Simultaneous Localization and Mapping(SLAM)is the foundation of autonomous navigation for unmanned systems.The existing SLAM solutions are mainly divided into the visual SLAM(vSLAM)equipped with camera and the lidar S...Simultaneous Localization and Mapping(SLAM)is the foundation of autonomous navigation for unmanned systems.The existing SLAM solutions are mainly divided into the visual SLAM(vSLAM)equipped with camera and the lidar SLAM equipped with lidar.However,pure visual SLAM have shortcomings such as low positioning accuracy,the paper proposes a visual-inertial information fusion SLAM based on Runge-Kutta improved pre-integration.First,the Inertial Measurement Unit(IMU)information between two adjacent keyframes is pre-integrated at the front-end to provide IMU constraints for visual-inertial information fusion.In particular,to improve the accuracy in pre-integration,the paper uses the RungeKutta algorithm instead of Euler integral to calculate the pre-integration value at the next moment.Then,the IMU pre-integration value is used as the initial value of the system state at the current frame time.We combine the visual reprojection error and imu pre-integration error to optimize the state variables such as speed and pose,and restore map points’three-dimensional coordinates.Finally,we set a sliding window to optimize map points’coordinates and state variables.The experimental part is divided into dataset experiment and complex indoor-environment experiment.The results show that compared with pure visual SLAM and the existing visual-inertial fusion SLAM,our method has higher positioning accuracy.展开更多
Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosph...Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.展开更多
The Soft X-ray Imager(SXI)is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.SMILE is a joint science mission between the European Space Agency(ESA)and the Chinese...The Soft X-ray Imager(SXI)is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.SMILE is a joint science mission between the European Space Agency(ESA)and the Chinese Academy of Sciences(CAS)and is due for launch in 2025.SXI is a compact X-ray telescope with a wide field-of-view(FOV)capable of encompassing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit.SXI is sensitive to the soft X-rays produced by the Solar Wind Charge eXchange(SWCX)process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere.SWCX provides a mechanism for boundary detection within the magnetosphere,such as the position of Earth’s magnetopause,because the solar wind heavy ions have a very low density in regions of closed magnetic field lines.The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours.SXI is led by the University of Leicester in the United Kingdom(UK)with collaborating organisations on hardware,software and science support within the UK,Europe,China and the United States.展开更多
Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)t...Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the CTP.However,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent applications.This study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)model.LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP.Additionally,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COT<10 to improve the algorithm's stability.The synergistic method was then applied to the directional polarized camera(DPC)and Polarized and Directionality of the Earth’s Reflectance(POLDER)measurements for evaluation,and the resulting retrieval accuracy of the POLDER-based measurements(RMSEPOLDER=205.176 hPa,RMSEDPC=171.141 hPa,R^(2)POLDER=0.636,R^(2)DPC=0.663,respectively)were higher than that of the MODIS and POLDER Rayleigh pressure measurements.The synergistic algorithm also showed good performance with the application of DPC data.This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.展开更多
Grasslands in northern China serve the country as both an ecological barrier and a livestock production base.There,installing enclosures has been becoming the major grassland restoration measure adopted by many local ...Grasslands in northern China serve the country as both an ecological barrier and a livestock production base.There,installing enclosures has been becoming the major grassland restoration measure adopted by many local governments.However,the effects of restoration on both ecological and production benefits of grassland remain unclear for implemented grassland restoration policies.Therefore,a representative rangeland in northern China,the Maodeng pasture in Inner Mongolia Autonomous Region was selected as the study area,and remote sensing monitoring analyses were carried out to quantify the ecological benefits and economic benefits from 2015 to 2021.The results showed that:1) in terms of ecological benefits,the grassland area with a grassland coverage rate of more than 60% accounts for 32.3% of the regional area,and 86.4% of its grassland grew significantly better than the same period in2015,showing a significant improvement in grassland growth.Using the average amount of carbon per unit area as the ecological benefit evaluation index,it increased by 27.1% to 32.48Tg C/yr from 2015 to 2021.2) In terms of economic benefits,both theoretical grass production and livestock carrying capacity increased from 2015 to 2021.Compared to 2015,the theoretical grass production in 2021 increased by 24.8% to 71 900 t.The livestock carrying capacity reached 52 100 sheep units in 2021,nearly 11 000 sheep units more than that in 2015.During the study period,multiple economic indicators(on a per capita basis of permanent residents) for the pastoral area of Xilinhot City to which the Maodeng pasture belongs,have grown steadily.Per capita total income rose from 29 630 yuan(RMB) in2015 to 62 859 yuan(RMB) in 2021.Relying on grassland resources to develop the pastoral ecology also broadens the potential economic development space.Overall,the establishment of the reserve and the experiment of implanting an enclosure policy have had a significant and positive impact on Maodeng pasture’s development from both an ecological and economic perspective.With the support of scientific evidence,enclosure policy can be extended to more than 110 000 km~2 of grasslands in northern China with similar precipitation and temperature conditions,enhancing the productive and ecological potential of grasslands.The above research results will contribute to the scientific formulation of grassland pasture quality improvement plans in northern China.展开更多
In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)prototype.The LUCCN primarily consists of two categories of instruments...In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)prototype.The LUCCN primarily consists of two categories of instruments,including ground-based and UAV-based in-situ measurement.We use the GMP343,a low-cost non-dispersive infrared sensor,in both ground-based and UAV-based instruments.The first integrated measurement campaign took place in Shenzhen,China,4 May 2023.During the campaign,we found that LUCCN’s UAV component presented significant data-collecting advantages over its ground-based counterpart owing to the relatively high altitudes of the point emission sources,which was especially obvious at a gas power plant in Shenzhen.The emission flux was calculated by a crosssectional flux(CSF)method,the results of which differed from the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC).The CSF result was slightly larger than others because of the low sampling rate of the whole emission cross section.The LUCCN system will be applied in future carbon monitoring campaigns to increase the spatiotemporal coverage of carbon emission information,especially in scenarios involving the detection of smaller-scale,rapidly varying sources and sinks.展开更多
Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small...Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.展开更多
Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.I...Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.展开更多
The study of extreme weather and space events has gained paramount importance in modern society owing to rapid advances in high technology.Understanding and describing exceptional occurrences plays a crucial role in m...The study of extreme weather and space events has gained paramount importance in modern society owing to rapid advances in high technology.Understanding and describing exceptional occurrences plays a crucial role in making decisive assessments of their potential impact on technical,economic,and social aspects in various fields.This research focuses on analyzing the hourly values of the auroral electrojet(AE)geomagnetic index from 1957 to 2019 by using the peak over threshold method in extreme value theory.By fitting the generalized Pareto distribution to extreme AE values,shape parameter indices were derived,revealing negative values that establish an upper bound for this time series.Consequently,it became evident that the AE values had reached a plateau,suggesting that extreme events exceeding the established upper limit are rare.As a result,although the need for diligent precautions to mitigate the consequences of such extreme events persists,surpassing the upper limit of AE values becomes increasingly challenging.It is also possible to observe an aurora in the middle-and low-latitude regions during the maximum period of the AE index.展开更多
A reasonable heat treatment process for TC4 ELI titanium alloy is crucial to tune microstructures to improve its explosion resistance.However,there is limited investigation on tuning microstructures of TC4 ELI to impr...A reasonable heat treatment process for TC4 ELI titanium alloy is crucial to tune microstructures to improve its explosion resistance.However,there is limited investigation on tuning microstructures of TC4 ELI to improve explosion resistance.Moreover,the current challenge is quantifying microstructural changes'effects on explosion resistance and incorporating microstructural changes into finite element models.This work aims to tune microstructures to improve explosion resistance and elucidate their anti-explosion mechanism,and find a suitable method to incorporate microstructural changes into finite element models.In this work,we systematically study the deformation and failure characteristics of TC4 ELI plates with varying microstructures using an air explosion test and LS-DYNA finite element modeling.The Johnson-Cook(JC)constitutive parameters are used to quantify the effects of microstructural changes on explosion resistance and incorporate microstructural changes into finite element models.Because of the heat treatment,one plate has equiaxed microstructure and the other has bimodal microstructure.The convex of the plate after the explosion has a quadratic relationship with the charge mass,and the simulation results demonstrate high reliability,with the error less than 17.5%.Therefore,it is feasible to obtain corresponding JC constitutive parameters based on the differences in microstructures and mechanical properties and characterize the effects of microstructural changes on explosion resistance.The bimodal target exhibits excellent deformation resistance.The response of bimodal microstructure to the shock wave may be more intense under explosive loading.The well-coordinated structure of the bimodal target enhances its resistance to deformation.展开更多
Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and c...Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.展开更多
The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions.With advancement of remote sensing and better unde...The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions.With advancement of remote sensing and better understanding of frozen soil dynamics,discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change.However,as an important data source of frozen soil processes,remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes.Although great progress has been made in remote sensing and frozen soil physics,yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies.In the present study,a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed.In order to reduce the uncertainty of the simulation,the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation.The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau.The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%.These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study.The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory.The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil.The average accuracy increased by about 5%after integrating remotely sensed information on the surface soil.The simulation accuracy was significantly improved,especially in transition periods between freezing and thawing of the surface soil.展开更多
Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanism...Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.展开更多
Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and incre...Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and increased greenhouse gas emissions.In response to the current lack of studies combining drought conditions and soil erosion processes,in this study,we developed a comprehensive Geographic Information System(GIS)-based approach to assess soil erosion and droughts,thereby revealing the relationship between soil erosion and droughts under an arid climate.The vegetation condition index(VCI)and temperature condition index(TCI)derived respectively from the enhanced vegetation index(EVI)MOD13A2 and land surface temperature(LST)MOD11A2 products were combined to generate the vegetation health index(VHI).The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed,southeastern Tunisia.The revised universal soil loss equation(RUSLE)model was applied to quantitatively estimate soil erosion.The relationship between soil erosion and droughts was investigated through Pearson correlation.Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016.The average soil erosion rate was determined to be 1.8 t/(hm2•a).The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts.The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion.The relationship between droughts and soil erosion had a positive correlation(r=0.3);however,the correlation was highly varied spatially across the watershed.Drought was linked to soil erosion in the Negueb watershed.The current study provides insight for natural disaster risk assessment,land managers,and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.展开更多
Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled t...Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.展开更多
Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images.However,as more and more remote sensing data is available,it is a new challenge to comprehensively u...Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images.However,as more and more remote sensing data is available,it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation.In addition,semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation,but existing methods usually study individual tasks separately,which leads to high computational resource overhead.To this end,we propose a Multi-Task learning framework for Multi-Modal remote sensing images(MM_MT).Specifically,we design a Cross-Modal Feature Fusion(CMFF)method,which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation.Besides,a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation(JSSHE),extracting common features in a shared network to save time and resources,and then learning task-specific features in two task branches.Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently,multi-task learning saves 20%of training time and achieves competitive performance with mIoU of 83.02%for semantic segmentation and accuracy of 95.26%for height estimation.展开更多
The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield base...The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.展开更多
Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected ...Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected to improve by removing these components.However,their infl uence in GPP estimations has not been quantitatively evaluated for deciduous forests.Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components(FAPAR_(green))for partitioning APAR green(photosynthetically active radiation absorbed by photosynthetic components).In this study,the enhanced vegetation index(EVI)estimated FAPAR_(green)and to separate the photosynthetically active radiation absorbed by photosynthetic components(APAR green)from total APAR observations(APAR_(total))at two deciduous forest sites.The eddy covariance-light use effi ciency(EC-LUE)algorithm was employed to evaluate the infl uence of non-photosynthetic components and to test the performance of APAR green in GPP estimation.The results show that the infl uence of non-photosynthetic components have a seasonal pattern at deciduous forest sites,large diff erences are observed with normalized root mean square error(RMSE*)values of APAR green-based GPP and APAR_(total)-based GPP between tower-based GPP during the early and end stages,while slight diff erences occurred during peak growth seasons.In addition,daily GPP estimation was significantly improved using the APAR green-based method,giving a higher coeffi cient of determination and lower normalized root mean square error against the GPP estimated by the APAR_(total)-based method.The results demonstrate the signifi cance of partitioning APAR green from APAR_(total)for accurate GPP estimation in deciduous forests.展开更多
The change in the ecological environment in the arid core area is a critical issue in the context of global warming.To study the paleoclimate evolution,precise identification of minerals deposited in Asia’s arid hint...The change in the ecological environment in the arid core area is a critical issue in the context of global warming.To study the paleoclimate evolution,precise identification of minerals deposited in Asia’s arid hinterland,Lop Nur Salt Lake,NW China was conducted.The hyperspectral data of the salt crust was sampled to identify the species and content of sedimentary minerals,and the multispectral photos were used to reconstruct the salt crust morphology using the unmanned aerial vehicles platform.The SUnSAL(sparse unmixing by variable splitting and augmented Lagrangian)method was employed to inverse the sedimentary mineral components along the shoreline.The heterogeneity of salt and clay minerals in bright and dark ear-shaped strips was evaluated.The paleoclimatic environment associated with salt lake extinction was reconstructed by analyzing paleoclimate records of sediments,spectral reflectance and morphology of the salt crust.Results show that:(1)the variations in the micro-geomorphology of the salt crust are obviously the reason for the formation of bright and dark ear-shaped strips and the differences in the species and relative content of the sedimentary minerals are the microscopic reason.The high ratio of sedimentary salt minerals to clay minerals(RS/C)contributes to the high reflectivity,and the salt crust presents a bright texture.The low RS/C results in the low reflectivity,salt crust presents a dark texture;(2)the bright and dark ear-shaped strips represent warm-arid and cold-humid climates.The shape of the Lop Nur Lake shoreline evolved due to alternating warm-dry and cold-humid paleoclimate changes.展开更多
Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evalua...Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.展开更多
基金supported by the China Postdoctoral Science Foundation under Grant 2019M653870XBNational Natural Science Foundation of Shanxi Province under Grants No.2020GY-003 and 2021GY-036+1 种基金National Natural Science Foundation of China under Grants 62001340Fundamental Research Funds for the Central Universities,China,XJS211306 and JC2007
文摘Simultaneous Localization and Mapping(SLAM)is the foundation of autonomous navigation for unmanned systems.The existing SLAM solutions are mainly divided into the visual SLAM(vSLAM)equipped with camera and the lidar SLAM equipped with lidar.However,pure visual SLAM have shortcomings such as low positioning accuracy,the paper proposes a visual-inertial information fusion SLAM based on Runge-Kutta improved pre-integration.First,the Inertial Measurement Unit(IMU)information between two adjacent keyframes is pre-integrated at the front-end to provide IMU constraints for visual-inertial information fusion.In particular,to improve the accuracy in pre-integration,the paper uses the RungeKutta algorithm instead of Euler integral to calculate the pre-integration value at the next moment.Then,the IMU pre-integration value is used as the initial value of the system state at the current frame time.We combine the visual reprojection error and imu pre-integration error to optimize the state variables such as speed and pose,and restore map points’three-dimensional coordinates.Finally,we set a sliding window to optimize map points’coordinates and state variables.The experimental part is divided into dataset experiment and complex indoor-environment experiment.The results show that compared with pure visual SLAM and the existing visual-inertial fusion SLAM,our method has higher positioning accuracy.
基金supported by the National Natural Science Foundation of China(Grant Nos.42322408,42188101,41974211,and 42074202)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDJ-SSW-JSC028)+1 种基金the Strategic Priority Program on Space Science,Chinese Academy of Sciences(Grant Nos.XDA15052500,XDA15350201,and XDA15014800)supported by the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y202045)。
文摘Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.
基金funding and support from the United Kingdom Space Agency(UKSA)the European Space Agency(ESA)+5 种基金funded and supported through the ESA PRODEX schemefunded through PRODEX PEA 4000123238the Research Council of Norway grant 223252funded by Spanish MCIN/AEI/10.13039/501100011033 grant PID2019-107061GB-C61funding and support from the Chinese Academy of Sciences(CAS)funding and support from the National Aeronautics and Space Administration(NASA)。
文摘The Soft X-ray Imager(SXI)is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.SMILE is a joint science mission between the European Space Agency(ESA)and the Chinese Academy of Sciences(CAS)and is due for launch in 2025.SXI is a compact X-ray telescope with a wide field-of-view(FOV)capable of encompassing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit.SXI is sensitive to the soft X-rays produced by the Solar Wind Charge eXchange(SWCX)process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere.SWCX provides a mechanism for boundary detection within the magnetosphere,such as the position of Earth’s magnetopause,because the solar wind heavy ions have a very low density in regions of closed magnetic field lines.The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours.SXI is led by the University of Leicester in the United Kingdom(UK)with collaborating organisations on hardware,software and science support within the UK,Europe,China and the United States.
基金the National Natural Science Foundation of China(Grant Nos.42025504,No.41905023)National Natural Science Youth Science Foundation(Grant No.41701406)Youth Innovation Promotion Association of Chinese Academy of Sciences(Grant No.:2021122).
文摘Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the CTP.However,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent applications.This study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)model.LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP.Additionally,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COT<10 to improve the algorithm's stability.The synergistic method was then applied to the directional polarized camera(DPC)and Polarized and Directionality of the Earth’s Reflectance(POLDER)measurements for evaluation,and the resulting retrieval accuracy of the POLDER-based measurements(RMSEPOLDER=205.176 hPa,RMSEDPC=171.141 hPa,R^(2)POLDER=0.636,R^(2)DPC=0.663,respectively)were higher than that of the MODIS and POLDER Rayleigh pressure measurements.The synergistic algorithm also showed good performance with the application of DPC data.This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.
基金Under the auspices of the Inner Mongolia Autonomous Region Science and Technology Achievement Transformation Special Project(No.2020CG0123)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA26050301-01)。
文摘Grasslands in northern China serve the country as both an ecological barrier and a livestock production base.There,installing enclosures has been becoming the major grassland restoration measure adopted by many local governments.However,the effects of restoration on both ecological and production benefits of grassland remain unclear for implemented grassland restoration policies.Therefore,a representative rangeland in northern China,the Maodeng pasture in Inner Mongolia Autonomous Region was selected as the study area,and remote sensing monitoring analyses were carried out to quantify the ecological benefits and economic benefits from 2015 to 2021.The results showed that:1) in terms of ecological benefits,the grassland area with a grassland coverage rate of more than 60% accounts for 32.3% of the regional area,and 86.4% of its grassland grew significantly better than the same period in2015,showing a significant improvement in grassland growth.Using the average amount of carbon per unit area as the ecological benefit evaluation index,it increased by 27.1% to 32.48Tg C/yr from 2015 to 2021.2) In terms of economic benefits,both theoretical grass production and livestock carrying capacity increased from 2015 to 2021.Compared to 2015,the theoretical grass production in 2021 increased by 24.8% to 71 900 t.The livestock carrying capacity reached 52 100 sheep units in 2021,nearly 11 000 sheep units more than that in 2015.During the study period,multiple economic indicators(on a per capita basis of permanent residents) for the pastoral area of Xilinhot City to which the Maodeng pasture belongs,have grown steadily.Per capita total income rose from 29 630 yuan(RMB) in2015 to 62 859 yuan(RMB) in 2021.Relying on grassland resources to develop the pastoral ecology also broadens the potential economic development space.Overall,the establishment of the reserve and the experiment of implanting an enclosure policy have had a significant and positive impact on Maodeng pasture’s development from both an ecological and economic perspective.With the support of scientific evidence,enclosure policy can be extended to more than 110 000 km~2 of grasslands in northern China with similar precipitation and temperature conditions,enhancing the productive and ecological potential of grasslands.The above research results will contribute to the scientific formulation of grassland pasture quality improvement plans in northern China.
基金supported by the National Key Research and Development Plan(Grant No.2021YFB3901000)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(YSBR-037)+2 种基金the International Partnership Program of the Chinese Academy of Sciences(060GJHZ2022070MI)the MOST-ESA Dragon-5 Programme for Monitoring Greenhouse Gases from Space(ID.59355)the Finland–China Mobility Cooperation Project funded by the Academy of Finland(No.348596)。
文摘In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)prototype.The LUCCN primarily consists of two categories of instruments,including ground-based and UAV-based in-situ measurement.We use the GMP343,a low-cost non-dispersive infrared sensor,in both ground-based and UAV-based instruments.The first integrated measurement campaign took place in Shenzhen,China,4 May 2023.During the campaign,we found that LUCCN’s UAV component presented significant data-collecting advantages over its ground-based counterpart owing to the relatively high altitudes of the point emission sources,which was especially obvious at a gas power plant in Shenzhen.The emission flux was calculated by a crosssectional flux(CSF)method,the results of which differed from the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC).The CSF result was slightly larger than others because of the low sampling rate of the whole emission cross section.The LUCCN system will be applied in future carbon monitoring campaigns to increase the spatiotemporal coverage of carbon emission information,especially in scenarios involving the detection of smaller-scale,rapidly varying sources and sinks.
文摘Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.
文摘Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.
文摘The study of extreme weather and space events has gained paramount importance in modern society owing to rapid advances in high technology.Understanding and describing exceptional occurrences plays a crucial role in making decisive assessments of their potential impact on technical,economic,and social aspects in various fields.This research focuses on analyzing the hourly values of the auroral electrojet(AE)geomagnetic index from 1957 to 2019 by using the peak over threshold method in extreme value theory.By fitting the generalized Pareto distribution to extreme AE values,shape parameter indices were derived,revealing negative values that establish an upper bound for this time series.Consequently,it became evident that the AE values had reached a plateau,suggesting that extreme events exceeding the established upper limit are rare.As a result,although the need for diligent precautions to mitigate the consequences of such extreme events persists,surpassing the upper limit of AE values becomes increasingly challenging.It is also possible to observe an aurora in the middle-and low-latitude regions during the maximum period of the AE index.
基金National Key Laboratory of Science and Technology on Materials under Shock and Impact(Grant No.WDZC2022-4)to provide fund for conducting experiments。
文摘A reasonable heat treatment process for TC4 ELI titanium alloy is crucial to tune microstructures to improve its explosion resistance.However,there is limited investigation on tuning microstructures of TC4 ELI to improve explosion resistance.Moreover,the current challenge is quantifying microstructural changes'effects on explosion resistance and incorporating microstructural changes into finite element models.This work aims to tune microstructures to improve explosion resistance and elucidate their anti-explosion mechanism,and find a suitable method to incorporate microstructural changes into finite element models.In this work,we systematically study the deformation and failure characteristics of TC4 ELI plates with varying microstructures using an air explosion test and LS-DYNA finite element modeling.The Johnson-Cook(JC)constitutive parameters are used to quantify the effects of microstructural changes on explosion resistance and incorporate microstructural changes into finite element models.Because of the heat treatment,one plate has equiaxed microstructure and the other has bimodal microstructure.The convex of the plate after the explosion has a quadratic relationship with the charge mass,and the simulation results demonstrate high reliability,with the error less than 17.5%.Therefore,it is feasible to obtain corresponding JC constitutive parameters based on the differences in microstructures and mechanical properties and characterize the effects of microstructural changes on explosion resistance.The bimodal target exhibits excellent deformation resistance.The response of bimodal microstructure to the shock wave may be more intense under explosive loading.The well-coordinated structure of the bimodal target enhances its resistance to deformation.
基金This study was supported by the National Natural Science Foundation of China(42271396)the Natural Science Foundation of Shandong Province(ZR2022MD017)+1 种基金the Key R&D Project of Hebei Province(22326406D)The European Space Agency(ESA)and Ministry of Science and Technology of China(MOST)Dragon(57457).
文摘Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.
基金This work was supported by the National Key R&D Program of(Grant No.2016YFA0602302).
文摘The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions.With advancement of remote sensing and better understanding of frozen soil dynamics,discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change.However,as an important data source of frozen soil processes,remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes.Although great progress has been made in remote sensing and frozen soil physics,yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies.In the present study,a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed.In order to reduce the uncertainty of the simulation,the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation.The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau.The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%.These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study.The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory.The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil.The average accuracy increased by about 5%after integrating remotely sensed information on the surface soil.The simulation accuracy was significantly improved,especially in transition periods between freezing and thawing of the surface soil.
基金This work was supported by grants from the National Natural Science Foundation of China(42101306,4217107)the Natural Science Foundation of Shandong Province(ZR2021MD047),the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA2002040203)+2 种基金the Open Fund of the Key Laboratory of National Geographic Census and Monitoring,Ministry of Natural Resources(MNR)(2020NGCM02)the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(KF-2020-05-001)the Major Project of the High Resolution Earth Observation System of China(GFZX0404130304).
文摘Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.
基金Chinese Academy of Sciences (CAS)The World Academy of Science (TWAS) for providing financial support
文摘Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and increased greenhouse gas emissions.In response to the current lack of studies combining drought conditions and soil erosion processes,in this study,we developed a comprehensive Geographic Information System(GIS)-based approach to assess soil erosion and droughts,thereby revealing the relationship between soil erosion and droughts under an arid climate.The vegetation condition index(VCI)and temperature condition index(TCI)derived respectively from the enhanced vegetation index(EVI)MOD13A2 and land surface temperature(LST)MOD11A2 products were combined to generate the vegetation health index(VHI).The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed,southeastern Tunisia.The revised universal soil loss equation(RUSLE)model was applied to quantitatively estimate soil erosion.The relationship between soil erosion and droughts was investigated through Pearson correlation.Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016.The average soil erosion rate was determined to be 1.8 t/(hm2•a).The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts.The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion.The relationship between droughts and soil erosion had a positive correlation(r=0.3);however,the correlation was highly varied spatially across the watershed.Drought was linked to soil erosion in the Negueb watershed.The current study provides insight for natural disaster risk assessment,land managers,and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.
基金the Shandong Key Research and Development Project,China(2018GNC110025)the National Natural Science Foundation of China(41871253)+2 种基金the Central Guiding Local Science and Technology Development Fund of Shandong—Yellow River Basin Collaborative Science and Technology Innovation Special Project,China(YDZX2023019)the Natural Science Foundation of Shandong Province,China(ZR2020QD016)the“Taishan Scholar”Project of Shandong Province,China(TSXZ201712)。
文摘Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.
基金National Key R&D Program of China(No.2022ZD0118401).
文摘Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images.However,as more and more remote sensing data is available,it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation.In addition,semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation,but existing methods usually study individual tasks separately,which leads to high computational resource overhead.To this end,we propose a Multi-Task learning framework for Multi-Modal remote sensing images(MM_MT).Specifically,we design a Cross-Modal Feature Fusion(CMFF)method,which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation.Besides,a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation(JSSHE),extracting common features in a shared network to save time and resources,and then learning task-specific features in two task branches.Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently,multi-task learning saves 20%of training time and achieves competitive performance with mIoU of 83.02%for semantic segmentation and accuracy of 95.26%for height estimation.
基金supported by the Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII)。
文摘The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.
基金funded by Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(No.CBAS2022IRP01)the National Earth System Science Data Sharing Infrastructure(No.2005DKA32300)the National Natural Science Foundation of China(No.41825002).
文摘Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected to improve by removing these components.However,their infl uence in GPP estimations has not been quantitatively evaluated for deciduous forests.Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components(FAPAR_(green))for partitioning APAR green(photosynthetically active radiation absorbed by photosynthetic components).In this study,the enhanced vegetation index(EVI)estimated FAPAR_(green)and to separate the photosynthetically active radiation absorbed by photosynthetic components(APAR green)from total APAR observations(APAR_(total))at two deciduous forest sites.The eddy covariance-light use effi ciency(EC-LUE)algorithm was employed to evaluate the infl uence of non-photosynthetic components and to test the performance of APAR green in GPP estimation.The results show that the infl uence of non-photosynthetic components have a seasonal pattern at deciduous forest sites,large diff erences are observed with normalized root mean square error(RMSE*)values of APAR green-based GPP and APAR_(total)-based GPP between tower-based GPP during the early and end stages,while slight diff erences occurred during peak growth seasons.In addition,daily GPP estimation was significantly improved using the APAR green-based method,giving a higher coeffi cient of determination and lower normalized root mean square error against the GPP estimated by the APAR_(total)-based method.The results demonstrate the signifi cance of partitioning APAR green from APAR_(total)for accurate GPP estimation in deciduous forests.
基金Supported by the National Natural Science Foundation of China(Nos.42071313,41571363)the Science and Technology Project for Black Soil Granary(No.XDA28080500)the Scientific Investigation of Natural and Cultural Heritage of Lop Nur Region(No.2014FY210500)。
文摘The change in the ecological environment in the arid core area is a critical issue in the context of global warming.To study the paleoclimate evolution,precise identification of minerals deposited in Asia’s arid hinterland,Lop Nur Salt Lake,NW China was conducted.The hyperspectral data of the salt crust was sampled to identify the species and content of sedimentary minerals,and the multispectral photos were used to reconstruct the salt crust morphology using the unmanned aerial vehicles platform.The SUnSAL(sparse unmixing by variable splitting and augmented Lagrangian)method was employed to inverse the sedimentary mineral components along the shoreline.The heterogeneity of salt and clay minerals in bright and dark ear-shaped strips was evaluated.The paleoclimatic environment associated with salt lake extinction was reconstructed by analyzing paleoclimate records of sediments,spectral reflectance and morphology of the salt crust.Results show that:(1)the variations in the micro-geomorphology of the salt crust are obviously the reason for the formation of bright and dark ear-shaped strips and the differences in the species and relative content of the sedimentary minerals are the microscopic reason.The high ratio of sedimentary salt minerals to clay minerals(RS/C)contributes to the high reflectivity,and the salt crust presents a bright texture.The low RS/C results in the low reflectivity,salt crust presents a dark texture;(2)the bright and dark ear-shaped strips represent warm-arid and cold-humid climates.The shape of the Lop Nur Lake shoreline evolved due to alternating warm-dry and cold-humid paleoclimate changes.
基金funded by Key Research Program of Frontier Sciences,CAS(ZDBS-LY-DQC012)the National Natural Science Foundation of China(41971321,41830108)+2 种基金XPCC Science and Technology Project(2022CB002-01)Open Fund of Key Laboratory of Oasis Eco-agriculture,XPCC(201801 and 202003)supported by Youth Innovation Promotion Association,CAS(Y2021047)。
文摘Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.
