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

Flood Extent Mapping Using Optical Remote Sensing Indices: A Case Study of the Upper Medjerda River Basin (Northwestern Tunisia) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Flood Extent Mapping Using Optical Remote Sensing Indices: A Case Study of the Upper Medjerda River Basin (Northwestern Tunisia) Ahmed Ezzine, Taoufik Hermassi, Emna Kochlef This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8625262/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract 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. Medjerda optical data flood mapping SAR data Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Floods are among the most destructive natural hazards worldwide, causing significant human, economic, and environmental losses each year. Their occurrence and intensity are controlled by a combination of climatic, hydrological, geomorphological, and anthropogenic factors, including extreme rainfall events, land-use changes, river morphology, and urban expansion within floodplains. In recent decades, climate change has further increased the frequency and severity of flood events, particularly in Mediterranean and semi-arid regions (Wade et al.,2004). In Tunisia, flooding represents a major natural risk, especially within the Medjerda River basin, the country’s largest and most important watershed. The Upper Medjerda Valley, extending from Ghardimaou to the Sidi Salem Dam, has experienced several severe flood events over the past decades, notably in 1973, 2003, 2012, and 2015 (Fehri, 2014 ). These events have repeatedly affected agricultural lands, infrastructure, and urban areas, highlighting the urgent need for effective flood monitoring and risk management strategies. Flood hazard assessment and mapping play a crucial role in understanding flood-generating processes, identifying flood-prone areas, and supporting early warning systems and mitigation planning. Traditionally, flood mapping has relied on in situ hydrological measurements and hydraulic modeling; however, these approaches are often limited by sparse gauge networks, high costs, and data availability constraints, particularly in developing regions. Remote sensing has emerged as a powerful and complementary tool for flood monitoring at regional to global scales. Optical and radar satellite data have been widely used to detect inundated areas, monitor flood dynamics, and assess flood impacts. Optical multispectral imagery, in particular, offers valuable information for flood mapping due to its ability to capture spectral differences between water and non-water surfaces. Several studies have demonstrated the effectiveness of spectral water indices derived from visible, near-infrared (NIR), and short-wave infrared (SWIR) bands for surface water detection and flood extent delineation (Albertini et al., 2022 ). Among these indices, 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) are commonly applied for water body extraction. Nevertheless, their performance may vary depending on land cover characteristics, water turbidity, vegetation density, and the presence of built-up areas, often leading to misclassification in complex environments such as agricultural floodplains and urbanized valleys (Bonn et al., 1993). Despite the growing number of studies on flood mapping using spectral indices, comparative analyses under semi-arid Mediterranean conditions remain limited, particularly for North African river basins. Moreover, many applications rely on a single index, which may not adequately address confusion between water, vegetation, and built-up surfaces during flood events. In this context, the objective of this study is to assess the capability of optical remote sensing data for flood extent mapping in the Upper Medjerda River Valley using Landsat-8 imagery. The study compares the performance of several widely used spectral water indices and investigates the added value of a combined index-based approach integrating water, vegetation, and built-up indices through a decision-tree framework. By focusing on a well-documented flood event that occurred in February 2015, this work aims to identify the most suitable spectral index for flood detection in the study area and to propose a transferable methodology for flood mapping in similar semi-arid environments. 2. Material and Methods 2.1 Study Area The Medjerda River is the largest and most important river system in Tunisia, draining a watershed of approximately 23,000 km², of which about 16,400 km² are located within Tunisian territory. The basin accounts for nearly half of the country’s annual surface water resources, with an average inflow of approximately 1,000 Mm³ (Rodier et al., 1981 ). The study area corresponds to the Upper Medjerda River Valley, extending from Ghardimaou to the Sidi Salem Dam (Fig. 1 ). This section of the basin is characterized by a wide alluvial floodplain and a complex hydrographic network that promotes flood expansion and prolonged water stagnation during high-flow events. Several major tributaries contribute to the hydrological regime of the basin, including Wadi Rarai and Wadi Bouhertma on the left bank, and Wadi Mellegue and Wadi Tessa on the right bank. The Upper Medjerda Valley has been affected by recurrent and severe flood events. While floods were historically quasi-cyclical, occurring approximately every ten years, their frequency and intensity have increased in recent decades. Among the most significant events are the floods of March 1973, January 2003, February 2012, and February 2015. This study focuses on the flood event of 22 February 2015, during which peak discharges reached approximately 750 m³/s at Ghardimaou, 470 m³/s at Jendouba, and 600 m³/s at Bou Salem hydrometric stations (Fig. 2 ). 2.2 Satellite Data and Pre-processing To analyze flood extent before and after the February 2015 event, optical satellite imagery was required for dates as close as possible to the flood occurrence. High spatial resolution commercial imagery (e.g., SPOT, IKONOS, QuickBird) was initially considered; however, no suitable acquisitions covering the study area were available during the flood period. As an alternative, freely available Landsat-8 Operational Land Imager (OLI) data provided by the United States Geological Survey (USGS) were used. Two cloud-free Landsat-8 scenes were selected: one acquired before the flood on 13 January 2015 and one acquired after the flood on 2 March 2015. Landsat-8 OLI imagery offers multispectral data with a spatial resolution of 30 m across visible, near-infrared (NIR), and short-wave infrared (SWIR) bands, and a swath width of approximately 185 km, making it suitable for regional-scale flood mapping (Table 1 ). Indeed, as highlighted by Claverie et al. ( 2018 ), the harmonization of Landsat datasets has significantly enhanced the capability for dense time-series monitoring, providing reliable surface reflectance products that are essential for accurate change detection and flood mapping. Radiometric and geometric pre-processing steps were applied to the selected images to ensure data consistency and comparability. These steps included radiometric calibration, conversion to surface reflectance, and co-registration of the pre- and post-flood images. The analysis was conducted over three representative sub-areas within the Upper Medjerda basin, selected based on flood exposure and land cover diversity (Fig. 3 ). Table 1 Characteristics of Landsat-8 images Bands Spectral bands Spatial resolution (m) Wavelength (µm) Band 1 Blue 30 0.45–0.5 Band 2 Green 30 0.52–0.6 Band 3 Red 30 0.63–0.69 Band 4 Near IR 30 0.75–0.9 Band 5 SWIR 30 1.5–1.7 Band 6/1 Thermal IR 60 10.4–12.5 Band 6/2 120 Band 7 SWIR 30 2.08–2.35 Band 8 Panchromatic 15 0.52–0.9 The processing of these images was carried out on 3 study sites belonging to the study basin presented in Fig. 2 . 2.3 Spectral Indices for Flood Detection Flood mapping was performed using a set of commonly applied multispectral indices designed to enhance surface water detection. These indices exploit the strong absorption of water in the near-infrared and short-wave infrared wavelengths, in contrast to higher reflectance values observed for vegetation, soil, and built-up surfaces. Five water-related indices were selected and computed from the Landsat-8 imagery: the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), Automated Water Extraction Index (AWEI), and Water Ratio Index (WRI). In addition, two auxiliary indices were used to characterize vegetation and built-up areas: the Soil Adjusted Vegetation Index (SAVI) and the Normalized Difference Built-up Index (NDBI). The mathematical formulations of these indices are presented in Table 2 . The selected indices were chosen based on their widespread use in flood and surface water studies and their ability to capture different spectral characteristics of water, vegetation, and urban surfaces. Particular attention was given to indices incorporating the SWIR band, as previous studies have shown improved performance in discriminating water bodies under complex land cover conditions. The following table illustrates the main indices used in flood assessement (Rokni et al., 2014 ). Table 2 Water indices Type of indices Indices Formulas Water indices Normalized Difference Water Index NDWI = (Green - PIR) / (Green + PIR) Modification of normalised difference water index MNDWI = (Green - MIR) / (Green + MIR) Normalized difference moisture index NDMI = (PIR-MIR) / (PIR + MIR) Automated Water Extraction Index AWEI = 4*(Green- MIR) - (0.25*PIR + 2.75* Thermal IR ) Water Ratio Index WRI = (Green + Red) / (PIR + MIR) Built-up area index Normalized Difference Urbanization Index ( Xu, 2006 ) NDBI = (MIR - PIR) / (MIR + PIR) Vegetation index Soil Adjusted Vegetation Index ( Huete, 1988 ) SAVI = [(PIR-R) * (1 + L)] / (PIR + R + L) (no vegetation cover: L = 1; moderate vegetation cover L = 0.5, very dense vegetation cover: L = 0) 2.4 Flood Mapping Methodology The proposed flood mapping methodology was implemented in two main steps. In the first step, each water-related spectral index was applied independently to the Landsat-8 images to evaluate its ability to delineate flooded areas. The resulting index maps were visually interpreted and compared with false-color composite images to assess their effectiveness in highlighting inundated surfaces and minimizing confusion with irrigated agricultural fields and built-up areas. In the second step, a combined index-based approach was developed to improve flood extent extraction. This approach integrates information from three complementary indices: MNDWI for water detection, SAVI for vegetation masking, and NDBI for built-up area identification. A decision-tree framework was implemented to sequentially exclude vegetation and urban pixels before extracting water surfaces. This integration aims to reduce misclassification errors commonly encountered when using a single index, particularly in heterogeneous floodplain environments. The resulting flood extent maps represent areas where water presence is detected with higher confidence, allowing for a clearer delineation of flood boundaries. The overall methodology is summarized in Fig. 7 and is designed to be simple, reproducible, and transferable to other flood-prone regions with similar environmental characteristics. 3. RESULTS AND DISCUSSION 3.1 Visual Analysis of Pre- and Post-Flood Conditions False-color composite images generated using the short-wave infrared (SWIR), near-infrared (NIR), and red bands were used to visually assess land surface conditions before and after the February 2015 flood event. In these composites, water surfaces appear in blue tones, allowing a clear distinction between inundated and non-inundated areas. As illustrated in Fig. 4 , the post-flood images reveal a significant overflow of the Medjerda riverbed and its tributaries, resulting in extensive inundation of adjacent floodplains and agricultural lands across the three selected study sites. In contrast, the pre-flood images show confined river channels and limited surface water extent. These visual observations provide a qualitative reference for evaluating the performance of the spectral indices applied in the following sections. 3.2 Performance of Individual Spectral Water Indices The five spectral water indices (NDWI, MNDWI, NDMI, AWEI, and WRI) were applied to the post-flood Landsat-8 imagery to extract flooded areas. The resulting index maps are presented in Fig. 5 . Visual comparison with the false-color composite images indicates that all indices are capable of detecting the main river channel and major flooded zones. However, noticeable differences exist in terms of boundary sharpness and confusion with non-water surfaces. Indices incorporating the SWIR band, particularly MNDWI and AWEI, provide clearer discrimination of flooded areas, whereas NDWI and NDMI tend to misclassify irrigated agricultural fields as water. 3.3 Validation of Flood Mapping Results To assess the reliability and accuracy of the flood extent maps generated from Landsat-8 data, a validation process was conducted using Sentinel-1 radar imagery acquired in February 2015 over the same study area (Ezzine et al., 2018 ). According to Bioresita et al. ( 2018 ), the synergy between optical and SAR data provides a more robust framework for flood monitoring, as SAR sensors can penetrate cloud cover while optical data offers superior spectral discrimination of surface features. The validation approach involves both a visual comparative analysis across three specific sites and a quantitative assessment through statistical indicators (Fig. 6 ). By comparing the total flooded surface areas and calculating confusion matrices, this section demonstrates the performance of the proposed optical indexing method against independent radar-based observations. The statistical analysis performed on the flooded areas visualized via Landsat-8 and Sentinel-1 data provides a method for validating flooded surfaces using optical data. Table 3 illustrates the areas of the flooded zones in both polarizations. Analysis of Table 3 indicates that the total surface area of flooded zones according to Landsat-8 data for the three study sites is 827.88 ha, while for the VH polarization, it is estimated at 792.36 ha. Table 3 Statistics of Flooded Areas Surface (ha) Total Surface (ha) Site 1 Site 2 Site 3 Flooded areas by Landsat-8 data 191,71 512,3 123.87 827.88 Flooded areas by Sentinel-1 data (VH Polarization) 187,2 493,06 112,1 792.36 Other statistical criteria, such as the Kappa coefficient and overall accuracy, were applied to evaluate the performance of Landsat imagery in classifying flooded areas. The calculation of these coefficients from the confusion matrices of Landsat-8 and Sentinel-1 data confirmed that Landsat-8 optical imagery identifies flooded areas in a highly relevant manner (Table 4 ). Table 4 Précision globale et coefficient kappa Flooded areas by Landsat-8 data / Flooded areas by Sentinel-1 data - VH Polarization) Overall Accuracy Kappa Coefficient Site 1 75.5% 0.74 Site 2 81.1% 0.83 Site 3 73.44 0.72 3.4 Comparison between NDWI and MNDWI A focused comparison between NDWI and MNDWI was carried out to assess the impact of spectral band selection on flood mapping accuracy. As shown in Fig. 7 , NDWI tends to overestimate flooded areas due to confusion with moist soil and irrigated crops. In contrast, MNDWI produces more coherent flood boundaries and a more realistic spatial distribution of inundated zones by exploiting the stronger absorption of water in the SWIR band. 3.5 Combined Index-Based Flood Mapping Approach To overcome the limitations observed in single-index approaches, a combined index-based methodology integrating MNDWI, SAVI, and NDBI was implemented using a decision-tree framework. The methodological workflow is illustrated in Fig. 8 . This approach allows for the exclusion of vegetation and built-up areas prior to water extraction, improving flood extent delineation in heterogeneous landscapes. The decision tree algorithm was designed to sequentially mask out vegetation and urban surfaces that often mimic the spectral signature of turbid floodwaters. By integrating SAVI and NDBI thresholds, we significantly reduced the false positives typically encountered in complex environments. This approach minimizes errors in heterogeneous landscapes (Li et al., 2022 ; Albertini et al., 2022 ), ensuring that the final flood map accurately reflects the inundation extent despite the presence of dense crops and built-up areas in the Medjerda valley. The resulting flood extent map generated using this approach is presented in Fig. 8 . The map shows a consistent and continuous delineation of flooded areas across the three study sites, with reduced misclassification of urban and vegetated surfaces. 3.6 Discussion and Implications Optical satellites represent a powerful instrument for flooded area mapping. In fact, the multi-band sensors allow the spectral signatures of different objects to be exploited and information to be derived through a direct visual interpretation of scenes of specific bands or color composites. In addition, multispectral imagery allows information about river morphology dynamics and land cover changes to be integrated in flood risk modelling and provides good spatial resolution for flood management applications, although with some limitations. The method proposed involves the use of simple and available access data landsat8. It holds the advantages of free access to information and an unsupervised algorithm that does not depend on training data efficiency, which makes the methodology a practical process for mapping floods in rivers. Among flooded area detection studies, the best mapping accuracy was achieved by those based on the SWIR and visible bands, particularly the MNDWI index. This superior performance, as observed in previous studies (Du et al., 2016 ), is primarily due to the use of the Short-Wave Infrared (SWIR) band, which is more sensitive to water content and less affected by background signals from urban features. Similarly, Munasinghe et al. compared different inundation mapping methodologies, including supervised and unsupervised classification techniques, a change detection approach, and two spectral indices, i.e., the MNDWI and NDWI, based on Landsat 8-OLI satellite imagery. He showed that both indices achieved satisfactory results. More recent researches combined Landsat images indices to evaluate channel–floodplain dynamics. They were able to extract the flooding extent, based on visual interpretation, achieving very good accuracy. The analyses presented here can, indeed, be transferred to other sites to identify the best methodology that can ensure a reliable flood-prone area delineation using multispectral sensors. 4. Conclusion Flooding has become an increasingly critical natural hazard in recent decades, particularly in Mediterranean and semi-arid regions where extreme rainfall events and land-use changes intensify flood impacts. This study investigated the potential of optical remote sensing data for flood extent mapping in the Upper Medjerda River Valley (northwestern Tunisia) using Landsat-8 imagery acquired before and after the February 2015 flood event. The comparative analysis of several widely used spectral water indices demonstrated that indices incorporating the short-wave infrared (SWIR) band provide improved discrimination of flooded areas. Among the tested indices, the Modified Normalized Difference Water Index (MNDWI) exhibited the most reliable performance, producing sharper flood boundaries and reducing confusion with irrigated agricultural fields and built-up surfaces. In addition, a combined index-based approach integrating MNDWI, the Soil Adjusted Vegetation Index (SAVI), and the Normalized Difference Built-up Index (NDBI) within a decision-tree framework proved effective in enhancing flood extent delineation in complex floodplain environments. The robustness of this approach was rigorously validated through a comparative analysis with Sentinel-1 SAR data (VH polarization), which served as a reference for ground-truth conditions. The results show a high level of agreement, with the methodology achieving overall accuracies ranging from 73.4% to 81.1% and Kappa coefficients up to 0.83. While the uncertainty analysis reveals a slight overestimation of the flooded surface by the optical sensor (approximately 4.5% compared to SAR), this is primarily attributed to mixed-pixel effects at a 30-meter resolution and the influence of soil moisture in saturated riparian zones. Despite the limitations inherent to optical remote sensing, such as cloud cover and reduced sensitivity under dense vegetation, the results confirm the relevance of Landsat-8 multispectral imagery for operational flood mapping. The proposed methodology, which benefits from freely available satellite data and requires no training samples, is highly transferable to other flood-prone regions. It provides a cost-effective and reliable tool to support flood risk assessment, land-use planning, and disaster management strategies in data-scarce environments. Declarations Ethics approval and consent to participate This study did not involve any experiments on humans or animals. Therefore, ethics approval and consent to participate are not applicable. Consent for publication No individual person’s data or images are included in this manuscript. Consent for publication is not applicable. Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author’s contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ahmed Ezzine, Taoufik Hermassi, and Emna Kochlef. The first draft of the manuscript was written by Ahmed Ezzine and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. References Albertini, C., Cioffi, F., Grimaldi, S., 2022. Detection of surface water and floods with multispectral satellites. Remote Sensing 14 (15), 3705. https://doi.org/10.3390/rs14153705 Albertini, C., Gioia, A., Iacobellis, V., Manfreda, S., 2022. Detection of surface water and floods with multispectral satellites. Remote Sensing 14 (23), 6005. https://doi.org/10.3390/rs14236005 Bioresita, F., Puissant, A., Stumpf, A., Malet, J.P., 2018. A sequential flood monitoring procedure using Sentinel-1 SAR and Sentinel-2 MSI imagery. 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International Journal of Remote Sensing 27 (14), 3025–3033. https://doi.org/10.1080/01431160600589179 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 19 Jan, 2026 First submitted to journal 17 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":179989,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area location\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/1b126d4437b1e26846b50c46.jpg"},{"id":101205489,"identity":"cf316ff5-b7ef-4694-aa86-c582bf65a1c8","added_by":"auto","created_at":"2026-01-27 09:49:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92608,"visible":true,"origin":"","legend":"\u003cp\u003eObserved flow at hydrometric stations\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/c6d75f808fb728f08a6e98f2.jpg"},{"id":101204364,"identity":"2c23b2df-aa44-4ee1-97e2-eb7bfdb8cc10","added_by":"auto","created_at":"2026-01-27 09:42:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213472,"visible":true,"origin":"","legend":"\u003cp\u003eStudy areas for Landsat-8 images processing\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/61060b425fe808143012c4c2.jpg"},{"id":101018835,"identity":"7b27d63b-4f10-4506-a818-1e6d05768b6d","added_by":"auto","created_at":"2026-01-24 00:34:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":229955,"visible":true,"origin":"","legend":"\u003cp\u003e(A), (B) and (C) studied areas before flood, (D), (E) and (F) studied areas after flood\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/34f55fbfb76fa4869a263910.jpg"},{"id":101018839,"identity":"32c5bd19-d870-484b-8c38-25202e84368f","added_by":"auto","created_at":"2026-01-24 00:34:27","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":223433,"visible":true,"origin":"","legend":"\u003cp\u003eRemote sensing indices for flooded areas extraction\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/4c545373c48f866a72b41815.jpg"},{"id":101204468,"identity":"fe74cee0-814b-49f8-aeeb-fd903ade7546","added_by":"auto","created_at":"2026-01-27 09:43:14","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":198309,"visible":true,"origin":"","legend":"\u003cp\u003e(a), (b), and (c) flooded areas in blue on the Landsat-8 image;\u003c/p\u003e\n\u003cp\u003e(d), (e), and (f) digitization of flooded areas from the Sentinel-1 image in VH polarization\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/60116217646b00f8a7b0cd68.jpg"},{"id":101018838,"identity":"a098f1d8-6011-44e7-8563-baf7e86708a2","added_by":"auto","created_at":"2026-01-24 00:34:27","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":116867,"visible":true,"origin":"","legend":"\u003cp\u003eDetection of flooded area using NDWI and MNDWI\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/d1914503a10cf76d98906d2f.jpg"},{"id":101203756,"identity":"0a157731-e17c-49f8-911c-a5f7fa1c2631","added_by":"auto","created_at":"2026-01-27 09:40:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":49335,"visible":true,"origin":"","legend":"\u003cp\u003eAdopted methodology for flooded area extraction via remote sensing indices\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/f60ee1c9ab39f0049bb67729.jpg"},{"id":101018849,"identity":"4d251460-f2c6-472e-bd14-f30790aab6e8","added_by":"auto","created_at":"2026-01-24 00:34:28","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":129789,"visible":true,"origin":"","legend":"\u003cp\u003eFig.8. Mapping of flooded area using remote sensing indices\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/475b028452607ecf4d60146d.jpg"},{"id":101880427,"identity":"6e00adf4-4dc8-4d5e-9b07-80c1c711e736","added_by":"auto","created_at":"2026-02-04 15:00:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2184093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8625262/v1/872eec4e-7c07-4b33-9410-a26c1dc767f7.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eFlood Extent Mapping Using Optical Remote Sensing Indices: A Case Study of the Upper Medjerda River Basin (Northwestern Tunisia)\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFloods are among the most destructive natural hazards worldwide, causing significant human, economic, and environmental losses each year. Their occurrence and intensity are controlled by a combination of climatic, hydrological, geomorphological, and anthropogenic factors, including extreme rainfall events, land-use changes, river morphology, and urban expansion within floodplains. In recent decades, climate change has further increased the frequency and severity of flood events, particularly in Mediterranean and semi-arid regions (Wade et al.,2004).\u003c/p\u003e \u003cp\u003eIn Tunisia, flooding represents a major natural risk, especially within the Medjerda River basin, the country\u0026rsquo;s largest and most important watershed. The Upper Medjerda Valley, extending from Ghardimaou to the Sidi Salem Dam, has experienced several severe flood events over the past decades, notably in 1973, 2003, 2012, and 2015 (Fehri, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These events have repeatedly affected agricultural lands, infrastructure, and urban areas, highlighting the urgent need for effective flood monitoring and risk management strategies.\u003c/p\u003e \u003cp\u003eFlood hazard assessment and mapping play a crucial role in understanding flood-generating processes, identifying flood-prone areas, and supporting early warning systems and mitigation planning. Traditionally, flood mapping has relied on in situ hydrological measurements and hydraulic modeling; however, these approaches are often limited by sparse gauge networks, high costs, and data availability constraints, particularly in developing regions.\u003c/p\u003e \u003cp\u003eRemote sensing has emerged as a powerful and complementary tool for flood monitoring at regional to global scales. Optical and radar satellite data have been widely used to detect inundated areas, monitor flood dynamics, and assess flood impacts. Optical multispectral imagery, in particular, offers valuable information for flood mapping due to its ability to capture spectral differences between water and non-water surfaces. Several studies have demonstrated the effectiveness of spectral water indices derived from visible, near-infrared (NIR), and short-wave infrared (SWIR) bands for surface water detection and flood extent delineation (Albertini et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong these indices, 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) are commonly applied for water body extraction. Nevertheless, their performance may vary depending on land cover characteristics, water turbidity, vegetation density, and the presence of built-up areas, often leading to misclassification in complex environments such as agricultural floodplains and urbanized valleys (Bonn et al., 1993).\u003c/p\u003e \u003cp\u003eDespite the growing number of studies on flood mapping using spectral indices, comparative analyses under semi-arid Mediterranean conditions remain limited, particularly for North African river basins. Moreover, many applications rely on a single index, which may not adequately address confusion between water, vegetation, and built-up surfaces during flood events.\u003c/p\u003e \u003cp\u003eIn this context, the objective of this study is to assess the capability of optical remote sensing data for flood extent mapping in the Upper Medjerda River Valley using Landsat-8 imagery. The study compares the performance of several widely used spectral water indices and investigates the added value of a combined index-based approach integrating water, vegetation, and built-up indices through a decision-tree framework. By focusing on a well-documented flood event that occurred in February 2015, this work aims to identify the most suitable spectral index for flood detection in the study area and to propose a transferable methodology for flood mapping in similar semi-arid environments.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe Medjerda River is the largest and most important river system in Tunisia, draining a watershed of approximately 23,000 km\u0026sup2;, of which about 16,400 km\u0026sup2; are located within Tunisian territory. The basin accounts for nearly half of the country\u0026rsquo;s annual surface water resources, with an average inflow of approximately 1,000 Mm\u0026sup3; (Rodier et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study area corresponds to the Upper Medjerda River Valley, extending from Ghardimaou to the Sidi Salem Dam (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This section of the basin is characterized by a wide alluvial floodplain and a complex hydrographic network that promotes flood expansion and prolonged water stagnation during high-flow events. Several major tributaries contribute to the hydrological regime of the basin, including Wadi Rarai and Wadi Bouhertma on the left bank, and Wadi Mellegue and Wadi Tessa on the right bank.\u003c/p\u003e \u003cp\u003eThe Upper Medjerda Valley has been affected by recurrent and severe flood events. While floods were historically quasi-cyclical, occurring approximately every ten years, their frequency and intensity have increased in recent decades. Among the most significant events are the floods of March 1973, January 2003, February 2012, and February 2015. This study focuses on the flood event of 22 February 2015, during which peak discharges reached approximately 750 m\u0026sup3;/s at Ghardimaou, 470 m\u0026sup3;/s at Jendouba, and 600 m\u0026sup3;/s at Bou Salem hydrometric stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Satellite Data and Pre-processing\u003c/h2\u003e \u003cp\u003eTo analyze flood extent before and after the February 2015 event, optical satellite imagery was required for dates as close as possible to the flood occurrence. High spatial resolution commercial imagery (e.g., SPOT, IKONOS, QuickBird) was initially considered; however, no suitable acquisitions covering the study area were available during the flood period.\u003c/p\u003e \u003cp\u003eAs an alternative, freely available Landsat-8 Operational Land Imager (OLI) data provided by the United States Geological Survey (USGS) were used. Two cloud-free Landsat-8 scenes were selected: one acquired before the flood on 13 January 2015 and one acquired after the flood on 2 March 2015. Landsat-8 OLI imagery offers multispectral data with a spatial resolution of 30 m across visible, near-infrared (NIR), and short-wave infrared (SWIR) bands, and a swath width of approximately 185 km, making it suitable for regional-scale flood mapping (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndeed, as highlighted by Claverie et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the harmonization of Landsat datasets has significantly enhanced the capability for dense time-series monitoring, providing reliable surface reflectance products that are essential for accurate change detection and flood mapping.\u003c/p\u003e \u003cp\u003eRadiometric and geometric pre-processing steps were applied to the selected images to ensure data consistency and comparability. These steps included radiometric calibration, conversion to surface reflectance, and co-registration of the pre- and post-flood images. The analysis was conducted over three representative sub-areas within the Upper Medjerda basin, selected based on flood exposure and land cover diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Landsat-8 images\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral bands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial resolution (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWavelength (\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u0026ndash;0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNear IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 6/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThermal IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.4\u0026ndash;12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 6/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.08\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanchromatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe processing of these images was carried out on 3 study sites belonging to the study basin presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Spectral Indices for Flood Detection\u003c/h2\u003e \u003cp\u003eFlood mapping was performed using a set of commonly applied multispectral indices designed to enhance surface water detection. These indices exploit the strong absorption of water in the near-infrared and short-wave infrared wavelengths, in contrast to higher reflectance values observed for vegetation, soil, and built-up surfaces.\u003c/p\u003e \u003cp\u003eFive water-related indices were selected and computed from the Landsat-8 imagery: the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), Automated Water Extraction Index (AWEI), and Water Ratio Index (WRI). In addition, two auxiliary indices were used to characterize vegetation and built-up areas: the Soil Adjusted Vegetation Index (SAVI) and the Normalized Difference Built-up Index (NDBI). The mathematical formulations of these indices are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe selected indices were chosen based on their widespread use in flood and surface water studies and their ability to capture different spectral characteristics of water, vegetation, and urban surfaces. Particular attention was given to indices incorporating the SWIR band, as previous studies have shown improved performance in discriminating water bodies under complex land cover conditions.\u003c/p\u003e \u003cp\u003eThe following table illustrates the main indices used in flood assessement (Rokni et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFormulas\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWater indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormalized Difference Water Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNDWI = (Green - PIR) / (Green\u0026thinsp;+\u0026thinsp;PIR)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModification of normalised difference water index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMNDWI = (Green - MIR) / (Green\u0026thinsp;+\u0026thinsp;MIR)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormalized difference moisture index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNDMI = (PIR-MIR) / (PIR\u0026thinsp;+\u0026thinsp;MIR)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutomated Water Extraction Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAWEI\u0026thinsp;=\u0026thinsp;4*(Green- MIR) - (0.25*PIR\u0026thinsp;+\u0026thinsp;2.75*\u003c/span\u003e Thermal IR\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Ratio Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eWRI = (Green\u0026thinsp;+\u0026thinsp;Red) / (PIR\u0026thinsp;+\u0026thinsp;MIR)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBuilt-up area index\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNormalized Difference Urbanization Index (\u003c/span\u003eXu, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNDBI = (MIR - PIR) / (MIR\u0026thinsp;+\u0026thinsp;PIR)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eVegetation index\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSoil Adjusted Vegetation Index (\u003c/span\u003eHuete, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSAVI = [(PIR-R) * (1\u0026thinsp;+\u0026thinsp;L)] / (PIR\u0026thinsp;+\u0026thinsp;R\u0026thinsp;+\u0026thinsp;L) (no vegetation cover: L\u0026thinsp;=\u0026thinsp;1; \u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emoderate vegetation cover L\u0026thinsp;=\u0026thinsp;0.5, \u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003every dense vegetation cover: L\u0026thinsp;=\u0026thinsp;0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Flood Mapping Methodology\u003c/h2\u003e \u003cp\u003eThe proposed flood mapping methodology was implemented in two main steps. In the first step, each water-related spectral index was applied independently to the Landsat-8 images to evaluate its ability to delineate flooded areas. The resulting index maps were visually interpreted and compared with false-color composite images to assess their effectiveness in highlighting inundated surfaces and minimizing confusion with irrigated agricultural fields and built-up areas.\u003c/p\u003e \u003cp\u003eIn the second step, a combined index-based approach was developed to improve flood extent extraction. This approach integrates information from three complementary indices: MNDWI for water detection, SAVI for vegetation masking, and NDBI for built-up area identification. A decision-tree framework was implemented to sequentially exclude vegetation and urban pixels before extracting water surfaces. This integration aims to reduce misclassification errors commonly encountered when using a single index, particularly in heterogeneous floodplain environments.\u003c/p\u003e \u003cp\u003eThe resulting flood extent maps represent areas where water presence is detected with higher confidence, allowing for a clearer delineation of flood boundaries. The overall methodology is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e and is designed to be simple, reproducible, and transferable to other flood-prone regions with similar environmental characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Visual Analysis of Pre- and Post-Flood Conditions\u003c/h2\u003e \u003cp\u003eFalse-color composite images generated using the short-wave infrared (SWIR), near-infrared (NIR), and red bands were used to visually assess land surface conditions before and after the February 2015 flood event. In these composites, water surfaces appear in blue tones, allowing a clear distinction between inundated and non-inundated areas.\u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the post-flood images reveal a significant overflow of the Medjerda riverbed and its tributaries, resulting in extensive inundation of adjacent floodplains and agricultural lands across the three selected study sites. In contrast, the pre-flood images show confined river channels and limited surface water extent.\u003c/p\u003e \u003cp\u003eThese visual observations provide a qualitative reference for evaluating the performance of the spectral indices applied in the following sections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Performance of Individual Spectral Water Indices\u003c/h2\u003e \u003cp\u003eThe five spectral water indices (NDWI, MNDWI, NDMI, AWEI, and WRI) were applied to the post-flood Landsat-8 imagery to extract flooded areas. The resulting index maps are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Visual comparison with the false-color composite images indicates that all indices are capable of detecting the main river channel and major flooded zones. However, noticeable differences exist in terms of boundary sharpness and confusion with non-water surfaces.\u003c/p\u003e \u003cp\u003eIndices incorporating the SWIR band, particularly MNDWI and AWEI, provide clearer discrimination of flooded areas, whereas NDWI and NDMI tend to misclassify irrigated agricultural fields as water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation of Flood Mapping Results\u003c/h2\u003e \u003cp\u003eTo assess the reliability and accuracy of the flood extent maps generated from Landsat-8 data, a validation process was conducted using Sentinel-1 radar imagery acquired in February 2015 over the same study area (Ezzine et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to Bioresita et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the synergy between optical and SAR data provides a more robust framework for flood monitoring, as SAR sensors can penetrate cloud cover while optical data offers superior spectral discrimination of surface features. The validation approach involves both a visual comparative analysis across three specific sites and a quantitative assessment through statistical indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). By comparing the total flooded surface areas and calculating confusion matrices, this section demonstrates the performance of the proposed optical indexing method against independent radar-based observations.\u003c/p\u003e \u003cp\u003eThe statistical analysis performed on the flooded areas visualized via Landsat-8 and Sentinel-1 data provides a method for validating flooded surfaces using optical data. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the areas of the flooded zones in both polarizations. Analysis of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that the total surface area of flooded zones according to Landsat-8 data for the three study sites is 827.88 ha, while for the VH polarization, it is estimated at 792.36 ha.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of Flooded Areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSurface (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c11\" namest=\"c10\" rowspan=\"2\"\u003e \u003cp\u003eTotal Surface (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlooded areas by Landsat-8 data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e512,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e123.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e827.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlooded areas by Sentinel-1 data (VH Polarization)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e493,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e112,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e792.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOther statistical criteria, such as the Kappa coefficient and overall accuracy, were applied to evaluate the performance of Landsat imagery in classifying flooded areas. The calculation of these coefficients from the confusion matrices of Landsat-8 and Sentinel-1 data confirmed that Landsat-8 optical imagery identifies flooded areas in a highly relevant manner (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePr\u0026eacute;cision globale et coefficient kappa\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFlooded areas by Landsat-8 data /\u003c/p\u003e \u003cp\u003eFlooded areas by Sentinel-1 data - VH Polarization)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKappa Coefficient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comparison between NDWI and MNDWI\u003c/h2\u003e \u003cp\u003eA focused comparison between NDWI and MNDWI was carried out to assess the impact of spectral band selection on flood mapping accuracy. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e, NDWI tends to overestimate flooded areas due to confusion with moist soil and irrigated crops.\u003c/p\u003e \u003cp\u003eIn contrast, MNDWI produces more coherent flood boundaries and a more realistic spatial distribution of inundated zones by exploiting the stronger absorption of water in the SWIR band.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Combined Index-Based Flood Mapping Approach\u003c/h2\u003e \u003cp\u003eTo overcome the limitations observed in single-index approaches, a combined index-based methodology integrating MNDWI, SAVI, and NDBI was implemented using a decision-tree framework. The methodological workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis approach allows for the exclusion of vegetation and built-up areas prior to water extraction, improving flood extent delineation in heterogeneous landscapes.\u003c/p\u003e \u003cp\u003eThe decision tree algorithm was designed to sequentially mask out vegetation and urban surfaces that often mimic the spectral signature of turbid floodwaters. By integrating SAVI and NDBI thresholds, we significantly reduced the false positives typically encountered in complex environments. This approach minimizes errors in heterogeneous landscapes (Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Albertini et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), ensuring that the final flood map accurately reflects the inundation extent despite the presence of dense crops and built-up areas in the Medjerda valley.\u003c/p\u003e \u003cp\u003eThe resulting flood extent map generated using this approach is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The map shows a consistent and continuous delineation of flooded areas across the three study sites, with reduced misclassification of urban and vegetated surfaces.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Discussion and Implications\u003c/h2\u003e \u003cp\u003eOptical satellites represent a powerful instrument for flooded area mapping. In fact, the multi-band sensors allow the spectral signatures of different objects to be exploited and information to be derived through a direct visual interpretation of scenes of specific bands or color composites.\u003c/p\u003e \u003cp\u003eIn addition, multispectral imagery allows information about river morphology dynamics and land cover changes to be integrated in flood risk modelling and provides good spatial resolution for flood management applications, although with some limitations. The method proposed involves the use of simple and available access data landsat8. It holds the advantages of free access to information and an unsupervised algorithm that does not depend on training data efficiency, which makes the methodology a practical process for mapping floods in rivers. Among flooded area detection studies, the best mapping accuracy was achieved by those based on the SWIR and visible bands, particularly the MNDWI index. This superior performance, as observed in previous studies (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), is primarily due to the use of the Short-Wave Infrared (SWIR) band, which is more sensitive to water content and less affected by background signals from urban features. Similarly, Munasinghe et al. compared different inundation mapping methodologies, including supervised and unsupervised classification techniques, a change detection approach, and two spectral indices, i.e., the MNDWI and NDWI, based on Landsat 8-OLI satellite imagery. He showed that both indices achieved satisfactory results. More recent researches combined Landsat images indices to evaluate channel\u0026ndash;floodplain dynamics. They were able to extract the flooding extent, based on visual interpretation, achieving very good accuracy. The analyses presented here can, indeed, be transferred to other sites to identify the best methodology that can ensure a reliable flood-prone area delineation using multispectral sensors.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eFlooding has become an increasingly critical natural hazard in recent decades, particularly in Mediterranean and semi-arid regions where extreme rainfall events and land-use changes intensify flood impacts. This study investigated the potential of optical remote sensing data for flood extent mapping in the Upper Medjerda River Valley (northwestern Tunisia) using Landsat-8 imagery acquired before and after the February 2015 flood event. The comparative analysis of several widely used spectral water indices demonstrated that indices incorporating the short-wave infrared (SWIR) band provide improved discrimination of flooded areas. Among the tested indices, the Modified Normalized Difference Water Index (MNDWI) exhibited the most reliable performance, producing sharper flood boundaries and reducing confusion with irrigated agricultural fields and built-up surfaces. In addition, a combined index-based approach integrating MNDWI, the Soil Adjusted Vegetation Index (SAVI), and the Normalized Difference Built-up Index (NDBI) within a decision-tree framework proved effective in enhancing flood extent delineation in complex floodplain environments. The robustness of this approach was rigorously validated through a comparative analysis with Sentinel-1 SAR data (VH polarization), which served as a reference for ground-truth conditions. The results show a high level of agreement, with the methodology achieving overall accuracies ranging from 73.4% to 81.1% and Kappa coefficients up to 0.83. While the uncertainty analysis reveals a slight overestimation of the flooded surface by the optical sensor (approximately 4.5% compared to SAR), this is primarily attributed to mixed-pixel effects at a 30-meter resolution and the influence of soil moisture in saturated riparian zones. Despite the limitations inherent to optical remote sensing, such as cloud cover and reduced sensitivity under dense vegetation, the results confirm the relevance of Landsat-8 multispectral imagery for operational flood mapping.\u003c/p\u003e \u003cp\u003eThe proposed methodology, which benefits from freely available satellite data and requires no training samples, is highly transferable to other flood-prone regions. It provides a cost-effective and reliable tool to support flood risk assessment, land-use planning, and disaster management strategies in data-scarce environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study did not involve any experiments on humans or animals. Therefore, ethics approval and consent to participate are not applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNo individual person\u0026rsquo;s data or images are included in this manuscript. Consent for publication is not applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor\u0026rsquo;s contribution\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ahmed Ezzine, Taoufik Hermassi, and Emna Kochlef. The first draft of the manuscript was written by Ahmed Ezzine and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbertini, C., Cioffi, F., Grimaldi, S., 2022. Detection of surface water and floods with multispectral satellites. \u003cem\u003eRemote Sensing\u003c/em\u003e 14 (15), 3705. https://doi.org/10.3390/rs14153705\u003c/li\u003e\n\u003cli\u003eAlbertini, C., Gioia, A., Iacobellis, V., Manfreda, S., 2022. 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Water feature extraction and change detection using multitemporal Landsat imagery. \u003cem\u003eRemote Sensing\u003c/em\u003e 6 (5), 4173\u0026ndash;4189.\u003c/li\u003e\n\u003cli\u003eWade, S., Diop, S., Faye, S., Rudant, J.P., Diallo, A.H., Dia, A.M., Gaffuri, J., Kouame, J., 2004. Les donn\u0026eacute;es des satellites d\u0026apos;observation de la Terre au service de la gestion des ressources en eau : \u0026eacute;tudes de cas au S\u0026eacute;n\u0026eacute;gal. \u003cem\u003eGeo Observatory\u003c/em\u003e 13, 1\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eXu, H., 2006. Modification of normalized difference water index (MNDWI) to enhance open water features in remotely sensed imagery. \u003cem\u003eInternational Journal of Remote Sensing\u003c/em\u003e 27 (14), 3025\u0026ndash;3033. https://doi.org/10.1080/01431160600589179\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Medjerda, optical data, flood mapping, SAR data","lastPublishedDoi":"10.21203/rs.3.rs-8625262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8625262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFloods 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.\u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e","manuscriptTitle":"Flood Extent Mapping Using Optical Remote Sensing Indices: A Case Study of the Upper Medjerda River Basin (Northwestern Tunisia)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 00:34:22","doi":"10.21203/rs.3.rs-8625262/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-21T12:19:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T10:19:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T05:21:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Geophysica","date":"2026-01-17T07:01:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"11633cbe-8bec-43d5-b0e4-7e9cfb20d7f6","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-02T20:15:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-24 00:34:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8625262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8625262","identity":"rs-8625262","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}