World Congress of Soil Science Logo 18th World Congress of Soil Science
July 9-15, 2006 - Philadelphia, Pennsylvania, USA
International Union of Soil Sciences

Monday, 10 July 2006 - Friday, 14 July 2006
131-9

This presentation is part of 131: 1.5A Diffuse Reflectance Spectroscopy, Soil Sensing, Remote Sensing and Image Analysis - Poster

Remote Sensing and Elevation Models on the Determination of Soil Attributes Contents.

ALINE M. GENÚ1, JOSÉ ALEXANDRE M. DEMATTÊ2, JOSÉ GERALDO de A. SOUSA Jr.3, and RODNEI RIZZO3. (1) University of São Paulo (USP), Escola Superior de Agricultura " Luiz de Queiroz" (ESALQ), Department of Soil Science and Plant Nutrition, Av. Padua Dias, 11 - CP 09 , 13.418-900, Piracicaba, SP, Brazil, (2) University of São (USP), Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ), Department of Soil Science and Plant Nutrition, Av. Padua Dias, 11 – CP 09, 13.418-900, Piracicaba, SP, Brazil, (3) University of São Paulo (USP), Escola Superior de Agricultura “Luiz de Queiroz” (ESALQ), Department of Soil Science and Plant Nutrition, Av. Padua Dias, 11 – CP 09, 13.418-900, Piracicaba, SP, Brazil

REMOTE SENSING AND ELEVATION MODELS ON THE DETERMINATION OF SOIL ATTRIBUTES CONTENTS

 

Aline M. Genú; José Alexandre M. Demattê; José Geraldo de A. Sousa Junior; Rodnei Rizzo

 

Department of Soil Science and Plant Nutrition, University of São Paulo - Escola Superior de Agricultura “Luiz de Queiroz”

Av. Padua Dias, 11 – PO Box 09, 13.418-900, Piracicaba, São Paulo – Brasil

amgenu@esalq.usp.br; jamdemat@carpa.ciagri.usp.br; jgsousa@esalq.usp.br; rodneir@hotmail.com

 

 

The determination of soil constituents and its contents is a challenge for researches. It is true that the quantification of soil attributes by a quick and non-destructive method with environmental quality is desired. Most models takes in account remote sensing methods, and others use the terrain models, but both separated. Thus, the present work has the objective to determine soil attributes contents by using a model with spectral (orbital ASTER image) and terrain (Digital Elevation Model) data together.

The study area is located in Rafard, SP, Brazil where 184 points were georeferenced and sampled. These samples were analyzed in laboratory for chemical, granulometric and mineralogical evaluation. The reflectance data was obtained from ASTER image (bands 1 to 8) and the DEM from contour lines by linear interpolation, both using the software ENVI. Afterwars, the terrain attributes (elevation, slope, and aspect) were generated for the entire area. All the informations, reflectance and terrain attributes, were collected at the same points at the field. Using spectral reflectance data, elevation, slope, and aspect were generated a linear multiple regression equation at software SAS for each chemical, mineralogical and granulometric attribute. The dependent variables were selected by a forward stepwise method.

The results show that mineralogical (SiO2, Fe2O3 and TiO2) and clay attributes obtained the best R2 and, all of them have at least one terrain attribute in its equation. CEC also presented a good R2 however, only ASTER bands were selected for its equation and was the only chemical attribute with a significative equation. Other researches obtained similar results using only orbital data (Coleman et al., 1993) or using geostatistical approaches (Odeh et al., 1995). Based on this result, it is possible to say that mineralogical and clay content attributes can be quantify through terrain and remote sensing data as to assist traditional soil mapping and soil laboratory analysis. Table 1 - Multiple regression equations to estimate soil attributes using spectral (ASTER bands) and topographic data.

Attribute

Model6

R2

Ca

29.37 - (490.32*B1) + (772.07*B4) + (673.04*B6) - (1392.27*B8)

0.4261

Mg

15.58 + (230.03*B4) + (251.81*B6) - (554.51*B8)

0.3452

H + Al1

85.72 + (595.63*B2) - (347.57*B3) - (214.58*B4)

0.2211

OM2

68.58 - (102.85*B5) - (0.07*EL)

0.2527

S3

40.59 - (584.73*B1) + (1089.18*B4) + (945.52*B6) - (2001.93*B8)

0.4181

CEC4

154.65 + (991.74*B2) - (956.42*B3) + (973.79*B4) + (1031.94*B6) - (2267.81*B8)

0.5122

m%5

8.97 + (467.73*B2) - (302.27*B5)

0.0841

SiO2

24.91 - (1301.66*B1) + (1890.66*B2) - (891.89*B3) + (1222.74*B4) - (1124.56*B5) + (735.81*B6) - (1243.10*B8) + (0.28*EL) - (0.04*AS)

0.5796

Fe2O3

(-23.01) - (2120.66*B1) + (1556.15*B2) - (472.42*B8) + (0.42*EL) - (0.06*AS)

0.5751

TiO2

6.42 - (423.75*B1) + (202.09*B2) + (0.05*EL) - (0.01*AS)

0.5086

Clay

109.39 - (5473.10*B1) + (5137.16*B2) - (2353.59*B8) + (1.18*EL) - (0.14*AS)

0.5186

Sand

(-133.03) - (2988.14*B2) + (4871.70*B8) + (0.22*AS)

0.3832

Silt

1064.18 + (3407.15*B1) - (3018.46*B7) - (1.18*EL)

0.1942

1 Soil acidity; 2 Organic matter; 3 Sum of bases; 4 Cation exchange capacity; 5 Aluminum saturation; 6 B1….B8: ASTER bands, EL: elevation, AS: aspect. References COLEMAN, T.L.; AGBU, P.A.; MONTGOMERY, O.L. Spectral differentiation of surface soils and soil properties: is it possible from space platforms? Soil Science, 155:283-293, 1993.

ODEH, I.O.A.; McBRATNEY, A.B.; CHITTLEBOROUGH, D.J. Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma, 67: 215-226, 1995.


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