Global climate change has led to significant changes in seasonal rhythm events of vegetation growth, such as spring onset and autumn senescence. Spatiotemporal shifts in these vegetation phenological metrics have been widely reported over the globe. Vegetation growth peak represents plant photosynthesis capacity and responds to climate change. At present, spatiotemporal changes in vegetation growth peak characteristics (timing and maximum growth magnitude) and their underlying governing mechanisms remain unclear at regional scales. In this study, the spatiotemporal dynamics of vegetation growth peak in northeast China (NEC) was investigated using long-term NDVI time series. Then, the effects of climatic factors and spring phenology on vegetation growth peak were examined. Finally, the contribution of growth peak to vegetation production variability was estimated. The results of the phenological analysis indicate that the date of vegetation green up in spring and growth peak in summer generally present a delayed trend, while the amplitude of growth peak shows an increasing trend. There is an underlying cycle of 11 years in the vegetation growth peak of the entire study area. Air temperature and precipitation before the growing season have a small impact on vegetation growth peak amplitude both in its spatial extent and magnitude (mainly over grasslands) but have a significant influence on the date of the growth peak in the forests of the northern area. Spring green-up onset has a more significant impact on growth peak than air temperature and precipitation. Although green-up date plays a more pronounced role in controlling the amplitude of the growth peak in forests and grasslands, it also affects the date of growth peak in croplands. The amplitude of the growth peak has a significant effect on the inter-annual variability of vegetation production. The discrepant patterns of growth peak response to climate and phenology reflect the distinct adaptability of the vegetation growth peak to climate change, and result in different carbon sink patterns over the study area. The study of growth peak could improve our understanding of vegetation photosynthesis activity over various land covers and its contribution to carbon uptake.

Coseismic off-fault deformation (OFD) measurements provide crucial insight into fault deformation behavior and shallow faulting damage. Although kinematic and dynamic characteristics of the causative faults associated with the 2023 Turkey-Syria earthquake doublet (Mw 7.8 and 7.6) were well determined, the OFD behavior and mechanism remain poorly constrained. Here we utilized high-resolution optical satellite geodesy to illuminate OFD distribution of the earthquake doublet. We found an average of ~80% and ~83% of coseismic fault deformation were localized on the primary fault planes of the Mw 7.8 and Mw 7.6 earthquakes, respectively; together, the remaining deformation manifested as OFD distributed within an average of 200-m- and 140-m-wide damage zones. Our observations revealed the OFD systematically increases in fault segments that have more complex geometry and slower coseismic rupture velocity, validating that both factors exhibit a significant facilitation on first-order variation of OFD during the earthquake doublet.

East China is one of the most active regions in terms of economic and social development, and with the accelerated urbanization process, environmental problems are becoming increasingly prominent. The objective, quantitative, and timely evaluation of spatial and temporal changes in ecological quality is of great significance for environmental protection and decision making. The remote sensing ecological index (RSEI) is an objective, fast, and easy ecological quality monitoring and evaluation technique which has been widely used in the field of ecological research, but it often involves problems of cloud occlusion and stitching difficulties when used to conduct large-scale and long-term monitoring. In this paper, based on the Google Earth Engine (GEE) platform, an RSEI was constructed using MODIS data products to evaluate the spatial and temporal changes in ecological quality in East China over the past 20 years. The study shows the following: (1) The mean RSEI values in 2000, 2005, 2010, 2015, and 2020 were 0.67, 0.55, 0.59, 0.58, and 0.63, respectively, with the mean values first decreasing and then showing a stable increasing trend. In Shanghai and Jiangsu, the mean RSEI values show a fluctuating characteristic of “falling and then rising”, and large respective decreases of 32.4% and 25.8% throughout the monitoring period. The RSEI values in Fujian Province showed a relatively stable upward trend during the study period (19% increase). (2) The RSEI spatially correlated clustering maps of the local indicators showed that the regions with a high degree of clustering are mainly located in Quzhou City, Zhejiang Province, Ningde City, Fujian Province, and northern Anhui Province (Bozhou and Huabei). With the promotion of ecological civilization and the enhancement of environmental protection awareness, the vegetation cover has significantly increased, which has led to the rise in RSEI values. The low values are mainly distributed in densely populated areas with more human activity, such as the central-eastern part of Jiangsu Province, central Anhui Province, Shanghai, and northern Zhejiang Province. With the development of cities, impervious surfaces occupy more and more ecological land, which eventually affects the regional RSEI values. (3) This research provides a promising method for the evaluation of spatial and temporal changes in ecological environment quality based on an RSEI and GEE. The image processing, based on GEE cloud computing, can help overcome the problems of missing remote sensing data, chromatic aberrations, and spatial and temporal inconsistency, which could greatly improve the efficiency of image processing and extend the application of the remote sensing ecological index to large-scale, long-term ecological monitoring. The research results can provide a reference for improving the applicability and accuracy of remote sensing ecological indices and provide a theoretical basis for ecological conservation and land management in the context of rapid urbanization.

Residual noise effect in interferometric synthetic aperture radar (InSAR) time-series data typically complicates the characterization of deformation behaviors on tectonic faults, especially postseismic deformation caused by multiple physical mechanisms. To reconstruct denoised postseismic deformation signal in noisy InSAR time-series (TSInSAR) data, we proposed a deep-learning-based neural network and trained it on noisy TSInSAR dataset, which was generated by adding Sentinel-1 TSInSAR noise onto synthetic postseismic deformation. The application results on experimental dataset validated that our neural network efficiently removed residual noise and automatically recovered clean postseismic deformation in noisy TSInSAR with any space and time sizes. Moreover, when applied to real TSInSAR data following the 2021 Maduo earthquake, our network provided accurate reconstructions of postseismic deformation in both time and space, demonstrating its generalization performance on actual data. In comparison with the original data, the denoised postseismic deformation exhibits greater consistency with the observations from a continuous global positioning system (cGPS). Interestingly, our denoised postseismic slip reveals spatial juxtaposition between the geometrically complex coseismic rupture segments and the heterogeneity of the early afterslip along the Maduo rupture. Overall, these successful applications highlight the network’s generalization performance to improve postseismic deformation signals.

The precise characterization of surface rupture zones and associated co-seismic displacement fields from large earthquakes provides critical insights into seismic rupture mechanisms, earthquake dynamics, and hazard assessments. Stereo-photogrammetric digital elevation models (DEMs), produced from high-resolution satellite stereo imagery, offer reliable global datasets that are suitable for the detailed extraction and quantification of vertical co-seismic displacements. In this study, we utilized pre- and post-event WorldView-2 stereo images of the 2024 Ms7.1 Wushi earthquake in Xinjiang to generate DEMs with a spatial resolution of 0.5 m and corresponding terrain point clouds with an average density of approximately 4 points/m². Subsequently, we applied the Iterative Closest Point (ICP) algorithm to perform differencing analysis on these datasets. Special care was taken to reduce influences from terrain changes such as vegetation growth and anthropogenic structures. Ultimately, by maintaining sufficient spatial detail, we obtained a three-dimensional co-seismic displacement field with a resolution of 15 m within grid cells measuring 30 m near the fault trace. The results indicate a clear vertical displacement distribution pattern along the causative sinistral–thrust fault, exhibiting alternating uplift and subsidence zones that follow a characteristic “high-in-center and low-at-ends” profile, along with localized peak displacement clusters. Vertical displacements range from approximately 0.2 to 1.4 m, with a maximum displacement of ~1.46 m located in the piedmont region north of the Qialemati River, near the transition between alluvial fan deposits and bedrock. Horizontal displacement components in the east-west and north-south directions are negligible, consistent with focal mechanism solutions and surface rupture observations from field investigations. The successful extraction of this high-resolution vertical displacement field validates the efficacy of satellite-based high-resolution stereo-imaging methods for overcoming the limitations of GNSS and InSAR techniques in characterizing near-field surface displacements associated with earthquake ruptures. Moreover, this dataset provides robust constraints for investigating fault-slip mechanisms within near-surface geological contexts.