Flood Extent Mapping Using Optical Remote Sensing Indices: A Case Study of the Upper Medjerda River Basin (Northwestern Tunisia)

Floods represent one of the most frequent and damaging natural hazards in northwestern Tunisia, particularly along the Upper Medjerda River Valley, which has experienced several major flood events in recent decades. Accurate and timely mapping of flood extent is therefore essential for effective risk management and decision-making. This study investigates the capability of optical remote sensing data for flood extent mapping using Landsat-8 imagery acquired before and after the February 2015 flood event. Several widely used spectral water indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), Normalized Difference Moisture Index (NDMI), and Water Ratio Index (WRI), were applied and compared to assess their effectiveness in discriminating flooded areas. The results indicate that indices incorporating the short-wave infrared (SWIR) band, particularly the MNDWI, provide improved delineation of flooded surfaces with sharper boundaries and reduced confusion with irrigated agricultural land and built-up areas. In addition, a decision-tree-based approach combining MNDWI, the Soil Adjusted Vegetation Index (SAVI), and the Normalized Difference Built-up Index (NDBI) was implemented to enhance flood extent extraction by minimizing misclassification related to vegetation and urban surfaces. The findings demonstrate the relevance of freely available Landsat-8 data and spectral index-based approaches for rapid flood mapping in semi-arid environments. This methodology can be transferred to similar flood-prone regions and provides a practical tool for supporting flood risk assessment and management strategies.