The density of soil observations is a major determinant of digital soil mapping (DSM) prediction accuracy. In this study, we investigated the effect of soil sampling density on the performance of DSM to predict topsoil particle-size distribution in the Mayenne region of France. We tested two prediction algorithms, namely ordinary kriging (OK) and quantile random forest (QRF). The study area is a region of ~5000 km² with the highest density of field soil observations in France (1 profile per 0.64 km²). The number of training sites was progressively reduced (from n = 7500 to n = 400, corresponding to 1 profile per 0.7 km² to 1 profile per 13 km²) to simulate the different density of observations. For OK and QRF, we tested random subsampling for splitting the data into training and testing datasets using k-fold cross validation. For QRF we also tested conditioned Latin hypercube sampling based on the point coordinates or the covariates. The results indicated that, with increasing density of observations, OK performed as well or even better than QRF, depending on the particle-size fraction. For silt prediction, OK was systematically better than QRF. However, the prediction intervals were much larger for OK than for QRF, and OK did not seem to estimate uncertainty correctly. Overall, the performance indicators increased with the density of observations with a threshold at about 1 profile per 2 km² which suggests that the main limitation of DSM prediction accuracy using QRF is the amount of data collected in the field, not the type of calibration sampling strategy. Future DSM activities should focus on gathering more field observations.

土壤观测的密度是数字土壤测绘（DSM）预测精度的主要决定因素。在这项研究中，我们调查了土壤采样密度对DSM性能的影响，以预测法国Mayenne地区的表层土壤粒径分布。我们测试了两种预测算法，即普通克里格（OK）和分位数随机森林（QRF）。研究区域是约5000 km ²的地区，是法国实地土壤观测值最高的地区（每0.64 km ²分布1个剖面）。培训地点的数量逐渐减少（从n  = 7500减少到n  = 400，对应于每0.7 km ²的1个轮廓到每13 km ^2的1个轮廓）以模拟不同密度的观测值。对于OK和QRF，我们测试了随机子采样，以使用k倍交叉验证将数据分为训练和测试数据集。对于QRF，我们还根据点坐标或协变量测试了条件拉丁超立方体采样。结果表明，随着观察密度的增加，OK的表现好于QRF，甚至优于QRF，具体取决于粒径分数。对于淤泥预测，OK在系统上优于QRF。但是，OK的预测间隔比QRF大得多，OK似乎无法正确估计不确定性。总体而言，性能指标随观察密度的增加而增加，阈值大约为每2 km ² 1个剖面这表明使用QRF进行DSM预测准确性的主要限制是在现场收集的数据量，而不是校准采样策略的类型。未来的DSM活动应着重于收集更多的现场观察结果。