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Land cover mapping of marshland areas from satellite images data is not a sim‐ ple process, due to the similarity of the spectral characteristics of the land cov‐ er. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Senti‐ nel 2B by ESA (European Space Agency) were used to classify the land cover of Al ‐Hawizeh marsh/Iraq ‐Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built ‐up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B imag‐ es provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.

geomatics and environmental engineering

An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq