GlobalSoilMap.net a new digital soil map of the world Alfred Hartemink (on behalf of the global consortium) ISRIC World Soil Information Wageningen
The use of soil information
GlobalSoilMap.net A digital soil map of the world
Aspect 1 Capture and Capitalise Soil maps Soil samples Reports, literature Soil data and information
Aspect 2 Soil properties (not yet classes) Key properties 0 5 cm 5 15 cm 15 30 cm 30 60 cm 60 100 cm 100 200 cm Effective depth 1. Organic Carbon (g/kg) 2. Sand (%), Silt (%), Clay (%) & coarse fragments (%) 3. ph 4. Depth to bedrock or restricting layer (m) From these attributes, the following two properties will be predicted using pedo transfer functions: 5. Bulk Density (kg/m3) 6. Available Water Capacity (given in mm/m) Optional: 7. ECEC (Cations plus exchangeable acidity mol/kg) 8. EC (Electrical conductivity ds/m)
Aspect 3 Showing Uncertainties AWC (mm) 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 > 19 Standard errors 0.00-0.25 0.25-0.50 0.50-0.75 0.75-1.00 1.00-1.50 1.50-2.00 2.00-2.50 2.50-3.00 3.00-4.00 > 4.00
0 5cm 30 60cm 60 100cm Upper prediction limit DSM prediction Lower prediction limit Variability of OC at selected across the Edgeroi study area.
Aspect 4 Fine resolution grid 90 by 90 m From the polygon to the grid
Aspect 4 Fine resolution grid 90 by 90 m 0 5 cm 5 15 cm 15 30 cm 30 60 cm 60 100 cm 100 200 cm Effective depth
Initially a legacy based approach Which soil data are available? Assign quality of soil data and coverage in the covariate space Detailed soil maps Soil Point data Detailed soil maps Almost no data with legends with legends and Soil Point data scorpan Full Cover? kriging Full Cover? Homosoil Yes No Yes No Soil maps: -Spatially weighted mean -Spatial disaggregation Soil data: - scorpan kriging Extrapolation from reference areas: -Soil maps -Soil point data Time -Spatially weighted mean -Spatial disaggregation Extrapolation from reference areas Spatially weighted mean
Predictions Soil observations or maps with legends scorpan layers Existing Soil maps Climate Landcover DEM Lithology S p = f (s,c,o,r,p,a,n) + e f Linear regression, Regression trees, Random forests, Neural networks, Expert system Etc.. Krige residuals (e) Sand Clay Bulk density Organic C CEC ph Inferred property e.g., AWC
The spline Modal profile Fit masspreserving spline Fitted Spline Estimate averages for spline at standardised depth ranges Spline averages at specified depth ranges
layer Soil C prediction 0 5 cm 5 15 cm 15 30 cm 30 60 cm 60 100 cm 100 200 cm
Reconstruct splines at every pixel 3 2 1 Carbon (%) 0-5cm High : 6.5 Low : 0.8
11 task groups 1.Specifications 2. Soil profile and map legacy data 3. Covariates 4. New Prediction Method Development 5. Application & Documentation of Existing Methods 6. Soil Information Model 7. Cyber Infrastructure 8. End user Engagement 9. Uncertainty and Accuracy 10. Operational production mapping 11. Global stratification (pedoecophysiographic)
Aspect 5 It s s a global project North America Latin America/ Caribbean Eurasia Jordan North Africa/West Asia Africa South Asia East Asia Oceania
Start up in December 2006
The African launch 13 13 th th Jan 2009
The Global launch 17 th Feb 2009 "Let there be no mistake about the significance of this wonderful project" Kofi Annan "Soil mapping is one of the pillars to the challenge of sustainable development" Jeffrey Sachs
Large differences USA Sub Sahara Africa 9.1 million km 2 23.9 million km 2 35,000 soil profiles 4,057 soil profiles That s s currently available!
Progress Scientific Publications 2006 2007 2008 2010
Activities in the node East Asia
Activities in the node East Asia Proof of concept studies with voluntary source. 90m resolution maps are being tested in the pilot areas >0.05mm <0.001mm <0.01mm
Activities in the node North America Hired two post docs Will produce soil property information. Training workshops Work closely with Canada and Mexico Produced 30 m resolution soil carbon map (1 m depth) using spatially weighted mean calculations from SSURGO and STASGO Information
Activities in the node L America Launch at Seventeenth Latin American Congress of Soil Science Node established Identify country coordinators Technical meetings planned for March Translation some standards texts in Spanish Plan training and capacity building
Activities in the node L America
Activities in the node Oceania Good connections Australia Indonesia New Zealand Neil McKenzie, Mike Grundy, Peter Wilson, David Jacquier, Budi Minasny, Brendan Malone and John Gallant met in Canberra to discuss scientific issues, including proof of concept areas, and developing the Oceania node. Meetings in New Zealand, and Indonesia with follow ups in Australia are planned. There will be data from Australia forthcoming for proof of concept studies Indonesian Soil Science Society Conference 20-22 November 2009 in Yogyakarta
Activities in the node Oceania From R. Viscarra Rossell (CSIRO) From B. Malone et al. (Geoderma, 2009) Agreed specifications: 90 m, the spline (six depth layers) and uncertainty. Figured out how to convert ASRIS coverage to a 90 m raster with depth data and uncertainty. Increased complexity the way forward, not multi resolution,
Some conclusions Importance of soils and soil information is recognised in big global issues (climate change, food production, environmental degradation, biodiversity, water scarcity, land disputes and war) Increasingly recognised: soils are not the problem but part of the solution
Some conclusions Increased demand for soil information by other scientific disciplines, policy and society Demand for raster based soil properties with uncertainties Coincides with a quantum leap in technology to produce accurate soil information in a timely manner Enormous opportunities for soil science, and also for a new generation to get engaged
Thank you for your attention Alfred Hartemink (on behalf of the global consortium) ISRIC World Soil Information Wageningen