Synthetic Aperture Radar Observations Research Articles

Internal solitary waves observation and feature extraction based on wavelet transform by Sentinel-1A in Lombok Strait, Indonesia

Lombok Strait is an essential pathway in trans-oceanic water mass transport. Due to this water mass transport flowing over Indonesian waters, the current is known as Indonesian Throughflow (ITF), which plays a role in the transfer of warm water masses from the Pacific to the Indian Ocean annually. Lombok Strait has intensive characteristics of the internal waves (IWs) generation because of the strong current that passes through the complex bathymetry along the strait area. IWs with large amplitude and nonlinear properties are known as Internal Solitary Waves (ISW) that can be detected by Synthetic Aperture Radar (SAR) images. A Wavelet Transform method for ISW feature extraction was applied to SAR images by Sentinel-1A (C-band). ISW packet characteristics can be distinguished from other phenomena based on their geometrical structure and shape. ISW packet pattern consists of light and dark lines which decrease intensity from front to rear. SAR observation detected 5 parameters (phase speed, soliton numbers, wavelength, first crest length, and propagation direction). The arc-like ISW in the Lombok Strait propagated to the North of the sill with average phase speeds of about 2.13 m/s and was frequently detected during the northwest monsoon (NWM). The detected soliton number is less than 6 solitons per packet with a wavelength of about 1 – 4 km, and the first crest length varies from about 12.16 km to more than 100 km. ISW detected in Sentinel-1A images were located at the bathymetry about 800 meters around the Lombok Strait area.

Joint Inversion of Sea Surface Wind and Current Velocity Based on Sentinel-1 Synthetic Aperture Radar Observations

Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94° and 35.74° for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors.

InSAR, Gravity, and Geomagnetic Observations Suggest the 2020 Mw 6.5 Stanley Earthquake Occurred on a Blind Strike-Slip and a Normal Fault

ABSTRACT The 2020 Mw 6.5 Stanley earthquake occurred in western North America and is the largest earthquake in Idaho state in the past ∼40 yr. The hypocenter is located at the western margin of the Centennial Tectonic Belt (CTB), which is a subprovince of the Basin and Range and characterized by a series of normal faults. In this study, the coseismic Interferometric Synthetic Aperture Radar observations, geophysical data, and aftershocks are jointly collected to estimate the seismogenic fault of the 2020 Stanley earthquake. The best-fitting fault models suggest the earthquake occurred on two blind faults. Slip on the main, west-dipping, seismogenic fault was sinistral and on the second fault predominantly normal slip with a minor component of right-lateral strike slip. Geophysical observations including gravity and geomagnetic data are consistent with the fault interpretations. The Coulomb failure stress (CFS) change indicates that the rupture of the main seismogenic fault had a positive triggering effect on the slip of the second fault and may have increased the rupture risk of the Sawtooth fault. Moreover, a potential seismic hazard risk zone with a notably low occurrence of aftershocks is found in the west of the central segment of the main seismogenic fault.

Study on LOS to Vertical Deformation Conversion Model on Embankment Slopes Using Multi-Satellite SAR Interferometry

Slant range geometry plays a crucial role in interpreting synthetic aperture radar (SAR) observations, especially in converting line-of-sight (LOS) surface deformations to actual vertical subsidence. This paper proposes a new conversion model to retrieve vertical settlements of the embankment slopes using the geometrical parameters of the dam and the SAR sensor. The simulation results highlight the impact of slope foreshortening and heading direction of the satellite on deformation retrieval. Various SAR data with different resolutions and bands are used to analyze the model’s performance, revealing a high conformity of the model with practical conversion parameters exceeding 80% for TerraSAR-X and Cosmo-SkyMed data.

Pre-existing structural control on the recent Holuhraun eruptions along the Bárðarbunga spreading center, Iceland

The active rift zones in Iceland provide unique insight into the geodynamic processes of divergent plate boundaries. The geodynamics of Iceland are studied intensively, particularly, by geophysical methods sensitive to active and/or visible structures such as earthquake seismic and Synthetic Aperture Radar observations or aerial photographs. However, older and less active structures, that may exert a strong control on the presently active geodynamics, are often buried beneath recent volcanic or sedimentary deposits and are—due to their passive mode—overseen by the typical geophysical investigations. Aeromagnetic surveys provide spatial information about subsurface magnetization contrasts relating to both active and inactive structures. However, the aeromagnetic data in Iceland were collected in the 1970-80s and are relevant only to large-scale regional rift studies. With the availability of reliable drones and light-weight atomic scalar sensors, high-quality drone magnetic surveys can provide an unprecedented spatial resolution of both active and passive structures of rift systems as compared to conventional airborne surveys. Here, we present the results of a drone-towed magnetic scalar field and scalar gradiometry study of the north-northeast trending Bárðarbunga spreading center to the north of the Vatnajökull ice cap, Iceland. Our results provide new information about the structural complexity of rift zones with evidence of densely-spaced, conjugate and oblique faults throughout the area. Evidence is shown of a hitherto unknown and prominent east-northeast trending fault structure that coincides with the northern tip of the main eruption edifice of the 1797 and 2014-15 Holuhraun volcanic events. We suggest that this pre-existing structure controlled the locus of vertical magma migration during the two Holuhraun events.

Characterization of internal solitary waves in the Andaman Sea and Arabian Sea using EOS-04 and sentinel observations

ABSTRACT In this study, we synergistically used Synthetic Aperture Radar (SAR) and optical observations to characterize the Internal Solitary Waves (ISW) in the Andaman Sea and Arabian Sea region. Total 25 scenes combined from EOS-04, Sentinel-1&2 satellites are considered for the year 2022. Various approaches, including Fast Fourier Transform (FFT), Multiple Image Comparison (MIC), Tidal Time Period (TTP) and Korteweg-de Vries (KdV) equations, were employed to characterize the ISW, and its spatial and seasonal variations are also analysed. The dominant wavelength of ISW over the Andaman Sea in June 2022 is found to be 6500 m travelling towards the Andaman coast. The overall phase speed of ISW in the Andaman Sea lies in the range of 1.87–2.60 ms−1. It shows a seasonal variation of around 16% between winter and summer months, where a significant variation in the seasonal mixed layer depth was noticed. The phase speed also decreased as the ISW travelled from deep water to shallower regions. In contrast, the dominant wavelength of ISW in the continental shelf region of the west coast of India (near Goa) was much smaller (~400 m) compared to the Andaman Sea. Here, the ISW phase speed estimated using MIC and TTP methods was ~ 0.56 ms−1 in February 2022. The KdV equation yielded lower estimates of phase speed compared to the other two methods. The use of multiple satellite images and various approaches possess an advantage in comprehending the spatial and temporal variations in the characteristic features of ISW.

Analysis of wave breaking on synthetic aperture radar at C-band during tropical cyclones

ABSTRACT The purpose of our work is to analyze the effect of wave breaking on dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) synthetic aperture radar (SAR) image in the C-band during tropical cyclones (TCs) based on the machine learning method. In this study, more than 1300 Sentinel-1 (S-1) interferometric-wide (IW) and extra wide (EW) mode SAR images are collocated with wave simulations from the WAVEWATCH-III (WW3) model during 400 TCs. The validation of the significant wave height (SWH) simulated using the WW3 model against Jason-2 altimeter data. The winds for S-1 SAR images are reconstructed using wind retrievals in VV and VH polarization. The non-polarized (NP) contribution σ wb caused by wave breaking is assumed to be the result of the SAR-measured normalized radar cross-section (NRCS) σ 0 minus the Bragg resonant roughness σ br without the distortion of rain cells during TCs. The σ br is simulated by imputing wave spectra from the WW3 model into the theoretical backscattering model. It is found that the ratio (σ wb /σ 0) in VV polarization is related to the wind speed, the wind direction relative with the flight orientation, and radar incidence angle. Following this rationale, the Adaptive Boosting (AdaBoost) model was used for the estimation of NP contribution σ wb during TCs and are implemented for more than 300 dual-polarized S-1 images to validate the model. It is found that for the comparison between the sum of simulation NRCS and SAR observations, the root mean squared error (RMSE) is 1.95 dB and the coefficient (COR) is 0.86, which is better than a 2.83 dB RMSE and a 0.67 COR by empirical model. It is concluded that the AdaBoost model has a good performance on NP component simulation during TCs.

SAR marine oil spill detection based on an encoder-decoder network

ABSTRACT Marine oil spills are currently among the most challenging problems in marine environments. Synthetic Aperture Radar (SAR), with all-day and all-weather observation, has demonstrated great potential in oil disaster monitoring. A simple encoder-decoder network model was proposed by using SAR images for effective oil spill detection. The proposed model incorporates an optimized U-Net architecture that reduces computational requirements while maintaining detection performance. This is achieved through shortened encoder and decoder stages, depthwise separable convolutions, group normalization, and bilinear interpolation-based upsampling. To improve the model’s generalization, the auto-learning rate and focal loss function are also included. Two public SAR datasets acquired by different sensors have been used to illustrate the efficiency of the proposed model. In addition, the polarimetric information has also been assessed for the detection of oil spill monitoring. The results show that the model can achieve high efficiency at a small model size. The sub-dataset with polarimetric features also achieves a high F1-score of 91.65% and an IoU of 84.59%.

Improved Orthorectification and Empirical Reduction of Topographic Effects in Monostatic Mini-RF S-band Observations of the Moon

The Miniature Radio Frequency instrument (Mini-RF) on the Lunar Reconnaissance Orbiter obtained widespread synthetic aperture radar observations of the Moon in the S band (12.6 cm), including nearly complete coverage at both lunar poles. The currently archived monostatic data have spatial offsets from the lunar reference frame, making them more difficult to compare to other data sets. To address this issue, we have developed a new algorithm for spatially controlling the Mini-RF S-band monostatic data set and orthorectifying these data onto lunar topography. Additionally, as the influence of incidence angle changes on radar observations is well known, we describe an empirical approach to account for variations in observation geometry and surface topography. Individual radar swaths and mosaics produced using this method more clearly show the variability in scattering behavior due to changes in lunar regolith properties and suppress some of the behavior arising from these topographic effects alone. Once these terrain effects are taken into account, we find that areas of permanent shadow at both poles have a higher median radar reflectivity than nonpermanently shadowed regions, but the polarization behavior of shadowed versus unshadowed areas is largely similar. The higher radar reflectivity in permanent shadow is likely the result of physical or compositional differences in these unique environments, though the precise cause remains uncertain. The results here illustrate how reducing the influence of topography and geometry effects in Mini-RF radar data may enable better characterization of lunar geologic units, regolith structure, and potential areas hosting volatile deposits at the lunar poles.

A Bayesian Source Model for the 2022 Mw6.6 Luding Earthquake, Sichuan Province, China, Constrained by GPS and InSAR Observations

Until the Mw 6.6 Luding earthquake ruptured the Moxi section of the Xianshuihe fault (XSHF) on 5 September 2022, the region had not experienced an Mw >6 earthquake since instrumental records began. We used Global Positioning System (GPS) and Sentinel-1 interferometric synthetic aperture radar (InSAR) observations to image the coseismic deformation and constrain the location and geometry of the seismogenic fault using a Bayesian method We then present a distributed slip model of the 2022 Mw6.6 Luding earthquake, a left-lateral strike-slip earthquake that occurred on the Moxi section of the Xianshuihe fault in the southwest Sichuan basin, China. Two tracks (T26 and T135) of the InSAR data captured a part of the coseismic surface deformation with the line-of-sight displacements range from ∼−0.16 m to ~0.14 m in the ascending track and from ~−0.12 m to ~0.10 m in the descending track. The inverted best-fitting fault model shows a pure sinistral strike-slip motion on a west-dipping fault plane with a strike of 164.3°. We adopt a variational Bayesian approach and account for the uncertainties in the fault geometry to retrieve the distributed slip model. The inverted result shows that the maximum slip of ~1.82 m occurred at a depth of 5.3 km, with the major slip concentrated within depths ranging from 0.9–11 km. The InSAR-determined moment is 1.3 × 1019 Nm, with a shear modulus of 30 GPa, equivalent to Mw 6.7. The published coseismic slip models of the 2022 Luding earthquake show apparent differences despite the use of similar geodetic or seismic observations. These variations underscore the uncertainty associated with routinely performed source inversions and their interpretations for the underlying fault model.

Alarming a tailings dam failure with a joint analysis of InSAR-derived surface deformation and SAR-derived moisture content

A tailings dam failure can be catastrophic. One constantly dumps or removes ore-related materials behind or within a dam before its closure, creating substantial surface change or deformation. The deformation can be assessed with multiple temporal synthetic aperture radar (SAR) observations and interferometric SAR (InSAR) techniques. The surface deformation is one prerequisite causing the failure. The water content increase is another primary factor that can reduce the surface friction among piled materials and induce further surface deformation. Intuitively, large deformation and high water content behind the tailings dam are concerns of a possible dam failure. Thus, we propose a new joint analysis of the InSAR-derived surface deformation and SAR-derived moisture content to achieve the objectives, delineating the stable/unstable status of a tailings dam and capturing a possible pre-failure signal of the dam. We study three types of tailing dams to verify the proposed analysis. The first is the Majiatian tailings dam in China, closed safely at the end of 2020. Our analysis determines that the dam status is stable. The second is the collapsed Córrego de Feijão Tailings Dam I in Brazil. The analysis identified a turning point from stable to unstable near December 2018, and the dam failure occurred in January 2019. In case three, an unstable status first at the Maanshan tailings dam was delineated. The findings were conveyed to parties interested in dam safety, and they carried out mitigation activities afterward. The analysis continued until February 2023, indicating a stable status of the tailings dam. The analysis is also robust to slight variations of moving window size around the proposed value. Thus, a valid joint analysis has been studied and is transferable to locations since the analysis and multitemporal SAR observations are available.

Present-Day Crustal Deformation of the Northwestern Tibetan Plateau Based on InSAR Measurements

In this study, The ENVISAT advanced synthetic aperture radar observations from 2003 to 2010 of a descending track covering an area of 100 km × 300 km were used to map the surface velocity field in northwestern Tibet. The derived line-of-sight (LOS) velocity map revealed that interseismic deformation was mainly located on the Altyn Tagh Fault (ATF) and other four immature subsidiary faults (i.e., Tashikule Fault, Muzitage-jingyuhe Fault, Heishibeihu Fault, and Woniuhu Fault). A 2D elastic screw dislocation model was used to interpret the interferometric synthetic aperture radar (InSAR) velocity profiles, which revealed the following results. (a) The oblique movement is partitioned between left-lateral slip at a rate of 6.3 ± 1.4 mm/y on the ATF and 5.9 ± 2.8 mm/y on the subsidiary faults. The low slip rate of the ATF indicates that the ATF does not drive the northeastward extrusion of material, with most of the extrusion occurring in the eastern interior of the plateau and the four subsidiary faults localizing the oblique convergence partitioned in the west. This can reasonably explain why catastrophic earthquakes and rapid slip do not occur all over along the ATF. (b) Based on the four subsidiary faults accommodating the oblique movement and the traces amalgamation with the EKLF (delineated Bayan Har plate boundary to the northeast), we concluded guardedly that the four subsidiary faults are the evoluting plate boundary of the Bayan Har block to the northwest. (c) The Tanan top-up structure had an uplift rate of ~0.6 mm/y at the south of the Tarim Basin.

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

Remote sensing imagery is one of the most widely used data sources for large-scale Earth observations with consistent spatial and temporal quality. However, the current usage scenarios of Earth’s surface remote sensing images, such as those generated from Landsat, Sentinel 2, and SAR, are largely limited to retrospective tasks, as they are often used to reconstruct existing phenomena, such as land use change, flood inundation, and wildfire. This study proposes MA-SARNet, a one-shot nowcasting framework built with a modified MA-Net structure and ResNet50 as the backbone, to predict the backscatter values of Synthetic-Aperture Radar (SAR) images using the previous SAR observations, precipitation, soil moisture, and geomorphic data layers as input. The model was trained, validated, and tested with SAR images collected during the catastrophic 2019 Midwest U.S. Floods that affected several states on the Missouri and Mississippi River tributaries. Compared to the benchmark performance, model predictions show an increase of 31.9% and 17.8% for the mean and median AAI (Assemble Accuracy Index) and an increase of 37.9% and 15.1% for the mean and median NSE (Nash-Sutcliffe Efficiency) on the test set. Results showed that the flood extent derived from backscatter predictions has less misclassifications caused by pixel-level noise compared to the flood map derived using the real backscatters. Results from spatial and temporal robustness tests demonstrate that the model has sufficient generalization potential and does not require further fine-tuning to work with new data, and therefore proves its usefulness in real-time Earth surface prediction tasks that are aimed at fast response to and mitigation for upcoming floods on a tight time schedule.

Satellite imagery in evaluating oil spill modelling scenarios for the Syrian oil spill crisis, summer 2021

The second-largest oil pollution incident in the Eastern Mediterranean Levantine basin, following the oil pollution crisis in Lebanon in 2006, is considered to be the oil leakage from the Syrian Baniyas power plant (summer 2021), during which 12,000 tons of oil were released. At the operational phase, the everyday predictions of oil drift were provided using the MEDSLIK and MEDSLIK-II models in the framework of an agreement between the Mediterranean Operational Network for Global Ocean Observing System (MONGOOS) and the Regional Marine Pollution Emergency Response Centre for the Mediterranean (REMPEC). In this work, we present a novel post-operational comprehensive model-based analysis, conducting a SAR validation in two model outputs: the MEDSLIK and the OpenDrift models. Each simulation is initiated with the operationally acquired EMSA-CSN and ESA SAR images. Moreover, the high-resolution met-ocean fields (CYCOFOS, SKIRON) are used to force the oil drift and transformation in both models. The spill was developed under the calm-wind conditions that prevailed during the incident. We found that the boundary sea currents developed on the periphery of the Lattakia eddies (anticyclonic and cyclonic) were responsible for the fast westward spreading of the oil spill offshore in the NE Levantine, the north-south pathway bifurcation, and re-landing of oil in the extended coastal area of Lattakia. Model outputs were validated against Synthetic-aperture radar (SAR) images with appropriate performance metrics, used for the first time, to assess the capacity of a reliable representation of oil spill drift. The intercomparison between the two oil spill models indicated that both models produce almost similar results, while their validation against the satellite SAR observations illustrates moderate accuracy.

Woody biomass increases across three contrasting land uses in Hurungwe, mid-Zambezi valley, Zimbabwe

Globally, Miombo woodlands store important quantities of carbon, with tree cover and carbon stocks strongly determined by human use. We assessed woodland cover and aboveground carbon (AGC) stocks of miombo along a utilisation gradient on three different land use types, that is, a national park, a buffer zone and a communal area. Woodland cover and carbon stock changes were assessed through mapping of AGC between 2007 and 2017 using Phased Array L-Band Synthetic Aperture Radar observations (ALOS-PALSAR 1 and 2). Woodland cover was higher in the national park and the buffer zone than in the communal area for both 2007 and 2017. In 2007, mean AGC stock was not significantly different (p = 0.05) across all three land use types. However, in 2017, mean AGC was significantly lower (p < 0.001) in the buffer zone and communal area than in the national park. In all three land use types, Miombo woodland cover and mean AGC gains outweighed losses over the 10-year period. AGC gains were significantly higher (p < 0.001) in the national park than in both the buffer zone and the communal area. Results of the study indicate that woodland cover and aboveground carbon increased in all three land use types despite the observed human disturbance over the study period. Both variables recorded a lower increase with elevated utilisation. The study concluded that sustainable resource utilisation is possible without loss of such ecosystem services as carbon sequestration and climate change mitigation.

Impact of SAR-based vegetation attributes on the SMAP high-resolution soil moisture product

NASA's Soil Moisture Active Passive (SMAP) mission employs L-band radiometer observations with a spatial resolution of ~33 km to produce global soil moisture products at various spatial resolutions and regular intervals. Among these products is the SMAP-Sentinel active-passive high-resolution soil moisture, available at spatial resolutions of 1 km and 3 km. The SMAP-Sentinel product combines data from the SMAP L-band radiometer and the Copernicus Sentinel-1A and Sentinel-1B C-band Synthetic Aperture Radar (SAR) observations. The SMAP-Sentinel high-resolution soil moisture product enables various hydrological and agricultural applications that were previously beyond the capability of coarser resolution radiometer-only products. However, the assessment of SMAP-Sentinel product over SMAP core validation sites (CVS) has revealed high errors and biases, particularly in agricultural regions. We suspect that the discrepancy may be related to the vegetation attribute parameters (e.g., vegetation optical depth, τ) used in the SMAP-Sentinel baseline Single-Channel Algorithm (SCA) microwave emission model (i.e., τ-ω model) for soil moisture retrievals, which might be out of sync due to reliance on NDVI climatology in the τ estimation. In this study, we leveraged and evaluated the advantage of using vegetation attribute information inherently available through the SMAP ratiometer and Sentinel- 1A/1B SAR backscatter as an alternative to NDVI climatology-derived τ in the SCA to enhance soil moisture retrievals. The SAR cross-polarization (vh) backscatter potentially contains vegetation attribute information that can be correlated with the coarse resolution τ derived from SMAP observations. Therefore, we hypothesized that including Sentinel- 1A/1B vh observations to derive high-resolution (1 km and 3 km) τ, and subsequently incorporating it into the SCA, can significantly improve the accuracy of the SMAP-Sentinel soil moisture product. We established a statistical relationship between Sentinel- 1A/1B SAR backscatter and the SMAP Dual-Channel Algorithm (DCA)-based τ retrieval at ~33 km, for a specific region and different landcover classes at a global extent, using seven years of observations (2015–2022). Further, we developed an algorithm to downscale the SMAP DCA τ of ~33 km to 1 km and 3 km resolutions based on the developed statistical relationships, enabling us to capture the actual temporal variability in vegetation attributes (i.e., τ) at higher resolutions. The downscaled τ values were then incorporated into the SCA to retrieve SMAP-Sentinel high-resolution (1 km and 3 km) soil moisture. The validation analysis of high-resolution soil moisture retrievals demonstrates that the inclusion of the downscaled τ approach significantly improved the performance of the SMAP-Sentinel product by reducing both bias and error standard deviation compared to the retrievals based on τ derived from the NDVI climatology.

Subsurface deformation monitoring with InSAR and elastic inversion modeling in west Texas

Recent Interferometric Synthetic Aperture Radar (InSAR) observations depict centimeter-level surface uplift/subsidence signals in west Texas that correlate spatially and temporally with increased fluid extraction and injection activities over the past decade. Linear subsidence features from InSAR data near the Pecos area, that align well with maximum horizontal stress orientations and recent clusters of seismic events, suggest fault motion is also associated with this deformation. In this study, we investigate the relationship between observed surface displacement and inferred subsurface fault slip and reservoir compaction/inflation by inversely solving a combined model of elastic dislocation (Okada, 1985, 1992) and poroelastic reservoir compaction/inflation model (Geertsma, 1973).Our geomechanical analysis coupled with InSAR observations are consistent with the idea that both fault slip and reservoir inflation/compaction are occurring associated with fluid injection and withdrawal in the Pecos area. Numerical results show that the linear surface displacement patterns can be accurately reproduced using clusters of finite slip on multiple elastic dislocations in the subsurface, but also matching the broader areal subsidence and uplift signals requires the addition of poroelastic subsurface inflation and depletion. The inverted areal reservoir pressure distribution correlates well with known injection and withdrawal operations in the area. The best-fit surface deformation locates most fault slips patches at depths of 1–3 km, which is shallower than most reported earthquakes in this area. The difference in the estimation of depth suggests there could be aseismic fault slippages.

Analysis of the Differences in Internal Solitary Wave Characteristics Retrieved from Synthetic Aperture Radar Images under Different Background Environments in the Northern South China Sea

Two internal solitary waves (ISWs) with very long fronts observed by synthetic aperture radar (SAR) images in the northern South China Sea (NSCS) are comparatively analyzed based on oceanic reanalysis data and the Korteweg–de Vries (KdV) theory. The differences in the environmental parameters, wave half-width, and amplitude of the two ISW fronts in the two distinct oceanic environments are studied. In the presence of a weak westward surface current of approximately 0.05 m/s, the values of the linear wave speed increase by up to 0.056 m/s, and the retrieved ISW amplitudes decrease by up to 14 m. On the contrary, for another background oceanic environment considering a relatively strong eastward surface current of approximately 0.2 m/s, there are decreases of up to 0.17 m/s in the linear wave speed and increases of up to 32 m in the retrieved amplitudes. However, the results retrieved from both the SAR observations commonly show that the ISW amplitudes along the fronts reach their maximums at roughly 21°N and decrease toward the southern and northern sides, in spite of their distinct background environments.

Analysis of the fault slip, creep, and coupling characteristics of the Maomaoshan-Laohushan-Haiyuan Fault using InSAR and GNSS measurements

Line-of-sight Interferometric Synthetic Aperture Radar (InSAR) observations are affected by long-wavelength errors and the inaccurate estimation of vertical deformation, thereby restricting horizontal slip rate estimations for strike-slip faults, such as the Maomaoshan-Laohushan-Haiyuan Fault (MMS-LHS-HY). To overcome this limitation, herein, InSAR (2016–2022) and Global Navigation Satellite System (GNSS) observations were combined and the Least Squares Variance Component Estimation (LS-VCE) method was used to obtain the Three-Dimensional (3-D) deformation field of the Haiyuan fault zone; subsequently, the slip-rate and coupling distributions were inverted. The results showed that: (1) Compared with the GNSS and leveling deformation velocities, the residuals of the 3-D deformation field obtained using the LS-VCE method combined with the GNSS and InSAR observations reached an RMS of 1 mm/a, significantly better than the deformation field obtained without GNSS data. (2) Vertical deformation from InSAR observations led to over- or under-estimation of the fault strike-slip rate, with the maximum influence on the shallow creep rate of 2.1 mm/a. (3) Based on the 3-D deformation field, fault slip rate, locking depth, and fault creep characteristics obtained from the elastic dislocation and elastic block models were consistent with each other. (4) The strike-slip rate of the MMS-LHS-HY fault gradually decreased from west to east, whereas the thrust rate gradually increased in the same direction. (5) Based on the 3-D deformation field, the creep range of the LHS fault was ∼40 km, with the mean shallow creep rate reaching 2.5 ± 0.1 mm/a, notably larger than the creep rate obtained by ascending InSAR observation alone.

A multi-scale algorithm for the NISAR mission high-resolution soil moisture product

This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) synthetic aperture radar (SAR) backscatter and coarse resolution modeled/reanalysis soil moisture products (∼ 9 [km]) to create a high-resolution (200 [m]) soil moisture product at a global extent. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The use of modeled/reanalysis soil moisture products with high temporal resolution gives an added advantage in reducing the temporal mismatch between the two different inputs used in the algorithm. In this study, the proposed algorithm is tested using L-band UAVSAR backscatter (σ°) data and Advanced Land Observing Satellite −2 (ALOS-2) SAR σ° as a substitute for the NISAR L-band SAR observations. The algorithm uses the L-band SAR σ° to disaggregate coarse resolution (∼9 [km]) reanalysis soil moisture of the European Centre for Medium-Range Weather Forecast (ECMWF) to a high-resolution of ∼200 [m] soil moisture product. The potential of the algorithm is demonstrated over three sites in different hydroclimatic regions of the world, such as India, the USA, and Canada. The high-resolution soil moisture estimates were compared with the in-situ soil moisture measurements available for three sites (North India, Southern California, and Carman, Manitoba, Canada). In North India, in-situ measurements are from paddy crops with high vegetation water content, the unbiased root-mean-square-error (ubRMSE) for the high-resolution soil moisture retrievals was found to be 0.036 [m3/m3] with a bias of −0.051 [m3/m3]. For the southern California site, the validation statistics shows low ubRMSE of 0.027 [m3/m3] and a low bias of 0.016 [m3/m3]. At the Carman, Manitoba test site of Canada, where in-situ soil measurements were available for multiple crop types, the comparison shows that the ubRMSE for all the crop types lies below 0.05 [m3/m3] with an average bias of <0.07 [m3/m3]. The result confirms that the proposed algorithm meets the NISAR mission's accuracy goals, i.e., 0.06 [m3/m3] ubRMSE over areas with vegetation water content (VWC) below 5 [kg/m2]. Rigorous validation work will still need to be carried out in the future based on the availability of L-band SAR datasets and after the launch of the NISAR satellite.