Inversion Method for Gross Primary Productivity and Related Equipment

Abstract

The present application discloses a method for inverting gross primary productivity and related devices, which relates to the technical field of remote sensing data processing. The method includes: obtaining land cover purity data, first spectral data, second spectral data, and first leaf area index data within a target area; screening the first spectral data and the first leaf area index data according to the land cover purity data to obtain third spectral data and second leaf area index data; establishing a leaf area index inversion model based on the third spectral data and the second leaf area index data; substituting the second spectral data into the leaf area index inversion model to obtain target leaf area index data; and determining the gross primary productivity data of the target area according to the target leaf area index data. The present application screens low-resolution data through land cover purity data and constructs an inversion model for application to high-resolution data, realizing an effective transition of cross-resolution data, thereby improving the accuracy of gross primary productivity inversion.

Description

Technical Field

[0001] The present application relates to the technical field of remote sensing data processing, and more specifically, to an inversion method of total primary productivity and related equipment. Background Art

[0002] With the development of remote sensing technology and geographic information systems, the method of inverting gross primary productivity (GPP) based on remote sensing data has been widely used. However, in the current inversion method, the adaptation problem between low-resolution data and high-resolution data is still a technical problem that needs to be solved urgently; traditional inversion methods often directly use low-resolution data for modeling, but due to the problems of insufficient resolution and poor data quality of low-resolution data itself, the accuracy of the inversion results cannot meet the actual needs.

[0003] In the prior art, leaf area index inversion based on spectral data has been widely used in the estimation of total primary productivity. Usually, the inversion of leaf area index relies on high-resolution spectral data to obtain more accurate results, but the acquisition cost of high-resolution data is high. In practical applications, the acquisition cost of low-resolution data is low and the coverage is wide. However, the quality of low-resolution data is usually poor, and there is a large amount of noisy data, which poses a challenge to the accurate inversion of primary productivity, thereby affecting the reliability and accuracy of the inversion results. How to effectively apply the modeling results of low-resolution data to the inversion of high-resolution data to achieve an effective transition across resolutions is still an important technical bottleneck. That is, there is a technical problem of low accuracy in the inversion of high-resolution total primary productivity in the prior art. Summary of the invention

[0004] A series of simplified concepts are introduced in the summary of the invention, which will be further described in detail in the detailed description. The summary of the invention of this application does not mean to attempt to define the key features and essential technical features of the technical solution claimed for protection, nor does it mean to attempt to determine the scope of protection of the technical solution claimed for protection.

[0005] The inversion method and related equipment for total primary productivity provided in this application can filter low-resolution data through land purity data, improve its quality, build an inversion model and apply it to high-resolution data, thereby achieving an effective transition across resolution data and improving the accuracy of total primary productivity inversion.

[0006] In a first aspect, the present application provides a method for inverting gross primary productivity, comprising: obtaining land cover purity data, first spectral data, second spectral data, and first leaf area index data within a target area, wherein the spatial unit area of the first spectral data, the land cover purity data, and the first leaf area index data is a first area, the spatial unit area of the second spectral data is a second area, and the first area is larger than the second area; screening the first spectral data and the first leaf area index data according to the land cover purity data to obtain third spectral data and second leaf area index data; establishing a leaf area index inversion model according to the third spectral data and the second leaf area index data, wherein the leaf area index inversion model is used to deduce leaf area index data based on spectral data; substituting the second spectral data into the leaf area index inversion model to obtain target leaf area index data; and determining the gross primary productivity data of the target area according to the target leaf area index data.

[0007] In some embodiments, the process of obtaining the land cover purity data includes: obtaining first land cover data and second land cover data within the target area, wherein the spatial unit area of the first land cover data is the first area, and the spatial unit area of the second land cover data is the second area; and determining the land cover purity data according to the corresponding relationship between the first land cover data and the second land cover data, wherein the spatial unit area of the land cover purity data is the first area.

[0008] In some embodiments, the method further includes: obtaining first normalized difference vegetation index data and second normalized difference vegetation index data within the target area, wherein the spatial unit area of the first normalized difference vegetation index data is the first area, and the spatial unit area of the second normalized difference vegetation index data is the second area; the establishing of the leaf area index inversion model according to the third spectral data and the second leaf area index data includes: dividing the first normalized difference vegetation index data into groups according to a preset numerical range of the second leaf area index data, and removing the upper quarter data and the lower quarter data of each group in the first normalized difference vegetation index data to obtain third normalized difference vegetation index data; screening the third spectral data and the second leaf area index data according to the third normalized difference vegetation index data to obtain fourth spectral data and third leaf area index data; establishing the leaf area index inversion model according to the third normalized difference vegetation index data, the fourth spectral data and the third leaf area index data; the substituting of the second spectral data into the leaf area index inversion model to obtain target leaf area index data includes: substituting the second normalized difference vegetation index data and the second spectral data into the leaf area index inversion model to obtain the target leaf area index data.

[0009] In some embodiments, the establishing of the leaf area index inversion model according to the third normalized difference vegetation index data, the fourth spectral data and the third leaf area index data includes: obtaining leaf area index quality control data corresponding to the third leaf area index data; screening the third leaf area index data according to the leaf area index quality control data to obtain fourth leaf area index data; screening out corresponding fifth spectral data and fourth normalized difference vegetation index data from the fourth spectral data and the third normalized difference vegetation index data according to the fourth leaf area index data; establishing the leaf area index inversion model according to the fourth normalized difference vegetation index data, the fifth spectral data and the fourth leaf area index data.

[0010] In some embodiments, the method further includes: obtaining sixth spectral data and seventh spectral data within the target area, wherein the sixth spectral data includes a first blue band, a first green band, a first red band, a near-infrared band, a first short-wave infrared 1 band, and a first short-wave infrared 2 band, and the seventh spectral data includes a second blue band, a second green band, a second red band, a narrow near-infrared band, a second short-wave infrared 1 band, and a second short-wave infrared 2 band; determining a first surface albedo according to the first blue band, the first green band, the first red band, the near-infrared band, the first short-wave infrared 1 band, and the first short-wave infrared 2 band; determining a second surface albedo according to the second blue band, the second green band, the second red band, the narrow near-infrared band, the second short-wave infrared 1 band, and the second short-wave infrared 2 band; determining a third surface albedo according to the first surface albedo and the second surface albedo; the determining the gross primary productivity data of the target area according to the target leaf area index data includes: determining the gross primary productivity data according to the third surface albedo and the target leaf area index data.

[0011] In some embodiments, the method further includes: obtaining meteorological reanalysis data within the target area, wherein the meteorological reanalysis data includes first minimum temperature data, first solar radiation amount data, and first saturation vapor pressure difference data; resampling the first minimum temperature data, the first solar radiation amount data, and the first saturation vapor pressure difference data to obtain second minimum temperature data, second solar radiation amount data, and second saturation vapor pressure difference data with a spatial unit area of the second area; the determining the gross primary productivity data according to the third surface albedo and the target leaf area index data includes: determining the gross primary productivity data according to the second minimum temperature data, the second solar radiation amount data, the second saturation vapor pressure difference data, the third surface albedo, and the target leaf area index data.

[0012] In some embodiments, the meteorological reanalysis data further includes precipitation data and wind speed data; the resampling of the first minimum temperature data, the first solar radiation amount data, and the first saturation vapor pressure deficit data to obtain second minimum temperature data, second solar radiation amount data, and second saturation vapor pressure deficit data with a spatial unit area of the second area includes: taking the first minimum temperature data, the first solar radiation amount data, and the first saturation vapor pressure deficit data as dependent variables in sequence, respectively screening another two types of data in the meteorological reanalysis data through correlation analysis to establish a first multi-scale geographically weighted regression model; resampling the first regression parameters, the first regression constant, and the first error term of the first multi-scale geographically weighted regression model to obtain second regression parameters, a second regression constant, and a second error term that match the second area; solving the dependent variable through a second multi-scale geographically weighted regression model established by using the second regression parameters, the second regression constant, and the second error term to obtain the second minimum temperature data, the second solar radiation amount data, and the second saturation vapor pressure deficit data.

[0013] In a second aspect, the present application further provides a gross primary productivity inversion device, including: a data acquisition unit, configured to acquire land cover purity data, first spectral data, second spectral data, and first leaf area index data within a target area, wherein the spatial unit areas of the first spectral data, the land cover purity data, and the first leaf area index data are all the first area, the spatial unit area of the second spectral data is the second area, and the first area is larger than the second area; a data screening unit, configured to screen the first spectral data and the first leaf area index data according to the land cover purity data to obtain third spectral data and a second leaf area index data; a model establishment unit, configured to establish a leaf area index inversion model according to the third spectral data and the second leaf area index data; a calculation parameter acquisition unit, configured to substitute the second spectral data into the leaf area index inversion model to obtain target leaf area index data; a productivity calculation unit, configured to determine the gross primary productivity data of the target area according to the target leaf area index data.

[0014] In a third aspect, the present application further provides an electronic device, including: a memory and a processor, and the processor is configured to implement the steps of the gross primary productivity inversion method described in the first aspect when executing a computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium, storing a computer program, and the computer program is configured to implement the steps of the gross primary productivity inversion method described in the first aspect when executed by a processor.

[0016] Fifth aspect, the present application also provides a computer program product, including a computer program or computer-executable instructions, which, when executed by a processor, implement the inversion method of the gross primary productivity provided by the embodiments of the present application.

[0017] In summary, the present application screens the first spectral data and the first leaf area index data through the land type purity data, thereby improving the quality of the low-resolution data with a spatial unit area of the first area, eliminating the low-quality data with a low land type purity, and improving the reliability and representativeness of the low-resolution data, making the corresponding relationship between the remaining spectral data and the leaf area index data more accurate, and providing a more reliable data basis for subsequent modeling; after establishing the leaf area index inversion model based on the low-resolution data, this model can be applied to the inversion of the high-resolution data with a spatial unit area of the second area; since the high-resolution data can provide more details, the inversion result will be more accurate, realizing the cross-resolution data utilization and the effective transition from the low-resolution data to the high-resolution data, thereby improving the accuracy of the leaf area index inversion. In summary, the inversion method of the gross primary productivity provided by the present application screens the low-resolution data through the land type purity data, improves its quality, constructs an inversion model and applies it to the high-resolution data, realizes the effective transition of the cross-resolution data, and improves the accuracy of the gross primary productivity inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to limit this specification. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0019] Figure 1 is a schematic flowchart of an inversion method of the gross primary productivity provided by the embodiments of the present application;

[0020] Figure 2 is a schematic composition structure diagram of a gross primary productivity inversion device provided by the embodiments of the present application;

[0021] Figure 3 is a schematic composition structure diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The terms in the description, claims, and drawings of this application, such as "first", "second", "third", "fourth", etc. (if any), are used to distinguish similar objects and do not describe a specific order or sequence. Therefore, it is understood that, under appropriate circumstances, these terms can be used interchangeably, so that the described embodiments can be implemented in different orders, unless there are special requirements in the drawings or descriptions. In addition, the terms "is" and "has" in this application and any of their variants are intended to inclusively include all possible constituent elements non-exclusively. For example, a process, method, system, product, or device that includes several steps or units does not necessarily have to be limited to the explicitly listed steps or units, but may also include other steps or units not explicitly listed, or steps or units inherent to the process, method, product, or device.

[0023] In this application, a "module" or "unit" refers to a computer program or a part of a computer program with a specific function, which works in cooperation with other relevant parts to achieve a predetermined goal. These modules or units can be implemented by software, hardware (such as processing circuits or memories), or a combination of both. One or more processors or memories can implement one or more modules or units. At the same time, each module or unit can also be a part of a larger module or unit.

[0024] The technical solutions in this application will be described in detail below with reference to the accompanying drawings in the embodiments. It should be noted that the described embodiments are only a part of this application, rather than all embodiments. In the following description, the "some embodiments" mentioned are only subsets of all possible embodiments, which can be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0025] Figure 1 It is a schematic flowchart of an inversion method for gross primary productivity provided by an embodiment of this application. Exemplarily, refer to Figure 1 The inversion method for gross primary productivity provided by the embodiment of this application may include the following steps 101 to 105:

[0026] Step 101, obtain the land class purity data, first spectral data, second spectral data, and first leaf area index data in the target area;

[0027] Among them, the spatial unit area of the first spectral data, land class purity data, and first leaf area index data is the first area, the spatial unit area of the second spectral data is the second area, and the first area is larger than the second area;

[0028] In some examples, the target area is the area for gross primary productivity inversion. The land cover purity data is numerical data used to characterize the purity or quality of various land surfaces within the target area, reflecting the proportion of a specific land cover within a particular area or pixel in the target area. The larger the proportion of a specific land cover in the area, the higher the land cover purity in that area; conversely, it indicates a higher degree of mixing. Exemplarily, specific land covers can include forests, farmlands, grasslands, etc.; remote sensing data classification techniques can be used to determine the land cover of each pixel and calculate the land cover purity data. Exemplarily, remote sensing data classification techniques can include supervised classification, unsupervised classification, and machine learning, etc. The first spectral data refers to the reflected spectral data obtained by remote sensing equipment in specific spectral bands. The first spectral data has a lower spatial resolution and is used to cover a larger area. The specific spectral bands can be spectral bands such as visible light and near-infrared; for example, the surface reflected spectral data obtained by remote sensing satellites such as the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) can be used to cover a large area, and the spatial resolution of each pixel can be 500 meters. The second spectral data is also remote sensing image data, but it has a higher spatial resolution; for example, remote sensing images can be obtained using remote sensing platforms such as Landsat 8, Sentinel-2, and the Satellite Pour l’Observation dela Terre (SPOT) series, and the spatial resolution of each pixel can be 30 meters. The Leaf Area Index (LAI) is the ratio of the total leaf area of plants to the ground area per unit ground area and is used to describe the density and health status of plant communities; the first LAI data refers to the LAI data estimated based on lower-resolution remote sensing data, and the first LAI data can be obtained through a remote sensing inversion model or by combining on-site measurements with remote sensing data. The spatial unit area refers to the actual area on the ground of the pixel or unit of remote sensing data. In a remote sensing image, each pixel represents a fixed-size ground area; for example, the first area corresponding to the spatial unit area of the first spectral data and the first LAI data is larger, such as a pixel of 500 meters × 500 meters, while the second area corresponding to the spatial unit area of the second spectral data is smaller, such as a pixel of 30 meters × 30 meters.

[0029] By implementing step 101, obtaining land cover purity data, first spectral data, second spectral data, and first leaf area index data can make full use of the characteristics of different data sources, provide more comprehensive ground information, and contribute to the inversion processing of gross primary productivity using cross-resolution data.

[0030] Step 102: According to the land cover purity data, screen the first spectral data and the first leaf area index data to obtain third spectral data and second leaf area index data.

[0031] Exemplarily, the third spectral data is the data screened from the first spectral data, and only the spectral data of areas with higher land cover purity will be retained. The second leaf area index data is the data screened from the first leaf area index data, and only the spectral data of areas with higher land cover purity will be retained.

[0032] Exemplarily, areas with higher land cover purity can be screened according to the land cover purity data, the corresponding first spectral data and first leaf area index data of these areas can be retained, and the relevant data of areas with low land cover purity can be removed to obtain third spectral data and second leaf area index data. The finally obtained data reflects higher-quality, more representative and accurate surface information and can be used for subsequent leaf area index inversion modeling.

[0033] By implementing step 102, removing the data of areas with lower land cover purity can effectively improve the quality of the first spectral data and the first leaf area index data, which are two low-resolution data, ensure that the corresponding relationship between the spectral data and the leaf area index data is more accurate, and ensure that the data used for modeling is more reliable.

[0034] Step 103: Establish a leaf area index inversion model according to the third spectral data and the second leaf area index data.

[0035] Among them, the leaf area index inversion model is used to deduce leaf area index data based on spectral data.

[0036] In some examples, the leaf area index inversion model is a mathematical model designed to use spectral data to predict or deduce the leaf area index of the target area. The leaf area index inversion model can be constructed by regression analysis, machine learning methods or statistical methods and trained using the third spectral data and the second leaf area index data obtained in step 102.

[0037] Exemplarily, methods such as Support Vector Machine (SVM), Random Forest (RF) or neural network can be used to train the model using the third spectral data so as to deduce the second leaf area index data.

[0038] Through the implementation of step 103, based on the screened data, that is, the third spectral data and the second leaf area index data, a leaf area index inversion model is established, which can realize the process of deducing the leaf area index data from the spectral data, provide a model basis for subsequent high-precision inversion, and improve the prediction accuracy of the model.

[0039] Step 104, substitute the second spectral data into the leaf area index inversion model to obtain the target leaf area index data;

[0040] In some examples, the target leaf area index data is obtained by substituting the second spectral data into a pre-established leaf area index inversion model and calculating and deducing.

[0041] Exemplarily, the second spectral data can be used as input. Through the inversion model, the leaf area index of the target area is calculated using the input spectral data, and the output target leaf area index data is the estimated leaf area index data of the target area, which represents the leaf area index value of each pixel in the area in the form of a grid. Exemplarily, the second spectral data can include the reflectance of visible light, infrared, near-infrared and other bands; the inversion model can use regression models, machine learning models, etc.

[0042] Through the implementation of step 104, the established inversion model is used to deduce the high-resolution data to obtain the target leaf area index data, which can more accurately reflect the actual situation of the target area.

[0043] Step 105, determine the gross primary productivity data of the target area according to the target leaf area index data;

[0044] Specifically, the gross primary productivity of the target area refers to the total amount of light energy converted into chemical energy by plants in the target area through photosynthesis within a specific time period, and can be effectively deduced by combining the light radiation transfer model with the target leaf area index data.

[0045] Through the implementation of step 105, according to the target leaf area index data, the gross primary productivity data of the large-scale target area is further calculated. Through the refined modeling and data screening in the previous steps, the accuracy of the gross primary productivity inversion prediction in step 105 is effectively improved.

[0046] In summary, in the embodiments of the present application, the first spectral data and the first leaf area index data are screened by the land type purity data, so as to improve the quality of the low-resolution data with a spatial unit area of the first area, eliminate the low-quality data with a low land type purity, and improve the reliability and representativeness of the low-resolution data, making the corresponding relationship between the remaining spectral data and the leaf area index data more accurate, and providing a more reliable data basis for subsequent modeling; after establishing the leaf area index inversion model based on the low-resolution data, this model can be applied to the inversion of the high-resolution data with a spatial unit area of the second area; since the high-resolution data can provide more details, the inversion result will be more accurate, realizing the cross-resolution data utilization and the effective transition from the low-resolution data to the high-resolution data, thereby improving the accuracy of the leaf area index inversion. In summary, the inversion method of the gross primary productivity provided by the embodiments of the present application screens the low-resolution data through the land type purity data, improves its quality, constructs an inversion model and applies it to the high-resolution data, realizes the effective transition of the cross-resolution data, and improves the accuracy of the gross primary productivity inversion.

[0047] In some embodiments, the process of obtaining the aforementioned land type purity data may include: obtaining the first land type data and the second land type data in the target area, where the spatial unit area of the first land type data is the first area, and the spatial unit area of the second land type data is the second area; determining the land type purity data according to the corresponding relationship between the first land type data and the second land type data, where the spatial unit area of the land type purity data is the first area.

[0048] In some examples, the first land type data is a land use / land cover data set of the target area, and its spatial resolution is the size of the "first area"; for example, the MODIS land cover product (MCD12Q1.061) data set, which is provided by the MODIS satellite of the National Aeronautics and Space Administration (NASA) of the United States, has a spatial resolution of 500 meters and provides land use / land cover types worldwide. The second land type data is another land use / land cover data set, and its spatial resolution is the size of the "second area"; for example, the Global Land Cover (GlobeLand) data set, which provides land use data with a resolution of 30 meters and can provide higher spatial details than the 500-meter resolution data. The land type purity can be calculated by comparing the MCD12Q1.061 with a resolution of 500 meters and the GlobeLand data with a resolution of 30 meters, these two data sets with different resolutions; the land type purity reflects the matching degree of the data with different resolutions at the same location; if the land type in the high-resolution (30-meter) data is very consistent within a large area of the low-resolution (500-meter) unit, the land type purity is relatively high.

[0049] Exemplarily, the land cover data in the MCD12Q1.061 dataset includes Water Bodies, Grasslands, Shrublands, Broadleaf Croplands, Savannas, Evergreen Broadleaf Forests, Deciduous Broadleaf Forests, Evergreen Needleleaf Forests, Deciduous Needleleaf Forests, Non-Vegetated Lands, and Urban and Built-up Lands; the land cover data in the GlobeLand dataset includes Cultivated Land, Forest, GrassLand, Shrubland, Wetland, Water Bodies, Tundra, Artificial Surfaces, Bareland, and Permanent Snow&Ice. The "Water Bodies" category in MCD12Q1.061 corresponds to the "Water Bodies" category in GlobeLand30; "Grasslands" corresponds to "GrassLand"; "Shrublands" corresponds to "Shrubland"; "Broadleaf Croplands" corresponds to "Cultivated Land"; "Savannas" corresponds to "Forest"; "Evergreen Broadleaf Forests", "Deciduous Broadleaf Forests", "Evergreen Needleleaf Forests", and "Deciduous Needleleaf Forests" all correspond to the "Forest" category; "Non-Vegetated Lands" corresponds to "Bareland"; and "Urban and Built-up Lands" corresponds to "Artificial Surfaces".

[0050] Through the implementation of the above embodiments, the first land cover data and the second land cover data (such as GlobeLand30, 30-meter resolution data) in the target area are obtained, and data fusion is performed through the corresponding relationship between the two, which can overcome the differences between data of different resolutions, balance between a larger scale (500 meters) and a smaller scale (30 meters), and eliminate low-quality and low-representative areas; Exemplarily, the first land cover data can be 500-meter resolution data such as MCD12Q1.061, and the second land cover data can be 30-meter resolution data such as GlobeLand30. Specifically, the first land cover data may have large spatial inhomogeneity due to its lower resolution, while the second land cover data provides more detailed information and can complement the deficiencies of the first land cover data; furthermore, by accurately determining the land cover purity data, the subsequent model establishment and prediction analysis are made more reliable.

[0051] In some embodiments, the foregoing method may further include: obtaining the first normalized difference vegetation index data and the second normalized difference vegetation index data in the foregoing target area, where the spatial unit area of the first normalized difference vegetation index data is the first area, and the spatial unit area of the second normalized difference vegetation index data is the second area; Step 103 may include: dividing the first normalized difference vegetation index data into groups according to the preset numerical interval of the second leaf area index data, and removing the upper quarter data and the lower quarter data of each group in the first normalized difference vegetation index data to obtain the third normalized difference vegetation index data; screening the third spectral data and the second leaf area index data according to the third normalized difference vegetation index data to obtain the fourth spectral data and the third leaf area index data; establishing a leaf area index inversion model according to the third normalized difference vegetation index data, the fourth spectral data, and the third leaf area index data; Step 104 may include: substituting the second normalized difference vegetation index data and the second spectral data into the leaf area index inversion model to obtain the target leaf area index data.

[0052] In some examples, the first normalized difference vegetation index data is the normalized difference vegetation index data calculated from remote sensing images. The second normalized difference vegetation index data is the normalized difference vegetation index data calculated from high-resolution remote sensing data. The leaf area index has a strong positive correlation with the normalized difference vegetation index. Within a specific leaf area index value range, the normalized difference vegetation index value often falls within a relatively stable normal range. Therefore, dividing the preset numerical range of the second leaf area index data can screen out more representative normalized difference vegetation index data. For example, the preset numerical range can be an interval of 0.2 m² / m² (such as 0 m² / m² - 0.2 m² / m², 0.2 m² / m² - 0.4 m² / m², etc.). In the implementation process, according to the preset numerical range of the second leaf area index data, the first normalized difference vegetation index data can be divided into several groups and the data within each group can be sorted. In each group, the upper quarter and the lower quarter of the first normalized difference vegetation index data can be removed to eliminate potential extreme values or abnormal data, resulting in the third normalized difference vegetation index data. This process can effectively reduce the noise in the data and enhance the robustness and stability of the subsequent model. The fourth spectral data and the third leaf area index data are the band data and index data selected from the third spectral data and the second leaf area index data that are consistent with the area / pixel where the third normalized difference vegetation index data is located, based on the third normalized difference vegetation index data.

[0053] Through the implementation of the above embodiments, by dividing the first normalized difference vegetation index data into groups and removing the upper quarter and the lower quarter of the abnormal samples, the noise and extreme values in the data can be effectively removed, making the subsequent model training more stable and the inversion result more accurate.

[0054] In some embodiments, establishing a leaf area index inversion model based on the third normalized difference vegetation index data, the fourth spectral data, and the third leaf area index data may include: obtaining leaf area index quality control data corresponding to the third leaf area index data; screening the third leaf area index data according to the leaf area index quality control data to obtain the fourth leaf area index data; screening from the fourth spectral data and the third normalized difference vegetation index data to obtain the fifth spectral data and the fourth normalized difference vegetation index data corresponding to the fourth leaf area index data; and establishing a leaf area index inversion model based on the fourth normalized difference vegetation index data, the fifth spectral data, and the fourth leaf area index data.

[0055] In some examples, the leaf area index quality control data is an indicator used to evaluate the quality of leaf area index data. Through these quality control data, it is possible to identify and eliminate leaf area index data points with poor quality or unreliability, ensuring the accuracy of subsequent data analysis. The leaf area index quality control data can come from the quality marks attached by the remote sensing platform when obtaining the leaf area index data. For example, the leaf area index data product (MOD15A2H) of MODIS provides leaf area index quality control data information. The fourth leaf area index data is more accurate leaf area index data obtained after quality control screening. The fifth spectral data and the fourth normalized difference vegetation index data are data sets selected from the spectral data corresponding to the fourth leaf area index data, which are consistent with the area / pixel where the fourth leaf area index data is located.

[0056] Exemplarily, in the leaf area index data of MODIS, the leaf area index quality control data is used to mark which pixel's leaf area index value is reliable. The fourth leaf area index data can be obtained by deleting the leaf area index quality control data outside the range of 0 to 8 and the data with odd leaf area index quality control data.

[0057] Through the implementation of the above embodiments, by performing quality control screening on the leaf area index data and removing low-quality or unreliable data points, the quality of the training data for establishing the leaf area index inversion model can be significantly improved, thereby improving the accuracy of the inversion result.

[0058] In some embodiments, the foregoing method may further include: obtaining the sixth spectral data and the seventh spectral data within the foregoing target area, wherein the sixth spectral data may include the first blue band, the first green band, the first red band, the near-infrared band, the first short-wave infrared 1 band, and the first short-wave infrared 2 band, and the foregoing seventh spectral data may include the second blue band, the second green band, the second red band, the narrow near-infrared band, the second short-wave infrared 1 band, and the second short-wave infrared 2 band; determining the first surface albedo according to the foregoing first blue band, first green band, first red band, near-infrared band, first short-wave infrared 1 band, and first short-wave infrared 2 band; determining the second surface albedo according to the second blue band, second green band, second red band, narrow near-infrared band, second short-wave infrared 1 band, and second short-wave infrared 2 band; determining the third surface albedo according to the first surface albedo and the second surface albedo; the foregoing step 105 may include: determining the gross primary productivity data according to the third surface albedo and the target leaf area index data.

[0059] In some examples, the first blue band, the first green band, the first red band, the near infrared band, the first shortwave infrared band 1 and the first shortwave infrared band 2 are derived from a certain type of remote sensing satellite image data, which can be obtained through remote sensing images; the second blue band, the second green band, the second red band, the narrow near infrared band, the second shortwave infrared band 1 and the second shortwave infrared band 2 are derived from remote sensing satellites or different sensor devices different from those used to obtain the sixth spectral data. Albedo is an indicator of reflectivity, which indicates the ratio of solar radiation reflected by the surface to incident radiation; the first surface albedo is calculated based on the sixth spectral data; the second surface albedo is calculated based on the seventh spectral data; the third surface albedo is a comprehensive albedo obtained by combining the first surface albedo and the second surface albedo, using weighted average or other data fusion methods; illustratively, the sixth spectral data can be the data of each band in the Landsat image, and the seventh spectral data can be the data of each band in the Sentinel-2 image. The total primary productivity data can be effectively derived using radiative transfer models and energy balance models by combining the third surface albedo and target leaf area index data.

[0060] Exemplarily, the first surface albedo can be calculated by the following formula:

[0061]

[0062] In the formula, is the first surface albedo, represents the first blue band, represents the first green band, represents the first red band, represents the near-infrared band, Indicates the first short-wave infrared 1 band, Indicates the first shortwave infrared 2 band.

[0063] The second surface albedo can be calculated by the following formula:

[0064]

[0065] In the formula, is the second surface albedo, represents the second blue band, represents the second green band, represents the second red band, represents a narrow near-infrared band, Indicates the second short-wave infrared 1 band, Indicates the second shortwave infrared 2 band.

[0066] By implementing the above embodiments, using the sixth spectral data and the seventh spectral data, the surface features that may be overlooked by single-source data can be effectively compensated. By calculating the first surface albedo and the second surface albedo and combining the two to obtain the third surface albedo, the surface reflection characteristics of the target area can be evaluated more precisely, and then the gross primary productivity data of the target area can be retrieved more accurately.

[0067] In some embodiments, the foregoing method may further include: obtaining meteorological reanalysis data within the foregoing target area, where the meteorological reanalysis data may include first minimum temperature data, first solar radiation amount data, and first saturation vapor pressure difference data; resampling the foregoing first minimum temperature data, first solar radiation amount data, and first saturation vapor pressure difference data to obtain second minimum temperature data, second solar radiation amount data, and second saturation vapor pressure difference data with a spatial unit area of the second area; determining the gross primary productivity data according to the third surface albedo and the target leaf area index data, which may include: determining the gross primary productivity data according to the second minimum temperature data, second solar radiation amount data, second saturation vapor pressure difference data, third surface albedo, and target leaf area index data.

[0068] In some examples, meteorological reanalysis data is the historical meteorological conditions reanalyzed by combining numerical weather prediction models and historical meteorological observation data. It usually includes multiple meteorological elements such as temperature, radiation amount, humidity, air pressure, etc., and has high temporal continuity and spatial coverage. The meteorological reanalysis data in the embodiments of the present application can often be extracted from the published data of meteorological agencies such as the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The first minimum temperature data, the first solar radiation amount data, and the first saturation vapor pressure deficit data are specific meteorological elements from the meteorological reanalysis data. The second minimum temperature data, the second solar radiation amount data, and the second saturation vapor pressure deficit data are the data after resampling the first minimum temperature data, the first solar radiation amount data, and the first saturation vapor pressure deficit data. The resampling process can be achieved through spatial interpolation or regridding operations. For example, meteorological data with a 1-kilometer resolution can be resampled to data with a 500-meter or 30-meter resolution and processed using Geographic Information System (GIS) software or programming tools. By combining the second minimum temperature, the second solar radiation amount, the second saturation vapor pressure deficit, the third surface albedo, and the target leaf area index data, using a multi-variable radiative transfer model and an energy balance model, and considering the effects of temperature, moisture, and radiation, the gross primary productivity data of the target area can be comprehensively derived.

[0069] Through the implementation of the above embodiments, by resampling and integrating multiple data sources such as remote sensing data and meteorological data, meteorological data with a lower resolution can be converted into a format compatible with high-resolution remote sensing data. Thus, by combining meteorological data with factors such as surface albedo and leaf area index, the gross primary productivity data of the target area can be estimated more accurately.

[0070] In some embodiments, the aforementioned meteorological reanalysis data may further include precipitation data and wind speed data; the resampling of the first minimum temperature data, the first solar radiation data, and the first saturation vapor pressure deficit data to obtain the second minimum temperature data, the second solar radiation data, and the second saturation vapor pressure deficit data with a second area as the spatial unit area may include: taking the first minimum temperature data, the first solar radiation data, and the first saturation vapor pressure deficit data as dependent variables in sequence, respectively screening another two types of data in the meteorological reanalysis data through correlation analysis to establish a first multi-scale geographically weighted regression model. During the establishment of the first multi-scale geographically weighted regression model, the normalized difference vegetation index data and the digital elevation data of the target area may also be taken as independent variables; resampling the first regression parameter, the first regression constant, and the first error term of the first multi-scale geographically weighted regression model to obtain a second regression parameter, a second regression constant, and a second error term that match the second area; solving the dependent variable through the second multi-scale geographically weighted regression model established by using the second regression parameter, the second regression constant, and the second error term to obtain the second minimum temperature data, the second solar radiation data, and the second saturation vapor pressure deficit data.

[0071] In some examples, the Geographically Weighted Regression (GWR) model is a spatial statistical model that can handle the spatial heterogeneity between independent and dependent variables in spatial data, that is, the regression relationships in different regions may be different; the Multi-Scale Geographically Weighted Regression (MGWR) is an extension of the geographically weighted regression, which conducts regression analysis at different scales to better capture the multi-level characteristics of spatial data; the first multi-scale geographically weighted regression model refers to the initial regression model constructed based on the first minimum temperature data, the first solar radiation data, and the first saturation vapor pressure deficit data; the first regression parameter, the first regression constant, and the first error term are obtained when constructing the first multi-scale geographically weighted regression model; the second multi-scale geographically weighted regression model is a new regression model obtained by resampling the first regression parameter, the first regression constant, and the first error term, and is used to process new spatial data; different from the first multi-scale geographically weighted regression model, the second multi-scale geographically weighted regression model will conduct analysis according to the new spatial unit to obtain a more refined spatial prediction.

[0072] Exemplarily, first, perform a correlation analysis on meteorological data to find the independent variable data that is most similar to the change trend of the first minimum temperature data; for example, solar radiation and precipitation may have a strong correlation with the minimum temperature; then use the minimum temperature data as the dependent variable, and solar radiation and precipitation as the independent variables to establish a first multi-scale geographically weighted regression model, and then resample the first regression parameter, the first regression constant, and the first error term of the first multi-scale geographically weighted regression model to obtain a second multi-scale geographically weighted regression model, and further obtain more refined second minimum temperature data; finally, repeat the above steps to perform similar regression analyses on solar radiation data and saturated vapor pressure difference data respectively, using them as the dependent variables in turn to obtain second solar radiation data and second saturated vapor pressure difference data.

[0073] Through the implementation of the above embodiments, using correlation analysis to screen the most relevant independent variables can ensure that the information used in the regression model is more relevant and improve the prediction accuracy; using a multi-scale geographically weighted regression model can reveal the relationship between the dependent variable and the independent variables in different geographical regions and at different scales, and can effectively integrate various meteorological data together, providing accurate basic data for the further inversion of gross primary productivity data.

[0074] Furthermore, as an implementation of the foregoing method embodiments, the present application also provides a gross primary productivity inversion device for implementing the foregoing method embodiments. This device embodiment corresponds to the foregoing method embodiments. For the convenience of reading, the details of the foregoing method embodiments will not be repeated one by one in this gross primary productivity inversion device embodiment, but it should be clear that the device in the embodiments of the present application can correspondingly implement all the content of the foregoing method embodiments. For example Figure 2As shown, the total primary productivity inversion device 20 includes: a data acquisition unit 201, a data screening unit 202, a model building unit 203, a calculation parameter acquisition unit 204 and a productivity calculation unit 205, wherein the data acquisition unit 201 is used to acquire land purity data, first spectral data, second spectral data and first leaf area index data in the target area, wherein the spatial unit area of ​​the first spectral data, the land purity data and the first leaf area index data is the first area, the spatial unit area of ​​the second spectral data is the second area, and the first area is greater than the second area; the data screening unit 202 is used to screen the first spectral data and the first leaf area index data according to the land purity data to obtain the third spectral data and the second leaf area index data; the model building unit 203 is used to establish a leaf area index inversion model according to the third spectral data and the second leaf area index data; the calculation parameter acquisition unit 204 is used to substitute the second spectral data into the leaf area index inversion model to obtain the target leaf area index data; the productivity calculation unit 205 is used to determine the total primary productivity data of the target area according to the target leaf area index data.

[0075] In some embodiments, the data acquisition unit 201 is also used to acquire first land class data and second land class data in the target area, wherein the spatial unit area of ​​the first land class data is a first area, and the spatial unit area of ​​the second land class data is a second area; based on the correspondence between the first land class data and the second land class data, the land class purity data is determined, wherein the spatial unit area of ​​the land class purity data is the first area.

[0076] In some embodiments, the data acquisition unit 201 is further used to acquire first normalized difference vegetation index data and second normalized difference vegetation index data in the target area, wherein the spatial unit area of ​​the first normalized difference vegetation index data is the first area, and the spatial unit area of ​​the second normalized difference vegetation index data is the second area; the model establishment unit 203 is further used to group the first normalized difference vegetation index data according to a preset numerical interval of the second leaf area index data, and remove the upper quarter data and the lower quarter data of each group in the first normalized difference vegetation index data to obtain third normalized difference vegetation index data; according to the third normalized difference vegetation index data, the third spectral data and the second leaf area index data are screened to obtain fourth spectral data and third leaf area index data; according to the third normalized difference vegetation index data, the fourth spectral data and the third leaf area index data, a leaf area index inversion model is established; the calculation parameter acquisition unit 204 is further used to substitute the second normalized difference vegetation index data and the second spectral data into the leaf area index inversion model to obtain target leaf area index data.

[0077] In some embodiments, the model building unit 203 is further configured to obtain leaf area index quality control data corresponding to the third leaf area index data; screen the third leaf area index data according to the leaf area index quality control data to obtain fourth leaf area index data; screen from the fourth spectral data and the third normalized difference vegetation index data to obtain fifth spectral data and fourth normalized difference vegetation index data corresponding to the fourth leaf area index data; and establish a leaf area index inversion model according to the fourth normalized difference vegetation index data, the fifth spectral data, and the fourth leaf area index data.

[0078] In some embodiments, the data acquisition unit 201 is further configured to obtain sixth spectral data and seventh spectral data within a target area, where the sixth spectral data includes a first blue band, a first green band, a first red band, a near-infrared band, a first short-wave infrared 1 band, and a first short-wave infrared 2 band, and the seventh spectral data includes a second blue band, a second green band, a second red band, a narrow near-infrared band, a second short-wave infrared 1 band, and a second short-wave infrared 2 band; determine a first surface albedo according to the first blue band, the first green band, the first red band, the near-infrared band, the first short-wave infrared 1 band, and the first short-wave infrared 2 band; determine a second surface albedo according to the second blue band, the second green band, the second red band, the narrow near-infrared band, the second short-wave infrared 1 band, and the second short-wave infrared 2 band; determine a third surface albedo according to the first surface albedo and the second surface albedo; and the productivity calculation unit 205 is further configured to determine total primary productivity data according to the third surface albedo and the target leaf area index data.

[0079] In some embodiments, the data acquisition unit 201 is further configured to obtain meteorological reanalysis data within a target area, where the meteorological reanalysis data includes first minimum temperature data, first solar radiation amount data, and first saturation vapor pressure difference data; resample the first minimum temperature data, the first solar radiation amount data, and the first saturation vapor pressure difference data to obtain second minimum temperature data, second solar radiation amount data, and second saturation vapor pressure difference data with a spatial unit area of a second area; and the productivity calculation unit 205 is further configured to determine total primary productivity data according to the second minimum temperature data, the second solar radiation amount data, the second saturation vapor pressure difference data, the third surface albedo, and the target leaf area index data.

[0080] In some embodiments, the meteorological reanalysis data further includes precipitation data and wind speed data; the data acquisition unit 201 is further configured to use the first minimum temperature data, the first solar radiation amount data, and the first saturation vapor pressure difference data as dependent variables in sequence, and respectively screen the other two types of data in the meteorological reanalysis data through correlation analysis to establish the first multi-scale geographically weighted regression model; resample the first regression parameter, the first regression constant, and the first error term of the first multi-scale geographically weighted regression model to obtain a second regression parameter, a second regression constant, and a second error term that match the second area; solve the dependent variable through the second multi-scale geographically weighted regression model established by using the second regression parameter, the second regression constant, and the second error term to obtain the second minimum temperature data, the second solar radiation amount data, and the second saturation vapor pressure difference data.

[0081] The present application also provides a computer-readable storage medium, which stores computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will be caused to execute any step of the total primary productivity inversion method provided by the present application.

[0082] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it may also be various devices including one or any combination of the above memories.

[0083] In some embodiments, the computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0084] In some embodiments, the computer-executable instructions may or may not correspond to a file in the file system, and may be stored as part of a file that stores other programs or data. For example, they may be stored in one or more scripts in a hypertext markup language (HTML) document, stored in a single file dedicated to the program in question, or stored in multiple cooperating files (such as files that store one or more modules, subroutines, or code portions).

[0085] In some embodiments, the computer-executable instructions may be deployed to execute on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0086] As Figure 3 shown, the present application also provides an electronic device 30, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, any step of the above-mentioned total primary productivity inversion method is implemented.

[0087] The present application also provides a computer program product, which includes a computer program or computer-executable instructions stored in a computer-readable storage medium. The processor of the electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and the processor executes the computer program or computer-executable instructions, so that the electronic device executes any step of the total primary productivity inversion method described above in the present application.

[0088] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for inversion of total primary productivity, characterized in that: include: Acquire land purity data, first spectral data, second spectral data and first leaf area index data in the target area, wherein the spatial unit areas of the first spectral data, the land purity data and the first leaf area index data are all first areas, the spatial unit area of ​​the second spectral data is second area, the first area is greater than the second area, and the land purity data is numerical data used to characterize the purity or quality of various types of land surfaces in the target area; According to the land type purity data, the first spectrum data and the first leaf area index data are screened to obtain third spectrum data and second leaf area index data; Establishing a leaf area index inversion model according to the third spectral data and the second leaf area index data, wherein the leaf area index inversion model is used to deduce the leaf area index data based on the spectral data; Substituting the second spectral data into the leaf area index inversion model to obtain target leaf area index data; Determining the total primary productivity data of the target area according to the target leaf area index data; The process of acquiring the land class purity data includes: acquiring the first land class data and the second land class data in the target area, wherein the spatial unit area of ​​the first land class data is the first area, and the spatial unit area of ​​the second land class data is the second area; determining the land class purity data according to the corresponding relationship between the first land class data and the second land class data, wherein the spatial unit area of ​​the land class purity data is the first area.

2. The inversion method of total primary productivity according to claim 1, characterized in that: The method further comprises: Acquire first normalized difference vegetation index data and second normalized difference vegetation index data in the target area, wherein the spatial unit area of ​​the first normalized difference vegetation index data is the first area, and the spatial unit area of ​​the second normalized difference vegetation index data is the second area; The step of establishing a leaf area index inversion model according to the third spectral data and the second leaf area index data comprises: The first normalized difference vegetation index data are divided into groups according to the preset value interval of the second leaf area index data, and the upper quarter data and the lower quarter data of each group in the first normalized difference vegetation index data are eliminated to obtain third normalized difference vegetation index data; According to the third normalized difference vegetation index data, the third spectral data and the second leaf area index data are screened to obtain fourth spectral data and third leaf area index data; Establishing the leaf area index inversion model according to the third normalized difference vegetation index data, the fourth spectral data and the third leaf area index data; Substituting the second spectral data into the leaf area index inversion model to obtain target leaf area index data includes: Substituting the second normalized difference vegetation index data and the second spectral data into the leaf area index inversion model, the target leaf area index data is obtained.

3. The inversion method of total primary productivity according to claim 2, characterized in that: The step of establishing the leaf area index inversion model according to the third normalized difference vegetation index data, the fourth spectral data and the third leaf area index data comprises: Acquire leaf area index quality control data corresponding to the third leaf area index data; According to the leaf area index quality control data, the third leaf area index data is screened to obtain fourth leaf area index data; Filtering the fourth spectral data and the third normalized difference vegetation index data to obtain fifth spectral data and fourth normalized difference vegetation index data corresponding to the fourth leaf area index data; The leaf area index inversion model is established according to the fourth normalized difference vegetation index data, the fifth spectral data and the fourth leaf area index data.

4. The inversion method of total primary productivity according to claim 1, characterized in that: The method further comprises: Acquire sixth spectral data and seventh spectral data within a preset target area, wherein the sixth spectral data includes a first blue band, a first green band, a first red band, a near infrared band, a first short-wave infrared 1 band, and a first short-wave infrared 2 band, and the seventh spectral data includes a second blue band, a second green band, a second red band, a narrow near infrared band, a second short-wave infrared 1 band, and a second short-wave infrared 2 band; Determine a first surface albedo according to the first blue band, the first green band, the first red band, the near infrared band, the first shortwave infrared 1 band, and the first shortwave infrared 2 band; determining a second surface albedo according to the second blue band, the second green band, the second red band, the narrow near infrared band, the second shortwave infrared 1 band, and the second shortwave infrared 2 band; Determining a third surface albedo according to the first surface albedo and the second surface albedo; Determining the total primary productivity data of the target area according to the target leaf area index data comprises: The total primary productivity data is determined based on the third surface albedo and the target leaf area index data.

5. The inversion method of total primary productivity according to claim 4, characterized in that: The method further comprises: Acquiring meteorological reanalysis data in the target area, wherein the meteorological reanalysis data includes first minimum temperature data, first solar radiation data, and first saturated air pressure difference data; Resampling the first minimum temperature data, the first solar radiation data, and the first saturated air pressure difference data to obtain second minimum temperature data, second solar radiation data, and second saturated air pressure difference data whose spatial unit area is the second area; Determining the total primary productivity data according to the third surface albedo and the target leaf area index data comprises: The total primary productivity data is determined according to the second minimum temperature data, the second solar radiation data, the second saturated air pressure difference data, the third surface albedo and the target leaf area index data.

6. The inversion method of total primary productivity according to claim 5, characterized in that: The meteorological reanalysis data also includes precipitation data and wind speed data; The resampling of the first minimum temperature data, the first solar radiation data, and the first saturated air pressure difference data to obtain second minimum temperature data, second solar radiation data, and second saturated air pressure difference data having a spatial unit area of ​​the second area includes: The first minimum temperature data, the first solar radiation data and the first saturated air pressure difference data are used as dependent variables in sequence, and the other two data in the meteorological reanalysis data are selected through correlation analysis to establish a first multi-scale geographically weighted regression model; Resampling the first regression parameter, the first regression constant and the first error term of the first multi-scale geographically weighted regression model to obtain a second regression parameter, a second regression constant and a second error term matching the second area; By using the second multi-scale geographically weighted regression model established by using the second regression parameter, the second regression constant and the second error term, the dependent variable is solved to obtain the second minimum temperature data, the second solar radiation data and the second saturated air pressure difference data.

7. A total primary productivity inversion device, characterized in that: include: A data acquisition unit, used for acquiring land purity data, first spectral data, second spectral data and first leaf area index data in a target area, wherein the spatial unit areas of the first spectral data, the land purity data and the first leaf area index data are all first areas, the spatial unit area of ​​the second spectral data is a second area, and the first area is larger than the second area; A data screening unit, configured to screen the first spectrum data and the first leaf area index data according to the land type purity data to obtain third spectrum data and second leaf area index data; A model building unit, used for building a leaf area index inversion model according to the third spectral data and the second leaf area index data; A calculation parameter acquisition unit, used for substituting the second spectral data into the leaf area index inversion model to obtain target leaf area index data; A productivity calculation unit, used for determining the total primary productivity data of the target area according to the target leaf area index data; The data acquisition unit is further used to acquire first land class data and second land class data in the target area, wherein the spatial unit area of ​​the first land class data is the first area, and the spatial unit area of ​​the second land class data is the second area; and the land class purity data is determined according to the corresponding relationship between the first land class data and the second land class data, wherein the spatial unit area of ​​the land class purity data is the first area.

8. An electronic device comprising: A memory and a processor, characterized in that the processor is used to implement the steps of the inversion method of total primary productivity as described in any one of claims 1 to 6 when executing the computer program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the inversion method of total primary productivity according to any one of claims 1 to 6 are implemented.

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