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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 10.4121/0c934ac6-2e95-4422-8360-d3a802766c71. 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. [ABSTRACT FROM AUTHOR]

In response to the growing societal awareness of the critical role of healthy soils, there is 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 modelling 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 25m resolution between 0 - 2m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 - 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations numbering between 3815 - 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, and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was highest, with the model efficiency coefficient (MEC) ranging between 0.6 - 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC = 0.27 - 0.78), and especially oxalate-extractable phosphorus (MEC = -0.11 - 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 of spatial aggregates. A step-by-step manual helps users decide whether BIS-4D is suitable for their intended purpose, an overview of all maps and their uncertainties can be found in the supplementary information (SI), openly available code and input data enhance reproducibility and future updates, and BIS-4D prediction maps can be easily downloaded at https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). BIS-4D fills the previous data gap of a 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. [ABSTRACT FROM AUTHOR]