Abstract
Spatial characterization of soil variability at a fine scale is a real need in the developing country where details information on chemical and physical soil properties are not often available. The objective of this study was to evaluate the SOC mapping by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the variability of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results of the study, carried out in a Sahelian region of Senegal, showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression . Carbon variability was also detailed scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression . Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon . The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Senegal.
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- ESA:
-
European Space Agency
- FAO:
-
Food and Agriculture Organization of the United Nations
- GDP:
-
Gross domestic product
- GPS:
-
Global positioning system
- INP:
-
Institut National de Pédologie
- LUCC:
-
Land use and cover change
- MAER:
-
Ministère de l’Agriculture et de l’Équipement Rural
- MLR:
-
Multiple linear regression
- NDVI:
-
Normalized difference vegetation index
- NDWI:
-
Normalized difference water index
- OK:
-
Ordinary kriging
- PLSR:
-
Partial least square regression
- RMSD:
-
Root mean square deviation
- RS:
-
Remote sensing
- SOC:
-
Soil organic carbon
- SWIR:
-
Short wave infrared
- USDA:
-
United States Department of Agriculture
- UTM:
-
Universal transverse mercator
- WRB:
-
World reference base for soil resources
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Loum, M., Dieye, A.B., Ndiaye, M., Mendy, F., Sow, S., Diagne, P.N. (2019). Remote Sensing and Sustainable Management of SOC in the Sahelian Area. In: Lal, R., Francaviglia, R. (eds) Sustainable Agriculture Reviews 29. Sustainable Agriculture Reviews, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-26265-5_5
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