Interoperability of -Band Sentinel-1 SAR and GRACE Satellite Sensors on PSInSAR-Based Urban Surface Subsidence Mapping of Varanasi, India

The Sentinel-1 is an active synthetic aperture radar (SAR) satellite with a <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>-band SAR sensor operating at a center frequency of 5.405 GHz and a wavelength of 5.55 cm. With the availability of freely available SAR datasets from Sentinel-1, the persistent scatterer SAR interferometry (PSInSAR)-based urban surface subsidence mapping has become easy. However, apart from geological causes, the major cause of urban surface subsidence is the over-exploitation of groundwater that results in piezometric pressure loss in the aquifers resulting in net subsidence. With the Gravity Recovery and Climate Experiment (GRACE) satellite sensor, the groundwater level fluctuations can be very easily studied temporally. But the coarse spatial resolution of GRACE data makes the study of groundwater fluctuations difficult to study for smaller watersheds. This study aims to correlate the PSInSAR average surface line of sight (LOS) displacement from Sentinel-1, with the average groundwater fluctuations from the GRACE sensor temporally from May 2017 to February 2022. The study also compared the correlation between PSInSAR displacement in both VV and VH polarizations and observed the <inline-formula> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> values to be 0.63 and 0.65 for VV and VH polarizations, respectively, with GRACE data. After that, using the displacement and groundwater level fluctuation data, an estimation of a gravimetric anomaly due to a decrease in groundwater level was carried out for Varanasi city. The <inline-formula> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula>, mean absolute error (MAE), and root-mean-square error (RMSE) were observed to be 0.84, 1.23, and 2.4, respectively, in gravimetric anomaly estimation, thus giving sufficient acceptance for interoperability of the two sensors.

[1]  T. Farr,et al.  Using Sentinel-1 and GRACE satellite data to monitor the hydrological variations within the Tulare Basin, California , 2022, Scientific Reports.

[2]  S. Gaur,et al.  Analysis of the Spatio-Temporal Variation of the Thermal Pattern of River Ganges in Proximity to Varanasi, India , 2022, Journal of the Indian Society of Remote Sensing.

[3]  Md. Moniruzzaman,et al.  Delineating Flood Zones upon Employing Synthetic Aperture Data for the 2020 Flood in Bangladesh , 2022, Earth Systems and Environment.

[4]  Rachel L. Gomes,et al.  Comparative Study of Groundwater-Induced Subsidence for London and Delhi Using PSInSAR , 2021, Remote. Sens..

[5]  R. Tiwari,et al.  Utilisation of spaceborne C-band dual pol Sentinel-1 SAR data for simplified regression-based soil organic carbon estimation in Rupnagar, Punjab, India , 2021, Advances in Space Research.

[6]  Liming Jiang,et al.  ARU-Net: Reduction of Atmospheric Phase Screen in SAR Interferometry Using Attention-Based Deep Residual U-Net , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[7]  H. K. Pandey,et al.  Groundwater Stress Analysis using GIS in Parts of Varanasi District, Uttar Pradesh, India , 2021, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences.

[8]  I. Michaelis,et al.  Observing Earth’s magnetic environment with the GRACE-FO mission , 2021, Earth, Planets and Space.

[9]  R. Tiwari,et al.  Spaceborne C-band SAR remote sensing–based flood mapping and runoff estimation for 2019 flood scenario in Rupnagar, Punjab, India , 2021, Environmental Monitoring and Assessment.

[10]  Praveen K. Thakur,et al.  Decadal Urban Land Use/Land Cover Changes and Its Impact on Surface Runoff Potential for the Dhaka City and Surroundings Using Remote Sensing , 2020, Remote. Sens..

[11]  R. Tiwari,et al.  Role of Space-Borne Remote Sensing Technology for Monitoring of Urban and Environmental Hazards , 2021, Earth and Environmental Sciences Library.

[12]  Zhenfeng Shao,et al.  Monitoring, analyzing and predicting urban surface subsidence: A case study of Wuhan City, China , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Daniele Perissin,et al.  Land Subsidence Susceptibility Mapping Using Persistent Scatterer SAR Interferometry Technique and Optimized Hybrid Machine Learning Algorithms , 2021, Remote. Sens..

[14]  Timo Balz,et al.  Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data , 2021, Remote. Sens..

[15]  Elias C. Massoud,et al.  Groundwater Depletion Signals in the Beqaa Plain, Lebanon: Evidence from GRACE and Sentinel-1 Data , 2021, Remote. Sens..

[16]  S. Gaur,et al.  Study of Methods Available for Groundwater and Surfacewater Interaction: A Case Study on Varanasi, India , 2021 .

[17]  I. Rousta,et al.  Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh , 2020, SN Applied Sciences.

[18]  Wolfgang Rauch,et al.  Impacts of urban development on urban water management - Limits of predictability , 2020, Comput. Environ. Urban Syst..

[19]  A. Tripathi,et al.  Synergetic utilization of sentinel-1 SAR and sentinel-2 optical remote sensing data for surface soil moisture estimation for Rupnagar, Punjab, India , 2020, Geocarto International.

[20]  A. S. Raghubanshi,et al.  Green space indicators in a social-ecological system: A case study of Varanasi, India , 2020 .

[21]  Zhigui Kang,et al.  Extending the Global Mass Change Data Record: GRACE Follow‐On Instrument and Science Data Performance , 2020, Geophysical Research Letters.

[22]  W. Niemeier,et al.  Ground Deformation in The Ciloto Landslides Area Revealed by Multi-Temporal InSAR , 2020, Geosciences.

[23]  F. Landerer,et al.  Continuity of Ice Sheet Mass Loss in Greenland and Antarctica From the GRACE and GRACE Follow‐On Missions , 2020, Geophysical Research Letters.

[24]  Pravin S. Rana,et al.  UN SDGs and Context of Holy-Heritage Cities in India: A Study of Ayodhya and Varanasi , 2020 .

[25]  Prafull Singh,et al.  Climate change effect on water resources in Varanasi district, India , 2019, Meteorological Applications.

[26]  W. Filho,et al.  Assessing the impacts of climate change in cities and their adaptive capacity: Towards transformative approaches to climate change adaptation and poverty reduction in urban areas in a set of developing countries. , 2019, The Science of the total environment.

[27]  Huadong Guo,et al.  Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017) , 2019, Remote Sensing of Environment.

[28]  Lorenzo Solari,et al.  Evaluation of subsidence induced by long-lasting buildings load using InSAR technique and geotechnical data: The case study of a Freight Terminal (Tuscany, Italy) , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[29]  Sachin Mishra,et al.  Impact of Municipal Solid Waste Landfill leachate on groundwater quality in Varanasi, India. , 2019, Groundwater for Sustainable Development.

[30]  C. Kuo,et al.  Quantifying Vertical Deformation in the Tigris–Euphrates Basin Due to the Groundwater Abstraction: Insights from GRACE and Sentinel-1 Satellites , 2019, Water.

[31]  Frank Flechtner,et al.  Contributions of GRACE to understanding climate change , 2019, Nature Climate Change.

[32]  A. Sarangi,et al.  Analysis of Drivers of Trends in Groundwater Levels Under Rice–Wheat Ecosystem in Haryana, India , 2019, Natural Resources Research.

[33]  F. Cian,et al.  Sentinel-1 for Monitoring Land Subsidence of Coastal Cities in Africa Using PSInSAR: A Methodology Based on the Integration of SNAP and StaMPS , 2019, Geosciences.

[34]  S. Singh,et al.  Decadal scale geomorphic changes and tributary confluences within the Ganga River valley in Varanasi region, Ganga Plain, India , 2019, Quaternary International.

[35]  Hui Lin,et al.  Mapping the Yellow River Delta land subsidence with multitemporal SAR interferometry by exploiting both persistent and distributed scatterers , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[36]  Anja du Plessis.  Current and Future Water Scarcity and Stress , 2019 .

[37]  Shashi Kumar,et al.  X-band persistent SAR interferometry for surface subsidence detection in Rudrapur City, India , 2018, Remote Sensing.

[38]  W. Dietrich,et al.  Quantification of the seasonal hillslope water storage that does not drive streamflow , 2018 .

[39]  Esmaeil Alizadeh,et al.  Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery , 2017, Arabian Journal of Geosciences.

[40]  P. Rai,et al.  Applying Remote Sensing and GIS in Study of Physical and Cultural Aspects of Varanasi District , 2017 .

[41]  Srinivas Bettadpur,et al.  High‐resolution CSR GRACE RL05 mascons , 2016 .

[42]  Ramon F. Hanssen,et al.  Phase Estimation for Distributed Scatterers in InSAR Stacks Using Integer Least Squares Estimation , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Giorgio Boni,et al.  Use of SAR Data for Detecting Floodwater in Urban and Agricultural Areas: The Role of the Interferometric Coherence , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[44]  N. J. Raju,et al.  Assessment of pollution potential of leachate from the municipal solid waste disposal site and its impact on groundwater quality, Varanasi environs, India , 2016, Arabian Journal of Geosciences.

[45]  Federico Raspini,et al.  PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization , 2016, Remote. Sens..

[46]  Frank Flechtner,et al.  What Can be Expected from the GRACE-FO Laser Ranging Interferometer for Earth Science Applications? , 2016, Surveys in Geophysics.

[47]  V. Chowdary,et al.  Spatio–temporal analysis of groundwater resources in Jalandhar district of Punjab state, India , 2015, Sustainable Water Resources Management.

[48]  Mengdao Xing,et al.  Sparse Regularization of Interferometric Phase and Amplitude for InSAR Image Formation Based on Bayesian Representation , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[49]  K. Raju,et al.  Water in Ancient Indian Perspective and Ponds of Varanasi as Water Harvesting Structures , 2015 .

[50]  Nicola Casagli,et al.  Multi-Temporal Evaluation of Landslide Movements and Impacts on Buildings in San Fratello (Italy) By Means of C-Band and X-Band PSI Data , 2015, Pure and Applied Geophysics.

[51]  Z. Su,et al.  Surface–groundwater interactions in hard rocks in Sardon Catchment of western Spain: An integrated modeling approach , 2014 .

[52]  Colm Jordan,et al.  Simulating SAR geometric distortions and predicting Persistent Scatterer densities for ERS-1/2 and ENVISAT C-band SAR and InSAR applications: Nationwide feasibility assessment to monitor the landmass of Great Britain with SAR imagery , 2014 .

[53]  Ty P. A. Ferré,et al.  Direct measurement of subsurface mass change using the variable baseline gravity gradient method , 2014 .

[54]  R. Tomás,et al.  Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain , 2013, Environmental Earth Sciences.

[55]  W. Dietrich,et al.  Rain, rock moisture dynamics, and the rapid response of perched groundwater in weathered, fractured argillite underlying a steep hillslope , 2012 .

[56]  A. Monti Guarnieri,et al.  Sentinel 1 SAR interferometry applications: The outlook for sub millimeter measurements , 2012 .

[57]  K. Novakowski,et al.  Extremely rapid and localized recharge to a fractured rock aquifer , 2009 .

[58]  U. Shukla,et al.  Migration of the Ganga river and its implication on hydro-geological potential of Varanasi area, U.P., India , 2008 .

[59]  M. Agrawal,et al.  Annual and seasonal variations in tropospheric ozone concentrations around Varanasi , 2008 .

[60]  S. Moretti,et al.  Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project , 2006 .

[61]  K. A. Haq.  Water Management in Dhaka , 2006 .

[62]  Walid Tabbara,et al.  An interferometric coherence optimization method in radar polarimetry for high-resolution imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[63]  X. Sánchez-Vila,et al.  Introductory review of specific factors influencing urban groundwater, an emerging branch of hydrogeology, with reference to Barcelona, Spain , 2005 .

[64]  Timothy R. Oke,et al.  Flux and turbulence measurements at a densely built‐up site in Marseille: Heat, mass (water and carbon dioxide), and momentum , 2004 .

[65]  Wenke Sun,et al.  A Network of Superconducting Gravimeters Detects Submicrogal Coseismic Gravity Changes , 2004, Science.

[66]  David E. Weinstein,et al.  A Search for Multiple Equilibria in Urban Industrial Structure , 2004 .

[67]  In-Sik Choi,et al.  Efficient radar target classification using adaptive joint time-frequency processing , 2000 .

[68]  C S Agarwal,et al.  Study of drainage pattern through aerial data in Naugarh area of Varanasi district, U.P. , 1998 .

[69]  Qiang Huang,et al.  Underwater target classification using wavelet packets and neural networks , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).