To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

For approximately 100 years, the Houston region has been adversely impacted by land subsidence associated with excessive groundwater withdrawals. The rapidly growing population in the Houston region means the ongoing subsidence must be vigilantly monitored. Interferometric synthetic aperture radar (InSAR) has become a powerful tool for remotely mapping land-surface deformation over time and space. However, the humid weather and the heavy vegetation have significantly degraded the performance of InSAR techniques in the Houston region. This study introduced an approach integrating GPS and Sentinel-1 InSAR datasets for mapping long-term (2015–2019) and short-term (inter-annual, seasonal) subsidence within the greater Houston region. The root-mean-square (RMS) of the detrended InSAR-displacement time series is able to achieve a subcentimeter level, and the uncertainty (95% confidence interval) of the InSAR-derived subsidence rates is able to achieve a couple of millimeters per year for 5-year or longer datasets. The InSAR mapping results suggest the occurrence of moderate ongoing subsidence (~1 cm/year) in nothwestern Austin County, northern Waller County, western Liberty County, and the city of Mont Belvieu in Champers County. Subsidence in these areas was not recognized in previous GPS-based investigations. The InSAR mapping results also suggest that previous GPS-based investigations overestimated the ongoing subsidence in southwestern Montgomery County, but underestimated the ongoing subsidence in the northeastern portion of the county. We also compared the InSAR- and GPS-derived seasonal ground movements (subsidence and heave). The amplitudes of the seasonal signals from both datasets are comparable, below 4 mm within non-subsiding areas and over 6 mm in subsiding (>1 cm/year) areas. This study indicates that groundwater-level changes in the Evangeline aquifer are the primary reason for ongoing long-term and seasonal subsidence in the Houston region. The former is dominated by inelastic deformation, and the latter is dominated by elastic deformation. Both could cause infrastructure damage. This study demonstrated the potential of employing the GPS- and InSAR-integrated method (GInSAR) for near-real-time subsidence monitoring in the greater Houston region. The near-real-time monitoring would also provide timely information for understanding the dynamic of groundwater storage and improving both long-term and short-term groundwater resource management.

Harris-Galveston Subsidence District (HGSD), in collaboration with several other agencies, has been operating a dense Global Navigation Satellite System (GNSS) network for subsidence and faulting monitoring within the Greater Houston region since the early 1990s. The GNSS network is designated HoustonNet, comprising approximately 250 permanent GNSS stations as of 2021. This paper documents the methods used to produce position time series, transform coordinates from the global to regional reference frames, identify outliers and steps, analyze seasonal movements, and estimate site velocities and uncertainties. The GNSS positioning methods presented in this paper achieve 2–4-mm RMS accuracy for daily positions in the north–south and east–west directions and 5–8-mm accuracy in the vertical direction within the Greater Houston region. Five-year or longer continuous observations are able to achieve submillimeter-per-year uncertainties (95% confidence interval) for both horizontal and vertical site velocities. Two decades of GNSS observations indicate that Katy in Fort Bend County, Jersey Village in northwestern Harris County, and The Woodlands in southern Montgomery County have been the areas most affected by subsidence ($1–2\mathrm{cm}/\mathrm{year}$ ) since the 2000s; the overall subsidence rate and the size of subsiding area ($>5\mathrm{mm}/\mathrm{year}$ ) have been decreasing as a result of the groundwater regulations enforced by HGSD and other local agencies. HoustonNet data and products are released to the public through HGSD. The primary products are the daily East-North-Up (ENU) position time series and site velocities with respect to the International GNSS Service (IGS) Reference Frame 2014 (IGS14), the stable Gulf of Mexico Reference Frame (GOM20), and the stable Houston Reference Frame (Houston20). The ENU position time series with respect to Houston20 are recommended for delineating subsidence and faulting within the Greater Houston region. The ENU time series with respect to GOM20 are recommended for studying subsidence and faulting within the Gulf coastal plain and sea-level changes along the Gulf Coast. The entire HoustonNet data set is reprocessed every a few years with updated positioning software, IGS and regional reference frames, and data analysis tools. We recommend that users use the most recent release of HoustonNet data products and avoid mixing old and new positions.

Cities in the northern Gulf of Mexico, such as Houston, have experienced one of the fastest rates of subsidence, with groundwater/hydrocarbon withdrawal being considered the primary cause. This work reports substantial ground subsidence in a few parts of Greater Houston and adjoining areas not reported before. Observation of surface deformation using interferometric synthetic aperture radar (InSAR) data obtained from Sentinel-1A shows total subsidence of up to 9 cm in some areas from 2016 to 2020. Most of the area within the Houston city limits shows no substantial subsidence, but growing suburbs around the city, such as Katy in the west, Spring and The Woodlands in the north and northwest, and Fresno in the south, show subsidence. In this study, we performed emerging hot spot analysis on InSAR displacement products to identify areas undergoing significant subsidence. To investigate the contributions of groundwater to subsidence, we apply optimized hot spot analysis to groundwater level data collected over the past 31 years from over 71,000 water wells and look at the correlation with fault surface deformation patterns. To evaluate the contribution of oil/gas pumping, we applied optimized hot spot analysis to known locations of oil and gas wells. The high rate of water pumping in the suburbs is the main driver of subsidence, but oil/gas withdrawal plays an important role in areas such as Mont Belvieu. Displacement time series shows that the Clodine, Hockley, and Woodgate faults are active, whereas the Long Point Fault shows no motion, although it was once very active.

Flooding and other natural calamities may wreak havoc on people's lives and property. Following the incident, a detailed estimate of the affected area is necessary to undertake rescue operations by deploying an emergency response team. Obtaining an accurate estimate of a flooded region has traditionally required many human resources, and it is time-consuming. In this work, a patch-based Convolutional Neural Network (CNN) model is proposed for quickly detecting floods on remote sensing data. Since SAR - synthetic aperture radar images are acquired with active sensors in any weather condition, these images are considered for flood mapping during both day and night. The SEN 12-FLOOD dataset consisting of SAR images covering the flood events in Western Africa, Iran, and Australia is used to assess the model's performance. The SAR images are divided into small patches, and these patches are fed to the network for training and prediction. The maximum vote is considered for deciding whether the region covered by an image is flooded or not. The proposed SAR-FloodN et outperforms the existing pre-trained models in flood detection, giving an accuracy of 95%.

National Capital Region (NCR, Delhi) in India is one of the fastest-growing metropolitan cities which is facing a severe water crisis due to increasing water demand. The over-extraction of groundwater, particularly from its unconsolidated alluvial deposits makes the region prone to subsidence. In this study, we investigated the effects of plummeting groundwater levels on land surface elevations in Delhi NCR using Sentinel-1 datasets acquired during the years 2014–2020. Our analysis reveals two distinct subsidence features in the study area with rates exceeding 11 cm/year in Kapashera—an urban village near IGI airport Delhi, and 3 cm/year in Faridabad throughout the study period. The subsidence in these two areas are accelerating and follows the depleting groundwater trend. The third region, Dwarka shows a shift from subsidence to uplift during the years which can be attributed to the strict government policies to regulate groundwater use and incentivizing rainwater harvesting. Further analysis using a classified risk map based on hazard risk and vulnerability approach highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of ground movement, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR.

This study investigated the rate of natural subsidence along the Texas coast using multidecadal to century tide gauge (TG) and global positioning system (GPS) data sets. The rates of land subsidence and sea level rise are aligned to the Gulf of Mexico (GOM) Reference Frame 2020 (GOM20), which is tied to the stable portion of the Gulf Coastal Plain. GOM20 provides a robust reference for ruling out regional ground movements associated with regional tectonics and glacial isostatic adjustment (GIA) and highlighting natural subsidence in the Gulf Coast Aquifer region. According to this study, the mean sea level rise rate within the GOM was $2.6\mathrm{mm}/\mathrm{year}$ with respect to GOM20 from the 1970s to the 2010s. Present land subsidence along the Texas coast is dominated by the natural subsidence varying from $0.7\mathrm{mm}/\mathrm{year}$ in the central coastal area (Port Mansfield, Corpus Christi, and Rockport) to $1.6\mathrm{mm}/\mathrm{year}$ in the southern coastal area (South Padre Island) to $1.5–3.5\mathrm{mm}/\mathrm{year}$ in the northern coastal area (Freeport, Galveston Island, Texas City, and Sabine Pass). The average natural subsidence rate along the 600-km Texas coastline is $1.4\mathrm{mm}/\mathrm{year}$ with respect to GOM20. Four scenarios (lowest, medium-low, medium-high, and highest) for future coastal submergence were developed by integrating the natural subsidence and sea level rise along the Texas coast with the global sea level scenarios. Our analysis projects that the average submergence along the Texas coastline from 2020 to 2100 will be greater than 0.3 m, and likely between 0.6 and 1.2 m, but is unlikely to exceed 2.0 m.

Wuhan, the largest city in central China, has experienced rapid urban development leading to land subsidence as well as environmental concerns in recent years. Although a few studies have analyzed the land subsidence of Wuhan based on ALOS-1, Envisat, and Sentinel-1 datasets, the research on long-term land subsidence is still lacking. In this study, we employed multi-temporal InSAR to investigate and reveal the spatiotemporal evolution of land subsidence over Wuhan with ALOS-1, Envisat, and Sentinel-1 images from 2007–2010, 2008–2010, 2015–2019, respectively. The results detected by InSAR were cross-validated by two independent SAR datasets, and leveling observations were applied to the calibration of InSAR-derived measurements. The correlation coefficient between the leveling and InSAR has reached 0.89. The study detected six main land subsidence zones during the monitoring period, with the maximum land subsidence velocity of −46 mm/a during the 2015–2019 analysis. Both the magnitude and the extent of the land subsidence have reduced since 2017. The causes of land subsidence are discussed in terms of urban construction, Yangtze river water level changes, and subsurface water level changes. Our results provide insight for understanding the causes of land subsidence in Wuhan and serve as reference for city management for reducing the land subsidence in Wuhan and mitigating the potential hazards.

The Himalayan main frontal thrust (MFT) accommodates most of the present-day Indo–Asia convergence with related periodic earthquakes. The seismicity and deformation mechanism varies considerably across the frontal Himalayas. We mapped a segment (Manzai Ranges) of the MFT at the western margin of the Himalayas and analyzed its deformation mechanism and active tectonics using geomorphic indices and the Interferometric Synthetic Aperture Radar (InSAR) Small Baseline Subset (SBAS) technique. Two frontal thrust faults (Khirgi and Jandola) were mapped using Sentinel-2B band ratios in the study area. Water gaps were present in the form of deflected streams at the tip of the growing anticlines. The C-band RADAR interferometry (Sentinel-1A) showed an average uplift of 5–9 mm/year in the satellite line of sight (LOS) from May 2018 to October 2019. The velocity profiles show an uplift variation across the anticlines and may be related to the displacement transfer from the zone of compression in the Manzai Ranges to the zone of transpression in the Pezu–Bhittani Ranges. Four types of morphometric analyses were carried out to assess the relative tectonic activity, namely mountain front sinuosity index (Smf), valley floor width to height ratio (V_f), normalized longitudinal river profile, and normalized channel steepness index (Ksn). The landscape response to active tectonics in the study area was recorded as a deep fluvial incision in V-shaped valleys, convex river profiles, topographic breaks as knickpoints, and a high Ksn index. The geomorphic parameters show a relative increase in tectonic uplift and deformation from the Kundi anticline to the Khirgi and Manzai anticline. We concluded that the frontal structures in the western Himalayas are still going through an active phase of deformation and landscape development with both seismic and aseismic creep.

Shandong peninsula, the largest peninsula of China, is prone to severe land subsidence hazards along the coastline. In this paper, we provide, for the first time, multi-scale and multi-dimensional time series deformation measurements of the entire Shandong peninsula with advanced time series Interferometric Synthetic Aperture Radar (InSAR) techniques. We derive the spatiotemporal evolutions of the land subsidence by integrating multi-track Sentinel-1A/B and RADARSAT-2 satellite images. InSAR measurements are cross validated by the independent deformation rate results generated from different SAR tracks, reaching a precision of less than 1.3 cm/a. Two-dimensional time series over the Yellow River Delta (YRD) from 2017 to 2019 are revealed by integrating time series InSAR measurements from both descending and ascending tracks. Land subsidence zones are mainly concentrated on the YRD. In total, twelve typical localized subsidence zones are identified in the cities of Dongying (up to 290 mm/a; brine and groundwater exploitation for industrial usage), Weifang (up to 170 mm/a; brine exploitation for industrial usage), Qingdao (up to 70 mm/a; aquaculture and land reclamation), Yantai (up to 50 mm/a; land reclamation) and Rizhao (up to 60 mm/a; land reclamation). The causal factors of localized ground deformation are discussed, encompassing groundwater and brine exploitation, aquaculture and land reclamation. Multi-scale surveys of spatiotemporal deformation evolution and mechanism analysis are critical to make decisions on underground fluid exploitation and land reclamation.

We have established a stable regional geodetic reference frame using long-history (13.5 years on average) observations from 55 continuously operated Global Navigation Satellite System (GNSS) stations adjacent to the Gulf of Mexico (GOM). The regional reference frame, designated as GOM20, is aligned in origin and scale with the International GNSS Reference Frame 2014 (IGS14). The primary product from this study is the seven-parameters for transforming the Earth-Centered-Earth-Fixed (ECEF) Cartesian coordinates from IGS14 to GOM20. The frame stability of GOM20 is approximately 0.3 mm/year in the horizontal directions and 0.5 mm/year in the vertical direction. The regional reference frame can be confidently used for the time window from the 1990s to 2030 without causing positional errors larger than the accuracy of 24-h static GNSS measurements. Applications of GOM20 in delineating rapid urban subsidence, coastal subsidence and faulting, and sea-level rise are demonstrated in this article. According to this study, subsidence faster than 2 cm/year is ongoing in several major cities in central Mexico, with the most rapid subsidence reaching to 27 cm/year in Mexico City; a large portion of the Texas and Louisiana coasts are subsiding at 3 to 6.5 mm/year; the average sea-level-rise rate (with respect to GOM20) along the Gulf coast is 2.6 mm/year with a 95% confidence interval of ±1 mm/year during the past five decades. GOM20 provides a consistent platform to integrate ground deformational observations from different remote sensing techniques (e.g., GPS, InSAR, LiDAR, UAV-Photogrammetry) and ground surveys (e.g., tide gauge, leveling surveying) into a unified geodetic reference frame and enables multidisciplinary and cross-disciplinary research.