Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

1. Land Health Surveillance
Information for decision making
Keith D Shepherd, Markus G Walsh, Ermias Betemariam
Remote Sensing – Beyond Images
Hotel Sevilla Palace, Mexico City, 14-15 December 2013

2. Surveillance Science Principles
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Define target population/area
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Measure frequency of problems and associated risk factors in populations
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Sample units
Probability sampling
Standardized measurement protocols
Case definitions
Rapid screening tests
Risk quantification
Operational surveillance systems built into policy and practice
UNEP. 2012. Land Health Surveillance: An Evidence-Based
Approach to Land Ecosystem Management. Illustrated with a Case Study in the West Africa Sahel.
United Nations Environment Programme, Nairobi.
http://www.unep.org/dewa/Portals/67/pdf/LHS_Report_lowres.pdf
Shepherd KD and Walsh MG (2007) Infrared spectroscopy—enabling an evidence-based diagnostic
surveillance approach to agricultural and environmental management in developing countries. Journal
of Near Infrared Spectroscopy 15: 1-19.

3. Land Health Surveillance
Sentinel sites
Randomized sampling schemes
Consistent field
protocol
Coupling with
Prevalence, Risk factors, Digital mapping remote sensing
Soil spectroscopy

4. AfSIS
✓60 primary sentinel sites
➡ 9,600 sampling plots
➡ 19,200 “standard” soil samples
➡ ~ 38,000 soil spectra
➡ 3,000 infiltration tests
➡ ~ 1,000 Landsat scenes
➡ ~ 16 TB of remote sensing data to date

5. Soil infrared spectra
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Mineral composition
Iron oxides
Organic matter
Water (hydration,
hygroscopic, free)
• Carbonates
• Soluble salts
• Particle size
distribution
 Functional
properties
1 = Fingerprint region e.g Si-O-Si stretching/bending
2 = Double-bond region (e.g. C=O, C=C, C=N)
3 = Triple bond (e.g. C≡C, C≡N)
4 = X–H stretching (e.g. O–H stretching)
NIR = Overtones; key features clay lattice and water OH; SOM affects overall
shape

6. Infrared spectroscopy
Dispersive VNIR
Handheld MIR ?
FT-NIR
FT-MIR Robotic
Mobile phone cameras
?
FT-MIR Portable
Shepherd KD and Walsh MG. (2002) Development
of reflectance spectral libraries for characterization of
soil properties. Soil Science Society of America
Journal 66:988-998.
Brown D, Shepherd KD, Walsh MG (2006). Global
soil characterization using a VNIR diffuse reflectance
library and boosted regression trees. Geoderma
132:273–290.
Terhoeven-Urselmans T, Vagen T-G, Spaargaren O,
Shepherd KD. 2010. Prediction of soil fertility
properties from a globally distributed soil midinfrared spectral library. Soil Sci. Soc. Am. J.
74:1792–1799

7. Spectral prediction performance

8. • Submit batch of spectra online
• Uncertainties estimated for each
sample
• Samples with large error submitted
for reference analysis
• Calibration models improve as more
samples submitted
• All subscribers benefit

9. Spectral Lab Network

10. Soil-Plant Spectral Diagnostics Lab
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500 visitors/yr again
338 instruction
13 PhD, 4 MSc
training

11. Markus Walsh

12. Soil property maps of
Africa at 1 km
Legacy soil
profiles12,000
locations

13. Africa Soil
Information Service
www.africasoils.net
Markus Walsh

15. Probability topsoil pH < 5.5 ... very acid soils
prob(pH < 5.5)
Africa Soil
Information Service
www.africasoils.net
Markus Walsh

16. Markus Walsh

18. Global-Continental Monitoring Systems
Applications
CGIAR pan-tropical sites
Vital signs
AfSIS
Vital Signs
Regional Information Systems
National surveillance
systems
Private sector soil testing
Ethiopia, Nigeria
Project baselines
SLM Cameroon
Parklands Malawi
Rangelands E/W Africa
Cocoa - CDI
MICCA EAfrica

19. What is the decision?
Decisions before Data
• Review of the Evidence on Indicators, Metrics and Monitoring
Systems.
http://r4d.dfid.gov.uk/output/192446/default.aspx
• A Survey and Analysis of the Data Requirements for Stakeholders
in African Agriculture
http://r4d.dfid.gov.uk/Output/193813/Default.aspx
• Government-level programmatic decisions
(fertilizer supply/blending; liming programmes)
• Farmer or local provider decisions (what
fertiliser to apply, where, when)

20. Explicit decision modelling
dealers.
• Uncertainties (risks)
represented
• Value of Information Analysis
• Preferences of stakeholders
Influence diagram
“Make fertilizer recommendations” use case
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Workflows that use consistent data model
Free and open source computer software
Automated analyses of remote sensing and market time series
Maps, monitoring and decision analysis products and services for Africa
Deployable via web and cellular/mobile services
Products to CKW’s, farmer groups, land management policy-makers,
government agencies and agro-input dealers

21. Outputs
Probabilistic impact
projections
Identification of high-value
variables
Aquifer size after 70 years of
abstraction (% of original)
Replenishment
Irrigation growth
Initial irrigated area
Water use per hectare
Aquifer size
Natural water use
Importance threshold

22. Smart data – Smart Decisions
• Inclusion of uncertain variables allows truly holistic
impact assessments
• Efficient way of organizing existing knowledge
• High information value variables are almost always not
those typically measured
• Identifies important metrics for monitoring
• Provides accumulated evidence for impact attribution
• Community of practice & capacity building in decision
analysis under uncertainty