Preprints
https://doi.org/10.5194/essd-2024-26
https://doi.org/10.5194/essd-2024-26
01 Feb 2024
 | 01 Feb 2024
Status: this preprint is currently under review for the journal ESSD.

BIS-4D: Mapping soil properties and their uncertainties at 25 m resolution in the Netherlands

Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink

Abstract. 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 25 m resolution between 0–2 m 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.

Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink

Status: open (until 24 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink

Data sets

BIS-4D: Maps of soil properties and their uncertainties at 25 m resolution in the Netherlands Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71

Georeferenced point data of soil properties in the Netherlands Anatol Helfenstein, Kees Teuling, Dennis J. J. Walvoort, Mirjam J. D. Hack-ten Broeke, Vera L. Mulder, Maarten van Doorn, and Gerard B. M. Heuvelink https://doi.org/10.4121/c90215b3-bdc6-4633-b721-4c4a0259d6dc

Spatially explicit environmental variables at 25 m resolution for spatial modelling in the Netherlands Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink https://doi.org/10.4121/6af610ed-9006-4ac5-b399-4795c2ac01ec

Model code and software

BIS-4D Anatol Helfenstein https://git.wur.nl/helfe001/bis-4d

Video supplement

4 Dimensional Information About the Skin of the Earth Anatol Helfenstein https://www.youtube.com/watch?v=ENCYUnqc-wo

Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink

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Short summary
Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here, we introduce BIS-4D, a statistical modelling platform that predicts nine essential soil properties and their uncertainties at 25m resolution in the surface 2m across the Netherlands. Using machine learning informed by up to 856000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was highest for clay, sand and pH.
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