Afsismidtermreviewconsortiumpresentationv2 110203031825-phpapp02

GlobalSoilMap.net: Update

GlobalSoilMap.net: AfSIS Objective 1

$18 million

Other grants and in-kind:

$8 million

Total budget: $300 million

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Key successes (objectives achieved) Consortium agreement signed & nodes

active Specifications prepared and agreed upon Soil legacy data for Africa (AfSIS)

Progress and results by node New tools and initiatives Training and capacity building Fund raising Future plans

Objective 1: Results & ActivitiesAfSIS Project Objective 1: Establishing the GlobalSoilMap.net global consortium

Consortium Agreement has been Signed

Slide Credit: Alfred Hartemink

Nodes are Established and Active

North America

LatinAmerica/Caribbean

Eurasia

Africa

East Asia

Oceania

SouthAsia

North Africa/West and Central AsiaCUMERC

(South Asia node is still pending)

Specifications Prepared and Agreed

Specifications – Soil properties (not classes)

Key soil properties

1. Organic Carbon (g/kg)2. Sand (g/kg), Silt (g/kg), Clay (g/kg) coarse fragments (g/kg)3. pH 4. Depth to bedrock or restricting layer (cm)

From these attributes, the following two properties will be predicted using pedo-transfer functions:

5. Bulk Density (Mg/m3)6. Available Water Capacity (given in mm/m)

Optional:7. ECEC (Cations plus exchangeable acidity mmol/kg)8. EC (Electrical conductivity mS/m)

0 - 5 cm5 – 15 cm15 – 30 cm

30 – 60 cm

60-100 cm

100-200 cmDepth to bedrockand Effective depth

Six Depths

Slide Credit: Alfred Hartemink

14,000 unique profiles

10,000 georeferenced

8,000 with lab data Error checked

profiles Positional accuracy Data value errors

Soil Legacy Data for Africa

Date Dec 2009 Added Dec 2010 Added Jan 2011Source (WISE3) Profiles Profiles & lab Profiles Profiles & labKenya 294 71 365 365 365 365Mali 20 260 280 280 170 450 450Malawi 3 2987 2990 853 2990 853Nigeria 37 190 227 218 393 620 600Tanzania 95 1031 1226 1226 20 1246 1236Total 2770 6742 9512 7365 583 10095 7927

Slide Credit: Johan Leenaars

Legacy Data Officer is locating, entering and checking data country by country

Progress and Results by Node

North America

LatinAmerica/Caribbean

Eurasia

Africa

East Asia

Oceania

SouthAsia

North Africa/West and Central AsiaCUMERC

(South Asia node is still pending)

North America Node

Detailed soil survey polygon data (SSURGO) and more generalized polygon data (STATSGO) have been converted to raster estimates of cumulative organic carbon content to 100 cm depth

USA is actively producing version 1.0 grid maps using polygon disaggregation methods

Slide Credit: Jon Hempel

North America Node

Generalized polygon data (STATSGO) have been converted to raster estimates of organic carbon content for the 6 depth intervals of the specifications

USA is actively producing version 1.0 grid maps using polygon disaggregation methods

Slide Credit: Nathan Odgers

North America NodeUSA is actively producing version 1.0 grid maps using polygon disaggregation methods

















Readily accessible gridded soils data have several advantages over vector data, such as easier integration with other land surface datasets. Currently, these data are being made available at a 30-meter resolution in the Albers Equal Area projection.

The compilation of the new database has been made possible as part of a National Atlas of Sustainable Ecosystem Services being developed under the leadership of the U.S. Environmental Protection Agency (EPA), along with many partner organizations including the NRCS and the U.S. Geological Survey.



We began with a 10-meter gridded version of SSURGO from NRCS and resampled to a 30-meter cell size for analysis and a 1,200-meter cell size for national display. Where SSURGO data were not available, the results were substituted from a similar analysis using the General Soil Map (STATSGO2) of the United States, a successor to the State Soil Geographic (STATSGO) Database (NRCS Soil Survey Staff, 1992; 1994).

The analysis of soil attributes starts on the component horizon (chorizon) table, aggregates the quantitative measures over the appropriate layers for a given analysis, and stores the result at the level of the component table (component). A weighted average of the component values is computed using the representative component percentage (comppct_r) as the area weighting factor, and the results are stored at the level of the map unit (mapunit table). These results are then copied to the spatial datasets where they are used to display maps.

Figure 11. Soil carbon content (from soil organic matter content). The carbon content is computed from the organic matter content, accounting for the bulk density, volume of rocks, and a conversion factor (0.58) for the mass of carbon per unit mass of organic matter. [om_r, dbthirdbar_r, and others]

1194 g C m-2

0


Figure 10. Clay percentage from the ratio of the mass of clay to the mass of soil fines. [claytotal_r, dbthirdbar_r, and others]

82

0


Figure 8. Sand percentage from the ratio of the mass of sand to the mass of soil fines (soil particles less than 2 mm diameter). [sandtotal_r, dbthirdbar_r, and others]

100

0


Figure 9. Silt percentage from the ratio of the mass of silt to the mass of soil fines. [silttotal_r, dbthirdbar_r, and others]

92

0


Figure 7. Bulk density is the mass of soil divided by the volume of soil for the fraction of soil with particles less than 2 mm in diameter (i.e., excluding rocks). The mass is measured on an oven-dry basis, and the volume at a water content with 33 kPa soil water tension. [dbthirdbar_r]

2.33

0.02


Figure 2. Average soil thickness is the maximum depth of soil recorded by soil scientists or the depth to bedrock, whichever is less. There may be “county boundaries” in the data because different soil surveys used different conventions on the maximum depth of soil to record in the database (e.g., 60 inches or 80 inches). [hzdepb_r] (SSURGO + STATSGO2 12/30/2009)

457 cm

0


NRCS – is developing methods to disaggregate polygon maps into component soils

GilpinPinevilleLaidigGuyandotteDekalb

Component Soils

CraigsvilleMeckesvilleCateacheShouns

Thompson et al. 2010

Each component has a single soil property value

Gilpin-Laidig

OtherPineville-Gilpin-Guyandotte

SSURGO Map Units

North America Node

North America NodeNRCS and University of Sydney have demonstrated methods for harmonizing soil maps

Original Non-harmonized Soil Maps Harmonized Soil Series Maps

Slide Credit: Alex McBratney

North America NodeUSA is conducting pilot projects (with Canada) to develop and assess new methods


Oceania NodeCSIRO have methods to convert polygon soil data to GlobalSoilMap.net specifications

ASRIS Approach: Deriving continuous depth soil properties from a soil

polygon map

Slide Credit: David Jacquier

Oceania NodeUniversity of Sydney has developed methods for predicting properties and uncertainty

The soil mapSoil sample designSoil sampling and analysisData analysisSoil map quality

DSM Soil Property Prediction Lower Prediction

Upper Prediction

Slide Credit: Budiman Minasny

Oceania NodeUniversity of Sydney has developed methods for predicting properties and uncertainty

0-5cm

Lower prediction limit

30-60cm

60-100cm

Upper prediction limit DSM predictionSlide Credit: Alex McBratney

Oceania NodeNew Zealand has produced a vision for integrating their S-map into GlobalSoilMap.net

New ZealandS-map

– new soil database

GlobalSoilMap provides• Methods• Covariate data sets • Capability development• Internat’l communication

S-map in New Zealand provides• Data for GSM

Propose: S-map at 15m ↔ generalise to GSM 90m Slide credit: Alan Hewitt

Oceania NodeNew Zealand has devised a strategy for applying different methods in different areas

Slide credit: Alan Hewitt

Latin America & Caribbean NodeLAC Node established a model for organizing countries at the node level

Slide credit:Lou Mendonça Santos The Rio Accord Declaration

West & Central Asia/North AfricaWorld’s first institute of Digital Soil Mapping opened at CUMERC December, 2010

Mahmoud AlFerihat, (2011)

A scheme showing 3-D soil mapping based on point pedon data for a pilot area in China

East Asia Node

Slide credit: Ganlin Zhang

East Asia node is evaluating 3-D SOM mapping for a pilot watershed in China

East Asia Node

Slide credit: Ganlin Zhang

3D mapping of SOM

Three horizontal slices at different depth: 1 cm, 50 cm, and 100 cm

vertical slices at four transects

Korea has developed and demonstrated methods of digital soil property mapping

East Asia NodeSlide credit: Song Young Hong

vertical slices at four transects Soil Carbon Prediction - Spectroscopy Soil Carbon Storage Mapping

◇ Soil Carbon Prediction and Mapping

○ Soil Spectrum Data Transform Library Prediction

○ PTF for BD C density Mapping Soil C Storage

Developing two prototype cases Slovakia national database at 100 x 100 m to

specifications Prepared by JRC

Denmark national database at 100 x 100 m to specifications

Prepared by Aarhus University Protype cases will be presented at the June, 2011

team meeting JRC is hosting the GlobalSoilMap.net team

meeting in June Providing facilities, organizational and logistical

support Contributed to the GlobalSoilMap.net test bed

activity Developed organic carbon WMS service for testbed

Eurasia node is underway now that the Consortium Agreement is signed and in force

Eurasia Node

Eurasia NodeEurasia node: from polygons to pixel based soil information systems

Slide credit: Luca Montanarella

PIXEL_id SMU % PIX AREA

STU % SMU AREA

Attributes

4526_2618 1 68 ZMB1 32

4526_2618 1 68 SDP1 23

4526_2618 1 68 ORN1 14

4526_2618 1 68 CTA1 10

4526_2618 1 68 CIA1 9

4526_2618 1 68 Bare dep. 13

4526_2618 2 32 PIP1 26

4526_2618 2 32 VAT1 24

4526_2618 2 32 CIA1 19

4526_2618 2 32 ZMB1 18

4526_2618 2 32 LCE1 13

GlobalSoilMap.net Exchange Format

SMU 1

PIXEL_id4526_2618

SMU 2

Eurasia NodeEurasia node: from polygons to pixel based soil information systems

Slide credit: Luca Montanarella

Multiscale EUropean Soil Information System (MEUSIS)

http://eusoils.jrc.it/projects/Meusis/main.html

1 km

10 km

100 km

90m

Multi-scale Soil Information system

First test results in Slovakia Multi-Scale European Soil Information System

(MEUSIS): A multi-scale method to derive soil indicators. Panagos, Van Liedekerke and Montanarella. Computational Geosciences, Springer (2010). Article in Press.

University of Sydney (McBratney, Minasny & Malone) Spline function – MATLAP and R-code to fit spline to

profile data Uncertainty – emperical method to estimate

uncertainty CSIRO – Canberra

Spline tool – stand alone Windows program for fitting the spline

AfSIS Sentinel site sampling strategy - design and

implementation Google mobile app for delivering on-site agronomic

advice

New Tools and InitiativesThe project has stimulated the development of new tools and protocols for DSM

Oceania NodeUniversity of Sydney has developed methods for predicting properties & uncertainties

• Standardisation of soil profile observation depths:- mass

preserving splines• Spatial Intersection of soil

information with available predictive covariates

• Data splitting: into calibration and validation datasets

• Fuzzy clustering of feature space

• model error estimation

• construction of cluster

prediction intervals

• Upper and lower prediction limits

• Construct prediction intervals

• Validate with PICP

• Estimate soil attribute concentration: model training rules

• construct prediction intervals• construct continuous depth representation of predictions and

uncertainties (upper and lower prediction limits:- mass preserving

splines

Model training and learning

Validate learning rules

R2, RMSE etc

Em

pirical u

ncertain

ty m

odel

Pre

dic

tive

mod

el

Map

pin

g p

red

icti

ons

and

as

soci

ated

p

red

icti

on

inte

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s

Data processing

Slide credit: Brendan Malone

Oceania NodeUniversity of Sydney has developed methods for fitting spline depth functions

‘Modal’ profile

Fit mass-preserving spline

Spline averages at specified depth ranges

Estimate averages for spline at standardised depth ranges, e.g., globalsoilmap depth ranges

Fitted Spline

Sun et al.(2010)

Oceania NodeUniversity of Sydney has developed methods for estimating uncertainty of predictions

-2 2

-1.2 1.0

-3 1.5

-6 6

Class A

Class B

Class C

ExtragradesClass A

Class B

Class C

Extragrades

Perform fuzzy k-means with extragrades clustering on the covariates

Calculate the distribution of errors for each fuzzy k-means class

Malone et al.(2011)

Oceania NodeUniversity of Sydney has developed methods for fitting spline depth functions

Class A Class B Class C Extragrades

ExampleLocation xPredicted value = 10mA = 0.6mB = 0.2mC = 0.15mExtragrades = 0.05

-2 2 -1.2 1.0 -3 1.5

Error:Lower Limit = 0.6 * -2 + 0.2 * -1.2 + 0.15 * -3 + 0.05 * -6 = -2.19LPL = 10 – 2.19 = 7.81

Upper Limit = 0.6 * 2 + 0.2 * 1.0 + 0.15 * 1.5 + 0.05 * 6 = 1.93UPL = 10 + 1.93 = 11.93

Prediction error is the weighted mean of the error

Malone et al.(2011)

Slide Credit: David Jacquier

Oceania NodeCSIRO has developed a stand alone Windows program for fitting spline depth functions

Randomization to minimize local biases that might arise from convenience sampling

Sentinel Site based on the Land Degradation Surveillance Frameworka spatially stratified, hierarchical, rand

omized sampling framework

Sentinel site (100 km2)

16 Clusters (1 km2)

10 Plots (1000 m2)

4 Sub-Plots (100 m2)

Africa Node (AfSIS)AfSIS has devloped a design and protocols for stratified hierarchical sampling

Slide Credit: Markus Walsh


Africa Node (AfSIS)AfSIS has devloped a design and protocols for stratified hierarchical sampling

Africa Node (AfSIS)AfSIS is developing a mobile phone based app with Google for on-site data sharing


Courses delivered CSIRO – Canberra,

June, 2010 JRC- Ispra, August,

2010 Embrapa – Rio, Sept,

2010 Courses planned

Korea – Spring, 2011 JRC – Ispra, June, 2011 MSc course, Univ of

Sydney

Course modules developed Data quality checking

Location accuracy checking

Property value checking

Bias in property values

Data harmonization (methods)

Standardization of methods

Data harmonization (depths)

Spline tool application Prediction methods

Regression kriging/ANN

Polygon disaggregation

DSM Training EffortsUniversity of Sydney is leading efforts to devlop and deliver DSM training modules

West & Central Asia/North Africa Node Replicate strategy from

LAC Meeting at

DSM/CUMERC Global In-kind

Contributions North America - $925 k Oceania - $158 k WCA/NA - $219 k LAC - $25 k AfSIS - $305 k Asia - $392 Institutes – $1.2 M

World Bank Washington – April

2010 50 Donor Agencies

invited Prospects identified

LAC Node – 3 Initiatives Google – short term

$300 k Harvest legacy soil

data IADB – medium term

$1.5 M Country-level DSM

pilots Moore – long term $15

M Operational

mapping

Fund Raising EffortsFund Raising Officer is developing node-specific fund faising strategies

Fund Raising Efforts

Nodes Supporting and coordinating institutesNorth

AmericaL America &

Carribean EurAsiaWest Asia-

North AfricaSub Sahara

Africa East Asia Oceania ISRICUniversity of Sydney IRD Total

Time in-kind 200,000 25,000 100,800 140,000 50,000 63,000 164,000 27,000 148,000 917,800

students/postdocs 300,000 70,000 120,000 94,500 584,500

funding visitors 2,000 9,000 11,000Travel & meetings 25,000 70,000 45,000 20,000 95,000 10,000 18,000 2,000 285,000

Grants 250,000 50,000 200,000 792,000 1,292,000

Equipment 150,000 49,000 199,000

Total 925,000 25,000 0 219,800 305,000 392,000 158,000 174,000 940,500 150,000 3,289,300(all amounts in US$)

In-Kind Contributions to 2010

The project is providing the impetus for significant in-kind contributions from nodes

ISRIC cyber-infrastructure development Server network Support for spatial databases

ISRIC concepts and support for harmonized global mapping Open Soil Profile Database – concept, design and

proposal Global Soil Information Facility - – concept, design

and proposal Multi-scale modelling prediction method – concept

and examples GlobalSoilMap.net coordination

Full week team workshop – JRC, Ispra June, 2011

Future Plans and ProposalsISRIC is implementing measures to support production and distribution of data

ISRIC Soil Portal

ISRIC – new plans & proposalsISRIC is developing cyber-infrastructure to host and serve geodata about soils

Slide credit: Hannes I. Reuter, 2011

ISRIC – new plans & proposalsISRIC Web Services: WCS – WFS - WMS

GeoNetwork – Open Source

GeoServer: WCS-WFS-WMS


ISRIC is developing proposals for collecting and hosting global soil profile and map data

Slide credit: Tom Hengl, 2011

ISRIC – new plans & proposals

ISRIC is devloping a vision and proposal for an open soil profiles database (OSPD)



ISRIC is developing a vision and proposal for an on-line soil map geo-referencer



Scan Geo-register through crowd sourcing

ISRIC is developing multi-scale methods for collating & harmonizing soil property maps



ISRIC is developing multi-scale methods for collating & harmonizing soil property maps



ISRIC – new proposalsISRIC is devloping multi-scale methods for harmonizing soil property maps


ISRIC – new proposalsISRIC is devloping multi-scale methods for harmonizing soil property maps


ISRIC contributions Node contributions Future plans and possibilities

Global training & capacity building in DSM

Global standards and methods for soils

Global platforms and systems for mapping soil

Implications Harmonization of soil science

globally

Conclusions

Original supporter

Provided grant to suppport work in Sub-Saharan Africa

To develop improved land management recommendations

Support inital development of the GlobalSoilMap.net project

Recognizing our Supporters

Technology

Afsismidtermreviewconsortiumpresentationv2 110203031825-phpapp02