Farmland soil carbon sequestration potential rapid assessment method and device and storage medium

Abstract

The invention relates to the technical field of farmland soil carbon sequestration evaluation, and provides a farmland soil carbon sequestration potential rapid evaluation method and device and a storage medium, and the method comprises the steps: constructing a first model and a second model according to fixed-point experiment data; dividing the target area into a plurality of grid units and obtaining grid observation data and grid prediction data corresponding to the grid units; obtaining initial soil organic carbon density distribution information according to the grid observation data and the first model; and according to the initial soil organic carbon density distribution information, the grid prediction data and a second model, obtaining prediction saturated soil organic carbon density distribution information, and further obtaining soil carbon sequestration potential spatial distribution information. The first model and the second model capture the relationship between the data, and adaptively obtain the corresponding processing result data based on the data condition of the grid unit, thereby obtaining the spatial distribution information of the soil carbon sequestration potential, and improving the evaluation accuracy. The second model considers the upper limit of soil carbon sequestration to avoid excessive evaluation of the organic carbon density.

Description

Technical Field

[0001] The present invention relates to the technical field of farmland soil carbon sequestration assessment, and in particular to a method, device and storage medium for rapid assessment of farmland soil carbon sequestration potential. Background Art

[0002] The assessment of soil carbon sequestration potential is of great significance for addressing climate change and reducing greenhouse gas emissions. As one of the world's largest carbon reservoirs, soil can significantly increase the storage of organic carbon in the soil through reasonable management measures, thereby reducing the concentration of carbon dioxide in the atmosphere and alleviating the problem of climate warming. Assessing the carbon sequestration potential of farmland soil can not only provide a scientific basis for carbon management, but also provide data support for agricultural sustainable development, soil improvement and other aspects.

[0003] Existing methods for assessing soil carbon sequestration potential mainly use extrapolation estimation based on soil survey data and scenario simulation based on process models. The former determines the carbon sequestration rate of farmland soil under one or more management measures based on soil carbon sequestration experimental data in local areas, and extrapolates the data to other regions or the global scale to assess the overall soil carbon sequestration potential; the latter simulates farmland ecology to determine the carbon sequestration rate of farmland soil under different management measures, and then assesses the soil carbon sequestration potential.

[0004] However, the extrapolation estimation method based on soil survey data relies on experimental data from local areas to extrapolate other external areas. Due to differences in regional soil properties, climate conditions, etc., there are obvious uncertainties in the extrapolated assessment, resulting in low assessment accuracy. In the scenario simulation method based on process models, the process model involves many parameters that are difficult to accurately determine. The application and adjustment of the process model requires long-term complex optimization, and a large amount of computing resources are required when applied. As the scope of the assessment area increases, the complexity of the process model will increase exponentially, resulting in an inability to adapt to large-scale estimates. In addition, existing process models do not take into account the natural upper limit of soil carbon sequestration, resulting in an overestimation of the soil carbon sequestration potential.

[0005] Therefore, how to more accurately and efficiently evaluate the carbon sequestration potential of farmland soil is an urgent problem that needs to be solved. Summary of the invention

[0006] The present invention provides a method, device and storage medium for quickly evaluating the carbon sequestration potential of farmland soil, so as to solve the problems of low accuracy and high calculation cost in evaluating the carbon sequestration potential of farmland soil in the prior art.

[0007] The present invention provides a method for rapidly evaluating the carbon sequestration potential of farmland soil, comprising: Obtaining fixed-point experimental data, regional observation data, and regional prediction data for the target area; According to the fixed-point experimental data, a first model is constructed based on actual carbon input, wherein the first model is used to obtain a historical soil organic carbon density change rate; According to the fixed-point experimental data, a second model is constructed based on the maximum carbon input, and the second model is used to predict the saturated soil organic carbon density; Divide the target area into a plurality of grid units according to preset grid parameters, obtain grid observation data corresponding to each of the grid units according to the regional observation data, and obtain grid prediction data corresponding to each of the grid units according to the regional prediction data; Acquiring initial soil organic carbon density distribution information of the target area according to the grid observation data and the first model; Acquire the predicted saturated soil organic carbon density distribution information of the target area according to the initial soil organic carbon density distribution information, the grid prediction data and the second model; The spatial distribution information of soil carbon sequestration potential in the target area is obtained according to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information.

[0008] According to a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention, the fixed-point experimental data includes fixed-point climate data, fixed-point soil property data, fixed-point soil organic carbon density data, fixed-point actual carbon input data, fixed-point maximum carbon input data, fixed-point water management data and fixed-point experimental time data; The regional observation data include regional climate data, regional soil property data, regional soil organic carbon density data, regional actual carbon input data, regional maximum carbon input data, regional water management data and regional observation time; The regional prediction data includes regional prediction climate data and regional prediction carbon input resource quantity data.

[0009] According to a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention, a linear regression equation is constructed based on the fixed-point soil organic carbon density data, with a preset unit time as an independent variable and with the soil organic carbon density of the corresponding time series in the fixed-point soil organic carbon density data as a dependent variable; Determine the soil organic carbon density change rate according to the slope of the linear regression equation; Determine the initial soil organic carbon density of the fixed point according to the fixed point soil organic carbon density data, and determine the median of the actual carbon input amount of the fixed point according to the actual carbon input amount data of the fixed point; A random forest model is constructed as the first model, using the fixed-point climate data, the fixed-point soil property data, the fixed-point initial soil organic carbon density, the fixed-point median of actual carbon input, the fixed-point water management data and the fixed-point experimental time data as independent variables, and the soil organic carbon density change rate as the dependent variable.

[0010] According to a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention, the second model is constructed based on the fixed-point experimental data and the maximum carbon input, comprising: According to the fixed-point climate data, determine the fixed-point annual average temperature and the fixed-point annual average rainfall during the observation period of the region; according to the fixed-point soil organic carbon density data, determine the fixed-point initial soil organic carbon density; according to the fixed-point maximum carbon input data, determine the fixed-point maximum carbon input median; according to the fixed-point soil organic carbon density data, determine the fixed-point final year soil organic carbon density under the fixed-point maximum carbon input management; Taking the average annual temperature at the fixed point, the average annual rainfall at the fixed point, the initial soil organic carbon density at the fixed point and the median of the maximum carbon input at the fixed point as independent variables, and taking the soil organic carbon density at the end of the fixed point as the dependent variable, a nonlinear regression model is constructed as the second model; The time corresponding to the predicted saturated soil organic carbon density output by the second model is associated with the regional observation time.

[0011] According to a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention, obtaining initial soil organic carbon density distribution information of the target area based on the grid observation data and the first model includes: Determine grid climate data, grid soil property data, grid soil organic carbon density data, grid actual carbon input data, grid water management data and grid observation time according to the grid observation data corresponding to the grid unit; Inputting the grid climate data, the grid soil property data, the grid soil organic carbon density data, the grid actual carbon input data, the grid water management data and the grid observation time into the first model, and obtaining the grid soil organic carbon density change rate corresponding to the grid unit; Calculating the grid initial soil organic carbon density corresponding to the grid unit according to the grid soil organic carbon density data, the grid soil organic carbon density change rate and the grid observation time; The initial soil organic carbon density distribution information is obtained according to the grid initial soil organic carbon density of each grid unit.

[0012] According to a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention, the predicted saturated soil organic carbon density distribution information of the target area is obtained according to the initial soil organic carbon density distribution information, the grid prediction data and the second model, including: According to the initial soil organic carbon density distribution information, the grid initial soil organic carbon density of each grid unit is used as the grid initial soil organic carbon density for prediction; Determining the grid predicted annual average temperature, the grid predicted annual average rainfall and the grid predicted maximum carbon input of the preset SSP scenario according to the grid predicted data and the regional observation time; The grid initial soil organic carbon density and the grid predicted annual average temperature, the grid predicted annual average rainfall and the grid predicted maximum carbon input corresponding to the preset SSP scenario are input into the second model to obtain the grid predicted saturated soil organic carbon density of the grid unit corresponding to the preset SSP scenario; The predicted saturated soil organic carbon density distribution information of the preset SSP scenario is obtained according to the grid predicted saturated soil organic carbon density of each of the grid units.

[0013] According to a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention, obtaining the spatial distribution information of soil carbon sequestration potential in the target area according to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information includes: According to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information, subtracting the grid predicted saturated soil organic carbon density corresponding to the grid unit under the same preset SSP scenario from the grid initial soil organic carbon density to obtain a grid carbon sequestration potential value; According to the grid carbon sequestration potential value of each of the grid cells, the spatial distribution information of the soil carbon sequestration potential of the preset SSP scenario is obtained.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for rapidly assessing the carbon sequestration potential of farmland soil as described in any one of the above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for rapidly assessing the carbon sequestration potential of farmland soil as described in any one of the above.

[0016] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements a method for rapid assessment of farmland soil carbon sequestration potential as described in any one of the above.

[0017] The present invention provides a method, device and storage medium for rapid assessment of farmland soil carbon sequestration potential, which at least have the beneficial effect of constructing a first model and a second model based on fixed-point experimental data in a target area. The target area is divided into a plurality of grid units, and calculation processing is performed in units of grid units, which is conducive to improving processing efficiency and facilitating the reflection of spatial distribution. Accordingly, the regional observation data and regional prediction data of the target area are rasterized to form grid observation data and grid prediction data corresponding to the grid units. The first model reflects the relationship between various experimental conditions including the actual carbon input and the rate of change of soil organic carbon density, and the initial soil organic carbon density distribution information under known observation conditions is obtained according to the grid observation data and the first model. The second model reflects the relationship between various experimental conditions including the maximum carbon input and the soil organic carbon density, and the second model combines the initial soil organic carbon density distribution information and the grid prediction data to obtain the predicted saturated soil organic carbon density distribution information under the maximum carbon input condition in the future. According to the difference between the initial soil organic carbon density information under the known observation conditions and the predicted saturated soil organic carbon density distribution information, the soil's future carbon sequestration potential is determined, and the spatial distribution information of the soil carbon sequestration potential in the target area is obtained. In this way, the fixed-point experimental data of the long-term experiment provides a basis for constructing the first model and the second model. The relationship between the data captured by the first model and the second model is extended to each grid unit in the target area, so that the processing result data corresponding to the grid unit can be obtained based on the data of the grid unit itself, and then the spatial distribution information of the soil carbon sequestration potential can be accurately obtained, which is conducive to improving the accuracy of the soil carbon sequestration potential assessment. At the same time, the second model constructed based on the maximum carbon input takes into account the upper limit of soil carbon sequestration, avoids overestimating the organic carbon density by predicting the distribution information of saturated soil organic carbon density, and is conducive to further improving the accuracy of soil carbon sequestration potential assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of a process for rapidly evaluating the carbon sequestration potential of farmland soil provided by the present invention.

[0020] Figure 2It is a schematic diagram of the data processing process of a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention.

[0021] Figure 3 It is a schematic diagram of the initial soil organic carbon density distribution information in one embodiment of a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention.

[0022] Figure 4 It is a schematic diagram of predicting the distribution information of saturated soil organic carbon density in one embodiment of a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention.

[0023] Figure 5 It is a schematic diagram of the spatial distribution information of soil carbon sequestration potential in one embodiment of a method for rapid assessment of farmland soil carbon sequestration potential provided by the present invention.

[0024] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Combine the following Figure 1 and Figure 2 A method for rapidly assessing the carbon sequestration potential of farmland soil according to the present invention is described, comprising: S100: Acquire fixed-point experimental data, regional observation data, and regional prediction data of the target area; S200: constructing a first model based on the actual carbon input according to the fixed-point experimental data, wherein the first model is used to obtain a historical soil organic carbon density change rate; S300: constructing a second model based on the fixed-point experimental data and the maximum carbon input, wherein the second model is used to predict saturated soil organic carbon density; S400: Dividing the target area into a plurality of grid units according to preset grid parameters, acquiring grid observation data corresponding to each of the grid units according to the regional observation data, and acquiring grid prediction data corresponding to each of the grid units according to the regional prediction data; S500: Acquire initial soil organic carbon density distribution information of the target area according to the grid observation data and the first model; S600: Acquire predicted saturated soil organic carbon density distribution information of the target area according to the initial soil organic carbon density distribution information, the grid prediction data and the second model; S700: Acquire the spatial distribution information of soil carbon sequestration potential in the target area according to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information.

[0027] According to the fixed-point experimental data in the target area, the first model and the second model are constructed. The target area is divided into multiple grid units, and the calculation and processing are performed in grid units, which is conducive to improving the processing efficiency and facilitating the reflection of spatial distribution. Accordingly, the regional observation data and regional prediction data of the target area are rasterized to form raster observation data and raster prediction data corresponding to the grid units. The first model reflects the relationship between various experimental conditions including the actual carbon input and the rate of change of soil organic carbon density. According to the grid observation data and the first model, the initial soil organic carbon density distribution information under the known observation conditions is obtained. The second model reflects the relationship between various experimental conditions including the maximum carbon input and the soil organic carbon density. The second model combines the initial soil organic carbon density distribution information and the grid prediction data to obtain the predicted saturated soil organic carbon density distribution information under the maximum carbon input conditions in the future. According to the difference between the initial soil organic carbon density information under the known observation conditions and the predicted saturated soil organic carbon density distribution information, the soil's future carbon sequestration potential is determined, and the spatial distribution information of the soil carbon sequestration potential in the target area is obtained.

[0028] In this way, the fixed-point experimental data of long-term experiments provide the basis for constructing the first model and the second model. The relationship between the data captured by the first model and the second model is extended to each grid unit in the target area, so that the processing result data corresponding to the grid unit can be obtained based on the data of the grid unit itself, and then the spatial distribution information of soil carbon sequestration potential can be accurately obtained, which is conducive to improving the accuracy of soil carbon sequestration potential assessment.

[0029] At the same time, the second model constructed based on the maximum carbon input takes into account the upper limit of soil carbon sequestration, avoiding overestimating the organic carbon density by predicting the distribution information of saturated soil organic carbon density, which is conducive to further improving the accuracy of soil carbon sequestration potential assessment.

[0030] In some embodiments of the present invention, the target area is divided into a plurality of grid cells according to a preset grid parameter, and the preset grid parameter may be 1 km×1 km, and the target area is divided into a plurality of 1 km×1 km grid cells. Correspondingly, the grid observation data and grid prediction data of the grid cells are determined according to the regional observation data and the regional prediction data. The preset grid parameter may be set according to the size of the target area and the resolution size of the evaluation.

[0031] It should be noted that the initial soil organic carbon density distribution information refers to the distribution information corresponding to the starting time point for carbon sequestration potential assessment, and does not refer to the distribution information at the initial time. For example, in some embodiments of the present invention, the fixed-point experimental data and regional observation data correspond to a time period from 1981 to 2020, and the initial soil organic carbon density distribution information can be the soil organic carbon density of each farmland distributed in the target area in 2020 (such as Figure 3 ), rather than referring to the initial distribution information in 1981, the predicted saturated soil organic carbon density can be the predicted soil organic carbon density of each farmland distributed in the target area in 2060 (such as Figure 4 The spatial distribution information of soil carbon sequestration potential obtained can reflect the soil carbon sequestration potential of each farmland between 2020 and 2060 (as shown in Figure 5 The above time periods are only schematic illustrations to facilitate understanding of the solution. Time periods can be set according to the acquired data time period and forecast requirements to obtain the spatial distribution information of soil carbon sequestration potential in the corresponding time period.

[0032] Actual carbon input refers to the total amount of carbon actually input directly into the soil, including crop residues, fertilizers, organic matter and all other carbon sources that are directly used to improve soil quality or increase soil organic carbon storage.

[0033] The fixed-point experimental data can include data obtained from experiments under different carbon input conditions. Under the maximum carbon input, according to experiments and data observations, the experimental site showed that the soil organic carbon density had reached a dynamic equilibrium state in the last year of the experiment, and the absorption and release of soil organic carbon reached a balance point. Even if the carbon input is increased, the total amount of soil organic carbon will not increase further, reaching the natural upper limit of soil carbon fixation, that is, it is in a carbon saturated state. The second model is constructed based on the maximum carbon input, which can take into account the upper limit of soil carbon fixation and predict the saturated soil organic carbon density assuming that the soil will be in a carbon saturated state in the future, thereby making the predicted saturated soil organic carbon density distribution information more accurate and avoiding overestimation.

[0034] refer to Figure 2 In some embodiments of the method for rapid assessment of farmland soil carbon sequestration potential of the present invention, the fixed-point experimental data include fixed-point climate data, fixed-point soil property data, fixed-point soil organic carbon density data, fixed-point actual carbon input data, fixed-point maximum carbon input data, fixed-point water management data, and fixed-point experimental time data; The regional observation data include regional climate data, regional soil property data, regional soil organic carbon density data, regional actual carbon input data, regional maximum carbon input data, regional water management data and regional observation time; The regional prediction data include regional prediction climate data and regional prediction farmland carbon input resource quantity data.

[0035] In some embodiments of the present invention, fixed-point experimental data can be obtained from books, reports, experimental site report data, etc., including: fixed-point climate data formed by the temperature and rainfall of the experimental point; fixed-point soil attribute data formed by the clay content, sand content and pH value of the soil; fixed-point soil organic carbon density data calculated based on the organic carbon content of the soil; fixed-point maximum carbon input data determined based on the environmental conditions of the experimental point; fixed-point actual carbon input data formed by crop residues, fertilizers, organic matter, etc. input into farmland management; fixed-point water management data formed by water management measures corresponding to whether the farmland is a paddy field or a dry field; and fixed-point experimental time data formed by the duration of the experiment and the time point of recording the above data.

[0036] In some embodiments of the present invention, regional observation data can be obtained from data centers, papers, remote sensing detection data sets, etc., such as the Global Change Science Research Center, the Qinghai-Tibet Plateau Science Data Center, GSDE (Global Soil Dataset), etc., and can be obtained at a resolution of 1 km and include: regional climate data formed by temperature and rainfall distribution in the target area; soil data sets of each farmland in the target area form regional soil attribute data; the input or resource amount of crop residues, roots, waste and organic matter in the target area forms regional actual carbon input data; the maximum organic carbon input of each farmland in the target area forms regional maximum carbon input data; the specific paddy field and dry field water management measures of each farmland in the target area form regional water management data; the recording time period of the above observation data forms regional observation time.

[0037] In some embodiments of the present invention, regional prediction data can be obtained from data centers, such as the Global Change Science Research Center, the Qinghai-Tibet Plateau Science Data Center, etc., including: the temperature and rainfall of different SSP (Shared Socioeconomic Pathway) scenarios in the target area form regional prediction climate data; the carbon resource amount of different farmlands in the target area forms regional prediction carbon resource data.

[0038] In some embodiments of the present invention, some of the data in the fixed-point experimental data and the regional observation data are soil organic matter content, and the soil organic carbon content cannot be directly obtained. The organic carbon can be calculated by the van Bemmelen conversion factor, and the calculation formula is as follows:

[0039] in, is the soil organic carbon content (g C kg -1 ); is the soil organic matter content (g kg -1); 0.58 is the van Bemmelen conversion factor.

[0040] The soil organic carbon density was then calculated based on the following formula:

[0041] in, is the soil organic carbon density (Mg C ha -1 ); is the soil organic carbon content (g C kg -1 ); is the soil sampling depth (cm); is the soil bulk density (g cm -3 ); (coarse fraction) is the proportion (%) of coarse particles larger than 2 mm in the soil.

[0042] In some embodiments of the present invention, the data of soil organic carbon density are normalized to 0 to 30 cm.

[0043] refer to Figure 2 In some embodiments of the method for rapid assessment of farmland soil carbon sequestration potential of the present invention, the first model is constructed based on the actual carbon input according to the fixed-point experimental data, including: According to the fixed-point soil organic carbon density data, a linear regression equation is constructed with a preset unit time as an independent variable and the soil organic carbon density of the corresponding time series in the fixed-point soil organic carbon density data as a dependent variable; Determine the soil organic carbon density change rate according to the slope of the linear regression equation; Determine the initial soil organic carbon density of the fixed point according to the fixed point soil organic carbon density data, and determine the median of the actual carbon input amount of the fixed point according to the actual carbon input amount data of the fixed point; A random forest model is constructed as the first model, using the fixed-point climate data, the fixed-point soil property data, the fixed-point initial soil organic carbon density, the fixed-point median of actual carbon input, the fixed-point water management data and the fixed-point experimental time data as independent variables, and the soil organic carbon density change rate as the dependent variable.

[0044] According to the preset unit time, such as year or month, a linear regression equation is constructed based on the soil organic carbon density of the corresponding year and month sequence in the fixed-point soil organic carbon density data to reflect the change in soil organic carbon density per unit time interval, and then the rate of change of soil organic carbon density is determined according to the slope of the linear regression equation fitted by the fitting data. Based on the fixed-point soil organic carbon density data, the initial soil organic carbon density of the initial year is determined, and based on the fixed-point actual carbon input data, the median of the actual carbon input at the fixed point is determined. The use of the median can more accurately reflect the carbon input. Based on the fixed-point climate data, fixed-point soil attribute data, fixed-point initial soil organic carbon density, fixed-point median of actual carbon input and fixed-point water management data as independent variables, the rate of change of soil organic carbon density as the dependent variable, a random forest model is constructed as the first model. The random forest model captures the relationship between various environmental conditions and the rate of change of soil organic carbon density, so that the corresponding rate of change of soil organic carbon density can be obtained for the subsequent grid observation data of different grid units, adapting to the characteristics of different grid units, avoiding extrapolation based on fixed-point test data alone, and helping to improve the accuracy of the assessment.

[0045] In some embodiments of the present invention, with year as the unit time and as the independent variable, and fixed-point soil organic carbon density data as the dependent variable, the constructed linear regression equation can be as follows:

[0046] in, Indicates the year; Indicates the year soil organic carbon density; is the fitting slope, which represents the annual change rate of soil organic carbon density, in Mg C ha -1 a -1 ; b is the intercept term.

[0047] In some embodiments of the present invention, the fixed-point experimental data include annual stubble, root and organic matter input data, and the fixed-point actual carbon input data can be calculated by the following formula:

[0048] in, is the total annual carbon input (Mg C ha -1 ), is the annual crop residue carbon input (Mg C ha -1 ), is the annual root carbon input, is the annual carbon input of organic matter (Mg C ha -1 ).

[0049] Similarly, the regional observation data, including the annual stubble, root and organic matter input data, can also be calculated using the above formula to obtain the actual regional carbon input data, and then obtain the grid actual input data.

[0050] The median of actual carbon input at a fixed point can be calculated using the following formula:

[0051] in, is the median carbon input; Indicates the year serial number; Indicates Annual carbon input; Represents the median function.

[0052] Similarly, the median of the actual carbon input of the grid can also be calculated using the above formula.

[0053] In some embodiments of the present invention, other machine learning algorithms may be used instead of the random forest algorithm to construct a model, such as vector machines, XGBoost and other models as the first model.

[0054] refer to Figure 2 In some embodiments of the method for rapid assessment of farmland soil carbon sequestration potential of the present invention, the second model is constructed based on the fixed-point experimental data and the maximum carbon input, including: According to the fixed-point climate data, determine the fixed-point annual average temperature and the fixed-point annual average rainfall during the observation period of the region; according to the fixed-point soil organic carbon density data, determine the fixed-point initial soil organic carbon density; according to the fixed-point maximum carbon input data, determine the fixed-point maximum carbon input median; according to the fixed-point soil organic carbon density data, determine the fixed-point final year soil organic carbon density under the fixed-point maximum carbon input management; Taking the average annual temperature at the fixed point, the average annual rainfall at the fixed point, the initial soil organic carbon density at the fixed point and the median of the maximum carbon input at the fixed point as independent variables, and taking the soil organic carbon density at the end of the fixed point as the dependent variable, a nonlinear regression model is constructed as the second model; The time corresponding to the predicted saturated soil organic carbon density output by the second model is associated with the regional observation time.

[0055] According to the fixed-point experimental data, the fixed-point average annual temperature, fixed-point average annual rainfall, fixed-point initial soil organic carbon density and fixed-point maximum carbon input median were used as independent variables, and the fixed-point final year soil organic carbon density under maximum carbon input management was used as the dependent variable to construct a nonlinear regression model as the second model. Based on the median of the fixed-point maximum carbon input, the upper limit of soil carbon sequestration, that is, the carbon saturation problem, was taken into account. The nonlinear regression equation constructed with the median of the fixed-point maximum carbon input as the independent variable can avoid overestimation of soil carbon sequestration potential and make the assessment more accurate and reliable.

[0056] It should be noted that the last year refers to the last year of historical data, which can also be understood as the year before the starting time point of the prediction. For example, if the data from 1981 to 2020 are used as historical data and the data for 2060 are predicted, the last year refers to 2020 and the first year refers to 1981. Correspondingly, the fixed-point initial soil organic carbon density refers to the soil organic carbon density in 1981, and the fixed-point last-year soil organic carbon density refers to the soil organic carbon density in 2020. Based on the above assumptions, the nonlinear regression model can reflect the changes in soil organic carbon density over a period of 40 years (1981 to 2020). The average annual temperature, average annual rainfall, median maximum carbon input from 2021 to 2060, and soil organic carbon density in 2021 are input into the nonlinear regression model, that is, input into the second model, and the soil organic carbon density in 2060 can be obtained accordingly to achieve the prediction effect.

[0057] In some embodiments of the present invention, the constructed nonlinear regression equation model may be as follows:

[0058] in, The soil organic carbon density in the last year under the management with maximum carbon input; is the intercept term of the regression equation; is the average annual temperature during the prediction period; is the average annual rainfall during the forecast period; is the soil organic carbon density in the initial year; is the median of the maximum carbon input; , is the coefficient of the mean annual temperature; , is the coefficient of average annual rainfall; , is the coefficient of initial soil organic carbon density; , is the coefficient of the median maximum carbon input.

[0059] In some embodiments of the present invention, other nonlinear statistical models may be used as the second model instead of the nonlinear regression equation model.

[0060] In some embodiments of the method for rapid assessment of farmland soil carbon sequestration potential of the present invention, obtaining the initial soil organic carbon density distribution information of the target area according to the grid observation data and the first model includes: Determine grid climate data, grid soil property data, grid soil organic carbon density data, grid actual carbon input data, grid water management data and grid observation time according to the grid observation data corresponding to the grid unit; Inputting the grid climate data, the grid soil property data, the grid soil organic carbon density data, the grid actual carbon input data, the grid water management data and the grid observation time into the first model, and obtaining the grid soil organic carbon density change rate corresponding to the grid unit; Calculating the grid initial soil organic carbon density corresponding to the grid unit according to the grid soil organic carbon density data, the grid soil organic carbon density change rate and the grid observation time; The initial soil organic carbon density distribution information is obtained according to the grid initial soil organic carbon density of each grid unit.

[0061] The regional observation data is rasterized based on the grid cells to form grid climate data, grid soil property data, grid soil organic carbon density data, grid actual carbon input data, grid water management data and grid observation time corresponding to the grid cells, which can meet the input data requirements of the first model. After the data required by the first model is input, the grid soil organic carbon density change rate of the grid cell is obtained, and then the grid soil organic carbon density data and grid observation time are combined to calculate the soil organic carbon density of the grid cell in the last year, that is, the grid initial soil organic carbon density. By aggregating the grid initial soil organic carbon density of each grid cell, the initial soil organic carbon density distribution information can be obtained, and the distribution of soil organic carbon in the target area is reflected at the resolution of the preset grid parameters.

[0062] In some embodiments of the present invention, the grid initial soil organic carbon density can be calculated by the following formula:

[0063] Where: is the grid initial soil organic carbon density; is the rate of change of grid soil organic carbon density (Mg C ha -1 ); is the soil organic carbon density (Mg C ha-1 ); is the grid observation time.

[0064] It is understandable that the data types included in the regional observation data and the grid observation data are the same, but the data cover different areas. Similarly, the data types included in the regional prediction data and the grid prediction data are the same, but the data cover different areas.

[0065] In some embodiments of the method for rapid assessment of farmland soil carbon sequestration potential of the present invention, obtaining the predicted saturated soil organic carbon density distribution information of the target area according to the initial soil organic carbon density distribution information, the grid prediction data and the second model includes: According to the initial soil organic carbon density distribution information, the grid initial soil organic carbon density of each grid unit is used as the grid initial soil organic carbon density for prediction; Determining the grid predicted annual average temperature, the grid predicted annual average rainfall and the grid predicted maximum carbon input of the preset SSP scenario according to the grid predicted data and the regional observation time; The grid initial soil organic carbon density and the grid predicted annual average temperature, the grid predicted annual average rainfall and the grid predicted maximum carbon input corresponding to the preset SSP scenario are input into the second model to obtain the grid predicted saturated soil organic carbon density of the grid unit corresponding to the preset SSP scenario; The predicted saturated soil organic carbon density distribution information of the preset SSP scenario is obtained according to the grid predicted saturated soil organic carbon density of each of the grid units.

[0066] The SSP (Shared Socioeconomic Pathway) scenario describes different future development paths for the global society, combining greenhouse gas emissions and climate management measures to assess environmental trends. Because future carbon input management, climate and other conditions will also change under different SSP scenarios, the predicted data of different SSP scenarios are considered separately to facilitate a more comprehensive assessment of soil carbon sequestration potential.

[0067] According to the grid prediction data, the preset SSP scenarios, such as SSP119, SSP245, SSP585, etc., are determined, and the corresponding grid predicted annual average temperature, grid predicted annual average rainfall and grid predicted maximum carbon input are input into the second model. At the same time, the grid initial soil organic carbon density of the last year is input into the second model as the initial soil organic carbon density of the initial year of prediction to obtain the grid predicted saturated soil organic carbon density of the maximum carbon input management under the corresponding SSP scenario. By aggregating the grid predicted saturated soil organic carbon density of each grid unit under the same SSP scenario, the predicted saturated soil organic carbon density distribution information under the SSP scenario can be obtained, which reflects the distribution of the predicted future soil organic carbon density under the SSP scenario.

[0068] In some embodiments of the present invention, the annual average temperature of a grid cell may be calculated by the following formula:

[0069] in, is the average annual temperature; is the temperature of the nth grid cell in the jth year.

[0070] In some embodiments of the present invention, the average annual rainfall of a grid cell may be calculated by the following formula:

[0071] in, is the average annual rainfall; is the rainfall in the jth year of the nth grid cell, which can be the total annual rainfall or the average monthly rainfall in the year.

[0072] It can be understood that the above-mentioned method of calculating the average annual temperature and average annual precipitation is applicable to historical data and forecast data, such as the average annual temperature and average annual precipitation corresponding to the historical data from 1981 to 2020, and the average annual temperature and average annual precipitation under each SSP scenario from 2021 to 2060.

[0073] The regional prediction data includes regional predicted farmland carbon input resource data, which, after being rasterized, form grid predicted farmland carbon input resource data corresponding to the grid unit. The grid predicted maximum carbon input can be determined based on the grid predicted farmland carbon input resource data and the corresponding coefficient of the preset SSP scenario. For example, under the SSP119 scenario, 50% of the grid predicted farmland carbon input resource is used as the grid predicted maximum carbon input; under the SSP245 scenario, 70% of the grid predicted farmland carbon input resource is used as the grid predicted maximum carbon input; under the SSP585 scenario, 90% of the grid predicted farmland carbon input resource is used as the grid predicted maximum carbon input.

[0074] It should be noted that the grid initial soil organic carbon density of the last year is used as the initial soil organic carbon density of the predicted initial year, and the grid initial soil organic carbon density obtained based on the change rate of the grid soil organic carbon density output by the first model is input into the second model as one of the input quantities, so that the first model and the second model can be coupled. In some embodiments of the present invention, the random forest model is coupled with the nonlinear regression equation model, and the output data of the random forest model is processed as one of the input data of the nonlinear regression equation model. In this way, the ability of random forest to capture the complex relationship of multiple data can be used to accurately obtain the corresponding grid soil organic carbon density change rate based on the grid observation data of the grid unit, and then the initial grid soil organic carbon density of the last year is calculated and input into the nonlinear regression equation. The reliability and explanatory advantages of the nonlinear regression equation model in long-term trend prediction are used to obtain the grid predicted saturated soil organic carbon density corresponding to the grid unit, and then the soil carbon sequestration potential of the grid unit can be evaluated according to the initial grid soil organic carbon density and the grid predicted saturated soil organic carbon density, which is conducive to improving the accuracy and reliability of the evaluation.

[0075] It is to be understood that the regional forecast data includes forecast data for multiple SSP scenarios.

[0076] In some embodiments of the method for rapid assessment of farmland soil carbon sequestration potential of the present invention, obtaining the spatial distribution information of soil carbon sequestration potential of the target area according to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information includes: According to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information, subtracting the grid predicted saturated soil organic carbon density corresponding to the grid unit under the same preset SSP scenario from the grid initial soil organic carbon density to obtain a grid carbon sequestration potential value; According to the grid carbon sequestration potential value of each of the grid cells, the spatial distribution information of the soil carbon sequestration potential of the preset SSP scenario is obtained.

[0077] The grid predicted saturated soil organic carbon density of each grid unit under the same SSP scenario is subtracted from the grid initial soil organic carbon density. The difference between the two, i.e., the grid carbon sequestration potential value, reflects the amount of organic carbon that the soil in the grid unit can fix in the future. The grid carbon sequestration potential values ​​of each grid unit are summarized to form the spatial distribution information of soil carbon sequestration potential under the SSP scenario. Obtaining the spatial distribution information of soil carbon sequestration potential under different SSP scenarios can more comprehensively understand the soil carbon sequestration potential under different future development situations, which is conducive to a more comprehensive and reliable assessment of soil carbon sequestration potential.

[0078] Figure 6An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the above-mentioned method for rapid assessment of farmland soil carbon sequestration potential.

[0079] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0080] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a method for rapid assessment of farmland soil carbon sequestration potential provided by the above methods.

[0081] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute a method for rapidly assessing the carbon sequestration potential of farmland soil provided by the above methods.

[0082] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0084] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0085] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0086] All actions to obtain signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the location and with the authorization given by the owner of the corresponding device.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapid assessment of farmland soil carbon sequestration potential, characterized in that: include: Obtaining fixed-point experimental data, regional observation data, and regional prediction data for the target area; According to the fixed-point experimental data, a first model is constructed based on actual carbon input, wherein the first model is used to obtain a historical soil organic carbon density change rate; According to the fixed-point experimental data, a second model is constructed based on the maximum carbon input, and the second model is used to predict the saturated soil organic carbon density; Divide the target area into a plurality of grid units according to preset grid parameters, obtain grid observation data corresponding to each of the grid units according to the regional observation data, and obtain grid prediction data corresponding to each of the grid units according to the regional prediction data; Acquiring initial soil organic carbon density distribution information of the target area according to the grid observation data and the first model; Acquire the predicted saturated soil organic carbon density distribution information of the target area according to the initial soil organic carbon density distribution information, the grid prediction data and the second model; The spatial distribution information of soil carbon sequestration potential in the target area is obtained according to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information.

2. A method for rapid assessment of farmland soil carbon sequestration potential according to claim 1, characterized in that: The fixed-point experimental data include fixed-point climate data, fixed-point soil property data, fixed-point soil organic carbon density data, fixed-point actual carbon input data, fixed-point maximum carbon input data, fixed-point water management data and fixed-point experimental time data; The regional observation data include regional climate data, regional soil property data, regional soil organic carbon density data, regional actual carbon input data, regional maximum carbon input data, regional water management data and regional observation time; The regional prediction data include regional prediction climate data and regional prediction farmland carbon input resource quantity data.

3. A method for rapid assessment of farmland soil carbon sequestration potential according to claim 2, characterized in that: The step of constructing a first model based on the fixed-point experimental data and actual carbon input includes: According to the fixed-point soil organic carbon density data, a linear regression equation is constructed with a preset unit time as an independent variable and the soil organic carbon density of the corresponding time series in the fixed-point soil organic carbon density data as a dependent variable; Determine the soil organic carbon density change rate according to the slope of the linear regression equation; Determine the initial soil organic carbon density of the fixed point according to the fixed point soil organic carbon density data, and determine the median of the actual carbon input amount of the fixed point according to the actual carbon input amount data of the fixed point; A random forest model is constructed as the first model, using the fixed-point climate data, the fixed-point soil property data, the fixed-point initial soil organic carbon density, the fixed-point median of actual carbon input, the fixed-point water management data and the fixed-point experimental time data as independent variables, and the soil organic carbon density change rate as the dependent variable.

4. A method for rapid assessment of farmland soil carbon sequestration potential according to claim 2, characterized in that: The step of constructing a second model based on the fixed-point experimental data and the maximum carbon input includes: According to the fixed-point climate data, determine the fixed-point annual average temperature and the fixed-point annual average rainfall during the observation period of the region; according to the fixed-point soil organic carbon density data, determine the fixed-point initial soil organic carbon density; according to the fixed-point maximum carbon input data, determine the fixed-point maximum carbon input median; according to the fixed-point soil organic carbon density data, determine the fixed-point final year soil organic carbon density under the fixed-point maximum carbon input management; Taking the average annual temperature at the fixed point, the average annual rainfall at the fixed point, the initial soil organic carbon density at the fixed point and the median of the maximum carbon input at the fixed point as independent variables, and taking the soil organic carbon density at the end of the fixed point as the dependent variable, a nonlinear regression model is constructed as the second model; The time corresponding to the predicted saturated soil organic carbon density output by the second model is associated with the regional observation time.

5. A method for rapid assessment of farmland soil carbon sequestration potential according to claim 2, characterized in that: The step of obtaining the initial soil organic carbon density distribution information of the target area according to the grid observation data and the first model includes: Determine grid climate data, grid soil property data, grid soil organic carbon density data, grid actual carbon input data, grid water management data and grid observation time according to the grid observation data corresponding to the grid unit; Inputting the grid climate data, the grid soil property data, the grid soil organic carbon density data, the grid actual carbon input data, the grid water management data and the grid observation time into the first model, and obtaining the grid soil organic carbon density change rate corresponding to the grid unit; Calculating the grid initial soil organic carbon density corresponding to the grid unit according to the grid soil organic carbon density data, the grid soil organic carbon density change rate and the grid observation time; The initial soil organic carbon density distribution information is obtained according to the grid initial soil organic carbon density of each grid unit.

6. A method for rapid assessment of farmland soil carbon sequestration potential according to claim 2, characterized in that: The step of acquiring the predicted saturated soil organic carbon density distribution information of the target area according to the initial soil organic carbon density distribution information, the grid prediction data and the second model includes: According to the initial soil organic carbon density distribution information, the grid initial soil organic carbon density of each grid unit is used as the grid initial soil organic carbon density for prediction; Determine the grid predicted annual average temperature, grid predicted annual average rainfall and grid predicted maximum carbon input of the preset SSP scenario according to the grid predicted data and the regional observation time; The grid initial soil organic carbon density and the grid predicted annual average temperature, the grid predicted annual average rainfall and the grid predicted maximum carbon input corresponding to the preset SSP scenario are input into the second model to obtain the grid predicted saturated soil organic carbon density of the grid unit corresponding to the preset SSP scenario; The predicted saturated soil organic carbon density distribution information of the preset SSP scenario is obtained according to the grid predicted saturated soil organic carbon density of each of the grid units.

7. A method for rapid assessment of farmland soil carbon sequestration potential according to claim 6, characterized in that: The step of obtaining the spatial distribution information of soil carbon sequestration potential in the target area according to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information includes: According to the initial soil organic carbon density distribution information and the predicted saturated soil organic carbon density distribution information, subtracting the grid predicted saturated soil organic carbon density corresponding to the grid unit under the same preset SSP scenario from the grid initial soil organic carbon density to obtain a grid carbon sequestration potential value; According to the grid carbon sequestration potential value of each of the grid cells, the spatial distribution information of the soil carbon sequestration potential of the preset SSP scenario is obtained.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, a method for rapid assessment of farmland soil carbon sequestration potential as described in any one of claims 1 to 7 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements a method for rapid assessment of farmland soil carbon sequestration potential as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements a method for rapid assessment of farmland soil carbon sequestration potential as described in any one of claims 1 to 7.

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