Accurately identifying the soil map unit component at a specific point‐location within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone‐based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non‐soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a sampling‐location based on the numerical similarity of soil property values between the sampled soil profile and the soil components mapped in that area. To evaluate this soil identification framework, we used a subset of the U.S. National Cooperative Soil Survey Soil Characterization Database (NCSS–SCD) as our soil profile test dataset and the U.S. Soil Survey Geographic (SSURGO) database as our reference dataset using profile data of soil components in the area surrounding each test profile. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of generalizing depth‐wise variability (i.e., depth‐support) and generalizing across feature space (i.e., soil properties). Three soil property groups (i.e., Novice, Expert, Expert‐Plus) representing different levels of detail and three types of depth‐support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class, rock fragment volume class, and soil color) resulted in nearly as high identification accuracy (46–53%) as that achieved with an Expert (48–57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert‐Plus dataset (53–60%). This study also showed minimal effect from the type of depth‐support used to represent depth‐wise variability. Furthermore, we evaluated several measures of soil functional similarity (i.e., ecological sites, land capability, taxonomic distance) which resulted in management relevant accuracies ranging from 65–89%. These findings support the utility of simple soil observations sampled at fixed depths for soil identification.

中文翻译：

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数值土壤分类支持公民科学家使用有限的简单土壤观测结果进行土壤鉴定

准确识别景观中特定点位置的土壤图单元组件对于实施可持续的土壤管理至关重要。基于智能手机的土壤剖面表征技术的最新发展，再加上改进的土壤分类数值算法，使非土壤科学家更容易对土壤剖面进行采样，表征和分类。这项研究的主要目的是评估一个可操作的土壤分类框架，用于根据采样土壤剖面和该区域测绘的土壤成分之间的土壤属性值的数值相似性，在采样地点识别土壤成分。为了评估这种土壤识别框架，我们使用了美国的一个子集 国家合作土壤调查土壤特征数据库（NCSS–SCD）作为我们的土壤剖面测试数据集，美国土壤调查地理（SSURGO）数据库作为我们的参考数据集，使用每个测试剖面周围区域的土壤成分剖面数据。使用代表不同泛化程度的土壤属性数据测试了数值相似性，无论是泛化深度方向变异性（即深度支持）还是跨特征空间泛化（即土壤属性）。评估了代表不同详细程度的三个土壤属性组（即，新手，专家，专家+）和三种类型的深度支持（即，遗传水平，深度间隔和深度函数）。使用一组简单的土壤属性输入（例如，新手：土壤质地分类，碎石体积分类，和土壤颜色）的识别准确率（46–53％）与专家（48–57％）数据集（包括更精确的确定（沙子，粉砂，粘土和岩石碎屑的百分比））获得的识别准确性几乎一样高，并且通过在Expert-Plus数据集中添加pH和有机物，几乎没有进一步的改善（53-60％）。这项研究还表明，用于表示深度变化的深度支持类型的影响最小。此外，我们评估了土壤功能相似性的几种测量方法（即，生态场所，土地能力，分类距离），这些方法导致管理相关精度为65％至89％。这些发现支持在固定深度采样的简单土壤观测结果对土壤识别的实用性。