Utilization of Open-Source SAR Data for Flood Detection: Insights from The Yamuna River Basin Flood

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Pangarkar, Shafiyoddin Sayyad, Pradnya Maheshmalkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7607174/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In early July 2023, the Yamuna River Basin in Delhi experienced severe flooding, primarily triggered by high-intensity rainfall and flash releases at the Okhla Barrage. The combination of rapid urbanization, impervious surfaces, and inadequate drainage infrastructure exacerbated flood impacts, leading to extensive inundation, property damage, and disruption of livelihoods. Accurate and timely flood mapping is essential for mitigating such urban flood risks and enhancing disaster preparedness. In this study, Sentinel-1 SAR data acquired before and during the flood were utilized to delineate inundated areas. The inherent sensitivity of SAR to surface water, due to reduced backscatter from specular reflection, makes it a reliable tool for identifying flood-prone zones. Flood extent was extracted through preprocessing and threshold-based band math operations. Additionally, Sentinel-2 harmonized data and Dynamic World classifications were integrated to assess Land Use/Land Cover (LULC) changes in the affected region. A coherence analysis using pre- and post-flood datasets further supported the detection of inundated zones. Results revealed significant impacts of flooding on different LULC classes, highlighting the vulnerability of built-up and agricultural areas. The study demonstrates the effectiveness of open-source SAR and optical datasets for flood detection and mapping in urban river basins, thereby offering a cost-effective and reliable approach to strengthen disaster response and urban resilience planning. SAR sentinel-1 Thresholding Flood Detection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION Natural disasters like floods often result in extensive destruction and loss, responsible for extensive damage to infrastructure, property, and human lives across the globe[ 1 ], [ 2 ]. India, owing to its diverse geography and climatic variability, is especially vulnerable to recurrent flooding events. Past occurrences of severe floods in the country have not only disrupted the economy but have also resulted in tragic loss of life. Therefore, a clear understanding of the factors that drive flooding, along with systematic efforts toward mitigation, is essential for effective disaster management. Human-induced factors such as rapid urbanization[ 3 ], deforestation, and unplanned development have significantly increased the extent of areas vulnerable to flooding. In addition, climate change reflected in the rising frequency of extreme precipitation events has emerged as a major driver intensifying the flooding problem. Urban areas such as Delhi face growing flood risks due to unplanned expansion, impervious surfaces, limited drainage capacity and the intensifying effects of climate change[ 4 ], [ 5 ]. The Yamuna River, which flows through Delhi, is highly susceptible to flooding during periods of heavy rainfall [ 3 ]and sudden water releases from upstream barrages. The July 2023 flood event highlighted these vulnerabilities, with inundation severely affecting settlements, agricultural zones, and critical infrastructure. Such events underscore the urgent need for reliable and timely flood mapping techniques to support effective disaster management and urban resilience planning. The flood information can be collected through both in-situ (on-site) measurements and remote sensing techniques using aerial or satellite platforms. However, relying exclusively on in-situ data for flood detection is often impractical, costly, time-consuming, and inefficient. Similarly, using aerial photography for flood assessment can involve significant expenses. Therefore, satellite remote sensing emerges as an efficient and practical solution for regularly monitoring large geographic areas and assessing flood extent. Several techniques exist for detecting floods using remote sensing data, including change detection, supervised and unsupervised classification, water indices such as NDWI, machine learning approaches, and object-based image analysis. While each method has its merits, they often involve high computational costs, require extensive training data, or are limited by weather conditions and cloud cover in optical imagery. In contrast, thresholding of SAR imagery offers a simple, efficient, and reliable solution for flood mapping. It enables rapid separation of water and non-water areas, is reproducible, and performs consistently even when water occupies only a small portion of the scene. Furthermore, SAR-based thresholding is unaffected by clouds or darkness, making it the most practical and effective technique for large-scale, operational flood monitoring. Thresholding of SAR imagery stands out for its simplicity, efficiency, and proven effectiveness in delineating the extent of inundation[ 6 ], [ 7 ]. Accuracy while getting threshold is most crucial step to accurate image classification. However, manually determining the threshold is time-consuming and impractical, especially when coverage of flood is very less amount in the image. In such cases, the distributions of water and background pixels often overlap significantly, and a clear bimodal distribution is typically absent. Therefore, automating the water extraction process becomes essential for reliable and efficient flood mapping[ 5 ], [ 8 ]. Google Earth Engine (GEE) and Sentinel-1 SAR data to map floods in Himachal Pradesh during July 2023. GEE's JavaScript-based code editor was employed to develop scripts for processing, retrieving, and mapping flood inundation. Data was filtered using parameters such as AOI, resolution, polarization, instrument mode, and orbit pass. A threshold of 1.25 was chosen based on experimental analysis to minimize false positives and negatives. By exporting the processed images, the flooded area within the AOI was calculated. The integration of GEE and SAR data proved effective for rapid and accurate flood mapping. [ 9 ] The July 2023 flood in the Yamuna River Basin provided a critical opportunity to evaluate the potential of satellite-based approaches for urban flood mapping. By integrating multi-temporal SAR and optical datasets, this study addresses the pressing need for cost-effective, reliable, and rapid techniques to delineate inundated areas and assess their impacts on different land use/land cover (LULC) classes. Furthermore, coherence analysis using pre- and post-flood SAR images provides an additional layer of validation, enhancing the reliability of flood detection. This study focuses on the July 2023 Yamuna River Basin flood event in Delhi. By employing pre- and post-flood Sentinel-1 SAR images, along with Sentinel-2 optical datasets, flood-affected areas were mapped, LULC impacts were assessed, and coherence analysis was performed to better understand inundation patterns. Choosing this study is therefore not only relevant to understanding the July 2023 event but also contributes more broadly to developing robust frameworks for flood disaster preparedness and urban resilience in other flood-prone regions of India and beyond. The findings demonstrate the potential of SAR-based approaches for rapid, reliable, and cost-effective flood detection, contributing to improved disaster preparedness and management strategies in urban environments. 2. STUDY AREA AND DATASET 2.1. Study Area The selected study area is Wazirabad, located in the northern part of Delhi, within the Yamuna River Basin. Wazirabad is an important urban locality that lies along the banks of the Yamuna River, one of the major rivers originating from the Yamunotri glacier in the Himalayas. The geographical coordinates of the area approximately range from 28.700° to 28.750° N latitude and 77.170° to 77.210° E longitude (Fig. 1 ). The Yamuna River plays a vital role in supplying water to Delhi and surrounding regions, supporting both domestic and industrial use. During the period from 9 to 14 July 2023, the region experienced exceptionally heavy rainfall exceeding 208.6 mm, which caused the water level of the Yamuna River to rise significantly. This sudden increase in water flow led to flooding in low-lying areas, inundation of roads, and damage to infrastructure, including bridges and power lines. Such events not only disrupted daily life but also posed serious threats to public safety and urban resilience. The flood episodes highlight the vulnerability of densely populated urban settlements in the Yamuna River Basin to extreme hydrological events, emphasizing the need for monitoring, early warning systems, and effective flood management strategies. Drainage network The Yamuna River, the longest tributary of the Ganges in India and the second largest in terms of discharge, flows for 1376 km and drains an area of 366,223 square kilometers, accounting for 40.2% of the entire Ganges basin.[ 10 ] Climatic condition Delhi has a climate that is primarily humid subtropical with strong monsoon influences, transitioning toward a hot semi-arid type, marked by significant seasonal fluctuations in both temperature and rainfall. The city records an average yearly precipitation of 774.4 mm.[ 10 ] 2.2. Dataset For this study, multi-temporal remote sensing data were acquired using the Sentinel-1A and Sentinel-2 satellites, along with Dynamic World Land Use and Land Cover (LULC) data. Sentinel-1A images were obtained in Interferometric Wide (IW) swath mode, including both Ground Range Detected (GRD) and Single Look Complex (SLC) products. The data were collected on two different dates: 30th June 2023 and 12th July 2023. In addition, Sentinel-2 Harmonized data and Dynamic World Land Use Land Cover data were used to complement the analysis. The dual-polarized (VV and VH) Sentinel-1A data are primarily used for assessing surface properties and land cover dynamics during different time intervals. The specific dataset IDs for the GRD and SLC products, as well as Sentinel-2, are provided in the table (Fig. 2 ) below. 3. METHODOLOGY Two images from Sentinel-1A, dated 30th June 2023 and 12th July 2023, were used for this study. The image from 12th July was considered the crisis image, while the 30th June image served as the archive image. The archive image helped distinguish permanent water bodies from flooded areas after preprocessing and terrain correction of both images. A subset covering Wazirabad and nearby regions was created using SNAP software for both dates. Precise orbit files were applied to compute the satellite orbits, followed by Thermal Noise Removal (TNR) and Border Noise Removal (BNR). Radiometric Calibration was performed to convert pixel values from integer digital numbers to backscatter coefficients (sigma nought). Speckle Filtering was applied to further reduce noise, and terrain correction transformed the coordinates from the satellite reference system to a geographic reference system for both images. A threshold value of -17.22E-2 was used to separate flooded areas in the crisis image from the rest of the scene. The detected flooded areas were overlaid onto the archive image to distinguish temporary flood zones from permanent water bodies. A binary flood map was generated and exported in KMZ format for 2D and 3D visualization. Sentinel-2 Harmonized data, along with Dynamic World Land Use Land Cover (LULC) data, were processed using Google Earth Engine (GEE) to generate LULC maps. Both the flood map and the LULC map were reprojected and converted into vector format using ArcGIS. Their spatial intersection allowed visualization and interpretation of the land use and land cover types submerged under the flooded region. Additionally, a coherence map of the study area was generated using Sentinel-1 SLC data from 30th June and 12th July 2023, by applying geocoding and coherence estimation operations. An RGB Composite map was also created using the same dataset by performing debursting, calibration, multi-looking, coherence generation, and image stacking through SNAP software (Vargas-Cuentas & Roman-Gonzalez, 2022). These coherence and composite maps were used to study the change in coherence within flooded areas and support image interpretation. The overall methodology is summarized in Figure A (Group A) and Figure B (Group B), which provide a visual overview of the processing workflow. 4. RESULT AND DISCUSSION Water bodies and flood-affected areas can be effectively identified using Synthetic Aperture Radar (SAR) sensors due to their unique backscattering characteristics. In radar imagery, water surfaces generally appear dark because they exhibit low backscatter. This behavior is primarily due to the smooth surface of water acting as a specular reflector. When radar waves encounter calm water, they are mostly reflected away from the sensor rather than back toward it, resulting in low signal return. Although water has a relatively high dielectric constant, which makes it capable of absorbing radar energy, its smooth surface geometry causes most of the incident radar waves to reflect in the forward direction. This leads to a significant reduction in the energy backscattered toward the sensor, particularly in VV (Vertical Transmit, Vertical Receive) polarization. In contrast, rough water surfaces, induced by wind or waves, may exhibit slightly higher backscatter values, but these are still much lower than those from urban areas, vegetation, or other complex land surfaces. Overall, flooded areas are consistently distinguishable by their low or negative backscatter values in sigma0 VV images, providing an effective means for flood detection. In this study, Sentinel-1A GRD images from 30th June 2023 and 12th July 2023 were used. The 12th July image was designated as the crisis image, representing the flood-affected condition, while the 30th June image served as the archive image, representing the pre-flood baseline. The archive image played a critical role in distinguishing permanent water bodies from temporary flood inundation. Both images were pre-processed using SNAP software, and a subset covering Wazirabad and nearby regions was extracted for detailed analysis. Figure 4 shows the backscattering coefficient band and range of before and after flood area. Initially, precise orbit files were applied to compute accurate satellite orbits, which significantly improve the geolocation accuracy of the images. Thermal Noise Removal (TNR) and Border Noise Removal (BNR) operations were performed to suppress instrument-induced noise and border artifacts, enhancing the overall image quality. Radiometric Calibration was then applied to convert the digital pixel values into backscatter coefficients (sigma nought), representing the radar reflectivity of the. Speckle Filtering was applied to further reduce the granular noise inherent in SAR images while preserving important structural information. Terrain Correction was then performed to project the images from the satellite’s slant range geometry into a geographic coordinate system, ensuring accurate spatial referencing. A thresholding technique was applied in SNAP software with a threshold value set at -17.22 to clearly separate flooded regions in the crisis image from other land cover types shown in Fig. 5 . The extracted flooded areas were then overlaid onto the archive image to differentiate permanent water bodies from newly flooded zones. A binary flood map was generated and exported in KMZ format to support both 2D and 3D visualization of the inundated regions. Using Sentinel-2 Harmonized data along with Dynamic World Land Use Land Cover (LULC) data, the land use of the study area was classified with the help of Google Earth Engine (GEE) shown in Fig. 6 . The generated LULC map provided detailed information about the different land cover types present in the study area. Both the flood map and LULC map were reprojected to a common coordinate system and vectorized using QGIS software. Overlay analysis was then performed to spatially intersect the flood-inundated areas with the LULC classes, allowing interpretation of how land cover types were affected by the flood. Visual analysis indicated that agricultural areas were the most affected land cover class, followed by artificial surfaces such as bare soil and built-up regions. For a more detailed temporal analysis, a coherence map of the study area was generated using Sentinel-1A SLC data from 30th June and 12th July 2023. The coherence estimation process involved geocoding the SLC images followed by coherence calculation to assess temporal stability in the area Which is shown in Figs. 7 and 8 . A low coherence value indicated areas that underwent significant change (such as flooding), while high coherence indicated stable surfaces. Additionally, an RGB Composite map was generated from the same SLC dataset through a sequence of debursting, radiometric calibration, multi-looking, coherence generation, and image stacking processes using SNAP software. Urban areas typically appear bright in sigma0 VV images due to their complex structural characteristics and material composition. Buildings, roads, and other man-made structures create multiple scattering effects, where radar waves bounce between vertical surfaces and the ground before returning to the sensor. This double-bounce scattering enhances the backscatter intensity. Furthermore, urban materials such as concrete, metal, and glass have a high dielectric constant, further increasing radar reflectivity compared to natural surfaces like soil or vegetation. VV polarization is particularly sensitive to vertical structures, making it ideal for detecting urban features and flooded regions in mixed land cover areas. Therefore, VV polarization was chosen for this study to effectively detect and analyze the spatial extent of flooding. The final quantitative analysis involved calculating the distribution of flood-inundated areas per LULC class. The above graph shows flood inundated areas per LULC class. The results indicated that the most severely affected class was agricultural land, followed by artificial surfaces, including built-up areas and bare soil. This detailed analysis provides important insights into flood vulnerability patterns, which can inform decision-making in flood risk management and mitigation. 5. CONCLUSION This study demonstrated an effective methodology for flood detection and land use land cover (LULC) impact assessment using multi-temporal remote sensing data from Sentinel-1A and Sentinel-2 satellites. The use of SAR imagery, particularly in VV polarization, proved to be highly efficient in identifying flooded areas due to the distinct low backscatter response of water surfaces. The combination of GRD and SLC data allowed not only flood mapping but also temporal coherence analysis, providing a deeper understanding of the surface changes caused by flooding events. The thresholding technique applied to the backscattering coefficient enabled accurate extraction of the flooded regions, while the overlay analysis with LULC maps provided clear insights into the types of land cover most affected by the flood. It was observed that agricultural areas suffered the highest impact, followed by artificial surfaces such as bare soil and built-up regions. This highlights the vulnerability of agricultural land to flood events, which has significant implications for regional planning and disaster management. Furthermore, the coherence map and RGB composite images contributed to a more precise interpretation of the flood dynamics, by visually highlighting areas of change and stability between the pre-flood and post-flood dates. The methodology established in this study can serve as a reliable framework for flood monitoring and assessment in other regions, especially in flood-prone areas where rapid and accurate flood mapping is crucial. Overall, the integration of Sentinel-1A and Sentinel-2 data, combined with advanced processing techniques and spatial analysis, provides a robust approach for understanding flood impacts and supports effective decision-making in flood risk management and mitigation strategies. Declarations Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Introduction part is written by Dr. Pradnya Maheshmalkar , figure layout adjustment by Dr. shafiyoddin sayyed and result discussion-processing part is carried out by miss rajeshwari pangarkar. Acknowledgements The authors extend their gratitude to the Indian Meteorological Department (IMD)[11] for furnishing the rainfall data, and to the European Space Agency (ESA) for granting complimentary access to Sentinel data products. References Dheeraj Raut, G.M., Sayyad, Sayyad, S.: Flood Mapping Using Microwave Remote Sensing. Interantional J. Sci. Res. Sci. Technol. 11 (13), 20–27 (2024) Thenkabail, P.S.: Remote Sensing Handbook, Volume I. CRC, Boca Raton (2024). 10.1201/9781003541141 Biswas, B., Ghute, B., Das, J.: Geoinformatics for Flood Risk Management. CRC, Boca Raton (2025). 10.1201/9781003533511 Amitrano, D., Di Martino, G., Di Simone, A., Imperatore, P.: Flood Detection with SAR: A Review of Techniques and Datasets, Remote Sens (Basel) , vol. 16, no. 4, p. 656, Feb. (2024). 10.3390/rs16040656 Wang, Z., Zhang, C., Atkinson, P.M.: Combining SAR images with land cover products for rapid urban flood mapping. Front. Environ. Sci. 10 (Oct. 2022). 10.3389/fenvs.2022.973192 Eudaric, J., Kreibich, H., Camero, A., Rafiezadeh Shahi, K., Martinis, S., Zhu, X.X.: A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness, Sci Rep , vol. 14, no. 1, p. 19290, Aug. (2024). 10.1038/s41598-024-69977-1 Juneja, A., Joseph, A., Murty, D.S.: GeoVadis. CRC, London (2025). 10.1201/9781003645917 Garg, V., Mathur, J., Bhatia, A.: Building Energy Simulation. CRC (2020). 10.1201/9780429354632 Singh, G., Rawat, K.S.: Mapping flooded areas utilizing Google Earth Engine and open SAR data: a comprehensive approach for disaster response. Discover Geoscience. 2 (1) (Apr. 2024). 10.1007/s44288-024-00006-4 Climates of Delhi: Accessed: Feb. 17, 2025. [Online]. Available: https://en.wikipedia.org/wiki?curid=8971610 Anusha, N., Bharathi, B.: Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data, The Egyptian Journal of Remote Sensing and Space Science , vol. 23, no. 2, pp. 207–219, Aug. (2020). 10.1016/j.ejrs.2019.01.001 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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1","display":"","copyAsset":false,"role":"figure","size":615311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation Map of Area of Interest\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/7cedd20d3b0fc1a45920b5b0.png"},{"id":92591374,"identity":"ed2d7862-f83f-48c6-be9e-af63fc36ec7e","added_by":"auto","created_at":"2025-10-01 11:57:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData Specifications\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/3852030d84a570c6c121cb2c.png"},{"id":92591740,"identity":"3c6f6fb0-1e3d-4a8b-97ff-d100ef841011","added_by":"auto","created_at":"2025-10-01 12:05:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethodology Workflow\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/aa89f806e15e338c859a45f9.png"},{"id":92591381,"identity":"c0611b34-b697-43d3-b143-6be907482198","added_by":"auto","created_at":"2025-10-01 11:57:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":779060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShows Backscattering Coefficient Band and Range of Before (Left Side image) and After (Right Side image) Flood area.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/8e734d74efa41ac41f00a0b1.png"},{"id":92591741,"identity":"11d5960b-f070-4a48-ac8b-51797f46f325","added_by":"auto","created_at":"2025-10-01 12:05:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":520761,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShows Pre (Left Side image) and Post (Right Side image) Flood Detection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/32d37f396b31a00a7bd6177d.png"},{"id":92591743,"identity":"729c18ca-8197-49c0-a6d4-3e41f4835ed7","added_by":"auto","created_at":"2025-10-01 12:05:40","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":246841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLULC Of Study Area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/0c5b05a4d261dc452572f87f.jpg"},{"id":92591380,"identity":"a08d5a22-fe14-4f0e-905c-a25fc6feda4c","added_by":"auto","created_at":"2025-10-01 11:57:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":193136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCoherence Map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/a145ca90dbe57051d7b4257b.jpg"},{"id":92591746,"identity":"0a8840d8-8ef3-4097-b433-cec9510f3c55","added_by":"auto","created_at":"2025-10-01 12:05:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":225392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRGB Composite of Study Area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/9df14f94fb4ac8b067efb0d7.jpg"},{"id":92591744,"identity":"4b8cac4b-41bd-497f-96b1-94cfd67b2af6","added_by":"auto","created_at":"2025-10-01 12:05:40","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":77020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLULC class wise flood inundated areas\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/eb9ffe73861b598280fd61e1.png"},{"id":94597793,"identity":"1d0b852d-8711-4807-9f3e-b97ad0d9b51d","added_by":"auto","created_at":"2025-10-28 18:49:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3448789,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7607174/v1/841d6ec1-8381-47ef-b0f5-985e36bbc59b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Utilization of Open-Source SAR Data for Flood Detection: Insights from The Yamuna River Basin Flood","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eNatural disasters like floods often result in extensive destruction and loss, responsible for extensive damage to infrastructure, property, and human lives across the globe[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. India, owing to its diverse geography and climatic variability, is especially vulnerable to recurrent flooding events. Past occurrences of severe floods in the country have not only disrupted the economy but have also resulted in tragic loss of life. Therefore, a clear understanding of the factors that drive flooding, along with systematic efforts toward mitigation, is essential for effective disaster management. Human-induced factors such as rapid urbanization[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], deforestation, and unplanned development have significantly increased the extent of areas vulnerable to flooding. In addition, climate change reflected in the rising frequency of extreme precipitation events has emerged as a major driver intensifying the flooding problem. Urban areas such as Delhi face growing flood risks due to unplanned expansion, impervious surfaces, limited drainage capacity and the intensifying effects of climate change[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Yamuna River, which flows through Delhi, is highly susceptible to flooding during periods of heavy rainfall [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]and sudden water releases from upstream barrages.\u003c/p\u003e\u003cp\u003eThe July 2023 flood event highlighted these vulnerabilities, with inundation severely affecting settlements, agricultural zones, and critical infrastructure. Such events underscore the urgent need for reliable and timely flood mapping techniques to support effective disaster management and urban resilience planning. The flood information can be collected through both in-situ (on-site) measurements and remote sensing techniques using aerial or satellite platforms. However, relying exclusively on in-situ data for flood detection is often impractical, costly, time-consuming, and inefficient. Similarly, using aerial photography for flood assessment can involve significant expenses. Therefore, satellite remote sensing emerges as an efficient and practical solution for regularly monitoring large geographic areas and assessing flood extent.\u003c/p\u003e\u003cp\u003eSeveral techniques exist for detecting floods using remote sensing data, including change detection, supervised and unsupervised classification, water indices such as NDWI, machine learning approaches, and object-based image analysis. While each method has its merits, they often involve high computational costs, require extensive training data, or are limited by weather conditions and cloud cover in optical imagery. In contrast, thresholding of SAR imagery offers a simple, efficient, and reliable solution for flood mapping. It enables rapid separation of water and non-water areas, is reproducible, and performs consistently even when water occupies only a small portion of the scene. Furthermore, SAR-based thresholding is unaffected by clouds or darkness, making it the most practical and effective technique for large-scale, operational flood monitoring. Thresholding of SAR imagery stands out for its simplicity, efficiency, and proven effectiveness in delineating the extent of inundation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Accuracy while getting threshold is most crucial step to accurate image classification. However, manually determining the threshold is time-consuming and impractical, especially when coverage of flood is very less amount in the image. In such cases, the distributions of water and background pixels often overlap significantly, and a clear bimodal distribution is typically absent. Therefore, automating the water extraction process becomes essential for reliable and efficient flood mapping[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGoogle Earth Engine (GEE) and Sentinel-1 SAR data to map floods in Himachal Pradesh during July 2023. GEE's JavaScript-based code editor was employed to develop scripts for processing, retrieving, and mapping flood inundation. Data was filtered using parameters such as AOI, resolution, polarization, instrument mode, and orbit pass. A threshold of 1.25 was chosen based on experimental analysis to minimize false positives and negatives. By exporting the processed images, the flooded area within the AOI was calculated. The integration of GEE and SAR data proved effective for rapid and accurate flood mapping. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe July 2023 flood in the Yamuna River Basin provided a critical opportunity to evaluate the potential of satellite-based approaches for urban flood mapping. By integrating multi-temporal SAR and optical datasets, this study addresses the pressing need for cost-effective, reliable, and rapid techniques to delineate inundated areas and assess their impacts on different land use/land cover (LULC) classes. Furthermore, coherence analysis using pre- and post-flood SAR images provides an additional layer of validation, enhancing the reliability of flood detection.\u003c/p\u003e\u003cp\u003eThis study focuses on the July 2023 Yamuna River Basin flood event in Delhi. By employing pre- and post-flood Sentinel-1 SAR images, along with Sentinel-2 optical datasets, flood-affected areas were mapped, LULC impacts were assessed, and coherence analysis was performed to better understand inundation patterns. Choosing this study is therefore not only relevant to understanding the July 2023 event but also contributes more broadly to developing robust frameworks for flood disaster preparedness and urban resilience in other flood-prone regions of India and beyond. The findings demonstrate the potential of SAR-based approaches for rapid, reliable, and cost-effective flood detection, contributing to improved disaster preparedness and management strategies in urban environments.\u003c/p\u003e"},{"header":"2. STUDY AREA AND DATASET","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Area\u003c/h2\u003e\u003cp\u003eThe selected study area is Wazirabad, located in the northern part of Delhi, within the Yamuna River Basin. Wazirabad is an important urban locality that lies along the banks of the Yamuna River, one of the major rivers originating from the Yamunotri glacier in the Himalayas. The geographical coordinates of the area approximately range from 28.700\u0026deg; to 28.750\u0026deg; N latitude and 77.170\u0026deg; to 77.210\u0026deg; E longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Yamuna River plays a vital role in supplying water to Delhi and surrounding regions, supporting both domestic and industrial use.\u003c/p\u003e\u003cp\u003eDuring the period from 9 to 14 July 2023, the region experienced exceptionally heavy rainfall exceeding 208.6 mm, which caused the water level of the Yamuna River to rise significantly. This sudden increase in water flow led to flooding in low-lying areas, inundation of roads, and damage to infrastructure, including bridges and power lines. Such events not only disrupted daily life but also posed serious threats to public safety and urban resilience. The flood episodes highlight the vulnerability of densely populated urban settlements in the Yamuna River Basin to extreme hydrological events, emphasizing the need for monitoring, early warning systems, and effective flood management strategies.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDrainage network\u003c/strong\u003e\u003cp\u003eThe Yamuna River, the longest tributary of the Ganges in India and the second largest in terms of discharge, flows for 1376 km and drains an area of 366,223 square kilometers, accounting for 40.2% of the entire Ganges basin.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClimatic condition\u003c/strong\u003e\u003cp\u003eDelhi has a climate that is primarily humid subtropical with strong monsoon influences, transitioning toward a hot semi-arid type, marked by significant seasonal fluctuations in both temperature and rainfall. The city records an average yearly precipitation of 774.4 mm.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Dataset\u003c/h2\u003e\u003cp\u003eFor this study, multi-temporal remote sensing data were acquired using the Sentinel-1A and Sentinel-2 satellites, along with Dynamic World Land Use and Land Cover (LULC) data. Sentinel-1A images were obtained in Interferometric Wide (IW) swath mode, including both Ground Range Detected (GRD) and Single Look Complex (SLC) products. The data were collected on two different dates: 30th June 2023 and 12th July 2023. In addition, Sentinel-2 Harmonized data and Dynamic World Land Use Land Cover data were used to complement the analysis. The dual-polarized (VV and VH) Sentinel-1A data are primarily used for assessing surface properties and land cover dynamics during different time intervals. The specific dataset IDs for the GRD and SLC products, as well as Sentinel-2, are provided in the table (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. METHODOLOGY","content":"\u003cp\u003eTwo images from Sentinel-1A, dated 30th June 2023 and 12th July 2023, were used for this study. The image from 12th July was considered the crisis image, while the 30th June image served as the archive image. The archive image helped distinguish permanent water bodies from flooded areas after preprocessing and terrain correction of both images. A subset covering Wazirabad and nearby regions was created using SNAP software for both dates. Precise orbit files were applied to compute the satellite orbits, followed by Thermal Noise Removal (TNR) and Border Noise Removal (BNR). Radiometric Calibration was performed to convert pixel values from integer digital numbers to backscatter coefficients (sigma nought). Speckle Filtering was applied to further reduce noise, and terrain correction transformed the coordinates from the satellite reference system to a geographic reference system for both images.\u003c/p\u003e\u003cp\u003eA threshold value of -17.22E-2 was used to separate flooded areas in the crisis image from the rest of the scene. The detected flooded areas were overlaid onto the archive image to distinguish temporary flood zones from permanent water bodies. A binary flood map was generated and exported in KMZ format for 2D and 3D visualization.\u003c/p\u003e\u003cp\u003eSentinel-2 Harmonized data, along with Dynamic World Land Use Land Cover (LULC) data, were processed using Google Earth Engine (GEE) to generate LULC maps. Both the flood map and the LULC map were reprojected and converted into vector format using ArcGIS. Their spatial intersection allowed visualization and interpretation of the land use and land cover types submerged under the flooded region.\u003c/p\u003e\u003cp\u003eAdditionally, a coherence map of the study area was generated using Sentinel-1 SLC data from 30th June and 12th July 2023, by applying geocoding and coherence estimation operations. An RGB Composite map was also created using the same dataset by performing debursting, calibration, multi-looking, coherence generation, and image stacking through SNAP software (Vargas-Cuentas \u0026amp; Roman-Gonzalez, 2022). These coherence and composite maps were used to study the change in coherence within flooded areas and support image interpretation.\u003c/p\u003e\u003cp\u003eThe overall methodology is summarized in Figure A (Group A) and Figure B (Group B), which provide a visual overview of the processing workflow.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4. RESULT AND DISCUSSION","content":"\u003cp\u003eWater bodies and flood-affected areas can be effectively identified using Synthetic Aperture Radar (SAR) sensors due to their unique backscattering characteristics. In radar imagery, water surfaces generally appear dark because they exhibit low backscatter. This behavior is primarily due to the smooth surface of water acting as a specular reflector. When radar waves encounter calm water, they are mostly reflected away from the sensor rather than back toward it, resulting in low signal return. Although water has a relatively high dielectric constant, which makes it capable of absorbing radar energy, its smooth surface geometry causes most of the incident radar waves to reflect in the forward direction. This leads to a significant reduction in the energy backscattered toward the sensor, particularly in VV (Vertical Transmit, Vertical Receive) polarization. In contrast, rough water surfaces, induced by wind or waves, may exhibit slightly higher backscatter values, but these are still much lower than those from urban areas, vegetation, or other complex land surfaces. Overall, flooded areas are consistently distinguishable by their low or negative backscatter values in sigma0 VV images, providing an effective means for flood detection.\u003c/p\u003e\u003cp\u003eIn this study, Sentinel-1A GRD images from 30th June 2023 and 12th July 2023 were used. The 12th July image was designated as the crisis image, representing the flood-affected condition, while the 30th June image served as the archive image, representing the pre-flood baseline. The archive image played a critical role in distinguishing permanent water bodies from temporary flood inundation. Both images were pre-processed using SNAP software, and a subset covering Wazirabad and nearby regions was extracted for detailed analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the backscattering coefficient band and range of before and after flood area.\u003c/p\u003e\u003cp\u003eInitially, precise orbit files were applied to compute accurate satellite orbits, which significantly improve the geolocation accuracy of the images. Thermal Noise Removal (TNR) and Border Noise Removal (BNR) operations were performed to suppress instrument-induced noise and border artifacts, enhancing the overall image quality. Radiometric Calibration was then applied to convert the digital pixel values into backscatter coefficients (sigma nought), representing the radar reflectivity of the. Speckle Filtering was applied to further reduce the granular noise inherent in SAR images while preserving important structural information. Terrain Correction was then performed to project the images from the satellite\u0026rsquo;s slant range geometry into a geographic coordinate system, ensuring accurate spatial referencing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA thresholding technique was applied in SNAP software with a threshold value set at -17.22 to clearly separate flooded regions in the crisis image from other land cover types shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The extracted flooded areas were then overlaid onto the archive image to differentiate permanent water bodies from newly flooded zones. A binary flood map was generated and exported in KMZ format to support both 2D and 3D visualization of the inundated regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing Sentinel-2 Harmonized data along with Dynamic World Land Use Land Cover (LULC) data, the land use of the study area was classified with the help of Google Earth Engine (GEE) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The generated LULC map provided detailed information about the different land cover types present in the study area. Both the flood map and LULC map were reprojected to a common coordinate system and vectorized using QGIS software. Overlay analysis was then performed to spatially intersect the flood-inundated areas with the LULC classes, allowing interpretation of how land cover types were affected by the flood. Visual analysis indicated that agricultural areas were the most affected land cover class, followed by artificial surfaces such as bare soil and built-up regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor a more detailed temporal analysis, a coherence map of the study area was generated using Sentinel-1A SLC data from 30th June and 12th July 2023. The coherence estimation process involved geocoding the SLC images followed by coherence calculation to assess temporal stability in the area Which is shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. A low coherence value indicated areas that underwent significant change (such as flooding), while high coherence indicated stable surfaces. Additionally, an RGB Composite map was generated from the same SLC dataset through a sequence of debursting, radiometric calibration, multi-looking, coherence generation, and image stacking processes using SNAP software.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUrban areas typically appear bright in sigma0 VV images due to their complex structural characteristics and material composition. Buildings, roads, and other man-made structures create multiple scattering effects, where radar waves bounce between vertical surfaces and the ground before returning to the sensor. This double-bounce scattering enhances the backscatter intensity. Furthermore, urban materials such as concrete, metal, and glass have a high dielectric constant, further increasing radar reflectivity compared to natural surfaces like soil or vegetation. VV polarization is particularly sensitive to vertical structures, making it ideal for detecting urban features and flooded regions in mixed land cover areas. Therefore, VV polarization was chosen for this study to effectively detect and analyze the spatial extent of flooding.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe final quantitative analysis involved calculating the distribution of flood-inundated areas per LULC class. The above graph shows flood inundated areas per LULC class. The results indicated that the most severely affected class was agricultural land, followed by artificial surfaces, including built-up areas and bare soil. This detailed analysis provides important insights into flood vulnerability patterns, which can inform decision-making in flood risk management and mitigation.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study demonstrated an effective methodology for flood detection and land use land cover (LULC) impact assessment using multi-temporal remote sensing data from Sentinel-1A and Sentinel-2 satellites. The use of SAR imagery, particularly in VV polarization, proved to be highly efficient in identifying flooded areas due to the distinct low backscatter response of water surfaces. The combination of GRD and SLC data allowed not only flood mapping but also temporal coherence analysis, providing a deeper understanding of the surface changes caused by flooding events.\u003c/p\u003e\u003cp\u003eThe thresholding technique applied to the backscattering coefficient enabled accurate extraction of the flooded regions, while the overlay analysis with LULC maps provided clear insights into the types of land cover most affected by the flood. It was observed that agricultural areas suffered the highest impact, followed by artificial surfaces such as bare soil and built-up regions. This highlights the vulnerability of agricultural land to flood events, which has significant implications for regional planning and disaster management. Furthermore, the coherence map and RGB composite images contributed to a more precise interpretation of the flood dynamics, by visually highlighting areas of change and stability between the pre-flood and post-flood dates. The methodology established in this study can serve as a reliable framework for flood monitoring and assessment in other regions, especially in flood-prone areas where rapid and accurate flood mapping is crucial.\u003c/p\u003e\u003cp\u003eOverall, the integration of Sentinel-1A and Sentinel-2 data, combined with advanced processing techniques and spatial analysis, provides a robust approach for understanding flood impacts and supports effective decision-making in flood risk management and mitigation strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\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 Contribution\u003c/h2\u003e\u003cp\u003eIntroduction part is written by Dr. Pradnya Maheshmalkar , figure layout adjustment by Dr. shafiyoddin sayyed and result discussion-processing part is carried out by miss rajeshwari pangarkar.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors extend their gratitude to the Indian Meteorological Department (IMD)[11] for furnishing the rainfall data, and to the European Space Agency (ESA) for granting complimentary access to Sentinel data products.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDheeraj Raut, G.M., Sayyad, Sayyad, S.: Flood Mapping Using Microwave Remote Sensing. 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Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en.wikipedia.org/wiki?curid=8971610\u003c/span\u003e\u003cspan address=\"https://en.wikipedia.org/wiki?curid=8971610\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnusha, N., Bharathi, B.: Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data, \u003cem\u003eThe Egyptian Journal of Remote Sensing and Space Science\u003c/em\u003e, vol. 23, no. 2, pp. 207\u0026ndash;219, Aug. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejrs.2019.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrs.2019.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SAR, sentinel-1, Thresholding, Flood Detection","lastPublishedDoi":"10.21203/rs.3.rs-7607174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7607174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn early July 2023, the Yamuna River Basin in Delhi experienced severe flooding, primarily triggered by high-intensity rainfall and flash releases at the Okhla Barrage. The combination of rapid urbanization, impervious surfaces, and inadequate drainage infrastructure exacerbated flood impacts, leading to extensive inundation, property damage, and disruption of livelihoods. Accurate and timely flood mapping is essential for mitigating such urban flood risks and enhancing disaster preparedness. In this study, Sentinel-1 SAR data acquired before and during the flood were utilized to delineate inundated areas. The inherent sensitivity of SAR to surface water, due to reduced backscatter from specular reflection, makes it a reliable tool for identifying flood-prone zones. Flood extent was extracted through preprocessing and threshold-based band math operations. Additionally, Sentinel-2 harmonized data and Dynamic World classifications were integrated to assess Land Use/Land Cover (LULC) changes in the affected region. A coherence analysis using pre- and post-flood datasets further supported the detection of inundated zones. Results revealed significant impacts of flooding on different LULC classes, highlighting the vulnerability of built-up and agricultural areas. The study demonstrates the effectiveness of open-source SAR and optical datasets for flood detection and mapping in urban river basins, thereby offering a cost-effective and reliable approach to strengthen disaster response and urban resilience planning.\u003c/p\u003e","manuscriptTitle":"Utilization of Open-Source SAR Data for Flood Detection: Insights from The Yamuna River Basin Flood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 11:57:35","doi":"10.21203/rs.3.rs-7607174/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cec7367-0b06-4e1c-bfed-0cc57f1b8f4f","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-28T18:05:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 11:57:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7607174","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7607174","identity":"rs-7607174","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}