Soil total carbon mapping, in Djerid Arid area, using ASTER multispectral remote sensing data combined with laboratory spectral proximal sensing data

Abstract

Spatial quantification of soil attributes is needed to assess and monitor soil resources. Our objective was to map soil total carbon (TC) over 580 ha bare soils, in Djerid arid area (SW Tunisia). One hundred and forty-four soil samples were collected in nodes of 200 m square grid and their spectra acquired in the laboratory at 400–2500 nm have served to radiometrically correct ASTER image using the empirical line method. TC was predicted using partial least squares regression-kriging model, based on the 144 spectra extracted from the nine visible-near infrared ASTER bands. Residual interpolation has improved prediction efficiency. Indeed, PLSR-kriging achieved higher R2 and lower RMSE than PLSR, respectively: 0.78 against 0.53 and 0.52% against 0.16%. Furthermore, spatial distribution of these quantifications has a physical significance. Our results offer relevant method for soil attribute mapping on large scale.