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@article{Altoom_Adam_Jackson_2025, title={Mapping cropland types in heterogeneous arid environments using machine learning algorithms and dataset variations on Google Earth Engine}, volume={11}, url={https://www.updatepublishing.com/journal/index.php/jaa/article/view/9024}, DOI={10.25081/jaa.2025.v11.9024}, abstractNote={<p>Monitoring crops ensures global, regional, and national food security. It entails gathering information such as the health and growth of crops and environmental conditions. This aids farmers in making well-informed decisions, enhancing productivity, and reducing environmental impact. This, in turn, leads to improved economic outcomes and long-term agricultural sustainability. In arid and semi-arid lands, effective crop monitoring is particularly critical due to the limiting factor of water availability. This study used Sentinel-2 (S2) image collection in Google Earth Engine (GEE) to mapping crop types in North Darfur State, Sudan during the 2022 growing season (July 1 to September 30, 2022), using support vector machine (SVM) and random forest (RF) classifiers. Eight vegetation indices (VIs), i.e., normalised difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), green-normalised difference vegetation index (GNDVI, weighted Difference vegetation index (WDVI), red edge NDVI (NDVIre), ratio-vegetation index (RVI), and normalised difference infrared index (NDII) were used as additional bands. The results show that the RF models produced an overall accuracy (OA) of 90-97% with a kappa coefficient (κ) of 0.87-0.96. The SVM models reported OA and κ values in the range of 84-95% and 0.81-0.94, respectively. Producer’s (PA) and user’s accuracies (UA) were in the range 83-97% and 81-98%, respectively. Highest F1 scores for both classifiers were 0.98. The findings of this study, along with the derived classification maps, would enable farmers, policymakers, and other stakeholders to make well-informed decisions regarding agricultural production, land use planning, and resource management in North Darfur and arid environments.</p>}, journal={Journal of Aridland Agriculture}, author={Altoom, Mohammed B. and Adam, Elhadi and Jackson, Colbert M.}, year={2025}, month={Feb.}, pages={1–16} }