Articles | Volume 7, issue 1
https://doi.org/10.5194/soil-7-217-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-7-217-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| Highlight paper
| 
14 Jun 2021
Original research article | Highlight paper |
| 14 Jun 2021
| 14 Jun 2021
SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty
ISRIC – World Soil Information, Wageningen, the Netherlands
Luis M. de Sousa
ISRIC – World Soil Information, Wageningen, the Netherlands
Niels H. Batjes
ISRIC – World Soil Information, Wageningen, the Netherlands
Gerard B. M. Heuvelink
ISRIC – World Soil Information, Wageningen, the Netherlands
Bas Kempen
ISRIC – World Soil Information, Wageningen, the Netherlands
Eloi Ribeiro
ISRIC – World Soil Information, Wageningen, the Netherlands
David Rossiter
ISRIC – World Soil Information, Wageningen, the Netherlands
Related authors
David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova
SOIL, 8, 559–586, https://doi.org/10.5194/soil-8-559-2022, https://doi.org/10.5194/soil-8-559-2022, 2022
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Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
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.
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
Earth Syst. Sci. Data, 16, 2941–2970, https://doi.org/10.5194/essd-16-2941-2024, https://doi.org/10.5194/essd-16-2941-2024, 2024
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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.
Luís Moreira de Sousa, Rául Palma, Bogusz Janiak, and Paul van Genuchten
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 29–34, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-29-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-29-2024, 2024
David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova
SOIL, 8, 559–586, https://doi.org/10.5194/soil-8-559-2022, https://doi.org/10.5194/soil-8-559-2022, 2022
Short summary
Short summary
Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
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
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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.
Niels H. Batjes, Eloi Ribeiro, and Ad van Oostrum
Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, https://doi.org/10.5194/essd-12-299-2020, 2020
Short summary
Short summary
This dataset provides quality-assessed and standardised soil data to support digital soil mapping and environmental applications at broadscale levels. 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 measures for geographic accuracy and a first approximation for the uncertainty associated with the various analytical methods.
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
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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.
Niels H. Batjes, Eloi Ribeiro, Ad van Oostrum, Johan Leenaars, Tom Hengl, and Jorge Mendes de Jesus
Earth Syst. Sci. Data, 9, 1–14, https://doi.org/10.5194/essd-9-1-2017, https://doi.org/10.5194/essd-9-1-2017, 2017
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Soil is an important provider of ecosystem services. Yet this natural resource is being threatened. Professionals, scientists, and decision makers require quality-assessed soil data to address issues such as food security, land degradation, and climate change. Procedures for safeguarding, standardising, and subsequently serving of consistent soil data to underpin broad-scale mapping and modelling are described. The data are freely accessible at doi:10.17027/isric-wdcsoils.20160003.
Jáchym Čepický and Luís Moreira de Sousa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 927–930, https://doi.org/10.5194/isprs-archives-XLI-B7-927-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-927-2016, 2016
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
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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
Soils and natural ecosystems
Mineral dust and pedogenesis in the alpine critical zone
Advancing studies on global biocrusts distribution
The soil knowledge library (KLIB) – a structured literature database on soil process research
Masked diversity and contrasting soil processes in tropical seagrass meadows: the control of environmental settings
Biocrust-linked changes in soil aggregate stability along a climatic gradient in the Chilean Coastal Range
Content of soil organic carbon and labile fractions depend on local combinations of mineral-phase characteristics
Effects of environmental factors and soil properties on soil organic carbon stock in a natural dry tropical area of Cameroon
The role of ecosystem engineers in shaping the diversity and function of arid soil bacterial communities
Disaggregating a regional-extent digital soil map using Bayesian area-to-point regression kriging for farm-scale soil carbon assessment
Opportunities and limitations related to the application of plant-derived lipid molecular proxies in soil science
Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales
A probabilistic approach to quantifying soil physical properties via time-integrated energy and mass input
Arctic soil development on a series of marine terraces on central Spitsbergen, Svalbard: a combined geochronology, fieldwork and modelling approach
Local versus field scale soil heterogeneity characterization – a challenge for representative sampling in pollution studies
Analysis and definition of potential new areas for viticulture in the Azores (Portugal)
The interdisciplinary nature of SOIL
Jeffrey S. Munroe, Abigail A. Santis, Elsa J. Soderstrom, Michael J. Tappa, and Ann M. Bauer
SOIL, 10, 167–187, https://doi.org/10.5194/soil-10-167-2024, https://doi.org/10.5194/soil-10-167-2024, 2024
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This study investigated how the deposition of mineral dust delivered by the wind influences soil development in mountain environments. At six mountain locations in the southwestern United States, modern dust was collected along with samples of soil and local bedrock. Analysis indicates that at all sites the properties of dust and soil are very similar and are very different from underlying rock. This result indicates that soils are predominantly composed of dust delivered by the wind over time.
Siqing Wang, Li Ma, Liping Yang, Yali Ma, Yafeng Zhang, Changming Zhao, and Ning Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2131, https://doi.org/10.5194/egusphere-2023-2131, 2023
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Biological soil crusts (cover a substantial proportion of dryland ecosystem and play crucial roles in ecological processes. Consequently, studying the spatial distribution of biocrusts holds great significance. This study aimed to stimulate global-scale investigations of biocrusts distribution by introducing three major approaches. Then, we summarized present understandings of biocrusts distribution. Finally, we proposed several potential research topics.
Hans-Jörg Vogel, Bibiana Betancur-Corredor, Leonard Franke, Sara König, Birgit Lang, Maik Lucas, Eva Rabot, Bastian Stößel, Ulrich Weller, Martin Wiesmeier, and Ute Wollschläger
SOIL, 9, 533–543, https://doi.org/10.5194/soil-9-533-2023, https://doi.org/10.5194/soil-9-533-2023, 2023
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Our paper presents a new web-based software tool to support soil process research. It is designed to categorize publications in this field according to site and soil characteristics, as well as experimental conditions, which is of critical importance for the interpretation of the research results. The software tool is provided open access for the soil science community such that anyone can contribute to improve the contents of the literature data base.
Gabriel Nuto Nóbrega, Xosé L. Otero, Danilo Jefferson Romero, Hermano Melo Queiroz, Daniel Gorman, Margareth da Silva Copertino, Marisa de Cássia Piccolo, and Tiago Osório Ferreira
SOIL, 9, 189–208, https://doi.org/10.5194/soil-9-189-2023, https://doi.org/10.5194/soil-9-189-2023, 2023
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The present study addresses the soil information gap in tropical seagrass meadows. The different geological and bioclimatic settings caused a relevant soil diversity. Contrasting geochemical conditions promote different intensities of soil processes. Seagrass soils from the northeastern semiarid coast are marked by a more intense sulfidization. Understanding soil processes may help in the sustainable management of seagrasses.
Nicolás Riveras-Muñoz, Steffen Seitz, Kristina Witzgall, Victoria Rodríguez, Peter Kühn, Carsten W. Mueller, Rómulo Oses, Oscar Seguel, Dirk Wagner, and Thomas Scholten
SOIL, 8, 717–731, https://doi.org/10.5194/soil-8-717-2022, https://doi.org/10.5194/soil-8-717-2022, 2022
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Biological soil crusts (biocrusts) stabilize the soil surface mainly in arid regions but are also present in Mediterranean and humid climates. We studied this stabilizing effect through wet and dry sieving along a large climatic gradient in Chile and found that the stabilization of soil aggregates persists in all climates, but their role is masked and reserved for a limited number of size fractions under humid conditions by higher vegetation and organic matter contents in the topsoil.
Malte Ortner, Michael Seidel, Sebastian Semella, Thomas Udelhoven, Michael Vohland, and Sören Thiele-Bruhn
SOIL, 8, 113–131, https://doi.org/10.5194/soil-8-113-2022, https://doi.org/10.5194/soil-8-113-2022, 2022
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Soil organic carbon (SOC) and its labile fractions are influenced by soil use and mineral properties. These parameters interact with each other and affect SOC differently depending on local conditions. To investigate the latter, the dependence of SOC content on parameters that vary on a local scale depending on parent material, soil texture, and land use as well as parameter combinations was statistically assessed. Relevance and superiority of local models compared to total models were shown.
Désiré Tsozué, Nérine Mabelle Moudjie Noubissie, Estelle Lionelle Tamto Mamdem, Simon Djakba Basga, and Dieudonne Lucien Bitom Oyono
SOIL, 7, 677–691, https://doi.org/10.5194/soil-7-677-2021, https://doi.org/10.5194/soil-7-677-2021, 2021
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Studies on soil organic carbon stock (SOCS) in the Sudano-Sahelian part of Cameroon are very rare. Organic C storage decreases with increasing latitude and more than 60 % of the SOCS is stored below the first 25 cm depth. In addition, a good correlation is noted between precipitation which decreases with increasing latitude and the total SOCS, indicating the importance of climate in the distribution of the total SOCS in the study area, which directly influence the productivity of the vegetation.
Capucine Baubin, Arielle M. Farrell, Adam Št'ovíček, Lusine Ghazaryan, Itamar Giladi, and Osnat Gillor
SOIL, 7, 611–637, https://doi.org/10.5194/soil-7-611-2021, https://doi.org/10.5194/soil-7-611-2021, 2021
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In this paper, we describe changes in desert soil bacterial diversity and function when two ecosystem engineers, shrubs and ant nests, in an arid environment are present. The results show that bacterial activity increases when there are ecosystem engineers and that their impact is non-additive. This is one of a handful of studies that investigated the separate and combined effects of ecosystem engineers on soil bacterial communities investigating both composition and function.
Sanjeewani Nimalka Somarathna Pallegedara Dewage, Budiman Minasny, and Brendan Malone
SOIL, 6, 359–369, https://doi.org/10.5194/soil-6-359-2020, https://doi.org/10.5194/soil-6-359-2020, 2020
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Most soil management activities are implemented at farm scale, yet digital soil maps are commonly available at regional/national scales. This study proposes Bayesian area-to-point kriging to downscale regional-/national-scale soil property maps to farm scale. A regional soil carbon map with a resolution of 100 m (block support) was disaggregated to 10 m (point support) information for a farm in northern NSW, Australia. Results are presented with the uncertainty of the downscaling process.
Boris Jansen and Guido L. B. Wiesenberg
SOIL, 3, 211–234, https://doi.org/10.5194/soil-3-211-2017, https://doi.org/10.5194/soil-3-211-2017, 2017
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The application of lipids in soils as molecular proxies, also often referred to as biomarkers, has dramatically increased in the last decades. Applications range from inferring changes in past vegetation composition to unraveling the turnover of soil organic matter. However, the application of soil lipids as molecular proxies comes with several constraining factors. Here we provide a critical review of the current state of knowledge on the applicability of molecular proxies in soil science.
Marleen de Blécourt, Marife D. Corre, Ekananda Paudel, Rhett D. Harrison, Rainer Brumme, and Edzo Veldkamp
SOIL, 3, 123–137, https://doi.org/10.5194/soil-3-123-2017, https://doi.org/10.5194/soil-3-123-2017, 2017
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We examined the spatial variability in SOC in a 10 000 ha landscape in SW China. The spatial variability in SOC was largest at the plot scale (1 ha) and the associations between SOC and land use, soil properties, vegetation, and topographical attributes varied across plot to landscape scales. Our results show that sampling designs must consider the controlling factors at the scale of interest in order to elucidate their effects on SOC against the variability within and between plots.
Christopher Shepard, Marcel G. Schaap, Jon D. Pelletier, and Craig Rasmussen
SOIL, 3, 67–82, https://doi.org/10.5194/soil-3-67-2017, https://doi.org/10.5194/soil-3-67-2017, 2017
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Here we demonstrate the use of a probabilistic approach for quantifying soil physical properties and variability using time and environmental input. We applied this approach to a synthesis of soil chronosequences, i.e., soils that change with time. The model effectively predicted clay content across the soil chronosequences and for soils in complex terrain using soil depth as a proxy for hill slope. This model represents the first attempt to model soils from a probabilistic viewpoint.
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.
Z. Kardanpour, O. S. Jacobsen, and K. H. Esbensen
SOIL, 1, 695–705, https://doi.org/10.5194/soil-1-695-2015, https://doi.org/10.5194/soil-1-695-2015, 2015
J. Madruga, E. B. Azevedo, J. F. Sampaio, F. Fernandes, F. Reis, and J. Pinheiro
SOIL, 1, 515–526, https://doi.org/10.5194/soil-1-515-2015, https://doi.org/10.5194/soil-1-515-2015, 2015
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Vineyards in the Azores have been traditionally settled on lava field terroirs whose workability and trafficability limitations make them presently unsustainable.
A landscape zoning approach based on a GIS analysis, incorporating factors of climate and topography combined with the soil mapping units suitable for viticulture was developed in order to define the most representative land units, providing an overall perspective of the potential for expansion of viticulture in the Azores.
E. C. Brevik, A. Cerdà, J. Mataix-Solera, L. Pereg, J. N. Quinton, J. Six, and K. Van Oost
SOIL, 1, 117–129, https://doi.org/10.5194/soil-1-117-2015, https://doi.org/10.5194/soil-1-117-2015, 2015
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This paper provides a brief accounting of some of the many ways that the study of soils can be interdisciplinary, therefore giving examples of the types of papers we hope to see submitted to SOIL.
Cited articles
Aitchison, J.: The statistical analysis of compositional data, Chapman &
Hall, London, UK, 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.,
78, 1953–1966, https://doi.org/10.2136/sssaj2014.05.0202, 2014. a
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J., de Lourdes Mendonca-Santos, M., Minasny, B., Montanarella, L., Odeh, I. O., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties, in: Advances in Agronomy, Academic Press, 93–134, https://doi.org/10.1016/B978-0-12-800137-0.00003-0, 2014. a, b, c
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., 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., Hallet, 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., McBratney, A., Bouma, J., Libohova, Z., de Forges, A. C. R.,
Morgan, C. L., Roudier, P., Poggio, L., and Mulder, V. L.: Impressions of
digital soil maps: The good, the not so good, and making them ever better,
Geoderma Regional, 20, e00255, https://doi.org/10.1016/j.geodrs.2020.e00255, 2020. a
Ballabio, C., Panagos, P., and Monatanarella, L.: Mapping topsoil physical
properties at European scale using the LUCAS database, Geoderma, 261,
110–123, 2016. a
Banwart, S., Black, H., Cai, Z., Gicheru, P., Joosten, H., Victoria, R., Milne, E., Noellemeyer, E., Pascual, U., Nziguheba, G., Vargas, R., Bationo, A., Buschiazzo, D., de Brogniez, D., Melillo, J., Richter, D., Termansen, M., van Noordwijk, M., Goverse, T., Ballabio, C., Bhattacharyya, T., Goldhaber, M., Nikolaidis, N., Zhao, Y., Funk, R., Duffy, C., Pan, G., la Scala, N.,
Gottschalk, P., Batjes, N., Six, J., van Wesemael, B., Stocking, M., Bampa,
F., Bernoux, M., Feller, C., Lemanceau, P., and Montanarella, L.: Benefits of
soil carbon: report on the outcomes of an international scientific committee
on problems of the environment rapid assessment workshop, Carbon Manag.,
5, 185–192, https://doi.org/10.1080/17583004.2014.913380, 2014. a
Barnes, M.: Aichi targets: Protect biodiversity, not just area, Nature, 526,
195–195, https://doi.org/10.1038/526195e, 2015. a
Barnes, R., Sahr, K., Evenden, G., Johnson, A., and Warmerdam, F.: dggridR: Discrete Global Grids for R, R package version 2.0.4, available at: https://github.com/r-barnes/dggridR/ (last access: 21 May 2021), 2016. a
Batjes, N.: ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global
grid (ver. 1.2), Report 2012/01, ISRIC – World Soil Information, available at:
http://www.isric.org/sites/default/files/isric_report_2012_01.pdf (last access: 21 May 2021), 2012. a
Batjes, N.: Harmonised soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61–68, https://doi.org/10.1016/j.geoderma.2016.01.034 2016. a
Batjes, N., Al-Adamat, R., Bhattacharyya, T., Bernoux, M., Cerri, C., Gicheru, P., Kamoni, P., Milne, E., Pal, D., and Rawajfih, Z.: Preparation of
consistent soil data sets for SOC modelling purposes: secondary SOTER data
sets for four case study areas, Agr. Ecosyst. Environ., 122,
26–34, https://doi.org/10.1016/j.agee.2007.01.005, 2007. a
Batjes, N. H.: Technologically achievable soil organic carbon sequestration in world croplands and grasslands, Land Degrad. Dev., 30, 25–32, https://doi.org/10.1002/ldr.3209, 2019. a
Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G., Zulkhe, M., Kalogirou, V., Seifert, F. M., and Arino, O.: Consistent global land cover maps for climate modelling communities: Current
achievements of the ESA's land cover CCI, in: Proceedings of the ESA Living
Planet Symposium, Edinburgh, Scotland, 9–13, 2013. a, b
Borrelli, P., Robinson, D. A., Fleischer, L. R., Lugato, E., Ballabio, C.,
Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V., Bagarello,
V., Oost, K. V., Montanarella, L., and Panagos, P.: An assessment of the
global impact of 21st century land use change on soil erosion, Nat. Commun., 8, 2013, https://doi.org/10.1038/s41467-017-02142-7, 2017. a
Bouma, J.: Engaging Soil Science in Transdisciplinary Research Facing
“Wicked” Problems in the Information Society, Soil Sci. Soc. Am. J., 79,
454–458, https://doi.org/10.2136/sssaj2014.11.0470, 2015. a
Brus, D. J.: Statistical sampling approaches for soil monitoring, Eur. J. Soil Sci., 65, 779–791, https://doi.org/10.1111/ejss.12176, 2014. a, b
Brus, D. J., Kempen, B., and Heuvelink, G.: 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
Buchhorn, M., Lesiv, M., Tsendbazar, N. E., Herold, M., Bertels, L., and Smets, B.: Copernicus Global Land Cover Layers – Collection 2,
Remote Sens.-Basel, 12, 1044, https://doi.org/10.3390/rs12061044, 2020. a
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X, and Mills, J.: Global land cover mapping at 30 m resolution: A
POK-based operational approach, ISPRS J. Photogramm., 103, 7–27, 2015. a
Chen, S., Mulder, V. L., Heuvelink, G. B. M., Poggio, L., Caubet, M.,
Román Dobarco, M., Walter, C., and Arrouays, D.: Model averaging for mapping
topsoil organic carbon in France, Geoderma, 366, 114237,
https://doi.org/10.1016/j.geoderma.2020.114237, 2020. a
Cowie, A. L., Orr, B. J., Castillo Sanchez, V. M., Chasek, P., Crossman, N. D., Erlewein, A., Louwagie, G., Maron, M., Metternicht, G. I., Minelli, S., Tengberg, A. E., Walter, S., and Welton, S.: Land in balance: The scientific conceptual framework for Land Degradation Neutrality, Environ. Sci. Policy, 79, 25–35, https://doi.org/10.1016/j.envsci.2017.10.011, 2018. a
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F.: A review of the global soil property maps for Earth system models, SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, 2019. a, b, c
de Sousa, L. M., Poggio, L., and Kempen, B.: Comparison of FOSS4G Supported
Equal-Area Projections Using Discrete Distortion Indicatrices,
ISPRS Int. J. Geo-Inf., 8, 351, https://doi.org/10.3390/ijgi8080351, 2019. a
de Sousa, L. M., Poggio, L., Dawes, G., Kempen, B., and Van Den Bosch, R.:
Computational Infrastructure of SoilGrids 2.0, in: International Symposium on
Environmental Software Systems, 24–31, 2020. a
Deutsch, C. and Journel, A.: GSLIB: Geostatistical Software Library and
User's Guide, edn. 2, Oxford University Press, New York, USA, 1998. a
Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J. E. M., Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., de Souza, N., Pintea, L., Brito, J. C., Llewellyn, O. A., Miller, A. G., Patzelt, A., Ghazanfar, S. A., Timberlake, J., Klöser, H., Shennan-Farpón, Y., Kindt, R., Lillesø, J.-P. B., van Breugel, P., Graudal, L., Voge, M., Al-Shammari, K. F., and Saleem, M.: An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm,
BioScience, 67, 534–545, https://doi.org/10.1093/biosci/bix014, 2017. a
Dorji, T., Odeh, I. O. A., Field, D. J., and Baillie, I. C.: Digital soil
mapping of soil organic carbon stocks under different land use and land cover
types in montane ecosystems, Eastern Himalayas,
Forest Ecol. Manag., 318, 91–102, https://doi.org/10.1016/j.foreco.2014.01.003, 2014. a
Ellili, Y., Walter, C., Michot, D., Pichelin, P., and Lemercier, B.: Mapping
soil organic carbon stock change by soil monitoring and digital soil mapping
at the landscape scale, Geoderma, 351, 1–8, https://doi.org/10.1016/j.geoderma.2019.03.005, 2019. a
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table
Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013. a
FAO: Digital Soil Map of the World and derived properties (ver. 3.5), Report
FAO Land and Water Digital Media Series 1, Food and Agriculture Organization of the United Nations (FAO), available at:
http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116 (last access: 21 May 2021),
1995. a
FAO: Guidelines for soil description, edn. 4, Report, Food and Agriculture Organization of the United Nations (FAO), available at:
http://www.fao.org/docrep/019/a0541e/a0541e.pdf (last access: 21 May 2021), 2006. a
FAO and ITPS: Status of the world's soil resources (SWSR) – Main report,
Report, Food and Agriculture Organization of the United Nations (FAO) and
Intergovernmental Technical Panel on Soils (ITPS), available at:
http://www.fao.org/3/a-i5199e.pdf (last access: 21 May 2021), 2015. a
FAO, IIASA, ISRIC, ISSCAS, and JRC: Harmonized World Soil Database (version
1.2), Report, edited by: Nachtergaele, F. O., van Velthuizen, H., Verelst,
L., Wiberg, D., Batjes, N. H., Dijkshoorn, J. A., van Engelen, V. W. P.,
Fischer, G., Jones, A., Montanarella, L., Petri, M., Prieler, S.,
Teixeira, E., and Xuezheng, S., Food and Agriculture Organization of the United Nations (FAO), International Institute for Applied Systems Analysis (IIASA), ISRIC – World Soil Information, Institute of Soil Science – Chinese Academy of Sciences (ISSCAS), Joint Research Centre of the European Commission (JRC), available at:
http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HWSD_Documentation.pdf (last access: 21 May 2021), 2012. a
FAO, IFAD, UNICEF, WFP, and WHO: The State of Food Security and Nutrition in
the World 2018, Building climate resilience for food security and nutrition,
Report, Food and Agriculture Organization of the United Nations (FAO), available at: http://www.fao.org/3/I9553EN/i9553en.pdf (last access: 21 May 2021), 2018. a
FAO-ISRIC: Guidelines for soil description, edn. 3, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 1986. a
FAO-Unesco: FAO-Unesco Soil Map of the World, 1:5 000 000 (Vol. 1 to 10),
United Nations Educational, Scientific, and Cultural Organization, Paris,
France, 1971–1981. a
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1 km spatial resolution
climate surfaces for global land areas, Int. J. Climatol.,
37, 4302–4315, 2017. a
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F., and Hamilton, S. K.: Development of a global inundation map at high spatial resolution from
topographic downscaling of coarse-scale remote sensing data,
Remote Sens. Environ., 158, 348–361, 2015. a
Goovaerts, P.: Geostatistics for Natural Resources Evaluation, Oxford
University Press, Oxford, UK, 500 pp., 1997. a
GRASS Development Team: Geographic Resources Analysis Support System (GRASS GIS) Software, version 7.8.0, available at: http://www.grass.osgeo.org (last access: 21 May 2021), 2020. a
Grunwald, S., Thompson, J. A., and Boettinger, J. L.: Digital soil mapping and modeling at continental scales: Finding solutions for global issues,
Soil Sci. Soc. Am. J., 75, 1201–1213, https://doi.org/10.2136/sssaj2011.0025, 2011. a
GSP and FAO: Pillar 4 Implementation Plan – Towards a Global Soil Information System, Report, Global Soil Partnership, available at:
http://www.fao.org/3/a-bl102e.pdf (last access: 21 May 2021), 2016. a
GSP and ITPS: Global soil organic carbon map (GSOCmap), Report Technical
Report, Global Soil Partnership (GSP) and International Panel on Soils
(ITPS), available at: http://www.fao.org/3/i8891en/I8891EN.pdf (last access: 21 May 2021), 2018. a
Guevara, M., Olmedo, G. F., Stell, E., Yigini, Y., Aguilar Duarte, Y., Arellano Hernández, C., Arévalo, G. E., Arroyo-Cruz, C. E., Bolivar, A., Bunning, S., Bustamante Cañas, N., Cruz-Gaistardo, C. O., Davila, F., Dell Acqua, M., Encina, A., Figueredo Tacona, H., Fontes, F., Hernández Herrera, J. A., Ibelles Navarro, A. R., Loayza, V., Manueles, A. M., Mendoza Jara, F., Olivera, C., Osorio Hermosilla, R., Pereira, G., Prieto, P., Ramos, I. A., Rey Brina, J. C., Rivera, R., Rodríguez-Rodríguez, J., Roopnarine, R., Rosales Ibarra, A., Rosales Riveiro, K. A., Schulz, G. A., Spence, A., Vasques, G. M., Vargas, R. R., and Vargas, R.: No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America, SOIL, 4, 173–193, https://doi.org/10.5194/soil-4-173-2018, 2018. a
Han, E., Ines, A. V. M., and Koo, J.: Development of a 10 km resolution global soil profile dataset for crop modeling applications,
Environ. Modell. Softw., 119, 70–83, https://doi.org/10.1016/j.envsoft.2019.05.012, 2019. a
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S.,
Tyukavina, A., Thau, D., Stehman, S., Goetz, S., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R.,G.:
High-resolution global maps of 21st-century forest cover change, Science,
342, 850–853, 2013. a
Harden, J. W., Hugelius, G., Ahlström, A., Blankinship, J. C., Bond-Lamberty,
B., Lawrence, C. R., Loisel, J., Malhotra, A., Jackson, R. B., Ogle, S.,
Phillips, C., Ryals, R., Todd-Brown, K., Vargas, R., Vergara, S. E., Cotrufo,
M. F., Keiluweit, M., Heckman, K. A., Crow, S. E., Silver, W. L., DeLonge,
M., and Nave, L. E.: Networking our science to characterize the state,
vulnerabilities, and management opportunities of soil organic matter,
Global Change Biol., 24, 705–718, https://doi.org/10.1111/gcb.13896, 2017. a
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM: A representation of rock properties at the Earth surface,
Geochem. Geophy. Geosy., 13, https://doi.org/10.1029/2012GC004370, 2012. a
Hengl, T., Mendes de Jesus, J., MacMillan, R. A., Batjes, N. H., Heuvelink,
G. B., Ribeiro, E. C., Samuel-Rosa, A., Kempen, B., Leenaars, J. G., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km – global soil information based on automated mapping, PLoS ONE, 9, e114788, https://doi.org/10.1371/journal.pone.0105992, 2014. a, b
Hengl, T., Leenaars, J. G. B., Shepherd, K. D., Walsh, M. G., Heuvelink, G. B. M., Mamo, T., Tilahun, H., Berkhout, E., Cooper, M., Fegraus, E., Wheeler,
I., and Kwabena, N. A.: Soil nutrient maps of Sub-Saharan Africa: assessment
of soil nutrient content at 250 m spatial resolution using machine learning,
Nutr. Cycl. Agroecosys., 109, 77–102, https://doi.org/10.1007/s10705-017-9870-x, 2017a. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748,
2017b. a, b, c, d, e
Heuvelink, G. B. M., Angelini, M. E., Poggio, L., Bai, Z., Batjes, N. H.,
van den Bosch, R., 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., 1–17, https://doi.org/10.1111/ejss.12998, 2020. a
Hounkpatin, O. K., de Hipt, F. O., Bossa, A. Y., Welp, G., and Amelung, W.:
Soil organic carbon stocks and their determining factors in the Dano
catchment (Southwest Burkina Faso), Catena, 166, 298–309, 2018. a
IPBES: Global assessment report on biodiversity and ecosystem services of the
Intergovernmental Science – Policy Platform on Biodiversity and Ecosystem
Services, edited by: Brondizio, E. S., Settele, J., Díaz, S., and Ngo, H. T.,
Report, IPBES Secretariat, Bonn, Germany, available at: https://www.ipbes.net/global-assessment-report-biodiversity-ecosystem-services (last access: 21 May 2021),
2019. a
IUSS Working Group WRB: World Reference Base for Soil Resources 2014, in: Update 2015 – International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, no. 106 in World Soil Resources Reports, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, available at: http://www.fao.org/3/i3794en/I3794en.pdf (last access: 21 May 2021), 2015. a
Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Kempen, B., and
de Sousa, L.: Global mapping of soil salinity change, Remote Sens. Environ., 231, 11260, https://doi.org/10.1016/j.rse.2019.111260, 2019. a
Janssen, P. H. M. and Heuberger, P. S. C.: Calibration of process-oriented
models, Ecol. Model., 83, 55–66, 1995. a
Karger, D., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R., Zimmermann, N., Linder, H., and Kessler, M.: CHELSA climatologies at high
resolution for the earth's land surface areas (Version 1.1), World Data
Center for Climate, 2016. a
Kempen, B., Brus, D. J., and de Vries, F.: Operationalizing digital soil
mapping for nationwide updating of the 1:50 000 soil map of the Netherlands, Geoderma, 241–242, 313–329, https://doi.org/10.1016/j.geoderma.2014.11.030, 2015. a
Kempen, B., Dalsgaard, S., Kaaya, A. K., Chamuya, N., Ruipérez-González, M.,
Pekkarinen, A., and Walsh, M. G.: Mapping topsoil organic carbon
concentrations and stocks for Tanzania, Geoderma, 337, 164–180,
https://doi.org/10.1016/j.geoderma.2018.09.011, 2019. a
Keskin, H. and Grunwald, S.: Regression kriging as a workhorse in the digital
soil mapper's toolbox, Geoderma, 326, 22–41,
https://doi.org/10.1016/j.geoderma.2018.04.004, 2018. a, b, c
Khaledian, Y. and Miller, B. A.: Selecting appropriate machine learning methods for digital soil mapping, Appl. Math. Model., 81, 401–418, 2020. a
Kuhn, M.: A Short Introduction to the caret Package, R Found Stat Comput, https://cran.microsoft.com/snapshot/2015-08-17/web/packages/caret/vignettes/caret.pdf (last access: 21 May 2021), 10 pp., 2015. a
Liu, F., Zhang, G.-L., Song, X., Li, D., Zhao, Y., Yang, J., Wu, H., and Yang, F.: High-Resolution and Three-Dimensional Mapping of Soil Texture of
China, Geoderma, 361, 114061, https://doi.org/10.1016/j.geoderma.2019.114061,
2020. a
Luo, Y., Ahlström, A., Allison, S., Batjes, N., Brovkin, V., Carvalhais, N.,
Chappell, A., Ciais, P., Davidson, E., Finzi, A., Georgiou, K., Guenet, B.,
Hararuk, O., Harden, J., He, Y., Hopkins, F. M., Jiang, L., Koven, C.,
Jackson, R., Jones, C., Lara, M., Liang, J., McGuire, A., Parton, W. J.,
Peng, C., Randerson, J., Salazr, A., Sierra, C., Smoth, M., Tian, H.,
Todd-Brown, K., Torn, M., van Groeningen, K., Wang, Y. P., Westm, O., Wei,
Y., Wieder, W., Xia, J., Xia, X., Xu, X., and Zhu, T.: Towards more realistic
projections of soil carbon dynamics by Earth System Models,
Global Biogeochem. Cy., 30, 40–56, https://doi.org/10.1002/2015GB005239, 2016. 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
Malhotra, A., Todd-Brown, K., Nave, L. E., Batjes, N. H., Holmquist, J. R.,
Hoyt, A. M., Iversen, C. M., Jackson, R. B., Lajtha, K., Lawrence, C.,
Vindušková, O., Wieder, W., Williams, M., Hugelius, G., and Harden, J.: The
landscape of soil carbon data: emerging questions, synergies and databases,
Prog. Phys. Geog., 43, 707–719, https://doi.org/10.1177/0309133319873309, 2019. a
Mallavan, B., Minasny, B., and Mcbratney, A.: Homosoil, a Methodology for Quantitative Extrapolation of Soil Information Across the Globe, in: Digital Soil Mapping, Progress in Soil Science, edited by: Boettinger, J. L., Howell, D. W., Moore, A. C., Hartemink, A. E., and Kienast-Brown, S., Springer, Dordrecht, The Netherlands, 137–150, https://doi.org/10.1007/978-90-481-8863-5_12, 2010. a
McBratney, A. B., Mendonça Santos, M. L., and Minasny, B.: On digital soil
mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003. a
Meyer, H. and Pebesma, E.: Predicting into unknown space? Estimating the area
of applicability of spatial prediction models, ArXiv, abs/2005.07939, 2020. a
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., and Nauss, T.: Improving
performance of spatio-temporal machine learning models using forward feature
selection and target-oriented validation, Environ. Modell. Softw., 101, 1–9, https://doi.org/10.1016/j.envsoft.2017.12.001, 2018. a
Minasny, B. and McBratney, A. B.: Digital soil mapping: A brief history and
some lessons, Geoderma, 264, 301–311, https://doi.org/10.1016/j.geoderma.2015.07.017, 2016. a, b
Mora-Vallejo, A., Claessens, L., Stoorvogel, J., and Heuvelink, G. B. M.: Small scale digital soil mapping in Southeastern Kenya, Catena, 76, 44–53,
https://doi.org/10.1016/j.catena.2008.09.008, 2008. a
Moulatlet, G. M., Zuquim, G., Figueiredo, F. O. G., Lehtonen, S., Emilio, T.,
Ruokolainen, K., and Tuomisto, H.: Using digital soil maps to infer edaphic
affinities of plant species in Amazonia: Problems and prospects,
Ecol. Evol., 7, 8463–8477, https://doi.org/10.1002/ece3.3242, 2017. a
Nauman, T. W. and Duniway, M. C.: Relative prediction intervals reveal larger
uncertainty in 3D approaches to predictive digital soil mapping of soil
properties with legacy data, Geoderma, 347, 170–184,
https://doi.org/10.1016/j.geoderma.2019.03.037, 2019. a
Nijbroek, R., Piikki, K., Söderström, M., Kempen, B., Turner, K., Hengari,
S., and Mutua, J.: Soil Organic Carbon Baselines for Land Degradation
Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in
Otjozondjupa, Namibia, Sustainability, 10, 1610, https://doi.org/10.3390/su10051610, 2018. a
NRCS National Soil Survey Center: Gridded Soil Survey Geographic
(gSSURGO) Database User Guide, Version 2.2, Tech. Rep., National Soil Survey Center, available at:
http://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf (last access: 21 May 2021), 2016. a
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A.: Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, 2018. a, b, c
Omuto, C., Nachtergaele, F., and Vargas Rojas, R.: State of the Art Report on
Global and Regional Soil Information: Where are we? Where to go?, Report,
Food and Agriculture Organization of the United Nations (FAO), available at: http://www.fao.org/docrep/017/i3161e/i3161e.pdf (last access: 21 May 2021), 2012. a
Oreskes, N.: Evaluation (Not Validation) of Quantitative Models, Environ. Health Persp., 106, 1453–1460, https://doi.org/10.1289/ehp.98106s61453, 1998. a
Pachepsky, Y. and Rawls, W. J. (Eds.): Development of Pedotransfer Functions in Soil Hydrology, in: Developments in Soil Science, Elsevier, 2004. a
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584,
2016. a
Pelletier, J., Broxton, P., Hazemberg, P., Zeng, X., Troch, P., Niu, G.,
Williams, Z., Brunke, M., and Gochis, D.: Global 1 km Gridded Thickness of
Soil, Regolith, and Sedimentary Deposit Layers, https://doi.org/10.3334/ORNLDAAC/1304, 2016. a
Piikki, K., Söderström, M., and Stadig, H.: Local adaptation of a national
digital soil map for use in precision agriculture,
Advances in Animal Biosciences, 8, 430–432, https://doi.org/10.1017/s2040470017000966, 2017. a
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, 2017a. a
Poggio, L. and Gimona, A.: Assimilation of optical and radar remote sensing
data in 3D mapping of soil properties over large areas, Sci. Total Environ., 579, 1094–1110, https://doi.org/10.1016/j.scitotenv.2016.11.078, 2017b. a, b
Poggio, L., Gimona, A., and Brewer, M. J.: Regional scale mapping of soil
properties and their uncertainty with a large number of satellite-derived
covariates, Geoderma, 209–210, 1–14, https://doi.org/10.1016/j.geoderma.2013.05.029, 2013. a
R Core Team: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
http://www.R-project.org/ (last access: 21 May 2021), 2020. a
Ribeiro, E., Batjes, N., and Van Oostrum, A.: Towards the standardization and harmonization of world soil data, in: Procedures Manual Report 2018, World Soil Information Service (WoSIS), Wageningen, The Netherlands, Report 2018/01, https://doi.org/10.17027/isric-wdcsoils.20180001, 2018. a
Robinson, N., Regetz, J., and Guralnick, R. P.: EarthEnv-DEM90: A
nearly-global, void-free, multi-scale smoothed, 90 m digital elevation model
from fused ASTER and SRTM data, ISPRS J. Photogramm., 87, 57–67, 2014. a
Rockstroem, J., Falkenmark, M., Lannerstad, M., and Karlberg, L.: The planetary water drama: Dual task of feeding humanity and curbing climate change, Geophys. Res. Lett., 39, L15401, https://doi.org/10.1029/2012gl051688, 2012. a
Rossiter, D. G.: Maps and Models Are Never Valid, but They Can Be Evaluated
(with Responses), Pedometron, 41, 19–28, available at:
http://www.pedometrics.org/Pedometron/Pedometron41.pdf (last access: 21 May 2021), 2017. a
Roudier, P., Burge, O. R., Richardson, S. J., McCarthy, J. K., Grealish, G.,
and Ausseil, A.-G.: National Scale 3D Mapping of Soil pH Using a Data
Augmentation Approach, Remote Sens.-Basel, 12, 2872, https://doi.org/10.3390/rs12182872, 2020. a
Sanderman, J., Hengl, T., and Fiske, G. J.: Soil carbon debt of 12 000 years of human land use, P. Natl. Acad. Sci. USA, 114, 9575–9580, https://doi.org/10.1073/pnas.1706103114, 2017. a
Sayre, R., Dangermond, J., Frye, C., Vaughan, R., Aniello, P., Breyer, S.,
Cribbs, D., Hopkins, D., Nauman, R., Derrenbacher, W., Wright, D. J., Brown, C., Convis, C., Smith, J. H., Benson, L., VanSistine, P., Warner, H., Cress, J. J., Danielson, J. J., Hamann, S. L., Cecere, T., Reddy, A. D., Burton, D., Grosse, A., True, D., Metzger, M., Hartmann, J., Moosdorf, N., Durr, H., Paganini, M., Defourny, P., Arino, O., Maynard, S., Anderson, M., and Comer, P.: A new map of
global ecological land units – an ecophysiographic stratification approach,
Association of American Geographers, Washington D.C., USA, 2014. a
Schoeneberger, P., Wysicki, D., Benham, E., and Broderson, W.: Field book for
describing and sampling soils (ver. 3.0), Natural Resources Conservation
Service, National Soil Survey Center, Lincoln, Nebraska, USA, available at:
http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_052523.pdf (last access: 21 May 2021),
2012. a, b
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data set for earth system modeling, J. Adv. Model. Earth Sy., 6, 249–263, https://doi.org/10.1002/2013MS000293, 2014. a
Shrestha, D. L. and Solomatine, D. P.: Machine learning approaches for
estimation of prediction interval for the model output, Neural Networks, 19,
225–235, https://doi.org/10.1016/j.neunet.2006.01.012, 2006. a
Smith, P., Soussana, J.-F., Angers, D., Schipper, L., Chenu, C., Rasse, D.,
Batjes, N. H., van Egmond, F., McNeill, S., Kuhnert, M., Arias-Navaro, C.,
Olesen, J. E., Chirinda, N., Fornara, D., Joosse, P., Wollenberg, L.,
Alvaro-Fuentes, J., and Cobena, A.: How to measure, report and verify soil
carbon change to realise the potential of soil carbon sequestration for
atmospheric greenhouse gas remova, Global Change Biol., 26, 219–241,
https://doi.org/10.1111/gcb.14815, 2019. a
Soussana, J.-F., Lutfalla, S., Ehrhardt, F., Rosenstock, T., Lamanna, C.,
Havlík, P., Richards, M., Wollenberg, E., Chotte, J.-L., Torquebiau, E.,
Ciais, P., Smith, P., and Lal, R.: Matching policy and science: Rationale for
the “4 per 1000 – soils for food security and climate” initiative,
Soil Till. Res., 188, 3–15, https://doi.org/10.1016/j.still.2017.12.002, 2017. a
Springmann, M., Clark, M., Mason-D'Croz, D., Wiebe, K., Bodirsky, B. L.,
Lassaletta, L., de Vries, W., Vermeulen, S. J., Herrero, M., Carlson, K. M.,
Jonell, M., Troell, M., DeClerck, F., Gordon, L. J., Zurayk, R., Scarborough,
P., Rayner, M., Loken, B., Fanzo, J., Godfray, H. C. J., Tilman, D.,
Rockström, J., and Willett, W.: Options for keeping the food system within
environmental limits, Nature, 562, 519–525, https://doi.org/10.1038/s41586-018-0594-0, 2018. a
Stockmann, U., Padarian, J., McBratney, A., Minasny, B., de Brogniez, D.,
Montanarella, L., Hong, S. Y., Rawlins, B. G., and Field, D. J.: Global soil
organic carbon assessment, Global Food Security, 6, 9–16,
https://doi.org/10.1016/j.gfs.2015.07.001, 2015. a
Stoorvogel, J. J., Bakkenes, M., Temme, A. J. A. M., Batjes, N. H., and ten Brink, B.: S-World: a Global Soil Map for Environmental Modelling,
Land Degrad. Dev., 28, 22–33, https://doi.org/10.1002/ldr.2656, 2017. a
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A.:
Conditional variable importance for random forests, BMC Bioinformatics, 9,
307, https://doi.org/10.1186/1471-2105-9-307, 2008. a
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013. a
Tóth, G., Jones, A., and Montanarella, L.: LUCAS Topsoil survey: methodology,
data and results, Report, Land Resource Management Unit – Soil Action,
European Commission Joint Research Centre Institute for Environment and
Sustainability, available at: https://esdac.jrc.ec.europa.eu/ESDB_Archive/eusoils_docs/other/EUR26102EN.pdf (last access: 21 May 2021),
2013. a
UNEP: The benefits of soil carbon – managing soils for multiple, economic,
societal and environmental benefits, United Nations Environmental
Programme, Nairobi, Kenya, 19–33, https://tinyurl.com/2vu4nsf4 (last access: 21 May 2021), 2012.
a
van Bussel, L. G. J., Grassini, P., Van Wart, J., Wolf, J., Claessens, L.,
Yang, H., Boogaard, H., de Groot, H., Saito, K., Cassman, K. G., and van Ittersum, M. K.: From field to atlas: Upscaling of location-specific yield gap estimates, Field Crop. Res., 177, 98–108,
https://doi.org/10.1016/j.fcr.2015.03.005, 2015. a
van der Esch, S., Brink, B. T., Stehfest, E., Bakkenes, M., Sewell, A.,
Bouwman, A., Meijer, J., Westhoek, H., and van den Berg, M.: Exploring future
changes in land use and land condition and the impacts on food, water,
climate change and biodiversity: Scenarios for the UNCCD Global Land Outlook,
Report, UNCCD, available at: https://tinyurl.com/yagvs9vu (last access: 21 May 2021), 2017. a
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., and Hochman, Z.: Yield gap analysis with local to global relevance – A review, Field Crop. Res., 143, 4–17, https://doi.org/10.1016/j.fcr.2012.09.009, 2013. a
Vitharana, U. W. A., Mishra, U., and Mapa, R. B.: National soil organic carbon estimates can improve global estimates, Geoderma, 337, 55–64,
https://doi.org/10.1016/j.geoderma.2018.09.005, 2019. a, b
Wan, Z.: MODIS land surface temperature products users' guide,
Institute for Computational Earth System Science, University of California,
Santa Barbara, California, USA, available at: https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf, 2006. a
Wilson, A. M. and Jetz, W.: Remotely sensed high-resolution global cloud
dynamics for predicting ecosystem and biodiversity distributions, PLoS
Biol., 14, e1002415, https://doi.org/10.1371/journal.pbio.1002415, 2016. a
WOCAT: Where the land is greener: Case studies and analysis of soil and water
conservation initiatives worldwide, CTA, UNEP, FAO and CDE, Berne, 2007. 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, b
Yigini, Y. and Panagos, P.: Assessment of soil organic carbon stocks under
future climate and land cover changes in Europe, Sci. Total Environ., 557–558, 838–850, https://doi.org/10.1016/j.scitotenv.2016.03.085, 2016. a
Yoo, A. B., Jette, M. A., and Grondona, M.: Slurm: Simple linux utility for
resource management, in: Workshop on Job Scheduling Strategies for Parallel
Processing, 44–60, 2003. a
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.
This paper focuses on the production of global maps of soil properties with quantified spatial...
