Breure T S, Prout J M, Haefele S M, Milne A E, Hannam J A, Moreno-Rojas S, Corstanje R
Rothamsted Research, Harpenden AL5 2JQ, United Kingdom.
Cranfield University, Cranfield, Bedfordshire MK43 0AL, United Kingdom.
Soil Tillage Res. 2022 Jan;215:105196. doi: 10.1016/j.still.2021.105196.
The prediction accuracy of soil properties by proximal soil sensing has made their application more practical. However, in order to gain sufficient accuracy, samples are typically air-dried and milled before spectral measurements are made. Calibration of the spectra is usually achieved by making wet chemistry measurements on a subset of the field samples and local regression models fitted to aid subsequent prediction. Both sample handling and wet chemistry can be labour and resource intensive. This study aims to quantify the uncertainty associated with soil property estimates from different methods to reduce effort of field-scale calibrations of soil spectra. We consider two approaches to reduce these expenses for predictions made from visible-near-infrared ((V)NIR), mid-infrared (MIR) spectra and their combination. First, we considered reducing the level of processing of the samples by comparing the effect of different sample conditions (in-situ, unprocessed, air-dried and milled). Second, we explored the use of existing spectral libraries to inform calibrations (based on milled samples from the UK National Soil Inventory) with and without 'spiking' the spectral libraries with a small subset of samples from the study fields. Prediction accuracy of soil organic carbon, pH, clay, available P and K for each of these approaches was evaluated on samples from agricultural fields in the UK. Available P and K could only be moderately predicted with the field-scale dataset where samples were milled. Therefore this study found no evidence to suggest that there is scope to reduce costs associated with sample processing or field-scale calibration for available P and K. However, the results showed that there is potential to reduce time and cost implications of using (V)NIR and MIR spectra to predict soil organic carbon, clay and pH. Compared to field-scale calibrations from milled samples, we found that reduced sample processing lowered the ratio of performance to inter-quartile range (RPIQ) between 0% and 76%. The use of spectral libraries reduced the RPIQ of predictions relative to field-scale calibrations from milled samples between 54% and 82% and the RPIQ was reduced between 29% and 70% for predictions when spectral libraries were spiked. The increase in uncertainty was specific to the combination of soil property and sensor analysed. We conclude that there is always a trade-off between prediction accuracy and the costs associated with soil sampling, sample processing and wet chemical analysis. Therefore the relative merits of each approach will depend on the specific case in question.
通过土壤近感技术预测土壤性质的准确性已使其应用更具实用性。然而，为了获得足够的准确性，通常在进行光谱测量之前将样品风干并研磨。光谱校准通常是通过对一部分田间样品进行湿化学测量，并拟合局部回归模型以辅助后续预测来实现的。样品处理和湿化学分析都可能耗费大量人力和资源。本研究旨在量化不同方法估算土壤性质所带来的不确定性，以减少土壤光谱田间尺度校准的工作量。我们考虑了两种方法来降低利用可见 - 近红外（(V)NIR）、中红外（MIR）光谱及其组合进行预测的成本。首先，我们通过比较不同样品条件（原位、未处理、风干和研磨）的影响，考虑降低样品的处理水平。其次，我们探索了使用现有光谱库来辅助校准（基于英国国家土壤清单中的研磨样品），以及在光谱库中加入一小部分来自研究田地的样品（即“加标”）的情况。针对这些方法中的每一种，我们在英国农田的样品上评估了土壤有机碳、pH值、粘土、有效磷和钾的预测准确性。对于研磨后的样品组成的田间尺度数据集，有效磷和钾只能得到适度的预测。因此，本研究没有发现证据表明在有效磷和钾的样品处理或田间尺度校准方面存在降低成本的空间。然而，结果表明，利用(V)NIR和MIR光谱预测土壤有机碳、粘土和pH值，在减少时间和成本方面具有潜力。与研磨样品的田间尺度校准相比，我们发现减少样品处理使性能与四分位间距之比（RPIQ）降低了0%至76%。使用光谱库相对于研磨样品的田间尺度校准，预测的RPIQ降低了54%至82%，当光谱库加标时，预测的RPIQ降低了29%至70%。不确定性的增加是特定于所分析的土壤性质和传感器的组合的。我们得出结论，在预测准确性与土壤采样、样品处理和湿化学分析相关成本之间始终存在权衡。因此，每种方法的相对优点将取决于具体情况。
