Soil property maps are important for land management and earth systems modeling. A new hybrid point‐disaggregation predictive soil property mapping strategy improved mapping in the Colorado River basin by increasing sample size approximately sixfold and can be applied to other areas with similar data, including the conterminous United States. Random forests related environmental raster layers representing soil‐forming factors with samples to predict pH, texture fractions, rock, electrical conductivity (ec), gypsum, CaCO₃, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at seven depths as well as depth to restrictive layer (resdept) and surface rock size and cover. Cross‐validation R² values averaged .53 (range, .20–.76). Mean absolute errors ranged from 3 to 98% of training data averages (mean, 41%). Models of pH, om, and ec had the best accuracy (R² > .6). Most texture fractions, CaCO₃, and SAR models had R² values from .5 to .6. Models of kwfact, dbovendry, resdept, rock models, gypsum, and awc had R² values from .4 to .5; near‐surface models tended to perform better. Very‐fine sands and 200‐cm estimates for other models generally performed poorly (R² = .2–.4), and sample size for the 200‐cm models was too low for reliable model building. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily. Average error increased in areas with higher relative prediction intervals (higher uncertainty), which also had low sampling densities, suggesting that additional sampling in these areas may improve prediction accuracy. Greater uncertainty was observed in areas with highly stratified shale parent materials and physiographic settings uncommon relative to the broader study area.

中文翻译：

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使用常规土壤调查数据的混合预测土壤性质的方法

土壤属性图对于土地管理和地球系统建模很重要。一种新的混合点分解预测土壤特性测绘策略通过将样本量增加大约六倍来改善了科罗拉多河流域的测绘，并可以应用于具有类似数据的其他地区，包括美国本土。随机森林相关的环境栅格图层代表土壤形成因子，并带有样本以预测pH值，质地分数，岩石，电导率（ec），石膏，CaCO ₃，钠吸附率（sar），可用水容量（awc），容重（dbovendry），可蚀性（kwfact）和有机物（om）在七个深度以及限制层的深度（resdept）和地表岩石的尺寸和覆盖层。交叉验证的R ²值平均为.53（范围为.20-.76）。平均绝对误差范围为训练数据平均值的3％至98％（平均值为41％）。pH，om和ec模型的精度最高（R ²  > .6）。大多数纹理分数，CaCO ₃和SAR模型的R ²值为0.5至0.6。车型kwfact，dbovendry，resdept，岩石的模型，石膏和AWC了[R ²值从.4到.5；近地表模型往往表现更好。通常，其他模型的极细沙粒和200cm的估算值表现不佳（R ² = 0.2–.4），并且200cm的模型的样本量太小，无法建立可靠的模型。还通过创建相对的预测间隔来开发不确定性估计，从而使最终用户可以轻松地评估不确定性。在具有相对较高的预测间隔（较高的不确定性）的区域中平均误差会增加，该区域的采样密度也较低，这表明在这些区域中进行附加采样可以提高预测精度。在页岩母物质高度分层和地貌背景相对于更广泛的研究区域而言并不常见的地区，存在更大的不确定性。