Soil Infrared SpectroscopyApplications in Africa

International Workshop

Soil Spectroscopy: the present and future of Soil Monitoring

FAO HQ, Rome, Italy, 4-6 December 2013

Soil spectroscopy to monitor the state of soil resources in the

present and in the future

Keith D Shepherd

Surveillance Science• Measure frequency of problems and associated risk factors in populations

using statistical sampling designs & standardized measurement protocolsUNEP. 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

Identify problem

Develop case

defintition

Develop

screening test(s)

Measure prevalence

(no. cases/area)

Measure incidence

(no. cases/area/time)

Confirm risk factors

Measure

environmental

correlates

Differentiate risk

factors

Infrared spectroscopy

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.

• Increase sample density

• Measure soil functional

properties at landscape

scales

• Direct prediction of soil-

plant responses to

management

Spectral shape relates to basic soil properties

• Mineral composition

• Iron oxides

• Organic matter

• Water (hydration,

hygroscopic, free)

• Carbonates

• Soluble salts

• Particle size distribution

Functional properties

Infrared spectroscopy

Dispersive VNIR FT-NIR FT-MIR Robotic FT-MIR Portable

Handheld MIR ?Mobile phone cameras

?

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 mid-

infrared spectral library. Soil Sci. Soc. Am. J.

74:1792–1799

CalibrationSoil organic carbon

Spectral pretreatments

• Derivatives, smoothing

Data mining algorithms:

• PLS +

• Support Vector Machines

• Neural networks

• Multivariate Adaptive

Regression Splines

• Boosted Regression

Trees

• Random Forests

• Bayesian Additive

Regression Trees

Training Out-of-bag

validation

Soil pH

R package soil.spec

Soil spectral file

conversion, data

exploration and

regression functions

✓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

AfSIS

Spectral libraries

Spectral prediction performance

Spectral Lab

Network

•IAMM, Mozambique

•AfSIS, Sotuba, Mali

•AfSIS, Salien, Tanzania

•AfSIS, Chitedze, Malawi

•CNLS, Nairobi, Kenya

•ICRAF, Nairobi, Kenya

•CNRA, Abidjan, Cote D’Ivoire

•KARI, Nairobi, Kenya

•ICRAF, Yaounde, Cameroon

•Obafemi Awolowo University,

Ibadan, Nigeria

•IAR, Zaria, Nigeria

•ATA, Addis Ababa, Ethiopia (+ 5

on order)

•IITA, Ibadan, Nigeria

•IITA, Yaounde, Cameroon

•ICRAF, Nairobi, Kenya

Planned

•Eggerton University,

Kenya

•MoA, Liberia

•IER, Arusha, Tanzania

•FMARD, Nigeria

•NIFOR, Nigeria

•CNLS, Nairobi

•BLGG, Kenya (mobile

labs)

• 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

Soil-Plant Spectral Diagnostics Lab

• 500 visitors/yr

• 338 instruction

• 13 PhD, 4 MSc training

Land Health Surveillance

Consistent field

protocol

Soil spectroscopyCoupling with remote

sensingPrevalence, Risk factors, Digital mapping

Sentinel sites

Randomized sampling schemes

Markus Walsh

Africa Soil

Information Servicewww.africasoils.net

Markus Walsh

Probability topsoil pH < 5.5 ... very acid soils

prob(pH < 5.5)Africa Soil

Information Servicewww.africasoils.net

Markus Walsh

Calibrating plant response to IR

http://afsis-dt.ciat.cgiar.org

IR applications Vital signs

Cocoa - CDIParklands Malawi

National surveillance

systems

Regional Information Systems

Project baselines

Ethiopia, Nigeria

Rangelands E/W AfricaSLM Cameroon MICCA EAfrica

Global-Continental Monitoring Systems

CGIAR pan-tropical sites

AfSIS

Private sector soil testing

Critical success factors• Consistent field sampling protocol

• Soil-Plant sample labeling, drying,

preparation, sub-sampling, shipping, back-up

storage

• Data management, linking

• Judicious selection of samples for reference

analysis

• Consistency of reference analyses

• Stable spectrometer technology and

protocols

• Training in all steps and follow-up support

http://worldagroforestry.org/research/land-health/spectral-diagnostics-laboratory