Abstract. In response to the growing societal awareness of the critical role of healthy soils, there has been an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high-resolution soil modeling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25 m resolution between 0 and 2 m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 and 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations amounting to between 3815 and 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross validation (CV) and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was the highest, with the model efficiency coefficient (MEC) ranging between 0.6 and 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC of 0.27 to 0.78), and especially oxalate-extractable phosphorus (MEC of −0.11 to 0.38) were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty in spatial aggregates. We provide an example of good practice to help users decide whether BIS-4D is suitable for their intended purpose. An overview of all maps and their uncertainties can be found in the Supplement. Openly available code and input data enhance reproducibility and help with future updates. BIS-4D prediction maps can be readily downloaded at https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). BIS-4D fills the previous data gap of the national-scale GlobalSoilMap product in the Netherlands and will hopefully facilitate the inclusion of soil spatial variability as a routine and integral part of decision support systems.

中文翻译：

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BIS-4D：以 25 米分辨率绘制荷兰土壤特性及其不确定性图


摘要。随着社会对健康土壤的关键作用的认识不断增强，对准确、高分辨率土壤信息的需求不断增加，以告知国家政策并支持可持续土地管理决策。尽管数字土壤测绘和 GlobalSoilMap 等举措取得了进步，但量化土壤变异性及其在空间、深度和时间上的不确定性仍然是一个挑战。因此，国家范围内的关键土壤特性地图通常仍然缺失，荷兰也是如此。为了应对这一挑战并填补这一数据空白，我们推出了 BIS-4D，这是一个面向荷兰的高分辨率土壤建模和绘图平台。 BIS-4D 可在 3D 空间中 0 米至 2 米深度之间以 25 米分辨率提供土壤质地（粘土、淤泥和沙子含量）、容重、pH 值、总氮、草酸盐可提取磷、阳离子交换容量及其不确定性图。此外，它还以相同的分辨率和深度范围提供 1953 年至 2023 年间土壤有机质及其不确定性的 3D 空间和时间图。统计模型使用机器学习，根据土壤观测数据（取决于土壤性质）和 366 个环境协变量，土壤观测数据数量介于 3815 到 855 950 之间。我们使用基于设计的概率样本统计推断和位置分组 10 倍交叉验证 (CV) 来评估均值和中值预测的准确性，并使用预测区间覆盖概率来评估预测不确定性。我们发现粘土、沙子和 pH 值图的准确性最高，模型效率系数 (MEC) 根据深度在 0.6 到 0.92 之间。淤泥、容重、土壤有机质、全氮和阳离子交换能力（MEC为0.27至0.78），尤其是草酸盐可萃取磷（MEC 为 -0.11 至 0.38）更难以预测。 BIS-4D 的主要限制之一是预测图不能用于量化空间聚合的不确定性。我们提供了一个良好实践的示例，以帮助用户确定 BIS-4D 是否适合其预期目的。所有地图及其不确定性的概述可以在补充中找到。公开可用的代码和输入数据增强了可重复性并有助于未来的更新。 BIS-4D 预测图可以在 https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 上轻松下载（Helfenstein 等人，2024a）。 BIS-4D 填补了荷兰国家级 GlobalSoilMap 产品之前的数据空白，并有望促进将土壤空间变异纳入决策支持系统的常规和组成部分。