Regional scale soil carbon density and carbon reserve estimation method, medium and equipment

Abstract

The invention discloses a regional scale soil carbon density and carbon reserve estimation method, medium and equipment, and relates to the field of soil survey statistics, and the regional scale soil carbon density and carbon reserve estimation method mainly comprises the following steps: extracting data required by a model, carrying out grid layer space superposition operation and statistical analysis, and calculating the carbon density and carbon reserve of the model; obtaining a soil carbon density spatial distribution diagram, further obtaining a grid-based soil carbon reserve spatial distribution diagram, and finally obtaining the soil carbon reserve of the whole research area by utilizing a grid statistical command. By implementing the regional scale soil carbon density and carbon reserve estimation method, medium and equipment provided by the invention, accurate estimation of the regional scale soil carbon reserve and carbon density can be effectively realized, and spatial distribution characteristics of the carbon reserve and carbon density can be visually displayed.

Description

Technical Field

[0001] The present invention relates to the field of soil survey statistics, and more specifically to a method, medium and equipment for estimating soil carbon density and carbon reserves at a regional scale. Background Art

[0003] At present, soil carbon storage estimation at home and abroad generally adopts soil type method, life zone research method, model estimation method and correlation estimation method. The soil type method requires detailed and accurate soil physical and chemical property data, and the estimation accuracy is not high; the life zone research method has many uncertain factors in statistics, the calculation error is large, and the credibility of the estimation result is not high; the model estimation method needs to obtain model parameters, and initialization is difficult; the correlation estimation method can only be extended to the regional scale because the statistical relationship needs to be verified. Although there are many soil carbon storage estimation methods, most of them estimate carbon storage by multiplying soil area and soil type carbon density. The area of ​​each type of soil cannot be accurately and comprehensively estimated during the research process. In addition, due to factors such as inconsistent data sources, it will seriously affect the accuracy of soil carbon storage estimation in a large scale. Summary of the invention

[0004] The purpose of the present invention is to provide a method, medium and equipment for estimating soil carbon density and carbon reserves at a regional scale, which can effectively realize the accurate estimation of soil carbon reserves and carbon density at a regional scale, and intuitively display the spatial distribution characteristics of carbon reserves and carbon density.

[0005] The present invention provides a method for estimating soil carbon density and carbon storage at a regional scale, comprising the following steps: S1: obtaining soil data of a study area by using field data collection and laboratory determination methods; S2: digitizing the soil data of the study area by using geographic information software to obtain a GIS spatial database with soil occurrence types as units; S3: obtaining a grid thematic layer by using a spatial analysis tool based on the GIS spatial database with soil occurrence types as units; S4: performing a grid superposition analysis on the grid thematic layer by using a spatial analysis tool to obtain a spatial distribution map of soil carbon density of each grid unit; S5: obtaining the soil carbon storage of each grid unit and a grid-based spatial distribution map of soil carbon storage based on the spatial distribution map of soil carbon density of each grid unit; S6: obtaining the total soil carbon storage and average carbon density of the study area by using a cell statistical method based on the grid-based spatial distribution map of soil carbon storage and the area of ​​the study area.

[0006] Furthermore, the soil data of the study area for the above regional-scale soil carbon density and carbon storage estimation method include soil profile coordinates, soil occurrence type, soil occurrence layer thickness and each soil layer's bulk density, carbon content, and volume percentage of gravel larger than 2 mm.

[0007] Furthermore, step S3 of the above-mentioned regional-scale soil carbon density and carbon stock estimation method specifically includes: using geographic information software to establish vector layer data, and using spatial analysis tools to divide the study area into raster units based on the vector layer data; using Kriging spatial interpolation analysis based on the GIS spatial database based on soil attributes to obtain a raster thematic layer, wherein the raster thematic layer includes soil occurrence type, bulk density, organic carbon content, total carbon content, gravel volume percentage and soil layer thickness.

[0008] Furthermore, step S4 of the regional scale soil carbon density and carbon storage estimation method specifically includes: using a spatial analysis tool to perform grid overlay analysis on the grid thematic layer to obtain a spatial distribution map of soil carbon density in each grid unit, such as the formula:

[0009] Among them, D j is the soil carbon density of cell j, kg / m 2 ; n is the number of soil layers, dimensionless; C i is the carbon content of the i-th soil layer, g / kg; ρ i is the soil bulk density of the i-th soil layer, g / cm 3 ;H i is the thickness of the ith soil layer, cm; g is the volume percentage of gravel larger than 2 mm, %.

[0010] Furthermore, step S5 of the regional scale soil carbon density and carbon storage estimation method specifically includes: obtaining the soil carbon storage of each grid unit and the grid-based soil carbon storage spatial distribution map according to the spatial distribution map of soil carbon density of each grid unit, such as the formula:

[0011] Among them, T j is the soil carbon storage of cell j, kg; D j is the soil carbon density of cell j, kg / m 2 ; S j is the area of ​​cell j, m 2 ; According to the soil carbon storage in each grid unit, a grid-based spatial distribution map of soil carbon storage can be obtained.

[0012] Furthermore, step S6 of the regional-scale soil carbon density and carbon storage estimation method specifically includes: according to the grid-based soil carbon storage spatial distribution map and the area of ​​the study area, using the cell statistical method, the total soil carbon storage and average carbon density of the entire study area are obtained, such as the formula:

[0013]

[0014] Where T is the total soil carbon storage in the study area, kg; m is the total number of cells in the study area, dimensionless; D j is the soil carbon density of cell j, kg / m 2 ; S j is the area of ​​cell j, m 2 ; is the average soil carbon density in the study area, kg / m 2 ; S is the total area of ​​the study area, m 2 .

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned regional-scale soil carbon density and carbon storage estimation method.

[0016] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the above-mentioned regional scale soil carbon density and carbon storage estimation method are implemented.

[0017] The method, medium and device for estimating soil carbon density and carbon storage at a regional scale provided by the present invention have the following beneficial effects: The present invention provides a method for utilizing a GIS spatial analysis model to extract data required by the model, perform spatial overlay operations on raster layers and statistical analysis, obtain a spatial distribution map of soil carbon density, and then obtain a raster-based spatial distribution map of soil carbon reserves. Finally, the raster statistical command is used to obtain the soil carbon reserves and average carbon density of the entire study area, thereby overcoming the scale expansion problem caused by extrapolating point scale to regional scale and facilitating coupling with other technologies. The method can effectively realize accurate estimation of soil carbon reserves and carbon density at the regional scale and intuitively display the spatial distribution characteristics of carbon reserves and carbon density. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 It is a flow chart of the method for estimating soil carbon density and carbon storage at a regional scale provided by the present invention; Figure 2 It is the grid thematic layer provided by the present invention; Figure 3 It is the spatial distribution map of soil carbon density in the study area provided by the present invention; Figure 4 It is the spatial distribution map of soil carbon storage in the study area provided by the present invention; Figure 5 It is a structural block diagram of the computer device provided by the present invention. DETAILED DESCRIPTION

[0019] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0020] Figure 1 A schematic diagram of a method for estimating soil carbon density and carbon reserves at a regional scale according to this embodiment is shown. In this embodiment, the method for estimating soil carbon density and carbon reserves at a regional scale comprises the following steps: S1: Use field data collection and laboratory measurement methods to obtain soil data in the study area; Specifically, soil data of the study area include soil profile coordinates, soil occurrence type, soil occurrence layer thickness and bulk density, carbon content, and volume percentage of gravel larger than 2 mm in each soil layer; Specifically, step S1 includes: conducting field ground surveys, digging soil profiles, measuring soil bulk density using the ring knife method, measuring soil carbon content and the volume percentage of gravel larger than 2 mm in the laboratory, and recording profile coordinates, soil occurrence type, soil occurrence layer thickness, organic carbon, total carbon and other physical and chemical property data; S2: Using geographic information software, the soil data of the study area were digitized to obtain a GIS spatial database based on soil occurrence types; Specifically, step S2 includes: using ArcGIS software to digitize the physical and chemical analysis data such as soil profile coordinates, soil occurrence type, soil occurrence layer thickness, bulk density of each soil layer, carbon content, and volume percentage of gravel larger than 2 mm obtained from field surveys, and establish a GIS spatial database based on soil occurrence type; S3: Based on the GIS spatial database with soil occurrence type as the unit, the raster thematic layer is obtained by using spatial analysis tools; Specifically, step S3 includes: using geographic information software to establish vector layer data, and using spatial analysis tools to divide the study area into grid units based on the vector layer data; using Kriging spatial interpolation analysis based on a GIS spatial database based on soil occurrence types, to obtain a grid thematic layer, wherein the grid thematic layer includes soil occurrence type, bulk density, organic carbon content, total carbon content, gravel volume percentage, and soil layer thickness; Specifically, step S3 can also be implemented in the following manner: using the established vector layer data, using the spatial analysis tool in ArcGIS software, dividing the study area into grid units according to the characteristics and needs of the study area, using Kriging spatial interpolation analysis, and outputting a grid thematic layer through interpolation data and field survey measured data, wherein the grid thematic layer includes soil occurrence type, bulk density, organic carbon content, total carbon content, gravel volume percentage and soil layer thickness; S4: Using spatial analysis tools, grid thematic layers are subjected to grid overlay analysis to obtain the spatial distribution map of soil carbon density in each grid unit; Specifically, step S4 includes: using a spatial analysis tool to perform grid overlay analysis on the grid thematic layer to obtain a spatial distribution map of soil carbon density in each grid unit, such as the formula:

[0021] Among them, D j is the soil carbon density of cell j, n is the number of soil layers, C i is the carbon content of the i-th soil layer, ρ i is the soil bulk density of the i-th soil layer, H i is the thickness of the i-th soil layer, g is the volume percentage of gravel larger than 2 mm; Specifically, the carbon density spatial distribution map of the synthetic analysis is output in the following way: using the raster image overlay operation function in the spatial analysis of ArcGIS, the thematic layer is subjected to raster overlay analysis according to the following formula to obtain the spatial distribution map of soil carbon density of each grid unit;

[0022] Among them, D j is the soil carbon density of cell j, n is the number of soil layers, C i is the carbon content of the i-th soil layer, ρ i is the soil bulk density of the i-th soil layer, H i is the thickness of the i-th soil layer, g is the volume percentage of gravel larger than 2 mm; S5: According to the spatial distribution map of soil carbon density in each grid unit, the soil carbon storage in each grid unit and the grid-based spatial distribution map of soil carbon storage are obtained; Specifically, step S5 includes: according to the spatial distribution map of soil carbon density in each grid unit, obtaining the soil carbon storage in each grid unit and the grid-based spatial distribution map of soil carbon storage, such as the formula:

[0023] Among them, T j is the soil carbon storage of cell j, D j is the soil carbon density of cell j, S j is the area of ​​cell j. According to the soil carbon storage in each grid cell, the grid-based spatial distribution map of soil carbon storage can be obtained; S6: According to the grid-based spatial distribution map of soil carbon storage and the area of ​​the study area, the total soil carbon storage and average carbon density of the study area were obtained using the cell statistical method; Specifically, step S6 includes: according to the grid-based soil carbon storage spatial distribution map and the area of ​​the study area, using the cell statistical method, obtaining the soil carbon storage and average carbon density of the entire study area, such as the formula:

[0024]

[0025] Where T is the total soil carbon storage in the study area, m is the total number of cells in the study area, and D j is the soil carbon density of cell j, S j is the area of ​​cell j, is the average soil carbon density in the study area, and S is the total area of ​​the study area.

[0026] In some embodiments, the above-mentioned regional-scale soil carbon density and carbon storage estimation method can also be implemented in the following manner.

[0027] In this embodiment, the method for estimating soil carbon density and carbon storage at a regional scale includes the following steps: Step 1. Field data collection: According to the soil occurrence type of the study area, soil sample collection points are arranged to ensure the representativeness and typicality of the points; through field ground surveys, soil profiles are excavated (generally 100 cm, if the soil layer thickness is less than 100 cm, it is the actual soil layer thickness), stratified sampling is carried out, soil bulk density is determined by the ring knife method, soil organic carbon / total carbon content and the volume percentage of gravel larger than 2 mm are determined in the laboratory, and the profile geographic coordinates and elevation, soil occurrence type, soil occurrence layer thickness, sample net wet weight and other data are recorded; the coordinate system uses the CGCS2000 coordinate system, and the carbon content is determined by high-frequency combustion-infrared absorption spectroscopy or potassium dichromate volumetric method; Step 2. Spatial data acquisition: Using ArcGIS software, digitize the physical and chemical analysis data such as soil profile coordinates, soil occurrence type, soil occurrence layer thickness and bulk density of each soil layer, organic carbon / total carbon content, and volume percentage of gravel larger than 2 mm obtained from field surveys, and establish a GIS spatial database based on soil occurrence type; Step 3. Thematic layer generation: Using the established vector layer data and the spatial analysis tool in ArcGIS software, determine the resolution suitable for the study area according to the characteristics and needs of the study area, divide the study area into grid units, and use Kriging spatial interpolation analysis to output soil occurrence type, bulk density, organic carbon / total carbon content, gravel volume percentage and soil thickness grid thematic layers through interpolation data and field survey measured data; Step 4. Calculation of soil carbon density and output of spatial distribution map: Using the raster image overlay operation function in the spatial analysis of ArcGIS, the soil carbon content, soil bulk density, volume percentage of gravel larger than 2 mm, and soil layer thickness thematic layers were overlaid and analyzed according to the following formula to obtain the soil carbon density distribution map of each grid unit;

[0028] Among them, D j is the soil carbon density of cell j, kg / m 2 ; n is the number of soil layers, dimensionless; C i is the carbon content of the i-th soil layer, g / kg; ρ i is the soil bulk density of the i-th soil layer, g / cm 3 ;H i is the thickness of the i-th soil layer, cm; g is the volume percentage of gravel larger than 2 mm, %; Step 5. Calculation of soil carbon storage and output of spatial distribution map: Based on the soil carbon density of each grid cell and the size of the grid area, the soil carbon storage of each grid cell can be obtained. The calculation formula is as follows:

[0029] Among them, T j is the soil carbon storage of cell j, kg; D j is the soil carbon density of cell j, kg / m 2 ; S j is the area of ​​cell j, m 2 Through formula calculation, a grid-based spatial distribution map of soil carbon storage can be obtained; Step 6. Estimation of soil carbon density and carbon storage in the study area: Using the Cell Statistics raster statistics command, the total soil carbon storage and average carbon density in the study area can be obtained. The calculation formula is as follows:

[0030]

[0031] Where T is the total soil carbon storage in the study area, kg; m is the total number of cells in the study area, dimensionless; D j is the soil carbon density of cell j, kg / m 2 ; is the average soil carbon density in the study area, kg / m 2 ; S j is the area of ​​cell j, m 2 ; S is the total area of ​​the study area, m 2 .

[0032] In another embodiment, the above-mentioned regional scale soil carbon density and carbon storage estimation method can also be implemented in the following manner. In this embodiment, the regional scale soil carbon density and carbon storage estimation method includes the following steps: Step 1: Field data collection 1.1 Sampling point layout: Soil sampling points are laid out and numbered in the study area. The sampling points are evenly distributed in the study area and cover all soil occurrence types, so as to control the entire study area. For typical soil occurrence types with a large area, soil sampling points need to be densely laid out; 1.2 Sample collection: In the vicinity of the sampling point and within the same soil type range, adopt S-type or X-type mixed sampling method, select 4-5 locations to dig 100cm deep (when the effective soil layer thickness is less than 100cm, the actual soil layer thickness shall prevail), and conduct layered mixed sampling at intervals of 20cm on each section. The sampling volume of the mixed sample at the same depth is about 1kg; at the same time, take 2-3 ring knife samples from each layer, label and number the samples and bring them back to the room for later processing; 1.3 Index determination: Record the geographic coordinates and elevation of the sampling point, soil occurrence type, thickness, net wet weight of the ring cutter sample and other physical and chemical properties on site; bake the collected ring cutter sample at 105℃ to constant weight, obtain the moisture content, and calculate the average bulk density of each soil layer; air-dry, crush and sieve the mixed sample with a 2mm sieve, and use the drainage method to determine the volume percentage of gravel (particle size > 2mm); use the high-frequency combustion-infrared absorption spectroscopy or potassium dichromate volumetric method to determine the organic carbon / total carbon content in the soil sample; Step 2: Spatial data acquisition 2.1 Establish a vector map of sampling points: In ArcGIS software, convert the geographic coordinates of each sampling point recorded on site into a spatial layer; 2.2 Digital processing of determining factor data: Existing studies have shown that the factors that determine soil carbon density and carbon storage are mainly soil bulk density, organic carbon and total carbon content, gravel volume and soil layer thickness. The measured indicator data obtained in step 1 are digitally input; 2.3 Establish GIS spatial database: In ArcGIS software, the physical and chemical analysis data such as soil occurrence type, bulk density, organic carbon / total carbon content, gravel volume percentage and soil layer thickness of the sampling points are used to establish a vector map to form a soil profile point spatial database; Step 3: Generate thematic layers 3.1 Determination of grid size: According to the characteristics of the study area and research needs, the grid size of thematic layers such as soil occurrence type, bulk density, organic carbon / total carbon content, gravel volume percentage and soil layer thickness is uniformly determined; 3.2 Vector-to-raster conversion: In the ArcGIS software Spatial Analyst module, the Kriging spatial interpolation method was used to obtain the soil occurrence type, bulk density, organic carbon / total carbon content, gravel volume percentage and soil thickness raster thematic layers to achieve vector-to-raster conversion; Step 4: Calculation of soil carbon density and output of spatial distribution map The raster image overlay function of the Spatial Analyst module in ArcGIS was used to perform raster overlay analysis according to the soil carbon density calculation formula (1) to obtain the spatial distribution map of soil carbon density based on raster units. Step 5: Calculation of soil carbon storage and output of spatial distribution map In ArcGIS, the Raster Math command in the Spatial Analyst Tools module was used to calculate the carbon storage of each cell according to the carbon storage calculation formula (2) to obtain the spatial distribution map of soil carbon storage based on grid cells. Step 6: Estimation of soil carbon density and carbon storage in the study area The total soil carbon storage and average soil carbon density in the study area were obtained using the Cell Statistics command in the Spatial Analyst module of ArcGIS according to formulas (3) and (4).

[0033] In another embodiment, the above-mentioned regional scale soil carbon density and carbon storage estimation method can also be implemented in the following manner. In this embodiment, the soil carbon density and carbon storage in the Chongling River Basin, a tributary of the Xiangjiang River, are studied, and 65 sampling points are arranged, covering the soil types of the entire study area, which is representative and typical; the regional scale soil carbon density and carbon storage estimation method includes the following steps: Step 1: Field data collection: Conduct field sample point profile excavation and sample collection, process samples in the laboratory, measure indicators such as bulk density, total carbon content, and gravel volume percentage, and obtain data such as the geographic coordinates and elevation of the sampling points, soil type, soil layer thickness, bulk density, carbon content, and gravel (greater than 2mm) volume percentage; Step 2: Spatial data acquisition: In ArcGIS software, the acquired geographic coordinates and elevation of the sampling points, soil type, soil thickness, bulk density, carbon content, gravel volume ratio and other data are used to establish a vector map to form a spatial database of soil profile points in the Chongling River Basin; Step 3: Thematic layer production: In the ArcGIS software Spatial Analyst module, the Kriging spatial interpolation method is used to obtain the geographic coordinates and elevation of the watershed profile sampling points, soil type, bulk density, carbon content, and gravel volume ratio raster thematic layers (such as Figure 2A, B, C, D) to realize the conversion between vector and raster, the grid unit size is 5km*5km; Step 4: Calculation of soil carbon density and output of spatial distribution map: Using the raster image overlay function of the Spatial Analyst module in ArcGIS, raster overlay analysis was performed according to the soil carbon density calculation formula (1) to obtain a spatial distribution map of soil carbon density based on raster units (e.g. Figure 3 ), the maximum value is 22.93kg / m 2 , located in the northwest of the basin; the minimum value is 7.5kg / m 2 , mainly concentrated in the north and south ends of the basin; Step 5: Calculation of soil carbon storage and output of spatial distribution map: In ArcGIS, use the Raster Math command in the Spatial Analyst Tools module to calculate the carbon storage of each cell according to the carbon storage calculation formula (2) to obtain a spatial distribution map of soil carbon storage based on grid cells (e.g. Figure 4 ); Step 6: Regional carbon density and carbon storage estimation: Using the Cell Statistics command in the Spatial Analyst module in ArcGIS, according to formula (3) and formula (4), the total soil carbon storage and average soil carbon density of the study area were obtained. The soil carbon storage at a depth of 1 meter in the Chongling River Basin was 75610501430 kg, and the average carbon density was 12.87 kg / m 2 .

[0034] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned regional-scale soil carbon density and carbon stock estimation method are implemented. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0035] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned regional-scale soil carbon density and carbon stock estimation method are implemented.

[0036] like Figure 5As shown, the computer device may include: at least one processor 121, such as a CPU (Central Processing Unit), at least one communication interface 123, a memory 124, and at least one communication bus 122. Among them, the communication bus 122 is used to realize the connection and communication between these components. Among them, the communication interface 123 may include a display screen (Display), a keyboard (Keyboard), and the optional communication interface 123 may also include a standard wired interface and a wireless interface. The memory 124 may be a high-speed RAM memory (Random Access Memory, volatile random access memory), or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 124 may also be at least one storage device located away from the aforementioned processor 121. Among them, the memory 124 stores an application program, and the processor 121 calls the program code stored in the memory 124 to perform any of the above method steps. Among them, the communication bus 122 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 122 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one line is used to represent it, but it does not mean that there is only one bus or one type of bus. Among them, the memory 124 may include a volatile memory (English: volatile memory), such as a random access memory (English: random-access memory, abbreviated: RAM); the memory may also include a non-volatile memory (English: non-volatile memory), such as a flash memory (English: flash memory), a hard disk drive (English: hard disk drive, abbreviated: HDD) or a solid-state drive (English: solid-state drive, abbreviated: SSD); the memory 124 may also include a combination of the above-mentioned types of memory. Among them, the processor 121 may be a central processing unit (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP. Among them, the processor 121 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit (English: application-specific integrated circuit, abbreviated: ASIC), a programmable logic device (English: programmable logic device, abbreviated: PLD) or a combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. Optionally, the memory 124 is also used to store program instructions. The processor 121 can call the program instructions to implement the regional scale soil carbon density and carbon storage estimation method of the present embodiment.

[0037] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for estimating soil carbon density and carbon storage at a regional scale, characterized in that: The following steps are involved: S1: Use field data collection and laboratory measurement methods to obtain soil data in the study area; S2: using geographic information software, digitizing the soil data of the study area to obtain a GIS spatial database based on soil occurrence types; S3: According to the GIS spatial database based on soil occurrence type, a raster thematic layer is obtained by using spatial analysis tools; S4: using a spatial analysis tool, performing grid superposition analysis on the grid thematic layer to obtain a spatial distribution map of soil carbon density in each grid unit; S5: according to the spatial distribution map of soil carbon density in each grid unit, obtaining the soil carbon storage in each grid unit and the grid-based spatial distribution map of soil carbon storage; S6: According to the grid-based spatial distribution map of soil carbon storage and the area of ​​the study area, the total soil carbon storage and average carbon density of the study area are obtained using a cell statistical method.

2. The method for estimating soil carbon density and carbon storage at a regional scale according to claim 1, characterized in that: The soil data of the study area include soil profile coordinates, soil occurrence type, soil occurrence layer thickness and each soil layer's bulk density, carbon content, and volume percentage of gravel larger than 2 mm.

3. The method for estimating soil carbon density and carbon storage at a regional scale according to claim 1, characterized in that: Step S3 specifically includes: Using geographic information software to establish vector layer data, and using spatial analysis tools to divide the study area into grid cells based on the vector layer data; According to the GIS spatial database based on soil occurrence type, Kriging spatial interpolation analysis is used to obtain a raster thematic layer, which includes soil occurrence type, bulk density, organic carbon content, total carbon content, gravel volume percentage and soil layer thickness.

4. The method for estimating soil carbon density and carbon storage at a regional scale according to claim 1, characterized in that: Step S4 specifically includes: using a spatial analysis tool to perform grid overlay analysis on the grid thematic layer to obtain a spatial distribution map of soil carbon density in each grid unit, such as the formula: ， Among them, D j is the soil carbon density of cell j, n is the number of soil layers, C i is the carbon content of the i-th soil layer, ρ i is the soil bulk density of the i-th soil layer, H i is the thickness of the ith soil layer, and g is the volume percentage of gravel larger than 2 mm.

5. The method for estimating soil carbon density and carbon storage at a regional scale according to claim 1, characterized in that: Step S5 specifically includes: according to the spatial distribution map of soil carbon density in each grid unit, obtaining the soil carbon storage in each grid unit and the grid-based spatial distribution map of soil carbon storage, such as the formula: ， Among them, T j is the soil carbon storage of cell j, D j is the soil carbon density of cell j, S j is the area of ​​cell j. According to the soil carbon storage in each grid cell, a grid-based spatial distribution map of soil carbon storage can be obtained.

6. The method for estimating soil carbon density and carbon storage at a regional scale according to claim 1, characterized in that: Step S6 specifically includes: according to the grid-based soil carbon storage spatial distribution map and the area of ​​the study area, using the cell statistical method, obtaining the total soil carbon storage and average carbon density of the entire study area, such as the formula: ， ， Where T is the total soil carbon storage in the study area, m is the total number of cells in the study area, and D j is the soil carbon density of cell j, S j is the area of ​​cell j, is the average soil carbon density in the study area, and S is the total area of ​​the study area.

7. 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 any method for estimating soil carbon density and carbon storage at a regional scale as described in claims 1-6 are implemented.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of any method for estimating soil carbon density and carbon storage at a regional scale as described in claims 1-6 are implemented.

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