Environmental Management Hazards 2423-415X 13 1 2026 06 20 Automatic and Fast Flood Monitoring Using Remote Sensing Data and the GEE System (Study Area: Abdollah Abad Watershed, Semnan Province) Automatic and Fast Flood Monitoring Using Remote Sensing Data and the GEE System (Study Area: Abdollah Abad Watershed, Semnan Province) 21 36 107600 10.22059/jhsci.2026.412893.932 FA Peyman Peymankhah PhD Student of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran 0009-0008-4957-9531 Meysam Argany Associate Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran 0000-0001-6577-4443 Saeid Hamzeh Associate Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran 0000-0001-9233-8460 Journal Article 2026 04 17 Objective: This study investigates the automated and rapid monitoring of floods using remote sensing data and the Google Earth Engine (GEE) system in the Abdullah Abad watershed, Semnan Province. The aim of this study is to develop an efficient and automated method for identifying and monitoring floods in the shortest possible time using satellite data and rapid processing within the GEE platform. Methodology: This research was conducted using time-series data from Sentinel-1 and Landsat-8 satellites, along with other auxiliary datasets, within the GEE system. To monitor floods, the physiographic characteristics of the watershed were first examined, and the hydrological soil groups of the region were extracted to analyze the relationship between geological features and flood occurrence. Subsequently, an interactive web-based user interface was developed within the Earth Engine Apps platform, which automatically identifies and displays flood-affected areas. This interface, by integrating radar and optical data, detects water surface changes and classifies flood probability into three levels: low, medium, and high. Findings: The results demonstrated that the developed method can accurately identify flood-affected areas. Comparing flood monitoring maps with hydrological soil group maps confirmed a direct correlation between soil permeability and flood intensity. Areas with low permeability exhibited the highest likelihood of flooding, with flood-affected regions mainly located upstream of Abdullahabad village. Additionally, the low-slope plains in the region provided favorable conditions for flood expansion. Conclusion: This study revealed that the use of satellite imagery and automated processing in online systems can serve as an efficient tool for rapid flood monitoring and crisis management. The proposed method enables timely access to critical information for planning preventive measures and emergency response actions. Objective: This study investigates the automated and rapid monitoring of floods using remote sensing data and the Google Earth Engine (GEE) system in the Abdullah Abad watershed, Semnan Province. The aim of this study is to develop an efficient and automated method for identifying and monitoring floods in the shortest possible time using satellite data and rapid processing within the GEE platform. Methodology: This research was conducted using time-series data from Sentinel-1 and Landsat-8 satellites, along with other auxiliary datasets, within the GEE system. To monitor floods, the physiographic characteristics of the watershed were first examined, and the hydrological soil groups of the region were extracted to analyze the relationship between geological features and flood occurrence. Subsequently, an interactive web-based user interface was developed within the Earth Engine Apps platform, which automatically identifies and displays flood-affected areas. This interface, by integrating radar and optical data, detects water surface changes and classifies flood probability into three levels: low, medium, and high. Findings: The results demonstrated that the developed method can accurately identify flood-affected areas. Comparing flood monitoring maps with hydrological soil group maps confirmed a direct correlation between soil permeability and flood intensity. Areas with low permeability exhibited the highest likelihood of flooding, with flood-affected regions mainly located upstream of Abdullahabad village. Additionally, the low-slope plains in the region provided favorable conditions for flood expansion. Conclusion: This study revealed that the use of satellite imagery and automated processing in online systems can serve as an efficient tool for rapid flood monitoring and crisis management. The proposed method enables timely access to critical information for planning preventive measures and emergency response actions. https://jhsci.ut.ac.ir/article_107600_21dc2ceb80622778fa73adb4a09d1ceb.pdf