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Optical and synthetic aperture radar (SAR) images, two standard Earth observation tools, can reflect the characteristics of the surface from different perspectives and provide complementary information for land use classification. However, because they belong to different modes and express land objects differently, it is challenging to effectively fuse and use them to perform a pixel-wise classification. Current methods only focus on the local receptive field to fuse deep features in a single dimension, which is too simple to fully exploit the correlation between different modes. Moreover, the appearance disparities between each modality may induce semantic misalignment and disrupt the conditions of features' fusion. To overcome the above problems, we introduce a spatial-aware circular module to generate a cross-modality receptive field and globally enhance the interaction between each pixel in the spatial dimension. Additionally, we recalibrate the features in the channel dimension to selectively refine and retain the essential things during the process, which can further achieve feature refinement. To reduce the impact of modal appearance disparities, we transform their high-level features into a common latent space and align their distributions to correlate the complementary cues hidden in each modality. The experimental results for the WHU-OPT-SAR dataset show that our method performed better than other state-of-the-art methods, with a mean intersection over union (mIoU) of 58.5% and an overall accuracy (OA) of 84.2%. Furthermore, the method obtained competitive results in Ezhou and Panjin, China. The results demonstrate our method's applicability. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) vortex structure can be estimated from wind radii (34, 50, and 64 kt). Accurate wind radii are essential for assessing the impact range of TCs. Combining observations from active and passive microwave remote sensing instruments can provide long-time series data for monitoring changes in TC wind structure. Here, the evolution of the wind radii for Typhoon In-fa (2021), from its genesis to its first landfall, is evaluated using data from spaceborne microwave radiometers and synthetic aperture radars (SARs). Our results show that the retrieved wind radii are relatively close to those from the Joint Typhoon Warning Center (JTWC), the coastal automatic weather stations in China, and the advanced scatterometer. The time series for the wind radii show significant increasing trends during the lifecycle of In-fa and a massive vortex structure before In-fa made landfall. The average wind radii observed before landfall are all likely in the 90th percentile of the 2001–2021 JTWC historical wind radii records, indicating that the vortex structure is extraordinarily large before In-fa made landfall and represents a historical extreme. The extremely large vortex structure may be attributable to the interaction between In-fa and an extremely strong monsoon gyre. [Display omitted] • Wind radii for Typhoon In-fa (2021) are evaluated by multiple spaceborne microwave radiometers and SARs. • The retrieved wind radii are close to those from JTWC, AWSs in China, and ASCAT. • The significant increasing trends of wind radii and a massive vortex structure before landfall are revealed. [ABSTRACT FROM AUTHOR]

Land reclamation in the Yan'an New District (YND) on the Chinese Loess Plateau is one of the largest earthworks projects in the world, involving the excavation of loess from ridges and the deposition of the material in adjacent valleys, flattening an area of more than 78 km2. It can take multiple years for the landscape to adjust to the new topography after the earthworks are completed, with subsidence in the fill areas and uplift in the areas of excavation. Understanding the pattern and extent of this differential vertical movement has great importance for ensuring the structural integrity of any infrastructure built on the site. We therefore studied the spatial deformation field of the YND from January 2015 to December 2018 using satellite synthetic aperture radar interferometry (InSAR) technology. Persistent scatterers (PS) and distributed scatterers (DS) were combined in a two-tier network to overcome problems with temporal decorrelation of the InSAR phase signals. The derived deformation field was validated with in-situ ground leveling and GNSS measurements, and interpreted using a forward geophysical model. The results indicate that our approach provides a more detailed understanding of the deformation than conventional PSI and SBAS methods. Subsidence of up to 87 mm/yr occurred over about 4.4 km2 of the fill region, and uplift of up to 26 mm/yr in the excavated areas. There is a strong linear relationship between these displacements and prior topographic elevation change. The primary cause of subsidence in fill areas is compaction of remoulded loess. Instantaneous elastic and gradual poroelastic deformation are the main causes of uplift in excavated areas. • An improved MTInSAR method obtained more details in the land reclamation areas. • Heterogeneous surface displacements happened in the YND following earthwork. • A linear correlation between the displacement and topographic elevation change. • Subsidence is dominated by the compaction and consolidation of remoulded loess. • The newly recognized uplift is poroelastic rebound by surface load redistribution. [ABSTRACT FROM AUTHOR]

• A SVR model was proposed to provide accurate moisture estimation for saline soil. • It does not require prior information like soil surface roughness and soil salinity. • The new SVR model outperformed RFR and MLR in soil moisture estimation. • Sentinel-1A provides a SAR data source to timely map coastal saline soil moisture. Accurate and timely information on soil moisture (SM) is crucial for better understanding the hydrological and ecological processes in humid saline regions. This study investigated the potential of using Sentinel-1A Synthetic Aperture Radar (SAR) imagery and Machine Learning Algorithms for SM mapping in China's east coast. This study used the recursive feature elimination (RFE) technique to select optimum SAR indices from the backscattering coefficients of the sampling date as well as the averaged backscattering coefficients of the neighbouring dates. The newly developed support vector regression (SVR) model provided more accurate SM estimation than that from the random forest regression (RFR) and multivariate linear regression (MLR) models. By using the leave-one-out cross-validation (LOOCV), the achieved coefficient of determination (R2), root mean square error (RMSE), and the relative RMSE (RRMSE) in SM estimation were 0.77, 1.82%, and 10.29%, respectively. The SVR model was able to map the SM effectively over a large area under the condition that the information of soil surface roughness, soil salinity, and land cover types was unknown. Results from this study demonstrate that high-temporal-resolution Sentinel-1A SAR, with its free-accessibility, offers a great source for the SVR model to provide frequent and accurate SM mapping over coastal saline regions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we used the available algorithm for soil moisture estimation over LOPEX05 (the Loess Plateau land surface process Experiment (2005)) area. The available algorithm used ENVISAT/ASAR AP mode VV polarization observational data at a low incidence angle and ground measured soil moistures. The ground measurements were performed in the summer of the 2005 during the LOPEX05 field campaign. The validated results indicate that an average difference between the soil moistures estimated from the microwave remote sensing and ground measurements is less than 0.02cm3/cm3, with a RMS error of 2.0%, and a maximum less than 0.04cm3/cm3. The algorithm was applied to the surface soil moisture mapping later. The results show that this algorithm is suitable for monitoring soil moisture information of the agricultural fields over the Chinese Loess Plateau, when ground land cover situation and the resolution of imagery data are taken into account. However, we also find that there are large differences over the steep slope region, the edge of mesa. The results are not acceptable for surface soil moisture estimation in these regions. Thus, the surface soil moisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future. [Copyright &y& Elsevier]

• Propose a methodology to retrieve 3-D multi-level mining deformation with OTD-InSAR. • Improve the deformation precision with about 82.6% in the south-north direction. • Eliminate large errors caused by ground fissures in the horizontal deformation map. • Present 3-D mining deformation with more details. Underground longwall mining activities usually induce three-dimensional (3-D) multi-level surface deformation, which could lead to serious natural hazards. Offset-tracking (OT) and differential interferometric synthetic aperture radar (DInSAR) technology (referred to as OTD-InSAR) can be used to observe the large-level and small-level mining deformations based on single InSAR pair, but the measured deformation is only along the radar line-of-sight (LOS) or the azimuth directions rather than in the 3-D directions. The conventional InSAR-based methods to resolve 3-D mining deformation have the strict restrictions and low south-north accuracy. In this paper, a novel methodology is proposed to utilize a multi-level deformation fusion model and the Lagrange multiplier method based on single OTD-InSAR pair (hereafter called MAOTD-InSAR) to estimate and optimize the 3-D multi-level surface deformation. The Daliuta coal mining area of China was selected to verify the feasibility and precision of the proposed method using high resolution TerraSAR-X (TSX) SAR images with six pairs. Results show that the root mean square errors (RMSEs) of MAOTD-InSAR against in-situ measured data are less than 0.2214 m, 0.1612 m and 0.1880 m in the east, north and vertical directions, respectively. The minimum RMSE in the horizontal direction could reach 0.0361 m with an improvement of about 82.6%, which particularly improve the accuracy in the south-north direction for the TSX satellite since its direction of azimuth deformation is approximately north direction, and this is also adequate for the other satellites in the near-polar orbit. In addition, an empirical model was generated with respect to the gradients of ground fissures on horizontal deformation, and this model enabled us to remove large errors caused by ground fissures. The verification results show that the proposed method provides higher precision and reliability for the resolved 3-D multi-level surface deformation, which presents 3-D mining deformation with more details from the edge to the center of the subsidence basin and will show some possibilities for new findings and interpretations in the process of coal mining. [ABSTRACT FROM AUTHOR]

• Propose a new fusion model to retrieve multi-level mining deformation. • Monitor the long-term mining deformation using the DInSAR and OT technology. • Present the prediction model to forecast deformation with insufficient SAR data. • Bring about a comprehensive analysis of the deformation evolution of the full workface. Caused by the underground longwall mining activities, ground deformation, which includes sudden large-level deformation, slow small-level deformation and the middle-level deformation between them, can lead to serious construction damage and ecological environment degradation. In this paper, we propose a new fusion model that is able to completely retrieve multi-level deformation with measured radar line-of-sight (LOS) deformation from offset-tracking (OT) and differential interferometric synthetic aperture radar (DInSAR) technique (referred to as OTD-InSAR). The results from OTD-InSAR show that small-level deformation on the edge of subsidence basin and large-level deformation in the central portion of subsidence basin are measured with high accuracy. The multi-level deformation fusion model considers the geographic information and SAR data information simultaneously to fuse large-level and small-level deformation and use inverse distance weighting method (IDW) to estimate the middle-level deformation. Due to the insufficient measured data in the actual situation, we present the prediction model to forecast the long period deformation evolution of full workface based on the exponent Knothe model and the fusion results. The 52,303 workface in Daliuta coal mining area in China was selected to verify the effectiveness of the fusion model and prediction model. The comparison between the fusion results and the in-situ measured data shows that the root mean square error (RMSE) is less than 0.23 m. Results of deformation prediction model are found to be very close to fusion results and the RMSE is less than 0.027 m. This research work has brought about a comprehensive analysis of the deformation evolution of the full workface with high precision from the beginning to the end of coal mining for about 18 months. As a result, the monitoring results can support the requirements of mining deformation monitoring in the management of surface disaster. [ABSTRACT FROM AUTHOR]

The area, distribution, and temporal dynamics of anthropogenic impervious surface (AIS) at large scale are significant for environmental, ecological and socio-economic studies. Remote sensing has become an important tool for monitoring large scale AIS, while it remains challenging for accurate extraction of AIS using optical datasets alone due to the high diversity of land covers over large scale. Previous studies indicated the complementary use of synthetic aperture radar (SAR) to improve the AIS estimation, while most of them were limited to local and small scales. The potential of SAR for large scale AIS mapping is still uncertain and underexplored. In this study, first, a machine learning framework incorporating both optical and SAR data based on Google Earth Engine platform was developed for mapping and analyzing the annual dynamics of AIS in China. Feature-level fusion for SAR and optical data across large scale was tested applicable considering the backscattering coefficients, texture measures and spectral characteristics. Improved accuracy (averaged 2% increased overall accuracy and averaged 4% increased Kappa coefficient) and better delineation between the bright impervious surface and bare land was observed comparing with using optical data alone. Second, comprehensive assessment was conducted using high-resolution samples from Google Earth, census data from China Statistic Yearbook and benchmark datasets from the GlobeLand30 and GHSL, demonstrating the feasibility and reliability of the proposed method and results. Last but not the least, we analyzed the spatial and temporal patterns of AIS in China from national, regional and provincial levels. • We incorporated SAR and optical images for large scale impervious surfaces mapping. • Results were validated with high resolution samples, census and benchmark data. • SAR generally improved the overall accuracy by 2%. • SAR better delineated the impervious surface from bare land. [ABSTRACT FROM AUTHOR]

The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan River watershed located in the North of China. One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge. This proposed mission is targeted to provide continuity for the current satellite L-band microwave observations, and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active, passive and optical remote sensing data. Multi-resolution, multi-angle and multi-spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin, and one time over a 165 km by 5 km area that includes the Xiaoluan River basin. The near surface soil moisture (0 cm–5 cm) was measured extensively on the ground in fifty 1 km by 2 km quadrats (targeted to compare with the airborne radiometer), and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations. Two networks were established for continuous measurement of the soil moisture and temperature profile (3 cm, 5 cm, 10 cm, 20 cm, 50 cm) and precipitation in the Shandian and Xiaoluan River basin, respectively. Supporting ground measurements also included ground temperature, vegetation water content, surface roughness, continuous measurement of microwave emission and backscatter at a pasture site, reflectance of various land cover types, evapotranspiration and aerosol observations. Preliminary results within the experimental area indicate that (1) the near surface soil moisture spatial variability at a 200 m scale was up to ~0.1 cm3/cm3 at an intermediate value of ~0.35 cm3/cm3. (2) The difference of soil and vegetation temperature in grass and croplands reach its maximum of 11 K around solar noon time, and the soil temperature gradient is largest at around 15 P.M. (3) Both the airborne and ground measurements cover a wide range of conditions. The L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm3/cm3 to 0.5 cm3/cm3. The sensitivity of both active and passive data to soil moisture is compared at corresponding spatial resolutions and show high information complementarity for better accuracy and resolution soil moisture retrieval. • First airborne optical and active-passive microwave experiment conducted in China • Demonstration of the active-passive microwave synergy at varying incidence angles • Addressing prelaunch science questions of the terrestrial water resources mission [ABSTRACT FROM AUTHOR]

A controlled-source electromagnetic method with a synthetic aperture is proposed to improve the ability to detect a deep target body in the subsurface. First, we deduced the formulas of three-dimensional diffusion equations in the frequency domain, and gave the expression of synthetic aperture of the controlled-source electromagnetic array. An amplitude weighting coefficient, a phase shift coefficient, and a synthetic aperture range are selected based on model tests, and then, the optimum value of the error range is determined. A typical geo-electrical model is established, and the electric response, the normalized amplitude of the background field, and the apparent resistivity with and without a synthetic aperture are compared. The results show that a synthetic aperture can focus electromagnetic energy and enhance the electromagnetic response of the target body. This is beneficial when extracting weak anomaly information for a deep geo-electrical body, especially for a resistive target object. Finally, we apply the synthetic aperture to field data for gold ore in northern China, and the amplitude of a deep resistive anomaly is strengthened. This demonstrates that our method can be effectively applied to real field data processing using a synthetic aperture. • Synthetic aperture technique applied to wave electromagnetic field is used to low frequency diffuse electromagnetic field. • The optimized amplitude coefficient and a phase shift coefficient can greatly improve the resolution of deep target body. • Weak anomaly of resistive target body can be strengthened greatly, which is beneficial for exploration of deep oil and gas. [ABSTRACT FROM AUTHOR]