Predictive soil property maps with prediction uncertainty at 30-meter resolution for the Colorado River Basin above Lake Mead

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30 meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 greater than 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

本数据集旨在展示新型预测制图方法，并为胡佛大坝上游的科罗拉多河流域提供全覆盖的30米分辨率网格化土壤属性图。研究采用随机森林模型（Random Forest model），将表征土壤形成因子的环境栅格图层（environmental raster layers）与野外采样数据相结合，生成可在采样点之间进行插值的预测制图结果。本次制图涵盖的土壤属性包括：土壤pH值（soil pH）、颗粒组成组分（砂粒sand、粉粒silt、粘粒clay、细砂fine sand、极细砂very fine sand）、岩石含量（rock）、电导率（electrical conductivity, EC）、石膏（gypsum）、碳酸钙（CaCO₃）、钠吸附比（sodium adsorption ratio, SAR）、有效持水量（available water capacity, AWC）、容重（bulk density, DBOVdry）、可蚀性（erodibility, KWFACT）、有机质（organic matter, OM），上述属性均设置了7个深度层级（0、5、15、30、60、100及200 cm）；此外还包含限制层深度（depth to restrictive layer, RESDEPT）以及地表岩石粒径与覆盖度（surface rock size and cover）。通过10折交叉验证（10-fold cross validation）估算模型精度与误差，结果显示各模型的决定系数（R²）介于0.20至0.76之间，平均值为0.52，标准差为0.12，模型性能存在一定差异。其中，土壤pH值、有机质与电导率模型的精度最优，R²均大于0.6；多数颗粒组成组分、碳酸钙以及钠吸附比模型的R²值介于0.5至0.6之间。可蚀性、容重、限制层深度、岩石含量、石膏以及有效持水量模型的R²值介于0.4至0.5之间，但表层模型的性能通常更优。极细砂组分模型以及其他属性的200 cm深度模型整体表现较差，R²值介于0.2至0.4之间；且200 cm深度模型的采样量过少，难以构建可靠的预测模型。本数据集使用的土壤采样数据中，90%以上采集于2000年之后，但也包含部分历史采样样本。此外，本数据集还通过构建相对预测区间（relative prediction intervals）生成了不确定性估算结果，便于终端用户便捷评估制图结果的不确定性。