Chapter Seven - Data Sources

• Analyzed GNSS calibration effectiveness on different InSAR algorithms. • Compared calibrated InSAR displacements with submillimeter precision NDTs. • Used polynomial regressions to model and correct InSAR displacement errors. • Validated improvement locally and globally using GNSS and levelling data. • Found linear adjustment to be the best justified option for calibration in most cases. This article presents an analysis of the effectiveness of performing a calibration through Global Navigation Satellite System (GNSS) data on different approaches of the Interferometric Synthetic Aperture Radar (InSAR). InSAR has become a key technique for monitoring ground surface, but its results often exhibit systematic biases and deviations from ground truth measurements. This study addresses the need for more reliable displacement data by evaluating a GNSS-based calibration approach across three InSAR processing approaches: i) Persistent Scattering Interferometry (PSI) method using the open-source software StaMPS, ii) PSI method using the commercial SARPROZ software and iii) the Quasi-PS method implemented with SARPROZ. Sentinel-1 A/B data from both ascending and descending orbits, covering the period from October 2017 to January 2019, were used. Displacement errors from the same points were modeled using polynomial regressions and calibrated using reference data from GNSS and levelling, both with submillimeter precision. The effectiveness of the calibration was assessed at two levels: locally (via a corner reflector) and globally (via a GNSS baseline several kilometers long). Results show that 25 out of 27 displacement time series required correction in the mean value, with StaMPS showing the greatest need for calibration. The calibration improved the original results at all scales and acted mainly on the mean difference, with linear calibration being the most robust and consistent option. These findings highlight the importance of incorporating ground truth data to enhance InSAR reliability and support its application in geodetic and structural monitoring.

This study proposes a new set of processing procedures based on the strain model and the Kalman filter (SM-Kalman) to obtain high-precision three-dimensional surface deformation time series from interferometric synthetic aperture radar (InSAR) and global navigation satellite system (GNSS) data. Implementing the Kalman filter requires the establishment of state and observation equations. In the time domain, the state equation is generated by fitting the pre-existing deformation time series based on a deformation model containing linear and seasonal terms. In the space domain, the observation equation is established with the assistance of the strain model to realize the spatial combination of InSAR and GNSS observation data at each moment. Benefiting from the application of the Kalman filter, InSAR and GNSS data at different moments can be synchronized. The time and measurement update steps are performed dynamically to generate a 3-D deformation time series with high precision and a high resolution in the temporal and spatial domains. Sentinel-1 SAR and GNSS datasets in the Los Angeles area are used to verify the effectiveness of the proposed method. The datasets include twenty-seven ascending track SAR images, thirty-four descending track SAR images and the daily time series of forty-eight GNSS stations from January 2016 to November 2018. The experimental result demonstrates that the proposed SM-Kalman method can produce high-precision deformation results at the millimeter level and provide two types of 3-D deformation time series with the same temporal resolution as InSAR or GNSS observations according to the needs of users. The new method achieves a high degree of temporal and spatial fusion of GNSS and InSAR data.

The Choushui River alluvial fan is an important agricultural production region in western Taiwan. This insufficient level of rainfall replenishment, combined with the unauthorized use of wells to irrigate crops, exacerbated land subsidence in the region. In this study, levelling, global navigation satellite system (GNSS), and interferometric synthetic aperture radar (InSAR) observation data were collected from 2018 to 2021 for analysis. The GNSS data were processed using the precise point positioning method, and the results were compared with data processed using the traditional static relative positioning method. The 3-year cumulative subsidence at the subsidence centre was approximately 9 cm in Changhua and 19 cm in Yunlin. To compensate for the lack of spatial resolution of GNSS and improve the accuracy of InSAR, the InSAR, GNSS, and levelling data were integrated, and these results were validated against levelling check points. They showed an average error of 0.4 cm/year in the annual subsidence rate. Overall, the GNSS had high precision and continuity, making it suitable for real-time early warning of land subsidence. InSAR obtained data with higher spatial resolution, allowing for early detection of new land subsidence areas and comprehensive monitoring of changes in land subsidence across the entire region.

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

Mass movements represent a serious threat to the stability of human structures and infrastructures, and cause loss of lives and severe damages to human properties every year worldwide. Built structures located on potentially unstable slopes are susceptible to deformations due to the displacement of the ground that at worst can lead to total destruction. Synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites and processed by multi-temporal interferometric SAR (MT-InSAR) techniques can measure centimeter to millimeter-level displacement with weekly to monthly updates, characterizing long-term large-scale behavior of the buildings and slopes. However, the spatial resolution and short wavelength weaken the performance of Sentinel-1 in recognizing features (i.e., single buildings) inside image pixels and maintaining the coherence in mountainous vegetated areas. We have proposed and applied a methodology that combines Sentinel-1 interferometry with ground-based geomatics techniques, i.e., global navigation satellite system (GNSS), terrestrial laser scanning (TLS) and terrestrial structure from motion photogrammetry (SfM), for fully assessing building deformations on a slope located in the north-eastern Italian pre-Alps. GNSS allows verifying the ground deformation estimated by MT-InSAR and provides a reference system for the TLS and SfM measurements, while TLS and SfM allow the behavior of buildings located in the investigated slope to be monitored in great detail. The obtained results show that damaged buildings are located in the most unstable sectors of the slope, but there is no direct relationship between the rate of ground deformation of these sectors and the temporal evolution of damages to a single building, indicating that mass movements cause the displacement of blocks of buildings and each of them reacts differently according to its structural properties. This work shows the capability of MT-InSAR, GNSS, TLS and SfM in monitoring both buildings and geological processes that affect their stability, which plays a key role in geohazard analysis and assessment.

The purpose of this research is to systematize contemporary high-precision geodesy methods and assess their efficiency for monitoring Earth surface deformations. The study reviews operational principles and technical characteristics of Global Navigation Satellite Systems (GNSS), Continuously Operating Reference Station (CORS) networks, Interferometric Synthetic Aperture Radar (InSAR) techniques, and hybrid approaches that combine satellite-based and terrestrial measurements. Examples of practical applications are provided for monitoring engineering structures, natural landscapes, and infrastructure facilities, including the integration of Unmanned Aerial Vehicles (UAVs) for photogrammetric surveys. The analysis covers accuracy parameters, temporal resolution, spatial coverage, cost efficiency, and operational constraints of each method. Specific attention is given to error sources such as atmospheric delays, multipath effects, and limitations in dense urban or forested environments. The research proposes a comparative framework for selecting optimal monitoring techniques based on object-specific conditions, required precision, and resource availability. It also identifies trends shaping the future of geodetic monitoring, including the implementation of GNSS Precise Point Positioning in Real Time Kinematic mode (PPP-RTK), artificial intelligence integration for automated deformation detection and classification, multi-sensor data fusion (GNSS, InSAR, LiDAR, photogrammetry), and expanded access to free satellite radar imagery. The study emphasizes the importance of engineering and technical support, covering advanced GNSS receivers, high-precision total stations, digital levels, and specialized processing software for both GNSS and InSAR data. GIS integration is highlighted as a critical step for visualizing and analyzing deformation patterns within unified spatial analytical environments. The findings confirm that combining multiple geodetic techniques significantly increases the reliability and spatial completeness of deformation monitoring, supporting timely decision-making in safety-critical and geodynamically unstable areas.

Studies of the earthquake cycle benefit from long‐term time‐dependent slip modeling, as it can be a powerful means to improve our understanding on the interaction of earthquake cycle processes such as interseismic, coseismic, post seismic, and aseismic slip. Observations from Interferometric Synthetic Aperture Radar (InSAR) allow us to model slip at depth with a higher spatial resolution than when using Global Navigation Satellite Systems (GNSS) alone. While the temporal resolution of InSAR has typically been limited, the recent fleet of SAR satellites including Sentinel‐1, COSMO‐SkyMED, and RADARSAT‐2 permits the use of InSAR for time‐dependent slip modeling at intervals of a few days when combined. With the vast amount of SAR data available, simultaneous data inversion of all epochs becomes challenging. Here we expanded the original network inversion filter to include InSAR observations of surface displacements in addition to GNSS. In the Network Inversion Filter (NIF) framework, geodetic observations are limited to those of a given epoch, with a stochastic model describing slip evolution over time. The combination of the Kalman forward filtering and backward smoothing allows all geodetic observations to constrain the complete observation period. Combining GNSS and InSAR allows modeling of time‐dependent slip at unprecedented spatial resolution. We validate the approach with a simulation of the 2006 Guerrero slow slip event. We highlight the importance of including InSAR covariance information and demonstrate that InSAR provides an additional constraint on the spatial extent of the slow slip.

Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes.

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.

The Wadi Hagul region in the eastern desert of Egypt is facing seismic hazards and increased human activity. This study uses remote sensing and geodetic methods to monitor and analyze recent deformation in the area. Interferometric Synthetic Aperture Radar (InSAR) data from the Sentinel-1A satellite and Global Navigation Satellite System (GNSS) data were combined to track surface movements and deformations accurately. The study analyzed InSAR data from February 4, 2020, to February 07, 2024, and GNSS data from the Wadi Hagul geodetic network established in July 2022 and monitored until January 2024. Despite the relatively short GNSS monitoring period, it provided valuable insights into recent deformation trends. By integrating data from ten GNSS stations, including International Geodetic stations (IGS), and InSAR scenes from the Sentinel-1A mission, the study estimated recent ground deformation in the region. The main objectives were to analyze recent crustal movements by identifying spatial and temporal patterns of deformation and assess implications for geological processes. In Key Findings, horizontal movement fluctuates between 0.5 and 2.5 ± 0.1 mm annually across the geodetic network. The estimated velocity of the area was 1.5–2 ± 0.5 mm per year. Integrating GNSS and InSAR data helped calculate movement rates along fault lines and create a fault map. In conclusion, the results suggest that while current deformation rates are moderate, they could increase significantly due to human activity, leading to higher seismic activity and potential earthquakes. Limiting human activity in the region is advisable to prevent negative impacts on nearby populated areas.

<p>Microwave signals passing through the troposphere are delayed by refraction. Its high variations, both in time and space, are caused mainly by water vapor. The tropospheric delay used to be considered only as a source of error that needed to be removed. Nowadays, these delays are also a source of interest, for example, tropospheric delays or integrated water vapor information are being assimilated into nowcasting or numerical weather prediction (NWP) models. Moreover, long time series of tropospheric observations have become an important source of information for climate studies. On the other hand, the meteorological data is supporting the space-geodetic community by providing models that can be used to reduce the troposphere impact on the signal propagation.</p><p>There are several ways of observing the troposphere, especially considering water vapor.  First one are the classical meteorological: in-situ measurements, radiosondes or radiometers, from which we can sense directly the amount of water vapor. Another, indirect way of observing the water vapor distribution is by using the Global Navigation Satellite Systems (GNSS). This method is called GNSS meteorology. Other microwave techniques such as Very Long Baseline Interferometry (VLBI), Interferometric Synthetic Aperture Radar (InSAR) or space-based Radio Occultations (RO) can also be used in a similar way to GNSS.</p><p>This contribution presents an overview of the troposphere sensing techniques with examples of their applications. We present a multi-comparison of the tropospheric products, i.e. refractivity, tropospheric delays in zenith and slant directions and integrated water vapor. The integration of the different data sources often leads to an improved accuracy of the tropospheric products but requires a careful preparation of data. The combination of the data sources allows for using techniques of complementary properties, for example InSAR with very high spatial resolution with GNSS observations of high temporal resolution. With the emergence of new technologies, some traditional ways of tropospheric measurements can be augmented with the new methods. For example, we have tested meteo-drones as an alternative to radiosondes. The comparisons with GNSS data shows a good agreement of the drone and microwave data, even better than with radiosondes.</p>

Terrestrial laser scanner (TLS) and interferometric synthetic aperture radar (InSAR) allow the acquisition of data on an observed surface with high spatial sampling rate. The data provided by TLS observation of a landslide ground surface can be used to generate a very detailed digital model of this surface, and multitemporal observations with TLS or continuous or multitemporal observation with InSAR can provide a reliable displacement map. In order to acquire useful information about the analogies, differences, and capabilities, as well as limitations of these techniques, a joint experimentation of TLS and InSAR was performed over two years in various sites in the Italian Alps. The results have indicated that these techniques can provide high‐quality data, can be very useful in the monitoring intended for the mitigation of hydrogeological risk in a wide range of cases, and must be supported by a topographical georeferenced network.

This article examines the combined use of UAV (Unmanned Aerial Vehicle) photogrammetry and TLS (Terrestrial Laser Scanning) to detect changes in coastal cliffs in the Strunjan Nature Reserve. Coastal cliffs present unique surveying challenges, including limited access, unstable reference points due to erosion, GNSS (Global Navigation Satellite System) signal obstruction, dense vegetation, private property restrictions and weak mobile data. To overcome these limitations, UAV and TLS techniques are used with the help of GNSS and TPS (Total Positioning Station) surveying to establish a network of GCPs (Ground Control Points) for georeferencing. The methodology includes several epochs of data collection between 2019 and 2024, using a DJI Phantom 4 RTK for UAV surveys and a Riegl VZ-400 scanner for TLS. The data processing includes point cloud filtering, mesh comparison and a DoD (DEM of difference) analysis to quantify cliff surface changes. This study addresses the effects of vegetation by focusing on vegetation-free regions of interest distributed across the cliff face. The results aim to demonstrate the effectiveness and limitations of both methods for detecting and monitoring cliff erosion and provide valuable insights for coastal management and risk assessment.

Interest in small unmanned aircraft systems (sUAS) for topographic mapping has significantly grown in recent years, driven in part by technological advancements that have made it possible to survey small- to medium-sized areas quickly and at low cost using sUAS aerial photography and digital photogrammetry. Although this approach can produce dense point clouds of topographic measurements, they have not been tested extensively to provide insights on accuracy levels for topographic mapping. This case study examines the accuracy of a sUAS-derived point cloud of a parking lot located at the Citizens Bank Arena (CBA) in Ontario, California, by comparing it to ground control points (GCPs) measured using global navigation satellite system (GNSS) data corrected with real-time kinematic (RTK) and to data from a terrestrial laser scanning (TLS) survey. We intentionally chose a flat surface due to the prevalence of flat scenes in sUAS mapping and the challenges they pose for accurately deriving vertical measurements. When the GNSS-RTK survey was compared to the sUAS point cloud, the residuals were found to be on average 18 mm and −20 mm for the horizontal and vertical components. Furthermore, when the sUAS point cloud was compared to the TLS point cloud, the average difference observed in the vertical component was 2 mm with a standard deviation of 31 mm. These results indicate that sUAS imagery can produce point clouds comparable to traditional topographic mapping methods and support other studies showing that sUAS photogrammetry provides a cost-effective, safe, efficient, and accurate solution for topographic mapping.

Accuracy assessment of integrated GNSS measurements with LIDAR mobile mapping data in urban environments