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https://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/169085Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.creator | Gasmi, A. | - |
| dc.creator | /Gomez, Cécile | - |
| dc.creator | /Chehbouni, Abdelghani | - |
| dc.creator | Dhiba, D. | - |
| dc.creator | Elfil, H. | - |
| dc.date | 2022 | - |
| dc.date.accessioned | 2022-04-27T17:37:46Z | - |
| dc.date.available | 2022-04-27T17:37:46Z | - |
| dc.identifier | https://www.documentation.ird.fr/hor/fdi:010084516 | - |
| dc.identifier | oai:ird.fr:fdi:010084516 | - |
| dc.identifier | Gasmi A., Gomez Cécile, Chehbouni Abdelghani, Dhiba D., Elfil H.. Satellite multi-sensor data fusion for soil clay mapping based on the spectral index and spectral bands approaches. 2022, 14 (5), p. 1103 [22 p.] | - |
| dc.identifier.uri | http://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/169085 | - |
| dc.description | 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. | - |
| dc.language | EN | - |
| dc.subject | spectral index | - |
| dc.subject | spectral band | - |
| dc.subject | multispectral remote sensing | - |
| dc.subject | multi-sensors data fusion | - |
| dc.subject | digital soil mapping | - |
| dc.subject | clay content | - |
| dc.title | Satellite multi-sensor data fusion for soil clay mapping based on the spectral index and spectral bands approaches | - |
| dc.type | text | - |
| Aparece en las colecciones: | Institut de Recherche pour le Développement - IRD - Cosecha | |
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