Leaf area index calculation method, device, electronic device and storage medium

Abstract

The present invention provides a leaf area index calculation method, device, electronic device and storage medium, belonging to the field of remote sensing image processing technology, the method comprising: calculating an initial satellite-scale vegetation index based on satellite images of a region to be measured; correcting the initial satellite-scale vegetation index using a predetermined scale-up deviation value of the region to be measured to obtain a corrected satellite-scale vegetation index of the region to be measured; inputting the corrected satellite-scale vegetation index into a first relationship model to obtain a satellite-scale leaf area index of the region to be measured. The present invention adopts a sky-ground collaborative approach, fully considering the scale difference between satellite and drone data, correcting the initial satellite-scale vegetation index calculated from satellite images by using a scale-up deviation value, effectively reducing the uncertainty of coupled utilization of multi-scale data, realizing coupled utilization of multi-scale data, and can be applied to leaf area index extraction of heterogeneous surfaces.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and in particular to a leaf area index calculation method, device, electronic equipment and storage medium. Background Art

[0002] The Leaf Area Index (LAI) is defined as the sum of the green leaf areas on one side of the plant per unit area, and is a key parameter for characterizing vegetation growth conditions. Ground measurement and satellite remote sensing modeling and inversion are important ways to obtain the leaf area index. However, the former (ground measurement) is time-consuming and labor-intensive, and is a point-to-surface measurement method that cannot accurately obtain the leaf area index of a large area. The latter (satellite remote sensing modeling and inversion) is based on earth observation technology, which can quickly obtain the leaf area index of a large area and a long time series, providing support for regional and even global vegetation growth monitoring. However, due to the mismatch between the resolution of satellite observations and the scale of ground measurement plots, there is still a large uncertainty in satellite data in the mapping of leaf area index in large areas. This is also a common problem in medium and low resolution satellite remote sensing modeling, which has not yet been effectively solved.

[0003] With the rapid development of drone technology, the use of drones equipped with multispectral or hyperspectral cameras to obtain near-ground high spatial resolution data can effectively link ground measurements with satellite observations and achieve scale-up from point to surface. However, most of the current satellite-scale leaf area index inversions using drones as intermediate platforms still use drone estimation results as near-ground prior information to expand the modeling sample set, without considering the scale effect problem in the process of step-by-step deduction between the sky and the ground, and without achieving true sky-ground coordination to achieve seamless scale-up from the ground to the air and from the air to the sky.

[0004] In view of this, there is an urgent need to provide a new leaf area index calculation method in order to achieve the coupled utilization of multi-scale data and improve the extraction accuracy of leaf area index. Summary of the invention

[0005] The present invention provides a leaf area index calculation method, device, electronic device and storage medium, which are used to solve the defect of the prior art that multi-scale data lacks coupling when calculating the leaf area index. The uncertainty of leaf area index inversion from satellite images can be effectively reduced by eliminating the scale deviation between multi-scale data such as sky, air and ground.

[0006] The present invention provides a method for calculating leaf area index, comprising the following steps:

[0007] Calculate the initial satellite-scale vegetation index based on the satellite image of the area to be measured;

[0008] The initial satellite-scale vegetation index is corrected by using a predetermined scale-up deviation value of the area to be measured to obtain a satellite-scale vegetation index of the area to be measured;

[0009] The corrected satellite-scale vegetation index is input into a first relationship model to obtain a satellite-scale leaf area index of the area to be measured. The first relationship model is a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index.

[0010] According to a leaf area index calculation method provided by the present invention, the scale-up deviation value is predetermined based on the following steps:

[0011] Determine the red band reflectivity and near infrared band reflectivity of the drone scale of the area to be measured based on the drone aerial image obtained after registration with the satellite image;

[0012] Determine the drone scale initial vegetation index according to the drone red band reflectance and near infrared band reflectance of the drone scale;

[0013] The UAV scale initial vegetation index of the area to be measured is aggregated by upscaling, wherein the upscaling is performed from the UAV scale corresponding to the UAV aerial image to the satellite scale of the satellite image;

[0014] The scale-up deviation value is determined according to the difference between the UAV scale initial vegetation index and the aggregated vegetation index.

[0015] According to a leaf area index calculation method provided by the present invention, the UAV-scale initial vegetation index of the area to be measured is subjected to scale-up aggregation processing, and the expression of the aggregated vegetation index of the area to be measured is:

[0016]

[0017] in, is the NDVI value of the kth pixel after aggregation processing; N and M are the pixel row and column numbers respectively; is the initial vegetation index at the UAV scale in the i-th row and j-th column before aggregation.

[0018] According to a leaf area index calculation method provided by the present invention, the initial satellite-scale vegetation index is corrected by using a predetermined scale-up deviation value of the area to be measured, and the expression of the satellite-scale vegetation index of the area to be measured is obtained as follows:

[0019]

[0020] in, is the initial satellite-scale vegetation index of the k-th pixel; bias is the bias value of the scale push.

[0021] According to a leaf area index calculation method provided by the present invention, the scale-up deviation value is obtained by solving the following steps:

[0022] Obtaining a second-order Taylor expansion of a function expression of the scale-up deviation value;

[0023] Determine the variance and covariance of the red band reflectance and the near infrared band reflectance at the drone scale;

[0024] The second-order Taylor expansion is solved according to the variance and covariance of the red band reflectivity and the near-infrared band reflectivity to determine the scale-up deviation value.

[0025] According to a leaf area index calculation method provided by the present invention, the expression for determining the scale-up deviation value according to the second-order Taylor expansion and the variance and the covariance is:

[0026]

[0027] NDVI p =f(NIR,R);

[0028] in, and is the second-order Taylor expansion of the second relational model, f(NIR,R) is the model function of the second relational model; VAR NIR , VAR R is the variance of R and NIR, COV NIR,R is the covariance of R and NIR.

[0029] According to a leaf area index calculation method provided by the present invention, the first relationship model is pre-constructed in the following manner:

[0030] Acquire first historical measurement data related to the drone-scale leaf area index of the area to be measured, wherein the first historical measurement data includes a mean value and a standard deviation of the drone-scale leaf area index;

[0031] The first historical measurement data and parameter information of different vegetation provided by an open source remote sensing database are used as a priori information set to set first initial parameters of a radiation transfer model;

[0032] The radiation transfer model is used to simulate and obtain vegetation spectral data under multiple different leaf area index values;

[0033] Calculating the vegetation index corresponding to each group of vegetation spectral data to determine the vegetation index under a plurality of different leaf area index values;

[0034] The first relationship model is obtained by fitting using vegetation indices under a plurality of different leaf area index values.

[0035] According to a leaf area index calculation method provided by the present invention, before setting the first initial parameter of the radiation transfer model, the method further includes:

[0036] Obtaining a UAV-scale vegetation index of the area to be measured based on the UAV aerial image of the area to be measured;

[0037] Inputting the UAV-scale vegetation index into a second relationship model to obtain the UAV-scale leaf area index of the area to be measured;

[0038] Adding the UAV-scale leaf area index of each pixel of the area to be measured and the UAV-scale vegetation index of each pixel as prior information to the prior information set for setting the first initial parameters of the radiation transfer model;

[0039] The second relationship model is a relationship model between the UAV scale vegetation index and the UAV scale leaf area index.

[0040] According to a leaf area index calculation method provided by the present invention, the second relationship model is pre-constructed in the following manner:

[0041] Acquire second historical measurement data of the ground-scale leaf area index of the area to be measured, wherein the second historical measurement data includes a mean value and a standard deviation of the ground-scale leaf area index;

[0042] Setting second initial parameters of the radiation transfer model according to the second historical measurement data and parameter information of different vegetation provided by an open source remote sensing database;

[0043] The radiation transfer model is used to simulate and obtain vegetation spectral data under multiple different leaf area index values;

[0044] Calculating the vegetation index corresponding to each group of vegetation spectral data to determine the vegetation index under a plurality of different leaf area index values;

[0045] The second relationship model is obtained by fitting using vegetation indices under a plurality of different leaf area index values.

[0046] According to a leaf area index calculation method provided by the present invention, the UAV-scale vegetation index is one of a normalized difference vegetation index, a ratio vegetation index and a vertical vegetation index.

[0047] The present invention also provides a leaf area index calculation device, comprising the following units: an image processing unit, an index correction unit and a leaf area index calculation unit, wherein:

[0048] An image processing unit, used for calculating an initial satellite-scale vegetation index based on satellite images of the area to be measured;

[0049] An index correction unit, used to correct the initial satellite-scale vegetation index using a predetermined scale-up deviation value of the area to be measured, so as to obtain the satellite-scale vegetation index of the area to be measured;

[0050] The leaf area index calculation unit is used to input the corrected satellite-scale vegetation index into a first relationship model to obtain the satellite-scale leaf area index of the area to be measured, wherein the first relationship model is a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index.

[0051] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the leaf area index calculation methods described above is implemented.

[0052] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the leaf area index calculation method as described above is implemented.

[0053] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the leaf area index calculation method described above is implemented.

[0054] The leaf area index calculation method, device, electronic device and storage medium provided by the present invention adopt a sky-ground collaborative approach, fully consider the scale difference between satellite and UAV data, and correct the initial satellite-scale vegetation index calculated from satellite images by pushing up the scale deviation value, thereby effectively reducing the uncertainty of coupled utilization of multi-scale data, realizing coupled utilization of multi-scale data, and can be applied to leaf area index extraction of heterogeneous surfaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 This is one of the flow charts of the leaf area index calculation method provided by the present invention.

[0057] Figure 2 This is the second flow chart of the leaf area index calculation method provided by the present invention.

[0058] Figure 3 This is one of the schematic diagrams of the aggregation processing of scaling up the vegetation index provided by the present invention.

[0059] Figure 4 This is the second schematic diagram of the aggregation processing of scaling up the vegetation index provided by the present invention.

[0060] Figure 5 It is a structural schematic diagram of the leaf area index calculation device provided by the present invention.

[0061] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0062] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] It should be noted that, in the description of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. The orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0064] The terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0065] The present invention provides a leaf area index calculation method for multi-scale collaboration between sky and ground. While using unmanned aerial vehicles to expand ground prior samples, the scale information between satellite and unmanned aerial vehicle platform data is extracted as prior spatial knowledge, and then the scale-up deviation value is determined to eliminate the scale deviation between multi-scale sky data and reduce the uncertainty of leaf area index inverted by satellite.

[0066] Combine the following Figure 1-Figure 6 The invention describes a leaf area index calculation method, device, electronic device and storage medium.

[0067] It should be noted that the executor of the task construction method provided by the present invention can be a server, a computer device, such as a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc.

[0068] Figure 1 This is one of the flow charts of the method for calculating the leaf area index provided by the present invention, such as Figure 1 As shown, the method includes the following steps 101, 102 and 103.

[0069] Step 101: Calculate an initial satellite-scale vegetation index based on satellite images of the area to be measured.

[0070] First, satellite images of the area to be measured are obtained using a space remote sensing satellite or other platform to determine the remote sensing image data related to the satellite image, which usually contains spectral information of multiple bands, such as spectral information of red light band and near infrared band.

[0071] Since the scattering and absorption of the atmosphere will affect the radiation flux of satellite images, atmospheric correction can be performed before calculating the vegetation index to eliminate the atmospheric effect. At the same time, to ensure the accuracy of the spatial position of the satellite image, it can be matched with the actual geographical location. In addition, the pixel value of the satellite image can be adjusted according to the actual detection needs so that it can truly reflect the radiation brightness or reflectivity of the ground object.

[0072] According to the needs of detection and the calculation formula of related indexes, select the appropriate band to calculate the vegetation index. In the calculation of vegetation index, the red band (R) and the near infrared band (NIR) are commonly used bands.

[0073] Taking the calculation of Normalized Differential Vegetation Index (NDVI) as an example, the calculation formula is:

[0074]

[0075] Among them, the NDVI value range is between -1 and 1. The closer the value is to 1, the higher the vegetation coverage is, the closer the value is to -1, the lower the vegetation coverage is, and the value close to 0 means no vegetation.

[0076] Of course, when calculating the vegetation index, it is necessary to pay attention to the impact of factors such as atmospheric correction, ground object mixing, image resolution, and vegetation type on the results. Different vegetation indices or algorithms may need to be used for different vegetation types and ecological environments.

[0077] Step 102: correct the initial satellite-scale vegetation index using a predetermined upscaling deviation value of the area to be measured to obtain the satellite-scale vegetation index of the area to be measured.

[0078] When using satellite image data to determine the Leaf Area Index (LAI), the related satellite remote sensing data usually has a large observation scale and covers a wide range, but the resolution is relatively low. This large-scale observation enables satellites to quickly obtain a wide range of surface information, but it also brings about the scale effect problem. The scale effect refers to the uncertainty in the interpretation and application of remote sensing data due to changes in the observation scale, which causes changes in the spatial distribution and attribute characteristics of the objects.

[0079] In addition, at the ground scale, the vegetation structure is complex and varied, including leaf size, shape, and arrangement. However, at the satellite scale, due to resolution limitations, these subtle vegetation structure features are often not accurately captured. This results in the satellite-scale vegetation index calculation possibly ignoring some important vegetation structure information, thus affecting the accuracy of LAI estimation.

[0080] In addition, at the satellite scale, due to its wide coverage, a pixel may contain multiple different types of objects (such as vegetation, water bodies, bare land, etc.). This mixed phenomenon of objects will make the spectral information in the satellite pixel complicated and difficult to accurately reflect the characteristics of a single object. In LAI estimation, the mixed ground objects will cause the calculation results of the vegetation index to be interfered by other ground object types, thereby reducing the accuracy.

[0081] Specifically, in the present invention, considering that in the calculation of vegetation index, due to the differences in resolution, observation angle, coverage, etc. between drone aerial images and satellite images, directly using the drone-scale vegetation index to perform scale-up and other methods to pre-construct a priori information set, fitting to obtain the first relationship model to carry out the derivation between the satellite-scale vegetation index and the satellite-scale leaf area index at the satellite scale, may lead to inaccurate results. Therefore, the present invention introduces a scale-up deviation value to correct the error generated in this process.

[0082] Among them, the scale-up deviation value is obtained based on comparative analysis between different scales (such as drone aerial images and satellite images), field surveys or historical data, which reflects the uncertainty in the interpretation and application of remote sensing data caused by scale changes. By introducing the scale-up deviation value, the initial satellite-scale vegetation index obtained through satellite image analysis can be corrected to reduce the impact of scale effects and ground object mixing, and improve the accuracy of subsequent LAI estimation, which is of great significance to the fields of ecological environment monitoring, agricultural resource management and climate change research.

[0083] Specifically, the correction process may involve using the scale-up deviation value as an adjustment factor or correction coefficient to perform operations (such as addition, multiplication, etc.) with the initial satellite-scale vegetation index to eliminate or reduce the error caused by scale changes.

[0084] It should be noted that the scale-up deviation value is used as a correction factor to calculate the initial satellite-scale vegetation index. The specific calculation method may vary depending on the application scenario and data characteristics, but the goal is to eliminate or reduce the error caused by scale changes. The corrected vegetation index will more accurately reflect the vegetation coverage of the measured area at the satellite scale, providing more reliable data support for ecological environment monitoring and resource management.

[0085] Generally speaking, the ground-scale leaf area index, drone-scale leaf area index, etc. can be used as historical measurement data, or other open source data sources or historical data can be used to verify and evaluate the revised vegetation index to ensure its accuracy and reliability.

[0086] Step 103: input the satellite-scale NDVI into the first relationship model to obtain the satellite-scale LAI of the area to be measured.

[0087] The first relationship model is a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index.

[0088] A large number of studies have shown that vegetation index has a linear or nonlinear relationship with LAI. Taking NDVI as an example, by establishing an empirical relationship between LAI and NDVI, the LAI of vegetation in the area to be analyzed can be quickly calculated. However, this is usually achieved by establishing a relationship between LAI and NDVI, such as performing simple regression modeling to generate empirical formulas for calculation, or by performing physical inversion based on radiation transfer models.

[0089] In order to derive LAI from NDVI, a relationship model needs to be established. This relationship model can be linear, nonlinear, or based on machine learning. The establishment of the first relationship model constructed in the present invention mainly relies on fitting the ground measured data (i.e., the LAI value observed on the ground) and the UAV scale leaf area index as prior information.

[0090] For example, in the leaf area index calculation method provided by the present invention, the first historical measurement data related to the drone-scale leaf area index of the same area to be measured is collected, and the first historical measurement data and the parameter information of different vegetation provided by the open source remote sensing database are used as a priori information set for fitting. The specific fitting process is described in detail in the subsequent embodiments. The establishment of a relationship model needs to take into account a variety of factors, such as vegetation type, soil type, climate conditions, etc. Therefore, it may be necessary to establish different relationship models for different regions or different vegetation types.

[0091] The accuracy and reliability of the model depend on the quantity and quality of the training data. Therefore, when building the model, it is necessary to collect as much ground-truth data as possible and conduct strict quality control on the data.

[0092] When using a model for prediction, you need to pay attention to the range of input data and the effective range of the model. If the input data exceeds the effective range of the model, it may lead to inaccurate prediction results.

[0093] The leaf area index calculation method provided by the present invention adopts a sky-ground collaborative approach, fully considers the scale difference between satellite and UAV data, and corrects the initial satellite-scale vegetation index calculated from satellite images by pushing up the scale deviation value, thereby effectively reducing the uncertainty of coupled utilization of multi-scale data and realizing coupled utilization of multi-scale data. The method can be applied to the extraction of leaf area index on heterogeneous surfaces.

[0094] Figure 2FIG. 2 is a flow chart of the method for calculating the leaf area index provided by the present invention. As an optional embodiment, Figure 2 As shown, the scale push-up deviation value is predetermined based on the following steps:

[0095] Determine the red band reflectivity and near infrared band reflectivity of the drone scale of the area to be measured based on the drone aerial image obtained after registration with the satellite image;

[0096] Determine the drone scale initial vegetation index according to the drone red band reflectance and near infrared band reflectance of the drone scale;

[0097] Performing a scale-up aggregation process on the initial vegetation index of the drone scale of the area to be measured, wherein the vegetation index of the area to be measured is scaled up from the drone scale corresponding to the drone aerial image to the satellite scale of the satellite image;

[0098] The scale-up deviation value is determined according to the difference between the UAV scale initial vegetation index and the aggregated vegetation index.

[0099] As an optional embodiment, the process of determining the scale-up deviation value is crucial to improving the accuracy of vegetation indices (such as NDVI) calculated from satellite images. The following is a detailed description of the steps for determining the scale-up deviation value based on the combination of drone aerial images and satellite images:

[0100] First, it is necessary to accurately spatially align the drone aerial images with the satellite images to ensure the consistency of their spatial positions. This usually involves steps such as unifying the geographic coordinate system, selecting the projection method, and geometric correction of the images.

[0101] In the present invention, the remote sensing image processing software ENVI (The Environment for Visualizing Images) can be used to geometrically align the UAV and satellite images based on the image to image method. Of course, the geometric alignment can also be processed by using ground control points combined with the image to map method of ENVI software to attach unified geometric projection information to the satellite images and the UAV aerial images.

[0102] Furthermore, the red band reflectivity and near-infrared band reflectivity at the drone scale can be used to calculate the initial NDVI at the drone scale according to the NDVI calculation formula.

[0103] The initial NDVI at the drone scale is aggregated and scaled up to push it from the drone scale to the satellite scale of the satellite image. This step usually involves pixel resampling, regional averaging or weighted aggregation to ensure that the information at the drone scale can be accurately reflected at the satellite scale.

[0104] After completing the scale-up and aggregation processing, the NDVI value of the measured area at the satellite scale is obtained. This NDVI value is obtained by aggregating the NDVI value at the drone scale, reflecting the vegetation coverage in a larger area.

[0105] Finally, the scale-up deviation value is determined based on the difference between the initial NDVI at the drone scale and the NDVI at the satellite scale. This scale-up deviation value reflects the change in NDVI value caused by scale change and can be used to correct the NDVI value at the satellite scale to improve its accuracy.

[0106] Figure 3 This is one of the schematic diagrams of the aggregation processing of the vegetation index for scaling up provided by the present invention, such as Figure 3 As shown, the UAV-scale initial vegetation index of the area to be measured is aggregated by upscaling, and the expression of the aggregate vegetation index of the area to be measured is:

[0107]

[0108] in, is the NDVI value of the kth pixel after aggregation processing; N and M are the pixel row and column numbers respectively; is the initial vegetation index at the UAV scale in the i-th row and j-th column before aggregation.

[0109] by Figure 3 Taking the figure as an example, assuming N = 4, M = 4, that is, the initial vegetation index of the drone scale of 16 pixels is aggregated into the NDVI value of one satellite scale pixel. Generally speaking, the values ​​of M and N are determined according to the ratio between the satellite scale and the drone scale.

[0110] Figure 4 This is the second schematic diagram of the aggregation processing of the vegetation index for scale-up provided by the present invention, such as Figure 4 As shown, it can be combined Figure 3 The scale-up aggregation process shown in the figure performs sliding sampling on all pixels at the drone scale according to a preset sliding window, where the size of the sliding window is determined by the values ​​of M and N. All the drone-scale initial vegetation indices in each sliding window are aggregated into a satellite-scale pixel NDVI value, so that the satellite-scale NDVI value of the area to be measured can be determined pixel by pixel.

[0111] Based on the content of the above embodiment, as an optional embodiment, the initial satellite-scale vegetation index is corrected by using the predetermined scale-up deviation value of the area to be measured, and the expression of the satellite-scale vegetation index of the area to be measured is obtained as follows:

[0112]

[0113] in, is the initial satellite-scale vegetation index of the k-th pixel; bias is the bias value of the scale push.

[0114] As an optional embodiment, the scale-up deviation value is obtained by solving the following steps:

[0115] Step 1: Obtain a second-order Taylor expansion of a function expression of the scale-up deviation value;

[0116] Step 2, determine the variance and covariance of the red band reflectance and the near infrared band reflectance at the drone scale;

[0117] Step 3: Solve the second-order Taylor expansion according to the variance and covariance of the red band reflectivity and the near-infrared band reflectivity to determine the scale-up deviation value.

[0118] Specifically, the function expression of the scale-up deviation value can be processed by Taylor Series Expansion Method (TEM) and the Taylor remainder can be ignored to obtain:

[0119]

[0120] in, and is the second-order Taylor expansion of the second relational model, f(NIR,R) is the model function of the second relational model; VAR NIR , VAR R is the variance of R and NIR, COV NIR,R is the covariance of R and NIR.

[0121] The second-order Taylor expansion is an approximate solution to the second-order expansion of the Taylor series. For a given model function f, its second-order Taylor expansion with respect to the variables NIR and R can be expressed as:

[0122] (1) About the second-order partial derivative of NIR It represents the second-order change rate of the representation function of the second relationship model in the NIR direction.

[0123] (2) The second-order partial derivative of R It represents the second-order rate of change of the representation function of the second relational model in the R direction.

[0124] (3) Mixed second-order partial derivatives of NIR and R It represents the cross-change rate of the representation function of the second relationship model in the NIR direction and the R direction.

[0125] And; VAR NIT , VAR R Represents the variance of NIR and R, COV NIR,R Describes the correlation between two variables in the NIR direction and the R direction.

[0126] The present invention can capture more subtle changes in the model function of the second relational model near a given variable through a second-order Taylor expansion, thereby improving the approximation accuracy of the second relational model. The relationship between the referenced variables is calculated by calculating the second-order partial derivative, which can deeply understand the mutual influence between the variables and the degree of their influence on the model function. Consider variable uncertainty: variance and covariance information can reflect the uncertainty of the variables, which helps to consider these uncertainties when predicting the model. Performing a second-order Taylor expansion at the same time can simplify the complexity of the second relational model, that is, performing a second-order Taylor expansion on the relationship between the referenced variables can provide a relatively simple approximation form, which is convenient for subsequent analysis and application.

[0127] Among them, the second relationship model describes the relationship between the red band reflectance (R) and the near-infrared band reflectance (NIR) and the leaf area index (LAI). This model is usually established based on a large amount of field measurement data and remote sensing data to predict or estimate the LAI of the area to be measured.

[0128] By considering the variance and covariance of the second-order Taylor expansion and the drone scale, the variation trend and uncertainty of the model function near a given variable can be described more accurately, thereby improving the accuracy of LAI estimation from the drone scale to the satellite scale. It is not only applicable to the upscaling of drone and satellite remote sensing data, but also to the upscaling of other remote sensing platforms and data sources, with strong adaptability and universality.

[0129] As an optional embodiment, refer to Figure 2 As shown, the first relationship model is pre-built in the following manner:

[0130] Acquire first historical measurement data related to the drone-scale LAI of the area to be measured, where the first historical measurement data includes the drone-scale LAI mean and LAI standard deviation;

[0131] The first historical measurement data and parameter information of different vegetation provided by an open source remote sensing database are used as a priori information set to set first initial parameters of a radiation transfer model;

[0132] The radiation transfer model is used to simulate and obtain vegetation spectral data under multiple different LAI values;

[0133] Calculate the NDVI corresponding to each set of vegetation spectral data to determine the NVDI under multiple different LAI values;

[0134] The first relationship model is obtained by fitting using NVDI under multiple different LAI values.

[0135] Specifically, after the drone-scale LAI of the measured area is calculated by the method provided in the above embodiment, the first historical measurement data related to the drone-scale LAI can be further calculated, mainly including the mean and standard deviation, and the expression is:

[0136]

[0137] Where n represents the total number of pixels measured by UAV-scale LAI; LAI i represents the UAV-scale LAI measurement value of the i-th pixel on the ground; LAIm is the mean value; LAIstd is the standard value; is the average value of n pixels.

[0138] Then, the first historical measurement data is collected, which may include the vegetation type, growth status, environmental factors, etc. of a specific area, as well as the mean and standard deviation of the initial LAI of the drone, etc. At the same time, different vegetation parameter information related to the vegetation type of the area to be measured is obtained from an open source remote sensing database (such as the LOPEX'93 database), such as the reflectivity and transmittance of the vegetation.

[0139] The first historical measurement data and the parameter information in the open source database are integrated to form a priori information set for setting the first initial parameters of the radiation transfer model, including: using the constructed priori information set to set the initial parameters of the radiation transfer model (such as the PROSAIL model, the 6S model, etc.) to use the radiation transfer model to simulate vegetation spectral data under different LAI values. Here, LAI is a key variable that represents the total area of ​​vegetation leaves per unit area. During the simulation process, a series of LAI values ​​(such as 0.5, 1.0, 1.5, ... 5.0, etc.) can be set, and then the corresponding vegetation spectral data is simulated for each LAI value.

[0140] Furthermore, for each set of simulated vegetation spectral data, the corresponding NDVI value is calculated using the NDVI calculation formula, so that the NDVI values ​​under multiple different LAI values ​​can be obtained.

[0141] Finally, using the NDVI values ​​under multiple different LAI values, statistical methods (such as linear regression, polynomial regression, nonlinear regression, etc.) are used to fit the first relationship model between LAI and NDVI. The fitted relationship model can be a mathematical expression for estimating the LAI value based on the NDVI value.

[0142] The expression of the first relationship model obtained by fitting is:

[0143] LA11=a1*eb1*NDVI;

[0144] Wherein, a1 and b1 are model parameters of the first relationship model, which are obtained by fitting in the above manner.

[0145] After the first relationship model is obtained, the satellite-scale NDVI obtained by the method provided in the above embodiment can be input into the first relationship model to calculate the satellite-scale LAI1 of the area to be measured.

[0146] Suppose vegetation monitoring is conducted in a temperate farmland area. First, we collect historical measurement data for the area, including crop type, leaf chlorophyll content, leaf area index, leaf inclination, etc. Then, we obtain information such as leaf water content and structural parameters of the forest in the area from the open source remote sensing database. Next, we use this information to set up a radiation transfer model and simulate vegetation spectral data with LAI ranging from 0.5 to 5.0. Finally, we calculate the NDVI value of each set of simulated data, and use the linear regression method to fit the relationship model between LAI and NDVI.

[0147] The leaf area index calculation method provided by the present invention can more accurately set the parameters of the radiation transmission model by combining the first historical measurement data and the open source remote sensing database information, thereby improving the accuracy of the simulated spectral data, and then the LAI value can be more accurately estimated by fitting the LAI and NDVI relationship model. Due to the use of open source remote sensing database information, this method can be applied to different regions and different types of vegetation, and has strong universality and scalability. Once the relationship model between LAI and NDVI is established, the remote sensing data can be used to quickly calculate the NDVI value, and then estimate the LAI value, to achieve real-time monitoring of the vegetation growth status.

[0148] Based on the content of the above embodiment, as an optional embodiment, before setting the first initial parameter of the radiation transfer model, the method further includes:

[0149] Obtaining the drone-scale NDVI of the area to be measured based on the drone aerial image of the area to be measured;

[0150] Inputting the drone-scale NDVI into a second relationship model to obtain the drone-scale LAI of the area to be measured;

[0151] Adding the drone-scale LAI of each pixel in the area to be measured and the drone-scale NDVI of each pixel as prior information to the prior information set for setting the first initial parameters of the radiation transfer model;

[0152] The second relationship model is a relationship model between drone-scale NDVI and drone-scale LAI.

[0153] The present invention can realize the coupled utilization of multi-scale data on the basis of fully considering the scale difference between satellite and drone data. Because drone scale data has the characteristics of high spatial resolution and strong real-time performance. Adding drone scale data as prior information to the model can make the model more adaptable to the actual situation of the area to be tested, and improve the applicability of the model in different regions and different types of vegetation; at the same time, drone scale data provides detailed spatial distribution information of vegetation coverage in the area to be tested. Adding this information as prior information to the model can realize refined monitoring and evaluation of vegetation in the area to be tested, and provide more scientific and accurate decision support for the fields of ecological protection and resource management.

[0154] In view of this, refer to Figure 2 As shown in the figure, the present invention can provide more detailed and accurate input information for the radiation transfer model by adding the drone-scale LAI and NDVI as prior information to the prior information set. This information can better reflect the actual condition of the vegetation in the measured area, thereby improving the accuracy of the model simulation spectral data.

[0155] The aerial images of the area to be measured are obtained by using drone aerial photography equipment, and then the aerial images are preprocessed, including radiation calibration, geometric correction, atmospheric correction and other steps to eliminate noise and distortion in the image. Based on the preprocessed aerial images, the vegetation index (such as NDVI, EVI, etc.) of each pixel is calculated. These vegetation indices reflect the growth status, coverage and other information of the vegetation in the pixel.

[0156] The calculated UAV-scale vegetation index is input into the second relationship model. This second relationship model is pre-established and describes the relationship between the UAV-scale vegetation index and the UAV-scale leaf area index. Through the second relationship model, the UAV-scale LAI value of each pixel in the measured area can be obtained, and these LAI values ​​provide detailed spatial distribution information of vegetation coverage in the measured area.

[0157] The drone-scale leaf LAI and the corresponding drone-scale NDVI of each pixel are added as prior information to the prior information set, which contains the detailed characteristic information of the vegetation in the measured area.

[0158] Finally, the set containing this prior information is used to set the first initial parameters of the radiation transfer model. These initial parameters will directly affect the accuracy and reliability of the spectral data simulated by the model.

[0159] The leaf area index calculation method provided by the present invention can realize the fusion and comprehensive utilization of multi-source data by combining UAV-scale data with the radiation transmission model, which can not only improve the utilization efficiency of data, but also provide more comprehensive and in-depth data support for research and application in related fields.

[0160] Based on the content of the above embodiment, as an optional embodiment, the second relationship model is pre-constructed in the following manner:

[0161] Acquire second historical measurement data of the ground-scale leaf area index of the area to be measured, wherein the second historical measurement data includes a mean value and a standard deviation of the ground-scale leaf area index;

[0162] Setting second initial parameters of the radiation transfer model according to the second historical measurement data and parameter information of different vegetation provided by an open source remote sensing database;

[0163] The radiation transfer model is used to simulate and obtain vegetation spectral data under multiple different leaf area index values;

[0164] Calculating the vegetation index corresponding to each group of vegetation spectral data to determine the vegetation index under a plurality of different leaf area index values;

[0165] The second relationship model is obtained by fitting using vegetation indices under a plurality of different leaf area index values.

[0166] The present invention provides a step of constructing a second relationship model, namely, a relationship model between drone-scale NDVI and drone-scale LAI, which is described in detail as follows:

[0167] You can go to the area to be measured and conduct field measurements to obtain the second historical measurement data of LAI at the ground scale. These data are usually obtained by precise ground measurement equipment, such as LAI-2000 or LAI-2200.

[0168] The second historical measurement data should contain enough sample points to reflect the diversity and representativeness of the vegetation in the measured area, usually including the mean and standard deviation of LAI, and other possible statistical information, which will not be described in detail in this embodiment.

[0169] According to the parameter information of different vegetation provided by the second historical measurement data and the open source remote sensing database, such as reflectivity, transmittance, etc., the second initial parameters of the radiation transfer model are set to simulate the spectral response of different vegetation types under different LAI values.

[0170] Use the set radiation transfer model to simulate vegetation spectral data under multiple different LAI values. The values ​​of these vegetation spectral data should cover the actual variation range of LAI in the area to be measured. For each LAI value, the radiation transfer model will output the corresponding spectral data, including the reflectance of key bands such as the red band and the near-infrared band. For each set of simulated vegetation spectral data, calculate the corresponding NDVI value according to the selected NDVI calculation formula.

[0171] This will generate a series of point sets corresponding to LAI values ​​and NDVI values ​​for the subsequent fitting of the second relationship model. Finally, statistical methods such as linear regression, polynomial regression, and machine learning algorithms are used to fit the second relationship model based on the point sets of LAI values ​​and NDVI values, which describes the relationship between drone-scale NDVI and drone-scale LAI.

[0172] Assuming that the area to be measured is a forest, the LAI-2000 device can be used to obtain ground-scale LAI data, and the parameters of the radiation transfer model can be set in combination with the information from the open source remote sensing database.

[0173] By simulating the spectral data under different LAI values ​​and calculating the corresponding NDVI values, we get a set of points that correlate LAI values ​​with NDVI values. Then we use the linear regression method to fit the second relationship model:

[0174] LAI2=a2*e b2*NDVI ;

[0175] Wherein, a2 and b2 are model parameters of the second relationship model, which are obtained by fitting in the above manner.

[0176] In this way, when we obtain drone aerial images of the area to be tested and calculate the NDVI, we can quickly invert the LAI through the model, thereby realizing real-time monitoring and evaluation of the forest growth status.

[0177] The leaf area index calculation method provided by the present invention combines the second historical measurement data of the ground-scale LAI and the open source remote sensing database information, so that the construction process of the second relationship model can fully utilize the advantages of the actual measurement data and remote sensing data, and improve the accuracy and reliability of model fitting. The construction of the second relationship model is completely data-driven, without the need for complex physical models or assumptions, which makes it more flexible and adaptable, and can adapt to different vegetation types and different environmental conditions. Once the second relationship model is constructed, the drone-scale NVDI can be quickly calculated using drone aerial images, and the drone-scale LAI can be inverted through it, which makes it possible to achieve real-time monitoring of vegetation growth status.

[0178] It should be noted that in the above embodiments of the present invention, the vegetation index NDVI is used as an example for explanation, which is not regarded as a specific limitation on the protection scope of the present invention. In the actual detection process, NDVI can also be replaced by other vegetation indices, such as the modified normalized difference vegetation index (Modified Normalized Difference Vegetation Index, MNDVI), the ratio vegetation index (Simple Ratio, SR), the vertical vegetation index (Perpendiculars Vegetation Index, PVI), etc.

[0179] Figure 5 FIG. 1 is a schematic diagram of the structure of the leaf area index calculation device provided by the present invention. Figure 5 As shown, the present invention also provides a leaf area index calculation device, which mainly includes but is not limited to an image processing unit 51, an index correction unit 52 and a leaf area index calculation unit 53, wherein:

[0180] The image processing unit 51 is mainly used to calculate the initial satellite-scale vegetation index based on the satellite image of the area to be measured.

[0181] The index correction unit 52 is mainly used to correct the initial satellite-scale vegetation index using a predetermined scale-up deviation value of the area to be measured, so as to obtain the satellite-scale vegetation index of the area to be measured.

[0182] The leaf area index calculation unit 53 is mainly used to input the corrected satellite-scale vegetation index into the first relationship model to obtain the satellite-scale leaf area index of the measured area. The first relationship model is a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index.

[0183] It should be noted that the leaf area index calculation device provided by the present invention can execute the leaf area index calculation method described in any of the above embodiments during specific operation, which will not be described in detail in this embodiment.

[0184] The leaf area index calculation device provided by the present invention adopts a sky-ground collaborative approach, fully considers the scale difference between satellite and UAV data, and corrects the initial satellite-scale vegetation index calculated from satellite images by pushing up the scale deviation value, thereby effectively reducing the uncertainty of coupled utilization of multi-scale data and realizing coupled utilization of multi-scale data. The device can be applied to the extraction of leaf area index on heterogeneous surfaces.

[0185] Figure 6 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the leaf area index calculation method, which includes: calculating the initial satellite scale vegetation index according to the satellite image of the area to be measured; correcting the initial satellite scale vegetation index using the predetermined scale push-up deviation value of the area to be measured to obtain the satellite scale vegetation index of the area to be measured; inputting the corrected satellite scale vegetation index into the first relationship model to obtain the satellite scale leaf area index of the area to be measured, wherein the first relationship model is a relationship model between the satellite scale vegetation index and the satellite scale leaf area index.

[0186] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0187] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the leaf area index calculation method provided by the above-mentioned embodiments, and the method includes: calculating an initial satellite-scale vegetation index based on satellite images of the area to be measured; correcting the initial satellite-scale vegetation index using a predetermined scale-up deviation value of the area to be measured to obtain the satellite-scale vegetation index of the area to be measured; inputting the corrected satellite-scale vegetation index into a first relationship model to obtain the satellite-scale leaf area index of the area to be measured, and the first relationship model is a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index.

[0188] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the leaf area index calculation method provided by the above-mentioned embodiments, the method comprising: calculating an initial satellite-scale vegetation index based on satellite images of the area to be measured; correcting the initial satellite-scale vegetation index using a predetermined scale-up deviation value of the area to be measured to obtain the satellite-scale vegetation index of the area to be measured; inputting the corrected satellite-scale vegetation index into a first relationship model to obtain the satellite-scale leaf area index of the area to be measured, the first relationship model being a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index.

[0189] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0190] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating leaf area index, characterized in that: include: Calculate the initial satellite-scale vegetation index based on the satellite image of the area to be measured; The initial satellite-scale vegetation index is corrected using a predetermined scale-up deviation value of the area to be measured to obtain a corrected satellite-scale vegetation index of the area to be measured; Inputting the corrected satellite-scale vegetation index into a first relationship model to obtain a satellite-scale leaf area index of the area to be measured, wherein the first relationship model is a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index; The scale push-up deviation value is predetermined based on the following steps: Determine the red band reflectivity and near infrared band reflectivity of the drone scale of the area to be measured based on the drone aerial image obtained after registration with the satellite image; Determine the drone scale initial vegetation index according to the drone red band reflectance and near infrared band reflectance of the drone scale; Performing a scale-up aggregation process on the drone-scale initial vegetation index of the area to be measured to obtain an aggregated vegetation index of the area to be measured; the scale-up is to push from the drone scale corresponding to the drone aerial image to the satellite scale of the satellite image; The scale-up deviation value is determined according to the difference between the UAV scale initial vegetation index and the aggregated vegetation index.

2. The method for calculating leaf area index according to claim 1, characterized in that: The aggregation processing of upscaling the UAV-scale initial vegetation index of the area to be measured is performed, and the expression of the aggregated vegetation index of the area to be measured is: in, is the NDVI value of the kth pixel after aggregation processing; N and M are the pixel row and column numbers respectively; is the initial vegetation index at the UAV scale in the i-th row and j-th column before aggregation.

3. The method for calculating leaf area index according to claim 2, characterized in that: The initial satellite-scale vegetation index is corrected by using the predetermined scale-up deviation value of the area to be measured, and the expression of the satellite-scale vegetation index of the area to be measured is obtained as follows: in, is the initial satellite-scale vegetation index of the k-th pixel; bias is the bias value of the scale push.

4. The method for calculating leaf area index according to claim 3, characterized in that: The scale-up deviation value is obtained based on the following steps: Obtaining a second-order Taylor expansion of a function expression of the scale-up deviation value; Determine the variance and covariance of the red band reflectance and the near infrared band reflectance at the drone scale; The second-order Taylor expansion is solved according to the variance and covariance of the red band reflectivity and the near-infrared band reflectivity to determine the scale-up deviation value.

5. The method for calculating leaf area index according to claim 4, characterized in that: The expression for determining the scale-up deviation value according to the second-order Taylor expansion and the variance and the covariance is: NDVI p =f(NIR,R); in, and is the second-order Taylor expansion of the second relational model, f(NIR,R) is the model function of the second relational model; VAR NIR , VAR R is the variance of R and NIR, COV NIR,R is the covariance of R and NIR.

6. The method for calculating leaf area index according to any one of claims 1 to 5, characterized in that: The first relational model is pre-constructed in the following manner: Acquire first historical measurement data related to the drone-scale leaf area index of the area to be measured, wherein the first historical measurement data includes a mean value and a standard deviation of the drone-scale leaf area index; The first historical measurement data and parameter information of different vegetation provided by an open source remote sensing database are used as a priori information set to set first initial parameters of a radiation transfer model; The radiation transfer model is used to simulate and obtain vegetation spectral data under multiple different leaf area index values; Calculating the vegetation index corresponding to each group of vegetation spectral data to determine the vegetation index under a plurality of different leaf area index values; The first relationship model is obtained by fitting using vegetation indices under a plurality of different leaf area index values.

7. The method for calculating leaf area index according to claim 6, characterized in that: Before setting the first initial parameters of the radiation transfer model, it also includes: Obtaining a UAV-scale vegetation index of the area to be measured based on the UAV aerial image of the area to be measured; Inputting the UAV-scale vegetation index into a second relationship model to obtain the UAV-scale leaf area index of the area to be measured; Adding the UAV-scale leaf area index of each pixel of the area to be measured and the UAV-scale vegetation index of each pixel as prior information to the prior information set for setting the first initial parameters of the radiation transfer model; The second relationship model is a relationship model between the UAV scale vegetation index and the UAV scale leaf area index.

8. The method for calculating leaf area index according to claim 7, characterized in that: The second relational model is pre-built in the following manner: Acquire second historical measurement data of the ground-scale leaf area index of the area to be measured, wherein the second historical measurement data includes a mean value and a standard deviation of the ground-scale leaf area index; Setting second initial parameters of the radiation transfer model according to the second historical measurement data and parameter information of different vegetation provided by an open source remote sensing database; The radiation transfer model is used to simulate and obtain vegetation spectral data under multiple different leaf area index values; Calculating the vegetation index corresponding to each group of vegetation spectral data to determine the vegetation index under a plurality of different leaf area index values; The second relationship model is obtained by fitting using vegetation indices under a plurality of different leaf area index values.

9. The method for calculating leaf area index according to claim 7, characterized in that: The UAV-scale vegetation index is one of a normalized difference vegetation index, a ratio vegetation index and a vertical vegetation index.

10. A leaf area index calculation device, characterized in that: include: An image processing unit, used for calculating an initial satellite-scale vegetation index based on satellite images of the area to be measured; An index correction unit, used to correct the initial satellite-scale vegetation index using a predetermined scale-up deviation value of the area to be measured, so as to obtain the satellite-scale vegetation index of the area to be measured; A leaf area index calculation unit, used for inputting the corrected satellite-scale vegetation index into a first relationship model to obtain the satellite-scale leaf area index of the area to be measured, wherein the first relationship model is a relationship model between the satellite-scale vegetation index and the satellite-scale leaf area index; The scale push-up deviation value is predetermined based on the following steps: Determine the red band reflectivity and near infrared band reflectivity of the drone scale of the area to be measured based on the drone aerial image obtained after registration with the satellite image; Determine the drone scale initial vegetation index according to the drone red band reflectance and near infrared band reflectance of the drone scale; Performing a scale-up aggregation process on the drone-scale initial vegetation index of the area to be measured to obtain an aggregated vegetation index of the area to be measured; the scale-up is to push from the drone scale corresponding to the drone aerial image to the satellite scale of the satellite image; The scale-up deviation value is determined according to the difference between the UAV scale initial vegetation index and the aggregated vegetation index.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the leaf area index calculation method according to any one of claims 1 to 9 is implemented.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the leaf area index calculation method according to any one of claims 1 to 9 is implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the leaf area index calculation method according to any one of claims 1 to 9 is implemented.

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