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

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

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

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

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 ...

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”

Map data into standard WoSIS model Relational data model implemented in PostgreSQL 44 interrelated tables http://www.isric.org/data/wosis

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

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)

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

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

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)

Serving standardised data to the user Dynamic: via WFS, see Wosis-distribution-set Static: CSV (zipped), e.g WoSIS_July_2016.zip http://dx.doi.org/10.17027/isric-wdcsoils.20160003. Batjes et al. 2016 (Earth System Sci. Data, subm.)

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

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. “2014 - Proof-of-concept” http://soilgrids.org Hengl T. et al. (2014) SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE 9(8): e105992 http://soilgrids1km.isric.org/

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) https://soilgrids.org “2014 - Proof-of-concept” Hengl et al. (2016, PLoS One, subm.)

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

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

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)

Towards interoperable web services

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

‘ISRIC welcomes cooperation on data sharing and use’ www.isric.org niels.batjes@isric.org