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 repository 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 chemical 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 following 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 designations,
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 million 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 10.17027/isric-wdcsoils.20190901
(Batjes et al., 2019).
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 fraction thereof is readily accessible (i.e.
shared) in a consistent format for the greater benefit of the international
community. This paper describes procedures for preserving,
quality-assessing, standardising and subsequently providing consistent
world soil data to the international community, as developed 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 collaborative 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 spectral 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 system
(GLOSIS; see GSP-SDF, 2018).
We follow the definition of harmonisation used by the Global Soil
Partnership (GSP, Baritz et al., 2014). It encompasses “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,
classification, and mapping; (b) soil analyses; (c) exchange of digital 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 standardisation of soil property definitions,
soil analytical method descriptions 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 GlobalSoilMap (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 procedures for their collection, analysis and
reporting are well understood. 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 precludes, 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
consider the “pH of a soil”. This requires information on sample
pretreatment, soil / solution ratio and description of solution (e.g.
H2O, 1 M KCl, 0.02 M CaCl2, 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
H2O) 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 snapshot (Batjes et al., 2017),
describes the data screening procedure, provides a detailed overview of the
database content, explains how the new set of standardised data can be
accessed 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 effects of tillage
practices on soil gaseous emissions (Lutz et al., 2019). In
turn, this type of information can help to inform global conventions
such as the UNCCD (United Nations 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.
WoSIS workflow
The overall workflow for acquiring, ingesting and processing data in WoSIS
has been described in an earlier paper (Batjes 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) import all datasets “as is” into PostgreSQL; (c) ingest the data into the
WoSIS data model, including basic data quality assessment 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).
Schematic representation of the WoSIS workflow for
safeguarding and processing disparate soils datasets.
As indicated, datasets shared with our centre are first stored in the ISRIC
Data Repository, together with their 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 defined here as at least a CC-BY (attribution) or
CC-BY-NC (attribution non-commercial) licence. Presently, this corresponds
with data for some 196 498 profiles (i.e. profiles that have the right
licence and data for at least one of the standard soil properties).
Alternatively, some datasets may only be used for digital soil mapping using
SoilGrids™, corresponding 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 accordance 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.
Data screening, quality control and standardisationConsistency checks
Soil profile data submitted for consideration in WoSIS were collated
according to various national or international standards and presented in
various formats (from paper to digital). Further, they are of varying degrees
of completeness, as discussed below. Proper documentation of the provenance
and identification of each dataset and, ideally, each observation 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 (consistently) 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) classification is considered
desirable (case 1) but not mandatory (case 2). Georeferenced profiles
for which only the classification is specified can still be useful for
mapping of soil taxonomic classes (case 3). Alternatively, profiles without
any geo-reference may still prove useful to develop pedotransfer functions
(case 4 and 5); however, they cannot be served through WFS (because there is
no geometry, x,y). The remaining 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 complement of soil
profiles.
Basic requirements for considering soil profiles in the
WoSIS standardisation workflow.
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.
Consistency in layer depth (i.e. sequential increase in the upper and lower
depth reported for each layer down the profile) 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 surface, 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 be
filtered out during auxiliary computations of soil organic carbon stocks,
for example.
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 collated
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 Survey 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 duplicates. Upon additional,
semi-automated checks concerning the first three layers (upper and lower
depth), i.e. sand, silt and clay content, the duplicates with the
least comprehensive component of attribute data are flagged and excluded
from further processing. When still in doubt at this stage, additional
visual checks are made with respect to other commonly reported soil
properties, such as pHwater 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.
Ensuring naming consistency
The next key stage has been the standardisation of soil property names to the
WoSIS conventions, as well as the standardisation of the soil analytical
methods descriptions themselves (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 considered further in the workflow, unless the observed
“inconsistencies” 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 Appendices D, E and F in Ribeiro
et al., 2018).
Presently, we standardise the following set of soil properties 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
(extractable P, total P and P retention).
Physical. Soil texture (sand, silt and clay), coarse fragments, 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 consistency checks (and
standardisation of the units of measurement to the target units; see
Appendix A). As such, we neither apply “gap-filling” procedures in WoSIS, e.g. when only the sand and silt fractions are reported, nor do we apply
pedotransfer 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 functions 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).
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 laboratory measurements for
possible consideration in digital soil mapping and subsequent earth system
modelling (Dai et al., 2019).
All profile coordinates in WoSIS are presented according 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
referencing 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 provided degrees, minutes, and seconds (DMS) then the geographic
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 approximate 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
inferred geometric accuracy of the profile locations in their applications
(Grimm and Behrens, 2010), since the soil observations and
covariates may not actually correspond (Cressie and Kornak, 2003) in both space and time (see Sect. 4, second paragraph).
As indicated, soil data considered in WoSIS have been analysed according to
a wide range of analytical procedures and in different laboratories. An
indication of the measurement uncertainty is thus desired; soil-laboratory-specific Quality Management Systems (van Reeuwijk,
1998), as well as laboratory proficiency-testing (PT, Magnusson and
Örnemark, 2014; Munzert et al., 2007; WEPAL, 2019), can provide this type
of information. Yet, calculation of laboratory-specific measurement
uncertainty for a single method or multiple analytical methods
will require several measurement rounds (years of observation) and solid
statistical analyses. Overall, such detailed information is not available
for the datasets submitted to the ISRIC data repository. Therefore, out of
necessity, we have distilled the desired information 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 measurements, 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 variability,
expressed as coefficient of variation) of 35 %. Alternatively, 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 presented 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 refined 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 international laboratory PT networks, such as GLOSOLAN and
WEPAL. Meanwhile, the present “first” estimates may already 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).
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) properties. The naming
conventions and standard units of measurement are provided in Appendix A,
and the file structure is provided in Appendix B.
Location of soil profiles provided in the “September 2019”
snapshot of WoSIS; see Appendix C for the number and density of profiles by country.
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 sampled and analysed: 1397 (0.7 %) profiles were
sampled before 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 digital soil
mapping.
The number of profiles per continent is highest for North America (73 604
versus 63 066 in the “2016” snapshot), followed by Oceania (42 918 versus 235), Europe
(35 311 versus 1,908), Africa (27 688 versus 17 153), South America (10 218
versus 8790), Asia (6704 versus 3089) and Antarctica (9, no change). These
profiles come from 173 countries; the average density of observations is
1.35 profiles per 1000 km2. The actual density of observations varies
greatly, both between countries (Appendix C) and within each country, with
the largest densities of “shared” profiles reported for Belgium (228 profiles per 1000 km2) and Switzerland (265 profiles per 1000 km2). There are still relatively few profiles for Central Asia, Southeast Asia, Central and Eastern Europe, Russia, and the northern circumpolar
region. The number of profiles by biome (R. J. Olson et al.,
2001) or broad climatic region (Sayre
et al., 2014), as derived from GIS overlays, 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 observations 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 surveys only
considered the topsoil (e.g. soil fertility surveys), 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). Alternatively, 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 standardised for SoilGrids™ applications due to licence
restrictions. Consequently, the corresponding profiles (∼42 000) are neither shown in Fig. 2 nor are considered in the descriptive
statistics in Appendix C.
Distributing the standardised data
Upon their standardisation, the data are distributed through ISRIC's SDI
(Spatial Data Infrastructure). This web platform is based on open-source
technologies and open web-services (WFS, WMS, WCS, CSW) following Open
Geospatial Consortium (OGC) standards and is aimed specifically at handling
soil data; our metadata are organised following standards of the
International Organization for Standardization (ISO-28258, 2013)
and are INSPIRE (2015) compliant. 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 download from GeoNetwork.
These processes are aimed at facilitating global data interoperability and
citeability in compliance with FAIR principles: the data should be
“findable, accessible, interoperable and reusable” (Wilkinson et al.,
2016). With partners, steps are being taken towards the development of
a federated and ultimately interoperable spatial 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 standard
(OGC, 2019).
The procedure for accessing the most current set of standardised 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
possible 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 snapshots of the standardised
data, in a tab-separated values format, with unique DOI's (digital object
identifier); as indicated, this paper describes the second WoSIS snapshot.
Discussion
The above procedures describe standardisation according to operational
definitions for soil properties. Importantly, it should be stressed here
that the ultimate, desired full harmonisation to an agreed reference method
y, for example, “pH H2O, 1:2.5 soil / water solution” for all “pH 1:xH2O” measurements, will first become feasible once the target method
(y) for each property has been defined and subsequently 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 according to a given reference method (Yi) and the
corresponding national methods (Xj) for pedotransfer function
development. In practice, however, such relationships will often be soil type and region specific (see Appendix C in GlobalSoilMap, 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
consider the end user's requirements in terms of quality and applicability
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.
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
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).
Conclusions
The second WoSIS snapshot provides consistent, standardised data for some
196 000 profiles worldwide. However, as described, there are still important
gaps in terms of geographic distribution as well as the range of soil taxonomic
units and/or properties represented. These issues will be addressed 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 spectroscopy 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 uncertainty associated with the
operationally defined analytical methods. This information may be used to
assess uncertainty in digital soil mapping and earth system modelling
efforts that draw on the present set of point data.
Capacity building and cooperation among (inter)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.
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.
CodePropertyUnitsProfilesLayersDescriptionAccuracy(± %)aLayer dataBDFI33Bulk density fine earth – 33 kPakg dm-314 92478 215Bulk density of the fine-earth fractionb, equilibrated at 33 kPa35BDFIADBulk density fine earth – air drykg dm-317868471Bulk density of the fine-earth fraction, air dried35BDFIFMBulk density fine earth – field moistkg dm-3527914 219Bulk density of the fine-earth fraction, field moist35BDFIODBulk density fine earth – oven drykg dm-325 124122 693Bulk density of the fine-earth fraction, oven dry35BDWS33Bulk density whole soil – 33 kPakg dm-326 268154 901Bulk density of the whole soil, including coarse fragments, equilibrated at 33 kPa35BDWSADBulk density whole soil – air drykg dm-300Bulk density of the whole soil, including coarse fragments, airdried35BDWSFMBulk density whole soil – field moistkg dm-300Bulk density of the whole soil, including coarse fragments, field moist35BDWSODBulk density whole soil – oven drykg dm-314 58875 422Bulk density of the whole soil, including coarse fragments, oven dry35CECPH7Cation exchange capacity – buffered at pH7cmol(c) kg-154 278295 688Capacity of the fine-earth fraction to hold exchangeable cations, estimated by buffering the soil at “pH 7”20CECPH8Cation exchange capacity – buffered at pH8cmol(c) kg-1642223 691Capacity of the fine-earth fraction to hold exchangeable cations, estimated by buffering the soil at “pH 8”20CFGRCoarse fragments gravimetric totalg per 100 g39 527203 083Gravimetric content of coarsefragments in the whole soil20CFVOCoarse fragments volumetric totalcm3 per 100 cm345 918235 002Volumetric content of coarse fragments in the whole soil30CLAYClay totalg per 100 g141 640607 861Gravimetric 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,c15ECECEffective cation exchange capacitycmol(c) kg-131 708132 922Capacity of the fine-earth fraction to hold exchangeable cations at the pH of the soil (ECEC). Conventionally approximated by summation of exchangeable bases (Ca2+, Mg2+, K+ and Na+) plus 1 N KCl exchangeable acidity (Al3+ and H+) in acidic soils25ELCO20Electrical conductivity – ratio 1:2dS m-1801044 596Ability of a 1:2 soil–water extract to conduct electrical current10
Continued.
CodePropertyUnitsProfilesLayersDescriptionAccuracy(± %)aELCO25Electrical conductivity –ratio 1:2.5dS m-1331315 134Ability of a 1:2.5 soil–water extract to conduct electrical current10ELCO50Electrical conductivity –ratio 1:5dS m-123 09390 944Ability of a 1:5 soil–water extract to conduct electrical current10ELCOSPElectrical conductivity – saturated pastedS m-119 43473 517Ability of a water-saturated soil paste to conduct electrical current (ECe)10NITKJDTotal nitrogen (N)g kg-165 35621 6362The sum of total Kjeldahl nitrogen (ammonia, organic andreduced nitrogen) and nitrate–nitrite10ORGCOrganic carbong kg-1110 856471 301Gravimetric content of organic carbon in the fine-earth fraction15PHAQpH H2Ounitless130 986613 322A measure of the acidity or alkalinity in soils, defined as the negative logarithm (base 10) of the activity of hydronium ions (H+) in water0.3PHCApH CaCl2unitless66 921314 230A measure of the acidity or alkalinity in soils, defined as the negative logarithm (base 10) of the activity of hydronium ions (H+) in a CaCl2 solution, as specified in the analytical method descriptions0.3PHKCpH KClunitless32 920150 447A measure of the acidity or alkalinity in soils, defined as the negative logarithm (base 10) of the activity of hydronium ions (H+) in a KCl solution, as specified in the analytical method descriptions0.3PHNFpH NaFunitless497825448A measure of the acidity or alkalinity in soils, defined as the negative logarithm (base 10) of the activity of hydronium ions (H+) in a NaF solution, as specified in the analytical method descriptions0.3PHPBYIPhosphorus (P) – Bray-Img kg-110 73540 486Measured according to the Bray-I method, a combination of HCl and NH4F to remove easily acid soluble P forms, largely Al and Fe phosphates (for acid soils)40PHPMH3Phosphorus (P) – Mehlich-3mg kg-114467242Measured according to theMehlich-3 extractant, a combination of acids (acetic [HOAc] and nitric [HNO3]), salts (ammonium fluoride [NH4F] and ammonium nitrate [NH4NO3]), and the chelating agent ethylenediaminetetraacetic acid (EDTA); considered suitable for removing P and other elements in acid and neutral soils25
Continued.
CodePropertyUnitsProfilesLayersDescriptionAccuracy(± %)aPHPOLSPhosphorus (P) – Olsenmg kg-121628434Measured according to the Olsen P method: 0.5 M sodium bicarbonate (NaHCO3) solution at a pH of 8.5 to extract P from calcareous, alkaline and neutral soils25PHPRTNPhosphorus (P) – retentionmg kg-1463623 917Retention measured according to the New Zealand method20PHPTOTPhosphorus (P) – totalmg kg-1402212 976Determined with a very strong acid (aqua regia and sulfuric acid or nitric acid)15PHPWSLPhosphorus (P) – water solublemg kg-12831242Measured in 1:x soil:water solution (mainly determines P in dissolved forms)15SANDSand totalg per 100 g105 547491 810The y to z mm fraction of the fine-earth fraction and z upperlimit, as specified in the analytical method description for the sand fraction (e.g. y=0.05 mm to z=2 mm)c15SILTSilt totalg per 100 g133 938575 913x 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)c15TCEQCalcium carbonate equivalent totalg kg-151 991222 242The content of carbonate in a liming material or calcareous soil calculated as if all of the carbonate is in the form of CaCO3 (in the fine-earth fraction), also known as inorganic carbon10TOTCTotal carbon (C)g kg-132 662109 953Gravimetric content of organic carbon and inorganic carbon in the fine-earth fraction10WG0006Water retention gravimetric – 6 kPag per 100 g8634264Soil moisture content by weight, at tension 6 kPa (pF 1.8)20WG0010Water retention gravimetric – 10 kPag per 100 g335714 739Soil moisture content by weight, at tension 10 kPa (pF 2.0)20WG0033Water retention gravimetric – 33 kPag per 100 g21 11696 354Soil moisture content by weight, at tension 33 kPa (pF 2.5)20WG0100Water retention gravimetric – 100 kPag per 100 g6963762Soil moisture content by weight, at tension 100 kPa (pF 3.0)20WG0200Water retention gravimetric – 200 kPag per 100 g441828 239Soil moisture content by weight, at tension 200 kPa (pF 3.3)20WG0500Water retention gravimetric – 500 kPag per 100 g3441716Soil moisture content by weight, at tension 500 kPa (pF 3.7)20WG1500Water retention gravimetric – 1500 kPag per 100 g34 365187 176Soil moisture content by weight, at tension 1500 kPa (pF 4.2)20WV0006Water retention volumetric – 6 kPacm3 per 100 cm3926Soil moisture content by volume, at tension 6 kPa (pF 1.8)20WV0010Water retention volumetric – 10 kPacm3 per 100 cm314695434Soil moisture content by volume, at tension 10 kPa (pF 2.0)20
Continued.
CodePropertyUnitsProfilesLayersDescriptionAccuracy(± %)aWV0033Water retention volumetric – 33 kPacm3 per 100 cm3598717 801Soil moisture content by volume, at tension 33 kPa (pF 2.5)20WV0100Water retention volumetric – 100 kPacm3 per 100 cm37472559Soil moisture content by volume, at tension 100 kPa (pF 3.0)20WV0200Water retention volumetric – 200 kPacm3 per 100 cm339Soil moisture content by volume, at tension 200 kPa (pF 3.3)20WV0500Water retention volumetric – 500 kPacm3 per 100 cm37031763Soil moisture content by volume, at tension 500 kPa (pF 3.7)20WV1500Water retention volumetric – 1500 kPacm3 per 100 cm3614917 542Soil moisture content by volume, at tension 1500 kPa (pF 4.2)20Site dataCSTXSoil classification Soil taxonomyclasses21 314n/aClassification of the soil profile, according to the specified edition (year) of USDA Soil Taxonomy, up to subgroup level when available–CWRBSoil classification WRBclasses26 664n/aClassification of the soil profile, according to the specified edition (year) of the World Reference Base for Soil Resources (WRB), up to qualifier level when available–CFAOSoil classification FAOclasses23 890n/aClassification of the soil profile, according to the specified edition (year) of the FAO-Unesco Legend, up to soil unit level when available–DSDSDepth of soil – sampledcm196 381n/aMaximum depth of soil described and sampled (calculated)–HODSHorizon designation–80 849396 522Horizon designation as provided in the source databased
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
Structure of the “September 2019” WoSIS snapshot
This Appendix describes the structure of the data files presented 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 letter 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 uncertainty 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 described and sampled, as well as information
on the soil classification 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), reference_soil_group_code, reference_soil_group_name, and the name(s) of the prefix
(primary) qualifier(s) and suffix (supplementary) 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).
Alternatively, for USDA Soil Taxonomy, the version (year), order, suborder,
great group and subgroup can be accommodated (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.
List of properties described in file wosis_201909_profiles.tsv, wosis_201909_layers_chemical.tsv and wosis_201909_layer_physicals.tsv.
File name/PropertyDescriptionwosis_201909_profiles.tsvThis file specifies the main characteristics of a soil profileprofile_idPrimary keydataset_idIdentifier for source datasetcountry_idISO code for country namecountry_nameCountry name (in English)geom_accuracyAccuracy 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 1latitudeLatitude in degrees (WGS84)longitudeLongitude in degrees (WGS84)dsdsMaximum depth of soil described and sampled (calculated)cfao_versionVersion of FAO legend (e.g. 1974 or 1988)cfao_major_group_codeCode for major group (in given version of the legend)cfao_major_groupName of major groupcfao_soil_unit_codeCode for soil unitcfao_soil_unitName of soil unitcwrb_versionVersion of World Reference Base for Soil Resourcescwrb_reference_soil_group_codeCode for WRB group (in given version of WRB)cwrb_reference_soil_groupFull name for reference soil groupcwrb_prefix_qualifierName for prefix (e.g. for WRB1988) or principal qualifier (e.g. for WRB2015)cwrb_suffix_qualifierName for suffix (e.g. for WRB1988) or supplementary qualifier (e.g. for WRB2015)cstx_versionVersion of USDA Soil Taxonomy (UST)cstx_order_nameName of UST ordercstx_suborderName of UST subordercstx_great_groupName of UST great groupcstx_subgroupName of UST subgroupwosis_201909_layer_chemical.tsv and wosis_ 201909_layer_physical.tsvThe 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_idIdentifier for profile, foreign key to `wosis_ 201909_ profiles'profile_layer_idUnique identifier for layer for given profile (primary key)upper_depthUpper depth of layer (or horizon; cm)lower_depthLower depth of layer (cm)layer_nameName of the horizon, as provided in the source datalitterFlag (Boolean) indicating whether this is considered a surficial litter layerxxxx_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_avgAverage, for above (it is recommended to use this value for “routine” modelling)xxxx_methodArray 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_dateArray listing the date of observation for each valuexxxx_dataset_idAbbreviation for source data set (e.g. WD-ISCN)xxxx_profile_codeCode for given profile in the source datasetxxxx_licenseLicence 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.
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 imported 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).
* 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).
Distribution of soil profiles by eco-region and by biome
Number of soil profiles by broad rainfall and temperature zone*.
BioclimateProfiles n%Arctic20.00Very cold:– Dry60.00– Semi-dry1390.07– Moist3660.19– Wet18390.94– Very wet9490.48Cold:– Dry90.00– Semi-dry5370.27– Moist20481.04– Wet10 9215.56– Very wet58712.99Cool:– Very dry90.00– Dry2170.11– Semi-dry70983.61– Moist43082.19– Wet32 92716.76– Very wet61863.15Warm:– Very dry250.01– Dry10070.51– Semi-dry14 7787.52– Moist68603.49– Wet28 59514.55– Very wet8530.43Hot:– Very dry400.02– Dry20471.04– Semi-dry14 7747.52– Moist57832.94– Wet18 6469.49– Very wet24111.23Very hot:– Very dry200.01– Dry5660.29– Semi-dry77273.93– Moist49352.51– Wet88954.53– Very wet31991.63No data19050.97
* Bioclimatic (rainfall and temperature) zones as defined by Sayre et al. (2014).
Number of soil profiles by biome*.
BiomeSoil profiles n%Boreal forests/taiga61293.1Deserts and xeric shrublands10 2125.2Flooded grasslands and savannas7790.4Mangroves6820.3Mediterranean forests, woodlands and scrub16 7598.5Montane grasslands and shrublands14020.7Temperate broadleaf and mixed forests63 91232.5Temperate conifer forests12 1536.2Temperate grasslands, savannas and shrublands25 35712.9Tropical and subtropical coniferous forests13540.7Tropical and subtropical dry broadleaf forests38081.9Tropical and subtropical grasslands, savannas and shrublands34 77917.7Tropical and subtropical moist broadleaf forests16 4928.4Tundra19771.0No data7030.4
* Biomes defined according to “Terrestrial Ecoregions of the World”
(TEOW) (D. M. Olson et al., 2001).
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 conflict 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 access: 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 benefitted from the constructive comments provided by the two
reviewers.
Financial support
ISRIC – World Soil Information, legally registered 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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