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Land subsidence is a worldwide geological hazard. Differential land subsidence has posed the major threat to urban infrastructure, linear rail transit and underground space development and utilization, and also restricted the sustainable development of the economy and society. This paper systematically elaborates on the research progresses on land deformation acquisition, evolution mechanism and simulation of land subsidence, and focuses on the land deformation acquisition technology based on InSAR monitoring and multi-source deformation data fusion, as well as the correlation analysis, statistical analysis, machine learning and other methods to analyze the relationship between the evolution of land subsidence and various influencing factors based on geotechnical experiment and long time series observation data. On this basis, the advantages and disadvantages of land subsidence simulation models such as groundwater flow field-land deformation model, mathematical statistical model and machine learning model are explored. It is found that multi-source deformation data fusion can improve the spatiotemporal resolution of land deformation. The differences in geological structure, lithology, groundwater exploitation, and dynamic and static loads are factors contributing to the differential evolution of land subsidence. The difficulty in balancing the computational efficiency and interpretability of mathematical models for land subsidence is the main problem in simulation. According to the literature review, the current researches mainly focus on land subsidence caused by groundwater over-exploitation. This paper further proposes the future research directions for land subsidence, under the background of climate change, new hydrological condition and dataset, and based on the fusion of data through remote sensing and field observations, integrating the latest progress of InSAR, GeoAI, cloud platform and other technologies to reveal the evolution mechanism of land subsidence considering the climate change and anthropic activities and provide technical support for regional land subsidence prevention and urban safety.

The invasion of Spartina alterniflora (S. alterniflora) has severely impacted China’s coastal ecosystems by disrupting native habitats and polluting waters, highlighting the necessity for accurate distribution mapping. Existing studies mainly rely on moderate-resolution remote sensing imagery, like Landsat and Sentinel-2, but caused by the limitations of spatial resolution, mixed pixels have led to the misclassification of small patches and boundaries. This study developed sub-meter phenological spectral features of S. alterniflora and improved the DeepLabv3+ model with a Class Feature Attention Mechanism (CFAM) to produce the first sub-meter S. alterniflora product for the Beibu Gulf of China in 2020-2021. The results indicated that the developed sub-meter spectral features and the improved DeepLabv3+ model could enhance classification performance. The total area of the sub-meter product in this study was 1,190.36 hectares, which was 83.17 hectares less than the 10-meter product. When benchmarked against the sub-meter product, the 10-meter product exhibited an omission of 314.36 hectares and a commission of 397.53 hectares, with a spatial discrepancy of 711.89 hectares. This method provides a new approach for fine-scale invasive species monitoring. The sub-meter S. alterniflora distribution product provides critical baseline data for monitoring and managing S. alterniflora in the Beibu Gulf.

The South-to-North Water Diversion Project Middle Route (SNWD-MR) annually diverts hundreds of millions of cubic meters of water from the Danjiangkou Reservoir to Beijing, significantly impacting the regional groundwater cycle. To conserve water resources, the government has implemented ecological water replenishment (EWR) in some water systems. This study aimed to study the coupled process of groundwater dynamics and land subsidence (LS) in Yongding River (YDR) basin (Beijing section) following the implementation of the SNWD-MR and EWR. First, an Interferometric Synthetic Aperture Radar (InSAR) time series technique was used to detect spatial and temporal distributions of surface displacement between 2015 and 2020 based on satellite images from Sentinel-1A/B. The results indicated a predominant LS deceleration trend during the InSAR observation period. The area with subsidence velocity below 20 mm/yr decreased significantly by 18.6% after SNWD-MR and EWR (from 2016 to 2020). Then, groundwater level fluctuations during the InSAR observation period were analyzed using groundwater measurement well data. Over 60% of these wells showed a rebound in groundwater levels after the implementation of SNWD-MR and EWR. The polynomial distribution lag model (PDL) revealed the association between LS and groundwater level changes, depicting both short- and long-term lag effect. The results showed significant spatial and temporal heterogeneity on the short- and long-term response between groundwater level change and regional subsidence. In addition, we calculated the differential subsidence slope based on the InSAR-derived LS and analyzed its impact on the river slope after SNWD-MR and EWR. The results show that two sections with significant severe differential subsidence slope occur between 0 and 14,000 m and between 54,000 and 78,000 m along the YDR. The slope change caused by LS may reduce the conveyance capacity of surface canals, and more attention should be paid in those two sections. These findings have an important scientific significance for management strategy for LS control against the background of SNWD-MR and EWR.

Land subsidence, marked by a decline in surface elevation, poses a significant threat to urban infrastructure and safety. Accurate subsidence information and a reliable prediction model are crucial for prevention and control. In this study, we used persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to obtain long-term land subsidence data and analyzed subsidence characteristics in Decheng District. By integrating hydrogeological and groundwater data, we developed a three-dimensional groundwater flow and one-dimensional compaction model through numerical simulation. Furthermore, the subsidence data monitored by PS-InSAR were used to further constrain and validate the model. The evolution trend of land subsidence under different groundwater exploitation scenarios was predicted and analyzed. The results showed that from May 2017 to December 2021, the cumulative maximum subsidence in Decheng District reached −173 mm. The subsidence area is mainly concentrated in the northern area, and its subsidence center is near Qiaoyuan Town. According to the Land Subsidence Prevention and Control Plan of Dezhou City, Shandong Province (2018–2025), we set up different groundwater mining scenarios with the goal that the rate of land subsidence in the key prevention and control area is less than 35 mm/yr in 2025.The Fluid-solid coupled model prediction analysis results indicated that a 30% reduction in groundwater exploitation is reasonable.

The fluctuations in groundwater levels (GWLs) in the Beijing Plain area are profoundly affected by precipitation (Pre) and groundwater (GW) overexploitation, altering regional water cycle processes and constraining regional economic and ecological development. The South-to-North Water Diversion Project (SWDP) has altered water supply structure in Beijing, leading to a new pattern in GWL fluctuations. This study quantitatively analyzes the evolution patterns of shallow GWLs and quantifies the response relationship between Pre-driven GWL-response on spatial and temporal scales before and after the SWDP. Using GWL and Pre data from 2000 to 2021, employs the coefficient of variation to analyze interannual variability, identifies variation points using the Pettitt and sliding T-test combined with correlation coefficient-based method for the classification of jump degree (R_Jump degree), subsequently divides the sub-sequences. The hydrological trend variation classification method based on correlation coefficient (R_Trend) is used to quantify the spatiotemporal evolution patterns of GWLs in each sub-sequence, and cross-correlation analysis is utilized to examine the time-lag characteristics of GWL responses to Pre in each sub-sequence. The results indicate: (1) GWLs exhibited jump variations in 2015 and 2017, with variation degrees mainly of moderate and strong, and correlation coefficients ranging from 0.18 to 0.67; (2) The overall trend of GWLs shifted from a significant decline to a substantial rebound before and after the SWDP. However, GWLs in the toe of the alluvial fan continued to show a decreasing trend until 2017; (3) The time lag between Pre and GWLs shortened by 6–10 months before and after the SWDP, with the toe of the alluvial fan showing a longer lag time due to over-exploitation. The Pre-GWL lag pattern mainly depends on GW extraction. Compared to existing methods, this paper proposes a research framework for systematically and comprehensively quantifying the evolution of GWLs and their response patterns to Pre, clearly revealing the natural-artificial water cycle processes in the Beijing Plain, providing scientific basis for ecological conservation and water resource management.

China has abundant straw resources, but challenges in utilization persist. Utilization rates need improvement, and environmental pollution from straw burning remains a significant issue. Accurate and intelligent remote sensing classification of straw types is crucial for enhancing straw utilization and preventing straw burning. This paper proposed a new approach for the intelligent classification of maize straw types, using the DenseNet201 deep transfer learning algorithm based on RGB images captured by Unmanned Aerial Vehicle (UAV). The sample labels dataset was established for maize straw types, utilizing DenseNet201 deep transfer learning algorithm to pre-train the sample set. This pre-training facilitated model transfer and parameter initialization. Subsequently, the second round of deep transfer learning was performed to construct the final maize straw type remote sensing classification models using DenseNet201 deep transfer learning algorithm. This model and results were subsequently compared with maize straw type classification by the ResNet50 and GoogLeNet deep transfer learning algorithms, as well as maize straw type classification using DenseNet201, ResNet50, and GoogLeNet deep learning algorithms. The results showed that the accuracy of the pre-trained maize straw type deep transfer learning remote sensing classification model surpassed that of the untrained maize straw type deep learning remote sensing classification model, resulting in an enhancement of accuracy by 8.59%, 7.38%, and 1.28%, respectively. The DenseNet201 deep transfer learning model for maize straw types exhibited the highest accuracy with the overall accuracy of 95.57%, and the kappa coefficient of 0.9410. Hence, the DenseNet201 deep transfer learning classification of maize straw types enabled the attainment of intelligent remote sensing recognition of maize straw types. The classification methodology, model, and results presented in this paper can serve as valuable technical references, offering essential information support for agricultural and environmental protection departments actively involved in the comprehensive utilization of straw resources and atmospheric environmental protection efforts.

Land subsidence is an environmental geological phenomenon mainly caused by groundwater overexploitation. Long-term overexploitation of groundwater not only causes compaction of aquifer thickness and surface deformation but also leads to the loss of aquifer water storage capacity. The skeleton water storage coefficient (Sk) is an important parameter for evaluating the water storage capacity of aquifer groups. This article proposes a new research framework for obtaining the Sk of different aquifer groups: combining permanent scatter for SAR interferometry technology and a multiscale geographic weighted regression model to obtain subsidence information for different aquifer groups, inverting the Sk of different aquifer groups from the spatial scale, and discussing the deformation characteristics of soil layers under different water head change modes to evaluate the deformation and water storage characteristics of different aquifer groups. This framework is applied to the land subsidence region of the Beijing Plain. We calculated that the settlement proportions of different compression layer groups were 14.75%, 23.65%, 33.44%, and 28.16%. Due to the different lithological compositions and groundwater exploitation of different aquifers, the Sk values exhibit different spatial distribution characteristics. With the continuous development of subsidence, the water storage performance of the aquifer group is continuously declining. These findings contribute to managing the sustainable use of groundwater resources and controlling subsidence. It is demonstrated that the research framework proposed in this article can serve as an effective tool for obtaining settlement information and the Sk of different aquifer groups.

Beijing is a city on the North China Plain with severe land subsidence. In recent years, Beijing has implemented effective measures to control land subsidence. Since this implementation, the development of time-series land subsidence in Beijing has slowed and has shown nonlinearity. Most previous studies have focused on the linear evolution of land subsidence; the nonlinear evolutionary patterns of land subsidence require further discussion. Therefore, we aimed to identify the evolution of land subsidence in Beijing, based on Envisat ASAR and Radarsat-2 images from 2003 to 2020, using permanent scatterer interferometric synthetic aperture radar (PS-InSAR) and cubic curve polynomial fitting methods. The dates of the extreme and inflection points were identified from the polynomial coefficients. From 2003 to 2020, the subsidence rate reached 138.55 mm/year, and the area with a subsidence rate > 15 mm/year reached 1688.81 km2. The cubic polynomials fit the time-series deformation well, with R2 ranging from 0.86 to 0.99 and the RMSE ranging from 1.97 to 60.28 mm. Furthermore, the subsidence rate at 96.64% of permanent scatterer (PS) points first increased and then decreased. The subsidence rate at 86.58% of the PS points began to decrease from 2010 to 2015; whereas the subsidence rate at 30.51% of the PS point reached a maximum between 2015 and 2019 and then decreased. The cumulative settlement continued to increase at 69.49% of the PS points. These findings imply that groundwater levels are highly correlated with the temporal evolution of subsidence in areas with pattern D (Vs+-, S+), with increasing and then decelerating rates and increasing amounts. In regions with a thickness of compressible clay layer over 210 m, subsidence follows pattern E (Vs+, S+), with increasing rates and amounts. Fractures such as the Gaoliying and Sunhe fractures significantly influence the spatial distribution of subsidence patterns, showing distinct differences on either side. Near the Global Resort Station, pattern E (Vs+, S+) intensifies in subsidence, potentially due to factors like land use changes and construction activities.

Abstract Flue-cured tobacco grading plays a crucial role in tobacco leaf purchase and the formulation of tobacco leaf groups. However, the traditional flue-cured tobacco grading mode is usually manual, which is time-consuming, laborious, and subjective. Hence, it is essential to research more efficient and intelligent flue-cured tobacco grading methods. Most existing methods suffer from the more classes less accuracy problem. Meanwhile, limited by different industry applications, the flue-cured tobacco datasets are hard to be obtained publicly. The existing methods employ relatively small and lower resolution tobacco data that are hard to apply in practice. Therefore, aiming at the insufficiency of feature extraction ability and the inadaptability to multiple flue-cured tobacco grades, we collected the largest and highest resolution dataset and proposed an efficient flue-cured tobacco grading method based on deep densely convolutional network (DenseNet). Diverging from other approaches, our method has a unique connectivity pattern of convolutional neural network that concatenates preceding tobacco feature data. This mode connects all previous layers to the subsequent layer directly for tobacco feature transmission. This idea can better extract depth tobacco image information features and transmit each layer’s data, thereby reducing the information loss and encouraging tobacco feature reuse. Then, we designed the whole data pre-processing process and experimented with traditional and deep learning algorithms to verify our dataset usability. The experimental results showed that DenseNet could be easily adapted by changing the output of the fully connected layers. With an accuracy of 0.997, significantly higher than the other intelligent tobacco grading methods, DenseNet came to the best model for solving our flue-cured tobacco grading problem.

Abstract Terrestrial water storage anomaly (TWSA), derived from Gravity Recovery and Climate Experiment (GRACE) satellites, has been widely used in hydrology studies. The inversion is commonly achieved by truncating and filtering spherical harmonic coefficients (SHC), whereby the result is characterized by leakage error and low resolution. It remains unclear whether machine learning methods can help resolve this challenging issue. In this study, we present a convolutional neural network (CNN) approach to correct TWSA from GRACE SHC by leveraging the knowledge of the leakage effect determined from global hydrological models (GHMs) and land surface models (LSMs). The CNN approach is implemented in three representative regions in China, that is, the human‐impacted Haihe River Basin, the nature‐impacted Yangtze River Basin and the model‐limited Tibetan Plateau. The results show the following: (a) The recovery performance of CNN at the basin scale is better than that at the grid scale, and the grid‐scale recovery is significantly influenced by the spatial heterogeneity of TWSA and the input GHM/LSMs; (b) The more accurate the GHM/LSMs used for training, the better the recovery performance of CNN; and (c) The trained model retains comparable performance in deriving the TWSA time series from GRACE SHC when compared to that derived from other methods (i.e., scaling factor and mass concentration solutions) with average r = 0.90 and RMSE = 21.50 mm. This study highlights the potential of machine learning to supplement conventional correction methods when deriving the TWSA from GRACE SHC by utilizing signal restoration knowledge learned from multiple accurate GHMs/LSMs.

The inevitable thick cloud contamination in Landsat images has severely limited the usability and applications of these images. Developing cloud removal algorithms has been a hot research topic in recent years. Many previous algorithms used one or multiple cloud-free image(s) in the same area acquired on other date(s) as reference image(s) to reconstruct missing pixel values. However, it remains challenging to determine the optimal reference image(s). In addition, abrupt land cover change can substantially degrade the reconstruction accuracies. To address these issues, we present a new cloud removal algorithm called Virtual Image-based Cloud Removal (VICR). For each cloud region, VICR reconstructs the missing surface reflectance by three steps: virtual image within cloud region construction based on time-series reference images, similar pixel selection using the newly proposed temporally weighted spectral distance (TWSD), and residual image estimation. By establishing two buffer zones around the cloud region, VICR allows automatic selection of the optimal set of time-series reference images. The effectiveness of VICR was validated at four testing sites with different landscapes (i.e. urban, croplands, and wetlands) and land change patterns (i.e. phenological change, abrupt change caused by flooding and tidal inundation), and the performances were compared with mNSPI (modified neighborhood similar pixel interpolator), WLR (weighted linear regression) and ARRC (AutoRegression to Remove Clouds). Experimental results showed that VICR outperformed the other algorithms and achieved higher Correlation Coefficients and lower Root Mean Square Errors in surface reflectance estimation at the four sites. The improvement is particularly noticeable at the sites with abrupt land change. By considering the difference in the contributions from the reference images, TWSD can select more reliable similar pixels to improve the prediction of abrupt change in surface reflectance. Moreover, VICR is more robust to different cloud sizes and to changing reference images. VICR is also computationally much faster than ARRC. The framework for time-series image cloud removal by VICR has great potential to be applied for large datasets processing.

Beijing has undergone severe settlement in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges: (1) the nonlinear characteristics of time-series subsidence has not been fully investigated; (2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas. To address the challenges, we presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then time-series deformation considering 40 features including thickness of the compressible layers, annual groundwater level, etc. The major results showed that (1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2 ≈0.99), suggesting obvious nonlinear temporal pattern of deformation; (2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE

Study region: Beijing-Tianjin-Hebei (BTH) region, China Study focus: BTH has long been suffering from severe and uneven land subsidence. Finding out the spatial evolution of land subsidence is helpful to collaborative prevention and control. We innovatively revealed the spatial distribution of land subsidence from the mountains to the coastal area along four hydrogeological profiles, and inferred the land subsidence mechanism on the overall scale, local plain scale and typical regional scale. Small Baseline Subsets Interferometric Synthetic Aperture Radar was employed to collect displacement information in the BTH region from January 2012 to January 2020. We obtained groundwater storage data from the Gravity Recovery and Climate Experiment. New hydrological insights for the region: On the overall scale, from 2012 to 2019, the Changping-TongZhou district of Beijing suffered the most serious land subsidence, with an annual subsidence rate of 159 mm and a cumulative subsidence as high as 928 mm. On the local plain scale, areas experiencing severe subsidence were generally located in the central plain. Fractures in the area obviously influenced the spatial distribution of subsidence. On the typical regional scale, a positive correlation was found between land subsidence and groundwater depletion. The relationship between precipitation and land subsidence in Hebei was the most convincing (R2 = 0.64). This study provides a scientific basis for regional coordination in land subsidence control. Data availability: Data can be provided by the corresponding authors upon request.

Abstract Land subsidence induced by groundwater level decline has spatiotemporal variations. Taking the Interferometric Synthetic Aperture Radar (InSAR) results and the groundwater subsidence data acquired by the monitoring stations as the source material, this paper aims to reveal the spatiotemporal heterogeneity of groundwater-land subsidence in Beijing plain by using the Wind Rose Map (WRM) method and the Change Point Analysis (CPA) method. The WRM results show that the amount and variation in subsidence differs in different directions. This method detected the formation of new subsidence centers and the slowdown of land subsidence in 2008. The CPA results show that obvious changes are detected in subsidence development at the Wangsiying (WSY), Tianzhu (TZ) and Wangjing (WJ) stations. However, there is a relatively stable trend of groundwater decline and land subsidence at the Tianzhu (TZ) station. The stages of land subsidence development show a significant response to groundwater. Moreover, changes in land subsidence also show delayed response behind the changes in groundwater level. The time-lag could be affected by the variation in amplitude of the groundwater level.

Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this area is significant. In this paper, we used the Small Baseline Subsets Interferometric Point Target Analysis (SBAS-IPTA) technique to process the Envisat-ASAR, Radarsat-2, and Sentinel-1A data and obtained the land subsidence of Cangzhou from 2004 to 2020. Additionally, we obtained winter wheat distribution information in Cangzhou using the Pixel Information Expert Engine (PIE-Engine) remote sensing cloud platform. On this basis, we analyzed the relationship between ground water level, winter wheat planting area, and the response of land subsidence according to the land use type and groundwater level monitoring data near the winter wheat growing area. The results show that during 2004–2020, the average annual subsidence rate of many places in Cangzhou was higher than 30 mm/year, and the maximum subsidence rate was 115 mm/year in 2012. From 2004 to 2020, the area of the subsidence funnel showed a trend of first increasing and then decreasing. In 2020, the subsidence funnel area reached 6.9 × 103 km2. The winter wheat planting area in the urban area showed a trend of first decreasing, then increasing and then decreasing, and it accounted for a large proportion in the funnel area. At the same time, we studied the relationship between the land subsidence rate and the water level at different burial depths and the response of winter wheat planting area. The results showed that the change of confined water level had a stronger response with the other two variables.

Beijing’s Sub-Administrative Center (BSAC) is located in the South-eastern Beijing Plain, which exhibits severe subsidence. The rapid urban expansion in recent years has aggravated land subsidence and threatens the safe operation of Beijing. First, this study applied the persistent scatterer-interferometric synthetic aperture radar (PS-InSAR) to extract BSAC subsidence time series data. Second, combined with the index-based built-up index (IBI), expansion intensity index (EII), and expansion gradient index (EGI), the spatiotemporal characteristics of urban expansion were retrieved from optical data. Finally, we examined the urban expansion effects on land subsidence at the regional and single-building scales. The results showed that the maximum subsidence velocity in the BSAC reached 121 mm/year from 2015 to 2018, and the urban construction land area increased by 22%. At the regional scale, there existed a positive correlation between land subsidence and EGI or EII. This indicated that urban expansion had a certain impact on land subsidence. Therefore, we further explored the relationship between construction and land subsidence at the single-building scale. The engineering construction effects on land subsidence were divided into three periods, namely, rapid settlement, rebound, and stable periods. Although construction had a significant influence on land subsidence, it did not cause subsidence mutation.

High-speed railways in the Beijing–Tianjin–Hebei (BTH) Plain are gradually becoming more widespread, covering a greater area. The operational safety of high-speed railways is influenced by the continuous development of land subsidence. It is necessary to predict the subsidence along the high-speed railways; thus, this work is of critical importance to the safety of high-speed railway operation. In this study, we processed Sentinel-1A data using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique to acquire the land subsidence in the typical BTH area. Then, we combined the Empirical Mode Decomposition (EMD) and Gradient Boosting Decision Tree (GBDT) methods (EMD-GBDT) to forecast land subsidence along high-speed railways. The results revealed that some parts of the high-speed railways in the BTH plain had passed through or approached the land subsidence area; the maximum cumulative subsidence of the Beijing–Shanghai, Tianjin–Baoding and Shijiazhuang–Jinan high-speed railways reached 326 mm, 384 mm and 350 mm, respectively. The forecasting accuracy for land subsidence along high-speed railways was enhanced by the EMD-GBDT model. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were 0.38 mm to 0.56 mm and 0.23 mm to 0.38 mm, respectively.

Tianjin is a coastal city of China. However, the continuous rise of the relative sea-level has brought huge hidden danger to Tianjin’s economic and social development. The land subsidence is the most important factor that influences relative sea-level rise. By analyzing the current situation of subsidence in Tianjin through PS-InSAR, it was found that the subsidence rate of the southern plain of Tianjin is slowing down as a whole. In addition, Wuqing and Jinghai sedimentary areas as well as other several subsidence centers have been formed. By establishing a regular grid of land subsidence and ground water to construct a geo-weighted regression model (GWR), it was found that Wuqing sedimentary area as a whole is positively correlated with TCA. According to the relative sea-level change, it can be predicted that the natural coastline of Tianjin will recede by about 87 km2 in 20 years. Based on the research results above, this paper, by using machine-learning method (XGBoost), has evaluated Tianjin’s urban safety and analyzed high-risk areas and main contributing factors. Potential risks to urban safety brought about by relative sea-level rise have been analyzed, which will improve the resilience of coastal areas to disasters.

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.

The Gravity Recovery and Climate Experiment (GRACE) satellites have been widely used to estimate groundwater storage (GWS) changes, yet their uncertainties related to the multi-source datasets used are rarely investigated. This study focuses on quantifying the uncertainties of GRACE GWS estimates in mainland China during 2003–2015, by generating a total of 3456 solutions from the combinations of multiple GRACE products and auxiliary datasets. The Bayesian model averaging (BMA) approach is used to derive the optimal estimates of GWS changes under an uncertainty framework. Ten river basins are further identified to analyze the estimated annual GWS trends and uncertainty magnitudes. On average, our results show that the BMA-estimated annual GWS trend in mainland China is −1.93 mm/yr, whereas its uncertainty reaches 4.50 mm/yr. Albeit the estimated annual GWS trends and uncertainties vary across river basins, we found that the high uncertainties of annual GWS trends are tied to the large differences between multiple GRACE data and soil moisture products used in the GWS solutions. These findings highlight the importance of paying more attention to the existence of multi-source uncertainties when using GRACE data to estimate GWS changes.

Over-exploitation of groundwater has caused severe land subsidence in Beijing during the past two decades. Since the middle route of South-to-North Water Diversion Project (SNWDP), the biggest water diversion project in China, started to deliver water to Beijing in December 2014, the groundwater shortage has been greatly alleviated. This study aims to analyze the impact of SNWDP on the spatiotemporal evolution of land subsidence in Beijing. Change in surface displacement in Beijing after SNWDP was retrieved and the spatiotemporal patterns of the change were analyzed based on long time-series Envisat Advanced Synthetic Aperture Radar (ASAR) (2004–2010), Radarsat-2 (2011–2014), and Sentinel-1 (2015–2017) satellite datasets using Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) techniques. Land subsidence unevenness index (LSUI) was proposed to represent the spatial unevenness of surface displacement. PS-Time approach was then adapted to examine the time series evolution of LSUI. The results showed that the InSAR measurements agree well with leveling measurements with R2 over 0.96. Although the maximum annual displacement rate reached −159.7 mm/year by 2017, over 57% of the area within 25 mm/year contour line showed decreasing or unchanged displacement rate after the south-north water delivered to Beijing. The settlement rate in Chaoyang-Dongbalizhuang (CD) subsidence center has decreased for 26 mm/year from 2011–2014 to 2015–2017. Only around 15% of the area experienced continued accelerating settlement rate through the three time periods, which was mainly located in the area with the compressible layer thickness over 190 m, while the magnitude of velocity increment considerably decreased after SNWDP. Land subsidence unevenness, represented by LSUI, developed more slowly after SNWDP than that during 2011–2014. However, LSUI at the edge of settlement funnel has kept developing and reached 1.7‰ in 2017. Decreasing groundwater level decline after SNWDP and the positive relationship (R2 > 0.74) between land subsidence and groundwater level clearly showed impacts of SNWDP on the alleviating land subsidence. Other reasons include geological background, increasing precipitation, and strict water management policies implemented during these years.

This is the first study to inverse leaf area index (LAI) with photon counting LiDAR (i.e., Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS). A new framework composed of three major steps was applied: noise removal algorithm, photon classification algorithm, and the LAI estimation model. To evaluate the feasibility and accuracy of ICESat-2 derived LAI, the MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product at the spatial resolution of 500-m over two sites (Amazon rainforest and Daxing’an mountains forests) were selected for comparison purpose. The results show satisfactory agreements (Amazon: R=0.693, RMSE=2.545; Daxing’an: R=0.626, RMSE=1.893; Two sites together: R=0.667, RMSE=2.433) between MODIS LAI and ICESat-2 derived LAI. Moreover, we also selected the LAI derived from Sentinel-2 to further validate the ICESat-2 derived LAI, and got better results than MODIS LAI (Amazon: R=0.752, RMSE=2.329; Daxing’an: R=0.704, RMSE=1.724). Our study found that: (1) The new applied framework can effectively inverse LAI with ICESat-2. (2) The ICESat-2 derived LAI are reliable. (3) ICESat-2 exists many advantages over MODIS in terms of LAI estimation, for example, it not only can alleviate the issue of saturation of MODIS when LAI is high, but also can mitigate the problem of poor inversion effect of MODIS where the vegetation types are diversity.

The over exploitation of groundwater has seriously restricted the healthy development of social economy in North China Plain (NCP). The study of the change in the groundwater storage at different time scales is helpful to understand the characteristics of groundwater dynamic changes and real-time groundwater management. Based on Gravity Recovery and Climate Experiment (GRACE) satellite products and Global Land Data Assimilation System (GLDAS) data, this paper analyses the seasonal and long-term trend changes of NCP groundwater storage from June 2003 to June 2017 by using adaptive Ensemble Empirical Mode Decomposition (EEMD) algorithm. Moreover, their relationships with precipitation and the South-to-North Water Diversion Middle Route Project (SWDP) are analysed. The main conclusions are as follows: (1) The change of groundwater storage in NCP mainly shows a long-term downward trend and the downward trend gradually increases from northeast to southwest, and the maximum decline rate appears in Handan, reaching 26.44 mm/yr; (2) The main spatial pattern in the seasonal variation of groundwater storage in the NCP has a good consistency and the response of seasonal groundwater variation to seasonal precipitation variation lags by 3–4 months; and (3) After implementing the SWDP, the long-term decline rate of groundwater storage is reduced and the average contribution rate of seasonal variation characteristics of groundwater storage increases from 20% to 28%. The research results of this paper are helpful to understand the dynamic change process of groundwater storage and lay a foundation for further exploring the reasons for the change of groundwater storage.

Groundwater overexploitation and loading of buildings have been the main factors triggering land subsidence along the west coast of Bohai Bay, China, since the 2000s. Uneven subsidence has been causing damage to buildings and civil facilities, loss of elevation, increasing the risk of flood and seawater intrusion, and threatening the safety of people’s lives and property. This paper analyzed the spatial and temporal features of land subsidence along the coastal area from 2003 to 2010 and from 2015 to 2020, respectively. The relations between the initiating factors and land subsidence were explored. Then, the simulation model of land subsidence was constructed through a deep learning method. During the process, multiple data were collected, including land satellite (Landsat), environmental satellite advanced synthetic aperture radar (ENVISAT ASAR) and Sentinel-1 images, leveling data, lithological data, and groundwater level data. The area occupied by buildings and vertical displacement were extracted by using supervised classification, small baseline subset (SBAS), and persistent scatterer interferometry (PSI) technologies. The gated recurrent unit (GRU) neural network was adopted to simulate the evolution of land subsidence. Results showed that the maximum annual vertical displacement rate decreased from −94 mm/yr during 2003–2010 to −87 mm/yr during 2015–2020. The correlation efficiency between the groundwater level of the third confined aquifer group and land subsidence was larger than the area occupied by buildings and the compressible layer thickness with subsidence. The constructed GRU neural network model can simulate subsidence from September 2019 to December 2019, with the overall RMSE and MAE being 3.16 mm and 2.19 mm, respectively. This work can facilitate an understanding of the evolution and prevention of land subsidence along the west coast of Bohai Bay, which will provide information for policy decisions and flood-fighting plans of the worldwide coastal cities.

Beijing-Tianjin High Speed Railway is the first high-speed railway in China. The Beijing section of it runs through areas affected by subsidence which threaten its safe operation. This study develops a new time series fusion method based on the minimum gradient difference of a fitting curve to produce time series subsidence along this section. Through blending Envisat ASAR and TerrSAR-X time series, the InSAR-derived subsidence and its spatial–temporal development was analyzed along the railway. The relationship between subsidence and its causes was then explored using a maximum entropy model. The study reveals that: (1) The subsidence dynamics identified using the new fusion method agrees with the ground deformation measurements; (2) The sections of most severe subsidence occur between kilometer point KP 11 and KP 21; and (3) The main hydrogeological factors affecting subsidence are the compressible deposit thickness and the groundwater level in the second confined aquifer. The new fusion method proposed improves the accuracy and reliability of subsidence time series. It extends the time span of subsidence monitoring. The approach is mainly applicable to areas with significant vertical deformation, and is particularly suitable for integrating multi-platform data with overlapping in time or with a short time gap.

Shandong Province (SDP) experienced serious land subsidence from March 2017 to December 2020. Exploring the response relationships between land subsidence and its inducing factors plays an important role in ensuring the development of the economy and residential safety. Firstly, we applied Persistent Scatterers Interferometric Aperture Radar (PS-InSAR) technology to 558 Sentinel-1 images to determine the land subsidence in SDP from March 2017 to December 2020. Secondly, we mosaicked the land subsidence monitoring results of five tracks to obtain a land subsidence map covering the whole SDP and validated the land subsidence monitoring results using Global Positioning System (GPS) monitoring results and leveling benchmark monitoring results observed in the same period. Finally, the response relationships between the land subsidence and its inducing factors in SDP were analyzed. The findings are as follows: (1) the PS-InSAR outcomes showed that the land subsidence was widely distributed in SDP and that the maximum land subsidence rate was −298.9 mm/year during the study period. (2) The PS-InSAR monitoring results coincide well with the GPS monitoring results and leveling benchmark monitoring results; the Pearson correlation coefficient (PCC) values between the PS-InSAR monitoring results and the GPS measurement results and leveling benchmark monitoring results were 0.97 and 0.98, respectively. We found that Spearman’s rank correlation coefficient (SRCC) values between any two adjacent tracks of the mosaic PS-InSAR monitoring results were greater than 0.95, indicating good consistency. (3) The long-term overexploitation of groundwater in middle and deep aquifers and mining of underground mineral resources are the main inducing factors of land subsidence in SDP when considering this problem on a large geographical scale. Moreover, the type of bridge material is an important inducing factor causing the large variation in the land subsidence of the bridge body within a small geographical range. These findings may provide scientific support for land subsidence control measures in SDP.

The long-term over-exploitation of groundwater has not only caused the compaction of aquifer thickness and surface deformation but has also further aggravated the loss of groundwater storage (GWS) in Beijing plain. The South-to-North Water Diversion Project (SNWDP) furnishes a new source of water for Beijing. By reviewing related studies, it was found that there are few studies on the realization of GWS estimation based on InSAR technology considering the lag effect. Therefore, in this study, firstly, the long-time series deformation characteristics of Beijing plain were obtained from 46 RadarSat-2 images using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology. Secondly, the seasonal components of surface deformation and hydraulic head change were extracted by means of multichannel singular spectrum analysis (MSSA), verifying the separation accuracy by means of Monto Carlo-SSA (MC-SSA). Finally, for the hydrodynamic delay (aquifer water supply/drainage) of the complex aquifer system, we introduced the time lag cross-correlation (TLCC) approach to correct the hysteresis response of seasonal deformation relative to the variation of the aquifer system head, so as to realize the estimation of aquifer storage properties and GWS loss, even unrecoverable GWS (UGWS). The results showed that the average annual variation of total GWS (TGWS) in Beijing plain was −6.702 × 107 m3, of which the depletion volume of UGWS was −6.168 × 107 m3, accounting for 92.03% of the TGWS. On a temporal scale, the depletion of UGWS lagged behind the total head change, with about one year of lag time. On a spatial scale, in contrast to the north of Beijing plain, the depletion of UGWS in the south only recovered briefly after 2015 and then continued to decline. This further indicated that the process of the decline of middle-deep confined head and long-term GWS loss caused by over-exploitation of groundwater was irreversible. These findings are of great significance to optimize the allocation of groundwater resources, reduce the harm of land subsidence and protect groundwater resources.

Beijing-Tianjin-Hebei (BTH) has been suffering from severe groundwater storage (GWS) consumption and land subsidence (LS) for a long period. The overexploitation of groundwater brings about severe land subsidence, which affects the safety and development of BTH. In this paper, we utilized multi-frame synthetic aperture radar datasets obtained by the Rardarsat-2 satellite to monitor land subsidence’s temporal and spatial distribution in the BTH from 2012 to 2016 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). In addition, we also employed the Gravity Recovery and Climate Experiment (GRACE) mascon datasets acquired by the Center for Space Research (CSR) and Jet Propulsion Laboratory (JPL) to obtain the GWS anomalies (GWSA) of BTH from 2003 to 2016. Then we evaluate the accuracy of the results obtained. Furthermore, we explored the relationship between the regional GWSA and the average cumulative subsidence in the BTH. The total volume change of subsidence is 59.46% of the total volume change of groundwater storage. Moreover, the long-term decreasing trend of the GWSA (14.221 mm/year) and average cumulative subsidence (17.382 mm/year) show a relatively high consistency. Finally, we analyze the heterogeneity of GWS change (GWSC) and LS change (LSC) in the four typical areas by the Lorenz curve model. The implementation of the South-to-North Water Diversion Project (MSWDP) affects the heterogeneity of GWSC and LSC. It can be seen that the largest heterogeneity of LSC lags behind the GWSC in the Tianjin-Langfang-Hengshui-Baoding area. The largest uneven subsidence in Beijing and Tianjin occurred in 2015, and the largest uneven subsidence in Hengshui-Baoding occurred in 2014. After that, the heterogeneity of subsidence gradually tends to stable.

Under the background of over-exploitation of groundwater and urban expansion, the land subsidence in the Beijing Plain has dramatically increased recently, and has demonstrated obvious mutation characteristics. Firstly, this paper used the land-use transfer matrix (LUTM) to quantify the urban expansion of Beijing, from 1990 to 2015. Secondly, the gravity center migration model (GCM) and standard deviation ellipse (SDE) methods were employed in order to quantitatively reveal the response relationship between urban expansion and land subsidence in the study area. Finally, the research innovatively combines multi-disciplinary (remote sensing, geophysical prospecting, spatial analysis, and hydrogeology), to analyze the mechanism of land subsidence mutation in the Beijing Plain, at multiple scales. The results showed the following: 1. The development direction of the urban expansion and the subsidence bowl (subsidence rate > 50 mm/year) were highly consistent, with values of 116.8° and 113.3°, respectively. 2. At the regional scale, the overall spatial distribution of subsidence mutations is controlled by the geological conditions, and the subsidence mutation time was mainly in 2005 and 2015. The area where mutation occurred in 2005 was basically located in the subsidence bowls, and the correlation between the confined water level and the subsidence rate was relatively high (r > 0.62). The area where the settlement mutation occurred in 2015, was mainly located outside the subsidence bowls, and the correlation between the confined water level and the subsidence rate was relatively low (r < 0.71). 3. In the typical subsidence area, the subsidence mutation occurred mostly in the places where the stratigraphic density is reduced, due to human activities (such as groundwater exploitation). Human activities caused the reduction in stratigraphic density, at 20 m and 90 m vertical depth in urban and rural areas, respectively. 4. At the local scale, clusters of subsidence mutation were located in the fault buffer zone, with a lateral influence range of nearly 1 km in Tongzhou. The scattered settlement mutation is distributed as a spot pattern, and the affected area is relatively small, which basically includes high-rise buildings.

In metropolitan areas, the static load of high-rise buildings may result in uneven settlement, which seriously threatens residents’ living safety. Studying the response relationship between the additional stress of high-rise buildings and foundation settlement plays an important role in ensuring the safe development of metropolitan cities. Firstly, based on Persistent Scatterers Interferometric Aperture Radar (PS-InSAR) technology, we used 68 descending TerraSAR-X images to obtain the surface settlement in the study area from April 2010 to October 2018, which were validated with leveling benchmark monitoring results. Secondly, we calculated the additional stress of the building loads to quantify its effect on the uneven settlement in the Central Business District (CBD) of Beijing. Finally, two sets of characteristic points were selected to analyze the response relationships between foundation settlement and additional stress generated by building loads. The findings show: (1) The surface settlement rate varied from −145.2 to 24 mm/year in the Beijing Plain. The InSAR results agree well with the monitoring results derived from the leveling benchmark; the Pearson correlation coefficients were 0.98 and 0.95 in 2011–2013 and 2015–2016, respectively. (2) The stress results show that the depth of the influence of the static load of high-rise buildings was 74.9 m underground in the CBD. (3) The spatial distribution pattern of the additional stress is consistent with the foundation settlement. A characteristic point with greater additional stress in the same group has a higher foundation settlement rate. This relationship has also been found between the uneven foundation settlement and additional stress gradients. These findings provide scientific support for mitigating economic losses due to foundation settlement caused by additional stresses derived from building loads.

Surface displacement is an common environmental geological phenomenon in the Beijing Plain. Research on surface displacement in the Beijing Plain has mainly focused on vertical surface displacement, whereas the horizontal displacement has scarcely been studied. To investigate the 3-D surface displacement in the Beijing Plain, we construct a leveling-constrained multidirectional PS-InSAR 3-D surface displacement estimation method to obtain the 3-D surface displacement information. The results show that the surface displacement in the study area during 2016–2018 was mainly vertical displacement with two main northern and southern subsidence centers; the vertical displacement ranged from −150 mm/year (down) to 5 mm/year (up), and the east–west horizontal displacement ranged from 20 mm/year (east) to 22 mm/year (west). Validation results show that the 3-D surface displacement estimation results agree well with leveling data and GPS data, indicating the reliability of the 3-D surface displacement datasets. The 3-D surface displacement results show that horizontal displacement is obvious in the areas with a large vertical displacement in the eastern Beijing Plain. Additionally, the horizontal displacement is directed toward the center of vertical displacement. The compressive strain is observed close to the centers of vertical displacement, whereas tensile strain occurs far from the centers of vertical displacement. The main cause of the 3-D surface displacement in the study area is the long-term groundwater overexploitation, especially deep groundwater exploitation. The spatial and temporal extents of displacement do not exactly match the locations of the groundwater sinks in different aquifers; instead, geological structures and stratigraphic/lithological conditions may have a combined effect. Moreover, the spatial and temporal distributions of surface displacement are closely related to ground fissure activity, and both influence each other.

Land subsidence induced by excessive groundwater withdrawal has caused serious social, geological, and environmental problems in Beijing. Rapid increases in population and economic development have aggravated the situation. Monitoring and prediction of ground settlement is important to mitigate these hazards. In this study, we combined persistent-scatterer interferometric synthetic aperture radar with Grey system theory to monitor and predict land subsidence in the Beijing plain. Land subsidence during 2003–2014 was determined based on 39 ENVISAT advanced synthetic aperture radar (ASAR) images and 27 RadarSat-2 images. Results were consistent with global positioning system, leveling measurements at the point level and TerraSAR-X subsidence maps at the regional level. The average deformation rate in the line-of-sight was from −124 to 7 mm/year. To predict future subsidence, the time-series deformation was used to build a prediction model based on an improved Grey-Markov model (IGMM), which adapted the conventional GM(1,1) model by utilizing rolling mechanism and integrating a k-means clustering method in Markov-chain state interval partitioning. Evaluation of the IGMM at both point level and regional scale showed good accuracy (root-mean-square error

The authors wish to make the following corrections to this paper [...]

In the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land subsidence. Here we choose groundwater level change, thickness of the Quaternary deposit and index-based built-up index (IBI) as influencing factors, and we use the influence factors to predict the subsidence amount in the Beijing Plain. The Sentinel-1 radar images and the persistent scatters interferometry (PSI) were adopted to obtain the information of land subsidence. By using Google Earth Engine platform and Landsat8 optical images, IBI was extracted. Groundwater level change and thickness of the Quaternary deposit were obtained from hydrogeological data. Machine learning algorithms Linear Regression and Principal Component Analysis (PCA) were used to investigate the relationship between land subsidence and influencing factors. Based on the results obtained by Linear Regression and PCA, a suitable machine learning algorithm was selected to predict the subsidence amount in the Beijing Plain in 2018 through influencing factors. In this study, we found that the maximum subsidence rate in the Beijing Plain had reached 115.96 mm/y from 2016 to 2018. The land subsidence was serious in eastern Chaoyang and northwestern Tongzhou. In addition, the area where thickness of the Quaternary deposit reached 150–200 m was prone to more serious land subsidence in the Beijing Plain. In groundwater exploitation, the second confined aquifer had the greatest impact on land subsidence. Through Linear Regression and PCA, we found that the relationship between land subsidence and influencing factors was nonlinear. XGBoost was feasible to predict subsidence amount. The prediction accuracy of XGBoost on the subsidence amount reached 0.9431, and the mean square error was controlled at 15.97. By using XGBoost to predict the subsidence amount, our research provides a new idea for land subsidence prediction.

To reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) band reflectance with optimal spatiotemporal continuity, three bidirectional reflectance distribution function (BRDF) models—the Ross-Thick-Li-Sparse Reciprocal (RTLSR) model, Gao model, and adjusted BF model—were used to retrieve MODIS-band reflectance for cloudy MODIS pixels according to different inversion conditions with a proposed filling algorithm. Then, a spatiotemporally continuous MODIS-band reflectance dataset for most of Asia with more than 98% spatiotemporal coverage was reconstructed from 2012 to 2015. The validation highlighted an evident improvement in filling cloudy MODIS observations; a reasonable spatial distribution, such as in South Asia and Southeast Asia; and acceptable precision for the filled MODIS pixels, with the root mean square error percentage (RMSE%) at 9.7–9.8% and 12–16% for the Gao and adjusted BF models, respectively. In the course of reconstructing the spatiotemporal continuous MODIS-band reflectance, the differences among the three models were discussed further. For a 16-day period with a stable and unchanged land surface, the RTLSR model, as a basic model, accurately derived land surface reflectance (no more than 10% RMSE% for MCD43C1 V006 band 1) and outperformed the other two models. When the inversion period is sufficiently long (e.g., 108 days, 188 days, 268 days, or a full year), the Gao/adjusted BF model provides better precision than the RTLSR model by considering the normalized difference vegetation index (NDVI) and soil moisture/NDVI as intermediate variables used to adjust the BRDF parameters in real time. The Gao model is optimal when the inversion period is sufficiently long. Based on combining the RTLSR model and Gao/adjusted BF model, we proposed a filling algorithm to derive a dataset of MODIS-band reflectance with optimal spatiotemporal continuity.

Groundwater resources have been exploited and utilized on a large scale in the North China Plain (NCP) since the 1970s. As a result of extensive groundwater depletion, the NCP has experienced significant land subsidence, which threatens geological stability and infrastructure health and exacerbates the risks of other geohazards. In this study, we employed multi-track Synthetic Aperture Radar (SAR) datasets acquired by the Sentinel-1A (S1A) satellite to detect spatial and temporal distributions of surface deformation in the NCP from 2016 to 2018 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). The results show that the overall ground displacement ranged from −165.4 mm/yr (subsidence) to 9.9 mm/yr (uplift) with a standard variance of 28.8 mm/yr. During the InSAR monitoring period, the temporal pattern of land subsidence was dominated by a decreasing tendency and the spatial pattern of land subsidence in the coastal plain exhibited an expansion trend. Validation results show that the S1A datasets agree well with levelling data, indicating the reliability of the InSAR results. With groundwater level data, we found that the distribution of subsidence in the NCP is spatially consistent with that of deep groundwater depression cones. A comparison with land use data shows that the agricultural usage of groundwater is the dominant mechanism responsible for land subsidence in the whole study area. Through an integrated analysis of land subsidence distribution characteristics, geological data, and previous research results, we found that other triggering factors, such as active faults, precipitation recharge, urbanization, and oil/gas extraction, have also impacted land subsidence in the NCP to different degrees.

As a common geological hazard, land subsidence is widely distributed in the Eastern Beijing Plain. The pattern of evolution of this geological phenomenon is controlled by many factors, including groundwater level change in different aquifers, compressible layers of different thicknesses, and static and dynamic loads. First, based on the small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique, we employed 47 ENVISAT ASAR images and 48 RADARSAT-2 images to acquire the ground deformation of the Beijing Plain from June 2003 to November 2015 and then validated the results using leveling benchmark monitoring. Second, we innovatively calculated additional stress to obtain static and dynamic load information. Finally, we evaluated the contribution rate of the influencing factors to land subsidence by using the Spearman’s rank correlation coefficient (SRCC) and extremely randomized trees (ERT) machine learning methods. The SBAS-InSAR outcomes revealed that the maximum deformation rate was 110.7 mm/year from 2003 to 2010 and 144.4 mm/year from 2010 to 2015. The SBAS-InSAR results agreed well with the leveling benchmark monitoring results; the correlation coefficients were 0.97 and 0.96 during the 2003–2010 and 2013–2015 periods, respectively. The contribution rate of the second confined aquifer to the cumulative land subsidence was 49.3% from 2003 to 2010, accounting for the largest proportion; however, its contribution rate decreased to 23.4% from 2010 to 2015. The contribution rate of the third confined aquifer to the cumulative land subsidence increased from 2003 to 2015. Although the contribution of additional stress engendered from static and dynamic loads to the cumulative land subsidence was slight, it had a significant effect on the uneven land subsidence, with a contribution rate of 33.8% from 2003 to 2010 and 23.1% from 2010 to 2015. These findings provide scientific support for mitigating hazards associated with land subsidence.

In urban areas, deformation of transportation infrastructures may lead to serious safety accidents. Timely and accurate monitoring of the structural deformation is critical for prevention of transportation accidents and assurance of construction quality, particularly in areas with regional land subsidence, such as the city of Beijing. In this study, we proposed a method for the detection of seasonal deformation of highway overpasses using the integration of persistent scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) techniques and seasonal indices, i.e., deformation concentration degree (DCD) and deformation concentration period (DCP) indices. Taking eastern Beijing urban area as a case study area, we first used the PS-InSAR technique to derive time series surface deformation based on 55 TerraSAR-X images during 2010–2016. Then, we proposed DCD and DCP indices to characterize seasonal deformation of 25 highway overpasses in the study area, with DCD representing to what degree the annual deformation is distributed in a year, and DCP representing the period on which deformation concentrates in the year. Our results showed that the maximum annual deformation rate reached −141.3 mm/year in Beijing urban area, and the PS-InSAR measurements agreed well with levelling measurements (R2 > 0.97). For PS pixels with DCD ≥ 0.3, the monthly deformation showed obvious seasonal patterns with deformation values during some months greater than those during the other months. DCP revealed that the settlement during autumn and winter was more serious than that in spring and summer. The seasonal patterns seemed to be related to the location, structure, and construction age of the overpasses. The upper-level overpasses, the newly constructed overpasses, and those located in the subsidence area (rate < −40 mm/year) tended to show a greater seasonal pattern. The seasonal deformation variations were also affected by groundwater-level fluctuation, temperature, and compressible layer.

Land subsidence threatens the stable operation of urban rail transit, including subways. Obtaining deformation information during the entire life-cycle of a subway becomes a necessary means to guarantee urban safety. Restricted by sensor life and cost, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar (MTI) technology has been unable to meet the needs of long-term sequence, high-resolution deformation monitoring, especially of linear objects. The multi-sensor MTI time-series fusion (MMTI-TSF) techniques has been proposed to solve this problem, but rarely mentioned. In this paper, an improved MMTI-TSF is systematically explained and its limitations are discussed. Taking the Beijing Subway Network (BSN) as a case study, through cross-validation and timing verification, we find that the improved MMTI-TSF results have higher accuracy (R2 of 98% and, Root Mean Squared Error (RMSE) of 4mm), and compared with 38 leveling points, the fitting effect of the time series is good. We analyzed the characteristics of deformation along the BSN over a 15-year periods. The results suggest that there is a higher risk of instability in the eastern section of Beijing Subway Line 6 (L6). The land subsidence characteristics along the subway lines are related to its position from the subsidence center, and the edge of the subsidence center presents a segmented feature.

Balancing groundwater supply and food production is challenging, especially in large regions where there is often insufficient information on the groundwater budget, such as in the North China Plain (NCP) and the Northeast China Plain (NECP), which are major food producing areas in China. This study aimed to understand this process in a simple but efficient way by using Gravity Recovery and Climate Experiment (GRACE) data, and it focused on historical and projected groundwater storage (GWS) changes in response to changes in grain-sown areas. The results showed that during 2003–2016, the GWS was depleted in the NCP at a rate of −17.2 ± 0.8 mm/yr despite a decrease in groundwater abstraction along with an increase in food production and a stable sown area, while in the NECP, the GWS increased by 2.3 ± 0.7 mm/yr and the groundwater abstraction, food production and the sown area also increased. The scenario simulation using GRACE-derived GWS anomalies during 2003–2016 as the baseline showed that the GWS changes in the NCP can be balanced (i.e., no decreasing trend in storage) by reducing the area of winter wheat and maize by 1.31 × 106 ha and 3.21 × 106 ha, respectively, or by reducing both by 0.93 × 106 ha. In the NECP, the groundwater can sustain an additional area of 0.62 × 106 ha of maize without a decrease in storage. The results also revealed that the current groundwater management policies cannot facilitate the recovery of the GWS in the NCP unless the sown ratio of drought-resistance wheat is increased from 90% to 95%. This study highlights the effectiveness of using GRACE to understanding the nexus between groundwater supply and food production at large scales.

The long-term overexploitation of groundwater leads to serious land subsidence and threatens the safety of Beijing-Tianjin-Hebei (BTH). In this paper, an interferometric point target analysis (IPTA) with small baseline subset InSAR (SBAS-InSAR) technique was used to derive the land subsidence in a typical BTH area from 2012 to 2018 with 126 Radarsat-2 and 184 Sentinel-1 images. The analysis reveals that the average subsidence rate reached 118 mm/year from 2012 to 2018. Eleven subsidence features were identified: Shangzhuang, Beijing Airport, Jinzhan and Heizhuanghu in Beijing, Guangyang and Shengfang in Langfang, Wangqingtuo in Tianjin, Dongguang in Cangzhou, Jingxian and Zaoqiang in Hengshui and Julu in Xingtai. Comparing the different types of land use in subsidence feature areas, the results show that when the land-use type is relatively more complex and superimposed with residential, industrial and agricultural land, the land subsidence is relatively more significant. Moreover, the land subsidence development patterns are different in the BTH areas because of the different methods adopted for their water resource development and utilization, with an imbalance in their economic development levels. Finally, we found that the subsidence changes are consistent with groundwater level changes and there is a lag period between land subsidence and groundwater level changes of approximately two months in Beijing Airport, Jinzhan, Jingxian and Zaoqiang, of three months in Shangzhuang, Heizhuanghu, Guangyang, Wangqingtuo and Dongguang and of four months in Shengfang.

Drought is a natural disaster caused by long-term water deficit. Because the growth of crops and vegetation is closely related to soil moisture environment, it is of great significance to study the soil drought and vegetation response. In this paper, the soil moisture availability index (SMAI) was developed for quantifying soil drought conditions. The effectiveness and the ability of SMAI to recognize drought events were analyzed, while the vegetation condition index (VCI) was used to characterize the vegetation status. Temporal and spatial variations of soil drought and vegetation condition as well as the impacts of drought on vegetation in North China during 2001–2015 were comprehensively examined. We firstly concluded that SMAI related well with standardized precipitation evapotranspiration index (SPEI), and drought events can be detected by SMAI. Next, the mean value of SMAI in North China showed a decreasing trend in recent 15 years. Finally, the SMAI positively correlated with VCI in most areas of North China, and the response of four types of vegetation to SMAI differed over time. The results of SMAI on vegetation would assist drought research and application in North China.

Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes—classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images—were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%–82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%–20.2% for the CNU area and 4.7%–12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples.

Land surface albedo data with high spatio-temporal resolution are increasingly important for scientific studies addressing spatially and/or temporally small-scale phenomena, such as urban heat islands and urban land surface energy balance. Our previous study derived albedo data with 2–4-day and 30-m temporal and spatial resolution that have better spatio-temporal resolution than existing albedo data, but do not completely satisfy the requirements for monitoring high-frequency land surface changes at the small scale. Downscaling technology provides a chance to further improve the albedo data spatio-temporal resolution and accuracy. This paper introduces a method that combines downscaling technology for land surface reflectance with an empirical method of deriving land surface albedo. Firstly, downscaling daily MODIS land surface reflectance data (MOD09GA) from 500 m to 30 m on the basis of HJ-1A/B BRDF data with 2–4-day and 30-m temporal and spatial resolution is performed: this is the key step in the improved method. Subsequently, the daily 30-m land surface albedo data are derived by an empirical method combining prior knowledge of the MODIS BRDF product and the downscaled daily 30-m reflectance. Validation of albedo data obtained using the proposed method shows that the new method has both improved spatio-temporal resolution and good accuracy (a total absolute accuracy of 0.022 and a total root mean squared error at six sites of 0.028).

Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m2, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg−Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was Populus euphratica. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (R2 = 0.741, RMSE = 0.487, %RMSE = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI).

Land subsidence is a global environmental geological hazard caused by natural or human activities. The high spatial resolution and continuous time coverage of interferometric synthetic aperture radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Beijing, the capital city of China, has suffered from land subsidence for decades since it was first recorded in the 1950s. It was reported that uneven ground subsidence and fractures have seriously affected the normal operation of the Beijing Capital International Airport (BCIA) in recent years before the overhaul of the middle runway in April 2017. In this study, InSAR time series analysis was successfully used to detect the uneven local subsidence and ground fissure activity that has been gradually increasing in BCIA since 2010. A multi-temporal InSAR (MT-InSAR) technique was performed on 63 TerraSAR-X/TanDem-X (TSX/TDX) images acquired between 2010 and 2017, then deformation rate maps and time series for the airport area were generated. Comparisons of deformation rate and displacement time series from InSAR and ground-leveling were carried out in order to evaluate the accuracy of the InSAR-derived measurements. After an integrated analysis of the distribution characteristics of land subsidence, previous research results, and geological data was carried out, we found and located an active ground fissure. Then main cause of the ground fissures was preliminarily discussed. Finally, it can be conducted that InSAR technology can be used to identify and monitor geological processes, such as land subsidence and ground fissure activities, and can provide a scientific approach to better explore and study the cause and formation mechanism of regional subsidence and ground fissures.

Since the 1970s, land subsidence has been rapidly developing on the Beijing Plain, and the systematic study of the evolutionary mechanism of this subsidence is of great significance in the sustainable development of the regional economy. On the basis of Interferometric Synthetic Aperture Radar (InSAR) results, this study employed the Mann–Kendall method for the first time to determine the mutation information of land subsidence on the Beijing Plain from 2004 to 2015. By combining the hydrogeological conditions, “southern water” project, and other data, we attempted to analyse the reasons for land subsidence mutations. First, on the basis of ENVISAT ASAR and RADARSAT-2 data, the land subsidence of the Beijing Plain was determined while using small baseline interferometry (SBAS-InSAR) and Persistent Scatterers Interferometry (PSI). Second, on the basis of the Geographic Information System (GIS) platform, vector data of displacement under different scales were obtained. Through a series of tests, a scale of 960 metres was selected as the research unit and the displacement rate from 2004 to 2015 was obtained. Finally, a trend analysis of land subsidence was carried out on the basis of the Mann–Kendall mutation test. The results showed that single-year mutations were mainly distributed in the middle and lower parts of the Yongding River alluvial fan and the Chaobai River alluvial fan. Among these mutations, the greatest numbers occurred in 2015 and 2005, being 1344 and 915, respectively. The upper and middle alluvial fan of the Chaobai River, the vicinity of the emergency water sources, and the edge of the groundwater funnel have undergone several mutations. Combining hydrogeological data of the study area and the impact of the south-to-north water transfer project, we analysed the causes of these mutations. The experimental results can quantitatively verify the mutation information of land subsidence in conjunction with time series to further elucidate the spatial-temporal variation characteristics of land subsidence in the study area.

Land subsidence in the Eastern Beijing Plain has a long history and is always serious. In this paper, we consider the time-series evolution patterns of the eastern of Beijing Plain. First, we use the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) technique, with Envisat and Radarsat-2 data, to monitor the deformation of Beijing Plain from 2007 to 2015. Second, we adopt the standard deviation ellipse (SDE) method, combined with hydrogeological data, to analyze the spatial evolution patterns of land subsidence. The results suggest that land subsidence developed mainly in the northwest–southeast direction until 2012 and then expanded in all directions. This process corresponds to the expansion of the groundwater cone of depression range after 2012, although subsidence is restricted by geological conditions. Then, we use the permutation entropy (PE) algorithm to reverse the temporal evolution pattern of land subsidence, and interpret the causes of the phenomenon in combination with groundwater level change data. The results show that the time-series evolution pattern of the land subsidence funnel edge can be divided into three stages. From 2009 to 2010, the land subsidence development was uneven. From 2010 to 2012, the land subsidence development was relatively even. From 2012 to 2013, the development of land subsidence became uneven. However, subsidence within the land subsidence funnel is divided into two stages. From 2009 to 2012, the land subsidence tended to be even, and from 2012 to 2015, the land subsidence was relatively more even. The main reason for the different time-series evolution patterns at these two locations is the annual groundwater level variations. The larger the variation range of groundwater is, the higher the corresponding PE value, which means the development of the land subsidence tends to be uneven.

Yongding River is the largest river flowing through Beijing, the capital city of China. In recent years, Yongding River Basin (YDRB) has witnessed increasing human impacts on water resources, posing serious challenges in hydrological and ecological health. In this study, remote sensing techniques and statistical time series approaches for hydrological studies were combined to characterize the dynamics and driving factors of reservoir water extents in YDRB during 1985–2016. First, 107 Landsat 4, 5, 7 and 8 images were used to extract surface water extents in YDRB during 1985–2016 using a combination of water indices and Otsu threshold algorithm. Significant positive correlation was found between water extents and the annual inflow for the two biggest reservoirs, the downstream Guanting and upstream Cetian reservoirs, proving their representativeness of surface water availability in this basin. Then, statistical time series approaches including trend-free pre-whitening Mann-Kendall trend test, Pettit change-point test and double mass curve method, which are frequently used in hydrological studies, were adopted to quantify the trend of reservoir water extents dynamics and the relative contributions of climate variability and human activities. Results showed that the water extents in both reservoirs exhibited significant downward trend with change point occurring in 2001 and 2005 for Guanting and Cetian, respectively. About 74%~75% of the shrinkage during the post-change period can be attributed to human activities, among which GDP, population, electricity power production, raw coal production, steel and crude iron production, value of agriculture output, and urban area were the major human drivers. Hydrological connectivity between the upstream Cetian and downstream Guanting reservoirs declined during the post-change period. Since 2012, water extents in both reservoirs recovered as a result of various governmental water management policies including the South-to-North Water Diversion Project. The methodology presented in this study can be used for analyzing the dynamics and driving mechanism of surface water resources, especially for un-gauged or poorly-gauged watersheds.