Detection and analysis of cyclonic events through satellite-based remote sensing techniques for coastal areas of Pakistan

Erosion induced coastal changes in Karasu, Türkiye: new evidence from GNSS and remote sensing data

Coastal regions are highly sensitive to both natural processes and anthropogenic pressures including settlement, tourism, industry, and transportation, in addition to the impacts of climate change. Continuous monitoring of these areas is therefore essential for sustainable coastal management and for enabling rapid response to potential erosion hazards. Such changes can be effectively monitored using geodetic and remote sensing techniques. The study area is located in the Karasu district of Sakarya Province, in the northwestern part of Türkiye, where significant coastal erosion has been observed. Erosion mitigation measures were initiated in 2010 following the identification of the problem. Since then, the shoreline has retreated approximately 100 m landward, although the construction of breakwaters has partially reduced the erosion rate. In this study, the current state of coastal change was investigated using open-access Sentinel-2 and Sentinel-1 datasets. The Normalized Difference Water Index (NDWI) was applied to optical imagery, while backscatter information derived from Synthetic Aperture Radar (SAR) data was used to delineate the land–water boundary. Additionally, shoreline data obtained from Global Navigation Satellite System (GNSS) observations were adopted as reference data for accuracy assessment. Based on GNSS surveys conducted at two-year intervals within the same period, an erosion area of 12,069 m² and an accretion area of 77,410 m² were quantified along an approximately 9 km coastal segment. The results indicate that shoreline extraction from Sentinel-2 imagery shows significantly higher consistency with GNSS-derived reference data compared to Sentinel-1 results. Overall, Sentinel-2 data provide more reliable and accurate shoreline delineation, suggesting that they are highly suitable for rapid and effective coastal erosion monitoring in support of sustainable coastal management.

Remote Sensing of Flood-Driven Water Quality Degradation Using Sentinel-2 MSI in Southern Brazil

Satellite-based remote sensing has become an essential tool for territorial monitoring and environmental analysis, particularly in the context of extreme events. The European Union’s Copernicus Programme provides open-access satellite data that support near-real-time environmental assessment. Among its missions, the Sentinel-2 satellites, equipped with the Multispectral Instrument (MSI), offer high spatial and spectral resolution through 13 spectral bands, enabling the detection and monitoring of water quality parameters in inland and coastal water bodies. In early May 2024, intense rainfall events in Rio Grande do Sul, southern Brazil, triggered one of the most severe flooding episodes recorded in the region over the past 40 years. The objective of this study is to assess the environmental impacts of this event by mapping the flooded areas and analysing changes in water quality using Sentinel-2 imagery. Sentinel-2 Level-1C (Top-of-Atmosphere) products were processed using the Copernicus Browser to evaluate variations in coloured dissolved organic matter (CDOM) and dissolved organic carbon (DOC). Temporal analysis based on true-colour composite images allowed the observation of a significant increase in water extent over a seven-day period. Temporal comparison of Sentinel-2 images acquired before, during, and after the flood consistently showed a progressive expansion of inundated areas, accompanied by spatially coherent increases in satellite-derived CDOM and DOC indicators during the flood peak, followed by a partial decrease after flood recession. Although no simultaneous field measurements were available for quantitative validation, the observed spatial–temporal patterns consistently indicate flood-induced deterioration of water quality. Elevated CDOM and DOC levels are commonly associated with increased organic matter inputs and may have implications for water availability and suitability for human and animal consumption. Given the expected increase in the frequency and intensity of extreme weather events driven by climate change, this study highlights the importance of satellite-based remote sensing for rapid environmental monitoring. The Sentinel satellite constellation demonstrates strong potential for near-real-time assessment, offering timely information and broad spatial coverage to support environmental management and decision-making.

A GeoAI framework for coastal flood risk assessment: integrating remote sensing and socioeconomic data

Bangladesh’s coastal zone is widely recognized as one of the most hazard-prone regions of the world. Within this vulnerable belt, Lakshmipur District stands for its long, highly exposed coastline and its recurrent experience of hydrometeorological hazards, including frequent floods, tidal surges, river overflows, and intense monsoon rainfall. The district’s flat topography and geographic setting further amplify its susceptibility to both seasonal and extreme flood events. Notably, two consecutive major floods in 2024 and 2025 affected approximately 600,000 and 50,000 people, respectively, with the 2024 event alone causing crop losses exceeding BDT 2.27 billion. In response to these escalating risks, this study develops a Geospatial Artificial Intelligence (GeoAI)-based flood risk mapping framework that integrates multi-source satellite Earth observation data and social datasets to map flood extents for 2024 and 2025 and to predict flood risk for Lakshmipur District. Flood extents were derived using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, while flood risk prediction employed a Random Forest classifier trained on elevation, slope, distance to rivers, precipitation, population, cropland, and built-up indices. The resulting probabilistic flood risk was classified into five categories ranging from very low to very high risk, with population and cropland exposure quantified at the union level. Model validation using 2025 flood data achieved an overall accuracy of 85.9% and a Cohen’s Kappa of 0.71, demonstrating strong predictive performance. The results highlight critical flood-risk and exposure hotspots, underscoring the utility of GeoAI-based approaches for enabling timely, efficient, and actionable flood risk assessments, particularly for vulnerable communities where early risk detection through mapping and modeling is essential for proactive disaster planning and targeted mitigation for natural disaster management.

High-resolution flood monitoring based on CYGNSS surface reflectivity: a case study of Sofala Province, Mozambique

Extreme precipitation associated with tropical cyclones frequently triggers severe flooding in low-lying coastal regions, highlighting the importance of timely and reliable flood monitoring. Conventional remote sensing approaches are often constrained by limited temporal resolution and cloud interference. To address these limitations, this study investigates the application of Cyclone Global Navigation Satellite System (CYGNSS) Level 1 observations for flood detection in Sofala Province, Mozambique, which was severely affected by Cyclone Idai in March 2019. Surface reflectivity (SR) is derived from delay Doppler map (DDM) measurements and employed to characterize surface water conditions. A threshold-based criterion is then applied to delineate inundated areas. The results are evaluated using Soil Moisture Active Passive (SMAP) soil moisture data, together with precipitation records and time series analysis. The findings indicate that SR responds sensitively to variations in surface water, enabling effective identification of flood extent. A strong consistency is observed between CYGNSS-derived results and SMAP observations, while CYGNSS additionally provides improved temporal resolution and all-weather monitoring capability. Time series analysis further reveals a clear linkage among precipitation, soil moisture dynamics, and flood evolution. Overall, this study demonstrates the effectiveness of GNSS-R observations for rapid flood monitoring, particularly in data-scarce regions.

Multi-Event Flood Inundation Detection Using Sentinel-1 SAR Ratio Image on Alluvial Plain: A Case Study of Klaten Regency, Indonesia

Periodic river flooding occurs in Klaten Regency, consistently striking within close time intervals; however, spatial documentation for this area is currently unavailable. This study applies the Ratio Image method to Sentinel-1 SAR imagery processed using Google Earth Engine, to delineate floodwater distribution during two flood events that occurred in Klaten Regency in 2026. The First event, on January 12–13, was caused by the overflow of the Jabang Bayi and Gawe Rivers, and the second, on March 3– 4 event caused by the breach of the Dengkeng River embankment. An empirical threshold of 1.5 was established based on the bimodal distribution observed in the histogram, after which post-processing steps were applied, comprising the masking of permanent water bodies, slope, HAND, land cover, and a connectivity filter. Detection results indicated a total inundation area of of 1,150.46 ha across 24 subdistricts for the January event and 666.24 ha across 23 subdistricts for the March event. All subdistricts reported by the Klaten Regency Disaster Management Agency (BPBD) for each respective event were successfully identified within these results, a level of agreement that points on the realiable performance of the ratio image method on alluvial plains irrespective of flood source.

An approach using very high-resolution remote sensing and geospatial tools for monitoring shoreline dynamics and assessing erosion risk in relation to hydrodynamic forcing along the central Algerian coastline