Improved spatiotemporal resolution of anti-scattering super-resolution label-free microscopy via synthetic wave 3D metalens imaging

Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear or indistinguishable.Additionally,live-cell imaging faces challenges in achieving high temporal resolution for fast-moving subcellular structures.Here,we present the principles of a synthetic wave microscopy(SWM)to extract three-dimensional information from thick unlabeled specimens,where photobleaching and phototoxicity are avoided.SWM exploits multiple-wave interferometry to reveal the specimen’s phase information in the area of interest,which is not affected by the scattering media in the optical path.SWM achieves~0.42λ/NA resolution at an imaging speed of up to 106 pixels/s.SWM proves better temporal resolution and sensitivity than the most conventional microscopes currently available while maintaining exceptional SR and anti-scattering capabilities.Penetrating through the scattering media is challenging for conventional imaging techniques.Remarkably,SWM retains its efficacy even in conditions of low signal-to-noise ratios.It facilitates the visualization of dynamic subcellular structures in live cells,encompassing tubular endoplasmic reticulum(ER),lipid droplets,mitochondria,and lysosomes.

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

Spatiotemporal pattern of climate change in the China-Myanmar Economic Corridor from 1901 to 2018

The China-Myanmar Economic Corridor(CMEC) is an important part of China's Belt and Road Initiative and an important area for global ecology and biodiversity. In this study, the annual and seasonal spatiotemporal patterns of temperature and precipitation in the CMEC over the past century were investigated using linear tendency estimation, the Mann-Kendall mutation test, the T-test, and wavelet analysis based on the monthly mean climatic data from 1901 to 2018 released by the Climatic Research Unit(CRU) of the University of East Anglia, UK. The results show that the CMEC demonstrated a trend of warming and drying over the past 100 years, and the rate of change in Myanmar was stronger than that in Yunnan Province of China. The warming rate was 0.039 ℃/10a. Precipitation decreased at a rate of -6.1 mm/10a. From the perspective of spatial distribution, temperature was high in the central and southern, low in the north of the CMEC, and the high-temperature centers were mainly distributed in the southern plain and river valley. Precipitation decreased from west to east and from south to north of the CMEC. From the perspective of the rate of change, warming was stronger in central and northern CMEC than in southern and northeastern CMEC. The rate of precipitation decline was stronger in the central and western regions than in the eastern region. This study provides a scientific reference for the CMEC to address climate change and ensure sustainable social and economic development and ecological security.

Spatiotemporal variations,influencing factors,and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency(EE)helps address energy constraints and promote environmental sustainability.This study comprehensively explores the spatiotemporal variations,influencing factors,and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier(G-S-MinDS),exploratory spatial data analysis(ESDA),multiscale geographically weighted regression(MGWR),and fuzzy set qualitative comparative analysis(fsQCA)methods.The findings are:①China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%,showing considerable promotion potential.②Industrial structure optimization,population agglomeration,economic development,and increased green coverage contribute positively,while government intervention and openness hinder China’s urban EE.③Four configurational promotion paths for enhancing China’s urban EE are identified,where among those paths population density is a core condition,while government intervention is not.This study provides valuable insights into substantially improving urban EE,emphasizing the need for targeted policies to address energy and environmental crises in China.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Unveiling the adaptation strategies of woody plants in remnant forest patches to spatiotemporal urban expansion through leaf trait networks

Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.

Spatiotemporal variations of parameters of internal solitary waves in the northern South China Sea

The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the dynamic parameters of the ISWs in the northern South China Sea(SCS)were studied based on the reanalysis of long-term temperature and salinity datasets.The results for spectrum analysis show that there are definite geographical differences for the periodic variation of the parameters:in shallow water,all parameters vary with a wave period of one year,while in deep water wave components of the parameters at other frequencies exist.Using wavelet analysis,the wavelet power spectral densities in deep water exhibited an inter-annual variation pattern.For example,the wave component of the dispersion coefficient with a wave period of about half a year reached its power peak once every two years.Based on previous work,this inter-annual variation pattern was deduced to be caused by dynamic processes.In further work on the regulatory mechanisms,empirical orthogonal function(EOF)decomposition was performed.It was found that the modes of the dispersion coefficient have different geographical distributions,explaining the reason why the wave components in different frequencies appeared in different locations.The numerical simulation results confirm that the variations in the parameters of the ISWs derived from the eKdV equation could affect the waveforms significantly because of changes in the polarity of the ISWs.Therefore,the periodic variations of the dynamic parameters are related to the geographical location because of dynamic processes operating.

Spatiotemporal mapping of(ultra‐)mafic magmatic mine areas:Implications of economic and political realities in China

The spatiotemporal extension/expansion of mine areas is affected by multiple factors.So far,very little has been done to examine the interaction between mine areas and political or economic realities.The(ultra‐)mafic magmatic mines in China played a specific role in supporting national development and providing an ideal research subject for monitoring their interrelationship.In this study,remote sensing and mining‐related GIS data were used to identify and analyze 1233(ultra‐)mafic magmatic mine area polygons in China,which covered approximately 322.96 km2 of land and included a V–Ti–Fe mine,a copper–nickel mine,a chromite mine,an asbestos mine,and a diamond mine.It was found that(1)the areal expansion of mines is significantly related to the mine types,perimeter,topography,and population density.(2)The mine area variation also reflects market and policy realities.The temporal expansion of the mine area from 2010 to 2020 followed an S‐shaped pattern(with the turning point occurring in 2014),closely related to iron overcapacity and tightened mining policies.(3)The complexity(D)of the mine area may reflect mine design and excavation practices.To be specific,lower D indicates early‐stage or artisanal/small‐scale mining,whereas higher D represents large‐scale mining.This study demonstrates that the detailed mapping of mine land can serve as an indicator to implement miningrelated market and policy changes.The(ultra‐)mafic mines area data set can be accessed at https://zenodo.org/record/7636616#.Y-p0uXaZOa0.

Extending the spatiotemporal resolution of super-resolution microscopies using photomodulatable °uorescent proteins

In the past two decades,various super-resolution(SR)microscopy techniques have been developed to break the di®raction limit using subdi®raction excitation to spatially modulate the°uorescence emission.Photomodulatable°uorescent proteins(FPs)can be activated by light of speci¯c wavelengths to produce either stochastic or patterned subdi®raction excitation,resulting in improved optical resolution.In this review,we focus on the recently developed photomodulatable FPs or commonly used SR microscopies and discuss the concepts and strategies for optimizing and selecting the biochemical and photophysical properties of PMFPs to improve the spatiotemporal resolution of SR techniques,especially time-lapse live-cell SR techniques.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT images.However,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited resources.To alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual blocks.In addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information.Finally,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels.Numerous experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT dataset.In detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,respectively.Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

Epidemiological Characteristics and Spatiotemporal Distribution Patterns of Human Norovirus Outbreaks in China, 2012–2018

Objective To clarify the epidemiological characteristics and spatial distribution patterns of human norovirus outbreaks in China, identify high-risk areas, and provide guidance for epidemic prevention and control.Methods This study analyzed 964 human norovirus outbreaks involving 50,548 cases in 26 provinces reported from 2012 to 2018. Epidemiological analysis and spatiotemporal scanning analysis were conducted to analyze the distribution of norovirus outbreaks in China.Results The outbreaks showed typical seasonality, with more outbreaks in winter and fewer in summer, and the total number of infected cases increased over time. Schools, especially middle schools and primary schools, are the most common settings of norovirus outbreaks, with the major transmission route being life contact. More outbreaks occurred in southeast coastal areas in China and showed significant spatial aggregation. The highly clustered areas of norovirus outbreaks have expanded northeast over time.Conclusion By identifying the epidemiological characteristics and high-risk areas of norovirus outbreaks, this study provides important scientific support for the development of preventive and control measures for norovirus outbreaks, which is conducive to the administrative management of high-risk settings and reduction of disease burden in susceptible areas.

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

Hyperspectral Image Super-Resolution Meets Deep Learning:A Survey and Perspective

Hyperspectral image super-resolution,which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation,aims to improve the spatial resolution of the hyperspectral image,which is beneficial for subsequent applications.The development of deep learning has promoted significant progress in hyperspectral image super-resolution,and the powerful expression capabilities of deep neural networks make the predicted results more reliable.Recently,several latest deep learning technologies have made the hyperspectral image super-resolution method explode.However,a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent.To this end,in this survey,we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary information.Then,we review the learning-based methods in three categories,including single hyperspectral image super-resolution,panchromatic-based hyperspectral image super-resolution,and multispectral-based hyperspectral image super-resolution.Subsequently,we summarize the commonly used hyperspectral dataset,and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively.Moreover,we briefly introduce several typical applications of hyperspectral image super-resolution,including ground object classification,urban change detection,and ecosystem monitoring.Finally,we provide the conclusion and challenges in existing learning-based methods,looking forward to potential future research directions.

SOFFLFM:Super-resolution optical fluctuation Fourierlight-field microscopy

Fourier light-field microscopy(FLFM)uses a microlens aray(MLA)to segment the Fourierplane of the microscopic objective lens to generate multiple two-dimensional perspective views,thereby reconstructing the threedimensional(3D)structure of the sample using 3D deconvo-lution calculation without scanning.However,the resolution of FLFM is stil limited by dif-fraction,and furthermore,it is dependent on the aperture division.In order to improve itsresolution,a super-resolution opticai fuctuation Fourier light-field microscopy(SOFFLFM)wasproposed here,in which the super-resolution optical fluctuation imaging(SOFI)with the abilityof super-resolution was introduced into FLFM.SOFFLFM uses higher-order cumulants statis-tical analysis on an image sequence collected by FLFM,and then carries out 3D deconvolutioncalculation to reconstruct the 3D structure of the sample.The theoretical basis of SOFFLFM onimproving resolution was explained and then verified with the simulations.Simulation resultsdemonstrated that SOFFLFM improved the lateral and axial resolution by more than V2 and 2times in the second-and fourth-order accumulations,compared with that of FLFM.

Super-resolution parameter estimation of monopulse radar by wide-narrowband joint processing

The angular resolution of radar is of crucial signifi-cance to its tracking performance.In this paper,a super-resolu-tion parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar.The range cells containing resolv-able scattering points are detected in the wideband mode,and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement.Then,the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters,and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy.Simu-lation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mis-match.

Spatiotemporal distribution of regional landslides and erosion rates in Southeastern Tibet

The difficulty in estimating ages of regional landslides hampers to assess frequency of landslides and hence to quantitatively assess regional landslide hazard.In this study,we used radiocarbon dating of organic sediment on boulder rock varnish to estimate landslide ages in Langxian(LX)arid region in southeastern Tibet.Samples of rock varnish with organic sediment were collected on site for radiocarbon dating,leading to landslide ages from 1880±30 to 18,430±30 yr B.P.To measure surface roughness characteristics of 109 remotely-mapped large bedrock landslide deposits,we estimated average standard deviation of slope(SDS)over an area of~640 km^(2) by calculating the slope gradient of each raster cell and using a rectangular moving window method in Arc Map from a 5 m-resolution Digital Elevation Model generated from helicopterobtained photographs.Combing estimated landslide ages(t)with average surface roughness of mapped landslide deposits(R)quantified by SDS,we fit an exponential landslide deposits surface roughness-age function(t=1.47×10^(6)×e^(-1).46R,r2=0.63)that was used to estimate regional landslide ages in LX.We conclude that three periods with clusters of regional landslides in LX were revealed by different surface roughness of landslide deposits combing roughness-age function,with the values of 5563-7455 yr B.P.,1724-4151 yr B.P.,and 960-1287 yr B.P..Furthermore,we used our estimates of landslide ages to quantify landslide erosion rates of three corresponding hillslopes in LX ranging from 0.50 to 2.42 mm yr-1.Although rock varnish radiocarbon dating provides us a feasible option for timing regional landslides of arid regions,the epistemic uncertainty in the dating method should arouse our attention,which could be reduced by increasing the number of samples.

Elevation,bedrock exposure,land use,interbedded limestone and clastic rock,and vegetation coverage dominate the spatiotemporal variability of soil erosion in karst basin

Soil erosion is a prominent environmental problem in karst regions.Exploring the spatiotemporal variability of soil erosion and the factors that influence soil erosion is of great significance for regional soil erosion prevention and control.However,the mechanisms influencing the characteristic features of the karst basins,such as bedrock exposure and lithology,still need to be further explored.This study used GIS technology,the Revised Universal Soil Loss Equation model,Getis–Ord Gi*,and partial least squares regression(PLSR)to identify the dominant factors influencing soil erosion and the spatiotemporal variability of soil erosion in 31 sub-basins of the Dabang River Basin(DRB),a typical karst area of Southwest China,from 2010 to 2020.The results indicated that soil erosion in the DRB from 2010 to 2020 was generally decreasing,the mean soil erosion in the DRB in 2010,2015 and 2020 was 18.46,16.51 and 15.29 t ha^(-1)a^(-1),respectively.During the study period,the area of slight erosion increased by 26.39%(706.54 km^(2)),while severe erosion enlarged by 26.36 km^(2).Spatially,the DRB was primarily affected by medium and slight soil erosion.The hot spot areas of soil erosion(key control areas)were mainly concentrated in the central and southern parts of the basin,decreasing each year,and the area of soil erosion hot pots has decreased from 43.22%to 20.60%.PLSR decoupling results show that elevation,bedrock exposure,land use type,interbedded limestone and clastic rock,and vegetation coverage were identified as the key variables affecting soil erosion,explaining 52.8%of soil erosion variability,with a high value of the Variable Importance on Projection(VIP)more than 1.These results can be used as a reference for comprehensive control of soil erosion and water loss in the basin.

Accelerate Single Image Super-Resolution Using Object Detection Process

Image Super-Resolution(SR)research has achieved great success with powerful neural networks.The deeper networks with more parameters improve the restoration quality but add the computation complexity,which means more inference time would be cost,hindering image SR from practical usage.Noting the spatial distribution of the objects or things in images,a twostage local objects SR system is proposed,which consists of two modules,the object detection module and the SR module.Firstly,You Only Look Once(YOLO),which is efficient in generic object detection tasks,is selected to detect the input images for obtaining objects of interest,then put them into the SR module and output corresponding High-Resolution(HR)subimages.The computational power consumption of image SR is optimized by reducing the resolution of input images.In addition,we establish a dataset,TrafficSign500,for our experiment.Finally,the performance of the proposed system is evaluated under several State-Of-The-Art(SOTA)YOLOv5 and SISR models.Results show that our system can achieve a tremendous computation improvement in image SR.

3D-CNNHSR: A 3-Dimensional Convolutional Neural Network for Hyperspectral Super-Resolution

Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited by the high computing requirements.The spatial resolution of HSI can be enhanced by utilizing Deep Learning(DL)based Super-resolution(SR).A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI,without losing the spectral content.The 3DCNNHSR model was tested for the Hyperion HSI.The pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the errors.The key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral features.By clustering contiguous spectral content together,a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is realized.The 3D-CNNHSR model was compared with a 2D-CNN model,additionally,the assessment was based on higherresolution data from the Sentinel-2 satellite.Based on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed,which is significantly less than previous studies.

Spatiotemporal changes of biogenic elements in the Changjiang River Estuary and adjacent waters in summer over the last decade

The long-term spatiotemporal changes of surface biogenic elements in the Changjiang River Estuary and adjacent waters during the summer of 2008–2016 were analyzed in this study.The concentrations of dissolved inorganic nitrogen(DIN),soluble reactive phosphate(PO_(4)^(3−))and silicate(SiO_(3)^(2−))were generally stable,with a slight decrease of DIN and PO_(4)^(3−),and a slight increase of SiO_(3)^(2−),which mainly occurred in the estuarine waters.The grey correlation analysis was carried out between biogenic elements and chlorophyll a(Chl-a).Results showed that compared with the absolute values of biogenic elements,the correlations between the concentration ratio of nitrogen to phosphorus(N/P),ratio of silicon to nitrogen(Si/N)and Chl-a were closer,indicating the important influence on phytoplankton by the structure of biogenic elements.The study area was generally in a state of potential P limitation,and could have potential impact on the phytoplankton community,triggering the shift of red tide dominant species from diatoms to dinoflagellates.