Synthetic Aperture Radar Measurements Research Articles

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model’s effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques.

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach

Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio methods were used to determine the importance and weighting of various factors and sub-factors affecting subsidence. Novel approaches, including the standard adaptive-network-based fuzzy inference system (ANFIS) algorithm and its integration with the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, and recall). Finally, Shapley additive explanations approach was used to explore the importance of features in modeling. The findings showed that the subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, the decline of the water table, and aquifer thickness played pivotal roles in LS occurrences. In addition, the ANFIS-PSO model classified approximately 50.26 % of the study area as high and very high susceptibility. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model produced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771. In addition, the RMSE values for the ANFIS-PSO model were lower. Given its remarkable accuracy, the ANFIS-PSO model was deemed suitable for evaluating subsidence susceptibility in the study area.

Enhancing InSAR accuracy: Unveiling more accurate displacement fields through 3-D troposphere tomography

Interferometric Synthetic Aperture Radar (InSAR) measurements are susceptible to tropospheric delay induced by changes in tropospheric temperature, pressure, and water vapor. While pressure and temperature temporal variations have a limited impact on phase gradients, variations in water vapor, which contribute to the wet component of tropospheric delay, play a significant role. Therefore, obtaining a more accurate estimation of the wet refractivity of the tropospheric layer is crucial for improving InSAR displacement field accuracy. This paper presents the utilization of Global Navigation Satellite Systems (GNSS) tropospheric tomography for reconstructing three-dimensional (3-D) wet refractivity. Various data sources are employed, including GNSS measurements, ERA5 reanalysis data, radiosonde observations, and Sentinel-1A radar acquisitions. ERA5 reanalysis data and radiosonde observations are used to calculate tropospheric hydrostatic delay and to evaluate the wet refractivity obtained from tomography, respectively. The effectiveness of tomography techniques in mitigating the tropospheric signal in unwrapped interferograms is compared with the total tropospheric delay obtained from Generic Atmospheric Correction Online Service (GACOS) products, which is one of the most common approach for tropospheric correction of InSAR measurements. To ensure a comprehensive analysis, the effects of the correction methods are investigated in two different weather conditions. Based on the results, GNSS tropospheric tomography reduces the mean Root Mean Square Error (RMSE) by about 1.2 cm compared to GACOS products. This technique proves to be highly effective in improving the accuracy of InSAR displacement field estimation, particularly under challenging weather conditions.

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

Model-based comparisons of near-coincident TerraSAR-X and COSMO-SkyMed VV-polarized SAR measurements over sea surface with and without oil slicks

ABSTRACT This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar (SAR) missions. Two SAR scenes were acquired with a temporal difference of about 36 minutes, under similar met-ocean conditions, during the North Sea’s Gannet Alpha oil spill accident. The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model (AIEM) while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance (MLB) to include the damping effect of oil slicks. Experimental results show that X-band co-polarized numerical predictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces. When dealing with oil-covered sea surfaces, the predicted backscattering reasonably agrees with TSX measurements, while it overestimates the CSK ones. This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.

Higher temperature sensitivity of retrogressive thaw slump activity in the Arctic compared to the Third Pole

Climate change exacerbates permafrost thawing, resulting exceptionally intense retrogressive thaw slump (RTS) activity in the Arctic and Third Pole. However, comparative assessments of permafrost characteristics and RTS sensitivity under warming climate at both poles are still lacking. Here, the severity and temperature sensitivity of RTS were presented and compared using Tasselled Cap (TC) trend analysis of time-series Landsat images and Interferometric Synthetic Aperture Radar (InSAR) measurement. RTS has a more severe growth trend in the Arctic cold permafrost region, also with a deformation rate of approximately 70 mm/year and cumulative displacement up to 120 mm. In comparison, the deformation rate in the Third Pole is approximately 50 mm/year. The RTS severity in the Arctic is about 1.5 times higher than in the Third Pole, primarily owing to different sensitivities of cold and warm permafrost under warming climate. The intensification and vulnerability of RTS have global implications on climatological processes, hydrology, carbon release and ground stability, thus calling for attention and effective governance action.

The 8 September 2023, MW 6.8, Morocco Earthquake: A Deep Transpressive Faulting Along the Active High Atlas Mountain Belt

AbstractOn 8 September 2023 an MW 6.8 earthquake struck the High Atlas Mountains of western Morocco, about 70 km southwest from Marrakesh, causing significant devastation and casualties. In this study, we investigate a comprehensive geodetic data set, employing interferometric synthetic aperture radar measurements to assess the fault segment responsible for the seismic event. Our findings suggest two potential fault scenarios: either a transpressive NNW‐dipping high‐angle (70°) fault related to the Tizi n'Test alignment or a transpressive SSW‐dipping low‐angle (22°) fault associated with the North Atlas Fault, with slip (up to 2.2 m) only occurring on deeper parts of the fault. While seismic catalogs couldn't definitively determine the dip direction of the fault, evidence from mainshock locations, gravity and heat flow data and modeling, and active shortening direction suggest the activation of a low‐angle south‐westerly dipping oblique thrust of the North Atlas fault during the 2023 Moroccan earthquake.

Characteristics of strata movement and method for runoff disaster management for shallow multiseam mining in gully regions: A case study

Multiseam mining in gully regions has resulted in severe and complex disaster chains, including crack development, mountain landslides, river blockage, and intensified mine water inflow. To prevent and control water inrush disasters, it is imperative to investigate the characteristics and mechanizations of overburden failure under the combined effect of gully terrain and repeated mining in coal seams. This study takes multiseam mining within the gully region of the Xiqu Coal Mine as a case study. The comprehensive analytical method that integrates surface exploration, Interferometric Synthetic Aperture Radar (InSAR) measurement, numerical simulation, and rainfall-runoff analysis, is used to anatomize the entire process of overburden failure induced by downward multiseam mining and the accumulation process of runoff in the gully region. The results reveal that compared to single-seam mining activities, repeated mining and the occurrence of key stratum (KS) structure failure triggered the comprehensive subsidence of the interburden, producing a cutting-type fracture and stepped subsidence, while the overburden exhibited severe damage. Compared to gentle surface topography, gully slopes are prone to shear sliding under the effects of mining subsidence and gravity, resulting in the connection of the downward cracks of the slope and the upward cracks of the overburden. In addition, if the accumulation formed by gully landslides due to repeated mining blocks the river channel and forms a barrier lake during the flood season, it may cause a surge in underground water. High-precision terrain synthesized by UAV tilt photogrammetry is used to simulate the rainfall inundation range and time of different durations during the “100-year return period” rainstorm in the study area. This study proposes a zoning prevention and control method for surface runoff disasters based upon the characteristics of surface deformation and surface inundation time; this method involves filling surface cracks in runoff areas and submerged and crack development areas, implementing slope cutting and control in potential landslide areas, and strengthening the riverbed in submerged and maximum settlement areas.

Geodetic source models of the 2016–2022 Menyuan Earthquake sequence (Northeastern Tibet) inferred from InSAR and optical observations

SUMMARY We study the 2016 January 21 (${{{M}}}_{\rm{w}}$ 5.9) and 2022 January 8 (${{{M}}}_{\rm{w}}$ 6.7) earthquake sequence that struck the Menyuan region in northwest China's Qinghai province. These two earthquakes are destructive events that occurred around/on the Lenglongling fault (LLLF). Here, we derive the line-of-sight displacement fields of the two earthquakes using Interferometric Synthetic Aperture Radar (InSAR) measurements of Sentinel-1 SAR data, and map the range and horizontal offset fields of the 2022 event using Sentinel-1 amplitude images and Planet-Lab optical images. Based on the offset maps, we determine the detailed surface rupture trace of the 2022 event. We perform slip inversions for the two earthquakes on triangle fault patches whose size increases with depth. Results show that the western branch segment of the 2022 event has a ∼0.5-m normal dip-slip motion. This result contradicts previous inferences on dip-slip sense of this branch segment. We identify a left-stepping fault structure with a ∼5-km step width in the transition zone between the Tuolaishan fault (TLSF) and LLLF, which may serve as a kinematic barrier to prevent further propagation of seismic rupture along the TLSF. Stress calculation shows that a stress drop of ∼0.4 bar produced by the 2016 event on a ∼5-km long LLLF segment may act as a negative stress barrier to suppress rupture propagation of the 2022 event toward the southeast of the LLLF.

On the extraction of the reservoirs’ waterline using polarimetric X-band SAR measurements: the case study of the San Giuliano reservoir, Italy

ABSTRACT According to the World Bank data catalogue, records of reservoirs and their associated dams are present summing up a capacity of km3 of water. They play a crucial role in providing potable and irrigation water and, therefore, it is of paramount interest to effectively monitor such critical infrastructures. An effective approach is based on satellite remote sensing and, in particular, on the Synthetic Aperture Radar (SAR). In this paper, we critically investigate the use of polarimetric SAR measurements for reservoirs’ waterline estimation. Measurements of the novel COSMO-SkyMed Second Generation (CSG) X-band quad-polarimetric SAR related to the San Giuliano reservoir, in the South of Italy, are used to carry out an electromagnetic analysis of the different polarimetric scattering returns. Experimental results show that the cross-polarized channel, as well as the inter-channel phase, are noisy and, therefore, uninformative when used to design coherent polarimetric waterline extraction methods. From an electromagnetic viewpoint, this is due to the peculiarities of the reservoirs that call for low surface roughness and negligible wave pattern that, at once, result in a joint combination of un-tilted Bragg scattering and specular reflection. This implies that a low co-polarized backscatter and a cross-polarized signal largely below the system noise floor are to be expected. As a consequence, waterline extraction approaches that do not exploit the inter-channel phase, the so-called incoherent approaches, are shown to outperform the coherent ones.

Multivariate Data Assimilation at a Partially Mixed Estuary

Abstract In 2013 and 2014, multiple field excursions of varying scope were concentrated on the Columbia River, a highly energetic, partially mixed estuary. These experiments included surface drifter and synthetic aperture radar (SAR) measurements during the ONR RIVET-II experiment, and a novel animal tracking effort that samples oceanographic data by employing cormorants tagged with biologging devices. In the present work, several different data types from these experiments were combined in order to test an iterative, ensemble-based inversion methodology at the mouth of the Columbia River (MCR). Results show that, despite inherent limitations of observation and model accuracy, it is possible to detect dynamically relevant bathymetric features such as large shoals and channels while originating from a linear, featureless prior bathymetry in a partially mixed estuary by inverting surface current and gravity wave observations with a 3D hydrostatic ocean model. Bathymetry estimation skill depends on two factors: location (i.e., differing estimation quality inside versus outside the MCR) and observation type (e.g., surface currents versus significant wave height). Despite not being inverted directly, temperature and salinity outputs in the hydrodynamic model improved agreement with observations after bathymetry inversion.

Machine Learning Applied to a Dual-Polarized Sentinel-1 Image for Wind Retrieval of Tropical Cyclones

In this work, three types of machine learning algorithms are applied for synthetic aperture radar (SAR) wind retrieval in tropical cyclones (TCs), and the optimal method is confirmed. In total, 30 Sentinel-1 (S-1) images in dual-polarization (vertical–vertical [VV] and vertical–horizontal [VH] were collected during the period from 2016 to 2021, which were acquired in interferometric-wide and extra-wide modes with pixels of 10 m and 40 m, respectively. More than 100,000 sub-scenes with a spatial coverage of 3 km are extracted from these images. The dependences of variables estimated from sub-scenes, i.e., VV-polarized and VH-polarized normalized radar cross-section (NRCS), as well as the azimuthal wave cutoff wavelength, on wind speeds from the stepped-frequency microwave radiometer (SFMR) and the soil moisture active passive (SMAP) radiometer are studied, showing the linear relations between wind speed and these three parameters; however, the saturation of VV-polarized NRCS and the azimuthal wave cutoff wavelength is observed. This is the foundation of selecting input variables in machine learning algorithms. Two-thirds of the collocated dataset (20 images) are used for training the process using three machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), Multi-layer Perceptron, and K-Nearest Neighbor, and the coefficients are fitted after training completion through 20 images collocated with SFMR and SMAP data. Another 10 images are taken for validation up to 70 m/s, yielding a 2.53 m/s root mean square error (RMSE) with a 0.96 correlation and 0.12 scatter index (SI) using XGBoost. The result is better than the >5 m/s error achieved using the existing cross-polarized geophysical model function and the other two machine learning algorithms; moreover, the comparison between wind retrievals using XGBoost and Level-2 CyclObs products shows about 4 m/s RMSE and 0.18 SI. This suggests that the machine learning algorithm XGBoost is an effective method for inverting the TC wind field utilizing SAR measurements in dual-polarization.

Computational Oil-Slick Hub for Offshore Petroleum Studies

The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies.

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

Monitoring variations in the extent of wet snow over space and time is essential for many applications, such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. The Synthetic Aperture Radar (SAR) measurements from the Sentinel-1 satellite help detect wet snow in almost all weather conditions. Most detection methods use a fixed threshold to a winter image ratio with one or two reference images (with no snow or dry snow). This study aimed to explore the potential of image segmentation methods from different families applied to Sentinel-1 SAR images to improve the detection of wet snow over the French Alps. Several segmentation methods were selected and tested on a large alpine area of 100 × 100 km2. The segmentation methods were evaluated over one season using total snow masks from Sentinel-2 optical measurements and outputs from forecasters’ bulletins combining model and in-situ observations. Different metrics were used (such as snow probability, correlations, Hamming distance, and structure similarity scores). The standard scores illustrated that filtering globally improved the segmentation results. Using a probabilistic score as a function of altitude highlights the interest in some segmentation methods, and we show that these scores could be relevant to calibrate the parameters of these methods better.

Research on the spatiotemporal prediction of mining deformation with subcritical extraction integrated with D-InSAR technology

The prediction of surface movement and deformation in the mining area is of great significance to the production of the mining. Since the differential interferometry synthetic aperture radar measurement (D-InSAR) technology cannot predict the deformation caused by mining face, and the existing probability integral method (PIM) prediction model can only predict final mining deformation under critical extraction. Therefore, on the basis of PIM prediction model, the simplified Boltzmann function and Gompertz time function were introduced to form the BPIM dynamic prediction model for the analysis of temporal and spatial characteristics of the mining area. The parameters of BPIM model are inversed by D-InSAR monitoring data combined with optimization algorithm, and the mining of the working face is followed by the dynamic forward prediction of the changes in temporal and spatial characteristics. In addition, we analyzed the sensitivity of the main parameters in the model to the predicted results and the variation relationship of the model subsidence coefficient with the mining of the working face. When the model was applied to the working face of Guobei coal mine, the RMSE of the BPIM dynamic prediction model is 25.1 mm. Constructed with the previous dynamic prediction model established by conventional methods, its deformation accuracy under the prediction of subcritical extraction on the surface of the mining area is improved by 58.7%. The results demonstrated that the BPIM dynamic model effectively avoids the problem of large prediction results under subcritical extraction, and can provide some suggestions for the planning and production of the mining area.

Inferring slip-surface geometry and volume of creeping landslides based on InSAR: A case study in Jinsha River basin

The slip-surface geometry and volume of landslides are fundamental for landslide modeling and mechanism interpretation. The movement of a landslide, which is generally controlled by the slip-surface geometry, can be obtained from interferometric synthetic aperture radar (InSAR) measurements. However, there is a lack of a general approach for inferring the slip-surface geometry and volume of landslides through the InSAR-derived deformation field. Here we developed a geometry-based method to determine the landslide slip-surface geometry and volume using InSAR measurements and applied it to the Jinsha River Basin, a landslide-prone area that poses a significant threat to residents and infrastructure. Through the InSAR-derived displacement, topography, and Google Earth images, 50 creeping landslides were identified in the study area. Based on the displacement field, the landslide slip-surface slope was inverted under the assumption that the landslide displacement was parallel to the slip-surface. Then the two-dimensional slip-surface depths and volumes of selected landslides were inferred using the slip-surface slope. Comparisons with the in-situ data suggest that the results obtained in this study are reliable. The mapped landslides in the study area have depths of approximately 16–160 m along the central axis and volumes ranging from 483,412 to 135,789,944 m3. The derived volume–area relationship and 2D slip-surface depth suggest that deep-seated landslide is a major landslide type in the study area. We conclude that our method can infer the slip-surface geometry of creeping landslides based on InSAR observation and our results have improved the understanding of the landslide mechanisms in the Jinsha River Basin, China.

First Onset of Unrest Captured at Socompa: A Recent Geodetic Survey at Central Andean Volcanoes in Northern Chile

AbstractWe report the first detection of unrest at Socompa volcano during our recent survey of Central Andean volcanos in Northern Chile using Interferometric Synthetic Aperture Radar measurements spanning January 2018 to October 2021. We find that Socompa volcano, whilst initially undeforming and no recorded eruptions for 7.2 kyr, shows a steady uplift (17.5 mm/yr) from November 2019, independently recorded by near‐field continuous Global Positioning System data. The deformation pattern can be fitted with pressure increase in an ellipsoidal source region stretching from 2.1 to 10.5 km, with a volume change rate of ∼6.2 × 106 m3/yr. Our observations of the onset of uplift suggest it is unlikely that a nearby Mw 6.8 deep intraslab earthquake on 3 June 2020 triggered the unrest. The deformation signal we detect indicates the initiation of unrest at Socompa, after at least two decades without measurable deformation, and many thousands of years without volcanic activity.

Analysis of the resolution of interferometric synthetic aperture radar data inversion and application of the inversion residual to identify shallow hazards

We have conducted a study to investigate the sensitivity of interferometric synthetic aperture radar (InSAR) measurement to subsurface strain change and developed an inversion framework to use InSAR data for subsurface surveillance. Using the spatial domain and spatial-frequency-domain analysis, we have determined that the earth behaves as a low-pass filter in its transmission of deformation from the subsurface to the earth’s surface. As a result of this low-pass filtering effect of the earth, the horizontal resolution of InSAR images is roughly equal to the depth of the targeted activity. Therefore, the goal of InSAR data inversion is to recover high-spatial-frequency subsurface strains from InSAR measurements. Because changes in the reservoir tend to have a lower spatial-frequency InSAR signature, high-spatial-frequency surface displacement can be associated with shallow overburden activities. Based on these insights, we have developed an inversion workflow that takes into account the overburden and reservoir strain changes and applied it to an InSAR data set from the San Joaquin Valley. Our results indicate that an inversion model that only considers strain changes in the reservoir produces large spatially localized inversion residuals in locations with known shallow overburden activity. A residual analysis reveals that the high-spatial-frequency anomalies in InSAR data can be used to identify shallow activities.

An investigation of spatial wind direction variability and its consideration in engineering models

Abstract. We propose that considering mesoscale wind direction changes in the computation of wind farm cluster wakes could reduce the uncertainty of engineering wake modeling tools. The relevance of mesoscale wind direction changes is investigated using a wind climatology of the German Bight area covering 30 years, derived from the New European Wind Atlas (NEWA). Furthermore, we present a new solution for engineering modeling tools that accounts for the effect of such changes on the propagation of cluster wakes. The mesoscale wind direction changes relevant to the operation of wind farm clusters in the German Bight are found to exceed 11∘ in 50 % of all cases. Particularly in the lower partial load range, which is associated with strong wake formation, the wind direction changes are the most pronounced, with quartiles reaching up to 20∘. Especially on a horizontal scale of several tens of kilometers to 100 km, wind direction changes are relevant. Both the temporal and spatial scales at which large wind direction changes occur depend on the presence of synoptic pressure systems. Furthermore, atmospheric conditions which promote far-reaching wakes were found to align with a strong turning in 14.6 % of the cases. In order to capture these mesoscale wind direction changes in engineering model tools, a wake propagation model was implemented in the Fraunhofer IWES wind farm and wake modeling software flappy (Farm Layout Program in Python). The propagation model derives streamlines from the horizontal velocity field and forces the single turbine wakes along these streamlines. This model has been qualitatively evaluated by simulating the flow around wind farm clusters in the German Bight with data from the mesoscale atlas of the NEWA and comparing the results to synthetic aperture radar (SAR) measurements for selected situations. The comparison reveals that the flow patterns are in good agreement if the underlying mesoscale data capture the velocity field well. For such cases, the new model provides an improvement compared to the baseline approach of engineering models, which assumes a straight-line propagation of wakes. The streamline and the baseline models have been further compared in terms of their quantitative effect on the energy yield. Simulating two neighboring wind farm clusters over a time period of 10 years, it is found that there are no significant differences across the models when computing the total energy yield of both clusters. However, extracting the wake effect of one cluster on the other, the two models show a difference of about 1 %. Even greater differences are commonly observed when comparing single situations. Therefore, we claim that the model has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.