Digital Soil Maps underlying the publication "high-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management"

This dataset contains digital soil maps (.tiff) of predicted soil contents of oxalate-extractable iron and aluminium at a 25 m spatial resolution across six depth layers (0-5 cm, 5-10 cm, 10-25 cm, 25-60 cm, 60-100 cm and 100-200 cm) for agricultural fields in the Netherlands. For each of these depth layers, there is a map of mean predictions, the 5th, 50th (median) and 95th quantile predictions, as well as the 90% prediction interval (PI90 = 95th - 5th quantile) and prediction interval ratio (PIR = PI90 / median). PI90 and PIR represent absolute and relative uncertainty predictions, respectively. The maps were created using Quantile Regression Forest models, which were calibrated using geo-referenced wet-chemical measurements (n = 12,110) and near-infrared (NIR) estimates (n = 102,393) of oxalate-extractable iron and aluminium and over 150 spatial covariates (spatially explicit environmental variables of soil forming factors). See publication for details, including the assessment of map quality using design-based statistical inference.<br>

本数据集包含荷兰农田范围内、空间分辨率为25米的数字土壤图（格式为.tiff），涵盖6个深度层（0-5 cm、5-10 cm、10-25 cm、25-60 cm、60-100 cm及100-200 cm）的草酸盐浸提铁与铝（oxalate-extractable iron and aluminium）预测土壤含量。针对每个深度层，数据集均提供均值预测图、5分位数、50分位数（中位数）及95分位数预测图，同时包含90%预测区间（Prediction Interval 90，简称PI90，即95分位数与5分位数之差）与预测区间比率（Prediction Interval Ratio，简称PIR，即PI90与中位数的比值）。其中PI90与PIR分别代表绝对与相对不确定性预测结果。该土壤图基于分位数回归森林（Quantile Regression Forest）模型生成，模型通过草酸盐浸提铁与铝的地理参考湿化学测量数据（n=12,110）、近红外（NIR）估算数据（n=102,393），以及超过150项空间协变量（即表征土壤形成因子的空间显性环境变量）进行校准。详细信息（包括采用设计型统计推断开展的地图质量评估）请参阅相关发表文献。