Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s Index
Patna, the capital of Bihar, is among the cities most severely affected by floods in India. It is primarily due to its geographic location, being bordered by the Ganga, Sone, and Punpun rivers, which significantly increases its vulnerability to flooding. Our study aims to quantify the dynamic nature...
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Copernicus Publications
2025-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1367/2025/isprs-archives-XLVIII-G-2025-1367-2025.pdf |
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| author | R. Soudagar A. K. Thakur |
| author_facet | R. Soudagar A. K. Thakur |
| author_sort | R. Soudagar |
| collection | DOAJ |
| description | Patna, the capital of Bihar, is among the cities most severely affected by floods in India. It is primarily due to its geographic location, being bordered by the Ganga, Sone, and Punpun rivers, which significantly increases its vulnerability to flooding. Our study aims to quantify the dynamic nature of Patna's floods using statistical parameters, including global correspondence based on Moran’s Index. The flood extents required for statistical analysis were generated by applying Otsu thresholding to Sentinel-1 Synthetic Aperture Radar (SAR) data in Google Earth Engine (GEE) for the 2023 monsoon period (July to October). Spline interpolation was used to smooth the data, generating a continuous curve that fits the original discrete measurements. Spatiotemporal analysis revealed significant variability in water extent, peaking at 484.13 sq. km on 3rd September and receding to 97.88 sq. km on 20th September. The correspondence values indicate a significant shift in flooded areas throughout the monsoon period. The reason may be attributed to the combined effect of change in local rainfall patterns, poor drainage system and poor flood management in the upper reaches of Ganga. Further, validation with high-resolution PlanetScope data shows an overall accuracy of 93.10% and an F1 score of 0.8416. Overall, the findings provide valuable insights into flood management and disaster preparedness in the region. |
| format | Article |
| id | doaj-art-38d7cafff9254e77b2ed3fedb02095b5 |
| institution | DOAJ |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
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| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-38d7cafff9254e77b2ed3fedb02095b52025-08-20T02:47:46ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-08-01XLVIII-G-20251367137210.5194/isprs-archives-XLVIII-G-2025-1367-2025Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s IndexR. Soudagar0A. K. Thakur1Department of Civil Engineering, Indian Institute of Technology Roorkee, Haridwar, IndiaDepartment of Civil Engineering, Indian Institute of Technology Roorkee, Haridwar, IndiaPatna, the capital of Bihar, is among the cities most severely affected by floods in India. It is primarily due to its geographic location, being bordered by the Ganga, Sone, and Punpun rivers, which significantly increases its vulnerability to flooding. Our study aims to quantify the dynamic nature of Patna's floods using statistical parameters, including global correspondence based on Moran’s Index. The flood extents required for statistical analysis were generated by applying Otsu thresholding to Sentinel-1 Synthetic Aperture Radar (SAR) data in Google Earth Engine (GEE) for the 2023 monsoon period (July to October). Spline interpolation was used to smooth the data, generating a continuous curve that fits the original discrete measurements. Spatiotemporal analysis revealed significant variability in water extent, peaking at 484.13 sq. km on 3rd September and receding to 97.88 sq. km on 20th September. The correspondence values indicate a significant shift in flooded areas throughout the monsoon period. The reason may be attributed to the combined effect of change in local rainfall patterns, poor drainage system and poor flood management in the upper reaches of Ganga. Further, validation with high-resolution PlanetScope data shows an overall accuracy of 93.10% and an F1 score of 0.8416. Overall, the findings provide valuable insights into flood management and disaster preparedness in the region.https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1367/2025/isprs-archives-XLVIII-G-2025-1367-2025.pdf |
| spellingShingle | R. Soudagar A. K. Thakur Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s Index The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s Index |
| title_full | Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s Index |
| title_fullStr | Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s Index |
| title_full_unstemmed | Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s Index |
| title_short | Quantifying Spatiotemporal Variability of Patna's 2023 Monsoon Flood Using Sentinel-1 SAR Data and Moran’s Index |
| title_sort | quantifying spatiotemporal variability of patna s 2023 monsoon flood using sentinel 1 sar data and moran s index |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1367/2025/isprs-archives-XLVIII-G-2025-1367-2025.pdf |
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