36 results about "Vegetation canopy" patented technology

The invention discloses a forestry restoration vegetation state identification method and system based on image identification. The method comprises the following steps: obtaining a standard image sequence; segmenting the visible light image through an improved SegFormer network to obtain vegetation areas of different scales, and calculating vegetation canopy temperature distribution characteristics based on the thermal infrared image; extracting vegetation index features based on the multispectral image to obtain multi-scale feature data; according to the multi-scale feature data, utilizing an improved GAT model to process a vegetation spatial relationship graph, learning a spatial dependency relationship between vegetation areas through a multi-head attention mechanism, and generating a vegetation feature vector containing spatial information; and classifying the vegetation states by using an improved multi-scale fusion classifier in combination with the vegetation feature vectors, outputting a vegetation recovery index, and classifying the forestry restoration vegetation states according to the vegetation recovery index. The whole forestry restoration monitoring process is automatic, the monitoring efficiency is improved, and the labor cost is greatly reduced.

The application discloses a kind of based on spectrum-process-terrain coordination's farmland low-lying quick extraction method of waterlogging, comprising the following steps: step one, satellite remote sensing image acquisition and pretreatment;Step two, waterlogging area grading mark;Step three, process quantity extraction;Step four, red edge area index calculation;Step five, terrain factor processing and feature combination;Step six, model training and optimal feature subset screening;The application is by constructing bare soil period soil moisture variation process quantity and growth period crop growth change process quantity, accurately depict the evolution track of waterlogging with growth stage, break through the limitation that single phase cannot reflect process difference;By constructing red edge area index, enhance the spectrum difference of waterlogging, distinguish bare soil period easily confused ground class, avoid growth period vegetation canopy shielding interference;Through terrain factor, reflect the influence of topography on water depth, holding water time length, improve the identification accuracy of waterlogging spatial heterogeneity;By using random forest algorithm, improve the accuracy of waterlogging grading.

The invention discloses a crop nitrogen nutrition monitoring and diagnosis method and device based on a multispectral remote sensing image, a medium and a product, and relates to the technical field of modern agricultural intelligence, and the method comprises the steps: determining a multispectral reflectivity image of a to-be-detected region based on multispectral remote sensing images of the to-be-detected region and a target radiation correction plate; determining a soil adjustment vegetation index grid map based on the multispectral reflectivity image so as to determine a vegetation canopy area; determining a near infrared-red edge index grid map based on the reflectivity grid map of the red edge wave band and the reflectivity grid map of the near infrared wave band; determining canopy coverage according to the pixel number of the vegetation canopy area and the pixel number of the multispectral remote sensing image; inputting the canopy coverage into a nitrogen nutrition diagnosis model to obtain an optimal near infrared-red edge index confidence interval; and determining a crop nitrogen nutrition deficiency distribution map based on the near infrared-red edge index grid map and the optimal near infrared-red edge index confidence interval. According to the invention, large-scale crop nitrogen nutrition monitoring and diagnosis are realized.

The invention relates to a leaf area index time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; calculating a suburb forest leaf area index LAI in the research area and performing time sequence sorting; calculating the economic forest leaf area index LAI of the research area and performing time sequence sorting; performing seasonal decomposition on the suburb forest time sequence data after adaptive filtering; performing seasonal decomposition on the economic forest time series data after adaptive filtering; merging the suburb forest time sequence data and the economic forest time sequence data; and outputting to obtain final optimized data. The invention further relates to a leaf area index time sequence processing system. According to the invention, a purification LAI time sequence product which clearly represents long-term trend, mutation and gradual change signals of respective vegetation canopy structures can be output.

The present application provides a high-vegetation-covered area target mining area range prediction method, device and electronic equipment, comprising: acquiring hyperspectral data and laser radar point cloud data of a high-vegetation-covered research area, and registering; performing point cloud data layering on the point cloud data; manufacturing each layer of point cloud data into a height interval mask; performing spatial layering on the hyperspectral data based on the height interval mask, and extracting a hyperspectral image of a vegetation canopy bottom; extracting stressed vegetation according to abnormal spectral information, and manufacturing a sample; dividing all samples into a training set and a verification set according to a proportion; constructing a high-vegetation-covered area target mining area range prediction model based on deep learning DeeplabV3+; performing training and verification of the prediction model through the training set and the verification set respectively; acquiring a hyperspectral image of a target high-vegetation-covered area, and predicting the target mining area range in the target high-vegetation-covered area through the trained prediction model. The present application can efficiently and accurately realize high-vegetation-covered target mining area range prediction.

The application provides a city surface area classification method and related equipment. The city surface area classification method comprises: obtaining a first target parameter of a to-be-determined city surface area, the first target parameter comprising a characteristic building height, an average building height, a building height standard deviation, a building coverage rate, an impervious surface ratio, a characteristic vegetation canopy height, an average vegetation canopy height, and a vegetation coverage rate of the to-be-determined city surface area; inputting an electronic map corresponding to the to-be-determined city surface area and the first target parameter into a city surface area classification model to obtain a city surface area type corresponding to the to-be-determined city surface area output by the city surface area classification model, the city surface area type being used for analysis or simulation of heat waves, air quality, and extreme weather of the to-be-determined city surface area. In the application, the fineness and determination accuracy of the city surface area type are improved.

The application discloses a canopy coverage considering aggregation index inversion method and correction method, relates to the technical field of remote sensing image processing and aggregation index inversion, collects remote sensing images of a vegetation canopy, extracts hot spot reflectivity and dark spot reflectivity respectively, calculates a normalized hot spot and dark spot index NDHD, calculates an initial value of an aggregation index CI according to a linear relationship between the NDHD and the CI, and corrects the initial value of the CI by using a canopy coverage Ccan, wherein an error between the initial value of the CI and a real value of the CI has a linear relationship with the Ccan. The application first establishes a CI correction model based on the Ccan, and solves the deviation problem of a traditional algorithm in a sample field with high or low Ccan. By improving a mainstream CI inversion algorithm based on the NDHD, the application significantly improves the precision of CI inversion, and provides more reliable technical support for forest ecological monitoring, carbon cycle research and remote sensing application.

This invention discloses a non-prior detection method and device for targets obscured by vegetation canopy based on UAV multispectral point clouds, belonging to the field of three-dimensional target detection technology in remote sensing. To address the problem of insufficient target samples due to vegetation canopy obstruction in target detection methods based on multispectral point cloud data, this invention first clusters non-ground points in the spectral point cloud, statistically analyzes the circumradius of each cluster, and extracts multi-scale spatial features of the multispectral point cloud based on the clustering results, constructing a spatial-spectral feature profile. Then, it constructs a radius-adaptive spherical shell model, obtains the local background point set within the spherical shell corresponding to any point, and further analyzes the p... i The spatial-spectral features are jointly expressed, and the residuals between all expressed results and the original features are calculated, thereby realizing the detection of occluded targets.

The application discloses a kind of estimation method and system of vegetation canopy combustible moisture content based on meteorology and remote sensing data, comprising the following steps: first, combustible moisture content and various meteorological data and remote sensing data and other several kinds of subsidiary data are selected as combustible moisture content estimation data.Meteorological data includes air temperature, relative humidity, rainfall and wind speed.Remote sensing data includes two vegetation indexes: enhanced vegetation index and normalized vegetation index.Subsidiary data includes: root zone soil moisture, vapor pressure difference, drought index, fire weather factor.Then the long time sequence characteristics of meteorological data are extracted.The size of time window is determined first, and the experimental results show that the correlation coefficient of most sites is relatively high under the time window of 90-210 days.90 days, 150 days and 210 days of time window are selected respectively to extract the time characteristics of four kinds of meteorological data.Secondly, the samples of experimental area are divided into five vegetation classifications, which are closed shrub, sparse shrub, multi-tree tropical grassland, tropical savanna and grassland.Finally, the data sets of the five different vegetation types are sequentially adjusted to obtain the respective estimation model.

The application relates to the technical field of vegetation canopy water content inversion, and provides a vegetation canopy water content inversion method, device, equipment, product and medium. The method comprises the following steps: inputting a candidate vegetation data set into a vegetation radiation transmission model, taking the minimum loss function of the vegetation radiation transmission model as a target, and obtaining a basic data set in the candidate vegetation data set; obtaining a high-dimensional feature data set based on the basic data set and a vegetation index data set; inputting the high-dimensional feature data set into a proxy model, obtaining optimal feature data set in the high-dimensional feature data set based on a score output by the proxy model, and calculating the vegetation canopy water content based on the optimal feature data set; and the loss function is a multi-dimensional physical constraint constructed based on a multi-source remote sensing data set. The application can effectively solve the problems of parameter compensation of a physical inversion mechanism, insufficient precision in a complex environment and low efficiency of feature selection in the prior art.

This invention provides a method, system, device, and medium for identifying vegetation lodging risk around power facilities, relating to the field of power facility risk identification. The method includes: extracting vegetation canopy data of the transmission corridor of the target power facility from satellite multispectral images; calculating the initial tilt angle and lodging direction; obtaining a list of potential lodging areas; acquiring visible light images of the transmission corridor using ground image acquisition equipment; extracting vegetation pixels and obtaining satellite normalized vegetation index values ​​for corresponding locations at multiple time phases to construct a seasonal fluctuation feature sequence; extracting texture features from corresponding locations in the ground visible light image; constructing a fused feature vector and inputting it into a convolutional neural network for vegetation type classification; and calculating the risk identification result for each vegetation pixel by combining the initial tilt angle and lodging direction. This invention improves the accuracy of vegetation lodging risk detection around power facilities.

The invention relates to the technical field of photogrammetry, in particular to a communication line engineering investigation design method and system adopting an unmanned aerial vehicle for surveying and mapping. The method comprises the following steps of: 1, acquiring multi-source data driven by communication constraints; 2, reconstructing a three-dimensional geographic scene, separating the ground surface and the vegetation elevation through laser point cloud, and generating a digital elevation model and a vegetation canopy height model; performing semantic classification on the obstacles based on the multispectral image; 3, dynamically optimizing the position of the tower, generating tower candidate points along the path, iteratively checking the sag safety distance by combining a digital elevation model and a lead catenary equation, and adjusting the position and the type of the tower according to the path corner constraint; 4, verifying the electromagnetic compatibility of the obstacle; and step 5, integrated output of engineering parameters. According to the method, the vegetation area needing to be cleared can be accurately recognized, excessive felling is avoided, meanwhile, the electromagnetic compatibility of the obstacle is verified, and the negative influence on the environment is avoided.

The present application relates to a kind of non-fisheye digital camera-based vegetation canopy structure measurement method, comprising the following steps: (1) the field of view of non-fisheye digital camera and the actual length and width corresponding to image are calculated;(2) shooting sample is carried out, and image is obtained;(3) the RGB color space of image is converted to LAB color space;(4) the A wave band in LAB color space is preprocessed, and the binary image of background and vegetation is obtained;(5) the FVC, CC, porosity, solar direct radiation and the FAPAR of sky diffuse radiation, the FAPAR of average solar direct radiation in a day, the proportion of diffuse radiation in total radiation, and then the instantaneous FAPAR is calculated;(6) the cumulative pore size distribution of each pixel direction, the vegetation width of ridge row crop and the distance between ridges are calculated;(7) CI, slightly error LAI and LAI_e, and accurate LAI and LAI_e are calculated;(8) average leaf angle is calculated.The present application has low labor cost, no image distortion and wide applicability.

This invention is applicable to the field of ecological environmental protection, providing a method and system for dynamic assessment of biodiversity in ecologically sensitive areas. The method includes: deploying a dynamic mobile monitoring network around the periphery of the ecologically sensitive area to collect data on human activity infiltration and time-series data of ecological parameters (vegetation canopy fragmentation, soil microbial activity decay rate, and frequency of stress behavior of indicator species); analyzing data fluctuation patterns through a time-series analysis model to identify early warnings of ecological degradation; performing reverse correlation of multi-source data based on satellite imagery, pollutant diffusion paths, and historical violations to determine the location and driving factors of degradation events; and integrating data quarterly to construct a dynamic resilience map and generate customized protection recommendations. This achieves early warning and precise source tracing of ecological degradation, improving the targeting and effectiveness of ecological protection strategies, and is applicable to the dynamic assessment and protection decision-making of biodiversity in ecologically sensitive areas.

The utility model provides full-automatic vegetation canopy spectrum auxiliary observation equipment for field trial observation. The full-automatic vegetation canopy spectrum auxiliary observation equipment for field trial observation comprises a spectrograph and a support, the spectrograph is connected with one end of a bifurcated optical fiber, the other two ends of the bifurcated optical fiber are connected with a first corrector and a second corrector respectively, the bifurcated optical fiber is provided with an electronic switch, and the electronic switch is connected with the support. The first corrector and the second corrector are both arranged on a support, and the first corrector is located under the second corrector. The spectrograph and the electronic switch are arranged in the heat preservation box; the device further comprises a controller, and the controller is connected with the spectrograph. The full-automatic vegetation canopy spectrum auxiliary observation equipment for field test observation has the advantages of being convenient to use and capable of automatically rotating by 360 degrees so as to realize automatic observation of different test fields.

The invention discloses a method for dynamically monitoring the vegetation coverage of an intertidal zone in an ecological restoration shoreline, belongs to the technical field of coastal zone ecological monitoring, and solves the problem of data distortion caused by tidal boundary drift, mixed vegetation types and topographic relief in monitoring of the vegetation coverage of the intertidal zone. Satellite and unmanned aerial vehicle images are synchronously acquired; dynamically delimiting an intertidal zone range based on real-time tidal data, generating a boundary vector layer and calculating a total area; overlapping the images and the boundaries, inputting the images and the boundaries into a convolutional neural network model, and outputting mangrove forest, salt marsh herbaceous plants and spartina alterniflora classification results; performing morphological optimization on a classification result, and calculating an initial vertical projection area; arranging sampling grids according to gradient distribution, actually measuring a vegetation canopy projection area and generating error correction factor optimization model parameters; using an optimization model to re-classify, calculate and calibrate the projection area; and finally calculating the effective coverage degree. The method is mainly used for accurate monitoring and acceptance authentication of vegetation coverage of coastal zone ecological restoration engineering.

The application discloses a kind of slope infiltration boundary reconstruction methods based on atmosphere-plant-soil collaborative monitoring, comprising: the test area of slope to be monitored is divided into the root zone close to trunk and the crown projection area away from trunk, and in two area open space, soil layer monitoring unit is arranged in pairs;Atmosphere layer and plant layer monitoring unit are arranged in test area;With single rainfall event as basic analysis unit, the data collected by each monitoring unit is time-synchronized, quality-controlled and event-processed, and the monitoring results are integrated as atmospheric input index, plant regulation index and soil hydraulic response index;Based on plant regulation index, determine the water distribution relationship after rainfall through vegetation canopy and trunk influence, and correspond the root zone throughfall and the trunk runoff converted by area to the root zone infiltration boundary, correspond the throughfall of the crown projection area to the infiltration boundary of the area, to reconstruct the partition infiltration boundary of two areas under single rainfall event.The result obtained by the present application is closer to the real situation.

The application discloses a crop yield prediction method and system integrating multi-source remote sensing data, optical images and radar images of a target crop plot are acquired, a crop canopy vertical height profile is constructed based on the backscattering characteristics of the radar images, a canopy geometric projection relationship is established in combination with a solar incident azimuth angle and a sensor angle, a canopy shielding area is identified through a three-dimensional light transmission simulation algorithm, and canopy transmission light distribution characteristics are derived according to the spatial distribution difference between vegetation and shadows, the transmission light distribution is used to correct optical image reflectivity, and the height profile is combined to generate comprehensive features of a vegetation canopy structure, a deep feature fusion network based on an attention mechanism is used to fuse the canopy space and height features, and thus high-precision crop yield prediction is realized. The method effectively overcomes the influence of illumination conditions and image geometric distortion on estimation accuracy, and improves the accuracy and stability of multi-source remote sensing data in crop yield prediction.

This invention relates to an experimental device and method for monitoring canopy interception in arbor vegetation. The device includes a test chamber with an internal partition connected to a sleeve. The sleeve has a top opening for the trunk of the plant under test to pass through, with the diameter of the top opening larger than a set value for the trunk diameter to allow only branch runoff rainwater to enter the sleeve. The sleeve has a bottom opening with a sealing element for a sealed connection with the trunk of the plant under test. A penetrating rainwater collection assembly is located around the outer periphery of the sleeve. The bottom end of the sleeve is connected to a first rainfall monitoring element via a water supply pipe. The penetrating rainwater collection assembly is connected to a second rainfall monitoring element via a water supply pipe. A rain nozzle is located at the top of the test chamber and connected to a water supply mechanism. The experimental device of this invention provides accurate measurement results and has strong applicability.

The invention provides an effective image screening method for vegetation hemisphere images based on transfer learning, and aims to solve the problem that invalid images depend on manual screening due to weather factors when vegetation canopy structure parameters are measured by a digital hemisphere method. Comprising the following steps: step 1, processing image data into an image format meeting the input requirements of a VGG16 model through pre-processing operations such as size adjustment and standardization; step 2, based on the thought of transfer learning, performing forward propagation on the image data by a VGG16 model pre-trained on an ImageNet data set to extract image depth features; and step 3, performing iterative training on an SVM algorithm image classifier based on the extracted image depth features, and realizing weight updating of a convolutional layer in an iteration process. The method can greatly improve the screening efficiency and accuracy of valid digital hemispherical images, achieves the automatic elimination of invalid images, and greatly reduces the labor cost.

The application provides a method for dividing surface evapotranspiration components based on dual-source Penman-Monteith, which comprises the following steps: step 1, collecting meteorological data; step 2, constructing function expressions of ecosystem Penman-Monteith, vegetation canopy Penman-Monteith and soil layer Penman-Monteith based on the collected meteorological data; step 3, substituting the Penman-Monteith expressions in step 2 into energy balance equation and water vapor balance equation to deduce plant transpiration and soil evaporation division factors; and step 4, calculating plant transpiration and soil evaporation according to the division factors. The application can accurately divide the ecosystem evapotranspiration into plant transpiration and soil evaporation by only using conventional meteorological data driving. The application can provide a theoretical basis for ecosystem evapotranspiration component simulation, technical support for improving the calculation efficiency of existing global models in the process of ecosystem evapotranspiration component simulation, and a scientific basis for ecological hydrology research.

The invention relates to the technical field of data processing, in particular to an earthwork accurate calculation method fusing BIM and oblique photography technology, and the method comprises the steps: determining an effective working plane region participating in earthwork metering, and correcting a live-action earth surface model according to the vegetation canopy parameters and rainfall erosion parameters of the effective working plane region, obtaining a first live-action earth surface model; resampling the BIM design earth surface model and the first live-action earth surface model into a plurality of grid units, and determining an update weight; according to the elevation difference of the first grid units with the elevation difference smaller than a first threshold value, updating the weight and the area of the single grid, determining a first excavation amount; and according to the elevation difference of the second grid units of which the elevation difference is greater than a first threshold value, updating the weight and the area of the single grid, determining a first filling amount. Therefore, the accuracy of the calculation result of the earth-rock volume is improved.

The invention discloses a forest wild animal habitat evaluation and ecological corridor planning method and a forest wild animal habitat evaluation and ecological corridor planning system. Relates to the technical field of ecological protection and forestry information. The method comprises the following steps: acquiring a forest point cloud and a multispectral image by using an unmanned aerial vehicle carrying a laser radar and a multispectral camera; preprocessing to generate a digital elevation model, a vegetation canopy height model and an orthoimage; generating a land utilization classification map based on the image and a canopy height model, and calculating a landscape index; generating a habitat suitability grid map by using a pre-trained multi-modal deep neural network; identifying core habitat plaques through spatial overlay analysis; constructing a landscape map taking the habitat plaques as nodes, performing connectivity analysis by using a pre-trained map attention network, constructing a dynamic resistance surface, and determining a key ecological corridor; and combining the high-resolution image to identify the breaking point, and outputting an ecological corridor planning scheme. According to the invention, high-precision habitat evaluation and data-driven corridor planning can be realized.

The application discloses a kind of water content prediction method and system based on airborne multispectral sensitive waveband combination, it is related to agricultural remote sensing and image processing technical field, including steps: based on the response characteristics of soil and plant canopy reflectance spectrum to water, combined with the physical mechanism of O-H bond vibration in water molecule, and considering the influence of atmospheric water vapor on near infrared spectrum to determine the bandwidth of all center wavelengths;The reflectance spectrum of soil and vegetation canopy is generated, the relationship between center wavelength and bandwidth is used to generate spectral response function, the simulated spectrum is converted to water sensitive waveband, and reflectivity and spectral index are calculated;The reflectance spectrum image of soil and vegetation canopy at selected position is collected, and the reflectance spectrum image is input into the water content prediction model to predict water content.The present application can effectively solve the problem of insufficient waveband selection and low monitoring accuracy when the existing airborne multispectral sensor performs water monitoring task by optimizing waveband selection and bandwidth allocation.

The present application provides a forest canopy height remote sensing estimation method and a computer readable medium. The present application uses multiple historical near-surface vegetation canopy height data of forest area as true label data, and uses a random forest model to correct the relative height parameters of the footprint point position of the spaceborne laser radar data; using multiple historical passive optical remote sensing data, synthetic aperture radar data, interferometric radar data, terrain data to calculate multi-band feature indicators as non-environmental variables, combined with a spatial weight matrix to calculate a spatial feature vector set to construct an environmental variable set, using the corrected vegetation canopy height parameters of the footprint point position of the spaceborne laser radar data as true label data, constructing an optimized light gradient boosting machine learning model, and realizing spatial filter value-based forest canopy height remote sensing estimation.

The present application relates to the technical field of agricultural water conservancy, desert ecological restoration and carbon sink forestry, and discloses a centrifugal desert vegetation rejuvenation and irrigation system and method, wherein the centrifugal irrigation device is internally provided with a cyclone separation zone for separating coarse sand in the water flow from liquid rich in fine particles through cyclone action, the centrifugal irrigation device is provided with a tangential water inlet, a coarse sand discharge outlet, a core flow guide pipe and shallow water outlet holes in communication with the cyclone separation zone; the intelligent control subsystem comprises a sensor group for collecting vegetation canopy normalized difference vegetation index (NDVI) and soil moisture content, a control valve group and a central controller in communication connection with the sensor group and the control valve group, and the present application effectively solves the problem of combination of high-sand flood irrigation and vegetation rejuvenation in desert areas, improves water resource utilization efficiency and promotes healthy growth of vegetation.

The application discloses a kind of ecological restoration coast line intertidal zone vegetation coverage dynamic monitoring method, belong to the technical field of coastal zone ecology monitoring, solve intertidal zone vegetation coverage monitoring because of tidal boundary drift, vegetation type mixed and topography fluctuation leads to data distortion problem. Including synchronous acquisition satellite and unmanned aerial vehicle image;Intertidal zone range is dynamically demarcated based on real-time tidal data, generates boundary vector layer and calculates total area;After image and boundary are superimposed, input convolutional neural network model and output mangrove, salt marsh herb and mutual flower rice classification result;Classification result is optimized to morphology, and initial vertical projection area is calculated;According to slope distribution, sampling grid is laid out, and vegetation canopy projection area is measured to generate error correction factor optimization model parameter;Calibration projection area is calculated by reclassifying using optimization model;Finally, effective coverage is calculated. Mainly used for the accurate monitoring and acceptance certification of vegetation coverage in coastal zone ecological restoration engineering.

This invention discloses an automatic canopy sample selection method in high spatial resolution remote sensing imagery. The method involves calculating the Normalized Difference Vegetation Index (NDVI) based on digitally quantized data; traversing each pixel of the NDVI and constructing an inner and outer window for each traversed pixel; calculating the NDVI variation coefficients of the pixels contained in the inner and outer windows; determining whether the currently traversed pixel is a canopy pixel; and determining whether a canopy pixel is a tree canopy pixel. This invention improves the efficiency of canopy information recognition, overcomes the problem of poor canopy sample quality caused by terrain shadows and canopy overlap in high spatial resolution remote sensing imagery, and avoids the influence of terrain shadows and canopy overlap on sample selection. This invention has good generalization ability and can accurately identify tree canopies at the single-tree scale for different scenarios, thus improving the quality of canopy samples.

This invention discloses a method for retrieving diurnal canopy water content by coupling optical and microwave remote sensing, belonging to the field of ecological and environmental remote sensing technology. The method includes: retrieving canopy water content data from optical images based on a canopy radiative transfer model; calculating vegetation optical thickness data based on a zero-order microwave radiative transfer model and a multi-channel collaborative inversion algorithm; acquiring vegetation optical thickness data, vegetation type, vegetation index, and meteorological data, and constructing a sample dataset after unifying the resolution; using the canopy water content data from optical images as ground truth labels, and combining the cumulative effect of rainfall and the lag effect of vegetation optical thickness, constructing an extreme gradient boosting machine learning estimation model; using this model to estimate the canopy water content in areas missing from the optical images, and filling in the missing areas to obtain a global diurnal, spatiotemporally continuous vegetation canopy water content dataset. This invention, employing the above method, achieves the complementary advantages of optical and microwave remote sensing, providing reliable technical support for monitoring vegetation moisture status.