Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2941-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2941-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Jun 2024
Data description paper |
| 25 Jun 2024
| 25 Jun 2024
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
Anatol Helfenstein
CORRESPONDING AUTHOR
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Vera L. Mulder
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Mirjam J. D. Hack-ten Broeke
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Maarten van Doorn
Nutriënten Management Instituut, Nieuwe Kanaal 7C, 6709 PA, Wageningen, the Netherlands
Environmental Systems Analysis Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, the Netherlands
Kees Teuling
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Dennis J. J. Walvoort
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Gerard B. M. Heuvelink
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
ISRIC – World Soil Information, P.O. Box 353, 6700 AJ, Wageningen, the Netherlands
Related authors
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
Short summary
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
Short summary
Short summary
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-618, https://doi.org/10.5194/essd-2024-618, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Current understanding of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate spatially-explicit new global maps of top- and subsoil τ, with quantified uncertainties for better user applications. The new insights from resulting maps facilitate modelling efforts of carbon cycle and support effective carbon management.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
Short summary
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, https://doi.org/10.5194/soil-7-217-2021, 2021
Short summary
Short summary
This paper focuses on the production of global maps of soil properties with quantified spatial uncertainty, as implemented in the SoilGrids version 2.0 product using DSM practices and adapting them for global digital soil mapping with legacy data. The quantitative evaluation showed metrics in line with previous studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
Short summary
Short summary
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Jairo Arturo Torres-Matallana, Ulrich Leopold, and Gerard B. M. Heuvelink
Hydrol. Earth Syst. Sci., 25, 193–216, https://doi.org/10.5194/hess-25-193-2021, https://doi.org/10.5194/hess-25-193-2021, 2021
Short summary
Short summary
This study aimed to select and characterise the main sources of input uncertainty in urban sewer systems, while accounting for temporal correlations of uncertain model inputs, by propagating input uncertainty through the model. We discuss the water quality impact of the model outputs to the environment, specifically in combined sewer systems, in relation to the uncertainty analysis, which constitutes valuable information for the environmental authorities and decision-makers.
Manoranjan Muthusamy, Alma Schellart, Simon Tait, and Gerard B. M. Heuvelink
Hydrol. Earth Syst. Sci., 21, 1077–1091, https://doi.org/10.5194/hess-21-1077-2017, https://doi.org/10.5194/hess-21-1077-2017, 2017
Short summary
Short summary
In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a small urban catchment are used in this study. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. Results from this study can be used for uncertainty analyses of hydrologic modelling.
Mirjam J. D. Hack-ten Broeke, Joop G. Kroes, Ruud P. Bartholomeus, Jos C. van Dam, Allard J. W. de Wit, Iwan Supit, Dennis J. J. Walvoort, P. Jan T. van Bakel, and Rob Ruijtenberg
SOIL, 2, 391–402, https://doi.org/10.5194/soil-2-391-2016, https://doi.org/10.5194/soil-2-391-2016, 2016
Short summary
Short summary
For calculating the effects of hydrological measures on agricultural production in the Netherlands a new comprehensive and climate proof method is being developed: WaterVision Agriculture (in Dutch: Waterwijzer Landbouw). End users have asked for a method that considers current and future climate, which can quantify the differences between years and also the effects of extreme weather events.
W. Marijn van der Meij, Arnaud J. A. M. Temme, Christian M. F. J. J. de Kleijn, Tony Reimann, Gerard B. M. Heuvelink, Zbigniew Zwoliński, Grzegorz Rachlewicz, Krzysztof Rymer, and Michael Sommer
SOIL, 2, 221–240, https://doi.org/10.5194/soil-2-221-2016, https://doi.org/10.5194/soil-2-221-2016, 2016
Short summary
Short summary
This study combined fieldwork, geochronology and modelling to get a better understanding of Arctic soil development on a landscape scale. Main processes are aeolian deposition, physical and chemical weathering and silt translocation. Discrepancies between model results and field observations showed that soil and landscape development is not as straightforward as we hypothesized. Interactions between landscape processes and soil processes have resulted in a complex soil pattern in the landscape.
D. R. Cameron, M. Van Oijen, C. Werner, K. Butterbach-Bahl, R. Grote, E. Haas, G. B. M. Heuvelink, R. Kiese, J. Kros, M. Kuhnert, A. Leip, G. J. Reinds, H. I. Reuter, M. J. Schelhaas, W. De Vries, and J. Yeluripati
Biogeosciences, 10, 1751–1773, https://doi.org/10.5194/bg-10-1751-2013, https://doi.org/10.5194/bg-10-1751-2013, 2013
Related subject area
Domain: ESSD – Land | Subject: Pedology
A China dataset of soil properties for land surface modelling (version 2, CSDLv2)
Mapping global distributions, environmental controls, and uncertainties of apparent top- and subsoil organic carbon turnover times
Distribution and sources of fallout 137Cs and 239+240Pu in Equatorial and Southern Hemisphere reference soils
An integrated dataset of ground hydrothermal regimes and soil nutrients monitored in some previously burned areas in hemiboreal forests in Northeast China during 2016–2022
Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
The patterns of soil nitrogen stocks and C : N stoichiometry under impervious surfaces in China
Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices
Harmonized Soil Database of Ecuador (HESD): data from 2009 to 2015
ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018
Colombian soil texture: building a spatial ensemble model
SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling
A repository of measured soil freezing characteristic curves: 1921 to 2021
A compiled soil respiration dataset at different time scales for forest ecosystems across China from 2000 to 2018
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
Short summary
Short summary
In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-618, https://doi.org/10.5194/essd-2024-618, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Current understanding of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate spatially-explicit new global maps of top- and subsoil τ, with quantified uncertainties for better user applications. The new insights from resulting maps facilitate modelling efforts of carbon cycle and support effective carbon management.
Gerald Dicen, Floriane Guillevic, Surya Gupta, Pierre-Alexis Chaboche, Katrin Meusburger, Pierre Sabatier, Olivier Evrard, and Christine Alewell
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-509, https://doi.org/10.5194/essd-2024-509, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Fallout radionuclides such as 137Cs and 239+240Pu are considered as critical tools in various environmental research. Here, we compiled reference soil data on these fallout radionuclides from the literature to build a comprehensive database. Using this database, we determined the distribution and sources of 137Cs and 239+240Pu. We also demonstrated how the database can be used to identify the environmental factors that influence their distribution using a machine-learning algorithm.
Xiaoying Li, Huijun Jin, Qi Feng, Qingbai Wu, Hongwei Wang, Ruixia He, Dongliang Luo, Xiaoli Chang, Raul-David Şerban, and Tao Zhan
Earth Syst. Sci. Data, 16, 5009–5026, https://doi.org/10.5194/essd-16-5009-2024, https://doi.org/10.5194/essd-16-5009-2024, 2024
Short summary
Short summary
In Northeast China, the permafrost is more sensitive to climate warming and fire disturbances than the boreal and Arctic permafrost. Since 2016, a continuous ground hydrothermal regime and soil nutrient content observation system has been gradually established in Northeast China. The integrated dataset includes soil moisture content, soil organic carbon, total nitrogen, total phosphorus, total potassium, ground temperatures at depths of 0–20 m, and active layer thickness from 2016 to 2022.
Niels H. Batjes, Luis Calisto, and Luis M. de Sousa
Earth Syst. Sci. Data, 16, 4735–4765, https://doi.org/10.5194/essd-16-4735-2024, https://doi.org/10.5194/essd-16-4735-2024, 2024
Short summary
Short summary
Soils are an important provider of ecosystem services. This dataset provides quality-assessed and standardised soil data to support digital soil mapping and environmental applications at a broad scale. The underpinning soil profiles were shared by a wide range of data providers. Special attention was paid to the standardisation of soil property definitions, analytical method descriptions and property values. We present three measures to assess "fitness for intended use" of the standardised data.
Songchao Chen, Zhongxing Chen, Xianglin Zhang, Zhongkui Luo, Calogero Schillaci, Dominique Arrouays, Anne Christine Richer-de-Forges, and Zhou Shi
Earth Syst. Sci. Data, 16, 2367–2383, https://doi.org/10.5194/essd-16-2367-2024, https://doi.org/10.5194/essd-16-2367-2024, 2024
Short summary
Short summary
A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across Europe using machine learning was generated. The proposed approach performed better in BD prediction and slightly better in SOC stock prediction than earlier-published PTFs. The outcomes present a meaningful advancement in enhancing the accuracy of BD, and the resultant topsoil BD and SOC stock datasets across Europe enable more precise soil hydrological and biological modeling.
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
Short summary
Short summary
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Qian Ding, Hua Shao, Chi Zhang, and Xia Fang
Earth Syst. Sci. Data, 15, 4599–4612, https://doi.org/10.5194/essd-15-4599-2023, https://doi.org/10.5194/essd-15-4599-2023, 2023
Short summary
Short summary
A soil survey in 41 Chinese cities showed the soil nitrogen (N) in impervious surface areas (ISA; NISA) was 0.59±0.35 kg m−2, lower than in pervious soils. Eastern China had the highest NISA but the lowest natural soil N in China. Soil N decreased linearly with depth in ISA but nonlinearly in natural ecosystems. Temperature was negatively correlated with C : NISA but positively correlated with natural soil C : N. The unique NISA patterns imply intensive disturbance in N cycle by soil sealing.
Lukas Rimondini, Thomas Gumbricht, Anders Ahlström, and Gustaf Hugelius
Earth Syst. Sci. Data, 15, 3473–3482, https://doi.org/10.5194/essd-15-3473-2023, https://doi.org/10.5194/essd-15-3473-2023, 2023
Short summary
Short summary
Peatlands have historically sequestrated large amounts of carbon and contributed to atmospheric cooling. However, human activities and climate change may instead turn them into considerable carbon emitters. In this study, we produced high-quality maps showing the extent of peatlands in the forests of Sweden, one of the most peatland-dense countries in the world. The maps are publicly available and may be used to support work promoting sustainable peatland management and combat their degradation.
Daphne Armas, Mario Guevara, Fernando Bezares, Rodrigo Vargas, Pilar Durante, Víctor Osorio, Wilmer Jiménez, and Cecilio Oyonarte
Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
Short summary
Short summary
The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 395–409, https://doi.org/10.5194/essd-15-395-2023, https://doi.org/10.5194/essd-15-395-2023, 2023
Short summary
Short summary
We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China (ChinaCropSM1 km) over 1993–2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimensions by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.
Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría
Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, https://doi.org/10.5194/essd-14-4719-2022, 2022
Short summary
Short summary
These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
Short summary
Short summary
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
Short summary
Short summary
The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
Short summary
Short summary
Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Hongru Sun, Zhenzhu Xu, and Bingrui Jia
Earth Syst. Sci. Data, 14, 2951–2961, https://doi.org/10.5194/essd-14-2951-2022, https://doi.org/10.5194/essd-14-2951-2022, 2022
Short summary
Short summary
We compiled a new soil respiration (Rs) database of China's forests from 568 studies published up to 2018. The hourly, monthly, and annual samples were 8317, 5003, and 634, respectively. Most of the Rs data are shown in figures but were seldom exploited. For the first time, these data were digitized, accounting for 82 % of samples. Rs measured with common methods was selected (Li-6400, Li-8100, Li-8150, gas chromatography) and showed small differences of ~10 %. Bamboo had the highest Rs.
Cited articles
Aarts, N. and Leeuwis, C.: The Politics of Changing the Dutch Agri-Food System, Journal of Political Sociology, 1, 1, https://doi.org/10.54195/jps.14922, 2023. a
Adhikari, K., Kheir, R. B., Greve, M. B., Bøcher, P. K., Malone, B. P., Minasny, B., McBratney, A. B., and Greve, M. H.: High-Resolution 3-D Mapping of Soil Texture in Denmark, Soil Sci. Soc. Am. J., 77, 860–876, https://doi.org/10.2136/sssaj2012.0275, 2013. a
Aitchison, J.: The statistical analysis of compositional data, Chapman and Hall, London, 1986. a
Akpa, S. I. C., Odeh, I. O. A., Bishop, T. F. A., and Hartemink, A. E.: Digital Mapping of Soil Particle-Size Fractions for Nigeria, Soil Sci. Soc. Am. J., 78, 1953–1966, https://doi.org/10.2136/sssaj2014.05.0202, 2014. a
Amirian-Chakan, A., Minasny, B., Taghizadeh-Mehrjardi, R., Akbarifazli, R., Darvishpasand, Z., and Khordehbin, S.: Some practical aspects of predicting texture data in digital soil mapping, Soil Till. Res., 194, 104289, https://doi.org/10.1016/j.still.2019.06.006, 2019. a, b
Appelhans, T., Detsch, F., Reudenbach, C., and Woellauer, S.: mapview: Interactive viewing of spatial data in R, Tech. rep., https://github.com/r-spatial/mapview (last access: 15 January 2024), 2023. a
Arets, E. J. M. M., Kolk, J. W. H. v. d., Hengeveld, G. M., Lesschen, J. P., Kramer, H., Kuikman, P. J., and Schelhaas, N. J.: Greenhouse gas reporting for the LULUCF sector in the Netherlands: methodological background, update 2020, WOt-technical report 168, Statutory Research Tasks Unit for Nature & the Environment (WOT Natuur & Milieu), Wageningen, the Netherlands, WOT Natuur & Milieu, https://doi.org/10.18174/517340, 2020. a, b
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B. M., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J., Mendonca-Santos, M. d. L., Minasny, B., Montanarella, L., Odeh, I. O. A., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: Chapter Three – GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties, in: Advances in Agronomy, edited by: Sparks, D. L., vol. 125, 93–134, Academic Press, https://doi.org/10.1016/B978-0-12-800137-0.00003-0, 2014a. a, b, c, d
Arrouays, D., McBratney, A., Minasny, B., Hempel, J., Heuvelink, G. B. M., MacMillan, R. A., Hartemink, A., Lagacherie, P., and McKenzie, N.: The GlobalSoilMap project specifications, in: Proceedings of the 1st GlobalSoilMap Conference, 9–12, https://doi.org/10.1201/b16500-4, 2015. a, b, c, d
Arrouays, D., Leenaars, J. G. B., Richer-de Forges, A. C., Adhikari, K., Ballabio, C., Greve, M., Grundy, M., Guerrero, E., Hempel, J., Hengl, T., Heuvelink, G. B. M., Batjes, N., Carvalho, E., Hartemink, A., Hewitt, A., Hong, S.-Y., Krasilnikov, P., Lagacherie, P., Lelyk, G., Libohova, Z., Lilly, A., McBratney, A., McKenzie, N., Vasquez, G. M., Mulder, V. L., Minasny, B., Montanarella, L., Odeh, I., Padarian, J., Poggio, L., Roudier, P., Saby, N., Savin, I., Searle, R., Solbovoy, V., Thompson, J., Smith, S., Sulaeman, Y., Vintila, R., Rossel, R. V., Wilson, P., Zhang, G.-L., Swerts, M., Oorts, K., Karklins, A., Feng, L., Ibelles Navarro, A. R., Levin, A., Laktionova, T., Dell'Acqua, M., Suvannang, N., Ruam, W., Prasad, J., Patil, N., Husnjak, S., Pásztor, L., Okx, J., Hallett, S., Keay, C., Farewell, T., Lilja, H., Juilleret, J., Marx, S., Takata, Y., Kazuyuki, Y., Mansuy, N., Panagos, P., Van Liedekerke, M., Skalsky, R., Sobocka, J., Kobza, J., Eftekhari, K., Alavipanah, S. K., Moussadek, R., Badraoui, M., Da Silva, M., Paterson, G., Gonçalves, M. d. C., Theocharopoulos, S., Yemefack, M., Tedou, S., Vrscaj, B., Grob, U., Kozák, J., Boruvka, L., Dobos, E., Taboada, M., Moretti, L., and Rodriguez, D.: Soil legacy data rescue via GlobalSoilMap and other international and national initiatives, GeoResJ, 14, 1–19, https://doi.org/10.1016/j.grj.2017.06.001, 2017. a
Arrouays, D., Mulder, V. L., and Richer-de Forges, A. C.: Soil mapping, digital soil mapping and soil monitoring over large areas and the dimensions of soil security – A review, Soil Security, 5, 100018, https://doi.org/10.1016/j.soisec.2021.100018, 2021. a
Bakker, J., Dessel, B. v., and Zadelhoff, F. V.: Natuurwaardenkaart 1988: natuurgebieden, bossen en natte gronden in Nederland, 266862, s-Gravenhage SDU, ISBN 90-12-06089-3, https://library.wur.nl/WebQuery/hydrotheek/266862 (last access: 16 January 2024), 1989. a
Baltensweiler, A., Walthert, L., Hanewinkel, M., Zimmermann, S., and Nussbaum, M.: Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland, Geoderma Regional, 27, e00437, https://doi.org/10.1016/j.geodrs.2021.e00437, 2021. a
Baumann, P., Helfenstein, A., Gubler, A., Keller, A., Meuli, R. G., Wächter, D., Lee, J., Viscarra Rossel, R., and Six, J.: Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring, SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, 2021. a
Been, T. H., Kempenaar, C., van Evert, F. K., Hoving, I. E., Kessel, G. J. T., Dantuma, W., Booij, J. A., Molendijk, L. P. G., Sijbrandij, F. D., and van Boheemen, K.: Akkerweb and farmmaps: Development of Open Service Platforms for Precision Agriculture, in: Precision Agriculture: Modelling, edited by: Cammarano, D., van Evert, F. K., and Kempenaar, C., Progress in Precision Agriculture, 269–293, Springer International Publishing, Cham, ISBN 978-3-031-15258-0, https://doi.org/10.1007/978-3-031-15258-0_16, 2023. a
Behrens, T., Förster, H., Scholten, T., Steinrücken, U., Spies, E.-D., and Goldschmitt, M.: Digital soil mapping using artificial neural networks, J. Plant Nutr. Soil Sci., 168, 21–33, https://doi.org/10.1002/jpln.200421414, 2005. a
Behrens, T., Schmidt, K., MacMillan, R. A., and Viscarra Rossel, R. A.: Multi-scale digital soil mapping with deep learning, Sci. Rep., 8, 15244, https://doi.org/10.1038/s41598-018-33516-6, 2018a. a
Behrens, T., Schmidt, K., Rossel, R. A. V., Gries, P., Scholten, T., and MacMillan, R. A.: Spatial modelling with Euclidean distance fields and machine learning, Eur. J. Soil Sci., 69, 757–770, https://doi.org/10.1111/ejss.12687, 2018b. a
BIJ12: Informatiemodel Natuur (IMNa), https://www.bij12.nl/onderwerpen/natuur-en-landschap/digitale-keten-natuur-ketensamenwerking/informatiemodel-natuur-imna/ (last access: 17 January 2024), 2019. a
Bouma, J. and Hartemink, A. E.: Soil science and society in the Dutch context, Netherlands Journal of Agricultural Science, 50, 133–140, 2003. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Breiman, L.: Manual on Setting Up, Using, and Understanding Random Forests v3.1, Technical report ftp://ftp.stat.berkeley.edu/pub/users/breiman/Using_random_forests_v3.1.pdf (last access: 14 January 2024), 2002. a
Brouwer, F. and Walvoort, D.: Basisregistratie Ondergrond (BRO) - actualisatie bodemkaart: Herkartering van de bodem in Eemland, WOt-technical report 155, WOT Natuur & Milieu, Wageningen, https://doi.org/10.18174/494728, 2019. a
Brouwer, F. and Walvoort, D.: Basisregistratie Ondergrond (BRO) Actualisatie bodemkaart: Herkartering van de veengebieden aan de flanken van de Utrechtse Heuvelrug, WOt-technical report 177, WOT Natuur & Milieu, Wageningen, https://doi.org/10.18174/521574, 2020. a
Brouwer, F., de Vries, F. d., and Walvoort, D. J. J.: Basisregistratie Ondergrond (BRO) actualisatie bodemkaart: Herkartering van de bodem in Flevoland, WOt technical report 143, WOT Natuur & Milieu, WUR, Wageningen, https://library.wur.nl/WebQuery/wurpubs/549064 (last access: 13 January 2024), 2018. a, b, c, d, e
Brouwer, F., Maas, G., Teuling, K., Harkema, T., and Verzandvoort, S.: Bodemkaart en Geomorfologische Kaart van Nederland: actualisatie 2020-2021 en toepassing: deelgebieden Gelderse Vallei-Zuid en -West en Veluwe-Zuid, WOt-rapport 134, WOT Natuur & Milieu, Wageningen, https://doi.org/10.18174/557455, 2021. a
Brouwer, F., Assinck, F., Harkema, T., Teuling, K., and Walvoort, D.: Actualisatie van de bodemkaart in degemeente Vijfheerenlanden: herkartering van de verbreiding van veen, WOt-rapport 151, WOT Natuur & Milieu, Wageningen, https://research.wur.nl/en/publications/actualisatie-van-de-bodemkaart-in-degemeente-vijfheerenlanden-her (last access: 25 January 2024), 2023. a, b
Brus, D., Hengl, T., Heuvelink, G., Kempen, B., Mulder, T. V., Olmedo, G. F., Poggio, L., Ribeiro, E., and Omuto, C. T.: Soil Organic Carbon Mapping Cookbook, edited by: Yigini, Y., Baritz, R., and Vargas, R. R., FAO, Rome, 1st edn., ISBN 978-92-5-130440-2, 2017. a
Brus, D. J.: Sampling for digital soil mapping: A tutorial supported by R scripts, Geoderma, 338, 464–480, https://doi.org/10.1016/j.geoderma.2018.07.036, 2019. a
Brus, D. J., Vašát, R., Heuvelink, G. B. M., Knotters, M., Vries, F. d., and Walvoort, D. J. J.: Towards a Soil Information System with quantified accuracy. A prototype for mapping continuous soil properties, Statutory Research Tasks Unit for Nature and the Environment 197, Alterra, Wageningen, 2009. a, b, c, d, e, f, g, h
Brus, D. J., Kempen, B., and Heuvelink, G. B. M.: Sampling for validation of digital soil maps, Eur. J. Soil Sci., 62, 394–407, https://doi.org/10.1111/j.1365-2389.2011.01364.x, 2011. a, b, c, d
Buchanan, S., Triantafilis, J., Odeh, I. O. A., and Subansinghe, R.: Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data, Geophysics, 77, WB201–WB211, https://doi.org/10.1190/geo2012-0053.1, 2012. a
Buringh, P., Stuer, G. G. L., and Vink, P.: Some techniques and methods of soil survey in the Netherlands, Netherlands Journal of Agricultural Science, 10, 17, 1962. a
BZK: Ministerie van Binnenlandse Zaken en Koninkrijksrelaties (BZK): Uitvoeringsregeling Meststoffenwet, https://wetten.overheid.nl/BWBR0018989/2022-11-17#Hoofdstuk9_Paragraaf8_Artikel103a (last access: 17 January 2024), 2022. a
Carré, F., McBratney, A. B., and Minasny, B.: Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping, Geoderma, 141, 1–14, https://doi.org/10.1016/j.geoderma.2007.01.018, 2007. a
Chagas, C. D. S., de Carvalho Junior, W., Bhering, S. B., and Calderano Filho, B.: Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions, CATENA, 139, 232–240, https://doi.org/10.1016/j.catena.2016.01.001, 2016. a
Chen, S., Arrouays, D., Leatitia Mulder, V., Poggio, L., Minasny, B., Roudier, P., Libohova, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de Forges, A. C., and Walter, C.: Digital mapping of GlobalSoilMap soil properties at a broad scale: A review, Geoderma, 409, 115567, https://doi.org/10.1016/j.geoderma.2021.115567, 2022. a, b, c, d, e, f, g, h, i
Chen, S., Saby, N. P. A., Martin, M. P., Barthès, B. G., Gomez, C., Shi, Z., and Arrouays, D.: Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping, Geoderma, 433, 116467, https://doi.org/10.1016/j.geoderma.2023.116467, 2023. a
Clement, J.: GIS Vierde Bosstatistiek: Gebruikersdocumentatie, Documentatie van bestanden, Tech. rep., Research Instituut voor de Groene Ruimte, Alterra, Wageningen, 2001. a
Cochran, W. G.: Sampling techniques, John Wiley & Sons, New York, 3rd edn., 1977. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a
de Bakker, H. and Schelling, J.: Systeem van bodemclassificatie voor Nederland: de hogere niveaus: With Engl. summary: A system of soil classification for the Netherlands, Centrum voor Landbouwpublikaties en Landbouwdocumentatie, Wageningen, the Netherlands, second, revised edn., ISBN 978-90-220-0997-0, 1989. a, b, c, d, e
de Bruin, S., Bregt, A., and Ven, M. V. D.: Assessing fitness for use: the expected value of spatial data sets, Int. J. Geogr. Inf., 15, 457–471, https://doi.org/10.1080/13658810110053116, 2001. a
de Bruin, S., Brus, D. J., Heuvelink, G. B. M., van Ebbenhorst Tengbergen, T., and Wadoux, A. M. J.-C.: Dealing with clustered samples for assessing map accuracy by cross-validation, Ecol. Inform., 69, 101665, https://doi.org/10.1016/j.ecoinf.2022.101665, 2022. a, b
de Gruijter, J. J., Walvoort, D. J. J., and van Gams, P. F. M.: Continuous soil maps – a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models, Geoderma, 77, 169–195, https://doi.org/10.1016/S0016-7061(97)00021-9, 1997. a
de Gruijter, J. J., van der Horst, J. B. F., Heuvelink, G. B. M., Knotters, M., and Hoogland, T.: Grondwater opnieuw op de kaart; methodiek voor de actualisering van grondwaterstandsinformatie en perceelsclassificatie naar uitspoelingsgevoeligheid voor nitraat, Alterra-rapport 915, Alterra, Wageningen, https://edepot.wur.nl/26169 (last access: 16 December 2023), 2004. a, b
de Gruijter, J. J., Brus, D., Bierkens, M., and Knotters, M.: Sampling for Natural Resource Monitoring, Springer, The Netherlands, ISBN 978-3-540-33161-2, 2006. a
de Vries, F. and Al, E. J.: De groeiplaatsgeschiktheid voor bosdoeltypen in beeld met ALBOS, Tech. Rep. 234, DLO-Staring Centrum, https://edepot.wur.nl/303208 (last access: 16 December 2023), 1992. a
de Vries, F., de Groot, W., Hoogland, T., and Denneboom, J.: De Bodemkaart van Nederland digitaal;. Toelichting bij inhoud, actualiteit en methodiek en korte beschrijving van additionel informatie, Alterra-rapport 811, Alterra, Research Instituut voor de Groene Ruimte, Wageningen, the Netherlands, 2003. a, b, c, d, e, f, g, h, i, j
de Vries, F., Brus, D. J., Kempen, B., Brouwer, F., and Heidema, A. H.: Actualisatie bodemkaart veengebieden: deelgebied en 2 in Noord Nederland, Alterra-rapport 2556, Alterra, Wageningen, https://edepot.wur.nl/314315 (last access: 16 December 2023), 2014. a
de Vries, F., Walvoort, D., and Brouwer, F.: Basisregistratie Ondergrond (BRO) Actualisatie bodemkaart; Herkartering van de eenheden met slappe kleilagen, Wageningen Environmental Research Rapport 2834, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/423728, 2017. a
de Vries, F., Brouwer, F., and Walvoort, D.: Basisregistratie Ondergrond (BRO) actualisatie bodemkaart: Herkatering westelijk veengebied Waterschap Drents Overijsselse Delta, Wageningen Environmental Research Rapport 2887, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/450341, 2018. a
Delmas, M., Saby, N., Arrouays, D., Dupas, R., Lemercier, B., Pellerin, S., and Gascuel-Odoux, C.: Explaining and mapping total phosphorus content in French topsoils, Soil Use Manage., 31, 259–269, https://doi.org/10.1111/sum.12192, 2015. a
Delta Programme: National Delta Programme 2024: Now for the Future, Tech. rep., Ministry of Infrastructure and Water Management, the Ministry of Agriculture, Nature and Food Quality, and the Ministry of the Interior and Kingdom Relations, Den Haag, https://english.deltaprogramma.nl/ (last access: 16 December 2023), 2023. a
Edelmann, C.: Soils of the Netherlands, vol. 53, North-Holland Publishing Company, Amsterdam, https://doi.org/10.1002/jpln.19510530307, 1950. a, b
Erisman, J. W.: Setting ambitious goals for agriculture to meet environmental targets, One Earth, 4, 15–18, https://doi.org/10.1016/j.oneear.2020.12.007, 2021. a
Eurofins Agro: Bemesting Wetgeving, https://www.eurofins-agro.com/nl-nl/bemesting-wetgeving (last access: 16 December 2023), 2024a. a
Eurofins Agro: BemestingsWijzer, https://www.eurofins-agro.com/nl-nl/bemestingswijzer (last access: 16 December 2023), 2024b. a
European Commission: A Soil Deal for Europe: 100 living labs and lighthouses to lead the transition towards healthy soils by 2030, Implementation Plan, European Commission, https://ec.europa.eu/info/sites/default/files/research_and_innovation/funding/documents/soil_mission_implementation_plan_final_for_publication.pdf (last access: 16 December 2023), 2021. a, b, c
Eurostat: Key figures on the European food chain, https://ec.europa.eu/eurostat/en/web/products-key-figures/w/ks-fk-22-001 (last access: 16 December 2023), 2022. a
EZK: Fysisch Geografische Regio's 2013; Ministerie van Economische Zaken en Klimaat (EZK; Ministry of Economic Affairs and Climate), https://nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/metadata/c8b5668f-c354-42f3-aafc-d15ae54cf170 (last access: 16 December 2023), 2013. a
EZK: Basisregistratie Gewaspercelen (BRP): 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019. Ministerie van Economische Zaken en Klimaat (EZK; Ministry of Economic Affairs and Climate), Agrarische Areaal Nederland, https://www.pdok.nl/introductie/-/article/basisregistratie-gewaspercelen-brp- (last access: 16 December 2023), 2019. a
Felix, R.: Bodemkartering voor 1943 – het geologisch perspectief, in: Van bodemkaart tot informatiesysteem, edited by: Buurman, P. and Sevink, J., 1–17, Wageningen Pers, Wageningen, 1995. a
Finke, P. A.: On digital soil assessment with models and the Pedometrics agenda, Geoderma, 171–172, 3–15, https://doi.org/10.1016/j.geoderma.2011.01.001, 2012. a
Finke, P. A., de Gruijter, J. J., and Visschers, R.: Status 2001 Landelijke Steekproef Kaarteenheden en toepassingen; Gestructureerde bemonstering en karakterisering Nederlandse bodems, Alterra-rapport 389, Alterra, Research Instituut voor de Groene Ruimte, Wageningen, 2001. a
GDAL/OGR contributors: GDAL/OGR geospatial data abstraction software library, https://gdal.org/ (last access: 16 December 2023), 2023. a
Gregoire, T. G. and Valentine, H. T.: Sampling Strategies for Natural Resources and the Environment, CRC Press, Boca Raton, USA, ISBN 978-0-203-49888-0, 2007. a
Grimm, R. and Behrens, T.: Uncertainty analysis of sample locations within digital soil mapping approaches, Geoderma, 155, 154–163, https://doi.org/10.1016/j.geoderma.2009.05.006, 2010. a
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V.: Gene Selection for Cancer Classification using Support Vector Machines, Mach. Learn., 46, 389–422, https://doi.org/10.1023/A:1012487302797, 2002. a
Hack-ten Broeke, M., van Beek, C. L., Hoogland, T., Knotters, M., Mol-Dijkstra, J. P., Schils, R., Smit, A., and de Vries, F.: Kaderrichtlijn Bodem; Basismateriaal voor eventuele prioritaire gebieden, Alterra-rapport 2007, Alterra, Wageningen, the Netherlands, 2009. a
Hack-ten Broeke, M. J. D., Mulder, H. M., Bartholomeus, R. P., van Dam, J. C., Holshof, G., Hoving, I. E., Walvoort, D. J. J., Heinen, M., Kroes, J. G., van Bakel, P. J. T., Supit, I., de Wit, A. J. W., and Ruijtenberg, R.: Quantitative land evaluation implemented in Dutch water management, Geoderma, 338, 536–545, https://doi.org/10.1016/j.geoderma.2018.11.002, 2019. a
Hartemink, A. E. and Sonneveld, M. P. W.: Soil maps of The Netherlands, Geoderma, 204–205, 1–9, https://doi.org/10.1016/j.geoderma.2013.03.022, 2013. a
Hastie, T., Tibshirani, R., and Friedman, J. H.: The elements of statistical learning: data mining, inference, and prediction, Springer series in statistics, Springer, New York, NY, 2nd edn., ISBN 978-0-387-84857-0, 978-0-387-84858-7, 2009. a
Hazeu, G., Schuiling, R., Thomas, D., Vittek, M., Storm, M., and Bulens, J. D.: Landelijk Grondgebruiksbestand Nederland 2021 (LGN2021): achtergronden, methodiek en validatie, Rapport 3235, Wageningen Environmental Research, Wageningen, https://research.wur.nl/en/publications/landelijk-grondgebruiksbestand-nederland-2021-lgn2021-achtergrond (last access: 16 December 2023), 2023. a
Hazeu, G. W., Vittek, M., Schuiling, R., Bulens, J. D., Storm, M. H., Roerink, G. J., and Meijninger, W. M. L.: LGN2018: een nieuwe weergave van het grondgebruik in Nederland, Tech. Rep. 3010, Wageningen Environmental Research, Wageningen, https://library.wur.nl/WebQuery/wurpubs/565896 (last access: 16 December 2023), 2020. a, b, c
Helfenstein, A.: 4 Dimensional Information About the Skin of the Earth, Wageningen University & Research [video], https://www.youtube.com/watch?v=ENCYUnqc-wo, last access: 21 June 2024a. a
Helfenstein, A.: BIS-4D. In Earth System Science Data, Zenodo [code], https://doi.org/10.5281/zenodo.12238785, 2024b. a
Helfenstein, A., Baumann, P., Viscarra Rossel, R., Gubler, A., Oechslin, S., and Six, J.: Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library, SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, 2021. a
Helfenstein, A., Mulder, V. L., Hack-ten Broeke, M. J., van Doorn, M., Teuling, K., Walvoort, D. J., and Heuvelink, G. B.: BIS-4D: Maps of soil properties and their uncertainties at 25 m resolution in the Netherlands, 4TU.ResearchData [data set], https://doi.org/10.4121/0C934AC6-2E95-4422-8360-D3A802766C71, 2024a. a, b, c
Helfenstein, A., Mulder, V. L., Hack-ten Broeke, M. J., van Doorn, M., Teuling, K., Walvoort, D. J., and Heuvelink, G. B.: Spatially explicit environmental variables at 25 m resolution for spatial modelling in the Netherlands, 4TU.ResearchData [data set], https://doi.org/10.4121/6AF610ED-9006-4AC5-B399-4795C2AC01EC, 2024b. a, b
Helfenstein, A., Mulder, V. L., Heuvelink, G. B. M., and Hack-ten Broeke, M. J. D.: Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands, Commun. Earth Environ., 5, 1–16, https://doi.org/10.1038/s43247-024-01293-y, 2024c. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Helfenstein, A., Teuling, K., Walvoort, D. J., Hack-ten Broeke, M. J., Mulder, V. L., van Doorn, M., and Heuvelink, G. B.: Georeferenced point data of soil properties in the Netherlands, 4TU.ResearchData [data set], https://doi.org/10.4121/C90215B3-BDC6-4633-B721-4C4A0259D6DC, 2024d. a
Hengl, T. and MacMillan, R. A.: Predictive Soil Mapping with R, OpenGeoHub foundation, Wageningen, the Netherlands, ISBN 978-0-359-30635-0, http://www.soilmapper.org (last access: 17 January 2024), 2019. a
Hengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M. G., Shepherd, K. D., Sila, A., MacMillan, R. A., Mendes de Jesus, J., Tamene, L., and Tondoh, J. E.: Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions, PLOS ONE, 10, e0125814, https://doi.org/10.1371/journal.pone.0125814, 2015. a
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., and Gräler, B.: Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables, PeerJ, 6, e5518, https://doi.org/10.7717/peerj.5518, 2018. a
Hessel, R., Stolte, J., and Riksen, M. J. P. M.: Huidige maatregelen tegen water- en winderosie in Nederland, Bodem, 21, 11–12, https://research.wur.nl/en/publications/huidige-maatregelen-tegen-water-en-winderosie-in-nederland-2 (last access: 17 January 2024), 2011. a
Heuvelink, G., Brus, D., De Vries, F., Kempen, B., Knotters, M., Vasat, R., and Walvoort, D.: Implications of digital soil mapping for soil information systems, in: 4th Global Workshop on Digital Soil Mapping, p. 6, Rome, Italy, https://edepot.wur.nl/160764 (last access: 17 January 2024), 2010. a
Heuvelink, G. B. M.: Uncertainty quantification of GlobalSoilMap products, in: GlobalSoilMap: Basis of the global spatial soil information system, 335–340, CRC Press, ISBN 978-1-315-77558-6, 2014. a
Heuvelink, G. B. M.: Uncertainty and Uncertainty Propagation in Soil Mapping and Modelling, in: Pedometrics, edited by: McBratney, A. B., Minasny, B., and Stockmann, U., Progress in Soil Science, 439–461, Springer International Publishing, Cham, ISBN 978-3-319-63439-5, https://doi.org/10.1007/978-3-319-63439-5_14, 2018. a, b, c
Heuvelink, G. B. M., Angelini, M. E., Poggio, L., Bai, Z., Batjes, N. H., Bosch, R. v. d., Bossio, D., Estella, S., Lehmann, J., Olmedo, G. F., and Sanderman, J.: Machine learning in space and time for modelling soil organic carbon change, Eur. J. Soil Sci., 72, 1607–1623, https://doi.org/10.1111/ejss.12998, 2020. a
Jukema, G., Ramaekers, P., and Berkhout, P.: De Nederlandse agrarische sector in internationaal verband: Editie 2023, Wageningen Economic Research, ISBN 978-94-6447-546-3, https://doi.org/10.18174/584222, 2023. a
Keller, A., Franzen, J., Knüsel, P., Papritz, A., and Zürrer, M.: Bodeninformations-Plattform Schweiz (BIP-CH), Thematische Synthese TS4 des Nationalen Forschungsprogramms “Nachhaltige Nutzung der Ressource Boden” (NFP 68), Schweizerischer Nationalfonds zur Förderung der wissenschaftlichen Forschung (SNF), Bern, 2018. a
Kempen, B., Brus, D. J., Heuvelink, G. B. M., and Stoorvogel, J. J.: Updating the 1:50 000 Dutch soil map using legacy soil data: A multinomial logistic regression approach, Geoderma, 151, 311–326, https://doi.org/10.1016/j.geoderma.2009.04.023, 2009. a, b
Kempen, B., Brus, D. J., and Stoorvogel, J. J.: Three-dimensional mapping of soil organic matter content using soil type–specific depth functions, Geoderma, 162, 107–123, https://doi.org/10.1016/j.geoderma.2011.01.010, 2011. a
Kempen, B., Brus, D. J., and Heuvelink, G. B. M.: Soil type mapping using the generalised linear geostatistical model: A case study in a Dutch cultivated peatland, Geoderma, 189–190, 540–553, https://doi.org/10.1016/j.geoderma.2012.05.028, 2012a. a
Kempen, B., Brus, D. J., Stoorvogel, J. J., Heuvelink, G. B. M., and de Vries, F.: Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps, Soil Sci. Soc. Am. J., 76, 2097–2115, https://doi.org/10.2136/sssaj2011.0424, 2012b. a
Kempen, B., Heuvelink, G. B. M., Brus, D., and Walvoort, D.: Towards GlobalSoilMap.net products for The Netherlands, GlobalSoilMap: Basis of the Global Spatial Soil Information System – Proceedings of the 1st GlobalSoilMap Conference, 85–90, https://doi.org/10.1201/b16500-19, 2014. a
Kempen, B., Brus, D. J., and de Vries, F.: Operationalizing digital soil mapping for nationwide updating of the 1:50,000
Keskin, H., Grunwald, S., and Harris, W. G.: Digital mapping of soil carbon fractions with machine learning, Geoderma, 339, 40–58, https://doi.org/10.1016/j.geoderma.2018.12.037, 2019. a
Khaledian, Y. and Miller, B. A.: Selecting appropriate machine learning methods for digital soil mapping, Appl. Math. Model., 81, 401–418, https://doi.org/10.1016/j.apm.2019.12.016, 2020. a
Knotters, M. and Vroon, H. R. J.: The economic value of detailed soil survey in a drinking water collection area in the Netherlands, Geoderma Regional, 5, 44–53, https://doi.org/10.1016/j.geodrs.2015.03.002, 2015. a, b
Knotters, M. and Walvoort, D.: Hoge resolutie, nauwkeurige kaarten?, https://basisregistratieondergrond.nl/doe-mee/begin-dag-bro-tje/bro-tjes-2020/15-oktober-hoge-resolutie-nauwkeurige-kaarten/ (last access: 16 October 2020), 2020. a
Knotters, M., Broeke, M. J. D. H.-t., Hinssen, P. J. W., Kolk, J. W. H. v. d., and Okx, J. P.: Betekenis van BRO/BIS Nederland voor WOT Natuur & Milieu: Een risicoanalyse, WOT Natuur & Milieu, https://research.wur.nl/en/publications/betekenis-van-brobis-nederland-voor-wot-natuur-amp-milieu-een-ris (last access: 17 January 2024), 2015a. a
Knotters, M., Okx, J., Hack-ten Broeke, M., and de Vries, F.: Bodem in beweging: BIS NEderland informeert, Bodem, 25, 11–13, 2015b. a
Kombrink, H., Doornenbal, J. C., Duin, E. J. T., Dulk, M. d., Veen, J. H. t., and Witmans, N.: New insights into the geological structure of the Netherlands; results of a detailed mapping project, Netherlands Journal of Geosciences, 91, 419–446, https://doi.org/10.1017/S0016774600000329, 2012. a
Koomen, A. and Maas, G.: Geomorfologische Kaart Nederland (GKN); Achtergronddocument bij het landsdekkende digitale bestand, Altera-rapport 1039, Alterra, Wageningen, 2004. a
Kortleve, A. J., Mogollón, J. M., Heimovaara, T. J., and Gebert, J.: Topsoil Carbon Stocks in Urban Greenspaces of The Hague, the Netherlands, Urban Ecosystems, 26, 725–742, https://doi.org/10.1007/s11252-022-01315-7, 2023. a
Kroes, J., Van Dam, J., Bartholomeus, R., Groenendijk, P., Heinen, M., Hendriks, R., Mulder, H., Supit, I., and Van Walsum, P.: SWAP version 4; Theory description and user manual, Report 2780, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/416321, 2017. a
Kuhn, M.: Building Predictive Models in R Using the Caret Package, J. Stat. Softw., 28, 1–26, https://doi.org/10.18637/jss.v028.i05, 2008. a
Kuhn, M.: Classification and Regression Training: package “caret”, GitHub [code], https://github.com/topepo/caret/, 2022. a
Kull, A., Kikas, T., Penu, P., and Kull, A.: Modeling Topsoil Phosphorus – From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping, Agronomy, 13, 1183, https://doi.org/10.3390/agronomy13051183, 2023. a
Lamigueiro, O. P. and Hijmans, R. J.: Visualization Methods for Raster Data: package “rasterVis”, GitHub [code], https://oscarperpinan.github.io/rastervis/ (last access: 17 January 2024), 2023. a
Lark, R. M. and Bishop, T. F. A.: Cokriging particle size fractions of the soil, Eur. J. Soil Sci., 58, 763–774, https://doi.org/10.1111/j.1365-2389.2006.00866.x, 2007. a
Lehmann, J., Bossio, D. A., Kögel-Knabner, I., and Rillig, M. C.: The concept and future prospects of soil health, Nat. Rev. Earth Environ., 1, 544–553, https://doi.org/10.1038/s43017-020-0080-8, 2020. a
Lemercier, B., Lagacherie, P., Amelin, J., Sauter, J., Pichelin, P., Richer-de Forges, A. C., and Arrouays, D.: Multiscale evaluations of global, national and regional digital soil mapping products in France, Geoderma, 425, 116052, https://doi.org/10.1016/j.geoderma.2022.116052, 2022. a
Li, J., Heap, A. D., Potter, A., and Daniell, J. J.: Application of machine learning methods to spatial interpolation of environmental variables, Environ. Model. Softw., 26, 1647–1659, https://doi.org/10.1016/j.envsoft.2011.07.004, 2011. a
Liu, L., Ji, M., and Buchroithner, M.: Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery, Sensors, 18, 3169, https://doi.org/10.3390/s18093169, 2018. a
Loiseau, T., Chen, S., Mulder, V. L., Román Dobarco, M., Richer-de Forges, A. C., Lehmann, S., Bourennane, H., Saby, N. P. A., Martin, M. P., Vaudour, E., Gomez, C., Lagacherie, P., and Arrouays, D.: Satellite data integration for soil clay content modelling at a national scale, Int. J. Appl. Earth Ob., 82, 101905, https://doi.org/10.1016/j.jag.2019.101905, 2019. a
Lookman, R., Vandeweert, N., Merckx, R., and Vlassak, K.: Geostatistical assessment of the regional distribution of phosphate sorption capacity parameters (FeOX and AlOX) in northern Belgium, Geoderma, 66, 285–296, https://doi.org/10.1016/0016-7061(94)00084-N, 1995. a, b
Ma, Y., Minasny, B., McBratney, A., Poggio, L., and Fajardo, M.: Predicting soil properties in 3D: Should depth be a covariate?, Geoderma, 383, 114794, https://doi.org/10.1016/j.geoderma.2020.114794, 2021. a, b
Malone, B., Searle, R., Malone, B., and Searle, R.: Updating the Australian digital soil texture mapping (Part 1*): re-calibration of field soil texture class centroids and description of a field soil texture conversion algorithm, Soil Res., 59, 419–434, https://doi.org/10.1071/SR20283, 2021. a
Malone, B. P., McBratney, A. B., and Minasny, B.: Spatial Scaling for Digital Soil Mapping, Soil Sci. Soc. Am. J., 77, 890–902, https://doi.org/10.2136/sssaj2012.0419, 2013. a
Malone, B. P., Minasny, B., and McBratney, A. B.: Using R for Digital Soil Mapping, Progress in Soil Science, Springer International Publishing, Cham, ISBN 978-3-319-44325-6 978-3-319-44327-0, https://doi.org/10.1007/978-3-319-44327-0, 2017. a
Maring, L., Vries, F. d., Brouwer, F., Groot, H., Kiden, P., Leeters, E. E. J. M., and Mol, G.: IMBOD deelactiviteit 5: inhoudelijke afstemming, Alterra-rapport 1817, Alterra, Wageningen, https://research.wur.nl/en/publications/imbod-deelactiviteit-5-inhoudelijke-afstemming (last access: 17 January 2024), 2009. a, b, c, d
Matos-Moreira, M., Lemercier, B., Dupas, R., Michot, D., Viaud, V., Akkal-Corfini, N., Louis, B., and Gascuel-Odoux, C.: High-resolution mapping of soil phosphorus concentration in agricultural landscapes with readily available or detailed survey data, Eur. J. Soil Sci., 68, 281–294, https://doi.org/10.1111/ejss.12420, 2017. a
McBratney, A., Mendonça Santos, M., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003. a, b
McKeague, J. A.: An evaluation of 0.1 m pyrophosphate and pyrophosphate-dithionite in comparison with oxalate as extractants of the accumulation products in podzols and some other soils, Ca. J. Soil Sci., 47, 95–99, https://doi.org/10.4141/cjss67-017, 1967. a
McKeague, J. A., Brydon, J. E., and Miles, N. M.: Differentiation of Forms of Extractable Iron and Aluminum in Soils, Soil Sci. Soc. Am. J., 35, 33–38, https://doi.org/10.2136/sssaj1971.03615995003500010016x, 1971. a
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn. Res., 7, 983–999, 2006. a
Meyer, H.: “caret” Applications for Spatial-Temporal Models: package “CAST”, GitHub [code], https://github.com/HannaMeyer/CAST (last access: 13 January 2024), 2023. a
Mulder, M., Walvoort, D., Brouwer, F., Tol-Leenders, D. V., and Verzandvoort, S.: Bodemgeschiktheidskaarten voor landbouw in de provincie Noord-Brabant: Een toepassing van Waterwijzer Landbouw, Rapport 3206, Wageningen Environmental Research, Wageningen, the Netherlands, https://research.wur.nl/en/publications/bodemgeschiktheidskaarten-voor-landbouw-in-de-provincie-noord-bra (last access: 18 January 2024), 2022. a
Mulder, V. L., Lacoste, M., Richer-de Forges, A. C., and Arrouays, D.: GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth, Sci. Total Environ., 573, 1352–1369, https://doi.org/10.1016/j.scitotenv.2016.07.066, 2016. a
Møller, A. B., Beucher, A. M., Pouladi, N., and Greve, M. H.: Oblique geographic coordinates as covariates for digital soil mapping, SOIL, 6, 269–289, https://doi.org/10.5194/soil-6-269-2020, 2020. a, b
Neyroud, J.-A. and Lischer, P.: Do different methods used to estimate soil phosphorus availability across Europe give comparable results?, J. Plant Nutr. Soil Sci., 166, 422–431, https://doi.org/10.1002/jpln.200321152, 2003. a
NHI: Nederlands Hydrologisch Instrumentarium (NHI), http://nhi-website-prd.fourdigits.nl:433/en/ (last access: 18 January 2024), 2023. a
Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A.: Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models, SOIL, 3, 191–210, https://doi.org/10.5194/soil-3-191-2017, 2017. a
Nussbaum, M., Zimmermann, S., Walthert, L., and Baltensweiler, A.: Benefits of hierarchical predictions for digital soil mapping – An approach to map bimodal soil pH, Geoderma, 437, 116579, https://doi.org/10.1016/j.geoderma.2023.116579, 2023. a
Odeh, I. O. A., Todd, A. J., and Triantafilis, J.: Spatial prediction of soil particle-size fractions as compositional data, Soil Sci,, 168, 501, https://doi.org/10.1097/01.ss.0000080335.10341.23, 2003. a
Padarian, J., Minasny, B., and McBratney, A. B.: Transfer learning to localise a continental soil vis-NIR calibration model, Geoderma, 340, 279–288, https://doi.org/10.1016/j.geoderma.2019.01.009, 2019a. a
Padarian, J., Minasny, B., and McBratney, A. B.: Using deep learning for digital soil mapping, SOIL, 5, 79–89, https://doi.org/10.5194/soil-5-79-2019, 2019b. a, b
Pahlavan-Rad, M. R. and Akbarimoghaddam, A.: Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran), CATENA, 160, 275–281, https://doi.org/10.1016/j.catena.2017.10.002, 2018. a
Pan, S. J. and Yang, Q.: A Survey on Transfer Learning, IEEE T. Knowl. Data Eng., 22, 1345–1359, https://doi.org/10.1109/TKDE.2009.191, 2010. a
Panagos, P., Montanarella, L., Barbero, M., Schneegans, A., Aguglia, L., and Jones, A.: Soil priorities in the European Union, Geoderma Regional, 29, e00510, https://doi.org/10.1016/j.geodrs.2022.e00510, 2022. a, b
Papadopoulos, G., Edwards, P., and Murray, A.: Confidence estimation methods for neural networks: a practical comparison, IEEE T. Neur. Netw., 12, 1278–1287, https://doi.org/10.1109/72.963764, 2001. a
Pawlowsky-Glahn, V. and Buccianti, A.: Compositional Data Analysis: Theory and Applications, John Wiley & Sons, Ltd, West Sussex, ISBN 978-1-119-97646-2, https://doi.org/10.1002/9781119976462, 2011. a
Pawlowsky-Glahn, V., Egozcue, J. J., and Tolosana-Delgado, R.: Modeling and Analysis of Compositional Data, John Wiley & Sons, West Sussex, ISBN 978-1-118-44306-4, 2015. a
Piikki, K., Wetterlind, J., Söderström, M., and Stenberg, B.: Perspectives on validation in digital soil mapping of continuous attributes – A review, Soil Use Manage., 37, 7–21, https://doi.org/10.1111/sum.12694, 2021. a, b
Poggio, L. and Gimona, A.: 3D mapping of soil texture in Scotland, Geoderma Regional, 9, 5–16, https://doi.org/10.1016/j.geodrs.2016.11.003, 2017. a
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a, b, c
QGIS Development Team: QGIS: A Free and Open Source Geographic Information System, https://www.qgis.org (last access: 18 January 2024), 2023. a
R Core Team: R: A language and environment for statistical computing, Tech. rep., R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 18 January 2024), 2023. a
RIVM: Grootschalige Concentratie- en Depositiekaarten Nederland (GCN, GDN), Rijksinstituut voor Volksgezondheid en Milieu (RIVM), https://www.rivm.nl/gcn-gdn-kaarten (last access: 18 January 2024), 2020. a
Román Dobarco, M., Arrouays, D., Lagacherie, P., Ciampalini, R., and Saby, N. P. A.: Prediction of topsoil texture for Region Centre (France) applying model ensemble methods, Geoderma, 298, 67–77, https://doi.org/10.1016/j.geoderma.2017.03.015, 2017. a
Ros, G. H., Verweij, S. E., Janssen, S. J. C., De Haan, J., and Fujita, Y.: An Open Soil Health Assessment Framework Facilitating Sustainable Soil Management, Environ. Sci. Technol., 56, 17375–17384, https://doi.org/10.1021/acs.est.2c04516, 2022. a, b
Ros, G. H., Haan, J. J. d., Fuchs, L. M., and Molendijk, L.: Bodembeoordeling van landbouwgronden voor diverse ecosysteemdiensten: ontwikkeling van de BLN, versie 2.0, Rapport / Wageningen University & Research, Business unit Open Teelten WPR-OT-1030, Wageningen Plant Research, Wageningen, https://doi.org/10.18174/634579, 2023. a, b
Sandri, M. and Zuccolotto, P.: A Bias Correction Algorithm for the Gini Variable Importance Measure in Classification Trees, J. Comput. Graph. Stat., 17, 611–628, https://doi.org/10.1198/106186008X344522, 2008. a
Sandri, M. and Zuccolotto, P.: Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms, Stat. Comput., 20, 393–407, https://doi.org/10.1007/s11222-009-9132-0, 2010. a
Schmidinger, J. and Heuvelink, G. B. M.: Validation of uncertainty predictions in digital soil mapping, Geoderma, 437, 116585, https://doi.org/10.1016/j.geoderma.2023.116585, 2023. a, b, c
Schoumans, O. F.: Description of the Phosphorus Sorption and Desorption Processes in Lowland Peaty Clay Soils, Soil Sci., 178, 291, https://doi.org/10.1097/SS.0b013e31829ef054, 2013. a
Schoumans, O. F. and Chardon, W. J.: Phosphate saturation degree and accumulation of phosphate in various soil types in The Netherlands, Geoderma, 237–238, 325–335, https://doi.org/10.1016/j.geoderma.2014.08.015, 2015. a, b, c
Schulte, R. P. O., Bampa, F., Bardy, M., Coyle, C., Creamer, R. E., Fealy, R., Gardi, C., Ghaley, B. B., Jordan, P., Laudon, H., O'Donoghue, C., Ó'hUallacháin, D., O'Sullivan, L., Rutgers, M., Six, J., Toth, G. L., and Vrebos, D.: Making the Most of Our Land: Managing Soil Functions from Local to Continental Scale, Front. Environ. Sci., 3, 81, https://doi.org/10.3389/fenvs.2015.00081, 2015. a
Scull, P., Franklin, J., Chadwick, O. A., and McArthur, D.: Predictive soil mapping: a review, Prog. Phys. Geogr. Earth and Environment, 27, 171–197, https://doi.org/10.1191/0309133303pp366ra, 2003. a, b
Seidel, M., Hutengs, C., Ludwig, B., Thiele-Bruhn, S., and Vohland, M.: Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations, Geoderma, 354, 113856, https://doi.org/10.1016/j.geoderma.2019.07.014, 2019. a
Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M., and Bajat, B.: Random Forest Spatial Interpolation, Remote Sens.-Basel, 12, 1687, https://doi.org/10.3390/rs12101687, 2020. a
Stettler, M., Keller, T., Weisskopf, P., Lamandé, M., Lassen, P., and Schjønning, P.: Terranimo – a web-based tool for evaluating soil compaction, Landtechnik, 69, 132–138, 2014. a
Stokstad, E.: Nitrogen crisis threatens Dutch environment – and economy, Science, 366, 1180–1181, https://doi.org/10.1126/science.366.6470.1180, 2019. a
Szatmári, G., Pásztor, L., and Heuvelink, G. B. M.: Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics, Geoderma, 403, 115356, https://doi.org/10.1016/j.geoderma.2021.115356, 2021. a, b
Taghizadeh-mehrjardi, R., Toomanian, N., Khavaninzadeh, A. R., Jafari, A., and Triantafilis, J.: Predicting and mapping of soil particle-size fractions with adaptive neuro-fuzzy inference and ant colony optimization in central Iran, Eur. J. Soil Sci., 67, 707–725, https://doi.org/10.1111/ejss.12382, 2016. a
Teuling, K., Knotters, M., van Tol-Leenders, T. P., Lesschen, J. P., and Reijneveld, J. A.: Nieuwe steekproefomvang voor landelijke monitoring koolstof en bodemkwaliteit, Vervolg op rapportages CC-NL en De staat van de Nederlandse landbouwbodems in 2018, Tech. rep., Wageningen Environmental Research, Wageningen, 2021. a
Tobler, W. R.: A Computer Movie Simulating Urban Growth in the Detroit Region, Economic Geography, 46, 234–240, https://doi.org/10.2307/143141, 1970. a, b
United Nations: Transforming our World: The 2030 Agenda for Sustainable Development, Tech. rep., United Nations, New York, NY, 2015. a
van Asselen, S., Erkens, G., Stouthamer, E., Woolderink, H. A. G., Geeraert, R. E. E., and Hefting, M. M.: The relative contribution of peat compaction and oxidation to subsidence in built-up areas in the Rhine-Meuse delta, The Netherlands, Sci. Total Environ., 636, 177–191, https://doi.org/10.1016/j.scitotenv.2018.04.141, 2018. a
van Dam, J. C., Huygen, J., Wesseling, J. G., Feddes, R. A., and Kabat, P.: Theory of SWAP version 2.0; Simulation of waterflow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment, Technical document 45, Wageningen Agricultural University and DLO Winand Staring Centre, Wageningen, the Netherlands, https://library.wur.nl/WebQuery/wurpubs/fulltext/222782 (last access: 19 January 2024), 1997. a
van Delft, B. and Maas, G.: Landschappelijke Bodemkartering (LBK): Achtergronden, toepassingen en technische documentatie, WOt-technical report 248, Wettelijke Onderzoekstaken Natuur & Milieu, Wageningen, https://doi.org/10.18174/641887, 2023. a, b
van den Akker, J. J. H. and Hoogland, T.: Comparison of risk assessment methods to determine the subsoil compaction risk of agricultural soils in The Netherlands, Soil Till. Res., 114, 146–154, https://doi.org/10.1016/j.still.2011.04.002, 2011. a
van den Akker, J. J. H., de Vries, F., Vermeulen, G., Hack-ten Broeke, M., and Schouten, T.: Risico op ondergrondverdichting in het landelijk gebied in kaart, Alterra-rapport 2409, Alterra, Wageningen, the Netherlands, 2012. a
van den Berg, F., Tiktak, A., Hoogland, T., Poot, A., Boesten, J., van der Linden, A. M. A., and Pol, J. W.: An improved soil organic matter map for GeoPEARL_NL: Model description of version 4.4.4 and consequence for the Dutch decision tree on leaching to groundwater, Tech. rep., Wageningen Environmental Research (Alterra), Wageningen, https://doi.org/10.18174/424920, 2017. a, b, c, d, e, f
van den Elsen, E., van Tol-Leenders, D., Teuling, K., Römkens, P., de Haan, J., Korthals, G., and Reijneveld, A.: De staat van de Nederlandse landbouwbodems in 2018: Op basis van beschikbare landsdekkende dataset (CC-NL) en bodem-indicatorenlijst (BLN), Tech. Rep. 3048, Wageningen Environmental Research, Wageningen, https://library.wur.nl/WebQuery/wurpubs/574884 (last access: 19 January 2024), 2020. a, b
van der Meulen, M., Doornenbal, J., Gunnink, J., Stafleu, J., Schokker, J., Vernes, R., van Geer, F., van Gessel, S., van Heteren, S., van Leeuwen, R., Bakker, M., Bogaard, P., Busschers, F., Griffioen, J., Gruijters, S., Kiden, P., Schroot, B., Simmelink, H., van Berkel, W., van der Krogt, R., Westerhoff, W., and van Daalen, T.: 3D geology in a 2D country: perspectives for geological surveying in the Netherlands, Netherlands J. Geosci., 92, 217–241, https://doi.org/10.1017/S0016774600000184, 2013. a
van der Westhuizen, S., Heuvelink, G. B. M., Hofmeyr, D. P., and Poggio, L.: Measurement error-filtered machine learning in digital soil mapping, Spatial Statistics, 47, 100572, https://doi.org/10.1016/j.spasta.2021.100572, 2022. a, b
van der Zee, S., van Riemsdijk, W., and de Haan, F.: Het protokol fosfaatverzadigde gronden I: toelichting, verslagen en mededelingen 1990-1A, Landbouwuniversiteit Wageningen, Landbouwuniversiteit Wageningen, Wageningen, https://edepot.wur.nl/394261 (last access: 19 January 2024), 1990. a
van Doorn, M., Helfenstein, A., Ros, G. H., Heuvelink, G. B. M., van Rotterdam-Los, D. A. M. D., Verweij, S. E., and de Vries, W.: High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management, Geoderma, 443, 116838, https://doi.org/10.1016/j.geoderma.2024.116838, 2024. a
van Leeuwen, C. C. E., Mulder, V. L., Batjes, N. H., and Heuvelink, G. B. M.: Statistical modelling of measurement error in wet chemistry soil data, Eur. J. Soil Sci., 73,e13137, https://doi.org/10.1111/ejss.13137, 2021. a
van Tol-Leenders, D., Knotters, M., Groot, W. d., Gerritsen, P., Reijneveld, A., van Egmond, F., Wösten, H., and Kuikman, P.: Koolstofvoorraad in de bodem van Nederland (1998–2018): CC-NL, Rapport 2974, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/509781, 2019. a, b
Varón-Ramírez, V. M., Araujo-Carrillo, G. A., and Guevara Santamaría, M. A.: Colombian soil texture: building a spatial ensemble model, Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, 2022. a
Vasenev, V. I., Stoorvogel, J. J., Vasenev, I. I., and Valentini, R.: How to map soil organic carbon stocks in highly urbanized regions?, Geoderma, 226–227, 103–115, https://doi.org/10.1016/j.geoderma.2014.03.007, 2014. a
Vasenev, V. I., Varentsov, M., Konstantinov, P., Romzaykina, O., Kanareykina, I., Dvornikov, Y., and Manukyan, V.: Projecting urban heat island effect on the spatial-temporal variation of microbial respiration in urban soils of Moscow megalopolis, Sci. Total Environ., 786, 147457, https://doi.org/10.1016/j.scitotenv.2021.147457, 2021. a
Vaysse, K. and Lagacherie, P.: Using quantile regression forest to estimate uncertainty of digital soil mapping products, Geoderma, 291, 55–64, https://doi.org/10.1016/j.geoderma.2016.12.017, 2017. a
Viscarra Rossel, R. A., Chen, C., Grundy, M. J., Searle, R., Clifford, D., and Campbell, P. H.: The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project, Soil Res., 53, 845, https://doi.org/10.1071/SR14366, 2015. a
Visschers, R., Finke, P. A., and de Gruijter, J. J.: A soil sampling program for the Netherlands, Geoderma, 139, 60–72, https://doi.org/10.1016/j.geoderma.2007.01.008, 2007. a
von Hippel, P. T.: Mean, Median, and Skew: Correcting a Textbook Rule, J. Stat. Educ., 13, 2, https://doi.org/10.1080/10691898.2005.11910556, 2005. a
Vos, P.: Origin of the Dutch coastal landscape: long-term landscape evolution of the Netherlands during the Holocene, described and visualized in national, regional and local palaeogeographical map series, Barkhuis, Groningen, ISBN 978-94-91431-82-1, 2015. a
Vos, P., Meulen, M. V. D., Weerts, H., and Bazelmans, J.: Atlas of the Holocene Netherlands, landscape and habitation since the last ice age, Amsterdam University Press, Amsterdam, https://www.cultureelerfgoed.nl/onderwerpen/bronnen-en-kaarten/overzicht/paleografische-kaarten (last access: 17 March 2020), 2020. a
Wadoux, A. M. J. C.: Using deep learning for multivariate mapping of soil with quantified uncertainty, Geoderma, 351, 59–70, https://doi.org/10.1016/j.geoderma.2019.05.012, 2019. a, b, c
Wadoux, A. M. J.-C. and Heuvelink, G. B. M.: Uncertainty of spatial averages and totals of natural resource maps, Methods Ecol. Evolut., 14, 1320–1332, https://doi.org/10.1111/2041-210X.14106, 2023. a
Wadoux, A. M. J.-C., Padarian, J., and Minasny, B.: Multi-source data integration for soil mapping using deep learning, SOIL, 5, 107–119, https://doi.org/10.5194/soil-5-107-2019, 2019. a
Wadoux, A. M. J. C., Heuvelink, G. B. M., de Bruin, S., and Brus, D. J.: Spatial cross-validation is not the right way to evaluate map accuracy, Ecol. Model., 457, 109692, https://doi.org/10.1016/j.ecolmodel.2021.109692, 2021a. a, b
Wadoux, A. M. J. C., Heuvelink, G. B. M., Lark, R. M., Lagacherie, P., Bouma, J., Mulder, V. L., Libohova, Z., Yang, L., and McBratney, A. B.: Ten challenges for the future of pedometrics, Geoderma, 401, 115155, https://doi.org/10.1016/j.geoderma.2021.115155, 2021b. a, b, c, d
Walvoort, D. J. J. and de Gruijter, J. J.: Compositional Kriging: A Spatial Interpolation Method for Compositional Data, Math. Geol., 33, 951–966, 2001. a
Wamelink, G. W. W., Walvoort, D. J. J., Sanders, M. E., Meeuwsen, H. A. M., Wegman, R. M. A., Pouwels, R., and Knotters, M.: Prediction of soil pH patterns in nature areas on a national scale, Appl. Veg. Sci., 22, 189–199, https://doi.org/10.1111/avsc.12423, 2019. a
Wang, Z. and Shi, W.: Mapping soil particle-size fractions: A comparison of compositional kriging and log-ratio kriging, J. Hydrol., 546, 526–541, https://doi.org/10.1016/j.jhydrol.2017.01.029, 2017. a
Withers, P. J. A., Sylvester-Bradley, R., Jones, D. L., Healey, J. R., and Talboys, P. J.: Feed the Crop Not the Soil: Rethinking Phosphorus Management in the Food Chain, Environ. Sci. Technol., 48, 6523–6530, https://doi.org/10.1021/es501670j, 2014. a
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R, J. Stat. Softw., 77, 1–17, https://doi.org/10.18637/jss.v077.i01, 2017. a
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 modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Earth system models and decision support systems greatly benefit from high-resolution soil...
Altmetrics
Final-revised paper
Preprint
Similar articles
A China dataset of soil properties for...
Shi et al.
An integrated dataset of ground...
Li et al.
Providing quality-assessed and...
Batjes et al.