空间分布是近红外光谱评价土壤属性预测的关键因素

IF 2.1 Q3 SOIL SCIENCE
Ricardo Canal Filho, J. Molin
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引用次数: 2

摘要

在土壤科学中,近红外(NIR)光谱正在进行大量测试,以直接在现场获取数据。使用这些光谱的机器学习(ML)模型可以进行校准,只添加来自一个领域的样本或收集不同的区域,以增加插入的数据并提高模型的准确性。预测模型的稳健性评估通常依赖于统计度量。然而,尽管农业生产决策取决于这些属性的空间变异性,但预测的土壤属性的空间分布如何受到影响的研究仍然很少。本研究的目的是使用在线近红外光谱在田间水平上预测土壤属性,评估统计指标以及预测中观察到的空间分布,将局部预测模型与从其他地区收集样本的模型进行比较。在实验场中共获得383个在线近红外光谱,用于预测粘土、沙子、有机物(OM)、阳离子交换容量(CEC)、钾(K)、钙(Ca)和镁(Mg)。为了建立ML校准,收集了来自实验场(本地数据集)的72个土壤光谱,其中59个样本来自附近的另一个区域,位于同一地质区域(地质数据集),该区域位于附近,60多个样本来自不同区域的另一区域(全球数据集)。使用k倍(k=10)交叉验证进行主成分回归。粘土模型报告了类似的预测误差,尽管局部模型的R2较低(0.17),但预测的空间分布证明了模型具有相似的性能。尽管三个数据集之间的OM模式具有可比性,但R2较低(0.75)的局部预测是最适合的。然而,对于次要的近红外响应属性,只有CEC可以成功预测,并且只能使用本地数据集,因为统计指标是兼容的,但地质和全球模型歪曲了该领域的空间模式。空间分布的农艺合理性被证明是评价田间土壤属性预测的关键因素。结果表明,局部校准是漫反射光谱近红外预测土壤属性的最佳建议,并且仅凭统计指标可能会错误地显示预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy
In soil science, near-infrared (NIR) spectra are being largely tested to acquire data directly in the field. Machine learning (ML) models using these spectra can be calibrated, adding only samples from one field or gathering different areas to augment the data inserted and enhance the models’ accuracy. Robustness assessment of prediction models usually rely on statistical metrics. However, how the spatial distribution of predicted soil attributes can be affected is still little explored, despite the fact that agriculture productive decisions depend on the spatial variability of these attributes. The objective of this study was to use online NIR spectra to predict soil attributes at field level, evaluating the statistical metrics and also the spatial distribution observed in prediction to compare a local prediction model with models that gathered samples from other areas. A total of 383 online NIR spectra were acquired in an experimental field to predict clay, sand, organic matter (OM), cation exchange capacity (CEC), potassium (K), calcium (Ca), and magnesium (Mg). To build ML calibrations, 72 soil spectra from the experimental field (local dataset) were gathered, with 59 samples from another area nearby, in the same geological region (geological dataset) and with this area nearby and more 60 samples from another area in a different region (global dataset). Principal components regression was performed using k-fold (k=10) cross-validation. Clay models reported similar errors of prediction, and although the local model presented a lower R2 (0.17), the spatial distribution of prediction proved that the models had similar performance. Although OM patterns were comparable between the three datasets, local prediction, with the lower R2 (0.75), was the best fitted. However, for secondary NIR response attributes, only CEC could be successfully predicted and only using local dataset, since the statistical metrics were compatible, but the geological and global models misrepresented the spatial patterns in the field. Agronomic plausibility of spatial distribution proved to be a key factor for the evaluation of soil attributes prediction at field level. Results suggest that local calibrations are the best recommendation for diffuse reflectance spectroscopy NIR prediction of soil attributes and that statistical metrics alone can mispresent the accuracy of prediction.
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