%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Gasmi, A.
%A Gomez, Cécile
%A Chehbouni, Abdelghani
%A Dhiba, D.
%A Elfil, H.
%T Satellite multi-sensor data fusion for soil clay mapping based on the spectral index and spectral bands approaches
%D 2022
%L fdi:010084516
%G ENG
%J Remote Sensing
%K spectral index ; spectral band ; multispectral remote sensing ; multi-sensors data fusion ; digital soil mapping ; clay content
%M ISI:000771442800001
%N 5
%P 1103 [22 ]
%R 10.3390/rs14051103
%U https://www.documentation.ird.fr/hor/fdi:010084516
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2022-04/010084516.pdf
%V 14
%W Horizon (IRD)
%X Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands' images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.
%$ 126 ; 068