For the better part of the 20th century pedologists mapped soil by drawing boundaries between different classes of soil which they identified from survey on foot or by vehicle, supplemented by air-photo interpretation, and backed by an understanding of landscape and the processes by which soil is formed. Its limitations for representing gradual spatial variation and predicting conditions at unvisited sites became evident, and in the 1980s the introduction of geostatistics and specifically ordinary kriging revolutionized thinking and to a large extent practice. Ordinary kriging is based solely on sample data of the variable of interest—it takes no account of related covariates. The latter were incorporated from the 1990s onward as fixed effects and incorporated as regression predictors, giving rise to kriging with external drift and regression kriging. Simultaneous estimation of regression coefficients and variogram parameters is best done by residual maximum likelihood estimation. In recent years machine learning has become feasible for predicting soil conditions from huge sets of environmental data obtained from sensors aboard satellites and other sources to produce digital soil maps. The techniques are based on classification and regression, but they take no account of spatial correlations. Further, they are effectively ‘black boxes’; they lack transparency, and their output needs to be validated if they are to be trusted. They undoubtedly have merit; they are here to stay. They too, however, have their shortcomings when applied to spatial data, which spatial statisticians can help overcome. Spatial statisticians and pedometricians still have much to do to incorporate uncertainty into digital predictions, spatial averages and totals over regions, and to take into account errors in measurement and spatial positions of sample data. They must also communicate their understanding of these uncertainties to end users of soil maps, by whatever means they are made.

在 20 世纪的大部分时间里，土壤学家通过在不同类别的土壤之间绘制边界来绘制土壤图，这些边界是他们通过步行或车辆调查确定的，辅以空中照片解释，并以对景观和土壤过程的理解为后盾形成了。它在表示渐进式空间变化和预测未访问地点条件方面的局限性变得明显，并且在 1980 年代引入地统计学，特别是普通克里金法，彻底改变了思想并在很大程度上改变了实践。普通克里金法仅基于感兴趣变量的样本数据，不考虑相关协变量。后者从 1990 年代开始作为固定效应并入回归预测因子，产生带有外部漂移的克里金法和回归克里金法。回归系数和变异函数参数的同时估计最好通过残差最大似然估计来完成。近年来，机器学习已经变得可行，可以根据从卫星上的传感器和其他来源获得的大量环境数据来预测土壤状况，从而生成数字土壤图。这些技术基于分类和回归，但它们没有考虑空间相关性。此外，它们实际上是“黑匣子”；他们缺乏透明度，如果要信任他们的输出，就需要对其进行验证。他们无疑是有优点的；他们在这里留下来。然而，它们在应用于空间数据时也有其缺点，空间统计学家可以帮助克服这些缺点。空间统计学家和计步器在将不确定性纳入数字预测、空间平均值和区域总数以及考虑样本数据的测量误差和空间位置方面仍有许多工作要做。他们还必须将他们对这些不确定性的理解传达给土壤图的最终用户，无论以何种方式制作。