Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches - Archive ouverte HAL Access content directly
Journal Articles Remote Sensing Year : 2022

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

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Abstract

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.
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Dates and versions

hal-03708710 , version 1 (29-06-2022)

Licence

Attribution - CC BY 4.0

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Anis Gasmi, Cécile Gomez, Abdelghani Chehbouni, Driss Dhiba, Hamza Elfil. Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches. Remote Sensing, 2022, 14 (5), ⟨10.3390/rs14051103⟩. ⟨hal-03708710⟩
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