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.

中文翻译：

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使用基于完整样品的新光谱 PTF 预测土壤容重


在大面积地区，土壤容重 （BD） 的样品采集和测量通常是劳动密集型的，而且成本高昂。相反，土壤光谱易于测量并有助于 BD 预测。然而，文献表明，在地面和/或筛分样品上扫描光谱时对土壤物理结构的破坏可能会阻碍光谱技术准确预测 BD 的能力。此外，由于一些与 BD 高度相关的土壤特性，例如土壤有机质 （SOM），在大多数土壤数据库中都是常规测量和可用的，因此与使用土壤特性或光谱相比，将它们与土壤光谱耦合可能会改善 BD 预测。因此，在这项研究中，我们提出了一种新的光谱 pedo-transfer 函数 （spectral PTF），它将测量的土壤在完整样品上的可见光和近红外光谱与其他土壤特性耦合，以准确预测 BD （BD = f （土壤光谱，土壤特性）），这与仅使用土壤特性 （BD = f （土壤特性）） 或仅使用光谱 （BD = f （土壤光谱）） 的传统 PTF 不同。本研究从东北地区 586 个地点采集了表土 （0–20 cm） 和底土 （20–40 cm） 样品，面积达 109 万 km2，以 SOM 含量高的黑土为特征。选择了五种常规测量的土壤特性：SOM、含水量 （MC）、沙子、淤泥和粘土，并使用偏最小二乘回归校准具有 1、2 和 3 土壤特性的各种光谱 PTF。交叉验证结果表明，当使用 SOM + MC + Silt 或 SOM + MC 时，传统 PTF 只能预测 R2 为 0.51、RMSE 为 0.11 g·cm−3 的底土 BD。 与底土相比，表土和所有层（表土 + 底土）的 BD 预测精度较低，当 BD 值高于 1.5 g·cm−3 时观察到饱和效应。出乎意料的是，尽管光谱是在完整样品上测量的，但土壤光谱并没有提供比传统 PTF 更高的 BD 预测精度。然而，将土壤特性添加到光谱 PTF 中提高了高 BD 值的预测精度和饱和效应。具有单一土壤特性 （MC） 的最佳光谱 PTF 显示出可接受的 BD 预测性能，R2≥0.49、RPD>1.4 和 RPIQ>1.7，无论样品是表土、底土还是所有层。此外，具有 2 或 3 种土壤特性的光谱 PTF 产生了略好的预测性能，并且在不同的土壤特性组合之间产生了更稳定的预测。这些结果表明，土壤性质和光谱对于 BD 预测是不可替代的。我们的研究证明了光谱 PTF 在准确预测 BD 方面的潜力，并为使用土壤光谱预测其他土壤特性提供了见解。