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Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations' data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city's gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China. [ABSTRACT FROM AUTHOR]

Ocean salinity is a pivotal aspect of the ocean dynamic environment. Spaceborne L-band radiometers, like SMOS, Aquarius, and SMAP, offer a comprehensive approach to mapping out large-scale ocean salinity patterns. As China prepares for the launch of the Chinese Ocean Salinity and Soil Moisture Mission (COSM), it is essential to delve into the intricacies of radio frequency interference (RFI) detection, localization, and mitigation. The L-band, in particular, is highly susceptible to RFI. COSM will carry not one but two advanced instruments: a 2-D L-band aperture synthesis microwave radiometer (LASMR) and a 1-D L-C-K band microwave imager combined active and passive (MICAP). This article delves into the current state of RFI research, particularly in recent years, and introduces a fusion method that integrates MICAP and LASMR for more accurate RFI detection, localization, and mitigation. This fusion method relies on an algorithm that constructs localization and intensity objective functions based on the principle of least squares. By optimizing these functions, we can pinpoint the precise location and intensity of RFI. The resulting minimum mitigation residual offers a blueprint for achieving optimal RFI detection, localization, and mitigation. The experimental results, achieved in a controlled anechoic chamber, confirm that this fusion method—when weighted by variance—boosts detection accuracy, refines localization precision, and minimizes residual mitigation errors compared with standalone MICAP or LASMR techniques. [ABSTRACT FROM AUTHOR]

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future. [ABSTRACT FROM AUTHOR]

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing. [ABSTRACT FROM AUTHOR]

Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice-mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data. On average, the user's, producer's, and overall accuracy values over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution maps explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at 10.57760/sciencedb.06963 (Shen et al., 2023). [ABSTRACT FROM AUTHOR]

Characterizing the coseismic slip behaviors of earthquakes could offer a better understanding of regional crustal deformation and future seismic potential assessments. On 18 December 2023, an Mw 6.0 earthquake occurred on the Lajishan–Jishishan fault system (LJFS) in the northeastern Tibetan Plateau, causing serious damage and casualties. The seismogenic fault hosting this earthquake is not well constrained, as no surface rupture was identified in the field. To address this issue, in this study, we use Interferometric Synthetic Aperture Radar (InSAR) data to investigate the coseismic surface deformation of this earthquake and invert both ascending and descending line-of-sight observations to probe the seismogenic fault and its slip characteristics. The InSAR observations show up to ~6 cm surface uplift caused by the Jishishan earthquake, which is consistent with the thrust-dominated focal mechanism. A Bayesian-based dislocation modeling indicates that two fault models, with eastern and western dip orientations, could reasonably fit the InSAR observations. By calculating the coseismic Coulomb failure stress changes (∆CFS) induced by both fault models, we find that the east-dipping fault scenario could reasonably explain the aftershock distributions under the framework of stress triggering, while the west-dipping fault scenario produced a negative ∆CFS in the region of dense aftershocks. Integrating regional geological structures, we suggest that the seismogenic fault of the Jishishan earthquake, which strikes NNE with a dip of 56° to the east, may be either the Jishishan western margin fault or a secondary buried branch. The optimal finite-fault slip modeling shows that the coseismic slip was dominated by reverse slip and confined to a depth range between ~5 and 15 km. The released seismic moment is 1.61 × 1018 N·m, which is equivalent to an Mw 6.07 earthquake. While the Jishishan earthquake ruptured a fault segment of approximately 20 km, it only released a small part of the seismic moment that was accumulated along the 220 km long Lajishan–Jishishan fault system. The remaining segments of the Lajishan–Jishishan fault system still have the capability to generate moderate-to-large earthquakes in the future. [ABSTRACT FROM AUTHOR]

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]

Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at 10 or 20-m resolution. The accuracy was examined by using 108195 survey samples and countylevel statistical data. On average, the user's, producer's, and overall accuracy over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution map explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.06963 (Shen et al., 2023). [ABSTRACT FROM AUTHOR]

Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level. The reasons for this rebound and the significant interannual variability over past 15 years are still open questions. Here, by using long-term time-series (2007–2022) optical and Synthetic Aperture Radar satellite observations (1000+ scenes), meteorological data and water quality statistics, the mechanism analysis was performed by exploring effects from natural factors and human activities. Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area (the Subei Shoal) during a critical period (from April to May 20) and the nutrient availability. Furthermore, by using these two parameters, a novel model for AMC prediction (R2 = 0.87, p < 0.01) is proposed and independently validated, which can reasonably explain the significant interannual variability (2014–2021) and agree well with the latest observation in 2022 (percentage difference 12%). Finally, suggestions are proposed for future disaster prevention and alleviation. This work may aid future bloom prediction and management measure optimization. [ABSTRACT FROM AUTHOR]

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data. [ABSTRACT FROM AUTHOR]

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam's substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage. [ABSTRACT FROM AUTHOR]

With the rapid economic development in China, coal resources are being exploited greatly, which easily causes geological disasters due to surface subsidence. Fast and accurate surface subsidence monitoring and forecasting in mining regions are important references to analyze surface variation laws and disaster warning. However, differential interferometric synthetic aperture radar (D-InSAR) in mine surface monitoring is highly sensitive to spatiotemporal baseline and atmospheric delay. In addition, traditional machine learning algorithms have complicated network structures and difficulties determining parameters. Small baseline subsets InSAR (SBAS-InSAR) and extreme learning machine (ELM) dynamic prediction were combined for corresponding experimental studies to address these problems. On the basis of SBAS-InSAR, surface subsidence monitoring data in mining areas in Pingdingshan City, China, were collected, and a comparative analysis of D-InSAR monitoring data was performed, which verified the validity of SBAS-InSAR monitoring. On the basis of SBAS-InSAR data, a prediction model was built by ELM. The model results were compared with the prediction results of back propagation (BP) neural network and support vector machine (SVM) through root mean square error (RMSE) and mean relative error (MRE). Results demonstrate that the surface subsidence prediction of SBAS-InSAR in the monitoring mining area can reach millimeter accuracy. The MRE values of ELM, BP, and SVM prediction are maintained within 2%, 5%, and 8%, and the RMSE values are less than 3 mm, 7 mm, and 10 mm, respectively, thereby indicating that ELM prediction has high accuracy and reliability. This study provides an important evidence for safe production and scientific disaster prevention and reduction in mining areas. [ABSTRACT FROM AUTHOR]

Because the penetration depth of electromagnetic waves in forests is large in the longer wavelength band, most traditional forest height estimation methods are carried out using polarimetric interferometry synthetic aperture radar (PolInSAR) data of the L or P band, and the estimation method is a three-stage method based on the random volume over ground (RVoG) model. For X-band electromagnetic waves, the penetration depth of radar waves in forests is limited, so the traditional forest height estimation method is no longer applicable. In view of the above problems, in this paper we propose a new forest height estimation strategy for airborne X-band PolInSAR data. Firstly, the sub-view interferometric SAR pairs obtained via frequency segmentation (FS) in the Doppler domain are used to extend the polarimetric interferometry coherence coefficient (PolInCC) range of the original SAR image under different polarization states, so as to obtain the accurate ground phase. For the determination of the effective volume coherence coefficient (VCC), part of the fitting line of the extended-range PolInCC distribution that is intercepted by the fixed extinction coherence coefficient curve (FECCC) of the fixed range is averaged to obtain the accurate effective VCC. Finally, the high-precision forest canopy height in the X-band is estimated using the effective VCC with the ground phase removed in the look-up table (LUT). The effectiveness of the proposed method was verified using airborne-measured data obtained in Shaanxi Province, China. The comparison was carried out using different strategies, in which we substituted one step of the process with the conventional method. The results indicated that our new strategy could reduce the root mean square error (RMSE) of the predicted canopy height vastly to 1.02 m, with a lower estimation height error of 12.86%. [ABSTRACT FROM AUTHOR]

In order to alleviate the conflict between populations and land resource, Tianjin adopted multi-phase reclamation projects to the formed large-scale artificial reclamation land. The reclamation areas, however, are prone to subsidence, which poses a significant threat to infrastructure as well as the safety and assets of the residents. The SBAS-InSAR was used to acquire surface deformation of Tianjin Binhai New Area from January 2017 to December 2022, analyze in depth the response relationship between land subsidence, reclamation project time, and land-use type. There is a strong correlation between surface deformation and reclamation time. Severe land subsidence occurred over newly reclaimed areas. In the offshore direction, the deformation values of the Nangang Industrial Zone, the Lingang Industrial Zone, and Hangu Harbor were −98 mm to −890 mm, 45 mm to −580 mm, and −140 mm to −290 mm, respectively. Significant differences in deformation were detected among different land-use types where reclamation projects were completed in the same time. Subsidence was positively correlated with surface load; in areas with higher surface loads, the surface settlement was also more severe. The average surface settlement for the heavy shipyard, with 67 grain storage tanks and 27 grain storage tanks, road, and bare land were −201 mm, −166 mm, −107 mm, −64 mm, and −43 mm, respectively. This study reveals significant differences of surface deformation in the reclamation completed at different times, and determines that the load is the main driving factor of settlement difference in the reclamation land completed at the same time. This has important guiding significance for preventing and controlling geological disasters in the reclamation area and later development planning. [ABSTRACT FROM AUTHOR]

The Chinese-French Oceanography SATellite (CFOSAT) jointly developed by the Chinese National Space Agency (CNSA) and the Centre National d'Etudes Spatiales (CNES) of France carries a wave spectrometer (Surface Waves Investigation and Monitoring, SWIM). SWIM has one nadir and five off-nadir beams to measure ocean surface waves. These near-nadir beams range from 0° to 10° at an interval of 2°. In this work, we investigated the performance of wave parameters derived from wave spectra measured by SWIM at off-nadir beams during the period 2020 to December 2022, e.g., incidence angles of 6°, 8° and 10°, which were collocated with the wave simulated by Simulating Waves Nearshore (SWAN). The validation of SWAN-simulated significant wave heights (SWHs) against National Data Buoy Center (NDBC) buoys of National Oceanic and Atmospheric Administration (NOAA) exhibited a 0.42 m root mean square error (RMSE) in the SWH. Our results revealed a RMSE of 1.02 m for the SWIM-measured SWH in the East Pacific Ocean compared with the SWH simulated by SWAN, as well as a 0.79 correlation coefficient (Cor) and a 1.17 squared error (Err) for the wave spectrum at an incidence angle of 10°, which are better than those (i.e., the RMSEs were > 1.1 m with Cors < 0.76 and Errs > 1.2) achieved at other incidence angles of SWH up to 14 m. This analysis indicates that the SWIM product is a relevant resource for wave monitoring over global seas. The collocated wave retrievals for more than 300 cases from Gaofen-3 (GF-3) synthetic aperture radar (SAR) images in China Seas were also used to verify the accuracy of SWIM-measured wave spectra. The energy of the SWIM-measured wave spectra represented by SWH was found to decrease with an increasing incidence angle in a case study. Moreover, the SWIM-measured wave spectra were most consistent with the SAR-derived wave spectra at an incidence angle of 10°, yielding a 0.77 Cor and 1.98 Err between SAR-derived and SWIM wave spectra under regular sea state conditions (SWH < 2 m). The error analysis indicates that the difference in SWH between SWIM at an incidence angle of 10° and SWAN has an increasing tendency with the growth in sea surface wind and sea state and it stabilizes to be 0.6 m at SWH > 4 m; however, the current and sea level have less influence on the uncertainties of the SWIM product. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on an analysis of current research regarding dune migration and deformation monitoring methods, high-precision ground-based synthetic aperture radar (GB-InSAR) interferometry was used for continuous dynamic monitoring of the migration and deformation of dunes in Changli Gold Coast in Hebei Province, China, a typical area of coastal dunes. Methods for interpretation and key data processing were investigated. The results showed that the maximum deformation of dunes reached 7 mm during the monitoring period and that the deformation of the topset of the dune was greater than that of the slope foot. With GB-InSAR technology, millimeter-scale deformation can be effectively recognized. This method can be applied to monitor real-time dynamic micro-deformation of coastal dunes because of its excellent precision. It offers good application prospects if popularized and applied in coastal dune migration monitoring. This study not only enriches the research methods and content for studying coastal wind–sand issues but also provides a scientific basis for the protection and development of the study area. [ABSTRACT FROM AUTHOR]

The Institute of Polymer Chemistry at Nankai University in China recently celebrated its 40th anniversary. The institute, founded in 1984 by Professor Binglin He, has become a well-established research institution with a focus on polymer synthetic chemistry, polymer physics, and the correlation between structure and properties in new functional polymer materials. It has 62 faculty members and offers master's and doctoral programs in polymer chemistry. The institute has published over 1000 research papers and received numerous awards for its contributions to polymer science. The special issue of Macromolecular Rapid Communications features 25 publications highlighting the institute's research in polymer science. The institute hopes that this publication will encourage more collaborations with institutions worldwide in the future. [Extracted from the article]

Most dams in China have been operating for a long time and are products of the economic and technical limitations at the time of construction. Due to decades of aging engineering and ancillary problems, these reservoirs pose great threats to the safety of local people and the development of the surrounding economy. In this study, the surface deformation information for the Banqiao Reservoir is monitored with the small baseline subset–synthetic aperture radar interferometry (SBAS-InSAR) method using 80 Sentinel-1A images acquired from 3 January 2020 to 20 August 2022. Additionally, ground measurements from the BeiDou ground-based deformation monitoring stations were collected to validate the InSAR results. Based on the InSAR results, the spatiotemporal deformation features of the dam were analyzed in detail. The results show that the deformation in most areas, including the dam in the study area, is relatively stable, and the regional deformation velocity of the Banqiao Reservoir dam and other hydraulic engineering facilities varies between −1 mm/y and −4 mm/y. The Ru River area has a relatively obvious subsidence trend, and the maximum subsidence velocity reaches 30 mm/y. The InSAR monitoring results are consistent with the change trend in the BeiDou ground-based deformation measurement results. The monitoring results for the reservoir dam area provide a reference for local sustainable development and geological disaster prevention. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface subsidence induced by coal mining is critical to safeguarding the environment and resources. However, the precision of current prediction models is often restricted by the lack of pertinent data or imprecise model parameters. To overcome these limitations, this study proposes an approach to predicting mine subsidence that leverages Interferometric Synthetic Aperture Radar (InSAR) technology and the long short-term memory network (LSTM). The proposed approach utilizes small baseline multiple-master high-coherent target (SBMHCT) interferometric synthetic aperture radar technology to monitor the mine surface and applies the long short-term memory (LSTM) algorithm to construct the prediction model. The Shigouyi coalfield in Ningxia Province, China was chosen as a study area, and time series ground subsidence data were obtained based on Sentinel-1A data from 9 March 2015 to 7 June 2016. To evaluate the proposed approach, the prediction accuracies of LSTM and Support Vector Regression (SVR) were compared. The results show that the proposed approach could accurately predict mine subsidence, with maximum absolute errors of less than 2 cm and maximum relative errors of less than 6%. The findings demonstrate that combining InSAR technology with the LSTM algorithm is an effective and robust approach for predicting mine subsidence. [ABSTRACT FROM AUTHOR]

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Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China. [ABSTRACT FROM AUTHOR]

Model-based incoherent polarimetric decomposition is a frequently used technique to analyze multilook data of polarimetric synthetic aperture radars (POLSARs). The purpose of this study is to analyze and compare different model-based incoherent polarimetric decomposition algorithms from the polarimetric information change aspect. For the input of a model-based incoherent polarimetric decomposition algorithm, polarimetric entropy was used to represent the polarimetric information of a coherency matrix. For the output of a model-based incoherent polarimetric decomposition algorithm, there are usually several decomposed components. To quantitatively represent their total polarimetric information, a new concept, generalized polarimetric entropy, was proposed which generalized the concept of polarimetric entropy based on the information entropy additivity of information theory. Generalized polarimetric entropy consists of two parts named as polarimetric power entropy and polarimetric residual entropy, respectively. Polarimetric power entropy describes the distribution status of the Span values of all decomposed components. Polarimetric residual entropy represents the residual randomness of all decomposed components. With the three new concepts, eight model-based incoherent polarimetric decomposition algorithms were compared and analyzed. Two real POLSAR images, respectively, derived by the E-SAR airborne system of Germany and the GF-3 satellite of China were used for the experiments. Experimental results had illustrated several useful conclusions. [ABSTRACT FROM AUTHOR]

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures. [ABSTRACT FROM AUTHOR]

A winter storm triggered a significant sea smoke with the northwesterly wind to the Yellow Sea, China, on 7 January 2021. The ocean responses to this event lasted about 3 days. Satellite observations show that the sea surface temperature dropped from 5.7° to 4.7°C on the following day and then recovered to the previous level; the chlorophyll-a, a bio-growth indicator, increased from 3.6 to 3.9 mg m−3 due to cooling-induced coastal upwelling between 7 and 9 January. Two buoys measurements showed that the air temperature dropped to −13.3°C and high relative humidity with a maximum value of 89.0% above the sea surface, creating favorable conditions for sea smoke generation. A Regional Ocean Modeling System (ROMS) and Weather Research and Forecasting (WRF) Model coupled model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and analyze the air–sea interaction. The 20°C temperature difference between extreme cold air (−13.3°C) and the relatively warm stable sea surface (4.7°–5.7°C) enhanced the seawater evaporation. In addition, we suppose the concentration of sea salt, a kind of condensation nucleus, with a particle diameter of 0.5–1.5 μm above the sea surface increased quickly on 7 January. The boiling-water-like sea surface was imaged on a synthetic aperture image. We developed an image analysis method to describe the cell-shaped texture characteristics imaged by Synthetic Aperture Radar (SAR). We also found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability. Significance Statement: On 7 January 2021, a significant sea smoke event happened in the Yellow Sea. The ocean response to the event lasted 3 days. First, on a synoptic scale, this study presents the comprehensive satellite observations of the sea surface temperature drop and chlorophyll-a increase associated with the sea smoke. Second, a coupled air–sea interaction model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and identify which condensation nucleus induced such heavy sea smoke. Third, we developed an image analysis method to analyze high-resolution synthetic aperture radar images and found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability. [ABSTRACT FROM AUTHOR]

The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure. [ABSTRACT FROM AUTHOR]

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Empirical wind-retrieval algorithms for synthetic aperture radar (SAR), such as the geophysical model function (GMF) CMOD5N in the C-Band, are currently well developed. The retrieval accuracy of GMFs, however, is lower in the presence of marine phenomena such as oceanic fronts and mesoscale eddies, making SAR wind retrieval challenging under these conditions. In this study, we proposed a scheme for wind retrieval from Chinese Gaofen-3 (GF-3) SAR images using an analytical approach to obtain the wind speed in the nearshore waters of China's seas. Approximately 300 images were acquired from 2018 to 2021 in the copolarization channels [vertical–vertical (VV) and horizontal–horizontal (HH)]. The images were collocated with the measurements from the Advanced Scatterometer (ASCAT) and the Chinese Haiyang-2B (HY-2B) scatterometer. Our analytical approach was based on the Bragg resonant and non-Bragg (NB) scattering model for the calculation of the normalized radar backscatter cross-section (NRCS), which accounted for the influence of wave-current interactions and breaking waves in nearshore waters. We used the sea surface currents obtained from the HYbrid Coordinate Ocean Model (HYCOM) to simulate the Bragg resonant roughness. We derived the contribution of the NB breaking waves from the SAR-measured NRCS values in both the VV and HH channels. By comparing the retrieval results obtained using the analytical approach with the scatterometer products, we concluded that the root mean square error (RMSE) of the wind speed was 2.01 m s−1 with a 0.91 correlation coefficient (COR), which was less than the 2.93 m s−1 RMSE and 0.72 COR for the GMF CMOD5N. The variation in the bias of the HYCOM currents was approximately 1 m s−1 for current speeds greater than 0.3 m s−1. These findings verified the stability of the analytical approach for SAR wind retrieval in complex sea states. [ABSTRACT FROM AUTHOR]

The dual-polarized Sentinel-1 synthetic aperture radar (SAR), which performs C-band SAR imaging day and night regardless of the weather, offers new opportunities for wetland cover monitoring in regions frequently covered by cloud. In this study, a decision tree (DT) classifier was applied to investigate the utility of Sentinel-1 for wetland classification in Poyang Lake, China. Six land cover classes were identified: water body, bare land, aquatic vegetation, cropland, forest, and urban area surrounding the lake. Two types of features were extracted from the dual-polarized SAR data, namely, the backscattering coefficients and the polarimetric decomposition components. Then, the DT classifier was trained and applied with backscattering features, polarimetric features, or a combination of the two features. The overall accuracy of all the classifiers was over 90% for the different feature combinations (98.02, 90.63, and 98.59%) for the classes in the lake area, compared with less than 78% (74.84, 63.01, and 77.29%) when the classes surrounding the lake were also considered, which demonstrates the potential of Sentinel-1 SAR data for wetland monitoring. The accuracy of different feature combinations increased in the following order: polarimetric features < backscattering features < combination of polarimetric and backscattering features. The artificial neural network, naïve Bayes, random forest, and adaptive boost algorithms were compared for the case of using backscattering features, and the adaptive boost algorithm showed lower performance than the other three algorithms. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has been widely used in landslide deformation monitoring in the past decade. It has the advantages of high monitoring accuracy, a wide range, and flexibility allowing all-weather continuous monitoring. The self-developed S-SAR synthetic aperture radar (slope radar) is the first completely domestic-made radar used in slope deformation monitoring equipment in China, and its performance and technical parameters are equal to or better than similar products made abroad. The characteristics of deformation data collected by S-SAR slope radar deployed in the front open pit mine are analyzed to further develop a spatio-temporal landslide prediction method which is applicable to the massive monitoring data within the monitoring range of slope radar. The intersection points of short-term moving average velocity curve and long-term moving average velocity curve of slope deformation, which are onset of acceleration (OOA) and termination of acceleration (TOA). When OOA occurs, the deformation will accelerate, and when TOA occurs, the deformation will tend to stabilize. The OOA can identify areas at risk in the monitored area before failure, so that the spatial position prediction of landslide early warning can be realized. Based on the assumptions of the inverse velocity method, a T-log (t) logarithmic model is established, and the updated monitoring data are corrected to approximate the time of failure, thus improving the accuracy of landslide location and time prediction. In an open-pit copper mine in Serbia, the accurate prediction of landslide location and time has been successfully applied, guaranteeing safe mining. [ABSTRACT FROM AUTHOR]

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Ground-based synthetic aperture radar (GBSAR) is a powerful tool used in monitoring structures, such as bridges and dams. However, despite the extremely short range of GBSAR interferometry, the atmosphere effects cannot be neglected. The permanent scatterer technique is an effective operational tool that utilizes a long series of SAR data and detects information with high accuracy. An algorithm based on the permanent scatterer technique is developed in accordance with the phase model used in GBSAR interferometry. In this study, atmospheric correction is carried out on a real campaign (Geheyan Dam, China). The atmosphere effects created using this method, which utilizes SAR data, can be reduced effectively compared to when plumb line data are used. [ABSTRACT FROM AUTHOR]

Reclaimed airports usually have fragile geological structures and are susceptible to the uneven ground settlements caused by filling-material consolidation, underground construction, and dynamic loading from takeoff and landing of aircrafts. Therefore, deformation monitoring is of great significance to the safe operation of reclaimed airports. This study adopts an improved permanent-scatterer interferometric synthetic-aperture radar strategy to map the spatiotemporal deformation of Shenzhen Bao'an International Airport in China using ascending and descending Envisat/ASAR data acquired from 2007 to 2010 and Sentinel-1 data from 2015 to 2019. The results show that uneven settlements of the airport concentrate in the new reclaimed land. Then we explore the settlement characteristics of each functional area. Furthermore, we separate out the dynamicload settlement of runway No. 2 and confirm the settlements caused by dynamic load. This study provides new ideas for studying deformation in similar fields, and technical references for the future construction of Shenzhen Airport. [ABSTRACT FROM AUTHOR]

In the process of developing mine resources, mining subsidence is inevitable. The D-InSAR (differential interferometric synthetic aperture radar) technology has been widely used to monitor large-scale ground subsidence in mining areas in recent years. However, the limitations of this technology mean that large-gradient ground subsidence cannot be monitored. This paper describes a weighted total least-squares method that can be used to determine the piecewise linear mapping between GNSS (global navigation satellite system) data and D-InSAR data. This mapping can be applied to optimize the conventional D-InSAR monitoring results, particularly in large-gradient subsidence areas, enabling the mining subsidence to be comprehensively evaluated. The proposed method is used to extract the surface subsidence information for a certain area of the Sihe mine in Shanxi Province, China. It is found that the optimized data is more reliable and accurate than the conventional D-InSAR monitoring data in the case of large-gradient subsidence. [ABSTRACT FROM AUTHOR]

Given the increasingly serious geological disasters caused by underground mining in the Hancheng mining area in China and the existing problems with mining subsidence prediction models, this article uses the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology to process 109 Sentinel-1A images of this mining area from December 2015 to February 2020. The results show that there are three subsidences: one in Donganshang, one in south of Zhuyuan village, and one in Shandizhaizi village. In the basin, the maximum annual average subsidence rate is 300 mm/a, and the maximum cumulative subsidence is 1000 mm. The SBAS-InSAR results are compared with Global Positioning System (GPS) observation results, and the correlation coefficient is 74%. Finally, a simulated annealing (SA) algorithm is used to estimate the optimal parameters of a support vector regression (SVR) prediction model, which is applied for mining subsidence prediction. The prediction results are compared with the results of SVR and the GM (1, 1). The minimum value of the coefficient of determination for prediction with SA-SVR model is 0.57, which is significantly better than that those of the other two prediction methods. The results indicate that the proposed prediction model offers high subsidence prediction accuracy and fully meets the requirements of engineering applications. [ABSTRACT FROM AUTHOR]

Plastic Mulching (PM) is criticized for causing environmental problems in spite of the benefits to increase crop yield. Accurate map of Plastic-Mulched Farmland (PMF) is demanded to monitor the usage of plastic mulches and to mitigate its environmental damages. However, mapping PMF with remote sensing data over large scale is still technically challenging due to its variable spectral characteristic. This paper proposed a Random Forest (RF) workflow to map PMF by coupling multi-resource satellite observations, including high- and medium- resolution optical imageries and Synthetic Aperture Radar (SAR) imageries. Under the context of two typical PMF regions, Jizhou and Guyuan, China, we test the performance of the proposed method. Results indicated that coupling optical and SAR remote sensing data improved the mapping accuracy significantly. There were different strategies for optimal data coupling. Coupling of three data, which are the GaoFen-1 (GF-1) data, Landsat-8 data and Radarsat-2 data, generated the highest mapping accuracy. Besides, coupling the GF-1 data with SAR data produced better accuracy than coupling the Landsat-8 data with SAR data. Compared with the accuracies derived from the coupling of two data (such as coupling GF-1 data with Radarsat-2 data and coupling Landsat-8 data with Radarsat-2 data), coupling the three data improved the overall accuracies by 0.39 to 4.93%. Compared with the mapping accuracy produced from Radarsat-2 data alone, the overall accuracies generated from the optimum coupling increased by 20.96 and 26.06%, respectively, in Jizhou and Guyuan. When compared with the mapping accuracy of GF-1 data alone, the overall accuracies generated from the optimum coupling improved by 4.77% both in Jizhou and Guyuan. In general, contribution of optical remote sensing data was greater than that of SAR data for mapping PMF. Whereas, the SAR data also made great contribution to improve the mapping accuracy of PMF. [ABSTRACT FROM AUTHOR]

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Highways built on soft clay subgrade are more prone to subsidence due to the geotechnical characteristics of soft clay. Monitoring ground movements in this area is significant for understanding the deformation dynamics and reducing maintenance cost as well. In this paper, small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) technique is exploited to obtain and investigate the time series ground surface deformation after the construction of a road embankment over soft clay settlement. Considering the important effect of temporal deformation models on the final accuracy of estimated deformation, both the linear velocity model and seasonal deformation model are utilized to conduct the comparative investigation of deformation time series. Two highways in Fuoshan, China—G1501 Guangzhou Belt Highway and Lungui Highway—were selected as the test area. Thirteen TerraSAR-X images acquired from October 2014 to November 2015 were analyzed. Comparative study based on two groups of analyses generated from the two models for both highways were conducted. Consequently, several feature points distributed near the two highways were analyzed in detail to understand the temporal evolution of the settlement. In order to evaluate the reliability of our measurements, the residual phase was analyzed to assess the modelling accuracy of the two models. In addition, leveling data were also used to validate the experimental results. Our measurements suggest that the seasonal model is more suitable for the test highways, with an accuracy of ±3 mm with respect to the leveling results. [ABSTRACT FROM AUTHOR]

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification. [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]

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]

Over the past 20 years, sonar imaging technology particularly for the high-technology sector has been a focus of research, in which many developed countries, especially those with coast lines, have been competing with each other. It has seen a rapid development with increasing widespread applications that has played an important and irreplaceable role in underwater exploration with great prospects for social, economic, scientific, and military benefits. The fundamental techniques underlying sonar imaging, including multi-beamforming, synthetic-aperture and inverse synthetic-aperture sonar, acoustic lensing, and acoustical holography, are described in this paper. This is followed by a comprehensive and systematic review on the advantages and disadvantages of these imaging techniques, applicability conditions, development trends, new ideas, new methods, and improvements in old methods over recent years with an emphasis on the situation in China, along with a bold and constructive prediction to some development characteristics of sonar imaging technology in the near future in China. The perspectives presented in this paper are offered with the idea of providing some degree of guidance and promotion of research on sonar imaging technology. [ABSTRACT FROM AUTHOR]

MDT recovery over coastal regions is challenging, as the mean sea surface (MSS) and geoid/quasi-geoid models are of low quality. The altimetry satellites equipped with the synthetic aperture radar (SAR) altimeters provide more accurate sea surface heights than traditional ones close to the coast. We investigate the role of using the SAR-based MSS in coastal MDT recovery, and the effects introduced by the SAR altimetry data are quantified and assessed. We model MDTs based on the multivariate objective analysis, where the MSS and the recently released satellite-only global geopotential model are combined. The numerical experiments over the coast of Japan and southeastern China show that the use of the SAR-based MSS improves the local MDT. The root mean square (RMS) of the misfits between MDT-modeled with SAR altimetry data and the ocean data is lower than that derived from MDT computed without SAR data—by a magnitude of 4–8 mm. Moreover, the geostrophic velocities derived from MDT modeled with the SAR altimetry data have better fits with buoy data than those derived from MDT modeled without SAR data. In total, our studies highlight the use of SAR altimetry data in coastal MDT recovery. [ABSTRACT FROM AUTHOR]

Wetlands play a key role in regional and global environments and are critically linked to many major issues such as climate change, water quality, hydrological and carbon cycles, and wildlife habitat and biodiversity. It is very important to measure water level changes and consequently water storage capacity changes in wetlands to enable wetland protection and reconstruction. In this study, HH polarization L-band synthetic aperture radar (SAR) data were used in conjunction with synchronous field measurements and investigations to investigate the potential to detect water level changes under different types of wetlands. After evaluating factors that influence interferometric coherence, the framework for measuring water level changes using interferometric synthetic aperture radar (InSAR) phase information is presented in this article. Additionally, the SAR data obtained are used to investigate InSAR-derived water level changes in Yellow River Delta wetlands. The results show that InSAR technology has great potential for application in mapping water level changes in coastal wetlands, and InSAR-derived water level changes can supply unprecedented spatial details. [ABSTRACT FROM AUTHOR]

The accuracy and applications of synthetic aperture radar interferometry (InSAR) are severely suppressed by tropospheric error. Numerical Weather Models (NWMs) and GPS-derived tropospheric delays have been widely used to correct the tropospheric error considering their complete spatial coverage or high accuracy. However, few studies focus on the fusion of both NWMs and GPS for the tropospheric error correction. In this study, we used the Weather Research and Forecasting (WRF) to obtain NWMs with a higher spatial-temporal resolution of 3 km and 20 s from both ERAI (79 km and 6 h) and ERA5 (0.25° and 1 h). After that, we utilized the WRF Data Assimilation (WRFDA) system to assimilate the GPS ZTD into these enhanced NWMs and generate merged NWMs products. The tropospheric correction effectiveness from different NWMs products was evaluated in a case in the Pearl River Delta region of China. The results showed that all the NWMs products could correct the stratified component in the interferogram but could not mitigate the turbulence well, even after improving the spatial-temporal resolution. As for the trend component, the merged NWMs products showed obvious superiority over other products. From the statistics perspective, the stdev of the interferogram decreased further over 20% by the merged NWMs products than other products when using both ERAI and ERA5, indicating the significant effectiveness of GPS ZTD assimilation. [ABSTRACT FROM AUTHOR]

This work investigated the large-scale ground deformations threatening the Northern Urumqi district, China, which are connected to groundwater exploitation and the seasonal freeze–thaw cycles that characterize this frozen region. Ground deformations can be well captured by satellite data using a multi-temporal interferometric synthetic aperture radar (Mt-InSAR) approach. The accuracy of the achievable ground deformation products (e.g., mean displacement time series and related ground displacement time series) critically depends on the number and quality of the selected interferograms. This paper presents a straightforward interferogram selection algorithm that can be applied to identify an optimal network of small baseline (SB) interferograms. The selected SB interferograms are then used to produce ground deformation products using the well-known small baseline subset (SBAS) Mt-InSAR algorithm. The developed interferogram selection algorithm (ISA) permits the selection of the group of SB data pairs that minimize the relative error of the mean ground deformation velocity. Experiments were carried out using a group of 102 Sentinel-1B SAR data collected from 12 April 2017 to 29 October 2020. This research study shows that the investigated farmland region is characterized by a maximum ground deformation rate of about 120 mm/year. Periodic groundwater overexploitation, coupled with irrigation and freeze–thaw phases, is also responsible for seasonal (one-year) ground displacement signals, with oscillation amplitudes up to 120 mm in the zones of maximum displacement. [ABSTRACT FROM AUTHOR]

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting. [ABSTRACT FROM AUTHOR]

Mining subsidence is time-dependent and highly nonlinear, especially in the Loess Plateau region in Northwestern China. As a consequence, and mainly in building agglomerations, the structures can be damaged severely during or after underground extraction, with risks to human life. In this paper, we propose an approach based on a combination of a differential interferometric synthetic aperture radar (DInSAR) technique and a support vector machine (SVM) regression algorithm optimized by grid search (GS-SVR) to predict mining subsidence in a timely and cost-efficient manner. We consider five Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) images encompassing the Dafosi coal mine area in Binxian and Changwu counties, Shaanxi Province. The results show that the subsidence predicted by the proposed InSAR and GS-SVR approach is consistent with the Global Positioning System (GPS) measurements. The maximum absolute errors are less than 3.1 cm and the maximum relative errors are less than 14%. The proposed approach combining DInSAR with GS-SVR technology can predict mining subsidence on the Loess Plateau of China with a high level of accuracy. This research may also help to provide disaster warnings. [ABSTRACT FROM AUTHOR]

Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method. The experimental results using our approach obtained the best performance in East Lake and Yanxi Lake regions with false alarm rates of 1.59% and 1.80%, total errors of 2.73% and 4.33%, overall accuracy of 97.27% and 95.67%, and Kappa coefficients of 0.6486 and 0.6275, respectively. Our results demonstrated that the proposed method is more suitable than the other compared methods for multi-temporal PolSAR data, and it can obtain both effective and accurate results. [ABSTRACT FROM AUTHOR]