Mohamed Yusuf
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Mohamed Yusuf has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Elias Cherenet Weldemariam
Department of GIS, Haramaya University, Dire Dawa P.O. Box 138, Ethiopia
1 shared publication - Esie Gebrewahd Gebre
School of Geography and Environmental Studies, Haramaya University, Harar, Ethiopia
1 shared publication - Firew Bekele Abebe
Department of Natural Resource Management, Haramaya University, Dire Dawa P.O. Box 138, Ethiopia
1 shared publication - Priyakant Sinha
Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
1 shared publication - Samuel Feyissa
Haramaya University, Dept. of Natural Resource Management, P.O Box138, Dire Dawa, Ethiopia, Ethiopia
1 shared publication
Latest Publications
- Open Access
- 28 October 2025
- Published by MDPI AG in Preprints.org
Mapping Soil Organic Carbon Potential Using Multisource Remote Sensing Indicators in Khat (Catha edulis) Dominated Landscapes in Eastern Ethiopia
Soil organic carbon (SOC) is a key component of terrestrial ecosystems, serving as an energy source for soil microorganisms and playing an essential role in climate regulation and ecosystem productivity. However, SOC stocks are highly influenced by land-use and land-cover changes. This study aims to estimate and map SOC using multispectral Sentinel-2 and RapidEye imagery combined with environmental, soil, and topographic variables across Khat-dominated landscapes in the Haramaya district, eastern Ethiopia. A total of 88 soil samples were collected and analyzed in the laboratory for organic carbon estimation. Two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGB), were applied to predict SOC, and their performances were evaluated using the coefficient of determination (R²), root mean square error (RMSE and mean absolute error (MAE). Laboratory-measured SOC values ranged from 0.83% to 3.9%. Both satellite datasets produced comparable predictions, with Sentinel-2 estimating slightly higher mean SOC values (~40mg/ha), while RapidEye(~38mg/ha) provided more spatially detailed and accurate maps due to its finer resolution. Among the algorithms tested, RF outperformed XGB, showing higher predictive accuracy and stability, particularly under heterogeneous landscape conditions. The results suggest that both Sentinel-2 and RapidEye data are suitable for SOC estimation and mapping, with higher-resolution imagery preferred for detailed spatial analysis. Future research should focus on optimising predictor selection and assessing the potential impacts of Khat cultivation on SOC variability and spatial distribution.
- 28 October 2025
- Published by MDPI AG in Preprints.org
Open AccessMapping Soil Organic Carbon Potential Using Multisource Remote Sensing Indicators in Khat (Catha edulis) Dominated Landscapes in Eastern Ethiopia
Soil organic carbon (SOC) is a key component of terrestrial ecosystems, serving as an energy source for soil microorganisms and playing an essential role in climate regulation and ecosystem productivity. However, SOC stocks are highly influenced by land-use and land-cover changes. This study aims to estimate and map SOC using multispectral Sentinel-2 and RapidEye imagery combined with environmental, soil, and topographic variables across Khat-dominated landscapes in the Haramaya district, eastern Ethiopia. A total of 88 soil samples were collected and analyzed in the laboratory for organic carbon estimation. Two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGB), were applied to predict SOC, and their performances were evaluated using the coefficient of determination (R²), root mean square error (RMSE and mean absolute error (MAE). Laboratory-measured SOC values ranged from 0.83% to 3.9%. Both satellite datasets produced comparable predictions, with Sentinel-2 estimating slightly higher mean SOC values (~40mg/ha), while RapidEye(~38mg/ha) provided more spatially detailed and accurate maps due to its finer resolution. Among the algorithms tested, RF outperformed XGB, showing higher predictive accuracy and stability, particularly under heterogeneous landscape conditions. The results suggest that both Sentinel-2 and RapidEye data are suitable for SOC estimation and mapping, with higher-resolution imagery preferred for detailed spatial analysis. Future research should focus on optimising predictor selection and assessing the potential impacts of Khat cultivation on SOC variability and spatial distribution.