Abstract. One challenge in soil mapping is the transfer of new techniques and methods into operational practice, integrating them with traditional field surveys, reducing costs, and increasing the quality of the soil maps. The latter is paramount, as they form the basis for many thematic maps. As part of a novel approach to soil mapping, we integrate various technologies and pedometric methodologies to create soil property maps for soil surveyors, which they can utilize as a reference before beginning their pedological fieldwork. This gives the surveyors considerably more detailed and accurate prior information, reducing the subjectivity inherent in soil mapping. Our approach comprises a novel soil sampling design that effectively captures spatial and feature spaces, mid-infrared spectroscopy, and spatial machine learning based on a comprehensive set of covariates generated through various feature engineering approaches. We employ multi-scale terrain attributes, temporal multi-scale remote sensing, and Euclidean distance fields to account for environmental correlation, spatial non-stationarity, and spatial autocorrelation in machine learning. Methods to reduce the uncertainties inherent to the spectral and spatial data were integrated. The new sampling design is based on a geographical stratification and focuses on the local soil variability. The method identifies spatially local minima and maxima of the feature space, which is fundamental to soil surveys at the specified scale. The k-means and Kennard-Stone algorithms were applied in a sequential manner within each cell of a hexagonal grid overlaying the study area. This approach permits a systematic sub-sampling from each cell to analyze predictive accuracy for varying sampling densities. We tested one to three samples per hectare. Our findings indicate that a sample size of two samples per hectare was sufficient for accurately mapping soil properties across 300 hectares. This markedly reduces the financial burden associated with subsequent projects, given the significant reduction in the time and resources required for surveying. The spectroscopic and spatial models were unbiased and yielded average R² values of 0.91 and 0.68–0.86, depending on mapping with or without pedotransfer models. Our study highlights the value of integrating robust pedometric technologies in soil surveys.

中文翻译：

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利用光谱学和空间机器学习进行精细土壤属性测绘


摘要。土壤测绘面临的一项挑战是将新技术和方法转化为操作实践，将其与传统的实地调查相结合，降低成本并提高土壤图的质量。后者至关重要，因为它们构成了许多专题地图的基础。作为土壤测绘新方法的一部分，我们整合了各种技术和计步方法，为土壤测量员创建土壤特性图，他们可以在开始土壤学实地工作之前将其用作参考。这为测量人员提供了更加详细和准确的先验信息，减少了土壤测绘固有的主观性。我们的方法包括一种新颖的土壤采样设计，可以有效地捕获空间和特征空间、中红外光谱和基于通过各种特征工程方法生成的一组全面的协变量的空间机器学习。我们采用多尺度地形属性、时间多尺度遥感和欧几里德距离场来解释机器学习中的环境相关性、空间非平稳性和空间自相关性。集成了减少光谱和空间数据固有的不确定性的方法。新的抽样设计基于地理分层，重点关注当地土壤的变化。该方法识别特征空间的空间局部最小值和最大值，这对于指定尺度的土壤调查至关重要。 k 均值和 Kennard-Stone 算法在覆盖研究区域的六边形网格的每个单元内按顺序应用。这种方法允许对每个细胞进行系统的二次采样，以分析不同采样密度的预测准确性。 我们每公顷测试一到三个样品。我们的研究结果表明，每公顷两个样本的样本量足以准确绘制 300 公顷土壤特性图。鉴于测量所需的时间和资源显着减少，这显着减轻了与后续项目相关的财务负担。光谱和空间模型是无偏的，产生的平均 R ²值为 0.91 和 0.68-0.86，具体取决于使用或不使用pedotransfer 模型的绘图。我们的研究强调了在土壤调查中整合强大的计步技术的价值。