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Earth Syst. Sci. Data, 12, 299–320, 2020
https://doi.org/10.5194/essd-12-299-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Standardised soil profile data to support global
mapping and modelling (WoSIS snapshot 2019)
Niels H. Batjes, Eloi Ribeiro, and Ad van Oostrum
ISRIC – World Soil Information, Wageningen, 6708 PB, the Netherlands
Correspondence:
Niels H. Batjes (niels.batjes@isric.org)
Received: 6 September 2019 – Discussion started: 16 September 2019
Revised: 17 December 2019 – Accepted: 9 January 2020 – Published: 10 February 2020
Abstract.
The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile
data to support digital soil mapping and environmental applications at broadscale levels. Since the release of the
first “WoSIS snapshot”, in July 2016, many new soil data were shared with us, registered in the ISRIC data repos-
itory and subsequently standardised in accordance with the licences specified by the data providers. Soil profile
data managed in WoSIS were contributed by a wide range of data providers; therefore, special attention was paid
to measures for soil data quality and the standardisation of soil property definitions, soil property values (and
units of measurement) and soil analytical method descriptions. We presently consider the following soil chemi-
cal properties: organic carbon, total carbon, total carbonate equivalent, total nitrogen, phosphorus (extractable P,
total P and P retention), soil pH, cation exchange capacity and electrical conductivity. We also consider the fol-
lowing physical properties: soil texture (sand, silt, and clay), bulk density, coarse fragments and water retention.
Both of these sets of properties are grouped according to analytical procedures that are operationally comparable.
Further, for each profile we provide the original soil classification (FAO, WRB, USDA), version and horizon des-
ignations, insofar as these have been specified in the source databases. Measures for geographical accuracy (i.e.
location) of the point data, as well as a first approximation for the uncertainty associated with the operationally
defined analytical methods, are presented for possible consideration in digital soil mapping and subsequent earth
system modelling. The latest (dynamic) set of quality-assessed and standardised data, called “wosis_latest”, is
freely accessible via an OGC-compliant WFS (web feature service). For consistent referencing, we also provide
time-specific static “snapshots”. The present snapshot (September 2019) is comprised of 196 498 geo-referenced
profiles originating from 173 countries. They represent over 832 000 soil layers (or horizons) and over 5.8 mil-
lion records. The actual number of observations for each property varies (greatly) between profiles and with
depth, generally depending on the objectives of the initial soil sampling programmes. In the coming years, we
aim to fill gradually gaps in the geographic distribution and soil property data themselves, this subject to the
sharing of a wider selection of soil profile data for so far under-represented areas and properties by our existing
and prospective partners. Part of this work is foreseen in conjunction within the Global Soil Information System
(GloSIS) being developed by the Global Soil Partnership (GSP). The “WoSIS snapshot – September 2019” is
archived and freely accessible at https://doi.org/10.17027/isric-wdcsoils.20190901 (Batjes et al., 2019).
Published by Copernicus Publications.
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1
Introduction
According to a recent review, so far over 800 000 soil profiles
have been rescued and compiled into databases over the past
few decades (Arrouays et al., 2017). However, only a frac-
tion thereof is readily accessible (i.e. shared) in a consistent
format for the greater benefit of the international commu-
nity. This paper describes procedures for preserving, quality-
assessing, standardising and subsequently providing consis-
tent world soil data to the international community, as de-
veloped in the framework of the Data or WoSIS (World
Soil Information Service) project since the release of the
first snapshot in 2016 (Batjes et al., 2017); this collabora-
tive project draws on an increasingly large complement of
shared soil profile data. Ultimately, WoSIS aims to provide
consistent harmonised soil data, derived from a wide range
of legacy holdings as well as from more recently developed
soil datasets derived from proximal sensing (e.g. soil spec-
tral libraries; see Terhoeven-Urselmans et al., 2010; Viscarra
Rossel et al., 2016), in an interoperable mode and preferably
within the setting of a federated, global soil information sys-
tem (GLOSIS; see GSP-SDF, 2018).
We follow the definition of harmonisation used by the
Global Soil Partnership (GSP, Baritz et al., 2014). It encom-
passes “providing mechanisms for the collation, analysis and
exchange of consistent and comparable global soil data and
information”. The following domains need to be considered
according to GSP’s definition: (a) soil description, classifi-
cation, and mapping; (b) soil analyses; (c) exchange of digi-
tal soil data; and (d) interpretations. In view of the breadth
and magnitude of the task, as indicated earlier (Batjes et
al., 2017), we have restricted ourselves to the standardisa-
tion of soil property definitions, soil analytical method de-
scriptions and soil property values (i.e. measurement units).
We have expanded the number of soil properties considered
in the preceding snapshot, i.e. those listed in the Global-
SoilMap (2015) specifications, gradually working towards
the range of soil properties commonly considered in other
global soil data compilation programmes (Batjes, 2016; FAO
et al., 2012; van Engelen and Dijkshoorn, 2013).
Soil characterisation data, such as pH and bulk density, are
collated according to a wide range of analytical procedures.
Such data can be more appropriately used when the proce-
dures for their collection, analysis and reporting are well un-
derstood. As indicated by USDA Soil Survey Staff (2011),
results differ when different analytical methods are used,
even though these methods may carry the same name (e.g.
soil pH) or concept. This complicates, or sometimes pre-
cludes, comparison of one set of data with another if it is not
known how both sets were collected and analysed. Hence,
our use of “operational definitions” for soil properties that
are linked to specific methods. As an example, we may con-
sider the “pH of a soil”. This requires information on sample
pretreatment, soil
/
solution ratio and description of solution
(e.g. H
2
O, 1 M KCl, 0.02 M CaCl
2
, or 1 M NaF) to be fully
understood. The pH level measured in sodium fluoride (pH
NaF), for example, provides a measure for the phosphorus
(P) retention of a soil, whereas pH measured in water (pH
H
2
O) is an indicator for soil nutrient status. Consequently, in
WoSIS, soil properties are defined by the analytical methods
and the terminology used, based on common practice in soil
science.
This paper discusses methodological changes in the
WoSIS workflow since the release of the preceding snap-
shot (Batjes et al., 2017), describes the data screening pro-
cedure, provides a detailed overview of the database content,
explains how the new set of standardised data can be ac-
cessed and outlines future developments. The data model for
the underpinning PostgreSQL database itself is described in
a recently updated procedures manual (Ribeiro et al., 2018);
these largely technical aspects are considered beyond the
scope of this paper.
Quality-assessed data provided through WoSIS can be
(and have been) used for various purposes. For example, as
point data for making soil property maps at various spatial-
scale levels, using digital soil mapping techniques (Arrouays
et al., 2017; Guevara et al., 2018; Hengl et al., 2017a, b;
Moulatlet et al., 2017). Such property maps, for example,
can be used to study global effects of soil and climate on leaf
photosynthetic traits and rates (Maire et al., 2015), generate
maps of root zone plant-available water capacity (Leenaars
et al., 2018) in support of yield gap analyses (van Ittersum
et al., 2013), assess impacts of long-term human land use on
world soil carbon stocks (Sanderman et al., 2017), or the ef-
fects of tillage practices on soil gaseous emissions (Lutz et
al., 2019). In turn, this type of information can help to in-
form global conventions such as the UNCCD (United Na-
tions Convention to Combat Desertification) and UNFCCC
(United Nations Framework Convention on Climate Change)
so that policymakers and business leaders can make informed
decisions about environmental and societal well-being.
2
WoSIS workflow
The overall workflow for acquiring, ingesting and processing
data in WoSIS has been described in an earlier paper (Bat-
jes et al., 2017). To avoid repetition, we will only name the
main steps here (Fig. 1). These are, successively, (a) store
submitted datasets with their metadata (including the licence
defining access rights) in the ISRIC Data Repository; (b) im-
port all datasets “as is” into PostgreSQL; (c) ingest the data
into the WoSIS data model, including basic data quality as-
sessment and control; (d) standardise the descriptions for the
soil analytical methods and the units of measurement; and
(e) ultimately, upon final consistency checks, distribute the
quality-assessed and standardised data via WFS (web feature
service) and other formats (e.g. TSV for snapshots).
As indicated, datasets shared with our centre are first
stored in the ISRIC Data Repository, together with their
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Figure 1.
Schematic representation of the WoSIS workflow for safeguarding and processing disparate soils datasets.
metadata (currently representing some 452 000 profiles) and
the licence and data-sharing agreement in particular, in line
with the ISRIC Data Policy (ISRIC, 2016). For the WoSIS
standardisation workflow proper, we only consider those
datasets (or profiles) that have a “non-restrictive” Creative
Commons (CC) licence as well as a defined complement
of attributes (see Appendix A). Non-restrictive has been de-
fined here as at least a CC-BY (attribution) or CC-BY-NC
(attribution non-commercial) licence. Presently, this corre-
sponds with data for some 196 498 profiles (i.e. profiles that
have the right licence and data for at least one of the stan-
dard soil properties). Alternatively, some datasets may only
be used for digital soil mapping using SoilGrids™, corre-
sponding with an additional 42 000 profiles, corresponding
to some 18 % of the total amount of standardised profiles
(
∼
238 000). Although the latter profiles are quality-assessed
and standardised following the regular WoSIS workflow, they
are not distributed to the international community in accor-
dance with the underpinning licence agreements; as such,
their description is beyond the scope of the present paper.
Finally, several datasets have licences indicating that they
should only be safeguarded in the repository; inherently,
these are not being used for any data processing.
3
Data screening, quality control and
standardisation
3.1
Consistency checks
Soil profile data submitted for consideration in WoSIS were
collated according to various national or international stan-
dards and presented in various formats (from paper to dig-
ital). Further, they are of varying degrees of completeness,
as discussed below. Proper documentation of the provenance
and identification of each dataset and, ideally, each obser-
vation or measurement is necessary to allow for efficient
processing of the source data. The following need to be
specified: profiles and layers referenced by feature (
x
–
y
–
z
)
and time (
t
), attribute (class, site, layer field and layer lab),
method, and value, including units of expression.
To be considered in the actual WoSIS standardisation
workflow, each profile must meet several criteria (Table 1).
First, we assess if each profile is geo-referenced, has (con-
sistently) defined upper and lower depths for each layer (or
horizon), and has data for at least some soil properties (e.g.
sand, silt, clay and pH). Having a soil (taxonomic) classi-
Table 1.
Basic requirements for considering soil profiles in the
WoSIS standardisation workflow.
Case
(
x, y
)
Layer
Soil
Classification
Keep
depth
properties
a
1
+
+
+
+
Yes
2
+
+
+
−
Yes
3
+
−
−
+
Yes
a
4
−
+
+
+
Yes/no
b
5
−
+
+
−
Yes/no
b
6
+
+
−
−
No
7
+
−
+
−
No
c
a
Such profiles may be used to generate maps of soil taxonomic classes using
SoilGrids™ (Hengl et al., 2017b).
b
Such profiles (geo-referenced solely according
to their country of origin) may be useful for developing pedotransfer functions. Hence,
they are standardised, though they are not distributed with the snapshot, as they lack
(
x, y
) coordinates.
c
Lacking information on the depth of sampling (i.e. layer), the
different soil properties cannot be meaningfully grouped to develop pedotransfer
functions.
fication is considered desirable (case 1) but not mandatory
(case 2). Georeferenced profiles for which only the classifi-
cation is specified can still be useful for mapping of soil tax-
onomic classes (case 3). Alternatively, profiles without any
geo-reference may still prove useful to develop pedotrans-
fer functions (case 4 and 5); however, they cannot be served
through WFS (because there is no geometry,
x, y
). The re-
maining cases (6 and 7) are automatically excluded from the
WoSIS workflow. This first broad consistency check led to
the exclusion of over 50 000 profiles from the initial comple-
ment of soil profiles.
Consistency in layer depth (i.e. sequential increase in the
upper and lower depth reported for each layer down the pro-
file) is checked using automated procedures (see Sect. 3.2).
In accord with current internationally accepted conventions,
such depth increments are given as “measured from the sur-
face, including organic layers and mineral covers” (FAO,
2006; Schoeneberger et al., 2012). Prior to 1993, however,
the beginning (zero datum) of the profile was set at the top
of the mineral surface (the solum proper), except for “thick”
organic layers as defined for peat soils (FAO-ISRIC, 1986;
FAO, 1977). Organic horizons were recorded as above and
mineral horizons recorded as below, relative to the mineral
surface (Schoeneberger et al., 2012, pp. 2–6). Insofar as is
possible, such “surficial litter” layers are flagged in WoSIS
as an auxiliary variable (see Appendix B) so that they may
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be filtered out during auxiliary computations of soil organic
carbon stocks, for example.
3.2
Flagging duplicate profiles
Several source materials, such as the harmonised WISE
soil profile database (Batjes, 2009), the Africa Soil Profile
Database (AfSP, Leenaars et al., 2014) and the dataset col-
lated by the International Soil Carbon Network (ISCN, Nave
et al., 2017) are compilations of shared soil profile data.
These three datasets, for example, contain varying amounts
of profiles derived from the National Cooperative Soil Sur-
vey database (USDA-NCSS, 2018), an important source of
freely shared, primary soil data. The original NCSS profile
identifiers, however, may not always have been preserved “as
is” in the various data compilations.
To avoid duplication in the WoSIS database, soil profiles
located within 100 m of each other are flagged as possible du-
plicates. Upon additional, semi-automated checks concern-
ing the first three layers (upper and lower depth), i.e. sand,
silt and clay content, the duplicates with the least compre-
hensive component of attribute data are flagged and excluded
from further processing. When still in doubt at this stage, ad-
ditional visual checks are made with respect to other com-
monly reported soil properties, such as pH
water
and organic
carbon content. This laborious, yet critical, screening process
(see Ribeiro et al., 2018) led to the exclusion of some 50 000
additional profiles from the initial complement of soil profile
data.
3.3
Ensuring naming consistency
The next key stage has been the standardisation of soil prop-
erty names to the WoSIS conventions, as well as the stan-
dardisation of the soil analytical methods descriptions them-
selves (see Appendix A). Quality checks consider the units
of measurement, plausible ranges for defined soil properties
(e.g. soil pH cannot exceed 14) using checks on minimum,
average and maximum values for each source dataset. Data
that do not fulfil the requirements are flagged and not consid-
ered further in the workflow, unless the observed “inconsis-
tencies” can easily be fixed (e.g. blatant typos in pH values).
The whole procedure, with flowcharts and option tables, is
documented in the WoSIS Procedures Manual (see Appen-
dices D, E and F in Ribeiro et al., 2018).
Presently, we standardise the following set of soil proper-
ties in WoSIS.
–
Chemical.
Organic carbon, total carbon (i.e. organic
plus inorganic carbon), total nitrogen, total carbonate
equivalent (inorganic carbon), soil pH, cation exchange
capacity, electrical conductivity and phosphorus (ex-
tractable P, total P and P retention).
–
Physical.
Soil texture (sand, silt and clay), coarse frag-
ments, bulk density and water retention.
It should be noted that all measurement values are reported
as recorded in the source data, subsequent to the above con-
sistency checks (and standardisation of the units of measure-
ment to the target units; see Appendix A). As such, we nei-
ther apply “gap-filling” procedures in WoSIS, e.g. when only
the sand and silt fractions are reported, nor do we apply pedo-
transfer functions to derive soil hydrological properties. This
next stage of data processing is seen as the responsibility of
the data users (modellers) themselves, as the required func-
tions or means of depth-aggregating the layer data will vary
with the projected use(s) of the standardised data (see Finke,
2006; Hendriks et al., 2016; Van Looy et al., 2017).
3.4
Providing measures for geographic and attribute
accuracy
It is well known that “soil observations used for calibration
and interpolation are themselves not error free” (Baroni et
al., 2017; Cressie and Kornak, 2003; Folberth et al., 2016;
Grimm and Behrens, 2010; Guevara et al., 2018; Hengl et
al., 2017b; Heuvelink, 2014; Heuvelink and Brown, 2006).
Hence, we provide measures for the geographic accuracy of
the point locations as well as the accuracy of the labora-
tory measurements for possible consideration in digital soil
mapping and subsequent earth system modelling (Dai et al.,
2019).
All profile coordinates in WoSIS are presented accord-
ing to the World Geodetic System (i.e. WGS84, EPSG code
4326). These coordinates were converted from a diverse
range of national projections. Further, the source referenc-
ing may have been in decimal degrees (DD) or expressed in
degrees, minutes, and seconds (DMS) for both latitude and
longitude. The (approximate) accuracy of georeferencing in
WoSIS is given in decimal degrees. If the source only pro-
vided degrees, minutes, and seconds (DMS) then the geo-
graphic accuracy is set at 0.01; if seconds (DM) are missing
it is set at 0.1; and if seconds and minutes (D) are missing it
is set at 1. For most profiles (86 %; see Table 2), the approx-
imate accuracy of the point locations, as inferred from the
original coordinates given in the source datasets, is less than
10 m (total
=
196 498 profiles; see Sect. 4). Typically, the
geo-referencing of soil profiles described and sampled before
the advent of GPS (Global Positioning Systems) in the 1970s
is less accurate; sometimes we just do not know the “true”
accuracy. Digital soil mappers should duly consider the in-
ferred geometric accuracy of the profile locations in their ap-
plications (Grimm and Behrens, 2010), since the soil obser-
vations and covariates may not actually correspond (Cressie
and Kornak, 2003) in both space and time (see Sect. 4, sec-
ond paragraph).
As indicated, soil data considered in WoSIS have been
analysed according to a wide range of analytical proce-
dures and in different laboratories. An indication of the mea-
surement uncertainty is thus desired; soil-laboratory-specific
Quality Management Systems (van Reeuwijk, 1998), as well
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Table 2.
Approximate accuracy of the profile locations.
Decimal
Decimal
Approximate
Number of
places
degrees
precision
profiles
n
%
7
0.0000001
1 cm
1345
0.7
6
0.000001
10 cm
84 945
43.2
5
0.00001
1 m
74 024
37.7
4
0.0001
10 m
9158
4.7
3
0.001
100 m
8108
4.1
2
0.01
1 km
10 915
5.6
1
0.1
10 km
6458
3.2
0
1
100 km
1545
0.8
as laboratory proficiency-testing (PT, Magnusson and Örne-
mark, 2014; Munzert et al., 2007; WEPAL, 2019), can pro-
vide this type of information. Yet, calculation of laboratory-
specific measurement uncertainty for a single method or
multiple analytical methods will require several measure-
ment rounds (years of observation) and solid statistical anal-
yses. Overall, such detailed information is not available for
the datasets submitted to the ISRIC data repository. There-
fore, out of necessity, we have distilled the desired infor-
mation from the PT literature (Kalra and Maynard, 1991;
Rayment and Lyons, 2011; Rossel and McBratney, 1998;
van Reeuwijk, 1983; WEPAL, 2019), in so far as technically
feasible. For example, accuracy for bulk density measure-
ments, both for the direct core and the clod method, has been
termed “low” (though not quantified) in a recent review (Al-
Shammary et al., 2018); using expert knowledge, we have
assumed this corresponds with an uncertainty (or variabil-
ity, expressed as coefficient of variation) of 35 %. Alterna-
tively, for organic carbon content the mean variability was
17 % (with a range of 12 % to 42 %) and for “CEC (cation
exchange capacity) buffered at pH 7” it was 18 % (range
13 % to 25 %) when multiple laboratories analyse a standard
set of reference materials using similar operational methods
(WEPAL, 2019). For soil pH measurements (log scale), we
have expressed the uncertainty in terms of “
±
pH units”.
Importantly, the figures for measurement accuracy pre-
sented in Appendix A represent first approximations. They
are based on the inter-laboratory comparison of well-
homogenised reference samples for a still relatively small
range of soil types. These indicative figures should be re-
fined once laboratory-specific and method-related accuracy
(i.e. systematic and random error) information is provided for
the shared soil data, e.g. by using the procedures described by
Eurachem (Magnusson and Örnemark, 2014). Alternatively,
this type of information may be refined in the context of inter-
national laboratory PT networks, such as GLOSOLAN and
WEPAL. Meanwhile, the present “first” estimates may al-
ready be considered to calculate the accuracy of digital soil
maps and of any interpretations derived from them (e.g. maps
of soil organic carbon stocks in support of the UNCCD Land
Degradation Neutrality, LDN, effort).
4
Spatial distribution of soil profiles and number of
observations
The present snapshot includes standardised data for
196 498 profiles (Fig. 2), about twice the amount represented
in the “July 2016” snapshot. These are represented by some
832 000 soil layers (or horizons). In total, this corresponds
with over 5.8 million records that include both numeric (e.g.
sand content, soil pH and cation exchange capacity) and class
(e.g. WRB soil classification and horizon designation) prop-
erties. The naming conventions and standard units of mea-
surement are provided in Appendix A, and the file structure
is provided in Appendix B.
Being a compilation of national soil data, the profiles were
sampled over a long period of time. The dates reported in the
snapshot will reflect the year the respective data were sam-
pled and analysed: 1397 (0.7 %) profiles were sampled be-
fore 1920, 218 (0.1 %) between 1921 and 1940, 7,657 (3.9 %)
between 1941 and 1960, 26,614 (13.5 %) between 1961 and
1980, 62 691 (31.9 %) between 1981 and 2000, and 31 084
(15.8 %) between 2001 and 2020, while the date of sampling
is unknown for 66 837 profiles (34.0 %). This information
should be taken into consideration when linking the point
data with environmental covariates, such as land use, in dig-
ital soil mapping.
The number of profiles per continent is highest for North
America (73 604 versus 63 066 in the “2016” snapshot), fol-
lowed by Oceania (42 918 versus 235), Europe (35 311 ver-
sus 1,908), Africa (27 688 versus 17 153), South America
(10 218 versus 8790), Asia (6704 versus 3089) and Antarc-
tica (9, no change). These profiles come from 173 coun-
tries; the average density of observations is 1.35 profiles per
1000 km
2
. The actual density of observations varies greatly,
both between countries (Appendix C) and within each coun-
try, with the largest densities of “shared” profiles reported for
Belgium (228 profiles per 1000 km
2
) and Switzerland (265
profiles per 1000 km
2
). There are still relatively few profiles
for Central Asia, Southeast Asia, Central and Eastern Eu-
rope, Russia, and the northern circumpolar region. The num-
ber of profiles by biome (R. J. Olson et al., 2001) or broad cli-
matic region (Sayre et al., 2014), as derived from GIS over-
lays, is provided in Appendix D for additional information.
There are more observations for the chemical data than the
physical data (see Appendix A) and the number of observa-
tions generally decreases with depth, largely depending on
the objectives of the original soil surveys. The interquartile
range for maximum depth of soil sampled in the field is 56–
152 cm, with a median of 110 cm (mean
=
117 cm). In this
respect, it should be noted that some specific purpose sur-
veys only considered the topsoil (e.g. soil fertility surveys),
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N. H. Batjes et al.: WoSIS snapshot 2019
Figure 2.
Location of soil profiles provided in the “September 2019” snapshot of WoSIS; see Appendix C for the number and density of
profiles by country.
while others systematically sampled soil layers up to depths
exceeding 20 m.
Present gaps in the geographic distribution (Appendices C
and D) and range of soil attribute data (Appendix A) will
gradually be filled in the coming years, though this largely
depends on the willingness or ability of data providers to
share (some of) their data for consideration in WoSIS. For the
northern boreal and Arctic region, for example, ISRIC will
regularly ingest new profile data collated by the International
Soil Carbon Network (ISCN, Malhotra et al., 2019). Alter-
natively, it should be reiterated that for some regions, such
as Europe (e.g. EU LUCAS topsoil database; see Tóth et al.,
2013) and the state of Victoria (Australia), there are holdings
in the ISRIC repository that may only be used and standard-
ised for SoilGrids™ applications due to licence restrictions.
Consequently, the corresponding profiles (
∼
42 000) are nei-
ther shown in Fig. 2 nor are considered in the descriptive
statistics in Appendix C.
5
Distributing the standardised data
Upon their standardisation, the data are distributed through
ISRIC’s SDI (Spatial Data Infrastructure). This web plat-
form is based on open-source technologies and open web-
services (WFS, WMS, WCS, CSW) following Open Geospa-
tial Consortium (OGC) standards and is aimed specifically
at handling soil data; our metadata are organised following
standards of the International Organization for Standardiza-
tion (ISO-28258, 2013) and are INSPIRE (2015) compli-
ant. The three main components of the SDI are PostgreSQL
+
PostGIS, GeoServer and GeoNetwork. Visualisation and
data download are done in GeoNetwork with resources from
GeoServer (https://data.isric.org, last access: 12 September
2019). The third component is the PostgreSQL database,
with the spatial extension PostGIS, in which WoSIS resides;
the database is connected to GeoServer to permit data down-
load from GeoNetwork. These processes are aimed at facil-
itating global data interoperability and citeability in com-
pliance with FAIR principles: the data should be “findable,
accessible, interoperable and reusable” (Wilkinson et al.,
2016). With partners, steps are being taken towards the de-
velopment of a federated and ultimately interoperable spa-
tial soil data infrastructure (GLOSIS) through which source
data are served and updated by the respective data providers
and made queryable according to a common SoilML stan-
dard (OGC, 2019).
The procedure for accessing the most current set of stan-
dardised soil profile data (“wosis_latest”), either from R or
QGIS using WFS, is explained in a detailed tutorial (Rossiter,
2019). This dataset is dynamic; hence, it will grow when new
point data are shared and processed, additional soil attributes
are considered in the WoSIS workflow, and/or when possi-
ble corrections are required. Potential errors may be reported
online via a “Google group” so that they may be addressed
in the dynamic version (register via: https://groups.google.
com/forum/#!forum/isric-world-soil-informationlast access:
15 January 2020).
For consistent citation purposes, we provide static snap-
shots of the standardised data, in a tab-separated values for-
mat, with unique DOI’s (digital object identifier); as indi-
cated, this paper describes the second WoSIS snapshot.
6
Discussion
The above procedures describe standardisation according
to operational definitions for soil properties. Importantly, it
Earth Syst. Sci. Data, 12, 299–320, 2020
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N. H. Batjes et al.: WoSIS snapshot 2019
305
should be stressed here that the ultimate, desired full har-
monisation to an agreed reference method
y
, for example,
“pH H
2
O, 1
:
2
.
5 soil
/
water solution” for all “pH 1
:
x
H
2
O”
measurements, will first become feasible once the target
method (
y
) for each property has been defined and subse-
quently accepted by the international soil community. A next
step would be to collate and develop “comparative” datasets
for each soil property, i.e. sets with samples analysed accord-
ing to a given reference method (
Y
i
) and the correspond-
ing national methods (
X
j
) for pedotransfer function devel-
opment. In practice, however, such relationships will often
be soil type and region specific (see Appendix C in Glob-
alSoilMap, 2015). Alternatively, according to GLOSOLAN
(Suvannang et al., 2018, p. 10) “comparable and useful soil
information (at the global level) will only be attainable once
laboratories agree to follow common standards and norms”.
In such a collaborative process, it will be essential to con-
sider the end user’s requirements in terms of quality and ap-
plicability of the data for their specific purposes (i.e. fitness
for intended use). Over the years, many organisations have
individually developed and implemented analytical methods
and quality assurance systems that are well suited for their
countries (e.g. Soil Survey Staff, 2014a) or regions (Orgiazzi
et al., 2018) and thus, pragmatically, may not be inclined to
implement the anticipated GLOSOLAN standard analytical
methods.
7
Data availability
Snapshot
“WoSIS_2019_September”
is
archived
for
long-term storage at ISRIC – World Soil Information,
the World Data Centre for Soils (WDC-Soils) of the
ISC (International Council for Science, formerly ICSU)
World Data System (WDS). It is freely accessible at
https://doi.org/10.17027/isric-wdcsoils.20190901
(Batjes
et al., 2019). The zip file (154 Mb) includes a “readme
first” file that describes key aspects of the dataset (see
also Appendix B) with reference to the WoSIS Procedures
Manual (Ribeiro et al., 2018), and the data itself in TSV
format (1.8 Gb, decompressed) and GeoPackage format
(2.2 Gb decompressed).
8
Conclusions
The second WoSIS snapshot provides consistent, standard-
ised data for some 196 000 profiles worldwide. However,
as described, there are still important gaps in terms of ge-
ographic distribution as well as the range of soil taxonomic
units and/or properties represented. These issues will be ad-
dressed in future releases, depending largely on the success
of our targeted requests and searches for new data providers
and/or partners worldwide.
–
We will increasingly consider data derived by soil spec-
troscopy and emerging innovative methods. Further,
long-term time series at defined locations will be sought
to support space–time modelling of soil properties, such
as changes in soil carbon stocks or soil salinity.
–
We provide measures for geographic accuracy of the
point data, as well as a first approximation for the un-
certainty associated with the operationally defined an-
alytical methods. This information may be used to as-
sess uncertainty in digital soil mapping and earth sys-
tem modelling efforts that draw on the present set of
point data.
–
Capacity
building
and
cooperation
among
(in-
ter)national soil institutes will be necessary to create
and share ownership of the soil information newly
derived from the shared data and to strengthen the
necessary expertise and capacity to further develop
and test the world soil information service worldwide.
Such activities may be envisaged within the broader
framework of the Global Soil Partnership and emerging
GLOSIS system.
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306
N. H. Batjes et al.: WoSIS snapshot 2019
Appendix A
Table A1.
Coding conventions and soil property names and their description, units of measurement, inferred accuracy, and number of profiles
and layers provided in the “WoSIS September 2019” snapshot. Soil properties are listed in alphabetical order using the property code.
Code
Property
Units
Profiles
Layers
Description
Accuracy
(
±
%)
a
Layer data
BDFI33
Bulk density fine earth –
33 kPa
kg dm
−
3
14 924
78 215
Bulk density of the fine-earth
fraction
b
, equilibrated at 33 kPa
35
BDFIAD
Bulk density fine earth – air
dry
kg dm
−
3
1786
8471
Bulk density of the fine-earth
fraction, air dried
35
BDFIFM
Bulk density fine earth –
field moist
kg dm
−
3
5279
14 219
Bulk density of the fine-earth
fraction, field moist
35
BDFIOD
Bulk density fine earth –
oven dry
kg dm
−
3
25 124
122 693
Bulk density of the fine-earth
fraction, oven dry
35
BDWS33
Bulk density whole soil –
33 kPa
kg dm
−
3
26 268
154 901
Bulk density of the whole soil,
including coarse fragments, equi-
librated at 33 kPa
35
BDWSAD
Bulk density whole soil –
air dry
kg dm
−
3
0
0
Bulk density of the whole soil,
including coarse fragments, air
dried
35
BDWSFM
Bulk density whole soil –
field moist
kg dm
−
3
0
0
Bulk density of the whole soil,
including coarse fragments, field
moist
35
BDWSOD
Bulk density whole soil –
oven dry
kg dm
−
3
14 588
75 422
Bulk density of the whole soil,
including coarse fragments, oven
dry
35
CECPH7
Cation exchange capacity –
buffered at pH7
cmol(c) kg
−
1
54 278
295 688
Capacity
of
the
fine-earth
fraction to hold exchangeable
cations, estimated by buffering
the soil at “pH 7”
20
CECPH8
Cation exchange capacity –
buffered at pH8
cmol(c) kg
−
1
6422
23 691
Capacity
of
the
fine-earth
fraction to hold exchangeable
cations, estimated by buffering
the soil at “pH 8”
20
CFGR
Coarse fragments gravimet-
ric total
g per 100 g
39 527
203 083
Gravimetric content of coarse
fragments in the whole soil
20
CFVO
Coarse fragments volumet-
ric total
cm
3
per 100 cm
3
45 918
235 002
Volumetric content of coarse
fragments in the whole soil
30
CLAY
Clay total
g per 100 g
141 640
607 861
Gravimetric content of
< x
mm
soil material in the fine-earth
fraction (e.g.
x
=
0
.
002 mm, as
specified in the analytical method
description)
b
,
c
15
ECEC
Effective cation exchange
capacity
cmol(c) kg
−
1
31 708
132 922
Capacity of the fine-earth frac-
tion to hold exchangeable cations
at the pH of the soil (ECEC).
Conventionally
approximated
by summation of exchangeable
bases (Ca
2
+
, Mg
2
+
, K
+
and
Na
+
) plus 1 N KCl exchangeable
acidity (Al
3
+
and H
+
) in acidic
soils
25
ELCO20
Electrical conductivity –
ratio 1
:
2
dS m
−
1
8010
44 596
Ability of a 1
:
2 soil–water ex-
tract to conduct electrical current
10
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307
Table A1.
Continued.
Code
Property
Units
Profiles
Layers
Description
Accuracy
(
±
%)
a
ELCO25
Electrical conductivity –
ratio 1
:
2
.
5
dS m
−
1
3313
15 134
Ability of a 1
:
2
.
5 soil–water ex-
tract to conduct electrical current
10
ELCO50
Electrical conductivity –
ratio 1
:
5
dS m
−
1
23 093
90 944
Ability of a 1
:
5 soil–water ex-
tract to conduct electrical current
10
ELCOSP
Electrical conductivity –
saturated paste
dS m
−
1
19 434
73 517
Ability of a water-saturated soil
paste to conduct electrical current
(EC
e
)
10
NITKJD
Total nitrogen (N)
g kg
−
1
65 356
21 6362
The sum of total Kjeldahl nitro-
gen (ammonia, organic and
reduced nitrogen) and nitrate–
nitrite
10
ORGC
Organic carbon
g kg
−
1
110 856
471 301
Gravimetric content of organic
carbon in the fine-earth fraction
15
PHAQ
pH H
2
O
unitless
130 986
613 322
A measure of the acidity or alka-
linity in soils, defined as the neg-
ative logarithm (base 10) of the
activity of hydronium ions (H
+
)
in water
0.3
PHCA
pH CaCl
2
unitless
66 921
314 230
A measure of the acidity or alka-
linity in soils, defined as the neg-
ative logarithm (base 10) of the
activity of hydronium ions (H
+
)
in a CaCl
2
solution, as specified
in the analytical method descrip-
tions
0.3
PHKC
pH KCl
unitless
32 920
150 447
A measure of the acidity or alka-
linity in soils, defined as the neg-
ative logarithm (base 10) of the
activity of hydronium ions (H
+
)
in a KCl solution, as specified
in the analytical method descrip-
tions
0.3
PHNF
pH NaF
unitless
4978
25448
A measure of the acidity or alka-
linity in soils, defined as the neg-
ative logarithm (base 10) of the
activity of hydronium ions (H
+
)
in a NaF solution, as specified
in the analytical method descrip-
tions
0.3
PHPBYI
Phosphorus (P) – Bray-I
mg kg
−
1
10 735
40 486
Measured according to the Bray-
I method, a combination of HCl
and NH
4
F to remove easily acid
soluble P forms, largely Al and
Fe phosphates (for acid soils)
40
PHPMH3
Phosphorus (P) – Mehlich-
3
mg kg
−
1
1446
7242
Measured according to the
Mehlich-3 extractant, a combi-
nation of acids (acetic [HOAc]
and nitric [HNO
3
]), salts (am-
monium fluoride [NH
4
F] and
ammonium nitrate [NH
4
NO
3
]),
and the chelating agent ethylene-
diaminetetraacetic acid (EDTA);
considered suitable for removing
P and other elements in acid and
neutral soils
25
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N. H. Batjes et al.: WoSIS snapshot 2019
Table A1.
Continued.
Code
Property
Units
Profiles
Layers
Description
Accuracy
(
±
%)
a
PHPOLS
Phosphorus (P) – Olsen
mg kg
−
1
2162
8434
Measured according to the Olsen
P method: 0.5 M sodium bicar-
bonate (NaHCO
3
) solution at a
pH of 8.5 to extract P from
calcareous, alkaline and neutral
soils
25
PHPRTN
Phosphorus (P) – retention
mg kg
−
1
4636
23 917
Retention measured according to
the New Zealand method
20
PHPTOT
Phosphorus (P) – total
mg kg
−
1
4022
12 976
Determined with a very strong
acid (aqua regia and sulfuric acid
or nitric acid)
15
PHPWSL
Phosphorus (P) – water sol-
uble
mg kg
−
1
283
1242
Measured in 1
:
x
soil:water solu-
tion (mainly determines P in dis-
solved forms)
15
SAND
Sand total
g per 100 g
105 547
491 810
The
y
to
z
mm fraction of the
fine-earth fraction and
z
upper
limit, as specified in the analyt-
ical method description for the
sand fraction (e.g.
y
=
0
.
05 mm
to
z
=
2 mm)
c
15
SILT
Silt total
g per 100 g
133 938
575 913
x
to
y
mm fraction of the fine-
earth fraction and
x
upper limit,
as specified in the analytical
method description for the clay
fraction (e.g.
x
=
0
.
002 mm to
y
=
0
.
05 mm)
c
15
TCEQ
Calcium carbonate equiva-
lent total
g kg
−
1
51 991
222 242
The content of carbonate in a lim-
ing material or calcareous soil
calculated as if all of the car-
bonate is in the form of CaCO
3
(in the fine-earth fraction), also
known as inorganic carbon
10
TOTC
Total carbon (C)
g kg
−
1
32 662
109 953
Gravimetric content of organic
carbon and inorganic carbon in
the fine-earth fraction
10
WG0006
Water retention gravimetric
– 6 kPa
g per 100 g
863
4264
Soil moisture content by weight,
at tension 6 kPa (pF 1.8)
20
WG0010
Water retention gravimetric
– 10 kPa
g per 100 g
3357
14 739
Soil moisture content by weight,
at tension 10 kPa (pF 2.0)
20
WG0033
Water retention gravimetric
– 33 kPa
g per 100 g
21 116
96 354
Soil moisture content by weight,
at tension 33 kPa (pF 2.5)
20
WG0100
Water retention gravimetric
– 100 kPa
g per 100 g
696
3762
Soil moisture content by weight,
at tension 100 kPa (pF 3.0)
20
WG0200
Water retention gravimetric
– 200 kPa
g per 100 g
4418
28 239
Soil moisture content by weight,
at tension 200 kPa (pF 3.3)
20
WG0500
Water retention gravimetric
– 500 kPa
g per 100 g
344
1716
Soil moisture content by weight,
at tension 500 kPa (pF 3.7)
20
WG1500
Water retention gravimetric
– 1500 kPa
g per 100 g
34 365
187 176
Soil moisture content by weight,
at tension 1500 kPa (pF 4.2)
20
WV0006
Water retention volumetric
– 6 kPa
cm
3
per 100 cm
3
9
26
Soil moisture content by volume,
at tension 6 kPa (pF 1.8)
20
WV0010
Water retention volumetric
– 10 kPa
cm
3
per 100 cm
3
1469
5434
Soil moisture content by volume,
at tension 10 kPa (pF 2.0)
20
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N. H. Batjes et al.: WoSIS snapshot 2019
309
Table A1.
Continued.
Code
Property
Units
Profiles
Layers
Description
Accuracy
(
±
%)
a
WV0033
Water retention volumetric
– 33 kPa
cm
3
per 100 cm
3
5987
17 801
Soil moisture content by volume,
at tension 33 kPa (pF 2.5)
20
WV0100
Water retention volumetric
– 100 kPa
cm
3
per 100 cm
3
747
2559
Soil moisture content by volume,
at tension 100 kPa (pF 3.0)
20
WV0200
Water retention volumetric
– 200 kPa
cm
3
per 100 cm
3
3
9
Soil moisture content by volume,
at tension 200 kPa (pF 3.3)
20
WV0500
Water retention volumetric
– 500 kPa
cm
3
per 100 cm
3
703
1763
Soil moisture content by volume,
at tension 500 kPa (pF 3.7)
20
WV1500
Water retention volumetric
– 1500 kPa
cm
3
per 100 cm
3
6149
17 542
Soil moisture content by volume,
at tension 1500 kPa (pF 4.2)
20
Site data
CSTX
Soil classification Soil tax-
onomy
classes
21 314
n/a
Classification of the soil profile,
according to the specified edition
(year) of USDA Soil Taxonomy,
up to subgroup level when avail-
able
–
CWRB
Soil classification WRB
classes
26 664
n/a
Classification of the soil profile,
according to the specified edition
(year) of the World Reference
Base for Soil Resources (WRB),
up to qualifier level when avail-
able
–
CFAO
Soil classification FAO
classes
23 890
n/a
Classification of the soil profile,
according to the specified edition
(year) of the FAO-Unesco Leg-
end, up to soil unit level when
available
–
DSDS
Depth of soil – sampled
cm
196 381
n/a
Maximum depth of soil described
and sampled (calculated)
–
HODS
Horizon designation
–
80 849
396 522
Horizon designation as provided
in the source database
d
a
Inferred accuracy (or uncertainty), rounded to the nearest 5 %, unless otherwise indicated (i.e. units for soil pH), as derived from the following sources: Al-Shammary et
al. (2018), Kalra and Maynard (1991), Rayment and Lyons (2011), Rossel and McBratney (1998), van Reeuwijk (1983), WEPAL (2019). These figures are first
approximations that will be fine-tuned once more specific results of laboratory proficiency tests, from national Soil Quality Management systems, become available.
b
Generally, the fine-earth fraction is defined as being
<
2
mm. Alternatively, an upper limit of 1 mm was used in the former Soviet Union and its satellite states (Katchynsky
scheme). This has been indicated in the file “wosis_201907_layers_chemical.tsv” and “wosis_201907_layer_physicals.tsv” for those soil properties where this differentiation
is important (see “sample pretreatment” in string “xxxx_method” in Appendix B).
c
Provided only when the sum of clay, silt and sand fraction is
≥
90
% and
≤
100
%.
d
Where available, the “cleaned” (original) layer and horizon designation is provided for general information; these codes have not been standardised as they vary widely
between different classification systems (Bridges, 1993; Gerasimova et al., 2013). When horizon designations are not provided in the source databases, we have flagged all
layers with an upper depth given as being negative (e.g.
−
10
to 0 cm under pre-1993 conventions; see text and the WoSIS Procedures Manual 2018; Ribeiro et al., 2018, p. 24,
footnote 9) in the source databases as likely being “litter” layers. n/a – not applicable
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N. H. Batjes et al.: WoSIS snapshot 2019
Appendix B: Structure of the “September 2019”
WoSIS snapshot
This Appendix describes the structure of the data files pre-
sented in the “September 2019” WoSIS snapshot:
–
wosis_201909_attributes.tsv
,
–
wosis_201909_profiles.tsv
,
–
wosis_201909_layers_chemical.tsv,
–
wosis_201909_layer_physicals.tsv
.
wosis_201909_attributes.tsv
. This file lists the four to six let-
ter codes for each attribute, whether the attribute is a site or
horizon property, the unit of measurement, the number of
profiles and layers represented in the snapshot, and a brief
description of each attribute, as well as the inferred uncer-
tainty for each property (Appendix A).
wosis_201909_profiles.tsv
. This file contains the unique
profile ID (i.e. primary key), the source of the data, country
ISO code and name, accuracy of geographical coordinates,
latitude and longitude (WGS 1984), point geometry of the
location of the profile, and the maximum depth of soil de-
scribed and sampled, as well as information on the soil classi-
fication system and edition (Table B1). Depending on the soil
classification system used, the number of fields will vary. For
example, for the World Soil Reference Base (WRB) system
these are as follows: publication_year (i.e. version), refer-
ence_soil_group_code, reference_soil_group_name, and the
name(s) of the prefix (primary) qualifier(s) and suffix (sup-
plementary) qualifier(s). The terms principal qualifier and
supplementary qualifier are currently used (IUSS Working
Group WRB, 2015); earlier WRB versions used prefix and
suffix for this (e.g. IUSS Working Group WRB, 2006). Al-
ternatively, for USDA Soil Taxonomy, the version (year),
order, suborder, great group and subgroup can be accom-
modated (Soil Survey Staff, 2014b). Inherently, the number
of records filled will vary between (and within) the various
source databases.
wosis_201909_layer_chemical.tsv
and
wosis_201909_layer_physical.tsv.
Data for the various
layers (or horizons) are presented in two separate files in
view of their size (i.e. one for the chemical and one for the
physical soil properties). The file structure is described in
Table B1.
Format.
All fields in the above files are delimited by tab,
with double quotation marks as text delimiters. File coding
is according to the UTF-8 unicode transformation format.
Using the data.
The above TSV files can easily be im-
ported into an SQL database or statistical software such as R,
after which they may be joined using the unique profile_id.
Guidelines for handling and querying the data are provided in
the WoSIS Procedures Manual (Ribeiro et al., 2018, pp. 45–
48); see also the detailed tutorial by Rossiter (2019).
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N. H. Batjes et al.: WoSIS snapshot 2019
311
Table
B1.
List
of
properties
described
in
file
wosis_201909_profiles.tsv
,
wosis_201909_layers_chemical.tsv
and
wosis_201909_layer_physicals.tsv
.
File name/Property
Description
wosis_201909_profiles.tsv
This file specifies the main characteristics of a soil profile
profile_id
Primary key
dataset_id
Identifier for source dataset
country_id
ISO code for country name
country_name
Country name (in English)
geom_accuracy
Accuracy of the geographical coordinates in degrees, e.g if degrees, minutes and seconds
are provided in the source then geom_accuracy is set at 0.01, if seconds are missing it is set
at 0.1, and if seconds and minutes are missing it is set at 1
latitude
Latitude in degrees (WGS84)
longitude
Longitude in degrees (WGS84)
dsds
Maximum depth of soil described and sampled (calculated)
cfao_version
Version of FAO legend (e.g. 1974 or 1988)
cfao_major_group_code
Code for major group (in given version of the legend)
cfao_major_group
Name of major group
cfao_soil_unit_code
Code for soil unit
cfao_soil_unit
Name of soil unit
cwrb_version
Version of World Reference Base for Soil Resources
cwrb_reference_soil_group_code
Code for WRB group (in given version of WRB)
cwrb_reference_soil_group
Full name for reference soil group
cwrb_prefix_qualifier
Name for prefix (e.g. for WRB1988) or principal qualifier (e.g. for WRB2015)
cwrb_suffix_qualifier
Name for suffix (e.g. for WRB1988) or supplementary qualifier (e.g. for WRB2015)
cstx_version
Version of USDA Soil Taxonomy (UST)
cstx_order_name
Name of UST order
cstx_suborder
Name of UST suborder
cstx_great_group
Name of UST great group
cstx_subgroup
Name of UST subgroup
wosis_201909_layer_chemical.tsv and
wosis_ 201909_layer_physical.tsv
The layer (horizon) data are presented in two separate files in view of their size, one for the
chemical and one for the physical soil properties. Both files have the same structure.
profile_id
Identifier for profile, foreign key to ‘wosis_ 201909_ profiles’
profile_layer_id
Unique identifier for layer for given profile (primary key)
upper_depth
Upper depth of layer (or horizon; cm)
lower_depth
Lower depth of layer (cm)
layer_name
Name of the horizon, as provided in the source data
litter
Flag (Boolean) indicating whether this is considered a surficial litter layer
xxxx_value
∗
Array listing all measurement values for soil property “xxxx” (e.g. BDFI33 or PHAQ) for
the given layer. In some cases, more than one observation is reported for a given horizon
(layer) in the source, for example, four values for TOTC:
{
1
:
5
.
4
,
2
:
8
.
2
,
3
:
6
.
3
,
4
:
7
.
7
}
xxxx _value_avg
Average, for above (it is recommended to use this value for “routine” modelling)
xxxx_method
Array listing the method descriptions for each value. The nature of this array varies with
the soil property under consideration, as described in the option tables for each analytical
method. For example, in the case of electrical conductivity (ELCO), the method is described
using sample pretreatment (e.g. sieved over 2 mm size, solution (e.g. water), ratio (e.g.,
1
:
5), and ratio base (e.g. weight/volume). Details for each method are provided in the
WoSIS Procedures Manual (Appendices D, E, and F in Ribeiro et al., 2018).
xxxx_date
Array listing the date of observation for each value
xxxx_dataset_id
Abbreviation for source data set (e.g. WD-ISCN)
xxxx_profile_code
Code for given profile in the source dataset
xxxx_license
Licence for given data, as indicated by the data provider (e.g. CC-BY).
(. . . )
The above “xxxx” fields are repeated for each soil property considered in Table A1.
∗
Name of attribute (“xxxx”) as defined under “code” in file
wosis_201909_attributes.tsv
.
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N. H. Batjes et al.: WoSIS snapshot 2019
Appendix C
Table C1.
Number of profiles by country and continent.
Continent
Country name
ISO
No. of
Area
Profile density
code
profiles
(km
2
)
(per 1000 km
2
)
Africa
Algeria
DZ
10
2 308 647
0.004
Angola
AO
1169
1 246 690
0.938
Benin
BJ
744
115 247
6.456
Botswana
BW
994
578 247
1.719
Burkina Faso
BF
2023
273 281
7.403
Burundi
BI
1063
26 857
39.58
Cameroon
CM
1306
465 363
2.806
Central African Republic
CF
88
619 591
0.142
Chad
TD
7
1 265 392
0.006
Côte d’Ivoire
CI
255
321 762
0.793
Democratic Republic of the Congo
CD
380
2 329 162
0.163
Egypt
EG
26
982 161
0.026
Ethiopia
ET
1712
1 129 314
1.516
Gabon
GA
47
264 022
0.178
Ghana
GH
432
238 842
1.809
Guinea
GN
128
243 023
0.527
Guinea-Bissau
GW
18
30 740
0.586
Kenya
KE
1601
582 342
2.749
Lesotho
LS
33
30 453
1.084
Liberia
LR
50
96 103
0.52
Libya
LY
14
1 620 583
0.009
Madagascar
MG
131
588 834
0.222
Malawi
MW
3049
118 715
25.683
Mali
ML
884
1 251 471
0.706
Mauritania
MR
13
1 038 527
0.013
Morocco
MA
113
414 030
0.273
Mozambique
MZ
566
787 305
0.719
Namibia
NA
1462
823 989
1.774
Niger
NE
520
1 182 602
0.44
Nigeria
NG
1402
908 978
1.542
Republic of the Congo
CG
71
340 599
0.208
Rwanda
RW
2007
25 388
79.052
Senegal
SN
312
196 200
1.59
Sierra Leone
SL
12
72 281
0.166
Somalia
SO
245
632 562
0.387
South Africa
ZA
874
1 220 127
0.716
South Sudan
SS
82
629 821
0.13
Sudan
SD
130
1 843 196
0.071
Swaziland
SZ
14
17 290
0.81
Togo
TG
9
56 767
0.159
Tunisia
TN
60
155 148
0.387
Uganda
UG
683
241 495
2.828
Tanzania
TZ
1915
939 588
2.038
Zambia
ZM
601
751 063
0.8
Zimbabwe
ZW
413
390 648
1.057
Antarctica
Antarctica
AQ
9
12 537 967
0.001
Asia
Afghanistan
AF
19
641 827
0.03
Armenia
AM
7
29 624
0.236
Arunachal Pradesh
∗
2
67 965
0.029
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N. H. Batjes et al.: WoSIS snapshot 2019
313
Table C1.
Continued.
Continent
Country name
ISO
No. of
Area
Profile density
code
profiles
(km
2
)
(per 1000 km
2
)
Azerbaijan
AZ
24
164 780
0.146
Bahrain
BH
2
673
2.97
Bangladesh
BD
207
139 825
1.48
Bhutan
BT
85
37 674
2.256
Cambodia
KH
409
181 424
2.254
China
CN
1648
9 345 214
0.176
Cyprus
CY
12
9249
1.297
Georgia
GE
17
69 785
0.244
Hong Kong
HK
2
1081
1.851
India
IN
199
2 961 118
0.067
Indonesia
ID
180
1 888 620
0.095
Iran
IR
2010
1 677 319
1.198
Iraq
IQ
14
435 864
0.032
Israel
IL
17
20 720
0.82
Jammu and Kashmir
∗
4
186 035
0.022
Japan
JP
198
373 651
0.53
Jordan
JO
47
89 063
0.528
Kazakhstan
KZ
12
2 841 103
0.004
Kuwait
KW
1
17 392
0.057
Kyrgyzstan
KG
1
199 188
0.005
Lao
LA
20
230 380
0.087
Lebanon
LB
10
10 136
0.987
Malaysia
MY
157
329 775
0.476
Mongolia
MN
9
1 564 529
0.006
Nepal
NP
142
147 437
0.963
Oman
OM
9
308 335
0.029
Pakistan
PK
45
788 439
0.057
Philippines
PH
81
296 031
0.274
South Korea
KR
23
99 124
0.232
Saudi Arabia
SA
7
1 925 621
0.004
Singapore
SG
1
594
1.683
Sri Lanka
LK
72
66 173
1.088
State of Palestine
PS
∗
18
6225
2.892
Syria
SY
68
188 128
0.361
Taiwan
TW
35
36 127
0.969
Tajikistan
TJ
5
142 004
0.035
Thailand
TH
482
515 417
0.935
Turkey
TR
69
781 229
0.088
United Arab Emirates
AE
12
71 079
0.169
Uzbekistan
UZ
9
449 620
0.02
Viet Nam
VN
29
327 575
0.089
Yemen
YE
284
453 596
0.626
Europe
Albania
AL
97
28 682
3.382
Austria
AT
128
83 964
1.524
Belarus
BY
92
207 581
0.443
Belgium
BE
7009
30 669
228.536
Bosnia and Herzegovina
BA
32
51 145
0.626
Bulgaria
BG
136
111 300
1.222
Croatia
HR
78
56 589
1.378
Czech Republic
CZ
664
78 845
8.422
Denmark
DK
74
44 458
1.664
Estonia
EE
242
45 441
5.326
Finland
FI
444
336 892
1.318
France
FR
1037
548 785
1.89
Germany
DE
4345
357 227
12.163
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314
N. H. Batjes et al.: WoSIS snapshot 2019
Table C1.
Continued.
Continent
Country name
ISO
No. of
Area
Profile density
code
profiles
(km
2
)
(per 1000 km
2
)
Greece
GR
370
132 549
2.791
Hungary
HU
1420
93 119
15.249
Iceland
IS
11
102 566
0.107
Ireland
IE
125
69 809
1.791
Italy
IT
575
301 651
1.906
Latvia
LV
102
64 563
1.58
Lithuania
LT
127
64 943
1.956
Luxembourg
LU
141
2621
53.802
Montenegro
ME
12
13 776
0.871
Netherlands
NL
320
35 203
9.09
North Macedonia
MK
20
25 424
0.787
Norway
NO
507
324 257
1.564
Poland
PL
618
311 961
1.981
Portugal
PT
460
91 876
5.007
Moldova
MD
35
33 798
1.036
Romania
RO
104
238 118
0.437
Russian Federation
RU
1410
16 998 830
0.083
Serbia
RS
69
88 478
0.78
Slovakia
SK
161
49 072
3.281
Slovenia
SI
67
20 320
3.297
Spain
ES
905
505 752
1.789
Svalbard and Jan Mayen Islands
SJ
4
63 464
0.063
Sweden
SE
583
449 212
1.298
Switzerland
CH
10 943
41 257
265.238
Ukraine
UA
409
600 526
0.681
United Kingdom
GB
1435
244 308
5.874
North America
Barbados
BB
3
433
6.928
Belize
BZ
29
21 764
1.332
Canada
CA
8516
9 875 646
0.862
Costa Rica
CR
560
51 042
10.971
Cuba
CU
53
110 863
0.478
Dominican Republic
DO
10
48 099
0.208
El Salvador
SV
38
20 732
1.833
Greenland
GL
6
2 165 159
0.003
Guadeloupe
GP
5
1697
2.947
Guatemala
GT
27
109 062
0.248
Honduras
HN
38
112 124
0.339
Jamaica
JM
76
10 965
6.931
Mexico
MX
7554
1 949 527
3.875
Netherlands Antilles
AN
4
790
5.066
Nicaragua
NI
26
128 376
0.203
Panama
PA
51
74 850
0.681
Puerto Rico
PR
280
8937
31.329
Trinidad and Tobago
TT
2
5144
0.389
United States of America
US
56 277
9 315 946
6.041
United States Virgin Islands
VI
49
352
139.069
Oceania
Australia
AU
42 758
7 687 634
5.562
Cook Islands
CK
1
241
4.142
Fiji
FJ
9
18 293
0.492
Guam
GU
15
544
27.579
Micronesia (Federated States of)
FM
78
740
105.397
New Caledonia
NC
2
18 574
0.108
New Zealand
NZ
53
270 415
0.196
Palau
PW
18
451
39.924
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N. H. Batjes et al.: WoSIS snapshot 2019
315
Table C1.
Continued.
Continent
Country name
ISO
No. of
Area
Profile density
code
profiles
(km
2
)
(per 1000 km
2
)
Papua New Guinea
PG
31
462 230
0.067
Samoa
WS
17
2835
5.996
Solomon Islands
SB
1
28 264
0.035
Vanuatu
VU
1
12 236
0.082
South America
Argentina
AR
244
2 780 175
0.088
Bolivia
BO
86
1 084 491
0.079
Brazil
BR
8883
8 485 946
1.047
Chile
CL
72
753 355
0.096
Colombia
CO
237
1 137 939
0.208
Ecuador
EC
94
256 249
0.367
French Guiana
GF
30
83 295
0.36
Guyana
GY
43
211 722
0.203
Paraguay
PY
1
399 349
0.003
Peru
PE
159
1 290 640
0.123
Suriname
SR
31
145 100
0.214
Uruguay
UY
132
177 811
0.742
Venezuela
VE
206
912 025
0.226
∗
Disputed territories. Country names and areas are based on the Global Administrative Layers (GAUL) database; see
http://www.fao.org/geonetwork/srv/en/metadata.show?id=12691 (last access: 8 January 2020).
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N. H. Batjes et al.: WoSIS snapshot 2019
Appendix D: Distribution of soil profiles by
eco-region and by biome
Table D1.
Number of soil profiles by broad rainfall and temperature
zone
∗
.
Bioclimate
Profiles
n
%
Arctic
2
0.00
Very cold:
– Dry
6
0.00
– Semi-dry
139
0.07
– Moist
366
0.19
– Wet
1839
0.94
– Very wet
949
0.48
Cold:
– Dry
9
0.00
– Semi-dry
537
0.27
– Moist
2048
1.04
– Wet
10 921
5.56
– Very wet
5871
2.99
Cool:
– Very dry
9
0.00
– Dry
217
0.11
– Semi-dry
7098
3.61
– Moist
4308
2.19
– Wet
32 927
16.76
– Very wet
6186
3.15
Warm:
– Very dry
25
0.01
– Dry
1007
0.51
– Semi-dry
14 778
7.52
– Moist
6860
3.49
– Wet
28 595
14.55
– Very wet
853
0.43
Hot:
– Very dry
40
0.02
– Dry
2047
1.04
– Semi-dry
14 774
7.52
– Moist
5783
2.94
– Wet
18 646
9.49
– Very wet
2411
1.23
Very hot:
– Very dry
20
0.01
– Dry
566
0.29
– Semi-dry
7727
3.93
– Moist
4935
2.51
– Wet
8895
4.53
– Very wet
3199
1.63
No data
1905
0.97
∗
Bioclimatic (rainfall and temperature)
zones as defined by Sayre et al. (2014).
Table D2.
Number of soil profiles by biome
∗
.
Biome
Soil profiles
n
%
Boreal forests/taiga
6129
3.1
Deserts and xeric shrublands
10 212
5.2
Flooded grasslands and savannas
779
0.4
Mangroves
682
0.3
Mediterranean forests, woodlands and scrub
16 759
8.5
Montane grasslands and shrublands
1402
0.7
Temperate broadleaf and mixed forests
63 912
32.5
Temperate conifer forests
12 153
6.2
Temperate grasslands, savannas and shrublands
25 357
12.9
Tropical and subtropical coniferous forests
1354
0.7
Tropical and subtropical dry broadleaf forests
3808
1.9
Tropical and subtropical grasslands, savannas and shrublands
34 779
17.7
Tropical and subtropical moist broadleaf forests
16 492
8.4
Tundra
1977
1.0
No data
703
0.4
∗
Biomes defined according to “Terrestrial Ecoregions of the World” (TEOW)
(D. M. Olson et al., 2001).
Earth Syst. Sci. Data, 12, 299–320, 2020
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Page 19
N. H. Batjes et al.: WoSIS snapshot 2019
317
Author contributions.
NB led the DATA (WoSIS) project and
wrote the body of the paper. ER provided special expertise on
database management and AO on soil analytical methods. All co-
authors contributed to the writing and revision of this paper.
Competing interests.
The authors declare that they have no con-
flict of interest.
Acknowledgements.
The development of WoSIS has been made
possible thanks to the contributions and shared knowledge of a
steadily growing number of data providers, including soil survey
organisations, research institutes and individual experts, for which
we are grateful; for an overview, please see https://www.isric.org/
explore/wosis/wosis-contributing-institutions-and-experts (last ac-
cess: 8 January 2020). We thank our colleagues Laura Poggio, Luis
de Sousa and Bas Kempen for their constructive comments on a
“pre-release” of the snapshot data. Further, the manuscript benefit-
ted from the constructive comments provided by the two reviewers.
Financial support.
ISRIC – World Soil Information, legally reg-
istered as the International Soil Reference and Information Centre,
receives core funding from the Dutch Government.
Review statement.
This paper was edited by David Carlson and
reviewed by Alessandro Samuel-Rosa and one anonymous referee.
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