Coastal Subsidence is a complex phenomenon with large spatiotemporal variability to natural processes and anthropogenic activities, leading to the potential inundation risk of major coastal cities worldwide with the increase in relative sea level rise and shoreline sinking (Shirzaei et al., 2021). India’s coastal metro-politician cities are vulnerable to future inundation risk due to the rising sea level. Kerala, a southern state in India with about 590 km of coastline covering vast habitats of rich biodiversity and occupants, has faced the impacts of coastal subsidence for the past few decades. There is a wide scope of detail investigating Kerala’s coastal land subsidence and its impact due to the rise in sea level using geodetic techniques. In this context, we explored the data from Sentinel-1 of ESA acquired along the descending track (Path 63 and 165) with a VV polarization for monitoring subsidence. The Vertical Land Motion (VLM) is analyzed using the Small BAseline Subset (SBAS) based MT-InSAR technique along the entire coastline of Kerala which is spread across 590 km (Berardino et al., 2002). A total of 1443 interferograms were generated by co-registering 326 single-look complex images and applying a spatial baseline threshold of 85 m and a temporal baseline threshold of 65 days. The result shows that most regions in Kerala are subsiding at a rate of > 5mm/year, with the Kuttanad region of Alappuzha showing a maximum subsidence of > 20mm/year. The tide gauge station of Kochi Willingdon Island shows a relative sea level trend of 1.97±mm/year. NASA’s Intergovernmental Panel on Climate Change (IPCC) AR6 report has projected a future sea level change of 0.71 meters by 2100, considering the socioeconomic scenario SSP3-7.0. The InSAR-derived VLM, projection data of sea level from the IPCC-AR6 report, and the high spatial resolution of the Digital Elevation Model (DEM) have been incorporated to map the low-lying fast subsiding zones that are prone to future flood inundation due to relative sea level rise for all the 14 districts of Kerala. We further aim to incorporate a U-net-based deep learning model to efficiently handle these terabytes of data and develop more accurate inundation maps. This study will be helpful for policymakers to take precautionary measures to prevent future inundation hazards over the shoreline. 

References

Shirzaei, M., et al. (2021). "Measuring, modelling and projecting coastal land subsidence." Nature Reviews Earth, Environment 2(1): 40-58.

Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti, (2002). "A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms." IEEE Transactions on Geoscience and Remote Sensing 40 (11): 2375–83. https://doi.org/10.1109/TGRS.2002.803792.