Soil class = abstract = the name from

which we retrieve various

Characteristics related with the dynamics

(water and nutrients) of the body of soil

Point

Soil Spectroscopy to monitor the state of soil

resources in the present and in the future

José Alexandre M. Demattê University of São Paulo, Brazil

jamdemat@usp.br

facilitate

Necessity: Food and Water

Precision agriculture

*Land use and Environment planning

*Soil and water Conservation

*Choose adequate areas for residue

disposal

*Allocation of the adequate plant in

the adequate soil with higher

Productivity

Can spectroscopy reach all this? Why?

Soil Attributes maps

Soil Class maps

Oxisol, Ultisol…

From one information =

several information

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

400 900 1400 1900 2400 2900

Refle

ctân

cia

Comprimento de onda (nm)

LR

Monitor soil quality

GENERAL

APPLICATIONS –

RESEARCH RESULTS

Soil hydrocarbons monitoring in petroleum installation by spectroscopy and hyperspectral image

Brazilian, Remote Sensing Simposium 2013, Foz do Iguacu, Brazil.

Pabon et al. (2013)

Jean-Philippe et al.

low

high

Geomorphological analyses coupled with Vis–NIR spectroscopy and geostatistics allowed to compare the

spatial distribution of SOM and water erosion processes in the Turbolo catchment.

Brazilian Team Research

2100 2150 2200 2250 2300 2350 2400 2450

Refle

câtn

cia -

contin

uo r

em

ovi

do

0.75

0.80

0.85

0.90

0.95

1.00

Comprimento de onda (nm)

2100 2150 2200 2250 2300 2350 2400 2450

0.88

0.90

0.92

0.94

0.96

0.98

1.00

2100 2150 2200 2250 2300 2350 2400 2450

0.86

0.88

0.90

0.92

0.94

0.96

0.98

1.00

K

Mg

Soil Chemistry: potassium

detection

Mineralogy

range

Ramirez-Lopes & Demattê (EJSS, in submission 2014)

HO

HO

Al+++

Al+++

Al+++

K+

K+

K+

K+

K+

K+ K+

K+

K+

R2 = 0,72

Soil chemical management: liming application

y = 1,0101x + 0,1685R² = 0,952

RMSE = 0,46

0

2

4

6

8

10

0 2 4 6 8 10

NC

est

imad

a (

t h

a-1

)

NC determinada (t ha-1)Determined Lime Necessity

Est

imate

d L

ime N

eces

sity

SLAUGHTERHOUSE

Blood goes to a drain

Wrong way

Soil chemical alteration due to slaughterhouse waste application as identified by spectral reflectance: an environmental

useful tool, J.A.M. Demattê, L.Ramirez Lopez, S.A. Araujo, F.S. Terra, J.C. Oliveirad, S.M.F. Maia, F.F.C. Mello

Submmission Geoderma, 2014

We have to monitor this!!

Soil samples were incubated with different doses of blood, and taken their spectra.

Sodium: altered from 0.8 to 9.8 mmolc dm-3

Different

concentration

Wavelength, nm

500 750 1000 1250 1500 1750 2000 2250

Refle

ctan

ce

0.0

0.1

0.2

0.3

0.4

0.5

33 mg kg-1

6729 mg kg-1

Wavelength, cm-1

40080012001600200024002800320036004000

Ref

lect

ance

0

2

4

6

8

10

12

68 mg kg-1

207 mg kg-1

1990 mg kg-1

6068 mg kg-1

Investigating Cr in soils applied with tannery sludge by Vis-Nir

and Mid-IR spectroscopy techniquesAraujo, S.; Vicente, S.; Demattê, J.A.M. (2014, submission Remote Sensing Environment)

0

600

1200

1800

2400

3000

3600

0 600 1200 1800 2400 3000 3600

Rv2 = 0.85

RMSEv= 324Rm

2 = 0.74

RMSEm= 408

Laboratory soil analysis Vis-NIR MID-IR

Determing soil properties in Amazonian Dark Earths by vis-nir-mid spectroscopy

S.R. Araújo, J.A.M. Demattê, M. Söderström, J.Eriksson, C.Isendahl – In submission EJSC

Exploratory (1:1.000.000)

Recognaissence (1:500.000)

Semidetailed (1:100.000)

Detailed (1:20.000) Most important for Agriculture

0,25%

mapped

500 samples

Areia % - Mapa digital

0 20 40 60 80 100

Are

ia %

- M

apa e

spectr

o-d

igital

0

20

40

60

80

100

Argila % - Mapa digital

0 20 40 60 80

Arg

ila %

- M

apa e

spectr

o-d

igital

0

20

40

60

80R

2

Ajust.= 0.96

RMSE = 3.26ME = -0.22m = 0.87Intercesção = 3.03

R2

Ajust.= 0.97

RMSE = 4.38ME = -0.04m = 0.86Intercesção = 10.03

A. B.

f = 8.06 + 281719.27 (1+e-(x+60.03)/-6.36)-1

R2Adj.= 0.85

Tamanho do grupo de amostras de calibração

0 100 200 300

SD

E -

SB

6

8

10

12

14

16

18

20

22

95% Intervalo de confiança

f = 1.88 + 0.02(1-e-0.017x)-2.37-0.017x)-2.37

R2Adj.= 0.98

Tamanho do grupo de amostras de calibração

0 100 200 300

SD

E -

Mg

1

2

3

4

5

6

7

95% Intervalo de confiança

f = 5.99 +1207029.3(1+e-(x+284.7)/-23.2)-1

R2Adj.= 0.81

Tamanho do grupo de amostras de calibração

0 100 200 300

SD

E -

Ca

5

6

7

8

9

10

11

12

95% Intervalo de confiança

f = 6.80 + 2.08 (1+e(-(x+77.53)/-24.34))-1

R2Adj.= 0.85

Tamanho do grupo de amostras de calibração

0 100 200 300

SD

E -

Are

ia

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

95% Intervalo de confiança

f = 5.86 + 1.07 (1+e(-(x+78.32)/-7.12))-1

R2Adj.= 0.85

Tamanho do grupo de amostras de calibração

0 100 200 300

SD

E -

Arg

ila

5.0

5.5

6.0

6.5

7.0

7.5

95% Intervalo de confiança

A B

DC

E

Detailed map

Lab: 500

Sensor: 166

Spatial attributes vs Optimal number of soil samples

6

16

26

36

46

56

66

10

30

50

70

90

N

0 1000 2000 3000 m

Argila, %

Areia, %

A. B.

C. D.

Sand

Clay

Observed Predicted

Ramirez-Lopes , Demattê, J.A.M., Terra, F.S. (2013- in submission Geoderma)

Wavelength, nm

Ref

lect

ance

fac

tor

Quantitave texture determination by spectral reflectance in Brazilian soils

for soil management M.H.D. Franceschini, M.V. Sato, J.A.M. Demattê, L.E.Vicente, C.R. Grego

In submission Brazilian Journal of Soil Science, 2013

Very clayey (>60% clay)

Clayey (35-60% clay)

Medium Clay (25-35% clay)

Very Sandy (<10% clay)

Sandy (10-15% clay)

Medium Sandy (15-25% clay)

- From top flat toward first shoulder

- Similar shape “in depth”)

low reflectance (rectilinear and flat ↑ weathering

-

Hematite

Hematite

Goethite

Crystalline

Crystalline

Crystalline

J.A. M. Demattê; F.S. Terra. Spectral Pedology: a new perspective on evaluation of soils along pedogenetic alterations. Geoder. 2014

- Shallowest unit (hillside): poor drainage

- Different intensities

- - Higher content of weathering minerals

Hematite

Soil profile spectral transformation along the toposequence (≠ pedogenesis)

- Foothill and second less steep hillside: restricted drainage (lower landscape position)

- Typic Argiudoll: argillic horizon (lessivage) and influence of Diabase

- Typic Hapludoll: feature at 1,900 nm larger than others (2:1 mineral predominance)

Hematite

Goethite

More crystalline

Less crystalline

Soil profile spectral transformation along the toposequence (≠ pedogenesis)

- 1st Terrain (flat depression): restricted drainage and accumulation of materials

- Slow weathering (large contribution of montmorillonite and vermiculite)

Intermediate crystallinity

Goethite

STRATEGIES

FOR SOIL CLASSIFICATION AND SOIL MAPPING

0,0

0,1

0,2

0,3

0,4

1 2 3 4 5 6Ban ds

Re

fle

cta

nc

e f

ac

tor

b.

different intensit ies

variat ions on the sp ect ral curve

tendency due to soil p rop erties

(mainly granulometric

and iron content differences)

AQ

PV

LE

PE

LR

T R

7

0 ,0

0 ,1

0 ,2

0 ,3

0 ,4

3 5 0 6 5 0 95 0 12 5 0 1 5 5 0 1 8 5 0 2 1 5 0 2 4 5 0

W av elen gth , n m

Re

fle

cta

nc

e f

ac

tor

p os it ive t endency

horiz ontal to negat ive t endency

a.

variat ion in the content and forms of iron

AQ

PVLE

PE

TR

LR

0 ,0

0 ,1

0 ,2

0 ,3

0 ,4

1 2 3 4 5 6

B a n d s

Re

fle

cta

nc

e

c .

c la y e y s o ils :

lo w e r in t e n s ity

s a n d y s o ils :

h ig h e r in t e n s ity A Q

P V

L i

L R

T R

P L

P E

L E

L V

7

Laboratory

vs Landsat

Demattê et al. (2009)

0.67Clay

0.72Fe2O3

0.9

0.95

clay

FeO2

Nanni and Demattê (2006) SSSAJ

Calibration of models

SPATIAL PREDICT OF CLASSES

THROUGH THE FUSION OF

INFORMATION

Elevation

Mean curvature

Inclination

etc...

TM Images

(Inf. superficial)

Band 1

Band 2

Band 3

...

Band 7

Fe2O3

TiO2

Ki

etc...

Parameter extraction

of relief by MDE

Coupling spectrally predicted soil weathering attributes, terrain parameters and satellite data for predictive mapping of tropical soils

J.A.M. Demattê, L.Ramírez-López, P.R. Fiorio. (Submitted Geoderma 2013)

Traditional map Predicted map

69% global accurracy

Demattê, J.A.M., Ramires-Lopez, L.; Fiorio, P.R. (2013-on going Geoderma)

Without EPO With EPO

RMSEP [g kg-1] 130 69

Bias [g kg-1] 19 95

RPD 2.0 1.0

Yufeg Ge and Cristine Morgan

showing the p-matrx can be used to correct field moist intact scans of soils

(Geoderma-in press 2014).

Jason Ackerson, Cristine Morgan Yufeng Ge, Budiman Minasny, and

Alex McBratney

Extraction of Water interference on soil spectra

(External Parameter Orthogonalization)

moist

Dried

EPO makes

correction

Soil properties mapping using Hyperespectral Spectir-image. Franceschini, M., Dematte, J.A.M. In submission 2014

- Bare Soil- Photosynthetic vegetation- Non-Photosynthetic vegetation

(a) Soil sampling in bare

soil sites (visual analysis)

(b) Estimation of fractional cover

using hyperspectral images

(c) Spectra near

sampling points

selected in pixels with

> 70% of bare soil

(d) Partial least squares

regression using spectra

extract from image as

predictor of soil properties

CTC 0.73 8.19 1.94 0.80 7.95 2.29

Clay 0.58 46.48 1.56 0.73 41.09 1.96

Sand 0.66 43.67 1.73 0.73 45.77 1.96

Calc. 0.44 7.31 1.36 0.60 6.40 1.61

Magn. 0.51 2.07 1.45 0.52 1.79 1.47

OM 0.53 2.85 1.47 0.47 3.86 1.40

Cross-valid.

(n = 60)Independent data

(n = 29)

R2 RMSE RPD R2 RMSE RPD

n = 89 samples

Vis-Nir: 357 bands

The first test with an on-the-go spectral sensor in Brazilian soils for liming. Franceschini, M.,

Dematte, J.A.M. –submission 2014

(a) Field plots where lime

was applied in variable rates

Lime Requirement (LR)

calculation = soil saturation

by cations

(b) On-the-go spectral measurements

and soil sampling

(c) Partial least squares

regression using spectra

measured on-the-go as

predictors

pH OM Calc. Magn. CTC V LR

R2 0.43 0.43 0.29 0.48 0.33 0.38 0.35

RMSE 0.51 1.97 8.78 4.51 6.15 14.47 1.36

RPD 1.34 1.34 1.20 1.40 1.23 1.28 1.25

Validation with independent data (n = 50)

n = 150

samples

USING A MOBILE REAL-TIME SOIL VISIBLE-NEAR INFRARED SENSOR FOR HIGH RESOLUTION SOIL PROPERTY MAPPING

Masakazu Kodaira, Sakae Shibusawa Geoderma, v. 199, p. 64-79, 2013.

Number of sample in database per state

Number of Institutions Involved 32

PA: 253

MA:400

MT : 2000

TO:3

GO: 10000

MG: 1550

DF: 50

MS: 3000

SP: 15.000

RS: 2000

AM: 200

AP: 1000

PB

PE:300

RJ

PR:4000

AC

SC: 1410

In

process

50 Researchers and Collaborators

Involved

Dinamics to use the

Data and a document

With agreement between researchers

11000 samples

Real time detection of soils

contamination at fieldReal time soil

mapping at field

Laboratories of soil analysis by spectral sensing with traditional methods

Real time on the go

spectral sensing

Merge spectroscopy

With other

parameteres

Real time detection by

satellite hyperspectral

sensing from orbital

or aerial data

Final considerations

Merge with Field. Most answers are there!

Continue efforts on statistics

Believe in Spectroscopy.

We are on the correct track

IMPORTANCE OF THE BIG PICTURE

SOIL MAPS DERIVED FROM THIS TECHNOLOGY FOR MORE FOOD

WITH SOIL ENVIRONMENT QUALITY

Thank you