SRSLab involves developing cutting-edge algorithms and advanced software for processing Synthetic Aperture Radar (SAR) datasets, of different Earth observation satellites, for measuring the Earth’s surface movements phenomena with mm-level accuracy and precision. Our contemporary research focused on deriving very high-resolution satellite data products for understanding groundwater dynamics and measuring induced land subsidence over India’s Indo-Gangetic Plains (IGP) region using advanced satellite radar interferometry techniques.
Research Interests
Multi-temporal InSAR technique using machine learning
Groundwater dynamics using satellite remote sensing
Mapping coastal flooding and land subsidence
Urban infrastructure monitoring
The Constrained-Network Propagation (C-NetP) technique is an advanced algorithm designed to improve the two-scale full-resolution SBAS-DInSAR performance for generating deformation time series. C-NetP follows a minimum-norm-constrained optimization problem based on a region-growing strategy for deformation measurement. The approach is more effective for medium-to-low coherence regions. The figure displays a comparison between the conventional two-scale SBAS with C-NetP results.
For more detail, check Ojha et al., 2015
The widespread decline of groundwater levels is associated with higher extraction rates. It is responsible for the large-scale deformation and infrastructure damages for many stressed aquifer systems in different regions across the globe. However, the InSAR-derived vertical land motion (VLM) integration with hydraulic head-level can provide a complete understanding of groundwater dynamics at the local-to-global scale. The figure shows various aquifer properties over the Center Valley of California with a groundwater depletion rate of about ~7km3/yr for 2007-2010.
For more details, check Ojha et al., 2018
Multi-temporal SAR interferometry (MT-InSAR) technique is suitable for urban infrastructure monitoring. However, a high-resolution SAR sensor (e.g., X-band of CSK) is more suitable for monitoring the intra-building movement in a densely urbanized region than a low-spatial resolution sensor (e.g., C-band ERS/ENVISAT). The figure-A displays a maximum displacement rate of about map 5 mm/yr over the south of Rome, Italy. The figure-B & C highlights a quantitative analysis of deformation time series between low to high-resolution SAR data.
For more detail, check Sansosti et al., 2015
The synthetic Aperture Radar (SAR) imaging technique can be useful in providing a semi-real-time mapping of coastal flooding and associated damage over a large area. The figure shows the investigation of Sentinel-1 SAR data using a probabilistic Bayesian approach for flood mapping affected in the Kerala state during August 2018. Extensive flooding was observed in the Alappuzha and Kottayam districts in the state.
For more details, check Sherpa et al., 2020