A new method, combining empirical modeling with time series Interferometric Synthetic Aperture Radar (InSAR) data, is proposed to provide an assessment of potential landslide volume and area. The ...method was developed to evaluate potential landslides in the Heitai river terrace of the Yellow River in central Gansu Province, China. The elevated terrace has a substantial loess cover and along the terrace edges many landslides have been triggered by gradually rising groundwater levels following continuous irrigation since 1968. These landslides can have significant impact on communities, affecting lives and livelihoods. Developing effective landslide risk management requires better understanding of potential landslide magnitude. Fifty mapped landslides were used to construct an empirical power-law relationship linking landslide area (AL) to volume (VL) (VL = 0.333 × AL1.399). InSAR-derived ground displacement ranges from −64 mm/y to 24 mm/y along line of sight (LOS). Further interpretation of patterns based on remote sensing (InSAR & optical image) and field survey enabled the identification of an additional 54 potential landslides (1.9 × 102 m2 ≤ AL ≤ 8.1 × 104 m2). In turn this enabled construction of a map that shows the magnitude of potential landslide activity. This research provides significant further scientific insights to inform landslide hazard and risk management, in a context of ongoing landscape evolution. It also provides further evidence that this methodology can be used to quantify the magnitude of potential landslides and thus contribute essential information towards landslide risk management.
•A new approach combining time-series InSAR with empirical model is proposed.•The volume and area of potential landslides are forecasted.•The approach is validated for recent landslides.•The approach contributes essential information to landslide risk assessment.
Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture ...Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. Recent developments in technology as well as improved computational power have resulted in unprecedented quantities of monitoring data, which can no longer be inspected manually. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging than many other applications. In this paper, we address this problem using synthetic interferograms to train the AlexNet CNN. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden (PPV of 100%). Thus, we demonstrate that training with synthetic examples can improve the ability of CNNs to detect volcano deformation in satellite images, and propose an efficient workflow for the development of automated systems.
•Training dataset problems are addressed using synthetic interferograms to train the CNN.•The CNN trained with synthetic data outperforms that trained with real data alone.•The CNN trained with a combined dataset achieves a final PPV of 82%.•Applying atmospheric corrections improve the detection performance to a PPV of 100%.
NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil ...moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products: i) high-resolution from radar (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1 km) and radar backscatter (~10 m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.
Interferometric synthetic aperture radar (InSAR) allows for mapping of crustal deformation on land with high spatial resolution and precision in areas with high signal-to-noise ratios. Efforts to ...obtain precise displacement time series globally, however, are severely limited by radar path delays within the troposphere. The tropospheric delay is integrated along the full path length between the ground and the satellite, resulting in correlations between the interferometric phase and elevation that can vary dramatically in both space and time. We evaluate the performance of spatially variable, empirical removal of phase-elevation dependence within SAR interferograms through the use of the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means clustering algorithm. We apply this method to both synthetic test data, as well as to C-band Sentinel-1a/b time series acquired over a large area in south-central Mexico along the Pacific coast and inland-an area with a large elevation gradient that is of particular interest to researchers studying tectonic- and anthropogenic-related deformation. We show that the clustering algorithm is able to identify cases where tropospheric properties vary across topographic divides, reducing total root mean square (rms) by an average of 50%, as opposed to a spatially constant phase-elevation correction, which has insignificant error reduction. Our approach also reduces tropospheric noise while preserving test signals in synthetic examples. Finally, we show the average standard deviation of the residuals from the best-fit linear rate decreases from approximately 3 to 1.5 cm, which corresponds to a change in the error on the best-fit linear rate from 0.94 to 0.63 cm/yr.
Satellite interferometric synthetic aperture radar (InSAR) has become a recognized and reliable surface deformation monitoring technology in recent years. However, the complexity of surface ...deformation, driven by various physical mechanisms, presents itself in different forms such as secular trend, cyclical fluctuations, and irregular variations in time series. Consequently, the limitations of conventional InSAR technology, which relies on linear deformation assumptions, make it challenging to meet the requirements of monitoring complex nonlinear deformation. In addition, the commonly used parameter inversion method based on the least-squares (LS) approach is unsuitable for non-Gaussian observation error distribution with gross errors. To address these issues, we propose an InSAR interferometric phase nonlinear function model that considers zero-mean second-order stochastic differential equations and periodic changes. This model can quantitatively describe the law of surface deformation driven by multiple physical factors. Furthermore, we utilize an efficient M-estimation (ME) method, known for its high robustness, to optimize the model parameters and mitigate the impact of non-Gaussian noise and/or gross errors in InSAR observation and data processing. By conducting simulation experiments, it verifies that the proposed method is more robust than the conventional InSAR method. Finally, the processing and analysis of Sentinel-1 data in the overlying rock glacier area confirm the effectiveness of the proposed method in extracting nonlinear surface deformation.
Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase ...closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA's airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very short temporal separation (~30 min). The algorithm is applied to six consecutive UAVSAR images collected in tidal wetlands of the Wax Lake Delta, Louisiana, USA. Validation of our water level change retrievals with in situ field observations was conclusive with high correlation and an RMSE generally smaller than 3 cm. Comparison of our algorithm with other phase unwrapping error correction methods shows significant improvement (30%-35% increase in the number of correctly unwrapped pixels) when applied to rapid changes in water level. The set of corrections presented in this work enables measurement of water level change in deltas and other areas where tides drive highly dynamic flooding of inland vegetated areas. Although demonstrated for water level change, the method is applicable to other InSAR datasets with large spatial gradients or observed discontinuities between coherent but spatially isolated areas.
Lakes and permafrost on Qinghai-Tibet Plateau (QTP) are both important indicators of climate change. Previous literatures have shown the usefulness of optical remote sensing in lake expansion ...monitoring and the effectiveness of synthetic aperture radar (SAR) interferometry (InSAR) in retrieving permafrost deformation on QTP. However, none of them incorporated both optical remote sensing and InSAR to investigate an event that may exhibit causal links between lake outburst and permafrost degradation. This study integrated both the Google Earth Engine (GEE) analysis on optical images and the small baseline subset (SBAS) processing on SAR datasets to evaluate the potential impact of a lake outburst event on permafrost degradation. The outburst of Zonag Lake (headwater lake) that occurred on 14 September 2011 was focused, and its consequential influence on the permafrost degradation surrounding Salt Lake (tailwater lake) was investigated. The GEE processing on Landsat and HJ-1 imageries allowed an efficient monitoring of the Salt Lake expansion over past 20 years. In addition, the SBAS-InSAR analysis on temporal Envisat and Sentinel-1 datasets further discovered the accelerated permafrost degradation surrounding Salt Lake after 2014. The results provide an evidence that on QTP the outburst of a headwater lake may significantly accelerate the permafrost degradation surrounding the tailwater lake. Such degradation may be attributed to the thermal alteration of the permafrost thawing-freezing cycle and the melting ground ice, along with the subsequent changes on hydrological connectivity and soil permeability. With the continuous trend of the permafrost degradation surrounding Salt Lake, potential risks may be further exposed to the regional environment and infrastructures such as the Qinghai-Tibet railway and highway, thus deserving a particular attention in the near future. The novelties of this study are: 1) technically, the preliminary attempt to integrate the GEE and InSAR techniques for a joint analysis of lake expansion and permafrost degradation, and 2) scientifically, the finding that lake outburst may accelerate permafrost degradation on QTP.
•Joint analysis of GEE and SBAS for lake and permafrost study over past 20 years•Salt Lake expanded with increased deformation after Zonag Lake outburst.•Lake outburst may accelerate permafrost degradation on QTP.
The differential ionospheric path delay is a major error source in L-band interferograms. It is superimposed to topography and ground deformation signals, hindering the measurement of geophysical ...processes. In this paper, we proceed toward the realization of an operational processor to compensate the ionospheric effects in interferograms. The processor should be robust and accurate to meet the scientific requirements for the measurement of geophysical processes, and it should be applicable on a global scale. An implementation of the split-spectrum method, which will be one element of the processor, is presented in detail, and its performance is analyzed. The method is based on the dispersive nature of the ionosphere and separates the ionospheric component of the interferometric phase from the nondispersive component related to topography, ground motion, and tropospheric path delay. We tested the method using various Advanced Land Observing Satellite Phased-Array type L-band synthetic aperture radar interferometric pairs with different characteristics: high to low coherence, moving and nonmoving terrains, with and without topography, and different ionosphere states. Ionospheric errors of almost 1 m have been corrected to a centimeter or a millimeter level. The results show how the method is able to systematically compensate the ionospheric phase in interferograms, with the expected accuracy, and can therefore be a valid element of the operational processor.
Long-term excessive groundwater exploitation for agricultural, domestic and stock applications has resulted in substantial ground subsidence in Arizona, USA, and especially in the Willcox Groundwater ...Basin. The land subsidence rate of the Willcox Basin has not declined but has rather increased in recent years, posing a threat to infrastructure, aquifer systems, and ecological environments. In this study, we first investigate the spatiotemporal characteristics of land subsidence in the Willcox Groundwater Basin using an interferometric synthetic aperture radar (InSAR) time series analytical approach with L-band ALOS and C-band Sentinel-1 SAR data acquired from 2006 to 2020. The overall deformation patterns are characterized by two major zones of subsidence, with the mean subsidence rate increasing with time from 2006 to 2020. An approach based on independent component analysis (ICA) was adopted to separate the mixed InSAR time series signal into a set of independent signals. The application of ICA to the Willcox Basin not only revealed that two different spatiotemporal deformation features exist in the basin but also filtered the residual errors in InSAR observations to enhance the deformation time series. Integrating the InSAR deformation and groundwater level data, the response of the aquifer skeletal system to the change in hydraulic head was quantified, and the hydromechanical properties of the aquifer system were characterized. Historical spatiotemporal storage loss from 1990 to 2020 was also estimated using InSAR measurements, hydraulic head and estimated skeletal storativity. Understanding the characteristics of land surface deformation and quantifying the response of aquifer systems in the Willcox Basin and other groundwater basins elsewhere are important in managing groundwater exploitation to sustain the mechanical health and integrity of aquifer systems.
•Spatiotemporal land subsidence in Willcox Basin in Arizona is mapped with InSAR.•ICA is implemented to mitigate atmosphere and other noise in InSAR.•ICA can separate signal into components with different spatiotemporal traits.•Inelastic deformation occurs due to the long-term excessive groundwater exploitation.•Skeletal storativity and groundwater storage loss are estimated.
Interferometric synthetic aperture radar (InSAR) has been a valuable tool for mapping topography and subtle deformations. However, dealing with a wide imaging integral angle (IIA), especially for ...low-frequency band unmanned aerial vehicle (UAV) InSAR systems, introduces challenges. The conventional interferometric phase model depends on the difference in two slant ranges between the synthetic aperture centers and the target in two observations. Accuracy limitations emerge when variations are encountered in differences of slant range history across the entire wide IIA. This article explores the impact of IIA on interferometric measurements and proposes a modified interferometric phase model to address these limitations. For a wide IIA, the proposed model focuses on the integral of differences in slant range history throughout IIA by considering the nonlinear trajectory of the UAV platform. Additionally, measurement models for the IIA are deduced, in which an additional scale factor expanded by the Bessel function is introduced. Simulated and experimental datasets are utilized to demonstrate improvements in the accuracy of topography and deformation measurements. These results validate the effectiveness of the modified model in overcoming the challenges posed by wide IIAs in UAV InSAR systems.
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