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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.

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

The shadow statistical method (SSM) used for extracting the significant wave height (SWH) from X-band marine radar images was further investigated because of its advantage of not requiring an external reference for calibration. Currently, a fixed shadow segmentation threshold is utilized to extract the SWH from a radar image based on the SSM. However, the retrieval accuracy of the SWH is not ideal for low wind speeds since the echo intensity of sea waves rapidly decays over distance. In order to solve this problem, an adaptive shadow threshold, which varies with echo intensity over distance and can accurately divide the radar image into shadow and nonshadow areas, is adopted to calculate the wave slope (WS) based on the texture feature of the edge image. Instead of using the averaged WS, the wave slope feature vector (WSFV) is constructed for retrieving the SWH since the illumination ratio and the calculated WS in the azimuth are different for shore-based radar images. In this paper, the SWH is calculated based on the constructed WSFV and classical support vector regression (SVR) technology. The collected 222 sets of X-band marine radar images with an SWH range of 1.0∼3.5 m and an average wind speed range of 5∼10 m/s were utilized to verify the performance of the proposed approach. The buoy record, which was deployed during the experiment, was used as the ground truth. For the proposed approach, the mean bias (BIAS) and the mean absolute error (MAE) were 0.03 m and 0.14 m when the ratio of the training set to the test set was 1:1. Compared to the traditional SSM, the correlation coefficient (CC) of the proposed approach increased by 0.27, and the root mean square error (RMSE) decreased by 0.28 m.

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 FROM AUTHOR]

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.

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.

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.

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.

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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.

The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km2 in size, and representing the entirety of Dease Strait, Coronation Gulf and Bathurst Inlet, Nunavut. The focus of this research was to assess the potential to operationalize shoreline sensitivity mapping to inform oil spill response and contingency planning. The impact of varying the training sample size and reducing model data load were evaluated. Results showed that acceptable accuracies could be achieved with relatively few training samples, but that higher accuracies and greater probabilities of correct class assignment were observed with larger sample sizes. Additionally, the number of inputs to the model could be greatly reduced without impacting overall performance. Optimized models reached independent accuracies of 91% for seven land cover types, and classification probabilities between 0.77 and 0.98 (values for latter represent per-class averages generated from independent validation sites). Mixed results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data.

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.

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).

The estimation of the Fraction of Absorbed Photosynthetically Active Radiation in forests (forest fAPAR) from multi-spectral Landsat-8 data is investigated in this paper using a physically based radiative transfer model (Invertible Forest Reflectance Model, INFORM) combined with an inversion strategy based on artificial neural nets (ANN). To derive the forest fAPAR for the Dabie mountain test site in China in 30 m spatial resolution (size approximately 3000 km2), a database of forest canopy spectral reflectances was simulated with INFORM taking into account structural variables such as leaf area index (LAI), crown coverage and stem density as well as leaf composition. To establish the relationship between forest fAPAR and the reflectance modeled by INFORM, a logarithmic relationship between LAI and fAPAR was used previously established using on-site field measurements. On this basis, predictive models between Landsat-8 reflectance and fAPAR were established using an artificial neural network. After calibrating INFORM for the test site, forty-two forest stands were used to validate the performance of the method. The results show that spectral signatures modeled by INFORM correspond reasonably well with the forest canopy reflectance spectra derived from Landsat data. Deviations increase with increasing angle between surface normal of the hilly terrain and sun incidence. The comparison of estimated and measured fAPAR (R2 = 0.47, RMSE = 0.11) demonstrates that INFORM can be inverted using neural nets to provide acceptable estimates of forest fAPAR. The accuracy of the predictions increased significantly when excluding pixels located in very steep terrain. This demonstrates that the applied topographic correction was not sufficiently accurate and should be improved for making optimum use of radiative transfer models such as INFORM.

Coal fires are a common and serious problem in most coal-bearing countries. Thus, it is very important to monitor changes in coal fires. Remote sensing provides a useful technique for investigating coal fields at a large scale and for detecting coal fires. In this study, the spreading direction of a coal fire in the Wuda Coal Field (WCF), northwest China, was analyzed using multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) thermal infrared (TIR) data. Using an automated method and based on the land surface temperatures (LST) that were retrieved from these thermal data, coal fires related to thermal anomalies were identified; the locations of these fires were validated using a coal fire map (CFM) that was developed via field surveys; and the cross-validation of the results was also carried out using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images. Based on the results from longtime series of satellite TIR data set, the spreading directions of the coal fires were determined and the coal fire development on the scale of the entire coal field was predicted. The study delineated the spreading direction using the results of the coal fire dynamics analysis, and a coal fire spreading direction map was generated. The results showed that the coal fires primarily spread north or northeast in the central part of the WCF and south or southwest in the southern part of the WCF. In the northern part of the WCF, some coal fires were spreading north, perhaps coinciding with the orientation of the coal belt. Certain coal fires scattered in the northern and southern parts of the WCF were extending in bilateral directions. A quantitative analysis of the coal fires was also performed; the results indicate that the area of the coal fires increased an average of approximately 0.101 km2 per year.

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. [ABSTRACT FROM AUTHOR]

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.

Beijing is severely affected by land subsidence, and rapid urbanisation and building construction might accelerate the land subsidence process. Based on 39 Envisat Advanced Synthetic Aperture Radar (ASAR) images acquired between 2003–2010, 55 TerraSAR-X images acquired between 2010–2016, and urban building information, we analysed the relationship between land subsidence and buildings at the regional, block, and building scales. The results show that the surface displacement rate in the Beijing urban area ranged from −109 mm/year to +13 mm/year between 2003–2010, and from −151 mm/year to +19 mm/year between 2010–2016; two subsidence bowls were mainly distributed in the eastern part of the Chaoyang District. The displacement rate agreed well with the levelling measurements, with an average bias of less than six mm/year. At the regional scale, the spatial pattern of land subsidence was mainly controlled by groundwater extraction, compressible layer thickness, and geological faults. Subsidence centres were located in the area around ground water funnels with a compressible layer depth of 50–70 m. The block-scale analysis demonstrated a clear correlation between the block construction age and the spatial unevenness of subsidence. The blocks constructed between 1998–2005 and after 2005 showed considerably more subsidence unevenness and temporal instability than the blocks constructed before 1998 during both time periods. The examination of the new blocks showed that the spatial unevenness increased with building volume variability. For the 16 blocks with a high building volume, variability, and subsidence unevenness, the building-scale analysis showed a positive relationship between building volume and settlement in most blocks, although the R2 was lower than 0.5. The results indicate that intense building construction in urban areas could cause differential settlement at the block scale in Beijing, while the settlement of single buildings could be influenced by the integrated effects of building volume, foundation structures, and the hydrogeological background.

Land subsidence is one of the most important geological hazards in Beijing, China, and its scope and magnitude have been growing rapidly over the past few decades, mainly due to long-term groundwater withdrawal. Interferometric Synthetic Aperture Radar (InSAR) has been used to monitor the deformation in Beijing, but there is a lack of analysis of the long-term spatiotemporal evolution of land subsidence. This study focused on detecting and characterizing spatiotemporal changes in subsidence in the Beijing Plain by using Persistent Scatterer Interferometry (PSI) and geographic spatial analysis. Land subsidence during 2003–2015 was monitored by using ENVISAT ASAR (2003–2010), RADARSAT-2 (2011–2015) and TerraSAR-X (2010–2015) images, with results that are consistent with independent leveling measurements. The radar-based deformation velocity ranged from −136.9 to +15.2 mm/year during 2003–2010, and −149.4 to +8.9 mm/year during 2011–2015 relative to the reference point. The main subsidence areas include Chaoyang, Tongzhou, Shunyi and Changping districts, where seven subsidence bowls were observed between 2003 and 2015. Equal Fan Analysis Method (EFAM) shows that the maximum extensive direction was eastward, with a growing speed of 11.30 km2/year. Areas of differential subsidence were mostly located at the boundaries of the seven subsidence bowls, as indicated by the subsidence rate slope. Notably, the area of greatest subsidence was generally consistent with the patterns of groundwater decline in the Beijing Plain.

Land subsidence is the disaster phenomenon of environmental geology with regionally surface altitude lowering caused by the natural or man-made factors. Beijing, the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence rates of more than 100 mm/year. In this study, we employ two SAR datasets acquired by Envisat and TerraSAR-X satellites to investigate the surface deformation in Beijing Plain from 2003 to 2013 based on the multi-temporal InSAR technique. Furthermore, we also use observation wells to provide in situ hydraulic head levels to perform the evolution of land subsidence and spatial-temporal changes of groundwater level. Then, we analyze the accumulated displacement and hydraulic head level time series using continuous wavelet transform to separate periodic signal components. Finally, cross wavelet transform (XWT) and wavelet transform coherence (WTC) are implemented to analyze the relationship between the accumulated displacement and hydraulic head level time series. The results show that the subsidence centers in the northern Beijing Plain is spatially consistent with the groundwater drop funnels. According to the analysis of well based results located in different areas, the long-term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction, while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics (seasonal characteristics) as the groundwater level changes. Moreover, according to the wavelet transformation, the land subsidence time series at monitoring well site lags several months behind the groundwater level change.

Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user’s and producer’s accuracies were 5–24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71–77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89–91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic.