Vegetation index day-by-day time sequence construction method and device

Abstract

The invention relates to a vegetation index day-by-day time sequence construction method and device, and the method comprises the steps: collecting image data collected by an observation foundation in a monitoring region and satellite remote sensing image data, and carrying out the data preprocessing; aiming at a target monitoring point, carrying out spatial matching on phenological camera data and a satellite remote sensing image, and constructing a regression equation; aiming at a target monitoring point, constructing a regression equation of flux tower data and a satellite remote sensing image; and based on a space matching and regression equation construction result, fusing foundation data and satellite remote sensing data, and reconstructing a vegetation index time sequence. Therefore, the problems that in the prior art, foundation and satellite observation data have differences in the aspects of spatial resolution, observation angles, wave band information and the like, space-time fusion of the foundation and the satellite observation data is difficult to effectively perform, and a continuous vegetation index time sequence with high space-time resolution cannot be constructed are solved.

Description

Technical Field

[0001] The present application relates to the field of satellite remote sensing technology, and in particular to a method and device for constructing a daily time series of vegetation indices. Background Art

[0002] With the development of Earth observation technology, remote sensing has been widely used in a variety of fields, including ecological and environmental monitoring, agricultural resource management, land use dynamics assessment, carbon flux estimation, and biodiversity conservation. Vegetation index time series, as an important parameter for measuring vegetation growth status and dynamic changes, are widely used to reflect vegetation growth, analyze crop phenology, assess ecosystem health, and model carbon sequestration potential.

[0003] Traditionally, remote sensing vegetation indices are calculated based on multi-temporal, multi-spectral satellite remote sensing imagery, relying on surface reflectance data provided by satellite platforms such as MODIS, Landsat, and Sentinel. However, traditional remote sensing data still face many limitations in practical applications. First, they are restricted by the orbital characteristics of the satellite platforms, with revisit periods typically ranging from several days to more than ten days. This is especially true for medium- and high-resolution (10-30 m) imagery, where the temporal density is significantly insufficient, making it difficult to capture rapidly occurring surface changes. Second, remote sensing imagery is easily affected by atmospheric conditions during acquisition, particularly in tropical and monsoon climate zones, where cloud cover and frequent rainfall reduce image availability, leading to frequent null values ​​and observation breakpoints in monitoring time series. Furthermore, differences between different satellite images in terms of band settings, radiation correction accuracy, and observation angles also pose challenges to data consistency analysis.

[0004] To overcome these challenges, ground-based observation technology has developed rapidly in recent years. Near-ground remote sensing observation methods, such as phenological cameras, ground-based flux towers, and automated sensor networks, have gradually emerged. These methods offer advantages such as high temporal resolution, fixed viewing angles, and low-cost operation. For example, phenological camera networks collect ground-based vegetation images at high frequencies (hourly or half-hourly), providing continuous and detailed information on phenological changes. These images reflect chlorophyll changes through image metrics such as the Green Chromatic Coordinate (GCC). Flux towers can use daily-scale net primary production (GPP) calculated from half-hourly CO2 flux data to reflect ground-based vegetation phenology. These metrics can serve as near-ground proxies for remotely sensed vegetation indices, providing a time series basis for remote sensing imagery within specific regions, with exceptional timeliness and sensitivity.

[0005] However, the existing technologies use differences between ground-based and satellite observation data in terms of spatial resolution, observation angle, and band information, making it difficult to effectively integrate the two in space and time, and unable to construct a continuous vegetation index time series with high space and time resolution, which urgently needs to be addressed. Summary of the Invention

[0006] The present application provides a method and apparatus for constructing a daily vegetation index time series to address the existing problems of differences in spatial resolution, observation angle, and band information between ground-based and satellite observation data, making it difficult to effectively integrate the two in time and space, and making it impossible to construct a continuous vegetation index time series with high temporal and spatial resolution.

[0007] The first aspect of the present application provides a method for constructing a daily time series of vegetation index, comprising the following steps: collecting flux tower data, phenological camera images and remote sensing data of a target detection area, and preprocessing the flux tower data, the phenological camera images and the remote sensing data to obtain corresponding preprocessed data, wherein the preprocessed data include vegetation index, green hue coordinates and total primary productivity; spatially matching the phenological camera images and the remote sensing data through the preprocessed data to obtain corresponding spatial matching results and a first regression equation; constructing a corresponding second regression equation according to the flux tower data and the remote sensing data, and based on the spatial matching results, the first regression equation and the second regression equation, and combining the flux tower data, the phenological camera images and the remote sensing data, to reconstruct the final daily time series of vegetation index in the target detection area.

[0008] Optionally, in one embodiment of the present application, the flux tower data, phenological camera images and remote sensing data of the target detection area are collected, and the flux tower data, the phenological camera images and the remote sensing data are preprocessed to obtain corresponding preprocessed data, wherein the preprocessed data include vegetation index, green hue coordinates and total primary productivity, including: obtaining the overlapping period of the flux tower data, the phenological camera images and the remote sensing data; performing a declouding operation on the remote sensing data within the overlapping period to calculate the vegetation index of each image; based on the phenological camera images and the flux tower data within the overlapping period, calculating the green hue coordinates of each frame of the phenological camera image and the total primary productivity corresponding to each site in the target detection area.

[0009] Optionally, in one embodiment of the present application, the spatial matching of the phenological camera image and the remote sensing data is performed through the preprocessed data to obtain corresponding spatial matching results and a first regression equation, including: performing a grid division operation on each frame of the phenological camera image to obtain multiple grids, and calculating the mean green hue coordinate corresponding to each grid in the multiple grids based on the green hue coordinate of each frame of the phenological camera image; performing a day-by-day division operation on each frame of the phenological camera image to obtain a daily phenological camera image, and calculating the target quantile of the mean green hue coordinate in each grid in the daily phenological camera image to generate the green hue coordinate of each grid. The method comprises the steps of: obtaining a daily time series of the remote sensing data; selecting remote sensing monitoring points corresponding to different vegetation types based on the remote sensing data, and obtaining a first available vegetation index observation value and a first available observation date for each remote sensing monitoring point; determining a satellite remote sensing observation value and each grid observation value corresponding to the first available vegetation index observation value and the first available observation date, and performing a linear regression operation based on the satellite remote sensing observation value and each grid observation value to obtain a regression result corresponding to each remote sensing monitoring point; determining a target grid in the regression result of each remote sensing monitoring point that meets preset correlation coefficient and mean square error requirements, and determining the spatial matching result and the first regression equation based on the target grid.

[0010] Optionally, in one embodiment of the present application, constructing a corresponding second regression equation based on the flux tower data and the remote sensing data includes: obtaining the flux tower position in the target detection area, and determining the corresponding flux monitoring point based on the flux tower position, and constructing a daily time series of total primary productivity corresponding to the flux monitoring point based on the flux tower data; performing smoothing and denoising on the daily time series of total primary productivity to obtain a denoised daily time series of total primary productivity; obtaining remote sensing data corresponding to the flux monitoring point, and obtaining a second available vegetation index observation value of the flux monitoring point and a second available observation date corresponding to the second available vegetation index observation value based on the remote sensing data and the denoised daily time series of total primary productivity; performing a linear regression operation on the remote sensing data corresponding to the second available observation date and the denoised daily time series of total primary productivity to obtain the second regression equation.

[0011] Optionally, in one embodiment of the present application, the method is based on the spatial matching result, the first regression equation and the second regression equation, and is combined with the flux tower data, the phenological camera image and the remote sensing data to reconstruct the final vegetation index daily time series of the target detection area, including: inputting the spatial matching result and the first available vegetation index observation value into the first regression equation to output the first vegetation index estimate of each remote sensing monitoring point on the same day; inputting the second available vegetation index observation value into the second regression equation to obtain the second vegetation index estimate of the same day corresponding to the flux monitoring point; based on the first available observation date and the second available observation date, determining that there is no remote sensing observation value and a date with remote sensing observations; determining a first daily vegetation index corresponding to the date without remote sensing observations based on the first daily vegetation index estimate and the second daily vegetation index estimate, and calculating an average of the remote sensing observations corresponding to the date with remote sensing observations, the first daily vegetation index estimate, and the second daily vegetation index estimate, so as to determine the second daily vegetation index corresponding to the date with remote sensing observations by the average; constructing an initial daily vegetation index time series based on the first daily vegetation index and the second daily vegetation index, and performing smoothing and denoising processing on the initial daily vegetation index time series to obtain a final daily vegetation index time series of the target detection area.

[0012] The second aspect of the present application provides a device for constructing a daily time series of vegetation index, including: an acquisition module for acquiring flux tower data, phenological camera images and remote sensing data of a target detection area, and preprocessing the flux tower data, the phenological camera images and the remote sensing data to obtain corresponding preprocessed data, wherein the preprocessed data includes vegetation index, green hue coordinates and total primary productivity; a matching module for spatially matching the phenological camera images and the remote sensing data through the preprocessed data to obtain corresponding spatial matching results and a first regression equation; a construction module for constructing a corresponding second regression equation based on the flux tower data and the remote sensing data, and based on the spatial matching results, the first regression equation and the second regression equation, and in combination with the flux tower data, the phenological camera images and the remote sensing data, to reconstruct the final daily time series of vegetation index of the target detection area.

[0013] Optionally, in one embodiment of the present application, the acquisition module includes: a first acquisition unit, used to acquire the overlapping period of the flux tower data, the phenological camera image and the remote sensing data; a declouding unit, used to perform a declouding operation on the remote sensing data within the overlapping period to calculate the vegetation index of each image; a first calculation unit, used to calculate the green hue coordinates of each frame of the phenological camera image and the total primary productivity corresponding to each site in the target detection area based on the phenological camera image and the flux tower data within the overlapping period.

[0014] Optionally, in one embodiment of the present application, the matching module includes: a division unit, used to perform a grid division operation on each frame of the phenological camera image to obtain a plurality of grids, and calculate the mean green hue coordinate corresponding to each grid in the plurality of grids according to the green hue coordinate of each frame of the phenological camera image; a generation unit, used to perform a daily division operation on each frame of the phenological camera image to obtain a daily phenological camera image, and calculate the target quantile of the mean green hue coordinate in each grid in the daily phenological camera image to generate a daily time series of green hue coordinates of each grid; a selection unit, used to select different vegetation types based on the remote sensing data. The invention provides a remote sensing monitoring point corresponding to the type, and obtains the first available vegetation index observation value and the first available observation date of each remote sensing monitoring point; a first linear regression unit is used to determine the satellite remote sensing observation value and each grid observation value corresponding to the first available vegetation index observation value and the first available observation date, and perform a linear regression operation based on the satellite remote sensing observation value and each grid observation value to obtain a regression result corresponding to each remote sensing monitoring point; a first determination unit is used to determine a target grid that meets the preset correlation coefficient and mean square error requirements in the regression result of each remote sensing monitoring point, and determine the spatial matching result and the first regression equation based on the target grid.

[0015] Optionally, in one embodiment of the present application, the construction module includes: a second acquisition unit, used to acquire the position of the flux tower in the target detection area, and determine the corresponding flux monitoring point according to the flux tower position, and construct a daily time series of total primary productivity corresponding to the flux monitoring point based on the flux tower data; a first denoising unit, used to perform smoothing and denoising on the daily time series of total primary productivity to obtain a denoised daily time series of total primary productivity; a third acquisition unit, used to acquire the remote sensing data corresponding to the flux monitoring point, and to acquire the second available vegetation index observation value of the flux monitoring point and the second available observation date corresponding to the second available vegetation index observation value based on the remote sensing data and the denoised daily time series of total primary productivity; a second linear regression unit, used to perform a linear regression operation on the remote sensing data corresponding to the second available observation date and the denoised daily time series of total primary productivity to obtain the second regression equation.

[0016] Optionally, in one embodiment of the present application, the construction module further includes: a first estimation unit for inputting the spatial matching result and the first available vegetation index observation value into the first regression equation to output the first vegetation index estimation value of each remote sensing monitoring point on the same day; a second estimation unit for inputting the second available vegetation index observation value into the second regression equation to obtain the second vegetation index estimation value of the same day corresponding to the flux monitoring point; a second determination unit for determining a date without remote sensing observation value and a date with remote sensing observation value based on the first available observation date and the second available observation date; a second calculation unit for calculating the vegetation index estimation value of the first day according to the first available vegetation index observation value. The first vegetation index for the day corresponding to the date without remote sensing observation values ​​is determined by using the index estimate value and the second vegetation index estimate value for the day, and the average of the remote sensing observation value corresponding to the date with remote sensing observation values, the first vegetation index estimate value for the day, and the second vegetation index estimate value for the day is calculated to determine the second vegetation index for the day corresponding to the date with remote sensing observation values ​​through the average value; a second denoising unit is used to construct an initial vegetation index daily time series based on the first vegetation index for the day and the second vegetation index for the day, and perform smoothing and denoising on the initial vegetation index daily time series to obtain a final vegetation index daily time series for the target detection area.

[0017] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing a daily time series of vegetation indices as described in the above embodiment.

[0018] A fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for constructing a daily time series of vegetation indices.

[0019] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned method for constructing a daily time series of vegetation indices.

[0020] Therefore, the embodiments of the present application have the following beneficial effects:

[0021] The embodiments of the present application can collect flux tower data, phenological camera images and remote sensing data of the target detection area, and preprocess the flux tower data, phenological camera images and remote sensing data to obtain corresponding preprocessed data, wherein the preprocessed data includes vegetation index, green hue coordinates and total primary productivity; spatially match the phenological camera images and remote sensing data through the preprocessed data to obtain corresponding spatial matching results and a first regression equation; construct a corresponding second regression equation based on the flux tower data and remote sensing data, and based on the spatial matching results, the first regression equation and the second regression equation, and in combination with the flux tower data, phenological camera images and remote sensing data, reconstruct the final vegetation index daily time series of the target detection area. The present application utilizes multi-source ground-based and satellite remote sensing data to achieve spectral feature daily time series reconstruction through data fusion, thereby improving the spatiotemporal accuracy of remote sensing applications. Thus, it solves the problems in the prior art that ground-based and satellite observation data have differences in spatial resolution, observation angle, band information, etc., making it difficult to effectively perform spatiotemporal fusion of the two and unable to construct a continuous vegetation index time series with high spatiotemporal resolution.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 This is a flowchart of a method for constructing a daily time series of vegetation indices according to an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the logical architecture of a method for constructing a daily time series of vegetation indices provided in one embodiment of the present application;

[0026] Figure 3 This is an example diagram of a device for constructing a daily time series of vegetation indices according to an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] Among them, 10 is a device for constructing a daily time series of vegetation index; 100 is an acquisition module, 200 is a matching module, 300 is a construction module; 401 is a memory, 402 is a processor, and 403 is a communication interface. DETAILED DESCRIPTION

[0029] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] The following describes the method and device for constructing a daily time series of vegetation index in an embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a method for constructing a daily time series of vegetation index, in which the flux tower data, phenological camera images and remote sensing data of the target detection area are collected, and the flux tower data, phenological camera images and remote sensing data are preprocessed to obtain corresponding preprocessed data, wherein the preprocessed data include vegetation index, green hue coordinates and total primary productivity; the phenological camera images and remote sensing data are spatially matched through the preprocessed data to obtain corresponding spatial matching results and a first regression equation; the corresponding second regression equation is constructed according to the flux tower data and remote sensing data, and based on the spatial matching results, the first regression equation and the second regression equation, and in combination with the flux tower data, phenological camera images and remote sensing data, the final daily time series of vegetation index in the target detection area is reconstructed. The present application utilizes multi-source ground-based and satellite remote sensing data to achieve daily time series reconstruction of spectral features through data fusion, thereby improving the spatiotemporal accuracy of remote sensing applications. This solves the problems in the existing technology, such as the differences between ground-based and satellite observation data in spatial resolution, observation angle, band information, etc., which make it difficult to effectively integrate the two in time and space, and it is impossible to construct a continuous vegetation index time series with high time and space resolution.

[0031] Specifically, Figure 1 This is a flowchart of a method for constructing a daily time series of vegetation indices provided in an embodiment of the present application.

[0032] like Figure 1 As shown in FIG, the method for constructing a daily time series of vegetation indices includes the following steps:

[0033] In step S101, flux tower data, phenological camera images, and remote sensing data of the target detection area are collected, and the flux tower data, phenological camera images, and remote sensing data are preprocessed to obtain corresponding preprocessed data, wherein the preprocessed data includes vegetation index, green hue coordinates, and total primary productivity.

[0034] The embodiment of the present application can first collect image data (i.e., flux tower data, phenological camera images) and satellite remote sensing image data collected by observation bases in the monitoring area, and perform data preprocessing on them.

[0035] Optionally, in one embodiment of the present application, flux tower data, phenological camera images and remote sensing data of the target detection area are collected, and the flux tower data, phenological camera images and remote sensing data are preprocessed to obtain corresponding preprocessed data, wherein the preprocessed data include vegetation index, green hue coordinates and total primary productivity, including: obtaining the overlapping time period of flux tower data, phenological camera images and remote sensing data; performing declouding operations on the remote sensing data within the overlapping time period to calculate the vegetation index of each image; based on the phenological camera images and flux tower data within the overlapping time period, calculating the green hue coordinates of each frame of phenological camera image and the total primary productivity corresponding to each site in the target detection area.

[0036] During the actual implementation process, the embodiments of the present application can first systematically collect ground-based and remote sensing observation data in the target monitoring area, where the ground-based data includes phenological camera image data and flux tower CO2 flux data, and the remote sensing data comes from multi-phase, multi-resolution satellite images, such as MODIS, Sentinel-2, Landsat, etc., and obtain the overlapping observation periods of multi-source ground-based and satellite remote sensing data that need to be fused.

[0037] Secondly, in the data preprocessing stage, the embodiments of the present application can perform cloud detection, geometric correction and radiation correction on satellite remote sensing images, and calculate vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), and unify them into observation results at the daily scale.

[0038] For the phenological camera images in the overlapping period, the images observed and taken during the day were collected and organized, and the green chromatic coordinate (GCC) observation results of each image were calculated; for the flux tower observation data in the overlapping period, the gross primary productivity (GPP) calculation results of the site were obtained and used as a representation of the ecological process.

[0039] In step S102, spatial matching is performed on the phenological camera image and the remote sensing data by preprocessing the data to obtain corresponding spatial matching results and a first regression equation.

[0040] Afterwards, the embodiment of the present application further needs to perform spatial matching of phenological camera data and satellite remote sensing images for the target monitoring point, and construct a first regression equation.

[0041] Optionally, in one embodiment of the present application, spatial matching is performed on phenological camera images and remote sensing data by preprocessing data to obtain corresponding spatial matching results and a first regression equation, including: performing a grid division operation on each frame of phenological camera image to obtain multiple grids, and calculating the mean green hue coordinate corresponding to each grid in the multiple grids based on the green hue coordinate of each frame of phenological camera image; performing a daily division operation on each frame of phenological camera image to obtain a daily phenological camera image, and calculating the target quantile of the mean green hue coordinate in each grid in the daily phenological camera image to generate the green hue coordinate of each grid. Daily time series; based on remote sensing data, select remote sensing monitoring points corresponding to different vegetation types, and obtain the first available vegetation index observation value and the first available observation date of each remote sensing monitoring point; determine the satellite remote sensing observation value and each grid observation value corresponding to the first available vegetation index observation value and the first available observation date, and perform linear regression operation based on the satellite remote sensing observation value and each grid observation value to obtain the regression result corresponding to each remote sensing monitoring point; determine the target grid in the regression result of each remote sensing monitoring point that meets the preset correlation coefficient and mean square error requirements, and determine the spatial matching result and the first regression equation based on the target grid.

[0042] It should be noted that, based on the above data collection and preprocessing results, the steps of performing spatial matching of phenological camera data with satellite remote sensing images and constructing a first regression equation for phenological camera data are as follows:

[0043] S1. Calculate the mean GCC (i.e., mean green hue coordinate) of each grid in each image of the phenological camera through grid division;

[0044] S2. Divide the phenological camera images into daily intervals and calculate the 90% quantile (i.e., target quantile) of the mean GCC value in each grid of the daily phenological camera images to form a daily GCC time series for each grid.

[0045] S3. Obtain pre-processed satellite remote sensing image data within the monitoring area, select corresponding monitoring points for different vegetation types based on the high spatial resolution remote sensing image, and obtain available vegetation index observation values ​​(i.e., first available vegetation index observation values) and available observation dates (i.e., first available observation dates) for each monitoring point;

[0046] S4. For each monitoring point, linear regression is performed using multiple data pairs consisting of satellite remote sensing observations corresponding to the available observation dates and phenological camera grid observations;

[0047] S5. The grid corresponding to the highest correlation coefficient and the lowest mean square error (i.e., the preset correlation coefficient and mean square error requirements) in the regression results of each monitoring point is used as the spatial matching result of each monitoring point between the phenological camera image and the satellite remote sensing image, and the first regression equation is obtained at the same time.

[0048] Specifically, in the specific implementation process, in order to achieve efficient fusion of PhenoCam and NSCam phenological camera image data and satellite remote sensing images and reconstruction of spectral feature sequences, the embodiments of the present application need to construct a stable and clearly structured daily GCC time series based on the phenological camera images, and achieve spatial matching and scale conversion with the satellite vegetation index through regression modeling.

[0049] First, the embodiment of the present application can perform spatial grid division on the RGB images captured by the phenological camera. Specifically, the embodiment of the present application divides each image into a number of equidistant grid cells (e.g., 10×10 grids) at a fixed resolution and extracts the GCC value within each grid. The GCC is calculated based on the pixel average of the three standard RGB channels using the following formula:

[0050]

[0051] Among them, R, G, and B represent the average brightness values ​​of the green, red, and blue channels of all pixels in the grid, respectively.

[0052] Therefore, through the above formula, the embodiment of the present application can eliminate the influence of the overall changes in light intensity and image brightness, and more accurately reflect the changes in chlorophyll content in the plant canopy.

[0053] Secondly, based on timestamp information, the present embodiment can classify all phenological images on a daily basis and extract the GCC value for each grid each day. To reduce the impact of outliers on the stability of the time series, the present embodiment can select the 90th percentile of the GCC value within each grid each day (i.e., the maximum value among the top 90% maximum values ​​after sorting) as the representative value, thereby constructing a robust daily GCC time series. This processing process can significantly suppress the impact of extreme values ​​caused by changes in lighting, camera angle, or local interference, thereby enhancing the continuity and phenological sensitivity of the time series.

[0054] Afterwards, in order to construct a spatial mapping relationship between ground-based observations and remote sensing images, the embodiment of the present application needs to select monitoring points that match the ground grid from Google Earth's high-spatial-resolution remote sensing images, and extract the available remote sensing vegetation index values ​​in the corresponding vegetation type areas (such as farmland, grassland, and woodland); the extracted remote sensing observation values ​​must meet the requirements of no cloud coverage, high image quality, and a clear imaging time, ultimately forming a set of vegetation index time series indexed by time, and synchronously recording the timestamp of each observation value.

[0055] For each monitoring point, the embodiment of the present application can extract the phenological camera GCC time series and the remote sensing vegetation index time series within the overlapping time period of the location, and construct a point-by-point data pair set (x i ,y i ), where x i is the GCC observation value of the day, y i is the observed value of vegetation index on the same day. On this basis, the embodiment of the present application can use the least squares method to perform linear regression modeling, and the regression model form is as follows:

[0056] y=ax+b

[0057] Here, z is the slope and b is the intercept.

[0058] It should be noted that this model can be used to map GCC values ​​to vegetation index estimates, serving as the core conversion function for subsequent remote sensing time series completion and reconstruction. To ensure the effectiveness and spatial consistency of the mapping model, the embodiment of this application uses a combined index of Pearson Correlation Coefficient (PCC) and Mean Squared Error (MSE) to screen for optimal grid matching; for each monitoring point, the PCC and MSE values ​​of its linear regression model with all grids are calculated, and the grid unit that satisfies both the maximum PCC and the minimum MSE is selected as the spatial matching result of the point in the phenological image.

[0059] Therefore, the determined “GCC-vegetation index” regression equation can be marked as the mapping function of the monitoring point in the subsequent time series reconstruction model.

[0060] In step S103, a corresponding second regression equation is constructed according to the flux tower data and remote sensing data, and based on the spatial matching results, the first regression equation and the second regression equation, and in combination with the flux tower data, phenological camera images and remote sensing data, the final vegetation index daily time series of the target detection area is reconstructed.

[0061] Furthermore, if Figure 2As shown, the embodiment of the present application can construct a second regression equation of flux tower data and satellite remote sensing images for the target monitoring point, and based on the spatial matching and regression equation construction results, fuse the ground-based data and satellite remote sensing data to reconstruct the vegetation index time series.

[0062] Therefore, the embodiments of the present application utilize multi-source ground-based and satellite remote sensing data, and through data fusion, achieve daily time series reconstruction of spectral features, thereby improving the spatiotemporal accuracy of remote sensing applications.

[0063] Optionally, in one embodiment of the present application, a corresponding second regression equation is constructed based on the flux tower data and the remote sensing data, including: obtaining the flux tower position in the target detection area, and determining the corresponding flux monitoring point based on the flux tower position, and constructing a daily time series of total primary productivity corresponding to the flux monitoring point based on the flux tower data; performing smoothing and denoising on the daily time series of total primary productivity to obtain a denoised daily time series of total primary productivity; obtaining remote sensing data corresponding to the flux monitoring point, and obtaining a second available vegetation index observation value of the flux monitoring point and a second available observation date corresponding to the second available vegetation index observation value based on the remote sensing data and the denoised daily time series of total primary productivity; performing a linear regression operation on the remote sensing data corresponding to the second available observation date and the denoised daily time series of total primary productivity to obtain a second regression equation.

[0064] Furthermore, the steps for constructing a regression equation for flux tower data and satellite remote sensing images in the embodiment of the present application are as follows:

[0065] S1. The location of the flux tower can be directly obtained as the monitoring point (i.e., the flux monitoring point), and the GPP daily time series of the observation point can be constructed based on the flux observation, and the time series smoothing algorithm can be used to smooth and denoise it.

[0066] S2. Obtaining pre-processed satellite remote sensing image data of the monitoring point to obtain an available vegetation index observation value (i.e., a second available vegetation index observation value) and an available observation date (i.e., a second available observation date) of the monitoring point;

[0067] S3. For the monitoring points, linear regression is performed using the satellite remote sensing observations and flux tower GPP observations corresponding to the available observation dates to obtain the second regression equation.

[0068] Specifically, after completing the spatial pairing and regression modeling of phenological camera images and remote sensing images, the embodiment of the present application can further introduce ground-based data sources of ecological processes, namely, carbon flux data observed by flux towers, to construct a more ecologically meaningful vegetation index time series reconstruction model.

[0069] First, the embodiment of the present application can directly set the geographical location of the flux tower as the target monitoring point. As a feasible method, the embodiment of the present application can extract the Net Ecosystem Exchange (NEE) time series of the site based on the original flux data provided by FLUXNET or a self-built flux observation network, and combine it with the standard carbon flux decomposition algorithm, using international general methods such as Marginal Distribution Sampling (MDS) or Artificial Neural Network (ANN) to decompose NEE into gross primary productivity (Gross Primary Production, GPP) and ecosystem respiration; the daily GPP values ​​obtained after processing constitute the vegetation productivity time series in the sense of ecological processes, with units of g Cm-2d-1 and a daily time scale, which is aligned with remote sensing NDVI.

[0070] Since GPP contains strong seasonal fluctuations and inter-day variations, the embodiment of the present application uses methods such as Savitzky-Golay to pre-smooth the original time series before model fitting to reduce the interference of intra-day fluctuations on regression performance.

[0071] Secondly, the embodiment of the present application can obtain remote sensing data of the pixel area where the flux tower monitoring point is located, using satellite image products that fully match the GPP observation time on a daily scale.

[0072] Specifically, the embodiment of the present application can spatially match the remote sensing image pixels corresponding to the coordinates of the flux tower, extract the NDVI, EVI or other vegetation index values ​​of the pixel in each time phase, and eliminate low-quality data such as clouds and shadows to ensure the accuracy and representativeness of remote sensing vegetation index observations, forming a set of remote sensing index time series with clear time labels.

[0073] Pair the above remote sensing index time series with the GPP daily time series, select the set of observation dates that overlap in time, and construct the data pair set (x i ,y i ), where x i is the GPP observation value of the day, y i is the observed value of vegetation index on the same day.

[0074] On this basis, the embodiment of the present application can apply the least squares regression method to construct a linear model, as shown in the following formula:

[0075] y=ax+b

[0076] Where a is the slope and b is the intercept.

[0077] It can be understood that the above model can be used to map GPP values ​​into vegetation index estimates as the core conversion function for subsequent remote sensing time series completion and reconstruction. Its regression performance can be comprehensively evaluated by correlation coefficient and mean square error, so as to be used for vegetation index inversion correction and time series reconstruction on subsequent remote sensing null value dates.

[0078] Optionally, in one embodiment of the present application, based on the spatial matching results, the first regression equation and the second regression equation, and in combination with the flux tower data, the phenological camera image and the remote sensing data, the final vegetation index daily time series of the target detection area is reconstructed, including: inputting the spatial matching results and the first available vegetation index observation value into the first regression equation to output the first vegetation index estimate of each remote sensing monitoring point on the same day; inputting the second available vegetation index observation value into the second regression equation to obtain the second vegetation index estimate of the same day corresponding to the flux monitoring point; determining the date with no remote sensing observation value and the date with remote sensing observation value based on the first available observation date and the second available observation date. The date of the remote sensing observation value; the first vegetation index for the day corresponding to the date without remote sensing observation value is determined based on the first vegetation index estimate value and the second vegetation index estimate value, and the mean of the remote sensing observation value, the first vegetation index estimate value and the second vegetation index estimate value corresponding to the date with remote sensing observation value is calculated to determine the second vegetation index for the date with remote sensing observation value through the mean; based on the first vegetation index and the second vegetation index, an initial vegetation index daily time series is constructed, and the initial vegetation index daily time series is smoothed and denoised to obtain the final vegetation index daily time series of the target detection area.

[0079] After completing the regression modeling of two types of ground-based observation data and remote sensing images from phenological cameras and flux towers, the embodiment of the present application enters the multi-source fusion and time series reconstruction stage. The core goal of this stage is to use the existing regression model to convert ground-based observation indicators such as GCC and GPP into estimated values ​​of remote sensing vegetation indices, thereby supplementing the time discontinuities caused by clouds, rain or revisit cycles in remote sensing images, forming a complete and continuous daily time series, and improving the smoothness and stability of the time series through filtering optimization.

[0080] In an embodiment of the present application, the steps for reconstructing a vegetation index time series by fusing multi-source ground-based and satellite remote sensing data are as follows:

[0081] S1. Call the linear regression models constructed by the phenological camera and flux tower respectively, use the daily observation values ​​of GCC based on the spatial matching result grid and the daily observation values ​​of GPP of the flux tower as the regression equation input, and calculate the output of the equation as the estimated vegetation index value of the monitoring point on the day (i.e., the first estimated vegetation index value on the day and the second estimated vegetation index value on the day);

[0082] S2: For dates without satellite remote sensing observations, the estimated vegetation index value obtained in S1 is used as the vegetation index observation result for that day (i.e., the first daily vegetation index). For dates with satellite remote sensing observations (i.e., satellite remote sensing provides high-quality observations on that day), the average of the observed value and the estimated value is calculated as the vegetation index for that day (i.e., the second daily vegetation index), thereby forming a preliminary daily vegetation index time series.

[0083] S3. In order to eliminate the sudden changes or sawtooth effects in the time series caused by data fusion, external disturbances or short-term outliers, the Savitzky-Golay and other time series filtering algorithms are used to smooth the preliminary daily time series of each monitoring point. While preserving the original curve shape as little as possible, the interference of high-frequency noise is reduced, thereby obtaining the reconstruction results of the daily time series of vegetation index with good temporal continuity, phenological sensitivity and remote sensing consistency (i.e., the final daily time series of vegetation index).

[0084] Therefore, the embodiments of the present application combine multi-source ground-based observation data, thereby being able to supplement the null values ​​of satellite remote sensing data caused by cloudy and rainy weather or data quality, and form accurate and dense vegetation index time series for different vegetation types.

[0085] According to the method for constructing a daily time series of vegetation indices proposed in an embodiment of the present application, flux tower data, phenological camera images, and remote sensing data of the target detection area are collected, and the flux tower data, phenological camera images, and remote sensing data are preprocessed to obtain corresponding preprocessed data, wherein the preprocessed data include vegetation index, green hue coordinates, and total primary productivity; the phenological camera images and remote sensing data are spatially matched with the preprocessed data to obtain corresponding spatial matching results and a first regression equation; a corresponding second regression equation is constructed based on the flux tower data and remote sensing data, and based on the spatial matching results, the first regression equation, and the second regression equation, and in combination with the flux tower data, phenological camera images, and remote sensing data, the final daily time series of vegetation indices in the target detection area is reconstructed. This application utilizes multi-source ground-based and satellite remote sensing data to achieve daily time series reconstruction of spectral features through data fusion, thereby improving the spatiotemporal accuracy of remote sensing applications.

[0086] Next, a device for constructing a daily time series of vegetation indices according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0087] Figure 3 4 is a block diagram of a device for constructing a daily time series of vegetation indices according to an embodiment of the present application.

[0088] like Figure 3 As shown, the vegetation index daily time series construction device 10 includes: a collection module 100 , a matching module 200 and a construction module 300 .

[0089] Among them, the acquisition module 100 is used to collect flux tower data, phenological camera images and remote sensing data of the target detection area, and preprocess the flux tower data, phenological camera images and remote sensing data to obtain corresponding preprocessed data, wherein the preprocessed data includes vegetation index, green hue coordinates and total primary productivity.

[0090] The matching module 200 is used to perform spatial matching on the phenological camera images and the remote sensing data by preprocessing the data to obtain corresponding spatial matching results and a first regression equation.

[0091] Construction module 300 is used to construct a corresponding second regression equation based on the flux tower data and remote sensing data, and based on the spatial matching results, the first regression equation and the second regression equation, and in combination with the flux tower data, phenological camera images and remote sensing data, to reconstruct the final vegetation index daily time series of the target detection area.

[0092] Optionally, in one embodiment of the present application, the acquisition module 100 includes: a first acquisition unit, a cloud removal unit and a first calculation unit.

[0093] The first acquisition unit is used to obtain the overlapping period of flux tower data, phenological camera images and remote sensing data.

[0094] The cloud removal unit is used to perform cloud removal operations on remote sensing data within overlapping time periods to calculate the vegetation index of each image.

[0095] The first calculation unit is used to calculate the green hue coordinates of each frame of the phenological camera image and the total primary productivity corresponding to each site in the target detection area based on the phenological camera image and flux tower data in the overlapping period.

[0096] Optionally, in one embodiment of the present application, the matching module 200 includes: a division unit, a generation unit, a selection unit, a first linear regression unit and a first determination unit.

[0097] Among them, the division unit is used to perform grid division operation on each frame of phenological camera image to obtain multiple grids, and calculate the mean green hue coordinate corresponding to each grid in the multiple grids according to the green hue coordinate of each frame of phenological camera image.

[0098] The generation unit is used to perform a daily division operation on each frame of the phenological camera image to obtain a daily phenological camera image, and calculate the target quantile of the mean green hue coordinate in each grid in the daily phenological camera image to generate a daily time series of the green hue coordinate of each grid.

[0099] The selection unit is used to select remote sensing monitoring points corresponding to different vegetation types based on remote sensing data, and obtain the first available vegetation index observation value and the first available observation date of each remote sensing monitoring point.

[0100] The first linear regression unit is used to determine the satellite remote sensing observation value and each grid observation value corresponding to the first available vegetation index observation value and the first available observation date, and perform a linear regression operation based on the satellite remote sensing observation value and each grid observation value to obtain a regression result corresponding to each remote sensing monitoring point.

[0101] The first determining unit is used to determine a target grid that meets preset correlation coefficient and mean square error requirements in the regression results of each remote sensing monitoring point, and determine a spatial matching result and a first regression equation based on the target grid.

[0102] Optionally, in one embodiment of the present application, the construction module 300 includes: a second acquisition unit, a first denoising unit, a third acquisition unit and a second linear regression unit.

[0103] Among them, the second acquisition unit is used to obtain the position of the flux tower in the target detection area, and determine the corresponding flux monitoring point according to the flux tower position, and construct a daily time series of total primary productivity corresponding to the flux monitoring point based on the flux tower data.

[0104] The first denoising unit is used to perform smoothing and denoising processing on the daily time series of total primary productivity to obtain a denoised daily time series of total primary productivity.

[0105] The third acquisition unit is used to obtain remote sensing data corresponding to the flux monitoring point, so as to obtain a second available vegetation index observation value of the flux monitoring point and a second available observation date corresponding to the second available vegetation index observation value based on the remote sensing data and the denoised daily time series of gross primary productivity.

[0106] The second linear regression unit is used to perform a linear regression operation on the remote sensing data corresponding to the second available observation date and the denoised daily time series of gross primary productivity to obtain a second regression equation.

[0107] Optionally, in one embodiment of the present application, the construction module 300 further includes: a first estimation unit, a second estimation unit, a second determination unit, a second calculation unit, and a second denoising unit.

[0108] The first estimation unit is used to input the spatial matching result and the first available vegetation index observation value into the first regression equation to output the first vegetation index estimation value of each remote sensing monitoring point on the same day.

[0109] The second estimation unit is used to apply the second available vegetation index observation value to the second regression equation to obtain a second vegetation index estimation value of the day corresponding to the flux monitoring point.

[0110] The second determining unit is configured to determine a date without remote sensing observation values ​​and a date with remote sensing observation values ​​based on the first available observation date and the second available observation date.

[0111] The second calculation unit is used to determine the first vegetation index for the day corresponding to the date without remote sensing observation values ​​based on the first vegetation index estimate value for the day and the second vegetation index estimate value for the day, and calculate the average of the remote sensing observation value, the first vegetation index estimate value for the day, and the second vegetation index estimate value for the day corresponding to the date with remote sensing observation values, so as to determine the second vegetation index for the day corresponding to the date with remote sensing observation values ​​through the average value.

[0112] The second denoising unit is used to construct an initial vegetation index daily time series based on the first vegetation index on the same day and the second vegetation index on the same day, and perform smoothing and denoising on the initial vegetation index daily time series to obtain a final vegetation index daily time series of the target detection area.

[0113] It should be noted that the above explanations of the embodiment of the method for constructing a daily vegetation index time series are also applicable to the apparatus for constructing a daily vegetation index time series of this embodiment, and will not be repeated here.

[0114] According to an embodiment of the present application, a device for constructing a daily vegetation index time series is provided, which includes an acquisition module 100 for collecting flux tower data, phenological camera images, and remote sensing data of a target detection area, and preprocessing the flux tower data, phenological camera images, and remote sensing data to obtain corresponding preprocessed data, wherein the preprocessed data includes vegetation index, green hue coordinates, and gross primary productivity; a matching module 200 for spatially matching the phenological camera images and remote sensing data using the preprocessed data to obtain corresponding spatial matching results and a first regression equation; a construction module 300 for constructing a corresponding second regression equation based on the flux tower data and remote sensing data, and based on the spatial matching results, the first regression equation, and the second regression equation, and in combination with the flux tower data, phenological camera images, and remote sensing data, to reconstruct the final daily vegetation index time series of the target detection area. This application utilizes multi-source ground-based and satellite remote sensing data to achieve daily spectral feature time series reconstruction through data fusion, thereby improving the spatiotemporal accuracy of remote sensing applications.

[0115] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0116] Memory 401 , processor 402 , and computer programs stored in the memory 401 and executable on the processor 402 .

[0117] When the processor 402 executes the program, the method for constructing a daily time series of vegetation indices provided in the above embodiment is implemented.

[0118] Furthermore, the electronic device further includes:

[0119] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0120] The memory 401 is used to store computer programs that can be run on the processor 402 .

[0121] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0122] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0123] Optionally, in a specific implementation, if the memory 401 , the processor 402 and the communication interface 403 are integrated on a chip, the memory 401 , the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0124] The processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0125] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing a daily time series of vegetation indices.

[0126] An embodiment of the present application further provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for constructing a daily time series of vegetation indices.

[0127] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0129] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0130] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0131] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0132] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0133] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0134] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for constructing a daily time series of vegetation indices, characterized in that: The following steps are involved: collecting flux tower data, phenological camera images, and remote sensing data of a target detection area, and preprocessing the flux tower data, the phenological camera images, and the remote sensing data to obtain corresponding preprocessed data, wherein the preprocessed data includes a vegetation index, a green hue coordinate, and a gross primary productivity; Performing spatial matching on the phenological camera image and the remote sensing data using the preprocessed data to obtain corresponding spatial matching results and a first regression equation; A corresponding second regression equation is constructed according to the flux tower data and the remote sensing data, and based on the spatial matching result, the first regression equation and the second regression equation, and in combination with the flux tower data, the phenological camera image and the remote sensing data, a final vegetation index daily time series of the target detection area is reconstructed.

2. The method according to claim 1, characterized in that The flux tower data, phenological camera images, and remote sensing data of the target detection area are collected, and the flux tower data, the phenological camera images, and the remote sensing data are preprocessed to obtain corresponding preprocessed data, wherein the preprocessed data includes vegetation index, green hue coordinates, and gross primary productivity, including: obtaining an overlapping period of the flux tower data, the phenological camera image, and the remote sensing data; performing a cloud removal operation on the remote sensing data within the overlapping period to calculate a vegetation index for each image; Based on the phenological camera images and the flux tower data within the overlapping period, the green hue coordinates of each frame of the phenological camera image and the total primary productivity corresponding to each site in the target detection area are calculated.

3. The method according to claim 2, characterized in that The spatial matching of the phenological camera image and the remote sensing data by the preprocessed data to obtain corresponding spatial matching results and a first regression equation includes: Performing a grid division operation on each frame of the phenological camera image to obtain a plurality of grids, and calculating a mean value of the green hue coordinate corresponding to each grid in the plurality of grids according to the green hue coordinate of each frame of the phenological camera image; Performing a daily division operation on each frame of the phenological camera image to obtain a daily phenological camera image, and calculating the target quantile of the mean value of the green hue coordinate in each grid in the daily phenological camera image to generate a daily time series of the green hue coordinate of each grid; Based on the remote sensing data, remote sensing monitoring points corresponding to different vegetation types are selected, and a first available vegetation index observation value and a first available observation date are obtained for each remote sensing monitoring point; Determining the satellite remote sensing observation value and each grid observation value corresponding to the first available vegetation index observation value and the first available observation date, and performing a linear regression operation based on the satellite remote sensing observation value and each grid observation value to obtain a regression result corresponding to each remote sensing monitoring point; A target grid that meets preset correlation coefficient and mean square error requirements in the regression results of each remote sensing monitoring point is determined, and the spatial matching result and the first regression equation are determined based on the target grid.

4. The method according to claim 3, characterized in that The constructing a corresponding second regression equation according to the flux tower data and the remote sensing data includes: Obtaining the location of a flux tower in the target detection area, determining a corresponding flux monitoring point according to the flux tower location, and constructing a daily time series of total primary productivity corresponding to the flux monitoring point based on the flux tower data; Performing smoothing and denoising on the daily time series of total primary productivity to obtain a denoised daily time series of total primary productivity; Acquire remote sensing data corresponding to the flux monitoring point, and acquire a second available vegetation index observation value of the flux monitoring point and a second available observation date corresponding to the second available vegetation index observation value based on the remote sensing data and the de-noised gross primary productivity daily time series; A linear regression operation is performed on the remote sensing data corresponding to the second available observation date and the denoised daily time series of gross primary productivity to obtain the second regression equation.

5. The method according to claim 4, characterized in that The reconstructing a final vegetation index daily time series of the target detection area based on the spatial matching result, the first regression equation, and the second regression equation, and combining the flux tower data, the phenological camera image, and the remote sensing data, includes: Inputting the spatial matching result and the first available vegetation index observation value into the first regression equation to output a first vegetation index estimation value of each remote sensing monitoring point on the same day; Applying the second available vegetation index observation value to the second regression equation to obtain a second vegetation index estimate for the day corresponding to the flux monitoring point; Determining a date without remote sensing observation values ​​and a date with remote sensing observation values ​​based on the first available observation date and the second available observation date; Determining a first vegetation index for the day corresponding to the date without remote sensing observations based on the first estimated vegetation index for the day and the second estimated vegetation index for the day, and calculating an average of the remote sensing observations corresponding to the date with remote sensing observations, the first estimated vegetation index for the day, and the second estimated vegetation index for the day, to determine a second vegetation index for the day corresponding to the date with remote sensing observations using the average; An initial vegetation index daily time series is constructed based on the first vegetation index for the day and the second vegetation index for the day, and smoothing and denoising are performed on the initial vegetation index daily time series to obtain a final vegetation index daily time series for the target detection area.

6. A device for constructing a daily time series of vegetation indices, characterized in that: include: an acquisition module, configured to acquire flux tower data, phenological camera images, and remote sensing data of a target detection area, and preprocess the flux tower data, the phenological camera images, and the remote sensing data to obtain corresponding preprocessed data, wherein the preprocessed data includes vegetation index, green hue coordinates, and gross primary productivity; A matching module, configured to perform spatial matching on the phenological camera image and the remote sensing data using the preprocessed data to obtain corresponding spatial matching results and a first regression equation; A construction module is used to construct a corresponding second regression equation based on the flux tower data and the remote sensing data, and based on the spatial matching result, the first regression equation and the second regression equation, and in combination with the flux tower data, the phenological camera image and the remote sensing data, to reconstruct the final vegetation index daily time series of the target detection area.

7. The device according to claim 6, characterized in that The acquisition module includes: a first acquisition unit, configured to acquire an overlapping period of the flux tower data, the phenological camera image, and the remote sensing data; a cloud removal unit, configured to perform a cloud removal operation on the remote sensing data within the overlapping period to calculate a vegetation index for each image; The first calculation unit is used to calculate the green hue coordinate of each frame of the phenological camera image and the total primary productivity corresponding to each site in the target detection area based on the phenological camera image and the flux tower data in the overlapping period.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing a daily time series of vegetation indices according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for constructing a daily time series of vegetation indices according to any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that The computer program is executed to implement the method for constructing a daily time series of vegetation indices according to any one of claims 1 to 5.

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