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Progress for the GlobalSoilMap.net-North American Node, Towards Global Soil Information: Activities with the GEO Task Global Soil Data Jon Hempel, USDA-NRCS
1. Progress for the GlobalSoilMap.net-North
American Node
Towards Global Soil Information: Activities with the GEO Task
Global Soil Data
Jon Hempel
USDA-NRCS
Global Soil Partnership Workshop 2012
FAO Headquarters
Rome, IT
20 – 23 March, 2012
2. It’s a global project
North America
Latin
America/
Caribbean
Eurasia
Africa
East Asia
Oceania
South
Asia
North Africa/West Asia
Jordan
3. Specifications Bob MacMillan
Soil Legacy Data Sharon Waltman/ Endre Dobos*
Covariates Janis Boettinger*/ Hannes Reuter*
New Methods Development Budiman Minasny*/ Alex McBratney*
Application of Existing Methods Tom Hengl*
Data Model Peter Wilson*
Cyber Infrastructure Sonya Ahmed/ Robert Gibb*
End User Engagement Phillip Owen*
Training Alex McBratney*/ Budiman Minasny*
Production Mapping Bob MacMillan
Global Stratification Philippe Lagacherie*
GlobalSoilMap.net Task Groups
4. Aspect 1--- Soil properties (not classes) presented in raster
format
Key properties
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)
0 - 5 cm
5 – 15 cm
15 – 30 cm
30 – 60 cm
60-100 cm
100-200 cm
Effective depth
9. Which soil data are available?
Define an area of interest
Detailed soil maps
with legends
-Spatially weighted
mean
-Spatial disaggregation
Extrapolation from
reference areas
Spatially weighted mean
Full Cover? Homosoil
Detailed soil maps
with legends
and Soil Point data
Soil Point data No data
NoYes
scorpan
kriging
Assemble environmental covariates
Soil maps:
-Spatially weighted mean
-Spatial disaggregation
Soil data:
- scorpan kriging
Extrapolation from
reference areas:
-Soil maps
-Soil point data
Full Cover?
NoYes
Increasing uncertainty in prediction
(depends on the quality of data and complexity of soil cover )
Assign quality of soil data and coverage in the covariate space
Digital Soil Mapping
Workflow
12. NA Node Partners
• USDA-Natural Resources Conservation Service
• Agriculture and Agri-Food Canada
• West Virginia University
• Purdue University
• Utah State University
• University of Florida
• University of California-Davis
• Environmental Protection Agency (EPA)
• US Geological Survey
• Mexico Partners
– Conafor-Comisión Nacional Forestal, is the National Forestry
Commission of Mexico and is an agency of the Secretariat of the
Environment and Natural Resources)
– INEGI- NEGI, Instituto Nacional de Estadística y Geografía (National
Institute of Statistic and Geography
13. NA Node Small scale digitized soil maps
available for 100% of NA node area
• CANADA-Soil Landscapes of Canada (SLC-
1:1,000,000)
• MEXICO-Carta Edafologica escala (1:250,000)
• USA-US General Soil Map (STATSGO2-
1:250,000)
14. NA Node Medium to Large Scale
digitized soil maps
available for 70% of the node
• CANADA Canada Land inventory (CLI)-
(Detailed Soil Surveys-1:10,000-1:250,000)
• MEXICO-Cartas Edafológicas registradas-
(1:50000)
• USA-SSURGO Soil Survey Geographic Database
(SSURGO-1:12,000-1:65,000)
15. Versioning of the NA Node Data
• Versions 0.1–0.4-STATSGO, SLC, CEE
• Versions 0.5–0.9-SSURGO, CLI, CER
• Versions 1.0+-Digital Soil Mapping predictive
modeling (point data, disaggregated soil
maps, covariate information)
17. Soil Landscapes of Canada
1:1M
• Organic C
• pH
• Clay
• Silt Spatially weighted means
• Sand At GSM specified depths
• Coarse Frags for Canada Ag areas
• Bulk Density
• AWC
• EC
Glenn Lelyk-AAFC
18.
19.
20.
21.
22.
23.
24. STATSGO-State Soil Geographic Overlay
1:250K
• Organic C
• pH
• Clay
• Silt Spatially weighted means
• Sand At GSM specified depths
• Coarse Frags
• Bulk Density
• AWC
• Soil Depth
Zamir Libahova, et. al
25.
26.
27.
28.
29.
30.
31. •Soil Depth
•Sand, Silt, Clay
•Coarse Fragments
•Depth to Bedrock
•Soil carbon
•Bulk Density Close to prescribed
•pH GSM.net depths
•Available Water Capacity (0-5, 5-20, 20-50
•Permeability 50-100, 100-150,
•hydrologic conductivity 150-200)
•Percentage hydric soils
EPA/USGS/NRCS Soil Property Maps from
SSURGO
Norman Bliss et. Al
32. SOC for conterminous US, 0 – 5 cm depth
Note: Legend colors are specific to each standard
layer and should not be compared between layers.
Red indicates low SOC values and blue indicates high
SOC values (grams per square meter)
33. SOC: 5 – 20 cm depth
Note: Legend colors are specific to each standard
layer and should not be compared between layers.
Red indicates low SOC values and blue indicates high
SOC values (grams per square meter)
34. SOC: 20 – 50 cm depth
Note: Legend colors are specific to each standard
layer and should not be compared between layers.
Red indicates low SOC values and blue indicates high
SOC values (grams per square meter)
35. SOC: 50 – 100 cm depth
Note: Legend colors are specific to each standard
layer and should not be compared between layers.
Red indicates low SOC values and blue indicates high
SOC values (grams per square meter)
36. SOC: 100 – 150 cm depth
Note: Legend colors are specific to each standard
layer and should not be compared between layers.
Red indicates low SOC values and blue indicates high
SOC values (grams per square meter)
37. SOC: greater than 150 cm depth
Note: Legend colors are specific to each standard
layer and should not be compared between layers.
Red indicates low SOC values and blue indicates high
SOC values (grams per square meter)
46. Conclusions
• Can use SSURGO, STATSGO, SLC to produce
property maps (wt ave) that meet GSM.net
specs
• Anywhere in the world with digitized soil maps
and attributed polygons can produce soil
property data
• Need work on uncertainty
• Research and develop
deconstruction/disaggregation technology