Black soil carbon sink potential assessment method and system based on grid precise zoning

Abstract

The present application relates to the field of administrative information technology, and provides a method and system for assessing the carbon sink potential of black soil based on grid precision zoning. By dividing the target study area into spatially continuous grid units, the environmental similarity of each grid unit is analyzed based on environmental variable data, and the target study area is divided into multiple sub-areas according to the degree of environmental similarity; the soil organic carbon content inversion model is used to obtain the soil carbon sequestration amount of each grid unit, and the difference between the maximum soil carbon sequestration amount of each sub-area and the soil carbon sequestration amount of each grid unit in the sub-area is used as the soil carbon sequestration potential value. The above scheme divides the grid units in the target study area according to environmental similarity, and can classify grid units with smaller environmental differences within the regional range into the same sub-area. By using grid precision zoning assessment means and integrating satellite remote sensing technology, the engineering application of the entire process of organic carbon sink potential estimation can be realized.

Description

Technical Field

[0001] The present application relates to the field of administrative management information technology, and in particular to a method and system for evaluating the carbon sink potential of black soil based on precise grid zoning. Background Art

[0002] Black soil has a high content of soil organic matter (usually between 3% and 10%). Its good soil structure and rich organic matter content give it a strong carbon sequestration capacity. In the context of global climate change, the carbon sequestration capacity of black soil is of great significance for mitigating climate change. Scientifically evaluating the carbon sequestration capacity of black soil, combined with reasonable land use and agricultural management measures, can further tap the carbon sequestration potential of black soil.

[0003] At present, soil carbon sink estimation is still mainly based on point monitoring to obtain data, which requires a lot of monitoring equipment or detection costs, and is time-consuming and labor-intensive. With the development of remote sensing technology, some studies have also introduced remote sensing images as the data basis, using direct estimation methods or vegetation index estimation methods to obtain regional-level soil carbon sink estimation results. However, the soil carbon cycle is significantly affected by environmental factors (temperature, rainfall, topography, etc.), and the soil itself has obvious heterogeneity. Traditional regional soil carbon sequestration potential assessment methods usually do not take into account soil heterogeneity, and it is difficult to ensure the accuracy of large-scale estimates.

[0004] Furthermore, the process from data collection, data processing, model building, carbon sink calculation to the generation of applicable results is complicated and tedious, with high technical barriers and a current lack of engineering means.

[0005] Therefore, there is an urgent need to innovate and invent a precise black soil organic carbon sink potential assessment system. Summary of the invention

[0006] The purpose of this application is to provide a black soil carbon sink potential assessment method and system based on grid precision zoning to solve or alleviate the problem of lack of accurate black soil soil organic carbon sink potential assessment means. At the same time, it solves or alleviates the problem that the existing organic carbon sink potential assessment method has high monitoring cost, complex process, and difficulty in large-scale engineering application.

[0007] In order to achieve the above objectives, this application provides the following technical solutions:

[0008] This application provides a method for assessing the carbon sink potential of black soil based on grid-based precise zoning, including:

[0009] Obtain satellite image data, sample point test data, and environmental variable data of the target study area; wherein the satellite image data divides the target study area into spatially continuous grid units according to a preset spatial resolution;

[0010] Based on the satellite image data of the target study area and the sample test data, a soil organic carbon content inversion model is constructed, and based on the soil organic carbon content inversion model, the soil carbon sequestration amount of each grid unit in the target study area is calculated;

[0011] Based on the environmental variable data, analyzing the environmental similarity of each grid unit, and dividing the target study area into a plurality of sub-areas based on the environmental similarity;

[0012] For each of the sub-regions, the maximum value of soil carbon sequestration of all grid cells within the sub-region is counted as the maximum limit of soil carbon sequestration in the sub-region;

[0013] For any grid cell, the difference between the maximum limit of soil carbon sequestration in the sub-region where the grid cell is located and the soil carbon sequestration amount of the grid cell is calculated as the soil carbon sequestration potential value of the grid cell.

[0014] Preferably, the environmental variable data includes raster data of N environmental parameters, and the spatial resolution of the raster data of the environmental parameters is the same as the spatial resolution of the satellite image data; N is a positive integer greater than or equal to 7;

[0015] Based on the environmental variable data, the environmental similarity of each grid unit is analyzed, and based on the environmental similarity, the target study area is divided into a plurality of sub-areas, including:

[0016] According to the preset environmental parameter value assignment classification rule, the grid units are classified according to the value of each environmental parameter of each grid unit to obtain N environmental type values ​​corresponding to each grid unit under the classification rule of each environmental parameter;

[0017] Compare the corresponding environmental type values ​​of any two grid cells under the classification rule of each environmental parameter. If the environmental type values ​​of the two grid cells meet the preset similarity conditions, the two grid cells are classified into the same sub-area.

[0018] Preferably, the similarity condition is specifically that: the N environment type values ​​of the two grid units are the same.

[0019] Preferably, after dividing the target research area into a plurality of sub-areas, the method further comprises:

[0020] Counting the number of grid cells contained in each of the sub-areas;

[0021] If the ratio of the number of grid cells contained in the first sub-area to the total number of grid cells in the target study area is less than a preset ratio threshold, all grid cells contained in the sub-area are divided into the second sub-area, wherein the first sub-area is any sub-area after the division, and the environmental difference between the first sub-area and the second sub-area is the smallest.

[0022] Preferably, the following steps are used to determine whether the difference between the environments of the first sub-area and the second sub-area is minimal:

[0023] Search for a sub-region with the same N-1 environment type values ​​as the first sub-region. If it exists, the found sub-region is called the third sub-region; if it does not exist, search for a sub-region with the same N-2 environment type values ​​as the first sub-region again, until the third sub-region is found;

[0024] Counting the number of third sub-regions, if there is only one third sub-region, taking the third sub-region as the second sub-region with the smallest environmental difference from the first sub-region;

[0025] If there are multiple third sub-regions, the distances from the center point of the first sub-region to the center points of the third sub-regions are calculated, and the third sub-region with the smallest distance is used as the second sub-region with the smallest environmental difference from the first sub-region.

[0026] Preferably, based on the satellite image data of the target study area and the sample test data, a soil organic carbon content inversion model is constructed, including:

[0027] Based on the satellite image data, extract the single-band reflectance of the grid unit where the sample point is located, and calculate multiple vegetation indices based on the single-band reflectance;

[0028] The single-band reflectance and the plurality of vegetation indices are screened by using the Pearson test method to obtain effective parameters required for constructing the model;

[0029] Based on the effective parameters, construct multiple candidate models;

[0030] The accuracy of the multiple candidate models is compared, and the model with the best accuracy is used as the final soil organic carbon content inversion model.

[0031] Preferably, the evaluation method further comprises: outputting a spatial distribution map and statistical chart of the soil organic carbon sink potential evaluation of the target study area to achieve visual display.

[0032] Preferably, the spatial distribution map includes one or more of the following: a spatial distribution map of organic carbon content, a spatial distribution map of organic carbon reserves, and a spatial distribution map of carbon sequestration potential.

[0033] Preferably, after obtaining the satellite image data, sample point test data and environmental variable data of the target study area, the method further includes:

[0034] Preprocessing the satellite image data by geometric correction, atmospheric correction, image fusion, and image cropping and splicing;

[0035] Performing standardization processing on the environmental variable data;

[0036] And performing data cleaning processing on the sample point test data.

[0037] The embodiment of the present application provides a black soil carbon sink potential assessment system based on grid precise zoning, and the system provides services based on the cloud, including:

[0038] A business data acquisition unit, configured to acquire satellite image data, sample point test data, and environmental variable data of a target study area based on a pre-configured standard data acquisition template;

[0039] The data storage and management unit is configured to store satellite image data, sample point test data, and environmental variable data of the target study area to achieve full process management and monitoring;

[0040] A system business application unit, wherein the system business application unit is used to implement the evaluation method described in any one of the above embodiments.

[0041] The technical solution of the embodiment of the present application has the following beneficial effects:

[0042] The black soil carbon sink potential assessment method based on grid precise zoning provided in the present application divides the target study area into spatially continuous grid units according to the spatial resolution of satellite image data, analyzes the environmental similarity of each grid unit based on environmental variable data, and divides the target study area into multiple sub-areas according to the degree of environmental similarity; uses the soil organic carbon content inversion model to obtain the soil carbon sequestration amount of each grid unit, and takes the difference between the maximum soil carbon sequestration amount of each sub-area and the soil carbon sequestration amount of each grid unit in the sub-area as the soil carbon sequestration potential value. Among them, the maximum value of soil carbon sequestration is used to characterize the carbon saturation level that the soil may reach, or the maximum amount of carbon that the soil may hold. The above scheme fully considers the impact of different environmental factors on the soil carbon cycle. First, the target study area is divided into a large number of grid units using satellite images, and the grid units are divided according to environmental similarity. The grid units with smaller environmental differences within the regional range can be classified into the same sub-area. Then, the soil carbon sequestration amount is estimated by grid unit, and the carbon saturation level that the soil may reach under various environmental conditions is determined by region. Finally, the soil carbon sequestration potential value is calculated with the grid unit as the basic unit, thereby ensuring the accuracy of large-scale estimation and providing a strong basis for land use and agricultural management measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:

[0044] Figure 1 A schematic flow chart of a method for assessing black soil carbon sink potential based on precise grid zoning according to some embodiments of the present application.

[0045] Figure 2 A schematic diagram of the implementation process of a method for assessing black soil carbon sink potential based on grid precise zoning provided according to some embodiments of the present application.

[0046] Figure 3 This is a schematic diagram of the structure of a black soil carbon sink potential assessment system based on grid precise zoning provided according to some embodiments of the present application.

[0047] Figure 4 A schematic diagram of a system business architecture provided according to some embodiments of the present application.

[0048] Figure 5 This is an example of a sub-region division result provided according to some embodiments of the present application. DETAILED DESCRIPTION

[0049] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desired that the present application includes such modifications and variations within the scope of the appended claims and their equivalents.

[0050] In the following description, the terms "first / second / third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present disclosure belongs. The terms used herein are only for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0052] Embodiment 1:

[0053] The present application embodiment provides a method for evaluating the carbon sink potential of black soil based on grid precise zoning, such as Figure 1 As shown, the evaluation method includes:

[0054] Step S101: Obtain satellite image data, sample point test data and environmental variable data of the target study area.

[0055] It should be noted that the target research area of ​​this embodiment can be a research area of ​​any range, such as a planting plot range, an experimental area range, a county-level area range, a city-level range, a provincial-level range, etc. This embodiment does not limit the range size of the target research area.

[0056] Preferably, the target study area is a large-scale area above the county level. A county-level area often includes a large number of planting areas and plots. In such a large-scale and large-area range, if a point monitoring method based on sampling points is used to obtain soil carbon sink estimation, it may take a lot of manpower and material resources, and it is difficult to obtain spatially continuous estimation results in the entire area. The traditional remote sensing estimation method does not take into account the spatial heterogeneity of the soil, and the estimation accuracy is still insufficient. Therefore, it is necessary to use the evaluation method provided in this embodiment to improve the accuracy of soil carbon sink estimation in a large-scale range.

[0057] In this embodiment, satellite image data, sample test data and environmental variable data are used as the basic data for soil carbon sink estimation. Among them, satellite image data can come from multiple satellites, and satellite types can include, for example, high-resolution series satellites, sentinel satellites, Landsat satellites, resource series satellites, etc., that is, satellite image data is multi-source data. The spatial resolution of a remote sensing image usually refers to the size of the smallest feature that can be distinguished by a remote sensing image or sensor on the ground. It is used to describe the pixel size of satellite image data or the resolution of ground cover. Different satellite types have different spatial resolutions. In this embodiment, according to the preset spatial resolution of each satellite, the satellite image can divide the target study area into spatially continuous grid units. The entire study area is composed of a large number of grid units, which serve as the basic units for subsequent soil carbon fixation and carbon sink potential estimation.

[0058] In addition to satellite image data, the data acquired in this embodiment also includes sample point test data and environmental variable data. Sample point test data, referred to as sample point data, is the geographical location and related soil attribute data corresponding to the collected soil sample. It is one of the data related to business applications and is also called sample data. Sample point data should at least include the longitude and latitude coordinates of the sample, as well as soil properties. Soil properties may include: organic matter content, pH value, etc. In addition, it may also include information such as the depth range of the sample and the specific collection time.

[0059] Environmental variable data, referred to as environmental data, includes information related to multiple environmental factors, such as temperature, rainfall, terrain, etc., each of which corresponds to an environmental factor.

[0060] Step S102: Based on the satellite image data and sample test data of the target study area, a soil organic carbon content inversion model is constructed, and based on the soil organic carbon content inversion model, the soil carbon fixation amount of each grid unit in the target study area is calculated.

[0061] In this embodiment, the soil organic carbon content inversion model is constructed based on satellite image data and sample test data. Satellite image data is used to provide characteristic information such as surface reflectivity and land cover type, and sample test data is used to provide the true value (label value) of soil organic carbon content. The soil organic carbon content inversion model is constructed by modeling the satellite image data and sample test data, wherein the modeling method adopted can be a regression method, a random forest, or a support vector machine, etc. This embodiment does not limit the specific modeling method.

[0062] The soil organic carbon content inversion model is used to calculate the soil carbon sequestration capacity of all grid cells, that is, to obtain the inversion results of the soil carbon sequestration capacity of each grid cell.

[0063] Step S103: Based on the environmental variable data, the environmental similarity of each grid unit is analyzed, and based on the environmental similarity, the target study area is divided into multiple sub-areas.

[0064] Considering that the soil carbon cycle is significantly affected by environmental factors, in this embodiment, the environmental similarity of each grid unit is analyzed according to the number of environmental variables, and the environmental parameters are analyzed and judged by each grid unit. Based on the environmental similarity, the target study area is divided into multiple sub-areas with the grid unit as the evaluation unit, and each sub-area is assigned a unique code (called the regional identification value) to distinguish the differences and similarities of environmental characteristics between different locations in the target study area, thereby reflecting the impact of environmental factors at different locations on the soil carbon cycle. The subsequent soil organic carbon sink potential assessment is carried out on the basis of the grid precision regional division of this embodiment.

[0065] It should be noted that there are many methods for measuring the environmental similarity between grid units. For example, the Euclidean distance between grid units can be calculated based on environmental factors to measure the environmental similarity between the two, or the Manhattan distance, correlation coefficient, etc. can be used to calculate the environmental similarity between each grid unit. This embodiment does not limit the method for measuring the environmental similarity between grid units.

[0066] Based on the environmental similarity of each grid cell, grid cells with similar environments can be grouped into the same sub-region, and grid cells with large environmental differences can be grouped into different sub-regions, thereby obtaining multiple sub-regions within the target study area. This allows differences in environmental characteristics between different sub-regions, while similarities in environmental characteristics within the same sub-region, laying the foundation for precise soil carbon sink assessment.

[0067] Step S104: For each sub-region, the maximum value of soil carbon sequestration of all grid cells within the sub-region is counted as the maximum limit of soil carbon sequestration in the sub-region.

[0068] Step S105: For any grid cell, the difference between the maximum limit of soil carbon sequestration in the sub-region where the grid cell is located and the soil carbon sequestration amount of the grid cell is calculated as the soil carbon sequestration potential value of the grid cell.

[0069] Based on the soil carbon sequestration of each grid unit estimated in step S102, combined with the sub-regions divided in step S103, the maximum soil carbon sequestration in each sub-region can be statistically calculated to reflect the carbon saturation level that the soil may reach under different environmental influences, or the maximum carbon content that the soil may contain. Then the maximum soil carbon sequestration of each grid unit and the soil carbon sequestration in its sub-region are calculated as the soil carbon sequestration potential value of the grid unit.

[0070] In summary, the method provided in this embodiment estimates soil carbon sequestration based on remote sensing technology, divides the target study area into different sub-areas using environmental variable data, and evaluates soil carbon sequestration and carbon sink potential in the target study area by combining grid units divided by the spatial resolution of satellite image data, which can accurately reflect the heterogeneity of soil carbon cycle under different environments. At the same time, the method provided in this embodiment gives full play to the advantages of satellite remote sensing technology in terms of efficiency, macroscopicity, timeliness, and rapidity, and saves manpower and material resources compared with traditional survey sampling, laboratory testing and other methods.

[0071] In some optional embodiments, after obtaining the satellite image data, sample test data and environmental variable data of the target study area, it also includes: pre-processing the satellite image data by geometric correction, atmospheric correction, image fusion, image cropping and splicing; standardizing the environmental variable data; and cleaning the sample test data.

[0072] For satellite image data, geometric correction, atmospheric correction, image fusion, image cropping and splicing are required. The purpose of geometric correction is to eliminate the geometric deformation caused by the changes in the earth's surface morphology and the satellite orbit position in the image. It can be achieved by methods including polynomial fitting and ground control point matching. Atmospheric correction is to eliminate the influence of atmospheric factors on remote sensing image observation, so that the radiation intensity of the ground objects in the image can more accurately reflect the surface characteristics. It can be corrected by combining atmospheric transmittance and reflectivity with atmospheric transfer function. Image fusion is to fuse image data from multiple bands or multiple sensors into a single image to obtain more information and improve image quality. The fused image can be a full-color image, a hyperspectral image, a multispectral image, etc. Image cropping refers to cutting out the part of interest from the original image according to specific requirements or areas (such as the shape and size of the target study area). Image splicing refers to combining multiple images according to certain rules and geometric transformations to generate a single large-size image. The splicing process can also include an image fusion step, that is, to smooth the transition of the image edges and reduce the traces of the splicing. By performing geometric correction, atmospheric correction, image cropping, splicing, fusion and other preprocessing on satellite image data, the quality and information content of remote sensing images can be improved, providing a more accurate data basis for subsequent ground object extraction, classification and analysis.

[0073] In actual operation, the geometric correction, atmospheric correction, image cropping, splicing, fusion and other preprocessing of satellite images can be achieved through professional remote sensing image processing software, such as ENVI, ERDAS Imagine, ArcGIS, etc. This embodiment does not limit the specific processing tools used for preprocessing.

[0074] For environmental variable data, it is necessary to perform standardization processing to unify the data scales between different variables, eliminate the dimensional differences between different variables, and make them have the same scale and importance for better comparison and analysis. In this embodiment, the environmental variable data is imported in the form of a grid, for example, it can be a Geotiff format or a tiff format, and each environmental variable corresponds to a Geotiff file. After being imported in the form of a grid, the grid data is standardized, and the specific standardization processing method used may include: Z-score standardization or minimum-maximum standardization method. This embodiment does not limit the specific method used for standardization.

[0075] For the sample test data, data cleaning processing is required. Here, data cleaning can include removing outliers, missing values ​​or erroneous values ​​in the sample data. Among them, an outlier refers to the value of a certain attribute of the sample that exceeds the reasonable range or does not conform to the actual situation. Whether there are outliers in the sample data can be identified through statistical analysis, visualization or professional knowledge, and it can be removed from the sample data based on the identification results. Missing values ​​refer to null values ​​in the sample data, and missing values ​​can be processed by filling, deleting or interpolating. Error values ​​refer to errors in the sample data. The sample data can be compared and analyzed with other data sources, or the errors in the sample data can be identified by using the knowledge of domain experts to ensure the quality and accuracy of the data. It should be noted that the content and method of the above-mentioned data cleaning processing are only exemplary, and other data cleaning techniques can be used as needed to further improve the quality of the sample data.

[0076] After obtaining high-quality satellite images through the aforementioned preprocessing, in some optional embodiments, step S102 of constructing a soil organic carbon content inversion model can be specifically implemented by the following steps: based on satellite image data, extracting the single-band reflectivity of the grid unit where the sample point is located, and calculating multiple vegetation indices based on the single-band reflectivity; using the Pearson test method to screen the single-band reflectivity and multiple vegetation indices to obtain effective parameters required for model construction; based on the effective parameters, constructing multiple candidate models; comparing the accuracy of multiple candidate models, and using the model with the best accuracy as the final soil organic carbon content inversion model.

[0077] It should be noted that satellite images usually include multiple bands (such as visible light band, infrared band, short-wave infrared band, etc.). Different bands correspond to different wavelength ranges on the electromagnetic radiation spectrum. The single-band reflectivity of satellite images refers to the surface reflectivity measured by the satellite sensor in a single band.

[0078] Since the sample point data is point data, and the satellite image is surface data, the grid unit where the sample point is located on the satellite image can be determined by superimposing the two, and then the single-band reflectivity of the grid unit can be extracted using the satellite image. In this embodiment, the single-band reflectivity includes at least: visible light band (i.e., red light, green light, blue light band) and near infrared band.

[0079] On the basis of obtaining the single-band reflectance, multiple index parameters are further extracted, namely vegetation indices, including but not limited to: Normalized difference vegetation index (NDVI), relative vegetation index (RVI), difference vegetation index (DVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI). The above index parameters are used as vegetation index parameters to be screened. The calculation methods of each index parameter are shown in Table 1. Table 1 is as follows:

[0080] Table 1 Index parameter calculation method

[0081]

[0082] In the calculation method of the above index parameters, NIR is the reflectance of the near infrared band, Red is the reflectance of the red light band, Blue is the reflectance of the blue light band, and L is the soil adjustment coefficient.

[0083] Since the correlations between each single-band reflectivity and each index parameter and the soil organic carbon content are different, in this embodiment, the Pearson (Person) test method is used to screen the single-band reflectivity and multiple vegetation indices to obtain the effective parameters required for building the model. That is, based on the results of the Person test, the bands and indices with high correlation with the sample soil organic carbon content are selected as effective bands and indices, and used as modeling parameters of the soil organic carbon content inversion model.

[0084] Taking into account the heterogeneity of soil organic carbon content distribution and the different applicable conditions of various modeling methods, this embodiment, after determining the effective parameters required to build the model, adopts the idea of ​​building multiple candidate models and then selecting the optimal model to construct the final soil organic carbon content inversion model to improve the accuracy of the inversion model.

[0085] Specifically, establishing a soil organic carbon content inversion model can include the following steps: using the screened single-band reflectance and vegetation index as variables, respectively constructing multiple candidate models, for example, a univariate linear regression model, a multivariate linear regression model, a curve regression model, and a support vector machine model can be constructed, and then the accuracy of the above multiple candidate models is compared, and the candidate model with the highest accuracy is selected as the final soil organic carbon content inversion model.

[0086] Furthermore, when comparing the accuracy, the coefficient of determination (R2 ) and root mean square error (RMSE) as the evaluation indicators of the model, R 2 The closer it is to 1, the higher the model prediction accuracy is, and the smaller the RMSE is, the smaller the model prediction value deviation is. By combining the two indicators to select the candidate model with the smallest prediction value deviation and the highest accuracy, the reliability of model selection can be further improved.

[0087] On the basis of constructing the soil organic carbon content inversion model, the established soil organic carbon content inversion model can be used to calculate the organic carbon content of each grid unit in the target study area, in units of (g / g). Then, according to the organic carbon content of the area where each grid unit is located, the soil carbon sequestration per unit volume of each grid unit in the target study area is estimated by formula (1):

[0088] C T =SOC i ×BD i ×(1-θ i )×H i ×a (1)

[0089] In the formula, C T is the soil carbon sequestration per unit volume of a grid unit (g), i represents the arable layer, SOC i represents the average content of soil organic carbon in the plough layer i (g / g), θ i is the volume percentage of gravel in tillage layer i (cm 3 / cm 3 ), H i is the soil thickness in the plough layer i (cm), a represents the area of ​​a grid unit (cm 2 ).

[0090] It should be noted that the tillage layer indicates thickness. Taking into account the influence of the tillage layer, the above-mentioned soil carbon sequestration amount is actually the soil carbon sequestration amount per unit volume.

[0091] In some optional embodiments, the environmental variable data includes raster data of N environmental parameters, and the spatial resolution of the raster data of the environmental parameters is the same as the spatial resolution of the satellite image data; N is a positive integer greater than or equal to 7; based on the environmental variable data, the environmental similarity of each grid cell is analyzed, and based on the environmental similarity, the target study area is divided into multiple sub-areas, including: according to the preset environmental parameter value assignment classification rules, the grid cells are classified according to the values ​​of each environmental parameter of each grid cell, and N environmental type values ​​corresponding to each grid cell under the classification rules of each environmental parameter are obtained; the environmental type values ​​corresponding to any two grid cells under the classification rules of each environmental parameter are compared, and if the environmental type values ​​of the two grid cells meet the preset similarity conditions, the two grid cells are classified into the same sub-area.

[0092] Considering that different environmental factors affect soil carbon cycle in different ways, the accuracy of sub-regional division can be improved by collecting N (N≥7) environmental parameters, where the number of environmental parameters is preferably 7 (N=7), that is, the seven environmental parameters with the most significant impact on organic carbon are selected, including: land use type, annual accumulated temperature, annual rainfall, soil type, soil texture, topography, and farming system. The above 7 environmental parameters are used as benchmark variables for precise zoning, which can not only fully reflect the effect of environmental images on soil carbon cycle, but also limit the number of environmental parameters within a certain range, reduce the complexity of data processing, and help improve the efficiency of carbon sink assessment under the whole process.

[0093] When there are N environmental parameters, the classification rules for assigning values ​​to each environmental parameter can be pre-set to classify the qualitatively described environmental parameters, and the grid cells can be clustered according to these classification rules to obtain a set of grid cells with similar environments, and then the adjacent grid cells in these grid cell sets can be merged into sub-areas.

[0094] In actual operation, the assignment method can be used to distinguish the various types under different environmental parameter classification rules, that is, to assign a value to each type, and the assigned value is called the environmental type value, so as to facilitate input and subsequent use.

[0095] It should be noted that different environmental parameters have different assignment classification rules, and each grid unit has a corresponding environmental type value under the classification rules of each environmental parameter. For example, land use types can be divided into five categories: cultivated land, forest land, garden land, grassland, and others. The value of cultivated land is "1", the value of forest land is "2", the value of garden land is "3", the value of grassland is "4", and the value of others is "5". For another example, for annual accumulated temperature, the value of annual accumulated temperature <2000℃ is "1", the value of annual accumulated temperature between 2000 and 2500℃ is "2", the value of annual accumulated temperature between 2500 and 3000℃ is "3", the value of annual accumulated temperature between 3000 and 3500℃ is "4", the value of annual accumulated temperature between 3500 and 4000℃ is "5", and the value of annual accumulated temperature >4000℃ is "6". The assignment classification rules of each environmental parameter are shown in Table 2, which is as follows:

[0096] Table 2 Classification rules for assigning values ​​to various environmental parameters

[0097]

[0098]

[0099] According to the above-mentioned environmental parameter assignment classification rules, each grid unit is analyzed and judged, and each grid unit is assigned a value. After the environmental parameters are standardized and assigned, they are converted into raster data with attribute assignments, so that the grid units obtain an attribute assignment (environmental type value) under each classification rule, and then the grid units at the same spatial position have attribute information of environmental parameter classification in multiple dimensions (one environmental parameter corresponds to one dimension). At this time, when judging the environmental similarity, the judgment conditions can be set directly based on the environmental type values ​​of each grid unit. By comparing the environmental type values ​​of any two grid units, the judgment results can be obtained quickly and efficiently, eliminating the complex calculation process of other measurement methods and further improving data processing efficiency.

[0100] In a specific example, the similarity condition can be: the N environment type values ​​of the two grid cells are the same. For example, assuming that the number of environmental parameters is 7, as long as all 7 environment type values ​​corresponding to the two grid cells are the same, the two environments are judged to be highly similar, and the two grid cells can be classified into the same sub-area, and a unique area identification value is assigned to the grid cells of the sub-area. Here, the area identification value can be in the form of a numerical value, such as integers 1, 2, 3, 4, 5... to distinguish the sub-areas to which each grid cell belongs.

[0101] Taking into account the spatial differences in environmental parameters, when the sub-areas are divided using "the N environmental type values ​​of two grid cells are the same" as the similarity condition, there may be a large difference in the number of grid cells in each sub-area. This phenomenon will lead to partitions with too few grid cells in practice. When the maximum value is selected as the upper limit in the subsequent carbon sink potential estimation, the upper limit may not be representative due to the small number of grid cells. Therefore, in some optional embodiments, after dividing the target study area into multiple sub-areas, it also includes: counting the number of grid cells contained in each sub-area; if the proportion of the number of grid cells contained in the first sub-area to the total number of grid cells in the target study area is less than a preset proportion threshold, all grid cells contained in the sub-area are classified into the second sub-area, wherein the first sub-area is any sub-area after the division, and the environmental difference between the first sub-area and the second sub-area is the smallest.

[0102] Furthermore, the following steps are used to determine whether the environment difference between the first sub-region and the second sub-region is the smallest: search for a sub-region with the same N-1 environment type values ​​as the first sub-region, and if it exists, the found sub-region is called the third sub-region; if it does not exist, search for a sub-region with the same N-2 environment type values ​​as the first sub-region again until the third sub-region is found; count the number of third sub-regions, and if there is only one third sub-region, take the third sub-region as the second sub-region with the smallest environment difference with the first sub-region; if there are multiple third sub-regions, calculate the distance from the center point of the first sub-region to the center point of each third sub-region, and take the third sub-region with the smallest distance as the second sub-region with the smallest environment difference with the first sub-region.

[0103] If the number of grid cells contained in the first sub-region accounts for less than the ratio of the number of grid cells in the target study area, it means that the number of grid cells contained in the first sub-region is too small and may not be representative. Here, illustratively, the preset ratio threshold may be 5%, that is, when the number of grid cells in any sub-region (i.e., the first sub-region) accounts for less than 5% of the number of grid cells in the target study area, it is determined that the maximum limit value of the sub-region may not be representative.

[0104] In view of the above problem, the solution provided by this embodiment is: find other sub-regions with the smallest environmental difference from the first sub-region, and assign all grid cells in the first sub-region to the second sub-region with the smallest environmental difference.

[0105] For example, assuming that the total number of environmental parameters is 7, when the statistical result of the number of grid cells in a certain sub-region accounts for less than 5% of the total number of grid cells in the target study area, it is determined whether there is a sub-region with 6 (i.e. 7-1) environmental variables identical to the sub-region and 1 environmental variable different. For the convenience of description, the found sub-region is called the third sub-region. After the determination, three results may appear: (1) If there is only one third sub-region, it is considered that the environmental difference between the first sub-region and the third sub-region is the smallest, that is, the third sub-region is regarded as the second sub-region with the smallest environmental difference from the first sub-region, and all grid cells of the first sub-region are classified into the second sub-region. (2) If there are multiple third sub-regions, the zonal characteristics of the environmental variables are fully considered, the distance between the center points of the multiple third sub-regions and the center point of the first sub-region is determined, and the partition with the smallest distance is selected as the second sub-region with the smallest environmental difference, and all grid cells of the first sub-region are classified into the second sub-region. (3) If there are no similar partitions with 6 identical environmental variables and 1 different environmental variable, that is, the number of third sub-regions is 0, then partitions with 5 identical environmental variables and 2 different environmental variables are selected for judgment and division. The division principle is the same as above until the third sub-region is found.

[0106] Figure 5 The sub-region division results obtained through the above process are shown in FIG. Figure 5 As shown in the figure, in the target study area, the sub-region with the area identification value of 1 contains a small number of grid units (the number accounts for less than 5%), so this sub-region is called the first sub-region, and then the above steps are used to find the second sub-region with the smallest environmental difference. Assuming that the area identification value of 2 and the area identification value of 4 both have 6 environmental variables the same as the first sub-region and 1 environmental variable different, the area identification value of 2 and the area identification value of 4 are called the third sub-region, and the distances between the first sub-region and the center points of the area identification value of 2 and the area identification value of 4 are calculated. The calculation results show that the area identification value of 2 is closer to the center point of the first sub-region, so all the grid cells of the first sub-region (that is, the area identification value of 1) are included in the area identification value of 2, that is, the area identification value of all the grid cells of the first sub-region is changed from 1 to 2.

[0107] After the grid precision regional division obtains multiple sub-regions, the carbon sequestration potential can be calculated based on the output of the precision zoning module - the zoning grid. That is, the soil carbon sequestration of all grid cells in the same sub-region, that is, the same regional identification value, is compared, and the highest value is the soil carbon sequestration maximum limit (C TH ), the difference between the soil carbon sequestration amount of each grid unit and the maximum limit of soil carbon sequestration in its sub-region is calculated as the soil carbon sequestration potential value (C Q ), calculate the carbon sequestration potential of all grid cells, and obtain the estimated carbon sequestration potential of the target study area. The above process is expressed as follows:

[0108] C Q =C TH -C T (2)

[0109]

[0110] In the formula, C Q is the soil carbon sequestration potential of a single grid unit (g), C TH is the maximum limit of soil carbon sequestration in the sub-region where the grid unit is located, C Qt is the total soil carbon sequestration potential (g) in the target study area.

[0111] In order to make the generated soil organic carbon carbon sink potential estimation results more intuitive and easy to understand, in some optional embodiments, the evaluation method further includes: outputting a spatial distribution map and statistical chart of the soil organic carbon carbon sink potential evaluation of the target study area to achieve visual display. Among them, the spatial distribution map includes one or more of the following: organic carbon content spatial distribution map, organic carbon reserve spatial distribution map, carbon fixation potential spatial distribution map.

[0112] Specifically, the spatial distribution map of organic carbon content, the spatial distribution map of organic carbon reserves, and the spatial distribution map of carbon sequestration potential can be displayed by superimposing remote sensing maps, and all calculation results can be selected. Statistical charts can be used to display the soil area and proportion of different levels of statistical distribution maps in the form of bar charts.

[0113] At the same time, the above results can be output in PDF and TIF formats and downloaded and saved locally.

[0114] By visualizing the results and displaying the estimation results visually, data users (decision makers) can quickly understand the meaning and trend of the data through visual perception alone without in-depth analysis of the data. They can more easily discover the relationships and mutual influences between the data, thereby gaining a deeper understanding of the information behind the data and making more scientific decisions.

[0115] To sum up, the black soil carbon sink potential assessment method based on grid precise zoning provided in this embodiment can realize multi-field empowerment application, that is, the organic carbon carbon sink potential assessment results can enable multi-field applications. By clarifying the soil carbon storage and carbon fixation potential, it can support broad engineering application prospects in related fields such as cultivated land resource utilization, agricultural production planning, and ecological environmental protection.

[0116] Embodiment 2:

[0117] The present application embodiment provides a black soil carbon sink potential assessment system based on grid precise zoning, and the assessment system provides services based on the cloud, such as Figure 3 , Figure 4 As shown, the evaluation system includes: a business data collection unit 301, a data storage and management unit 302 and a system business application unit 303. Among them:

[0118] The business data acquisition unit 301 is configured to acquire satellite image data, sample point test data and environmental variable data of the target study area based on a pre-configured standard data acquisition template;

[0119] The data storage and management unit 302 is configured to store satellite image data, sample point test data, and environmental variable data of the target study area to achieve full process management and monitoring;

[0120] The system service application unit 303 is used to implement the evaluation method provided in any of the above embodiments.

[0121] The black soil carbon sink potential assessment system based on grid precise zoning provided in this embodiment has a schematic diagram as shown in FIG. Figure 4As shown, it is mainly composed of three parts: business data collection unit 301, data storage and management unit 302, and system business application unit 303. The system can realize the full-process engineering application of soil organic carbon carbon sink potential estimation from business data collection, management, model calculation, estimation result generation to result visualization.

[0122] Specifically, the business data collection unit 301 formulates a standard data collection template based on the business data to be collected, and completes data entry and archiving based on the data standard. The collected data may include: environmental data (for evaluating regional environment), image data, sample point data, geographic information data, soil attribute data, etc. The data types include vector data, raster data, text data, etc. The collection step is to select a template based on the collection data type, enter or import relevant data according to the template prompts, and the system automatically archives the data.

[0123] Data storage and management unit 302, all collected business data is stored in the database in this step, and the whole process management and monitoring of data business application can be realized.

[0124] All data collected by the business data collection unit 301 are stored in a comprehensive database, which is composed of an image database, a business database, a basic database, a model database and a support database.

[0125] in:

[0126] The image database is used to store multi-source satellite image data. The database can support the storage of commonly used satellites at home and abroad. The satellite types mainly include Gaofen series satellites, Sentinel satellites, Landsat satellites, and Resource series satellites.

[0127] Business database: used for all data related to system applications, such as sample point data, soil test data, environmental variable data, etc.

[0128] Basic database: used to store basic information data such as administrative boundaries and water areas.

[0129] Model database, used to store relevant models such as organic carbon sink potential assessment.

[0130] Support database: used to store system users, permissions, logs and other data.

[0131] On the basis of completing data warehousing, the data warehousing and management unit 302 also has the ability to manage and monitor the entire data process, and realizes full-process data management by providing functions such as data access, data cleaning, data storage, data analysis, data publishing, and data security.

[0132] Specifically, each module implements the following functions:

[0133] The data connection module develops data connection templates based on different data types and manages the connection process, thereby realizing the rapid collection of large amounts of business data and reserving interfaces for possible business data types.

[0134] The data cleaning module formulates rules according to the business data type, reviews and verifies the original access data to ensure the consistency, accuracy and reliability of the data, which is used for the organic carbon sink potential assessment business.

[0135] The data storage module manages and queries the data volume, data type, and data storage status of the incoming data.

[0136] The data analysis module conducts statistical analysis on the carbon sink potential assessment process and result data, and analyzes sample conditions, environmental variable data, organic matter content levels, carbon sink capacity classification, etc.

[0137] Data publishing module, publishing of spatial and non-spatial data, especially slicing and publishing of spatial data (vector and raster data), so as to realize the visualization of calculation results.

[0138] Data security module: In order to ensure the security of user data, the data center should take measures from multiple aspects, mainly including: user data isolation, data access control, data backup, vulnerability scanning and other means of control.

[0139] The system business application unit 303 is the core of the evaluation system, which is used to obtain the data collected by the business data collection unit 301 and perform business application processing. It can include five modules: data preprocessing, regional division, soil carbon estimation, carbon sequestration potential assessment and results visualization. The functions of each module run through the system business process. Among them:

[0140] The data preprocessing module includes a data standardization submodule and a data quality inspection submodule, which realizes the preprocessing of three types of data: sample point data, image data, and environmental variable data. It should be noted that the data preprocessing module also includes extracting the latitude and longitude coordinates of the sampling point and the measured value of organic matter content from the input sample point data. The extracted results will be used as sample data and input into the soil organic carbon content inversion model.

[0141] The regional division module includes environmental factor analysis and regional delineation submodules. The environmental factor analysis submodule is used to analyze the environmental similarity of each grid unit, and the regional delineation submodule is used to divide the target study area into multiple sub-areas based on environmental similarity.

[0142] The soil carbon estimation module includes inversion model construction, model verification, and carbon estimation submodules, wherein the inversion model construction submodule is used to implement the following steps: based on satellite image data, extract the single-band reflectance of the grid unit where the sample point is located, and calculate multiple vegetation indices based on the single-band reflectance; use the Pearson test method to screen the single-band reflectance and multiple vegetation indices to obtain the effective parameters required for model construction; based on the effective parameters, build multiple candidate models. The model verification submodule is used to implement the following steps: compare the accuracy of multiple candidate models, and use the model with the best accuracy as the final soil organic carbon content inversion model. The carbon estimation submodule is used to calculate the soil carbon fixation of each grid unit in the target study area based on the established soil organic carbon content inversion model.

[0143] The carbon sequestration potential assessment module includes an assessment model construction and a potential assessment submodule, wherein the assessment model construction submodule is used to construct the logic of the assessment model, including obtaining the maximum limit of soil carbon sequestration and calculating the soil carbon sequestration potential value. The potential assessment submodule implements soil carbon sequestration potential assessment based on the logical process determined by the assessment model construction submodule.

[0144] The results visualization module includes digital mapping, data statistics and visualization configuration submodules. The digital mapping submodule is used to generate spatial distribution maps related to the soil organic carbon sink potential assessment, the data statistics submodule is used to generate statistical charts related to the soil organic carbon sink potential assessment, and the visualization configuration submodule is used to set visualization parameters and visualize the spatial distribution map and statistical charts according to the set parameters.

[0145] In summary, the evaluation system provided in this embodiment provides a full-process service from data collection, data processing, model construction, carbon sink calculation to the generation of applicable results based on the cloud, which can be applied to scenarios including carbon fixation analysis of farmland ecosystems, low-carbon agricultural construction, and planting structure adjustment. For example, the cloud service provided by this embodiment can be used to quickly obtain information such as soil organic carbon content and carbon fixation potential in any target study area, and then evaluate the carbon cycle and carbon storage in the farmland ecosystem; the cloud service provided by this embodiment can also be used to adjust and optimize agricultural management practices, reduce greenhouse gas emissions, and improve carbon fixation capacity by changing agricultural production and management methods, thereby reducing the impact of agriculture on climate change; the cloud service provided by this embodiment can also be used to adjust and optimize the planting structure of crops, and improve the carbon fixation capacity of farmland by optimizing crop types and promoting green varieties. The above application can be realized by simply obtaining cloud services, with low technical barriers, greatly simplifying the carbon sink potential assessment process, and providing a set of engineering, operational, and easy-to-implement technical methods for further tapping the carbon sink potential of black soil.

[0146] The evaluation system provided in this embodiment uses a grid-based precise zoning evaluation method and integrates satellite remote sensing technology to achieve engineering application of the entire process of organic carbon sink potential estimation.

[0147] In addition, the evaluation system provided in this embodiment can realize the engineering of the entire process of organic carbon sink potential evaluation, and the evaluation work can be completed through simple platform operations, greatly improving work efficiency.

[0148] Example applications:

[0149] Reference Figure 2 Taking the estimation of the carbon sequestration potential of cultivated land in a county (assuming it is County A) as an example, the black soil carbon sink potential assessment method based on grid precise zoning provided in this application can be implemented through the following steps:

[0150] Step 1: Data input.

[0151] Input the satellite image data, county range vector, sample point test data and environmental variable data of County A. Input data in the business data acquisition unit 301 of the evaluation system. Satellite image data can be selected from mainstream satellites at home and abroad. Taking the Landsat satellite source as an example, input the full-band data of Landsat satellite images taken in a suitable time period.

[0152] Import the entered sample test data (Excel table format) into the system. The Excel table data at least contains the sample number, sample longitude and latitude, and soil organic carbon content fields. At the same time, determine the proportion of sample points for modeling (i.e., training set) and sample points for precision testing (i.e., validation set) during input.

[0153] Environmental variable data were imported into the system in raster form (Geotiff format) and used for similar area division in the process of carbon sequestration potential assessment.

[0154] Step 2: Data preprocessing.

[0155] Preprocess the input data, standardize the data, and output data in the specified format.

[0156] If the imported satellite image data has not been preprocessed, the data preprocessing module will perform preprocessing work such as geometric correction, atmospheric correction, image fusion, image cropping and splicing, and the preprocessed satellite image will be input into the inversion model for calculation.

[0157] The environmental variable data are standardized and assigned values ​​according to the environmental parameter assignment classification rules shown in Table 2. Taking land use type as an example, if the attribute of a grid cell in the land use data is cultivated land, the land use type of the grid cell is assigned a value of "1", and if the attribute of the next grid cell is grassland, the land use type of the grid cell is assigned a value of "4".

[0158] Clean and delete null values ​​and outliers in the sample data.

[0159] Step 3: Area demarcation.

[0160] Carry out precise regional division of County A. The division principle is that if the 7 environmental type values ​​of two grid cells are all the same, then the two grid cells are judged to be in the same sub-region, and the two grid cells are assigned a unified code of the sub-region, that is, the regional identification value. For example, the environmental variables of a grid cell within the scope of County A are judged, and all the environmental type values ​​of the grid cell are extracted. Assuming that the 7 environmental type values ​​of the grid cell are: "1", "3", "3", "1", "4", "1", "1", it can be known that the sub-region to which the grid cell belongs is: plain cultivated land, annual accumulated temperature 2500 ~ 3000 ℃, annual rainfall 600 ~ 800mm, black soil, medium soil texture, no fallow throughout the year. Determine whether this sub-region has the same 7 environmental type values ​​as other sub-regions. If they are the same, they are classified as the same sub-region. If they are not the same, they are identified as a new sub-region and assigned a value. After this step is completed, County A will be divided into multiple sub-regions. Since the environmental variables themselves have zonal characteristics, the zoning results based on environmental variables usually also reflect zonal characteristics. For details, please refer to Figure 3 The results of the sub-region division are shown. It is assumed that County A can be initially divided into 7 regions based on environmental variable analysis, with regional identification values ​​ranging from 1 to 7. The carbon sink potential will be assessed based on this division.

[0161] Step 4: Select valid parameters.

[0162] Taking Landsat8 image as an example, the reflectance of red, infrared and blue bands (i.e. single-band reflectance) of Landsat8 image was extracted, and five vegetation indices were calculated according to the index parameter calculation method shown in Table 1. The calculated vegetation index and single-band reflectance were used as candidate model parameters. Parameters highly correlated with soil organic carbon (sampling point data) were selected as effective parameters for modeling. Principal component analysis (i.e. Pearson test method) was used to select parameters. During the analysis, the variable with the largest weight value in the main component (component eigenvalue greater than 1) was selected as the effective parameter.

[0163] Step 5: Construct a soil organic carbon content inversion model.

[0164] The effective parameters selected in step 4 and the organic matter sample data are used to form a model sample set (including training set and validation set), and multiple candidate models are constructed, such as regression model (multiple linear regression, curve regression) and support vector machine model. The model accuracy is tested using the validation set. By comparing R 2The optimal model was selected by using the RMSE as the final soil organic carbon content inversion model. The soil organic matter content of all grid cells in the entire area of ​​County A was calculated using this model and the raster data was output.

[0165] Step 6: Assessment of soil carbon sequestration potential in cultivated land.

[0166] Substitute the soil organic matter content (raster data) obtained in step 5 into the soil carbon storage calculation formula (Formula 1) to obtain the soil carbon storage, which is raster data. Estimate the carbon sequestration potential by region based on the sub-regions divided in step 3. The difference between the soil carbon storage of a grid cell and the maximum soil carbon sequestration amount of the sub-region where the grid cell is located is the soil carbon sequestration potential value of the grid cell. After completing the calculation of the soil carbon sequestration potential values ​​of all grid cells, output the carbon sequestration potential raster data of County A.

[0167] Step 7: Output the result data.

[0168] Based on the raster data obtained in the above steps, the calculation results can be viewed on the visual business interface of the evaluation system. You can choose to view the spatial distribution map of organic carbon content, the spatial distribution map of organic carbon reserves, the spatial distribution map of carbon fixation potential and related statistical charts.

[0169] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for assessing the carbon sink potential of black soil based on grid-based precise zoning, characterized in that: include: Obtain satellite image data, sample point test data, and environmental variable data of the target study area; wherein the satellite image data divides the target study area into spatially continuous grid units according to a preset spatial resolution; Based on the satellite image data of the target study area and the sample test data, a soil organic carbon content inversion model is constructed, and based on the soil organic carbon content inversion model, the soil carbon sequestration amount of each grid unit in the target study area is calculated; Based on the environmental variable data, analyzing the environmental similarity of each grid unit, and dividing the target study area into a plurality of sub-areas based on the environmental similarity; For each of the sub-regions, the maximum value of soil carbon sequestration of all grid cells within the sub-region is counted as the maximum limit of soil carbon sequestration in the sub-region; For any grid cell, calculate the difference between the maximum limit of soil carbon sequestration in the sub-region where the grid cell is located and the soil carbon sequestration amount of the grid cell as the soil carbon sequestration potential value of the grid cell; Based on the satellite image data of the target study area and the sample test data, a soil organic carbon content inversion model is constructed, including: Based on the satellite image data, extract the single-band reflectance of the grid unit where the sample point is located, and calculate multiple vegetation indices based on the single-band reflectance; The single-band reflectance and the plurality of vegetation indices are screened by using the Pearson test method to obtain effective parameters required for constructing the model; Based on the effective parameters, construct multiple candidate models; Comparing the accuracy of the multiple candidate models, and taking the model with the best accuracy as the final soil organic carbon content inversion model; The environmental variable data includes raster data of N environmental parameters, the spatial resolution of the raster data of the environmental parameters is the same as the spatial resolution of the satellite image data; N is a positive integer greater than or equal to 7; Based on the environmental variable data, the environmental similarity of each grid unit is analyzed, and based on the environmental similarity, the target study area is divided into a plurality of sub-areas, including: According to the preset environmental parameter value assignment classification rule, the grid units are classified according to the value of each environmental parameter of each grid unit to obtain N environmental type values ​​corresponding to each grid unit under the classification rule of each environmental parameter; Compare the corresponding environmental type values ​​of any two grid cells under the classification rule of each environmental parameter. If the environmental type values ​​of the two grid cells meet the preset similarity conditions, the two grid cells are classified into the same sub-area.

2. The evaluation method according to claim 1, characterized in that: The similarity condition is specifically that the N environment type values ​​of the two grid cells are the same.

3. The evaluation method according to claim 1, characterized in that: After dividing the target study area into a plurality of sub-areas, the following steps are further included: Counting the number of grid cells contained in each of the sub-areas; If the ratio of the number of grid cells contained in the first sub-area to the total number of grid cells in the target study area is less than a preset ratio threshold, all grid cells contained in the sub-area are divided into the second sub-area, wherein the first sub-area is any sub-area after the division, and the environmental difference between the first sub-area and the second sub-area is the smallest.

4. The evaluation method according to claim 3, characterized in that: The following steps are used to determine whether the difference between the environment of the first sub-area and the environment of the second sub-area is minimal: Search for a sub-region with N-1 environment type values ​​identical to those of the first sub-region. If such a sub-region exists, the sub-region thus found is referred to as the third sub-region. If such a sub-region does not exist, search for a sub-region with N-2 environment type values ​​identical to those of the first sub-region again until the third sub-region is found. Counting the number of third sub-regions, if there is only one third sub-region, taking the third sub-region as the second sub-region with the smallest environmental difference from the first sub-region; If there are multiple third sub-regions, the distances from the center point of the first sub-region to the center points of the third sub-regions are calculated, and the third sub-region with the smallest distance is used as the second sub-region with the smallest environmental difference from the first sub-region.

5. The evaluation method according to claim 1, characterized in that: The evaluation method also includes: outputting a spatial distribution map and statistical chart of the soil organic carbon sink potential evaluation of the target study area to achieve visual display.

6. The evaluation method according to claim 5, characterized in that: The spatial distribution map includes one or more of the following: a spatial distribution map of organic carbon content, a spatial distribution map of organic carbon reserves, and a spatial distribution map of carbon fixation potential.

7. The evaluation method according to claim 1, characterized in that: After obtaining satellite image data, sample test data and environmental variable data of the target study area, it also includes: Preprocessing the satellite image data by geometric correction, atmospheric correction, image fusion, and image cropping and splicing; Performing standardization processing on the environmental variable data; And performing data cleaning processing on the sample point test data.

8. A black soil carbon sink potential assessment system based on grid precise zoning, characterized in that: The system provides services based on the cloud, including: A business data acquisition unit, configured to acquire satellite image data, sample point test data, and environmental variable data of a target study area based on a pre-configured standard data acquisition template; The data storage and management unit is configured to store satellite image data, sample point test data, and environmental variable data of the target study area to achieve full process management and monitoring; A system business application unit, wherein the system business application unit is used to implement the evaluation method according to any one of claims 1 to 7.

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