Jorge Etchevers Barra (COLPOS) Carlos Cruz Gaistardo (INEGI) Fernando Paz Pellat (PMC)

NIR Reflectance Spectroscopyin Soil Surveys oriented a Toward Monitoring in Mexico 2016

Why are we here?

To expose our advances and adhere to universal

protocols are generated here, so that our samples,

processes and results can be integrated properly in the

near future at the Digital Soil Map of the World.

Why NIR in Mexico?

Utility: Reduce costs and time by avoiding the

use of strong acids and oxidizing agents.

Targets:

+ Organic and inorganic carbon and nitrogen

+ 13C and 15N Stable Isotopes

+Soil fertility

+ Pesticides and Contaminats

+ Streamline soil clasiffication and other maps

What are the processes?

Fieldwork with

one protocol

Treatment of

samples and

generation of

subsets

Analysis of the

reference samples

in TOC

Spectral collection

Data manipulation

Chemometric analysis

Scatter correction

Calibration evaluation

Auxiliary predictors

Validation models

vs field and

sensors

Digital soil mapping

Validation maps vs

field and sensors

Monitoring in soils

(Iterations, Reports

and Verification)

Preprocess

2009-2012Process NIR

2013-2014Postprocess

2015-2016

First step: Field work

FOREST AND SOIL NATIONAL INVENTORY 2009-2012

+ 12,500 study units

For each unit:

+ 1 reference sample (TOC,NIR,CIELab)

+ 18 organic samples (NIR)

+ 18 mineral samples (NIR)

+ 2m distance between soil samples

400m2

Where are the samples?

soil study units

samples:+12,500 references (DA)+72,000 fresh litter (HO)+56,000 fermentation layer (F)+74,200 mineral soil (0-30cm)+56,200 mineral soil (30-60cm)

Total 270,900 soil samples (verified)

+120 geomorphology units

+25 climate units (Koppen md EG)

+90 vegetation types

+18 land uses

+26 soil groups at least (WRB 2014)

675 samples for 10,000 km2

Wich Soil Analysis?

*En 2014 y solo las muestras con mayor probabilidad de mejorar los modelos de calibración.

+Basics

Coarse fragmentsBulk density

Color by CIELab

Carbonates

Organic Carbon

Sand, Lime and Clay

+Experimentals*

Dispersable clay

Extensivity Lineal Coef

CE, pH, C:N

Ca, Mg, K, Na

Volcanic glass

Fe and Al

Subsets for NIR

Stock of Soil Samples

Organic Mineral

HojarascaSapric, Folic

FermentationHistic

Superficial 0-30cm

Subsuperficial30-60cm

+Color CIELab

+Slope in specific transects

+Carbonates

+Crown forest

+COS (TOC)

+Clay activity and content**

About 250 subsets

+ Species dominants*

+Slope in specific transects

+Rain of the last month of

survey

+Bared soil (%) by value in the

photo classification

About 300 subsets

*Especies con más de 50 repeticiones en el stock total de muestras. **Experimental

Key subset: Color CIELab+ Field spectrometry. Three-dimensional modeling of color in the visible band.

L: luminosity. 0=black 100=white.a: red axis. >a is >oxids concentrationb: green axis. >b is >reduction processC:saturation o chromaticity of colorh: color angle specific

Key subset: Color CIELab+ Field spectrometry. Three-dimensional modeling of color in the visible band.

32 subsets for field color

L [0-25, 26-50, 51-75 76-100]

a [<18, >=18]

b [<12, >=12]

C [<15, >=15]

Key subset: Crown forest+ Internal software for Distance, Azimut, Normal diameter and Crown forest

Grass and few scrubs (Acacia)TOC= 0.25%

NIR*= 0.49% NIR**= 0.62%Crown= 8.42%

Scrub (Prosopis)TOC= 0.88%

NIR*= 0.96% NIR**= 1.17%Crown= 54.35%

Primary Forest (Quercus)TOC= 3.92%

NIR*= 3.97% NIR**= 4.02%Crown= 89.37%

*Valor del NIR de la misma muestra de referencia TOC (punto 0). **Valor promedio del NIR en las muestras aledañas (puntos 1 al 8)

Reference Values for NIRVIS and Near Infrared Sensors Schimadzu TOC 5050 and TOC VCSN Carbon and Nitrogen

TOC for main sites (12,500 already) and NIR for the repetition sites.

Important: No samples is thrown away.

All are conserved for further analyses.

Reference Values for NIRCross Validation References into Values TOC vs Field Parameters

TIPOC- INTERVALO DE CARBONO. COUNT- NUMERO DE MUESTRAS. MIN_TOC100- VALOR MINIMO DE CARBONO TOC. MAX_TOC100- VALOR MAXIMO DE CARBONO TOC. AVE_TOC100- VALOR PROMEDIO DE CARBONO TOC. AVE_ALTITU-PROMEDIO DE ALTITUD. AVE_PENDIE- PROMEDIO DE PENDIENTE. AVE_MAX_AR- PROMEDIO DE ARBOLES TOTALES EN EL SITIO DONDE SE HIZO EL ESTUDIO (3,4,2,1). AVE_AVE_DI- PROMEDIO DEL DIAMETRO NORMAL EN EL SITIO. AVE_AVE_AL. PROMEDIO DE LA ALTURA TOTAL EN EL SITIO. AVE_AVE_DI- PROMEDIO DEL DIAMETRO DE COPA EN EL SITIO (SOLO ARBOLES MAYORES A 7.5CM DN). AVE_SUM_DI- PROMEDIO DE LA SUMA TOTAL DE LOS DIAMETROS DE COPA EN EL SITIO (SOLO ARBOLES CON DN> 7.5CM). AVE_AVE_HO- PROMEDIO DE LA SUMA DE COMBUSTIBLES MENORES (1 A 100 HORAS) EN EL SITIO. AVE_SUM_DO- PROMEDIO DE LA SUMA DEL DOSEL ENCONTRADO EN EL SITIO EN LOS TRANSECTOS DE LOS SUELOS. AVE_L- PROMEDIO DE MATIZ DEL COLOR (0= NEGRO ABSOLUTO, 100= BLANCO ABSOLUTO). LAS VARIABLES MAS RELACIONADAS CON EL AUMENTO DE CARBONO SON EL DIAMETRO DE COPA, EL NUMERO DE COMBUSTIBLES MENORES Y EL COLOR.

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22mil spectra actually (Dic 2013)

A segment of samples are analyzed in triplicate

to minimize sampling errors. Spectra were

stored as the log 1/R. R= reflectance.

SPECTRAL COLLECTION

Thermo Nicolet con Sphera integradora

NIR Process

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SPECTRAL COLLECTION

Complet band: 400-2400 nm

Most frecuent band: 1100-2000 nm

with intervals each 2 nm y 32 scans

for both (references and samples)

All samples have a detailed field

references.

NIR Process

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NIR ProcessSPECTRAL COLLECTION

No. 12330-S1-06Sin coberturaCO= 0.01

No. 13024-S1-01400 msnmScrub (MET)CO= 0.42%

No. 18-S2-011447 msnmPinus (BP)CO=3.15%

Analyze available

data and identify

key correlations.

Data Manipulation

+PRINCIPAL COMPONENTS ANALYST (PCA)

For detect outliers

For separation of analytical and spectral data into orthogonal

components whose linear combinations approximated the

original data to a select degree of accuracy (Naes et al 2002)

+H STATISTIC (MAHALANOBIS DISTANCE)

For detect anomalous spectra

H-value >3, is not belonging to the population from wich the

ecuations were development.

Note

Next prediction is only for controlled

experiments (n<100) at local scales Mexico

preliminarities of the national exercise.

Later we will present the predictions for more

large scales.

Chemometric Analysis

For calibration equations -> two methods of regression are

used.

+MPLS. Modified partial least squares.

Better than PSL algorithm -> for standarize residuals of each

wavelenght. (Martens and Naes, 2006).

Prediction error for soils:+/- 0.45 gC/kg y +/-0.09g N/kg

+DPLS. Discriminant partial least squares.

Found colinear variables, data noise, overlapped classes. If

DPLS=2 (perfect relation), DPLS=1 (no relation).

Scatter correction

+MSC. Multiplicative scatter correction, assumed to be

caused by particle-size differences.

+Detrend (DT). Reduce confounding effects of basiline shift

and curvature on spectral differences, in addition to

multicolinearity. (Dahoa, Lister and Barnes, 1995).

+Tratamiento adicional (four digits)

First, Number of derivates

Second. Is the gap over wich the derivative is calculated

Third, number of data points

Fourth, is the second smooting

Calibration Evaluation

+Internal Evaluation

RATIO-PERFORMANCEDEVIATION (RPD)

RPD= St deviation reference / St error predictor (SEP) of NIR

model. If RPD > 2.5 , Very well!

Calibration Evaluation

+External evaluation

Calibrations with new references (TOC) are compared, and

residual analysis is applied, and apply the RMSE (Root

Means Standart Errors) and the t Student test.

t>= 2.5, samples removed!!!!

Where,

t= residuals / error prediction o error of cross validation

(SECV)

Iterations Field-Laboratory-SIG

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Conglomerate16519-S1With 8 spectra in topsoilc/2m distance

The overtones really explain the differences between soils?

What differences are more reliable to explain the use of NIR?

There are consistent with soil micromorphology?

There are opportunities to link this information to the satellite radiometry?

Efforts

Programa Mexicano del Carbono

Colegio de Postgraduados

Comisión Nacional Forestal

Instituto Nacional de Estadística y Geografía

Delaware University

Knowledgements

Join Research Centre

Université Catholique de Louvain La Neuve

Organización de las Naciones Unidas para la

Agricultura y la Al

Roma, December 04th 2013