1. 19/06/2018
WoSIS – World Soil Information Service Niels Batjes, Eloi Ribeiro, Ad van Oostrum, Johan Leenaars and Jorge Mendes de Jesus
“Enhancing data infrastructure services to sustain Earth Sciences researchers’ needs for a robust science”, Session 32 at SciDataCon 2016 (13 Sept., Denver, CO)
Rev. 25/01/2016

2. Source infographic: FAO 2015
Context: Soil is a non-renewable natural resource on a human life scale. Need to feed some 9 billion people in 2050.
Soil is an important provider of ecosystem services, but it remains one of the least well described data sets.
Source infographic: FAO 2015

3. From soil data rescue to use of quality-assessed data ...
WoSIS

4. Screen, standardise and harmonise soil data towards a global standard
19/06/2018
WoSIS workflow
Screen, standardise and harmonise soil data towards a global standard

5. Data providers
WoSIS is made possible thanks to a wide range of international partners
The ‘access rights’ (licences) and lineage must be specified in the metadata
and many others ...

6. Import data ‘as is’ into PostgreSQL
USDA-NRCS
CanSis
Preserve data with the original naming and coding conventions, abbreviations, domains etc. in PostgreSQL format
SOTER
“Safeguarding soil collections”

7. Map data into standard WoSIS model
Relational data model implemented in PostgreSQL
44 interrelated tables

8. General considerations
The source data are based on diverse (inter)national standards
Generally, limited quality information provided with the source (analytical) data
The challenge is to develop procedures to bring this disparate information into a consistent common form to facilitate use.
Lineage:
Datasets, reports & maps
Soil observations and measurements:
Feature (georeferenced profiles & layers)
Attribute (x-y-z & time; map, class, site, layer-field, layer-lab)
Method
Value, including units of expression

9. Areas of harmonization
Present focus in
WoSIS
Areas of harmonization (GSP)
“Providing mechanisms for the collation, analysis and exchange of consistent and comparable global soil data and information”
(Baritz et al. 2014, Global Soil Partnership)

10. Standardization of soil (analytical) method descriptions
Initial focus on soil properties considered in the GlobalSoilMap consortium specifications

11. Standardization of soil analytical method descriptions
‘A soil property is best described by key elements of the (laboratory) procedure applied’ (Soil Survey Staff, 2011)
Similarly, in WoSIS: ‘Major characteristics of commonly used methods for determining a given soil property are characterised’ (Ribeiro et al. 2015)
Code confidence in the ‘translation’: ‘Low’, ‘Medium’, ‘High’
Standardize units of measurements
GSM spec.: pHwater, 1:5

12. Harmonize to reference method ‘Y’ (not yet undertaken in WoSIS)
“Make the data comparable, as if assessed by a single given (reference) method”
“There is generally no universal equation for converting from one method to another in all situations” (GlobalSoilMap 2013)
“Each regional node will need to develop and apply node-specific conversions (towards the GSP-adopted standard methods and soils), building on comparative analyses of say archived samples” (Baritz et al., 2014)

13. Serving standardised data to the user
Dynamic: via WFS, see Wosis-distribution-set
Static: CSV (zipped), e.g WoSIS_July_2016.zip
Batjes et al (Earth System Sci. Data, subm.)

14. Location of point data served from WoSIS (July 2016)
WoSIS only serves data with at least a CC-BY or CC-BY-NC licence (>100,000 profiles)
See: ISRIC Data Policy

15. SoilGrids1km (30x30 arcsec)
19/06/2018
SoilGrids1km (30x30 arcsec)
Predictions for 7 soil properties, 6 depths, with 90%-confidence interval. Also predictions for 2 classes: WRB and USDA classification.)
Initial results were based on still “fairly coarse” linear models; 2014 SoilGrids1km should be seen as proof-of-concept of the automated, and scalabe SoilGrids approach.
“ Proof-of-concept”
Hengl T. et al. (2014) SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE 9(8): e105992

16. Soil maps derived from machine learning (SoilGrids250m)
19/06/2018
Soil maps derived from machine learning (SoilGrids250m)
Digital soil mapping:
Soil profile data (WoSIS)
Co-variate layers (e.g. DEM, land cover,…)
Model: Machine learning
3D-predictions of soil properties & classes at unobserved locations in the landscape
Products at 250m  1km, 5km, 10km …
pH water (1:10)
“ Proof-of-concept”
Hengl et al. (2016, PLoS One, subm.)

17. Use of soil information
19/06/2018
Work in progress
Use of soil information
Data sources
NEW DATA
(crowd sourcing;
Soilinfo App)
Data standardisation and harmonisation (WoSIS)
Data analysis
Soil information (SoilGrids)
Earth system modelling, food security, ... .
Use of soil
information
POLICY / DECISION MAKING

18. Interoperability and webservices (SDI under development)
WoSIS
Work in progress ...

19. OGC soil data interoperability experiment
Data providers:
CSIRO, AU
Landcare Research, NZ
ISRIC, NL (WoSIS)
Three organizations sharing soil data in an inter-operable manner
Development and testing of a Soil Markup Language (soilML)
Provides basis for a technical solution to a federated soil database
On the way to a new OGC standard? (OGC Eng. Report 6-088r1t)

20. Towards interoperable web services

21. Conclusions
Processing of disparate source databases pose many and diverse challenges
An important bottleneck remains the willingness of many data providers to freely share their data
Future releases will consider a broader range of soil data and their standardization/harmonization
With partners we are working towards global data inter- operability (IUSS WG SIS, OGC, GODAN, GSP…)
ISRIC will continue to serve the world with consistent quality-assessed data through its evolving SDI

22. ‘ISRIC welcomes cooperation on data sharing and use’