Predicting the soil bulk density using a new spectral PTF based on intact samples

Sample collection and measurement of soil bulk density (BD) are often labor-intensive and expensive in large regions. Conversely, soil spectra are easy to measure and facilitate BD prediction. However, the literature suggests that the damage to the physical structure of soil during scanning spectra on the ground and/or sieved samples might hinder the capacity of spectral technology to accurately predict BD. In addition, because some soil properties that have high correlations with BD, such as the soil organic matter (SOM), are routinely measured and available in most soil databases, coupling them with soil spectra may improve BD prediction compared to using soil properties or spectra. Therefore, in this study, we propose a novel spectral pedo-transfer function (spectral PTF) that couples the measured visible and near-infrared spectra of soils on intact samples and other soil properties to accurately predict the BD (BD = f (soil spectra, soil properties)), which is different from the traditional PTF that uses only soil properties (BD = f (soil properties)) or spectra alone (BD = f (soil spectra)). In this study, we collected topsoil (0–20 cm) and subsoil (20–40 cm) samples from 586 sites in Northeast China, covering a large area of 1.09 million km² characterized by black soils with high SOM contents. Five routinely measured soil properties were selected: SOM, moisture content (MC), Sand, Silt, and Clay, and various spectral PTFs with one, two, and three soil properties were calibrated using the partial least square regression. The cross-validation results show that the traditional PTF can only predict BD for subsoil with an R² of 0.51 and an RMSE of 0.11 g·cm⁻³ when using SOM + MC + Silt or SOM + MC. Compared to subsoil, topsoil and all layers (topsoil + subsoil) had a lower BD prediction accuracy, and a saturation effect was observed for BD values above 1.5 g·cm⁻³. Unexpectedly, the soil spectra did not provide a higher BD prediction accuracy than traditional PTFs, although the spectra were measured on intact samples. However, adding soil properties to the spectral PTF improved the prediction accuracy and saturation effect for high BD values. The optimal spectral PTF with a single soil property (MC) showed an acceptable BD prediction performance with R²≥0.49, RPD>1.4, and RPIQ>1.7 regardless of whether the sample was topsoil, subsoil, or all layers. Furthermore, the spectral PTF with two or three soil properties yielded a slightly better prediction performance and a more stable prediction among different combinations of soil properties. These results indicate that soil properties and spectra are irreplaceable for BD prediction. Our study demonstrates the potential of spectral PTFs for the accurate prediction of BD and offers insights into the prediction of other soil properties using soil spectra.