawback of calibration transfer is the need to share a common set of soils across instruments.…”

Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?

“…This is an important finding and warrants further investigation into the causes of the bias, especially at low SOC values, and into ways of overcoming the bias. Other calibration transfer approaches such as spectral space transformation might overcome this issue [18].…”

“…Building large training sets with high quality laboratory data associated with the spectra, such as in the studies listed above, is not an option available to most analytical labs. There is growing interest in applying predictive models built on these large libraries to spectra acquired on other instruments [17,18]. Due to differences in instrument configuration and environmental conditions during spectral acquisition, variations in spectra will arise.…”

A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols

“…Dangal and Sanderman [18] and Sanderman et al [19] showed that applying a calibration transfer function (TF) to the SSLs in question enabled the merging of two SSLs from readings obtained by two diferent spectrometers. Moreover, Pittaki-Chrysodonta et al [20] recently suggested that the spectral TF idea could be more efcient at minimizing the root mean square error (RMSE) in the spectral assessment of soil properties. It is interesting to note that Francos and Ben-Dor [21] presented a TF concept based on a random forest (RF) [22] algorithm to predict soil surface refectance in the feld using laboratory spectral measurements of Mediterranean soils from diferent countries (Italy, Israel, and Greece).…”