Access to this full-text is provided by PLOS.
Content available from PLOS One
This content is subject to copyright.
RESEARCH ARTICLE
SoilGrids250m: Global gridded soil
information based on machine learning
Tomislav Hengl
1
*, Jorge Mendes de Jesus
1
, Gerard B. M. Heuvelink
1
, Maria Ruiperez
Gonzalez
1
, Milan Kilibarda
2
, Aleksandar Blagotić
3
, Wei Shangguan
4
, Marvin N. Wright
5
,
Xiaoyuan Geng
6
, Bernhard Bauer-Marschallinger
7
, Mario Antonio Guevara
8
,
Rodrigo Vargas
8
, Robert A. MacMillan
9
, Niels H. Batjes
1
, Johan G. B. Leenaars
1
,
Eloi Ribeiro
1
, Ichsani Wheeler
10
, Stephan Mantel
1
, Bas Kempen
1
1ISRIC — World Soil Information, Wageningen, the Netherlands, 2Faculty of Civil Engineering, University of
Belgrade, Belgrade, Serbia, 3GILab Ltd, Belgrade, Serbia, 4School of Atmospheric Sciences, Sun Yat-sen
University, Guangzhou, China, 5Institut fu¨r Medizinische Biometrie und Statistik, Lu¨beck, Germany,
6Agriculture and Agri-Food Canada, Ottawa (Ontario), Canada, 7Department of Geodesy and
Geoinformation, Vienna University of Technology, Vienna, Austria, 8University of Delaware, Newark (DE),
United States of America, 9LandMapper Environmental Solutions Inc., Edmonton (Alberta), Canada,
10 Envirometrix Inc., Wageningen, the Netherlands
*tom.hengl@isric.org
Abstract
This paper describes the technical development and accuracy assessment of the most
recent and improved version of the SoilGrids system at 250m resolution (June 2016
update). SoilGrids provides global predictions for standard numeric soil properties
(organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions
and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in
addition to predictions of depth to bedrock and distribution of soil classes based on the
World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in
total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of
158 remote sensing-based soil covariates (primarily derived from MODIS land products,
SRTM DEM derivatives, climatic images and global landform and lithology maps), which
were used to fit an ensemble of machine learning methods—random forest and gradient
boosting and/or multinomial logistic regression—as implemented in the Rpackages
ranger,xgboost,nnetand caret. The results of 10–fold cross-validation show that
the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation
with an overall average of 61%. Improvements in the relative accuracy considering the
amount of variation explained, in comparison to the previous version of SoilGrids at 1 km
spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use
of machine learning instead of linear regression, (2) to considerable investments in prepar-
ing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further
development of SoilGrids could include refinement of methods to incorporate input uncer-
tainties and derivation of posterior probability distributions (per pixel), and further automa-
tion of spatial modeling so that soil maps can be generated for potentially hundreds of soil
variables. Another area of future research is the development of methods for multiscale
merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 1 / 40
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Hengl T, Mendes de Jesus J, Heuvelink
GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić
A, et al. (2017) SoilGrids250m: Global gridded soil
information based on machine learning. PLoS ONE
12(2): e0169748. doi:10.1371/journal.
pone.0169748
Editor: Ben Bond-Lamberty, Pacific Northwest
National Laboratory, UNITED STATES
Received: August 1, 2016
Accepted: December 21, 2016
Published: February 16, 2017
Copyright: ©2017 Hengl et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: SoilGrids are
available under the Open Database License (ODbl)
v1.0 and can be downloaded from www.soilgrids.
org and/or ftp.soilgrids.org without restrictions.
SoilGrids250m data has already been released in
July 2016 (see: http://www.isric.org/content/isric-
releases-upgraded-soilgrids-system-two-times-
improved-accuracy-predictions) Access to
SoilGrids maps is provided via a soil web mapping
portal at SoilGrids.org; through a Web Coverage
Service (WCS); and via the SoilInfo App, hence
access to data is without restrictions. All the code
50 m spatial resolution) so that increasingly more accurate, complete and consistent
global soil information can be produced. SoilGrids are available under the Open Data
Base License.
Introduction
There is a growing demand for detailed soil information, especially for global estimation of
soil organic carbon [1–3] and for modeling agricultural productivity [4,5]. Spatial information
about soil water parameters is likely to become increasingly critical in areas affected by climate
change [6]. Soils and soil information are also particularly relevant for the Sustainable Devel-
opment goal target 15.3 of achieving Land Degradation Neutrality (LDN), as specified by the
United Nations Convention to Combat Desertification (UNCCD; http://www.unccd.int), and
are one of the main areas of interest of the FAO’s Global Soil Partnership initiative [7]. Fol-
berth et al. [8] have recently discovered that accurate soil information might be the key to pre-
dicting either buffering or amplifying impacts of climate change on food production.
To reduce the gap between soil data demand and availability, ISRIC (International Soil
Reference Information Centre)—World Soil Information released a Global Soil Information
system called “SoilGrids”. The first version of SoilGrids (predictions at 1 km spatial resolu-
tion released in 2014), was, at the time, a ‘proof of concept’ demonstrating that global compi-
lations of soil profiles can be used in an automated framework to produce complete and
consistent spatial predictions of soil properties and classes [9]. Since the launch of the system
in 2014, several colleagues have recognized and reported some of the limitations of the first
version of the system. Mulder et al. [10] observed, using more detailed soil profile data and
maps, that SoilGrids likely overestimated all low values for organic carbon content in France.
Likewise, Griffiths et al. [11] reported underestimation of the pH in comparison to UK
national data. The overestimation of low values happened mainly as an effect of limited fit-
ting success (so that both high and low values are smoothed out). In addition, many of the
artifacts visible in the Harmonized World Soil Database (HWSD) [12], which was used as
one of the covariates to produce the first version of SoilGrids, e.g. country borders, were
propagated to SoilGrids1km. Some users have also expressed concerns that the first version
of SoilGrids did not provide predictions for arid and desert areas and hence can be consid-
ered an incomplete product [13].
To address these criticisms and concerns, we have re-designed and re-implemented Soil-
Grids with a particular emphasis on addressing methodological limitations of SoilGrids1km.
Hence, our main objective was to build a more robust system with improved output data qual-
ity; especially considering spatial detail and attribute accuracy of spatial predictions. We imple-
mented the following six key improvements:
1. We replaced linear models with tree-based, non-linear machine learning models to account
for non-linear relationships—especially for modeling soil property–depth relationships—
but also to be able to better represent local soil–covariate relationships. Predictions are now
primarily data-driven. Much less time is spent on choosing models, which also reduces the
complexity of producing updates.
2. We replaced single prediction models with an ensemble framework i.e. we use at least two
methods for each soil variable to reduce overshooting effects.
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 2 / 40
used to generate SoilGrids250m predictions is fully
documented via: https://github.com/
ISRICWorldSoil/SoilGrids250m/.
Funding: ISRIC is a non-profit organization
primarily funded by the Dutch government. The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript. GILAB DOO provided support in
the form of salaries for author AB, but did not have
any additional role in the study design, data
collection and analysis, decision to publish, or
preparation of the manuscript. The specific roles of
this author are articulated in the ‘author
contributions’ section.
Competing interests: Aleksandar Blagotićis
employee and web-developer of GILAB DOO. There
are no patents, products in development or
marketed products to declare. This does not alter
our adherence to all the PLOS ONE policies on
sharing data and materials.
3. We extended the initial list of covariates to include a wider diversity of MODIS land prod-
ucts and to better represent factors of soil formation. The spatial resolution of covariates
was increased from 1 km to 250 m with the expectation that finer resolution will help
increase the prediction accuracy.
4. We re-implemented the global soil mask using state-of-the-art land cover products [14].
The current soil mask now includes all previously excluded dryland and sand dune areas so
that most of the land mask (>95%) is represented.
5. The global compilation of soil profiles and samples used for model training was also
extended. We added extra points for the Russian Federation, Brazil, Mexico and the Arctic
circle; and re-visited data harmonization issues.
6. We created and inserted expert-based pseudo-points for a selection of parameters to mini-
mize extrapolation effects in undersampled geographic areas lacking field observations,
such as deserts, semi-deserts, glaciers and permafrost areas.
We present here the technical development and accuracy assessment of the updated Soil-
Grids system at 250 m resolution. In the following sections we describe the workflows used to
generate spatial predictions and report results of model fitting and accuracy assessment based
on 10–fold cross-validation. We conclude the article by suggesting some possible applications
of this new data set and identifying possible future improvements. SoilGrids250m map layers
are available for download via www.SoilGrids.org under the Open Database License (ODbL).
GeoTiffs can also be obtained from ftp://ftp.soilgrids.org/data/.
Methods and materials
Target variables
SoilGrids provides predictions for the following list of standard soil properties and classes [9]:
• Soil organic carbon content in ‰ (g kg
−1
),
• Soil pH in H
2
O and KCl solution,
• Sand, silt and clay (weight %),
• Bulk density (kg m
−3
) of the fine earth fraction (<2 mm),
• Cation-exchange capacity (cmol + /kg) of the fine earth fraction,
• Coarse fragments (volumetric %),
• Depth to bedrock (cm) and occurrence of Rhorizon,
• World Reference Base (WRB) class—at present, we map 118 unique soil classes, e.g. Plinthic
Acrisols, Albic Arenosols, Haplic Cambisols (Chromic), Calcic Gleysols and similar [15].
This is about four times as many classes as in the previous version of SoilGrids,
• United States Department of Agriculture (USDA) Soil Taxonomy suborders—i.e. 67 soil
classes [16].
We generated predictions at seven standard depths for all numeric soil properties (except
for depth to bedrock and soil organic carbon stock): 0 cm, 5 cm, 15 cm, 30 cm, 60 cm, 100 cm
and 200 cm, following the vertical discretisation as specified in the GlobalSoilMap specifica-
tions [17]. Averages over (standard) depth intervals, e.g. 0–5 cm or 0–30 cm, can be derived by
taking a weighted average of the predictions within the depth interval using numerical
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 3 / 40
integration, such as the trapezoidal rule:
1
baZb
afðxÞdx 1
ðbaÞ
1
2X
N1
k¼1
xkþ1xk
fðxkÞ þ fðxkþ1Þ
ð1Þ
where Nis the number of depths, x
k
is the k-th depth and f(x
k
) is the value of the target variable
(i.e., soil property) at depth x
k
. For example, for the 0–30 cm depth interval, with soil pH values
at the first four standard depths equal to 4.5, 5.0, 5.3 and 5.0, the pH is estimated as 1
302
50ð Þ 4:5þ5:0ð Þ þ 15 5ð Þ 5:0þ5:3ð Þ þ 30 15ð Þ 5:3þ5:0ð Þ½ =30 0:5¼5:083
(Fig 1).
Based on predictions of soil organic carbon content, bulk density, and coarse fragments, we
also derived soil organic carbon stock (tha
−1
) for the six GlobalSoilMap standard depth inter-
vals following the standard approach [9,18]. Fig 2 shows an example of observed vs predicted
values and corresponding derived soil organic carbon stock for 0–1 m and 1–2 m depths.
Model fitting and spatial prediction of depth to bedrock is based also on water well drilling
data. Model fitting and spatial prediction of soil depth to bedrock variables is explained in
detail in Shangguan et al. [19].
We set the reference soil surface at the air/soil boundary, as per FAO [20], hence all soil
material is included. Some national soil survey teams (and also earlier versions of the FAO
Fig 1. Standard soil depths following the GlobalSoilMap.net specifications and example of numerical
integration following the trapezoidal rule.
doi:10.1371/journal.pone.0169748.g001
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 4 / 40
standards) define 0 cm depth at the start of the mineral soil, i.e. just below the Oor the P(peat)
horizon. Consider for example the following sample soil profile from Canada [21]:
hor top bottom bd orgcarb
LFH -12 0 0.07 48.1
Ae 0 11 1.3 0.6
AB 11 25 1.53 0.4
Bt 25 44 1.62 0.4
which shows that the vertical coordinates of the organic layer of this soil site are negative (LFH
indicates Litter—Fermentation—Humus); orgcarbindicates soil organic carbon, bd is the
bulk density and topand bottomare the upper and lower horizon depth in cm). Therefore,
to avoid vertical mismatches between different national systems, all systems that put the zero
level at the start of the mineral soil have been adjusted to a reference with the zero level at the
air/soil boundary. For the example soil profile from Canada this means that 12 cm was added
to all top and bottom values (in the example above, there is a significant discontinuity in values
in organic carbon that drops from 48.1% to 0.6% within 12 cm of depth).
Fig 2. Example of soil variable-depth curves: Original sampled soil profiles (black rectangles) vs predicted SoilGrids values at seven standard
depths (broken red line), and predicted soil organic carbon stock for depth intervals 0–100 and 100–200 cm. Locations of points from the USDA
National Cooperative Soil Survey Soil Characterization database: mineral soil S1991CA055001(-122.37˚W, 38.25˚N), and anorganic soil profile
S2012CA067002(-121.62˚W, 38.13˚N).
doi:10.1371/journal.pone.0169748.g002
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 5 / 40
Input profile data
For model building, we used soil profile data from ca. 150,000 unique sites spread over all con-
tinents (Fig 3; see acknowledgments for a full list). These have been imported, cleaned and
merged into a single global compilation of soil points with unique column names and IDs.
Preparation of the global compilation of standardized soil training points took several
months of work. The translation and cleaning up of soil properties and soil classes took a large
amount of time. About 15–20% of the original soil profile data was only reported using a
national classification system, e.g. the Canadian and Brazilian classification systems. Since
some information is better than none, where possible we translated national classification sys-
tems to the two international (World Reference Base and USDA) classification systems. For
translation we used published correlation tables either reported in Krasilnikov et al. [22] or
reported on the agency websites; see e.g. correlation of Canadian Soil Taxonomy published
(http://sis.agr.gc.ca/cansis/taxa/) and correlation of the Brazilian classification system (http://
www.pedologiafacil.com.br/classificacao.php). We also consulted numerous local soil classifi-
cation experts and requested their feedback and corrections in the (online) correlation tables
(distributed via Google spreadsheets). Some national classification systems, such as the Austra-
lian soil classification system, are simply too different from the USDA and WRB systems to
allow satisfactory correlation. These data were therefore not used. The full list of correlation
tables is available from ISRIC’s github account at https://github.com/ISRICWorldSoil.
Another time-consuming operation was merging laboratory measurements and field obser-
vations and their harmonization to a standard format. In some cases missing values in the
original tables had been coded as "0" values, which can have a serious influence on prediction
models; in other cases we implemented and applied functions to locate and correct typos and
other gross errors. Some variables, such as soil organic carbon, needed to be converted either
from soil organic matter (e.g. divide by 1.724) and/or by removing CaCO
3
(Calcium carbon-
ates) from total carbon. Nevertheless, the majority of soil variables from various national soil
Fig 3. Input profile data: World distribution of soil profiles used for model fitting (about 150,000 points shown on the map; see
acknowledgments for a complete list of data sets used). Yellow points indicate pseudo-observations. For the majority of points shown on this map,
laboratory data can be accessed from ISRIC’s World Soil Information Service (WoSIS) at http://wfs.isric.org/geoserver/wosis/wfs.
doi:10.1371/journal.pone.0169748.g003
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 6 / 40
profile data bases appeared to be compatible and relatively easy to merge—soil scientists across
continents do measure similar things, but often express the results using different measure-
ment units, vocabularies and standards.
We imported all original tables as-is, next documented all conversion functions through R
scripts (available via ISRIC’s github account), to accommodate reproducible research and facil-
itate that conversion functions may, in the future, be further modified and improved. The
majority of the points (excluding LUCAS points and other data sets with specific restricting
terms of use) and legends used for model building and for producing SoilGrids are also avail-
able for public use via ISRIC’s WoSIS Web Feature Service (http://www.isric.org/data/wosis)
and/or the ISRIC’s institutional github account.
Expert-based pseudo-observations
Even though the input training point data are extensive and cover most continents and cli-
matic zones, some large areas that have extreme climatic conditions and/or have very
restricted access, are significantly undersampled. This occurs largely in the following five types
of areas:
1. Semi-arid and arid lands, deserts and sand dunes,
2. Mountain tops, steep slopes of mountains and similar inaccessible areas,
3. Areas covered by ice and/or snow, i.e. glaciers,
4. Inaccessible tropical forest,
5. Areas governed by totalitarian and hostile regimes, with military conflicts or war.
It might seem obvious to soil surveyors that there is no soil organic carbon in the top 2 m of
the active sand dunes of the Sahara, but any model fitted without observations in the Sahara
could result in dubious extrapolation and questionable predictions. In addition, relationships
across transitional areas—from semi-arid zones to deserts—can be difficult to represent with-
out enough points at both edges of the feature space. Some sand dunes in the USA have been
actually sampled and analyzed in the laboratory. For example, Lei [23] has shown that sand
dunes in the Mojave desert have an average pH of 8.1, 98% sand and 0% organic carbon.
Again, although it might seem obvious that deserts consist mainly of sand, and that steep
slopes without vegetation are either very shallow or show bedrock at the surface, the model is
not aware of such expert knowledge and hence such features need to be ‘numerically repre-
sented’ in the calibration dataset. We therefore decided, instead of masking out all such areas
from soil mapping, to insert pseudo-observations and fill gaps in the feature space for the first
four of the five types of areas listed above, i.e. to add pseudo-observations to the training data-
set, which we then use for model building.
We used the following data sources to delineate sand dunes, bare rock and glaciers and pro-
duce their respective land masks:
• Sand dunes mask—To delineate the global distribution of sand dunes we used mean annual
long-term surface temperature generated from the MODIS LST data product (MOD11A2),
long-term MODIS Mid-Infrared (MIR) band (MCD43A4) and a slope map. After visual
inspection of the border of the Sahara desert, it was clear that sand dunes can be relatively
accurately delineated using MIR reflectance, mean daily annual temperature (>25˚C) and a
slope map (<25 rad).
• Bare rock mask—To delineate bare rock we also used the MODIS MIR band (MCD43A4)
and a slope map. Bare rock or dominantly rocky areas show high MIR surface reflectance
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 7 / 40
and are associated with steep slopes (>32 rad). To the initial mask map estimated using
MODIS MIR band and slope map, we also added bare rock areas from more detailed maps
available for some countries, such as Iceland and northern Europe [19].
• Glaciers mask—To represent global distribution of glaciers we used the GLIMS Geospatial
Glacier Database [24].
For each of the three masks we then generated randomly 100–400 points based on the rela-
tive global extent and assigned soil properties and soil classes accordingly (e.g. in the case of
WRB’s Protic Arenosols for sand dunes, Lithic and Rendzic Leptosols for bare rock areas,
Cryosols for areas adjacent to glaciers; in the case of USDA’s Psamments for sand dunes, Orth-
ents for bare rock areas and Turbels for glaciers; for sand dunes we also inserted estimated val-
ues of 0 soil organic carbon, 98% sand and 0% coarse fragments). For model training for
predicting soil classes we also used pseudo-observations generated from the best available soil
polygon maps: for poorly accessible tropical forest areas, such as Indonesia, we used the Land
information system of Kalimantan [25], and for northern latitudes, i.e. to represent permafrost
soils, the Northern Circumpolar Soil Carbon Database was used [26].
When inserting pseudo-observations we tried to follow three simple rules of thumb to min-
imize any negative effects:
• keep the relative percentage of pseudo-points small i.e. try not to exceed 1–2% of the total
number of training points,
• only insert pseudo-points for which the actual ground value is known with high confidence,
e.g. sand content in sand dune areas,
• if polygon maps are used to insert pseudo-observations, we tried to use the most detailed soil
polygon maps and focus on polygons with very high thematic purity.
Soil covariates
As covariate layers for producing SoilGrids250m predictions we used an extensive stack of
covariates, which are primarily based on remote sensing data. These include (see e.g. Fig 4):
• DEM-derived surfaces—slope, profile curvature, Multiresolution Index of Valley Bottom
Flatness (VBF), deviation from Mean Value, valley depth, negative and positive Topographic
Openness and SAGA Wetness Index—all based on the global merge of SRTMGL3 DEM and
GMTED2010 [27]. All DEM derivatives were computed using SAGAGIS[28],
• Long-term averaged monthly mean and standard deviation of the MODIS Enhanced Vege-
tation Index (EVI). Derived using a stack of MOD13Q1 EVI images [29],
• Long-term averaged mean monthly surface reflectances for MODIS bands 4 (NIR) and 7
(MIR). Derived using a stack of MCD43A4 images [30],
• Long-term averaged monthly mean and standard deviation of the MODIS land surface tem-
perature (daytime and nighttime). Derived using a stack of MOD11A2 LST images [31],
• Long-term averaged mean monthly hours under snow cover based on a stack of MOD10A2
8-day snow occurrence images [32],
• Land cover classes (cultivated land, forests, grasslands, shrublands, wetlands, tundra, artifi-
cial surfaces and bareland cover) for the year 2010 based on the GlobCover30 product by the
National Geomatics Center of China [14]. Upscaled to 250 m resolution and expressed in
percent of pixel coverage,
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 8 / 40
• Monthly precipitation images derived as the weighted average between the WorldClim
monthly precipitation [33] and GPCP Version 2.2 [34],
• Long-term averaged mean monthly hours under snow cover. Derived using a stack of
MOD10A2 8-day snow occurrence images,
Fig 4. Examples of covariates used to generate SoilGrids: TWI is the Topographic Wetness Index (values multiplied by 100), EVI is the MODIS
Enhanced Vegetation Index (values multiplied by 10,000), s.d. LST is the long-term standard deviationof MODIS Land Surface Temperatures
(values in Celsius degrees). Location: San Francisco bay area, California. Size of the bounding box is 300 by 300 km.
doi:10.1371/journal.pone.0169748.g004
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 9 / 40
• Lithologic units (acid plutonics, acid volcanic, basic plutonics, basic volcanics, carbonate
sedimentary rocks, evaporite, ice and glaciers, intermediate plutonics, intermediate volca-
nics, metamorphics, mixed sedimentary rocks, pyroclastics, siliciclastic sedimentary rocks,
unconsolidated sediment) based on Global Lithological Map GLiM [35],
•Landform classes (breaks/foothills, flat plains, high mountains/deep canyons, hills, low
hills, low mountains, smooth plains) based on the USGS’s Map of Global Ecological Land
Units [36].
• Global Water Table Depth in meters; after Fan et al. [37],
• Long-term averaged mean monthly MODIS Flood Water based on the NRT Global MODIS
Flood Mapping Flood Water product (http://oas.gsfc.nasa.gov/floodmap/),
• Landsat-based estimated distribution of Mangroves; after Giri et al. [38],
• Average soil and sedimentary-deposit thickness in meters; after Pelletier et al. [39].
These covariates were selected to represent factors of soil formation according to Jenny
[40]: climate, relief, living organisms, water dynamics and parent material. Out of the five
main factors, water dynamics and living organisms (especially vegetation dynamics) are not
trivial to represent as these operate over long periods of time and often exhibit chaotic behav-
iour. Using reflectance bands such as the mid-infrared MODIS bands from a single day, would
have little use to soil mapping for areas with dynamic vegetation, i.e. with strong seasonal
changes in vegetation cover. To account for seasonal fluctuation and for inter-annual varia-
tions in surface reflectance, we instead used long-term temporal signatures of the soil surface
derived as monthly averages from long-term MODIS imagery (15 years of data). We assume
here that, for each location in the world, long-term average seasonal signatures of surface
reflectance or vegetation index provide a better indication of soil characteristics than only a
single snapshot of surface reflectance. Computing temporal signatures of the land surface
requires a considerable investment of time (comparable to the generation of climatic images vs
temporary weather maps), but it is possibly the only way to represent the cumulative influence
of living organisms on soil formation.
For processing the covariates we used a combination of Open Source GIS software, primar-
ily SAGAGIS [28], Rpackages raster[41], sp [42], GSIF and GDAL [43] for reprojecting,
mosaicking and merging tiles. SAGAGIS and GDAL were found to be highly suitable for pro-
cessing large data as parallelization of computing was relatively easy to implement.
We updated the 1 km global soil mask map using the most detailed 30 m resolution global
land cover map from 2010. This was combined with the global water mask [44] and the global
sea mask map based on the SRTM DEM [45] to produce one consistent global soil mask that
includes all land areas, expect for: (a) fresh water bodies such as lakes and rivers, and (b) per-
manent ice.
Spatial prediction framework
Spatial prediction, i.e. fitting of models and generation of maps, was fully implemented via the
Renvironment for statistical computing. The process of generating SoilGrids predictions con-
sists of four main steps (see Fig 5):
• overlay points and covariates and prepare regression matrix,
• fit spatial prediction models,
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 10 / 40
Fig 5. The (data-driven) statistical framework used for generating SoilGrids. SoilGrids are primarily based on publicly released soil profile
compilations, NASA’s MODIS and SRTM data products and Open Source software compiled with the ATLAS library: R(including contributed
packages), and Open Source Geospatial Foundation (OSGeo) supported software tools.
doi:10.1371/journal.pone.0169748.g005
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 11 / 40
• apply spatial prediction models using tiled raster stacks (covariates),
• assess accuracy using cross-validation.
For practical purposes, we implemented these steps separately for each of the following
groups of soil variables:
•WRB soil groups and USDA soil suborders were modelled using ensemble models based
on nnet::multinom(which fits multinomial log-linear models via neural networks)
[46] and ranger::ranger(fits random forest) functions [47]. We mapped probabilities
of occurrence for each individual soil class (118 probability maps for WRB and 67 for
USDA),
•Soil properties (organic carbon, bulk density, CEC, pH, soil texture fractions and coarse
fragments) were modelled as 3D variables using an ensemble of ranger::rangerand
xgboost::xgboost(fits Gradient Boosting Tree) [48]. Soil depth is used as a covariate,
so that the resulting models predict values of a target variable for any given depth, i.e. in
3D,
• Depth to bedrock was also modelled using ranger::rangerand xgboost::xgboost
functions, but the output is a 2D map.
To optimize the model tuning parameters we consistently used the caret::trainfunc-
tion [49], which is also suited for big data. The fine-tuning of the parameters is summarized in
the following three steps:
1. Randomly subset the regression matrix to e.g. 15,000 observations (usually 5–10% of the
total size),
2. Fit and validate a list of models for a combination of tuning parameters,
3. Select the optimal parameters (i.e. those that produce the lowest RMSE using repeated
cross-validation) and fit the final model using all observations.
Models for WRB and USDA classes are defined as:
R>TAXNWRB*DEMMRG5 + SLPMRG5 + . . . + ASSDAC3
where DEMMRG5 + SLPMRG5 + . . . + ASSDAC3 are the covariate layers, TAXNWRBis the
observed taxonomic class in the WRB system (target variable). An example of a soil property
model is given by:
R>ORCDRC*Depth + DEMMRG5 + . . . + ASSDAC3
where DEMMRG5 + . . . + ASSDAC3 are the covariate layers, ORCDRCis the value of organic
carbon observed (target variable), and Depthis the sampling / observation depth.
For each variable we fitted a separate model and merged predictions from at least two mod-
els to minimize overshooting effects [50]. The merging of predictions is done by using the
average model accuracy estimated during the fine-tuning of model parameters, i.e. as a
weighted average [50]:
fðxÞ ¼ PM
k¼1wkfkðxÞ
PM
k¼1wk
;wk¼1
s2
k;CV ð2Þ
where
fðxÞis the final ensemble prediction, Mis the number of models, w
k
is the model weight
and s2
k;CV is the model squared prediction error obtained using cross-validation. In practice,
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 12 / 40
both ranger::rangerand xgboost::xgboostreport about the same error in most
cases, hence the final prediction is often close to the unweighted average.
We also applied post-processing, mainly to remove artifacts: in the case of soil classes, we
filter out all classes theoretically impossible to occur in a given area, such as Gypsisols in arctic
climatic zones, using a simple soil-climate matrix (documented on the project github). For tex-
ture fractions we also applied a standardization function to ensure that all predictions are
between 0 and 100, and that the fractions sum up to 100%, e.g.:
Sandc½% ¼ Sand
ðSand þSilt þClayÞ100 ð3Þ
where Sand
c
is the corrected sand content.
SoilGrids can be considered as a Big Data project, especially in terms of data volumes and
variety. The total size of all input and output data used to generate SoilGrids exceeds 30 TiB, so
that a first step in preparing SoilGrids250m was to obtain a Synology 12-Bay NAS storage
server with 60 TiB space. Handling such a large data set presented major challenges consider-
ing computational complexity and network bandwidth limitations. To optimize computing
performance, especially spatial overlay, model fitting, predictions and export of predictions,
we used exclusively parallelized versions of functions. For prediction, parallelization is already
implemented internally via the rangeror xgboostsoftware; for other processes we primar-
ily used the snowfallpackage [51].
All processing was implemented on a single dedicated high performance server with 256
GiB RAM, 8 TiB hard disk space, 48 cores (Intel Xeon 2xE5-2690v3 24c/48t 2.6–3.5 GHz) and
running on Ubuntu 15.10 (Willy Werewolf) OS and R-cran 3.2.3 using ATLAS (Automatically
Tuned Linear Algebra Software) 3.11.38 library. Even after parallelization, producing predic-
tions for all soil variables and all depths took 10+ days of continuous computing, i.e. about 12
thousand CPU hours (about 90% of the computing time is invested in generating predictions).
Because the current system is fully scalable, the next update of SoilGrids could be completed in
shorter time frames, e.g. by boosting the number of computer cores, although this might also
greatly increase the production costs.
The tiling system
For tiling, we used the Equi7 Grid system [52] which splits the global land mass into seven sep-
arate planar grids (Europe and Asia are split into two land masses with some small overlap).
The Equi7 Grid system was selected for several practical reasons [52]:
1. The projections of the Equi7 Grid are equidistant and hence suitable for various geographic
analyses, especially for derivation of buffer distances and for hydrological DEM modeling,
i.e. to derive all DEM-based soil covariates,
2. Areal and shape distortions stemming from the Equi7 Grid projection are relatively small,
yielding a small grid oversampling factor,
3. The Equi7 Grid systems ensures an efficient raster data storage while suppressing inaccura-
cies during spatial transformation. Especially for high-resolution global data, these are
important features.
The global soil mask at 250 m resolution contains about 1.6 billion pixels (Africa: 330 mil-
lion, Europe: 110 million, North America: 230 million, South America: 210 million, Antartica:
0.05 million, Oceania: 140 million, Asia: 360 million). We provide the final outputs in both the
Equi7 Grid system and in geographical WGS84 coordinates. Final global mosaics in the
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 13 / 40
WGS84 system were produced by reprojecting all pixels using GDAL warp and translate func-
tions [43]. The ground resolution of 250 m corresponds to a geographical resolution of 1/480
decimal degrees. An image representing the whole world at this resolution comprises 172kcol-
umns and 72k rows.
Final predictions are available both as mosaics and as 1˚ tiles (16,360 tiles to represent the
world land mask); tiles are considered more suitable for users interested in regional and
national data, and mosaics (at resolutions of 20 km, 1 km and 250 m) are deemed suitable for
global modellers.
Accuracy assessment
For accuracy assessment of both numeric and categorical variables we used 10–fold repeated
cross-validation. Each model is re-fitted 10 times using 90% of the data and predictions
derived from the fitted models are compared with observations of the remaining 10%. For
each of the 14 numeric soil properties we derived the coefficient of determination (R
2
—the
amount of variation explained by the model), mean error (ME) and root mean squared error
(RMSE). The amount of variation explained by the model is derived as:
R2¼1SSE
SST
100% ð4Þ
where SSE is the sum of squared errors at cross-validation points and SST is the total sum of
squares. A coefficient of determination close to 1 indicates a perfect model, i.e. 100% of vari-
ation has been explained by the model. Numeric variables with skew distributions were log-
transformed prior to modeling and hence for these variables we report the amount of varia-
tion explained by the model after log-transformation. Also for the cross-validation correla-
tion plots we used either log or linear scale depending on whether log-transformation was
applied.
For predictions of soil WRB and USDA classes we calculated the map purity (0–100%) for
the dominant soil class at cross-validation points and weighted kappa metrics [53] as imple-
mented in the psychpackage. For the predicted probabilities of soil class occurrences (0–1
probability values) we also derived the area under the receiver operating characteristic curve
(AUC) and the True Positive Rate (TPR) statistic as implemented in the ROCRpackage [54,
55]. Values of TPR range from 0 to 1. Values of AUC close to 1 show high prediction perfor-
mance, while values around 0.5 and below are considered poor.
For soil WRB and USDA classes we also generated global maps of the scaled Shannon
Entropy Index using the per-class probability maps [56,57]:
HsðxÞ ¼ X
K
k¼1
pkðxÞ logKðpkðxÞÞ ð5Þ
where Kis the number of possible classes, log
K
is the logarithm to base Kand p
k
is probability
of class k. The scaled Shannon Entropy Index (H
s
) is in the range from 0–1, where 0 indicates
no ambiguity (one of the p
k
equals one and all others are zero) and 1 indicates maximum
confusion (all p
k
equal 1
K) [58]. Note that the scaled Shannon Entropy Index should not be
confused with classification accuracy assessment: H
s
is an internal accuracy measure derived
from the model and not based on comparison of predictions with (cross-)validation data,
such as the purity and kappa metrics. For Shannon index of 0 at some location accuracy
could still be completely wrong because the soil class at that location could actually be a dif-
ferent one.
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 14 / 40
Results
Model fitting
Summary results of model fitting are given in Figs 6and 7and Tables 1and 2. The ranger
package reports model fitting success via the R-square based on Out-of-bag (OOB) samples,
i.e. the amount of variation explained by the model, which ranged from a low of 0.59 for coarse
fragments to a high of 0.85 for soil pH. R-square estimated using xgboost (derived using
repeated cross-validation) was lower, ranging from 0.37 for coarse fragments to 0.60 for soil
Fig 6. Fitted variable importance plots for target variables. Generated as an average of predictions using the ranger and xgboostpackages (for soil
types results are based on the rangermodel only). DEPTH.f is depth from soil surface, TMOD3 and NMOD3are mean monthly temperatures daytime
and nighttime (red color), TWI,DEM,VBFand VDP are DEM-parameters (bisque color), MMOD4 are mean monthly MODIS NIR band reflectances (cyan
color), PMRG3are mean monthly precipitation (blue color), EMOD5 are mean monthly EVI derivatives (dark green color), VWMOD1are monthly MODIS
Precipitable Water Vapor images (orange color), CGLC5are land cover classes (light green color), and ASSDAC3 is the average soil and sedimentary-
deposit thickness (brown color).
doi:10.1371/journal.pone.0169748.g006
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 15 / 40
pH. On average, the two packages report R-square values between 0.4–0.8 with an overall aver-
age of 0.60. This number corresponds closely to our results produced using 10–fold cross vali-
dation with repeated fitting. Comparing these new results to average R-square values of 0.38
for the original SoilGrids1km predictions reveals a significant improvement of close to + 50%.
The trainfunction of the package caretusually picked a relatively high Mtry parame-
ter (number of variables randomly sampled as candidates at each split) as optimal for soil
properties: the optimized values ranged from 18 for coarse fragments to 22 for all other soil
properties. Higher Mtryis recommend for cases where the number of covariates is large and
Fig 7. Examples of relationships for target variables and the most important covariates: (top row) bulk density in kg m
−3
, (middle row) soil pH,
and (bottom row) soil organic carbon in permilles (on log scale). Plots show target variables and the top three most important covariates as reported
by the random forest model. DEPTH.fis the observed depth from soil surface, T09MOD3 is mean monthly temperature for September, TMDMOD3is mean
annual temperature, PRSMRG3 is total annual precipitation, M04MOD4is mean monthly MODIS NIR band reflectance for April, P07MRG3 is mean monthly
precipitation for July, T01MOD3is mean monthly temperature for January, and T02MOD3 is mean monthly temperature for February.
doi:10.1371/journal.pone.0169748.g007
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 16 / 40
multiple variables influence the target variables with equal importance [59]. For the Gradient
Boosting Tree method, trainalways selected the same combination of tuning parameters for
all soil properties: nrounds = 100,max_depth = 3,eta= 0.4,gamma = 0,colsam-
ple_bytree = 0.8 and min_child_weight= 1. This may be because we limited the
combinations of tuning parameters to 10 to speed up processing speed. Higher values for
xgboosttuning parameters are indicative of higher-level complexity of the model: many
Table 1. SoilGrids average prediction error for key soil properties based on 10–fold cross-validation. N = “Number of samples used for training”, ME =
“Mean Error”, MAE = “Mean Absolute Error”, RMSE = “Root Mean Squared Error” and R-square = “Coefficient of determination” (amount of variation explained
by the model). For variables with a skew distribution, such as organic carbon, coarse fragments and CEC, the accuracy statistics are also provided on log-
scale
.
Variable name N Min Max ME MAE RMSE R-square RMSE
R-square
Soil organic carbon
(gravimetric)
605,054 0 520 -0.292 10.2 32.8 63.5% 0.715 68.8%
pH index
(H
2
O solution)
604,019 2.1 11.0 -0.002 0.4 0.5 83.4%
Sand content
(gravimetric)
616,762 1% 94% -0.037 9.0 13.1 78.6%
Silt content
(gravimetric)
613,750 2% 74% 0.023 6.7 9.8 79.4%
Clay content
(gravimetric)
625,159 2% 68% -0.102 6.6 9.5 72.6%
Coarse fragments
(volumetric)
303,139 0% 89% -0.104 5.5 10.9 55.9% 1.185 64.3%
Bulk density
(fine earth fraction)
140,596 250 2870 -1.574 108.3 164.7 75.8%
Cation-exchange capacity
(fine earth fraction)
393,585 0 234 -0.071 5.5 10.3 64.5% 0.483 67.0%
Depth to bedrock
(in cm)
1,580,798 0 125,000 -29 678 835 54.0% 1.12 42.8%
doi:10.1371/journal.pone.0169748.t001
Table 2. Mapping performance of SoilGrids250m compared to summary results for SoilGrids1km [9]. Amount of variation explained by models (Eq 4),
i.e. prediction accuracy for soil types was determined using 10–fold cross-validation. GSIF = “Global Soil Information Facilities”.
Variable name Type Units GSIF
code
Amount of var. explained
(SoilGrids1km)
Amount of var. explained
(SoilGrids250m)
Relative
improvement
Soil organic carbon 3D g kg
−1
ORCDRC 22.9% 68.8% 200%
pH index
(H
2
O solution)
3D 10
−1
PHIHOX 50.5% 83.4% 65%
Sand content
(gravimetric)
3D kg kg
−1
SNDPPT 23.5% 78.6% 234%
Silt content
(gravimetric)
3D kg kg
−1
SLTPPT 34.9% 79.4% 127%
Clay content
(gravimetric)
3D kg kg
−1
CLYPPT 24.4% 72.6% 198%
Coarse fragments
(volumetric)
3D cm
3
cm
−3
CRFVOL - 64.3% -
Bulk density
(fine earth fraction)
3D kg m
−3
BLD 31.8% 75.8% 138%
Cation-exchange
capacity
(fine earth fraction)
3D cmol +
/kg CEC 29.4% 67.0% 128%
Depth to bedrock 2D cm BDT - 42.8% -
doi:10.1371/journal.pone.0169748.t002
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 17 / 40
relationships between soil properties and covariates are non-linear and a greater number of
splits is possibly required to represent this complexity.
Fig 6 shows the top 15 soil covariates for each target variable. This indicates that, for exam-
ple, spatial pattern of soil pH is primarily influenced by precipitation and surface reflectance
(MODIS Medium-Infrared band 6 for months April and May especially). Also, for most vari-
ables depth emerges as the most important covariate, especially for soil organic carbon, bulk
density and coarse fragments. For soil types and soil textures, DEM-parameters, i.e. soil form-
ing factors of relief, especially flow-based DEM-indices, emerge as second-most dominant
covariates. These results largely correspond with conventional soil survey knowledge (survey-
ors have been using relief as a key guideline to delineate soil bodies for decades), but it is
encouraging to have these findings supported by statistical modeling of real data on a global
scale.
Although lithology is not in the list of top 15 most important predictors, spatial patterns of
lithologic classes can often be distinctly recognized in the output predictions. This is especially
true for soil texture fractions and coarse fragments. In general, for predicting soil chemical
properties, climatic variables (especially precipitation) and surface reflectance seem to be the
most important, while for soil classes and soil physical properties it is a combination of relief,
vegetation dynamics and parent material.
Fig 7 shows some individual relationships between target variables and several of the most
important covariates. For soil pH we observe that the relationship with total annual rainfall is
close to linear; for soil organic carbon and depth the relationship is linear on a log-log scale.
Many such individual correlations can also be interpreted and understood in terms of pedo-
logic knowledge. For example, higher MIR reflectance may be associated with high concentra-
tion of salts in soil and hence higher pH; higher rainfall and cooler climates often result in
higher organic carbon content because the speed of organic matter accumulation is higher
than the speed of decomposition. For the majority of soil variables, however, relationships are
not clearly linear and often many soil covariates are equally important.
We have also investigated possibilities for using kriging of residuals to improve predictions
of soil properties. Because the majority of spatial variation has been explained by covariates
and machine learning models, it appears that no significant spatial autocorrelation structure
can be observed for residuals (i.e. almost all variograms show pure nugget effect structure) at
distances <300 km for almost all continents and all variables. Although locally, where the
point density is high, kriging of residuals could still be beneficial for mapping of CEC and
depth to bedrock, overall kriging of residuals for global land mass does not seem to be neces-
sary nor is it practical to implement for billions of pixels: it would only marginally improve the
accuracy of predictions at high computing costs.
Accuracy assessment
Table 1 shows summary results of cross-validation for soil properties (global assessment). In
all cases there is no large overestimation of values, although for organic carbon and CEC the
models seem to somewhat under-estimate the overall mean. For log-transformed variables we
applied the accuracy assessment in the log-transformed space which yields asymmetric predic-
tion intervals after back-tranformation. For example, predictions for organic carbon are
±0.715 in log-space, which means that the 90% probability prediction interval for a case where
the soil organic carbon prediction equals 20‰ (2%) is 6–65‰; for a case where the soil organic
carbon prediction equals 150‰ it is 46–485‰. Prediction intervals are hence still fairly wide,
which might make SoilGrids of limited usability for detailed spatial modeling e.g. at farm level.
Note also that because there is significant spatial clustering of the training points, it is possible
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 18 / 40
that the validation results might be somewhat more optimistic than if we had validated predic-
tions by using points collected following some (objective) probability sampling, as described in
Brus et al. [60]. On the other hand, the cross-validation results do not show any serious sys-
tematic over- or underestimation (ME close to zero), which is also visible from the correlation
plots (Fig 8).
Table 2 shows results for SoilGrids250m in comparison with the previous system at 1 km res-
olution. Improvements in average RMSE are between 30–80% and can largely be attributed to
the use of machine learning algorithms in place of multiple linear regression, but also to invest-
ments in preparing finer resolution covariates and additional and improved soil profile data.
Fig 8. Correlation (density) plots produced as a result of 10–fold cross-validation. See also Table 1 for more details.
doi:10.1371/journal.pone.0169748.g008
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 19 / 40
The most challenging variables to model with this set of covariates are coarse fragments
and depth to bedrock, although in no case is the R-square <50%. Nevertheless, the RMSE is
still relatively high in comparison to many local soil mapping projects. Users should thus be
aware that the uncertainty levels are still relatively high. There are also still problems with over-
estimation of low values, clearly visible for example in the case of soil organic carbon content.
Overall, predictions for most properties are unbiased, i.e. most predictions are fairly symmet-
ric around the 1:1 line (Fig 8).
For soil classes, out-of-bag average prediction accuracy, reported by the packages, was
between 20–28% for the WRB classification system and between 34–48% for the USDA sys-
tem. The 10–fold cross-validation results showed that the weighted kappa for WRB classes is
42%, with map purity 28%; for USDA classes the kappa is 57%, while the map purity is 48%.
Although WRB classes seem to be somewhat more challenging to model than USDA subor-
ders, this comparison should be considered within the context of: (a) the number of classes,
and (b) similarity between classes. The WRB classification contains about two times more
classes than USDA suborders, and many WRB classes with highest confusion fall in taxo-
nomically similar groups. Further evaluation of classification accuracy has shown that, at the
level of WRB soil groups, map purity jumps to 60%, i.e. it becomes comparable to the USDA
system. Remaining WRB soil groups with map purity <50% are Planosols, Phaeozems and
Ferrasols.
A more detailed assessment of prediction accuracy derived using the ROCR package, i.e. per
each individual class, shows that the average TPR is about 0.93 for USDA soil suborders
(Table 3), and about 0.90 for WRB classes (Table 4). Also maps of the scaled Shannon Entropy
index (Fig 9) indicate that produced soil class maps for USDA soil classification system are less
uncertain than for the WRB system: WRB classification is critically uncertain for Australia and
India, parts of Africa and highlands of Latin America. Maps of uncertainty closely reflect
extrapolation areas and could be potentially very useful for planning new soil surveys aimed at
mapping soil types. For example, Fig 10 shows that the highest confusion (lowest prediction
accuracy) is systematically connected with distribution of river valleys, urban areas and hill-
slopes.
In summary, the cross-validation results for predicting class probabilities indicate relatively
high correspondence between prediction probabilities and observed soil types, which is also
confirmed visually by overlaying observed classes and prediction probabilities. Nevertheless, it
appears from Tables 3and 4that for some classes, such as Cambisols, Luvisols, Fluvisols and
Planosols in the WRB system, and Aquepts, Fluvents and Aquents in the USDA Soil Taxo-
mony system, the confusion of predictions with other classes is still relatively high.
Discussion
In the following sections we address some remaining discussion points and suggest ways to
improve SoilGrids and embark on new research directions. Although we have reached current
effective limits imposed by software capabilities and availability of remote sensing data sources,
the accuracy of SoilGrids could still be improved. Globally, by adding more covariates based
on the most recent remote sensing data (see Fig 11), and locally, by combining global predic-
tions with local prediction models. Global models could be further improved especially by
revising (even re-designing) each of the three main components of the system:
• Soil training data,
• Statistical / Machine Learning models, and
• Covariate layers.
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 20 / 40
Table 3. Classification accuracy for predicted USDA class probabilities based on 10–fold cross-validation, ordered according to number of occur-
rences. ME = “Mean Error”, TPR = “True Positive Rate”, AUC = “Area Under Curve”, N = “Number of occurrences”, USDA = “United States Department of
Agriculture” soil classification system. The 1st and 2nd most probable classes are taken from the confusion matrix.
Name ME (%) TPR AUC N 1st class 2nd class
Udalfs 0.0 0.88 0.93 6326 Udalfs Udults
Udults 0.0 0.91 0.95 4997 Udults Udalfs
Udolls 0.1 0.91 0.93 3901 Udolls Udalfs
Ochrepts 0.1 0.89 0.91 2720 Ochrepts Udalfs
Aqualfs 0.0 0.89 0.91 2594 Aqualfs Udalfs
Aquolls 0.1 0.89 0.90 2450 Udolls Aquolls
Udox 0.0 0.93 0.95 2229 Ustox Udox
Ustolls -0.2 0.95 0.97 2042 Ustolls Borolls
Borolls 0.1 0.97 0.98 2029 Borolls Albolls
Ustox 0.1 0.93 0.95 2024 Ustox Udox
Orthents 0.1 0.88 0.89 1911 Orthents Udults
Aquepts 0.0 0.87 0.88 1734 Aquolls Aquepts
Psamments 0.1 0.90 0.92 1725 Psamments Udults
Fluvents 0.1 0.84 0.85 1579 Udults Udalfs
Orthods 0.1 0.97 0.98 1538 Orthods Ochrepts
Udepts 0.1 0.90 0.91 1429 Udepts Udults
Aquents -0.1 0.84 0.85 1342 Aquepts Udalfs
Ustalfs -0.1 0.95 0.96 1332 Ustalfs Ustolls
Xerolls 0.0 0.97 0.98 1319 Xerolls Xeralfs
Argids -0.1 0.98 0.99 907 Argids Xerolls
Turbels 0.1 0.99 1.00 787 Turbels Orthels
Orthels 0.0 0.97 0.98 648 Ochrepts Orthels
Xeralfs -0.3 0.97 0.98 615 Xeralfs Xerolls
Usterts -0.2 0.97 0.98 590 Usterts Ustolls
Albolls -0.2 0.92 0.93 589 Borolls Aquolls
Xerepts -0.3 0.99 0.99 588 Xerepts Xeralfs
Arents -0.2 0.99 0.99 554 Arents Ustox
Aquults -0.2 0.94 0.94 380 Udults Aquults
Cambids -0.1 0.99 0.99 362 Cambids Argids
Humults -0.1 0.98 0.98 348 Humults Udults
Hemists -0.2 0.93 0.93 347 Ochrepts Hemists
Torrox -0.3 0.98 0.99 334 Ustox Torrox
Saprists -0.4 0.93 0.93 319 Saprists Udalfs
Histels -0.3 0.99 0.99 302 Histels Turbels
Aquods 0.0 0.94 0.94 301 Orthods Udults
Calcids -0.1 0.98 0.99 301 Argids Calcids
Ustults 0.0 0.99 0.99 286 Ustults Ustalfs
Fibrists 0.0 0.96 0.97 250 Fibrists Udults
Udands 0.0 0.99 0.99 234 Udands Udox
Xerands 0.0 0.99 0.99 231 Xerands Xerolls
Aquerts -0.2 0.95 0.95 226 Aqualfs Udalfs
Xererts 0.0 0.96 0.96 184 Xererts Xerolls
Uderts 0.0 0.95 0.96 177 Udults Uderts
Ustepts 0.0 0.97 0.97 175 Ustolls Ustepts
Cryands 0.0 0.99 0.99 161 Cryands Ochrepts
(Continued)
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 21 / 40
Increasing and improving the quality and quantity of the training data
The most fruitful avenue for improving the current predictions is likely in improving the qual-
ity and quantity of soil profile data. ISRIC has invested decades in obtaining, digitizing, clean-
ing up and standardizing soil profile data. A large portion of these data (about 80,000 unique
points) is publicly available via ISRIC’s Web Feature Service WoSIS (http://wfs.isric.org/
geoserver/wosis/wfs) [61]; remaining soil profile data sets not publicly available via ISRIC’s
WoSIS WFS can be obtained by contacting the corresponding original data providers as listed
in the Acknowledgment section. This collection of soil profile data is of similar scope and util-
ity when compared to other international data initiatives in meteorology (e.g. Global Historical
Climatology Network) and biodiversity (http://gbif.org).
Although the training data shown in Fig 3 appear to be quite dense, there are still large gaps
in terms of representation of the feature space. Tropics, wetlands, semi-arid to hyper-arid
areas and mountains are still largely under-represented. There are undoubtedly millions of soil
field observations in the world unused for global soil mapping activities that could be collated
and used to improve predictions. FAO’s Global Soil Partnership (http://www.fao.org/
globalsoilpartnership/) has set as one of its main objectives the preparation of an international
compilation of reference soil profiles to help catalyze using soil data for decision making.
Hence, there are already some initiatives in this direction.
Harmonization of soil laboratory data and soil descriptive variables is another area that will
need to be improved. For example, we had to standardize soil depths for several databases by
re-aligning 0 depth to soil surface. Some soil databases only contain information about the
mineral soil and put the zero level at the start of the mineral soil. But such soils might have an
Table 3. (Continued)
Name ME (%) TPR AUC N 1st class 2nd class
Cryepts 0.0 0.98 0.98 150 Ochrepts Cryepts
Humods 0.0 0.92 0.92 149 Orthents Orthods
Cryods -0.1 0.99 1.00 133 Orthods Cryods
Torrerts -0.1 0.98 0.98 106 Ustolls Torrerts
Cryolls -0.2 0.99 0.99 79 Borolls Cryolls
Gelods -0.7 1.00 1.00 78 Turbels Gelods
Gypsids -0.1 0.99 0.99 70 Argids Gypsids
Vitrands -0.3 0.98 0.98 62 Vitrands Ochrepts
Torrands -0.3 0.99 0.99 60 Xerolls Torrands
Durids -0.3 0.99 0.99 59 Argids Xerolls
Xerults -0.3 0.97 0.97 53 Xeralfs Humults
Rendolls -0.5 0.93 0.93 41 Udalfs Ochrepts
Salids -0.6 0.94 0.94 37 Argids Fluvents
Cryalfs -0.7 1.00 1.00 32 Ochrepts Borolls
Folists -0.5 0.99 0.99 30 Orthods Cryods
Gelands -1.0 0.97 0.97 26 Gelods Turbels
Perox -0.7 0.99 0.99 21 Udults Perox
Aquands -0.7 0.98 0.98 19 Xerolls Udands
Ustands -0.9 1.00 1.00 17 Ustalfs Orthents
Aquox -1.0 0.98 0.98 16 Udults Udox
Cryids 0.99 0.99 8 Argids Borolls
Gelepts 0.83 0.83 6 Ochrepts Turbels
doi:10.1371/journal.pone.0169748.t003
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 22 / 40
Table 4. Classification accuracy for predicted WRB class probabilities based on 10–fold cross-validation,ordered according to number of occur-
rences. ME = “Mean Error”, TPR = “True Positive Rate”, AUC = “Area Under Curve”, N = “Number of occurrences”, WRB = “World Reference Base” soil clas-
sification system. The 1st and 2nd most probable classes are taken from the confusion matrix.
Name ME (%) TPR AUC N 1st class 2nd class
Haplic Cambisols 0.1 0.78 0.81 5619 Haplic Cambisols Haplic Cambisols (Dystric)
Haplic Luvisols 0.1 0.86 0.88 2975 Haplic Luvisols Haplic Cambisols
Haplic Acrisols 0.0 0.93 0.94 2607 Haplic Acrisols Haplic Ferralsols
Haplic Ferralsols -0.1 0.94 0.95 1887 Haplic Ferralsols Haplic Acrisols
Haplic Fluvisols -0.1 0.88 0.89 1776 Haplic Fluvisols (Calcaric) Haplic Fluvisols (Eutric)
Haplic Calcisols 0.0 0.93 0.94 1745 Haplic Calcisols Calcaric Regosols
Haplic Kastanozems 0.0 0.96 0.97 1718 Haplic Kastanozems Haplic Luvisols
Gleyic Luvisols 0.0 0.92 0.93 1686 Albic Luvisols Gleyic Luvisols
Aric Regosols -0.2 0.91 0.92 1488 Calcaric Regosols Haplic Leptosols
Haplic Chernozems 0.1 0.96 0.97 1394 Haplic Chernozems Haplic Kastanozems
Albic Luvisols -0.1 0.94 0.95 1389 Gleyic Luvisols Haplic Luvisols
Calcaric Regosols -0.2 0.92 0.93 1379 Aric Regosols Haplic Leptosols
Haplic Podzols 0.1 0.96 0.97 1359 Haplic Podzols Haplic Cambisols
Haplic Cambisols (Dystric) 0.0 0.90 0.91 1334 Haplic Cambisols Haplic Podzols
Haplic Cambisols (Calcaric) -0.1 0.92 0.92 1173 Haplic Cambisols Haplic Calcisols
Haplic Phaeozems -0.1 0.90 0.91 1114 Haplic Phaeozems Haplic Chernozems
Haplic Lixisols 0.0 0.92 0.93 1094 Haplic Lixisols (Chromic) Haplic Lixisols
Haplic Leptosols 0.0 0.91 0.92 1092 Haplic Leptosols Haplic Leptosols (Eutric)
Haplic Gleysols 0.0 0.88 0.89 1054 Haplic Gleysols (Eutric) Haplic Cambisols
Haplic Vertisols 0.1 0.93 0.93 1040 Haplic Vertisols (Eutric) Haplic Vertisols
Haplic Arenosols 0.1 0.91 0.92 935 Haplic Arenosols Haplic Cambisols
Ferralic Arenosols 0.1 0.96 0.97 920 Ferralic Arenosols Haplic Ferralsols
Haplic Cryosols 0.0 0.99 1.00 884 Haplic Cryosols Haplic Cambisols
Haplic Cambisols (Eutric) 0.1 0.84 0.85 857 Haplic Cambisols Haplic Luvisols
Haplic Alisols 0.1 0.94 0.95 827 Haplic Acrisols Haplic Cambisols
Luvic Phaeozems -0.4 0.90 0.91 741 Luvic Phaeozems Haplic Luvisols
Rendzic Leptosols 0.0 0.94 0.95 695 Haplic Cambisols Rendzic Leptosols
Haplic Fluvisols (Calcaric) 0.0 0.94 0.94 692 Haplic Fluvisols Haplic Calcisols
Petric Calcisols 0.1 0.97 0.98 679 Petric Calcisols Haplic Calcisols
Haplic Regosols (Eutric) 0.1 0.86 0.87 677 Haplic Cambisols Haplic Luvisols
Lithic Leptosols 0.0 0.93 0.93 655 Haplic Ferralsols Haplic Acrisols
Umbric Gleysols 0.0 0.91 0.92 621 Mollic Gleysols Calcic Gleysols
Mollic Gleysols 0.0 0.91 0.91 575 Umbric Gleysols Calcic Gleysols
Haplic Vertisols (Eutric) 0.1 0.95 0.95 568 Haplic Vertisols Haplic Kastanozems
Haplic Gypsisols 0.1 0.98 0.98 565 Haplic Gypsisols Aric Regosols
Haplic Solonetz 0.1 0.92 0.92 539 Gleyic Solonetz Solodic Planosols
Calcic Gleysols 0.0 0.90 0.91 514 Umbric Gleysols Mollic Gleysols
Haplic Nitisols (Rhodic) 0.1 0.94 0.95 492 Haplic Ferralsols Haplic Acrisols
Haplic Fluvisols (Eutric) 0.1 0.91 0.91 465 Haplic Fluvisols Haplic Ferralsols
Haplic Lixisols (Chromic) 0.0 0.97 0.97 441 Haplic Lixisols Haplic Ferralsols
Calcic Vertisols -0.1 0.93 0.93 437 Calcic Vertisols Haplic Vertisols
Calcic Kastanozems -0.1 0.95 0.95 415 Haplic Kastanozems Haplic Luvisols
Leptic Regosols 0.1 0.96 0.96 404 Petric Calcisols Haplic Luvisols
Haplic Luvisols (Chromic) -0.1 0.93 0.93 396 Haplic Luvisols Haplic Ferralsols
Haplic Solonchaks 0.1 0.95 0.95 383 Haplic Solonchaks (Sodic) Haplic Solonchaks
(Continued)
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 23 / 40
Table 4. (Continued)
Name ME (%) TPR AUC N 1st class 2nd class
Luvic Chernozems -0.1 0.93 0.93 377 Haplic Kastanozems Luvic Phaeozems
Acric Ferralsols 0.0 0.97 0.98 371 Haplic Ferralsols Acric Ferralsols
Fibric Histosols 0.0 0.96 0.96 371 Fibric Histosols Haplic Acrisols
Calcic Luvisols 0.0 0.92 0.92 369 Haplic Cambisols Haplic Luvisols
Calcic Chernozems 0.0 0.94 0.94 358 Calcic Chernozems Haplic Cambisols
Aluandic Andosols 0.0 0.97 0.97 341 Aluandic Andosols Haplic Cambisols
Luvic Calcisols 0.0 0.95 0.95 322 Haplic Calcisols Haplic Kastanozems
Protic Arenosols 0.0 0.99 0.99 322 Protic Arenosols Haplic Leptosols
Haplic Albeluvisols -0.1 0.97 0.97 321 Haplic Albeluvisols Haplic Cambisols
Mollic Solonetz -0.1 0.94 0.94 312 Gleyic Solonetz Haplic Kastanozems
Haplic Acrisols (Ferric) -0.1 0.95 0.95 310 Haplic Acrisols Haplic Cambisols
Haplic Planosols (Eutric) 0.0 0.89 0.89 310 Haplic Podzols Haplic Acrisols
Haplic Gleysols (Eutric) -0.1 0.93 0.93 306 Haplic Gleysols Haplic Acrisols
Ferralic Cambisols 0.0 0.93 0.93 301 Haplic Ferralsols Haplic Acrisols
Cryic Histosols -0.1 0.99 0.99 299 Cryic Histosols Haplic Cryosols
Gleyic Solonetz -0.2 0.94 0.94 282 Mollic Solonetz Haplic Solonetz
Haplic Cambisols (Humic) 0.0 0.92 0.93 273 Haplic Cambisols Haplic Acrisols
Leptic Phaeozems 0.1 0.97 0.97 267 Leptic Phaeozems Haplic Luvisols
Haplic Regosols (Dystric) 0.0 0.87 0.87 262 Haplic Cambisols Haplic Acrisols
Haplic Leptosols (Eutric) 0.0 0.93 0.93 261 Haplic Leptosols Haplic Calcisols
Acric Plinthosols 0.1 0.97 0.97 251 Haplic Acrisols Haplic Ferralsols
Hemic Histosols 0.0 0.97 0.97 250 Hemic Histosols Albic Luvisols
Endogleyic Cambisols 0.0 0.87 0.87 249 Haplic Cambisols Haplic Acrisols
Haplic Cambisols (Chromic) 0.0 0.93 0.93 242 Haplic Cambisols Haplic Ferralsols
Vertic Cambisols 0.1 0.88 0.88 241 Haplic Ferralsols Haplic Cambisols
Leptic Luvisols 0.2 0.96 0.97 229 Haplic Luvisols Leptic Phaeozems
Solodic Planosols 0.0 0.96 0.96 222 Haplic Solonetz Haplic Kastanozems
Hypoluvic Arenosols 0.0 0.96 0.96 205 Hypoluvic Arenosols Haplic Arenosols
Leptic Cambisols 0.1 0.94 0.94 199 Haplic Luvisols Petric Calcisols
Umbric Ferralsols 0.1 0.96 0.96 192 Haplic Ferralsols Haplic Acrisols
Gleyic Podzols 0.1 0.95 0.95 176 Gleyic Podzols Haplic Acrisols
Turbic Cryosols 0.0 0.99 1.00 168 Haplic Cryosols Turbic Cryosols
Vitric Andosols 0.1 0.97 0.97 166 Haplic Cambisols Aluandic Andosols
Haplic Acrisols (Humic) 0.0 0.96 0.96 164 Haplic Acrisols Haplic Cambisols
Haplic Fluvisols (Arenic) 0.0 0.98 0.98 163 Haplic Fluvisols Ferralic Arenosols
Stagnic Luvisols 0.0 0.93 0.93 163 Haplic Cambisols Haplic Luvisols
Mollic Leptosols 0.0 0.90 0.90 162 Petric Calcisols Haplic Leptosols
Haplic Acrisols (Alumic) 0.0 0.98 0.98 156 Haplic Acrisols Haplic Ferralsols
Plinthic Acrisols 0.0 0.94 0.94 152 Haplic Acrisols Plinthic Acrisols
Calcic Solonetz 0.0 0.93 0.93 149 Haplic Calcisols Haplic Kastanozems
Haplic Ferralsols (Xanthic) 0.0 0.96 0.96 146 Haplic Ferralsols Haplic Acrisols
Vertic Luvisols -0.1 0.93 0.94 140 Haplic Cambisols Haplic Luvisols
Haplic Lixisols (Ferric) -0.1 0.96 0.96 134 Haplic Lixisols Haplic Acrisols
Mollic Vertisols 0.0 0.96 0.96 133 Mollic Vertisols Haplic Cambisols
Haplic Solonchaks (Sodic) -0.1 0.97 0.97 130 Haplic Solonchaks Haplic Arenosols
Sapric Histosols -0.1 0.90 0.90 128 Haplic Cambisols Fibric Histosols
Haplic Ferralsols (Rhodic) -0.1 0.96 0.96 125 Haplic Ferralsols Haplic Acrisols
(Continued)
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 24 / 40
organic layer as well. Since the thickness of the organic horizon of these soil profiles is not
reported, their vertical coordinates could not be corrected. There are many situations like this
that require careful analysis of harmonization steps, so that any serious over or under-estima-
tion can be avoided.
It is also fundamentally important that we do not limit ourselves to legacy soil profile data
only. The soil science community needs to actively begin investing in collecting new soil pro-
file field observations, especially in the previously mentioned ecological and climatic zones
that have been under-sampled. For example, the AfSIS project (http://africasoils.net) has spent
already half a decade on collecting new samples for Africa. We believe that there is great poten-
tial in undertaking various types of feature space distribution analysis (see e.g. Minasny et al.
[62] and Fitzpatrick et al. [63]) and optimizing new sampling of additional soil profiles using,
for example, Latin Hypercube sampling principles. By adding only a few hundred new points
that are carefully allocated in extrapolation areas, the accuracy of predictions is likely to
improve more rapidly than if we double the number of points in areas already well repre-
sented. Collection of the new samples could even be implemented via crowd labour or crowd-
sourcing systems so that also local soil surveyors / enthusiasts could get involved (we are
currently testing using MySoil observations contributed by non-specialists, kindly donated to
SoilGrids by the British Geological Survey).
Table 4. (Continued)
Name ME (%) TPR AUC N 1st class 2nd class
Calcic Gypsisols -0.4 0.96 0.96 124 Calcaric Regosols Haplic Calcisols
Haplic Cambisols (Sodic) -0.1 0.98 0.98 120 Haplic Cambisols Ferralic Arenosols
Haplic Calcisols (Sodic) -0.4 0.98 0.98 115 Haplic Calcisols Haplic Cambisols
Haplic Fluvisols (Dystric) -0.2 0.93 0.93 107 Haplic Fluvisols Haplic Ferralsols
Haplic Gleysols (Dystric) -0.3 0.91 0.91 100 Haplic Gleysols Haplic Ferralsols
Gypsic Solonchaks -0.4 0.98 0.98 98 Haplic Gypsisols Calcaric Regosols
Haplic Luvisols (Ferric) -0.2 0.95 0.95 98 Haplic Luvisols Haplic Lixisols
Haplic Arenosols (Calcaric) -0.3 0.94 0.94 97 Haplic Arenosols Haplic Calcisols
Umbric Albeluvisols -0.2 0.99 1.00 97 Umbric Albeluvisols Haplic Albeluvisols
Alic Nitisols -0.1 0.98 0.98 70 Haplic Acrisols Alic Nitisols
Haplic Andosols -0.2 0.93 0.93 67 Aluandic Andosols Haplic Luvisols
Haplic Planosols (Dystric) 0.0 0.92 0.92 62 Ferralic Arenosols Haplic Ferralsols
Luvic Stagnosols 0.0 0.99 0.99 61 Haplic Cambisols Gleyic Luvisols
Haplic Umbrisols 0.0 0.93 0.93 57 Haplic Cambisols Haplic Acrisols
Albic Arenosols -0.1 0.93 0.93 54 Haplic Acrisols Haplic Arenosols
Lixic Plinthosols -0.2 0.94 0.94 49 Haplic Ferralsols Haplic Acrisols
Leptic Umbrisols -0.4 0.97 0.97 40 Haplic Luvisols Haplic Leptosols
Petric Durisols -0.3 1.00 1.00 39 Petric Durisols Haplic Phaeozems
Cutanic Alisols -0.4 0.98 0.98 34 Haplic Cambisols Cutanic Alisols
Endogleyic Planosols -0.2 0.93 0.93 34 Haplic Acrisols Haplic Luvisols
Haplic Regosols (Sodic) -0.5 0.97 0.97 34 Haplic Vertisols Leptic Regosols
Luvic Planosols -0.4 0.88 0.88 29 Haplic Luvisols Haplic Cambisols
Calcic Histosols -0.9 0.94 0.94 18 Haplic Acrisols Haplic Gleysols
Vetic Acrisols -1.6 0.90 0.90 15 Haplic Acrisols Haplic Ferralsols
Histic Albeluvisols -2.3 1.00 1.00 13 Umbric Albeluvisols Fibric Histosols
Vitric Cryosols -2.3 1.00 1.00 13 Vitric Cryosols Haplic Cambisols
doi:10.1371/journal.pone.0169748.t004
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 25 / 40
Improving the modeling framework
A major improvement from SoilGrids1km to SoilGrids250m is that we now consistently use
machine learning techniques to generate predictions. In the previous version of SoilGrids we
used various types of (Generalized) Linear Models in combination with natural splines to
model soil property-depth relationships, but this resulted in soil property-depth relationships
that were the same across the globe, which is unrealistic and suboptimal. To tackle such prob-
lems we now use dominantly tree-based models—random forest and gradient tree boosting—
to account for local relationships between soil variables and covariates. Fig 2 (left) shows that,
Fig 9. Maps of scaled Shannon Entropy index (Eq 5) for USDA and WRB soil classification maps.
doi:10.1371/journal.pone.0169748.g009
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 26 / 40
indeed, predictions produced using tree-based models adjust locally to observed values. The
current version of SoilGrids is thus, we contend, able to better represent both global and local
patterns.
It has already been demonstrated that random forest can outperform linear models, espe-
cially in being able to better represent complex non-linear relationships in large data sets [64–
66]. Likewise, gradient tree boosting has already won several Kaggle.com competitions (Kaggle
is a platform for predictive modeling and analytics competitions on which companies and
researchers post their data and statisticians and data miners from all over the world compete
to produce the best models). However, tackling the complexities of data size has been a major
challenge. In the case of SoilGrids, the regression matrices had up to one million point pairs
with over 150 covariates, hence their size and complexity well exceeds what can be handled
with desktop computers. Ultimately, we decided to primarily rely on three Rpackages—
caret[49], ranger[47] and xgboost[48]—that have proven to be capable of processing
huge raster stacks. By using these three open source packages and a single dedicated server
Fig 10. Example of scaled Shannon Entropy index for USDA and WRB soil classification maps with a zoom in on USA state Illinois near the
city of Chicago. This figure uses the same legend as used in Fig 9.
doi:10.1371/journal.pone.0169748.g010
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 27 / 40
(current costs of about $800 / month) we were able to optimize and fit all models needed to
generate SoilGrids within a few hours, and to generate all predictions for the entire world
within 12 days.
Machine learning (ML) greatly simplifies model fitting: basically, a soil surveyor does not
need to suggest or impose any relationships—the analyst only needs to list a target variable
and covariates, and machine learning does the ‘magic’ of optimizing model parameters. On
the one hand this is an attractive property because using the ML framework for global soil
mapping allows mapping hundreds of soil variables in parallel with little human interaction.
On the other hand it has also risks and limitations:
• ML is sensitive to noise and errors in the data. Even a few typos in the input values can result
in significant blunders in output maps,
• The computational intensity of ML, when compared to fitting linear models or similar, is an
order of magnitude greater. As the number of training points grows, the computational load
Fig 11. List of some remote sensing data of relevance for global soil mapping projects (i.e. with a near to global
coverage and with remote sensing technology of interest to soil mapping). Landsat 8 is part of the Landsat Data
Continuity Mission (LDCM) maintained by NASA and the United States Geological Survey (USGS). ALOS Global Digital
Surface Model is a product of the Japanese Aerospace Exploration Agency. Sentinel–1,2 is the Earth observation
mission developed by the European Space Agency as part of the Copernicus Programme. WorldDEM™is a commercial
product distributed by Airbus Defence and Space.
doi:10.1371/journal.pone.0169748.g011
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 28 / 40
grows exponentially. At some stage, it becomes currently infeasible and overly expensive to
compute predictions using machine learning,
• Extrapolation of models fitted using ML remains risky. Without using pseudo-points to fill-
in data gaps in feature space for some parameters, machine learning can potentially produce
worse maps (on average for most of the soil mask) than linear models,
• Because the sampling locations are clearly biased towards agricultural areas, and because
most of the training points come from the developed world (especially USA), it is very well
possible that SoilGrids predictions are significantly biased in undersampled parts of the
world. In principle, the best solution to this problem is to continuously add more points
from undersampled areas, especially in Africa (tropical soils and wetlands) and the Russian
Federation,
• With ML approaches it is difficult to derive spatially explicit measures of the prediction accu-
racy. We calculated accuracy measures using 10–fold cross-validation, but these are only
global measures.
• ML approaches have a high degree of “black box” modeling and it is difficult to incorporate
knowledge of soil forming processes in the prediction algorithm. But perhaps we can also
learn from ML models by closer inspection and interpretation of how dominant covariates
influence soil property and soil class predictions.
Could machine learning put soil mappers out of work? Probably not. Solid knowledge of
soil science, spatial statistics and/or geostatistics in projects such as SoilGrids is needed more
than ever. For example, it is clear that in order to improve SoilGrids, more focus will need to
be put on improving the feature space representation (adding extra samples) and on improv-
ing visualization and interpretation of complex relationships. Such improvements are not pos-
sible without understanding principles of spatial sampling and soil-environment relationships.
Expert knowledge on soil-landscape relations and soil distribution remains important to eval-
uate the results and assess if predicted spatial patterns make sense from a pedological view-
point. Even though the existing machine learning methods have proven to show improved
predictive performance, much work remains to make them more robust, less sensitive to blun-
ders, incorporate soil-landscape process knowledge and make them more suited for input data
of variable accuracy.
With the current version of SoilGrids, we have also not yet adequately addressed the prob-
lem of vertical soil stratigraphy. At this stage, we remain unable to properly model how some
soil horizons show smooth transition of soil properties, and some show clear and abrupt dis-
continuities (as in geological layers on a meso-scale). In the next update of SoilGrids we hope
to improve modeling and prediction of occurrence of diagnostic soil horizons (e.g. Histic,
Nitic, Albic etc) in 3D, so that transitions between horizons can be represented more
accurately.
Preparation and conversion of soil class input data could also be much improved. Several
research groups [67,68] are now looking into automating soil classification (i.e. by using auto-
mated or semi-automated soil classification software). Eberhardt [69], for example, demon-
strated using German soil profile data that soil classification can be completely automatized.
Future versions of SoilGrids could also try to derive soil classes by applying exact rules per
pixel, instead of trying to predict them from point data. This might be an ambitious project—
often the classification systems (keys and rules of classification) can be very detailed and
require a comprehensive combination of diagnostic properties, laboratory data, soil-moisture
and temperature regimes, etc. in order to deduce the correct classification. This is without
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 29 / 40
considering the sensitivity of such classifiers to data gaps and uncertainties. Incorporating
uncertainty into such complex soil classification algorithms is yet another challenge. So far, we
have managed to produce global maps of the scaled Shannon Entropy index (Fig 9) that clearly
indicate under-represented areas. A sensible approach to improving predictions of soil types
would be to set the sampling intensity proportional to the Shannon Entropy index or
completely focus on areas where the Shannon Entropy index is >80%. In that sense, there
seems to be slightly more work needed for the WRB classification system than for the USDA
system.
We have also so far explicitly avoided trying to model posterior distributions of target var-
iables, i.e. map uncertainty for each soil variable. Although tools for modeling uncertainty in
ML methods already exist (see e.g. Meinshausen [70]), these are hundreds of times more
computationally intensive and will probably need to be re-implemented in some high-per-
formance computing infrastructure. One future objective is to implement a framework to
model uncertainties of all predictions using a robust statistical framework, such as quantile
regression forests, but this might be highly challenging, especially when the data volumes
grow larger.
Another opportunity for improvement lies in using spatiotemporal modeling [71,72] vs
purely spatial modeling. Stockmann et al. [2] recently made progress in modeling global soil
organic carbon dynamics, mainly using time-series of MODIS land cover images, but numer-
ous challenges remain:
• There might not be enough well-distributed soil profile data in the time-domain that support
fitting of spatiotemporal (and/or dynamic) models. As we move back further in the past,
there are fewer and fewer observations, so potential time-domain gaps are possibly an order
of magnitude more serious than spatial data gaps,
• Some soil properties such as soil water content, soil temperature, and even soil nutrients,
change not simply within seasons, but also within weeks or days. At this stage, global fitting
of spatiotemporal models for such variables that vary at short time scales might remain unat-
tainable (until new global soil monitoring networks are established),
• Legacy soil profile data exhibit a significant noise (diversity of methods, laboratories) so that,
for example for soil organic carbon, where temporal dynamics are slow, it will be difficult to
detect real changes in time in a situation where the signal-to-noise ratio is low,
• It is almost impossible to properly validate spatiotemporal predictions produced for past
periods of time. There are very few and sparse validation soil data collected using objective
probability sampling designs (as described in Brus et al. [60]). Eventually, we might never
know how accurate our models are in predicting the past status of soil from 50 or 100 years
ago. One possible solution to this problem is linking soil science more directly with paleon-
tology and archeology, but this will probably not work for all soil variables.
Predicting at resolutions finer than 250 meter
Because the algorithms and software we have used in this work are already optimized for
processing large data, this opens a possibility to further speed up model fitting and predic-
tion and to generate predictions at ever finer resolutions. Fig 11 identifies some new remote
sensing data land products of relevance to global soil mapping. Note that some remote sens-
ing products, such as Landsat 8 and ASTER (distributed as scenes), require significant pro-
cessing capacities before they can be assembled and prepared for use in global soil mapping.
Nevertheless, considering the amount of remote sensing data available publicly today, we
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 30 / 40
anticipate that the Open Source software used in this work will soon (12–24 months) be able
to support generation of 30 m resolution SoilGrids, provided that enough resources exist to
cover the costs of preparing soil covariates and producing global predictions at these fine
resolutions.
Presently, the biggest challenges for upgrading SoilGrids to finer resolution are the
resources required to prepare all required remote sensing input data and computational capac-
ity needed to make fine resolution predictions globally. The software seems to be much less of
a problem. Although Rhas been often criticized for not being suited for large GIS layers, our
experience with SoilGrids has convinced us that, with proper combination of parallelization
and tiling of objects, and by using packages implemented in C++ or similar, equally efficient
computing can be achieved with rangerand xgboost(hence within R) or by using soft-
ware such as h2o(based on Java). The remaining bottleneck of Rwe experienced was the
size of models produced using random forest—the objects often exceeded 5–10 GiB and as
such require significant RAM during predictions. Such memory problems in Rcould possibly
be solved via the following two strategies:
1. Disk caching: by using the ff or a similar packages to save the forests on disk,
2. Efficient tree representation: transform trees to a simpler structure with the same output.
In the case of random forest, the number of trees required for a given accuracy depends on
the number of rows and columns, i.e. the number of observations (n) and covariates (p). Usu-
ally, for many rows only few trees are required, while for pnproblems (for example in
genetics) many more trees are needed. It should be generally fine to reduce the number of
trees to fewer than 300 but this could be at the expense of loss in accuracy. Lopes [73] shows a
framework, based on bootstrapping, to detect an optimal number of trees given some error
threshold. For example, in many cases, even 150 trees is sufficient to achieve stable results after
which a trade-off between computation time and accuracy offers no additional advantages.
We have not tried fine-tuning the number of trees per property (we consistently use 300 trees
as a practical compromise between precision and computing time) because this would have
been an additional load to the project.
Another serious challenge to producing finer resolution SoilGrids is the current lack of
adequately detailed geological data, i.e. data to represent the underlying lithology and min-
eralogy. We have thus far used the Global Lithological Map (GLiM) [35] as the key layer to
represent parent material, but this layer is probably even coarser than 1 km resolution
remote sensing covariates, and still contains numerous artifacts such as country/state bor-
ders. Although the OneGeology initiative is of obvious interest to global soil mapping proj-
ects, it has not, so far, delivered any globally consistent and complete information on parent
material. Likewise, the latest most accurate DEM of the world (WorldDEM™) is an order of
magnitude more accurate and more detailed than the SRTM DEM [74] and as such would
be an ideal covariate for many regional and global soil mapping projects. However, it will
likely remain a commercial product available to larger business only (civil engineering and
mineral exploration), and hence of limited use to global soil mappers. In that sense, USA’s
NASA and USGS, with its MODIS, Landsat and similar civil-applications missions will
likely remain the main source of spatial covariate data to support global soil mapping
initiatives.
Other potentially useful covariates for predicting soil properties and classes could be maps
of paleolithic i.e. pre-historic climatic conditions of soil formation, e.g. glacial landscapes and
processes, past climate conditions and similar. These could likely become significant predic-
tors of many current soil characteristics. Information on pre-historic climatic conditions and
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 31 / 40
land use is unfortunately often not available, especially not at detailed cartographic scales,
although there are now several global products that represent, for example, dynamics of land
use / changes of land cover (see e.g. HYDE data set by Klein et al. [75]) through the past 1500+
years. As the spatial detail and completeness of such pre-historic maps increases, they will
become potentially interesting covariates for global soil modeling.
Merging global and local: A system for automated soil image fusion
SoilGrids is not expected to be as accurate or relevant as locally produced maps and models
that make use of considerably greater amounts of local point data and finer local covariates.
This is especially the case for OECD countries that can draw upon orders of magnitude
more soil profile data than were used in this work (for illustration, it is estimated that
German Federal agencies alone have in possession 2–3 million complete soil profiles).
Comparison of SoilGrids with similar national or continent-wide products shows that there
is a general match in spatial patterns for many physical and chemical soil properties,
although there are still substantial differences (Fig 12). This indicates that promising possi-
bilities exist for further combination of local and global predictions (see further
discussion).
For both Tasmania and California, SoilGrids seems to show somewhat smoother predic-
tions, with some smoothing of higher and lower values, which is especially visible in the cross-
histogram scatter plots (Fig 12). SoilGrids tends to overestimate soil pH for parts of Tasmania
covered with rainforests mainly. There were not many ground observations to support the pre-
diction models for those areas, hence some systematic deviation could be expected and will
likely occur in other similar areas as well. We did not run a systematic comparison of values
for all soil properties, but Fig 12 indicates that merging SoilGrids250m with 100m resolution
predictions using higher density of local soil profiles could help to gradually improve accura-
cies locally and to fill gaps in locally generated predictions.
Mulder et al. [10] correctly recognized that, in many areas in the world, locally produced
predictions of soil properties could likely be significantly more accurate than SoilGrids. Our
hope is, nevertheless, that SoilGrids250m will be used by national and regional soil data pro-
duction teams with, or as a supplement to, local data, and that ultimately most users will use
merged (ensemble) global-local predictions for final decision making. We especially recom-
mend the following two frameworks for combining global and local data:
1. SoilGrids predictions as covariate layers for producing finer resolution local predictions of
soil properties (i.e. as an input for downscaling),
2. Ensemble predictions = SoilGrids + local soil spatial prediction models combined.
Option 2, i.e. produce ensemble predictions for smaller areas for which finer resolution
and/or higher quality soil covariates are available, is possibly the most attractive option consid-
ering that local and global predictions can then be generated independently. In that sense, Soil-
Grids could also be considered to be just one (the coarsest) component of a global soil
variation curve (Fig 13). But how many components to use to represent soil variation? Are two
components enough? How to optimally merge components where the accuracy is unknown
(not enough ground data for validation)? These will be areas of further research. In that con-
text, Malone et al. [77] recently made progress in testing and developing methods for merging
predictions from polygon-based maps and maps derived using spatial predictions. However,
running such models in an automated way for large areas (i.e. a system for an automated soil
image fusion) might take years before an operational system for global soil data fusion is fully
functional.
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 32 / 40
Conclusions
Soil has long been considered one of the least developed global environmental layers with data
available only at coarse resolutions and with limited accuracy [78,79]. ISRIC—World Soil
Information has a vision and a mission to produce soil information and map products that are
globally complete and consistent, scientifically robust, open, transparent and reproducible,
continuously improved and updated, easy to discover and access, easy to use and meaningful
to users. With this next generation SoilGrids250m we hope to continue to demonstrate prog-
ress in the production and distribution of improved global soil map products and to motivate,
especially non-soil scientists, to use these new soil data in their models and spatial planning,
i.e. directly as input for generation of soil functional properties and agro-ecological variables
and indicators to support decision making. With its Open Data license and web-services, we
aim to serve quality soil information freely and universally for science, society and a sustain-
able future.
Fig 12. Comparison between predicted soil pH: (above) SoilGrids (our predictions) for part of California and predictions based on the SSURGO
data set (for 0–200 cm depth interval) developed by the National Cooperative Soil Survey, (below) SoilGrids(our predictions) for Tasmania and
predictions based on the Soil and Landscape Grid of Australia [76] (for 0–5 cm depth interval). The correlation coefficients between the two data
sources are 0.79 and 0.71, respectively. Crosses on the map indicate soil profiles used for generating SoilGrids.
doi:10.1371/journal.pone.0169748.g012
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 33 / 40
We have demonstrated, using a series of cross-validation tests, that the new version of
SoilGrids represents a significant improvement upon the previous products at 1 km resolu-
tion, especially in terms of spatial detail and attribute accuracy. Future work is required to
determine if these improvements in accuracy could also help produce more accurate Global
Gridded Crop Models (GGCMs) that allow for more reliable estimates of impact of climate
change and land degradation on food production [8]. Data accessibility problems with Soil-
Grids have also been addressed: SoilGrids are now available for viewing in fusion with satel-
lite imagery via the data portal SoilGrids.org (Fig 14). SoilGrids rasters can also be
downloaded via FTP for smaller areas; at point locations through the SoilInfo App and the
REST SoilGrids. There should be fewer and fewer obstacles for ecologists, agronomists,
hydrologists, climatologists, foresters and spatial planners to discover, obtain and use soil
data in their daily work.
Fig 13. SoilGrids can be considered the ‘coarsest’ component of the global soil variation ‘signal’ curve. Other components, e.g. finer
products based on local / more detailed 250–100 m resolution imagery, could be added to produce a merged product.
doi:10.1371/journal.pone.0169748.g013
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 34 / 40
Acknowledgments
SoilGrids are based on numerous soil profile data sets that have been kindly contributed by
various national and international agencies: the USA National Cooperative Soil Survey Soil
Characterization database (http://ncsslabdatamart.sc.egov.usda.gov/) and profiles from the
USA National Soil Information System (http://soils.usda.gov/technical/nasis/), Land Use/Land
Cover Area Frame Survey (LUCAS) Topsoil Survey database [80] (http://eusoils.jrc.ec.europa.
eu/content/lucas-2009-topsoil-data), Africa Soil Profiles database [81], Australian National
Soil Information by CSIRO Land and Water [82,83], Mexican National soil profile database
[84] provided by the Mexican Instituto Nacional de Estadı
´stica y Geografı
´a / CONABIO, Bra-
zilian national soil profile database [85] provided by the University of São Paulo, Chinese
National Soil Profile database [86] provided by the Institute of Soil Science, Chinese Academy
of Sciences, soil profile archive from the Canadian Soil Information System [87] and Forest
Ecosystem Carbon Database (FECD), ISRIC-WISE [88], The Northern Circumpolar Soil Car-
bon Database [26], eSOTER profiles [89], SPADE [90], Unified State Register of soil resources
RUSSIA (Version 1.0. Moscow—2014), National Database of Iran provided by the Tehran
University, points from the Dutch Soil Information System (BIS) prepared by Wageningen
Environmental Research, and others. We are also grateful to USA’s NASA, USGS and USDA
agencies for distributing vast amounts of remote sensing data (especially MODIS land prod-
ucts and SRTM DEM), and to the Open Source software developers of the packages ranger,
Fig 14. Basic design and functionality of SoilGrids.org: Soil web-mapping browser that provides interactive viewing of 3D soil layers.
Reference administrative data, basic functionality and output data license of SoilGrids.org are primarily based on OpenStreetMap.
doi:10.1371/journal.pone.0169748.g014
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 35 / 40
xgboost,caret,raster,GDAL,SAGAGISand similar, and without which SoilGrids
would most likely not be possible. Every effort has been made to trace copyright holders of the
materials used to produce SoilGrids spatial predictions. Should we, despite all our efforts have
overlooked contributors please contact ISRIC and we shall correct this unintentional omission
without any delay and will acknowledge any overlooked contributions and contributors in
future updates.
Author Contributions
Conceptualization: TH JMJ GBMH MK WS MAG RAM JGBL IW BK.
Data curation: MRG WS XG RAM NHB JGBL SM.
Formal analysis: TH GBMH MK WS MNW BK.
Funding acquisition: TH GBMH.
Investigation: TH JMJ GBMH MRG WS BK.
Methodology: TH JMJ GBMH MRG MK WS MNW BBM RAM SM BK.
Project administration: TH JMJ MK.
Resources: JMJ MK AB BBM ER.
Software: TH JMJ MRG MK AB WS MNW ER BK.
Supervision: TH GBMH.
Validation: MRG XG MAG RV RAM NHB JGBL ER IW SM.
Visualization: TH JMJ AB MK ER.
Writing – original draft: TH JMJ GBMH WS MNW BBM RAM NHB IW BK.
Writing – review & editing: TH JMJ GBMH MK WS MNW BBM RAM NHB IW BK.
References
1. Scharlemann JPW, Tanner EVJ, Hiederer R, Kapos V. Global soil carbon: understanding and managing
the largest terrestrial carbon pool. Carbon Management. 2014; 5(1):81–91. doi: 10.4155/cmt.13.77
2. Stockmann U, Padarian J, McBratney A, Minasny B, de Brogniez D, Montanarella L, et al. Global soil
organic carbon assessment. Global Food Security. 2015; 6:9–16. http://dx.doi.org/10.1016/j.gfs.2015.
07.001.
3. Aksoy E, Yigini Y, Montanarella L. Combining Soil Databases for Topsoil Organic Carbon Mapping in
Europe. PLoS ONE. 2016; 11(3):1–17. doi: 10.1371/journal.pone.0152098
4. Shani U, Ben-Gal A, Tripler E, Dudley LM. Plant response to the soil environment: An analytical model
integrating yield, water, soil type, and salinity. Water resources research. 2007; 43(8). doi: 10.1029/
2006WR005313
5. Shepherd KD, Shepherd G, Walsh MG. Land health surveillance and response: A framework for evi-
dence-informed land management. Agricultural Systems. 2015; 132:93–106. doi: 10.1016/j.agsy.2014.
09.002
6. Pogson M, Hastings A, Smith P. Sensitivity of crop model predictions to entire meteorological and soil
input datasets highlights vulnerability to drought. Environmental Modelling & Software. 2012; 29(1):37–
43. doi: 10.1016/j.envsoft.2011.10.008
7. Montanarella L, Vargas R. Global governance of soil resources as a necessary condition for sustainable
development. Current Opinion in Environmental Sustainability. 2012; 4(5):559–564. doi: 10.1016/j.
cosust.2012.06.007
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 36 / 40
8. Folberth C, Skalsky
´R, Moltchanova E, BalkovičJ, Azevedo LB, Obersteiner M, et al. Uncertainty in soil
data can outweigh climate impact signals in global crop yield simulations. Nature Communications.
2016; 7. doi: 10.1038/ncomms11872
9. Hengl T, Mendes de Jesus J, MacMillan RA, Batjes NH, Heuvelink GBM, Ribeiro E, et al. SoilGrids1km
—Global Soil Information Based on Automated Mapping. PLoS ONE. 2014; 9(e105992). doi: 10.1371/
journal.pone.0105992 PMID: 25171179
10. Mulder V, Lacoste M, Richer-de Forges A, Martin M, Arrouays D. National versus global modelling the
3D distribution of soil organic carbon in mainland France. Geoderma. 2016; 263:16–34. doi: 10.1016/j.
geoderma.2015.08.035
11. Griffiths RI, Thomson BC, Plassart P, Gweon HS, Stone D, Creamer RE, et al. Mapping and validating
predictions of soil bacterial biodiversity using European and national scale datasets. Applied Soil Ecol-
ogy. 2016; 97:61–68. doi: 10.1016/j.apsoil.2015.06.018
12. FAO/IIASA/ISRIC/ISS-CAS/JRC. Harmonized World Soil Database (version 1.2). Rome: FAO; 2012.
13. Domisch S, Amatulli G, Jetz W. Near-global freshwater-specific environmental variables for biodiversity
analyses in 1 km resolution. Scientific data. 2015; 2. doi: 10.1038/sdata.2015.73 PMID: 26647296
14. Chen J, Chen J, Liao A, Cao X, Chen L, Chen X, et al. Global land cover mapping at 30 m resolution: A
POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing. 2015;
103:7–27. doi: 10.1016/j.isprsjprs.2014.09.002
15. IUSS Working Group WRB. World reference base for soil resources 2006: a framework for international
classification, correlation and communication. World soil resources reports No. 103. Rome: Food and
Agriculture Organization of the United Nations; 2006.
16. U S Department of Agriculture. Keys to Soil Taxonomy. 12th ed. U.S. Government Printing Office;
2014.
17. Arrouays D, Grundy MG, Hartemink AE, Hempel JW, Heuvelink GBM, Young Hong S, et al. Chapter
Three—GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties. In: Sparks DL, editor.
Soil carbon. vol. 125 of Advances in Agronomy. Academic Press; 2014. p. 93–134.
18. Nelson DW, Sommers LE. Total carbon, organic carbon, and organic matter. In: Page AL, Miller RH,
Keeney DR, editors. Methods of soil analysis, Part 2. 2nd ed. Agron. Monogr. 9. Madison, WI: ASA
and SSSA; 1982. p. 539–579.
19. Shangguan W, Hengl T, de Jesus JM, Yuan H, Dai Y. Mapping the global depth to bedrock for land sur-
face modeling. Journal of Advances in Modeling Earth Systems. 2016; p. n/a–n/a.
20. FAO. Guidelines for soil profile description. 4th ed. Rome: Food and Agriculture Organization of the
United Nations; 2006.
21. Shaw C, Bhatti JS, Sabourin KJ. An Ecosystem Carbon Database for Canadian Forests. Information
report. Edmonton, Alberta: Canadian Forest Service, Northern Forestry Centre; 2005.
22. Krasilnikov P, Marti JJI, Arnold R, Shoba S, editors. A Handbook of Soil Terminology, Correlation and
Classification. Earthscan LLC; 2009.
23. Lei SA. Soil Properties of the Kelso Sand Dunes in the Mojave Desert. The Southwestern Naturalist.
1998; 43(1):47–52.
24. Raup B, Racoviteanu A, Khalsa SJS, Helm C, Armstrong R, Arnaud Y. The GLIMS geospatial glacier
database: a new tool for studying glacier change. Global and Planetary Change. 2007; 56(1):101–110.
doi: 10.1016/j.gloplacha.2006.07.018
25. Mantel S, Wo
¨sten H, Verhagen J, et al. Biophysical land suitability for oil palm in Kalimantan, Indonesia.
ISRIC Report 2007/01. ISRIC—World Soil Information; 2007.
26. Hugelius G, Tarnocai C, Broll G, Canadell JG, Kuhry P, Swanson DK. The Northern Circumpolar Soil
Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern
permafrost regions. Earth System Science Data. 2013; 5(1):3–13.
27. Danielson JJ, Gesch DB. Global multi-resolution terrain elevation data 2010 (GMTED2010). Open-File
Report 2011–1073. U.S. Geological Survey; 2011.
28. Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, et al. System for Automated Geoscientific
Analyses (SAGA) v. 2.1.4. Geoscientific Model Development. 2015; 8(7):1991–2007. doi: 10.5194/
gmd-8-1991-2015
29. Savtchenko A, Ouzounov D, Ahmad S, Acker J, Leptoukh G, Koziana J, et al. Terra and Aqua MODIS
products available from NASA GES DAAC. Advances in Space Research. 2004; 34(4):710–714. doi:
10.1016/j.asr.2004.03.012
30. Mira M, Weiss M, Baret F, Courault D, Hagolle O, Gallego-Elvira B, et al. The MODIS (collection V006)
BRDF/albedo product MCD43D: temporal course evaluated over agricultural landscape. Remote Sens-
ing of Environment. 2015; 170:216–228. doi: 10.1016/j.rse.2015.09.021
SoilGrids250m: Global gridded soil information
PLOS ONE | DOI:10.1371/journal.pone.0169748 February 16, 2017 37 / 40
31. Wan Z. MODIS land surface temperature products users’ guide. ICESS, University of California; 2006.
32. Hall DK, Riggs GA. Accuracy assessment of the MODIS snow products. Hydrological Processes. 2007;
21(12):1534–1547.
33. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate sur-
faces for glo