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High resolution mapping of soil carbon in arid environment by regression-kriging combining ground based spectrometric data and Aster images.

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HAL Id: hal-00729678

https://hal-agrocampus-ouest.archives-ouvertes.fr/hal-00729678

Submitted on 6 Mar 2013

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High resolution mapping of soil carbon in arid environment by regression-kriging combining ground

based spectrometric data and Aster images.

Hamouda Aichi, Youssef Fouad, Christian Walter, Zohra Lili Chabaâne, Hervé Nicolas, Mustapha Sanna

To cite this version:

Hamouda Aichi, Youssef Fouad, Christian Walter, Zohra Lili Chabaâne, Hervé Nicolas, et al.. High

resolution mapping of soil carbon in arid environment by regression-kriging combining ground based

spectrometric data and Aster images.. European Geosciences Union (EGU) General Assembly, May

2010, Vienne (AUT), Austria. �hal-00729678�

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Geophysical Research Abstracts Vol. 12, EGU2010-9055, 2010 EGU General Assembly 2010

© Author(s) 2010

High resolution mapping of soil carbon in arid environment by

regression-kriging combining ground based spectrometric data and Aster images

Hamouda Aïchi (1,2,3), Youssef Fouad (1,2), Christian Walter (1,2), Zohra Lili Chabaane (3), Hervé Nicolas (1,2), and Mustapha Sanaa (3)

(1) Agrocampus Ouest, UMR 1069, Sol Agro et hydrosystèmes Spatialisation, Rennes, France([email protected]), (2) INRA, UMR 1069, Sol Agro et hydrosystèmes Spatialisation, Rennes, France([email protected]), (3) Institut National Agronomique de Tunisie (INAT), Tunis, Tunisie

Spatial quantification of soil properties is required to manage and monitor soil resources. Our aim was to set

up a soil properties mapping approach, by means of Partial Least Squares Regression-kriging (PLSR-kriging),

while combining information contained in the 9 Visible-Near infrared spectral bands of an ASTER image and a

collection of 144 soil samples spectra acquired in laboratory across the 400-2500 nm range. This approach was

tested in the Djerid arid region (SW Tunisia) to map total carbon (totC) over 580 ha of bare soils. Surface soil

samples were collected across nodes of a 200 m squared grid, dried and sifted to 2 mm, before we analysed totC

and acquired Vis-NIR spectra. We calibrated a PLSR model, based on the 144 spectra derived from the Aster

image, previously corrected radiometrically with the empirical line method. This model, when applied to the 9

bands of the sub-image, generates a first spatial quantification of totC. Residues have been calculated at sampled

grid nodes. The interpolation of residues by ordinary kriging produces a raster layer which, when added to the

first layer, offers a final prediction map. Residuals interpolation has improved PLSR prediction accuracy. Indeed,

we obtained respectively (R

2

= 0.78 and RMSE = 0.16%) versus (R

2

= 0.53 and RMSE = 0.52%). Furthermore

the spatial distribution of these quantifications has a physical significance. From our results emerge interesting

perspectives for mapping soil carbon over large territories in arid environment.

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