A method for calculating soil erosion carbon pool loss

Abstract

The present application belongs to the technical field of soil environmental analysis, and provides a method for calculating the loss of soil erosion carbon pool, by collecting the historical data of soil erosion carbon pool and the climate condition data of the corresponding time; under the climate condition data of different dimensions, analyzing the change relationship between the historical data of carbon pool and the climate condition data, and determining the environmental dimension dependence; according to the environmental dimension dependence, combined with the overall change trend of the historical data of carbon pool, obtaining the climate condition anomaly; according to the climate condition anomaly, combined with the least squares fitting coefficient in the SG algorithm, determining the denoising fitting coefficient of the SG algorithm; adding the denoising fitting coefficient to the least squares fitting result matrix of the SG algorithm, obtaining the denoising optimization result; according to the denoising optimization result, obtaining the data without noise influence and retaining the original characteristics, and evaluating the loss of soil erosion carbon pool. The present invention has the function of optimizing the fitting result of the least squares method in the SG algorithm, thereby playing the role of retaining the characteristic data.

Description

Technical Field

[0001] The present application relates to the technical field of soil environmental analysis, and in particular to a method for calculating the loss of soil erosion carbon pool. Background Art

[0002] Soil erosion carbon pool loss refers to the loss of carbon pool (carbon stored in the soil) caused by soil erosion. This concept involves many fields such as soil science, ecology, and environmental science, and is particularly important when studying the impact of soil erosion on the carbon cycle. Quantitative calculation of soil erosion carbon pool loss data can help researchers more accurately estimate the amount of carbon pool loss caused by soil erosion in the carbon cycle, and then provide a scientific basis for formulating strategies to reduce carbon emissions and increase carbon storage. In the process of analyzing carbon pool loss, the reliability and accuracy of the data will be affected by different environmental noises. In the prior art, the Savitzky-Golay filter algorithm (SG filter algorithm) is usually used to remove such noise, thereby optimizing the data and improving the credibility of the carbon pool loss calculation.

[0003] The SG filter algorithm is used to denoise the historical data of soil erosion carbon pool (such as soil organic carbon content in different time periods) to increase the credibility of the data. During data analysis, the historical data of soil erosion carbon pool will fluctuate due to environmental differences in different time periods (such as temperature and precipitation in climatic conditions, etc.) (for the specific impact of climatic conditions on soil organic carbon content, see "Journal of Peking University (Natural Science Edition)" July 2017, Vol. 53, No. 4). Such data fluctuations are normal fluctuations. However, when the SG filter algorithm is used to denoise the data, such data fluctuations will be treated as noise and removed, thereby losing the effective data change characteristics of soil organic carbon. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a method for calculating the amount of carbon pool loss due to soil erosion.

[0005] According to a method for calculating the loss of soil erosion carbon pool provided in this application, the method includes:

[0006] Collect historical data on soil erosion carbon pools and climate conditions at the corresponding time;

[0007] Under the climate condition data of different dimensions, analyzing the change relationship between the carbon pool historical data and the climate condition data to determine the environmental dimension dependency;

[0008] According to the environmental dimension dependency and in combination with the overall change trend of the carbon pool historical data, the climate condition anomaly is obtained;

[0009] According to the climate condition anomaly, combined with the least square fitting coefficient in the SG algorithm, the denoising fitting coefficient of the SG algorithm is determined;

[0010] Adding the denoising fitting coefficients to the least squares fitting result matrix of the SG algorithm to obtain a denoising optimization result;

[0011] According to the denoising and optimization results, data without noise influence and retaining original characteristics are obtained to evaluate the loss of soil erosion carbon pool.

[0012] In some embodiments of the present invention, the carbon pool historical data includes soil organic carbon content, and the climate condition data includes temperature and precipitation.

[0013] In some embodiments of the present invention, under the climate condition data of different dimensions, analyzing the change relationship between the carbon pool historical data and the climate condition data to determine the environmental dimension dependency includes:

[0014] Analyzing a first relationship between the soil organic carbon content change corresponding to two adjacent time points and the corresponding time temperature change;

[0015] Analyzing a second relationship between the change in soil organic carbon content corresponding to two adjacent time points and the change in precipitation at the corresponding time;

[0016] The environmental dimension dependency is determined based on the first relationship and the second relationship.

[0017] In some embodiments of the present invention, the quantitative formula of the environmental dimension dependency is:

[0018] In the formula, Indicates time The following is the environmental dimension dependence corresponding to the soil organic carbon content data; Indicated in Soil organic carbon content value at time; Indicated in Soil organic carbon content value at time; Indicated in Temperature value at the moment; Indicated in Temperature value at the moment; Indicated in Precipitation value at the moment; Indicated in Precipitation value at time; represents a symbolic function; represents the weight coefficient; Indicates the number of neighborhood points involved in the calculation, Represents the normalization function.

[0019] In some embodiments of the present invention, the The value range is [0.2~0.45].

[0020] In some embodiments of the present invention, according to the environmental dimension dependency and in combination with the overall change trend of the carbon pool historical data, the climate condition anomaly is obtained, including:

[0021] Analyzing a third relationship between the difference in soil organic carbon content corresponding to two adjacent time points and the average value of the difference in soil organic carbon content corresponding to all two adjacent time points in different seasons;

[0022] The inverse of the mean of multiple neighborhood data of the environmental dimension dependency is calculated, and combined with the third relationship, the climate condition anomaly is obtained.

[0023] In some embodiments of the present invention, the quantitative formula of the climate condition anomaly is:

[0024] In the formula, Indicates time The amount of climate change corresponding to the soil organic carbon content data under the Indicates time The following is the environmental dimension dependence corresponding to the soil organic carbon content data; Indicates time Soil organic carbon content and time under The difference between the soil organic carbon content values ​​under Indicates time Soil organic carbon content and time The difference between the soil organic carbon content values ​​under Indicates the total amount of data involved in calculating seasonal changes; Indicates a constant The exponent of the base.

[0025] In some embodiments of the present invention, the quantization formula of the denoising fitting coefficient is:

[0026] In the formula, Indicates the updated time The soil organic carbon content under the condition is involved in the calculation of the data coefficients when the least squares fitting is used; Indicates at time The soil organic carbon content under the condition is involved in the calculation of the data coefficients when the least squares fitting is used; Indicates time The amount of climate change corresponding to the soil organic carbon content data under the Indicates increment; Indicates a constant The exponent of the base.

[0027] In some embodiments of the present invention, according to the denoising optimization result, data without noise influence and retaining original characteristics is obtained to evaluate the loss of soil erosion carbon pool, including:

[0028] In the denoising optimization results, soil organic carbon content data after denoising at relevant time points are collected;

[0029] Based on the soil organic carbon content data after the denoising process, the soil carbon pool storage is estimated by using the initial soil organic carbon content data as a baseline;

[0030] The soil carbon pool storage at different time points is compared to estimate the soil erosion carbon pool loss between different time points.

[0031] In some embodiments of the present invention, the estimation formula of soil carbon pool storage is:

[0032] .

[0033] It can be seen from the above embodiments that the method for calculating the amount of soil erosion carbon pool loss provided in the embodiments of the present application has the following beneficial effects:

[0034] The present invention collects the historical data of the carbon pool of soil erosion and the climate condition data of the corresponding time; under the climate condition data of different dimensions, analyzes the change relationship between the historical data of the carbon pool and the climate condition data, and determines the environmental dimension dependence; according to the environmental dimension dependence, combined with the overall change trend of the historical data of the carbon pool, obtains the climate condition anomaly; according to the climate condition anomaly, combined with the least squares fitting coefficient in the SG algorithm, determines the denoising fitting coefficient of the SG algorithm; adds the denoising fitting coefficient to the least squares fitting result matrix of the SG algorithm to obtain the denoising optimization result; according to the denoising optimization result, obtains the data without noise influence and retains the original characteristics, and evaluates the loss of the soil erosion carbon pool. The present invention obtains the environmental dimension dependence according to the historical data of the carbon pool combined with the change of the climate data at the corresponding time, and obtains the climate condition anomaly according to the environmental dimension dependence, and then determines the denoising fitting coefficient of the SG algorithm, optimizes the denoising result, obtains the historical data of the carbon pool without noise influence and retains the original characteristics, and accurately evaluates the loss of the carbon pool.

[0035] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A schematic diagram of the basic flow of a method for calculating the amount of carbon pool loss from soil erosion provided in an embodiment of the present application;

[0038] Figure 2 A basic flow chart of a method for determining environmental dimension dependency provided in an embodiment of the present application;

[0039] Figure 3 A basic flow chart of a method for determining anomalies of climate conditions provided in an embodiment of the present application;

[0040] Figure 4 A schematic diagram of the basic process of a method for assessing soil erosion carbon pool loss provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0042] A method for calculating the loss of carbon pool from soil erosion provided by this embodiment will be described in detail below with reference to the accompanying drawings.

[0043] Figure 1 A schematic diagram of the basic flow of a method for calculating the amount of carbon pool loss from soil erosion provided in an embodiment of the present application; Figure 2 A basic flow chart of a method for determining environmental dimension dependency provided in an embodiment of the present application; Figure 3 A basic flow chart of a method for determining anomalies of climate conditions provided in an embodiment of the present application; Figure 4 A schematic diagram of the basic process of a method for assessing soil erosion carbon pool loss provided in an embodiment of the present application.

[0044] like Figure 1 As shown, a method for calculating the loss of soil erosion carbon pool includes the following steps:

[0045] S100: Collect historical data on soil erosion carbon pools and climate conditions at the corresponding time.

[0046] This embodiment aims to denoise the historical data of carbon pools in soil erosion, thereby playing a role in optimizing the data analysis processes such as the prediction of historical data of carbon pools and the calculation of the loss of carbon pools in soil erosion. Therefore, before performing data denoising, it is necessary to collect historical data of carbon pools in soil erosion and climate condition data at the corresponding time. The historical data of carbon pools include soil organic carbon content, and the climate condition data include the temperature and precipitation when the soil organic carbon content is collected. Among them, the collection period follows the periodic law of historical data of carbon pools in soil erosion. The data type is a discrete data value, the horizontal axis of the data is time, and the vertical axis is soil organic content, temperature and precipitation, etc.

[0047] At this point, data collection for denoising is completed.

[0048] The historical data of soil erosion carbon pools (such as soil organic carbon content) are highly correlated with climate conditions data. Specifically, warming and precipitation significantly reduce the content of extractable organic carbon in the soil, and there is no interaction between warming and precipitation on the soil organic carbon content, that is, they are independent of each other; and, after principal component analysis, the convergence of the overall soil pattern is mainly due to changes in precipitation, not warming.

[0049] In order to retain the characteristic data change mode in the denoising process of the historical data of the carbon pool of soil erosion, the specific denoising process of the SG algorithm is optimized. For the SG algorithm, the core part of its denoising is to perform least squares fitting on a number of data, and then output the filtering results. The fitting process is more sensitive to data fluctuations. The denoising effect is better for the fluctuation part, so the normal characteristic fluctuation part of the original data cannot be well preserved. In order to optimize the fitting process, it is necessary to determine the characteristic data that belongs to normal fluctuations. For the soil organic carbon content, its value has a linear change relationship with temperature, precipitation, etc., so here we choose to analyze the changes in the historical data of the carbon pool of some dimensions to obtain the environmental dimension dependence. The greater the environmental dimension dependence of the data, the more its fluctuation belongs to normal changes. Then, further, the lower the climate condition anomaly of such data, the less it belongs to noise, and the higher the corresponding least squares fitting coefficient, the purpose is to retain the characteristics of data changes and optimize the denoising effect.

[0050] Specifically, the environmental dimension dependency is obtained according to the carbon pool historical data combined with the change of climate data at the corresponding time, and the climate condition anomaly is obtained according to the environmental dimension dependency, and then the SG algorithm denoising fitting coefficient is determined, and the denoising result is optimized, including steps S200~S500.

[0051] S200: Analyze the changing relationship between carbon pool historical data and climate condition data under different dimensions of climate condition data to determine the environmental dimension dependence.

[0052] There is a certain connection between the changes in the historical data of the carbon pool of soil erosion and the climate condition data. Specifically, the soil organic carbon content will decrease with the increase of precipitation and the rise of temperature, among which the increase of precipitation is the main reason. Therefore, once the soil organic carbon content data produces a relatively abnormal fluctuation, if the precipitation and temperature data at the corresponding time have large fluctuations, or complementary (that is, one increase and one decrease) fluctuations, then the environmental dimension dependence corresponding to the historical data of the carbon pool will be higher, because such changes are consistent with the characteristics of the soil data in this scenario. On the contrary, the environmental dimension dependence corresponding to the historical data of the carbon pool is lower, and it is believed that the fluctuation of the historical data of the carbon pool is caused by data noise.

[0053] The embodiment of the present invention uses soil organic carbon content as an example for analysis. The analysis method for other historical data of the carbon pool is the same. The historical discrete time series data of the carbon pool obtained in step S100 and the discrete time series data of the climate conditions at the corresponding time are obtained. The corresponding discrete time series data are specifically the soil organic carbon content and the precipitation and temperature at the corresponding time. The environmental dimension dependence corresponding to different data points of the soil organic carbon content is calculated. Further, if Figure 2 As shown in the figure, under different dimensions of climate condition data, the relationship between the historical data of carbon pools and climate condition data is analyzed to determine the dependence on environmental dimensions, including:

[0054] S201: Analyze the first relationship between the change in soil organic carbon content corresponding to two adjacent time points and the corresponding time temperature change.

[0055] Get in Soil organic carbon content at time The temperature value corresponding to that moment , and in Soil organic carbon content at time The temperature value corresponding to that moment ,in The value range is [ , ], Indicates participation in calculation The number of neighboring points at a given moment, in the embodiment of the present invention The value of can be 10, that is, analysis The data change relationship of the 10 neighboring points before and after the moment. and Changes in soil organic carbon content at two adjacent time points and temperature changes ,analyze and Changes in soil organic carbon content at two adjacent time points And the corresponding time temperature change The first relationship between , for the first relationship conduct Symbolic function processing, obtain , The sign function takes a value of 1 when the value is greater than 0 and a value of -1 when the value is less than 0. Time neighborhood point range[ , The sum of all data points in ] is .

[0056] S202: Analyze the second relationship between the change in soil organic carbon content corresponding to two adjacent time points and the change in precipitation at the corresponding time.

[0057] Get in Soil organic carbon content at time The precipitation value corresponding to this moment , and in Soil organic carbon content at time The precipitation value corresponding to this moment ,in The value range is [ , ], Indicates participation in calculation The number of neighboring points at a given moment, in the embodiment of the present invention The value of can be 10, that is, analysis The data change relationship of the 10 neighboring points before and after the moment. and Changes in soil organic carbon content at two adjacent time points and precipitation value changes ,analyze and Changes in soil organic carbon content at two adjacent time points and the corresponding precipitation changes The second relationship between , for the second relationship conduct Symbolic function processing, obtain , The sign function takes a value of 1 when the value is greater than 0 and a value of -1 when the value is less than 0. Time neighborhood point range[ , The sum of all data points in ] is .

[0058] S203: Determine the environmental dimension dependency according to the first relationship and the second relationship.

[0059] Since the soil organic carbon content decreases with increasing precipitation and temperature, the sign function corresponding to the first and second relationships is and The output value of is -1. In order to better express the environmental dimension dependency, the embodiment of the present invention takes the opposite of these two parts. Since precipitation change is the main reason for the change in soil organic carbon content, when determining the environmental dimension dependency, the first relationship corresponding to the temperature value and the second relationship corresponding to the precipitation value are assigned weight coefficients. and 1- , in order to amplify the proportion of the main influencing factors, here is the weight proportion of increasing the dependence of the relationship between soil organic carbon content and precipitation changes on the environmental dimension.

[0060] Based on the above analysis, according to the first relationship and the second relationship, the quantitative formula for the environmental dimension dependence is determined as follows:

[0061] In the formula, Indicates time The following is the environmental dimension dependence corresponding to the soil organic carbon content data; Indicated in Soil organic carbon content value at time; Indicated in Soil organic carbon content value at time; Indicated in Temperature value at the moment; Indicated in Temperature value at the moment; Indicated in Precipitation value at the moment; Indicated in Precipitation value at time; Represents a sign function. When the value in the sign function is greater than 0, the output value is 1, and when it is less than 0, the output value is -1; represents the weight coefficient, which is used to increase the weight ratio of the relationship between soil organic carbon content and precipitation change on the environmental dimension. In this embodiment, The value range of can be [0.2~0.45]. =0.4; Indicates the number of neighborhood points involved in the calculation. In this embodiment The value can be 10; Represents the normalization function.

[0062] For the former , Indicated in and The difference in soil organic carbon content between two adjacent time points is intended to reflect the changing relationship of soil organic carbon content. Different difference results are obtained when different values ​​are taken; Indicated in and The temperature change relationship between two adjacent time points; then, the ratio result is It reflects the changing relationship between the two dimensions at that time. When the soil organic carbon content increases, the difference result is negative. If the temperature decreases, that is, the difference result is positive, then the ratio result is less than 0, and the sign function output is -1. And, corresponding to the scene, it reflects the relationship: as the temperature increases, the soil organic carbon content in the soil decreases, or while the soil organic carbon content decreases, the temperature increases, then it can reflect the normal change relationship. At this time, the soil organic carbon content data fluctuates at a normal value, then the corresponding environmental dimension dependence is higher. Similarly, for the latter This part aims to quantify the relationship between the change of soil organic carbon content and precipitation. If the changes of the two meet the characteristics of the scenario logic: that is, the soil organic carbon content decreases and the precipitation increases, or the soil organic carbon content increases and the precipitation decreases, then the ratio result is less than 0 and the output value is -1. In addition, the scenario logic mentions that after the analysis of the principal components, the convergence of the overall soil pattern is mainly due to changes in precipitation, not warming, so the weight coefficient The value of magnifies the latter's proportion in calculating the dependency of the environmental dimension. The ratio results of the corresponding soil organic carbon content data in the neighborhood are summed up, and the summation results are After normalization, the final environmental dimension dependency value is obtained. And it is considered that the environmental dimension dependency value In The data is environment-dependent.

[0063] In addition, it should be noted that when the temperature or precipitation does not change at two adjacent time points, that is, or If the corresponding soil organic carbon content does not change, it means that the soil organic carbon content is synchronized with the temperature or precipitation (neither changes), that is, the soil organic carbon content at this time has a high degree of dependence on the environmental dimension. or The output value is -1; if the corresponding soil organic carbon content changes, it means that the soil organic carbon content is not synchronized with the temperature or precipitation, and the change in soil organic carbon content is not caused by environmental changes, that is, the change in soil organic carbon content at this time has no environmental dimension dependence, then or The output value is 1.

[0064] At this point, the environmental dimension dependence was determined by combining historical data of carbon pools in different dimensions.

[0065] S300: Based on the environmental dimension dependency and the overall trend of carbon pool historical data, the climate condition anomaly is obtained.

[0066] The higher the degree of dependence on the environmental dimension of the data point, the lower the corresponding climate condition anomaly, because they have the characteristics of scene changes. The data fluctuation or mutation is not caused by noise, but by changes in the environment, such as precipitation. At this time, the more normal the corresponding data is, the more we need to retain such data during the filtering process. Therefore, the lower the climate condition anomaly, the higher the least squares fitting coefficient in the corresponding SG algorithm should be, aiming to retain the characteristics of the data in the filtering and denoising process. Further, in the process of calculating the climate condition anomaly, in order to prevent the change relationship between the abnormal data and the environmental data from being exactly the same as the normal data, it is necessary to analyze the overall data change trend excluding the neighborhood data. For example, if the precipitation in a certain place is more in summer than in winter, then the soil organic carbon content will show an upward trend from summer to winter. The more the data value conforms to this upward trend, the lower the corresponding climate condition anomaly, and the higher the corresponding denoising fitting coefficient.

[0067] Based on the above analysis, if Figure 3 As shown in the figure, based on the environmental dimension dependency and combined with the overall change trend of the carbon pool historical data, the climate condition anomaly is obtained, including:

[0068] S301: Analyze the third relationship between the difference in soil organic carbon content corresponding to two adjacent time points and the average value of the difference in soil organic carbon content corresponding to all two adjacent time points in different seasons.

[0069] Get in The time point and its previous time point The difference between the soil organic carbon content at the corresponding time points ; and obtain the average value of the difference between all adjacent data involved in calculating seasonal changes ,in Indicates the total amount of data involved in calculating seasonal changes. For example, if the data collection cycle is once every 1 / 8 month, then the total amount of data from summer to winter is 48, that is, the points involved in the summation traverse two quarters. Then analyze the difference in soil organic carbon content corresponding to two adjacent time points The third relationship between the average value of the soil organic carbon content difference corresponding to all two adjacent time points in different seasons is .

[0070] S302: Calculate the inverse of the mean of multiple neighborhood data of the environmental dimension dependency, and combine with the third relationship to obtain the climate condition anomaly.

[0071] Since the environmental dimension dependence and the climate condition anomaly are inversely proportional, the inverse of the mean of multiple neighborhood data of the environmental dimension dependence is calculated. Combined with the third relationship, the quantitative formula of the climate condition anomaly is obtained as follows:

[0072] In the formula, Indicates time The amount of climate change corresponding to the soil organic carbon content data under the Indicates time The following is the environmental dimension dependence corresponding to the soil organic carbon content data; Indicates time Soil organic carbon content and time under The difference between the soil organic carbon content values ​​under Indicates time Soil organic carbon content and time under The difference between the soil organic carbon content values ​​under Indicates the total amount of data involved in calculating seasonal changes; Indicates a constant The exponent of the base.

[0073] For the former As the coefficient of the latter summation, its value is inversely proportional to the size of the environmental dimension dependence. Here, the average of the 7 neighborhood data is taken as the size of the environmental dimension dependence of this point, aiming to eliminate part of the noise in the calculation process (equivalent to mean filtering). The change logic is the same as above, that is, the greater the environmental dimension dependence, the smaller the corresponding climate condition anomaly. Because the greater the environmental dimension dependence, the soil organic carbon content data and precipitation data show more regular change characteristics.

[0074] For the ratio summation part , the numerator does not participate in the overall summation calculation, and the denominator is used to calculate the overall change trend of several points in different seasons. The points involved in the summation traverse two quarters. The purpose is to ensure that the precipitation and temperature will change with the seasons during the data acquisition process. The difference of such data is summed and averaged to obtain the mean of the data difference (or slope) during the seasonal change process, which is used as the denominator. In this case, the numerator is time Soil organic carbon content and time under The difference between the soil organic carbon content values ​​under the following conditions is the result. If the two difference results are similar, the corresponding ratio result is closer to 1. Corresponding to the scene explanation: From summer to winter, the precipitation decreases significantly. The less water, the higher the soil organic carbon content. In this process, the difference in soil organic carbon content is always greater than 0 and is similar, because the precipitation is gradually decreasing. When a certain time node When the corresponding difference result is close to or equal to the mean of the overall difference, it proves that the data is normal change data, and its surrounding data is also normal change data, so the corresponding climate condition anomaly is lower. Although the data fluctuates at this time, it may be caused by sudden changes in climate conditions, such as concentrated rainfall in certain days causing a sudden change in soil organic carbon content, so the difference between the ratio and 1 is smaller, that is, The closer it is to 1, the lower the anomaly of climate conditions.

[0075] because The value of is [0,1], so The value range of is greater than or equal to 1; The value range of is also greater than or equal to 1, so, The value range is greater than or equal to 1.

[0076] S400: Determine the denoising fitting coefficient of the SG algorithm according to the climate condition anomaly and the least square fitting coefficient in the SG algorithm.

[0077] According to the climate conditions , combined with the least squares fitting coefficient in the SG algorithm , determine the denoising fitting coefficient of the SG algorithm The quantitative formula is:

[0078] In the formula, Indicates the updated time The soil organic carbon content under the condition is involved in the calculation of the data coefficients when the least squares fitting is used; Indicates at time The soil organic carbon content under the condition is involved in the calculation of the data coefficients when the least squares fitting is used; Indicates time The amount of climate change corresponding to the soil organic carbon content data under the Indicates increment; Indicates a constant The exponent of the base.

[0079] From the above analysis, we know The value range of is greater than or equal to 1, so The value of is (0,1]. As in time The soil organic carbon content under the condition is involved in the calculation of the least squares fitting data coefficient weight, climate condition anomaly The closer it is to 1, The larger the value, the more the updated The larger the data coefficient of the soil organic carbon content involved in the calculation of the least squares fitting, the higher the proportion after the least squares fitting, which serves the purpose of retaining important data characteristics. Indicates the increment, and the value can be =1.5, the data in the abnormal position have higher priority when calculating the least squares. Here it is to increase the preset coefficient of priority. Of course, it can also be not given and will not affect the final result.

[0080] So far, the anomaly of climate conditions and the denoising fitting coefficient of the SG algorithm are obtained by analyzing the dependence on environmental dimensions.

[0081] S500: adding the denoising fitting coefficient to the least square fitting result matrix of the SG algorithm to obtain the denoising optimization result.

[0082] Obtain the denoising fitting coefficient obtained in the above steps, which is intended to optimize the denoising result obtained by least squares fitting of the historical data of the carbon pool. The specific steps are: add (product) the denoising fitting coefficient to the corresponding element value in the least squares fitting result matrix of the SG algorithm to obtain the optimization result.

[0083] So far, by analyzing the relationship between the historical data of carbon pools and the climate conditions data of different dimensions, the corresponding environmental dimension dependence has been obtained. The determination of the environmental dimension dependence is conducive to obtaining the nature of data fluctuations; by analyzing the environmental dimension dependence, the climate condition anomaly is obtained, the SG algorithm denoising fitting coefficient is determined, and the denoising optimization result is obtained.

[0084] S600: Based on the denoising optimization results, data without noise influence and retaining the original characteristics are obtained to evaluate the loss of soil erosion carbon pool.

[0085] The denoising optimization result obtained in the above steps is obtained. The denoising optimization result performs denoising processing on the basis of ensuring the characteristics of the original data, eliminating the influence of noise on data analysis and thus obtaining the carbon pool loss. The denoised data is used to calculate and count the carbon pool loss.

[0086] like Figure 4 As shown in the figure, according to the denoising optimization results, the data without noise influence and retaining the original characteristics are obtained to evaluate the loss of soil erosion carbon pool, including:

[0087] S601: In the denoising optimization results, soil organic carbon content data after denoising at relevant time points are collected.

[0088] S602: Based on the denoised soil organic carbon content data, the soil carbon pool storage is estimated using the initial soil organic carbon content data as a baseline.

[0089] Based on the denoised soil organic carbon content data, the soil carbon pool storage was estimated using the initial soil organic carbon content data as a baseline. The soil carbon pool storage estimation formula is:

[0090] .

[0091] S603: Compare the soil carbon pool storage at different time points and estimate the soil erosion carbon pool loss between different time points.

[0092] Compare soil carbon pool storage at different time points and estimate the amount of soil carbon pool loss due to soil erosion between different time points. If data from multiple time points are available, time series analysis can be used to help understand the trend and pattern of carbon pool loss.

[0093] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0094] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the article or device including the element. Relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0095] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention invented herein. This application is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not invented by the present invention.

[0096] It should be understood that the present invention is not limited to the above description, and various modifications and changes may be made without departing from the scope thereof, and various modifications and changes made without departing from the scope thereof are within the protection scope of the present invention.

Claims

1. A method for calculating soil erosion carbon pool loss, characterized in that: The method comprises: Collect historical data on soil erosion carbon pools and climate conditions at the corresponding time; Under the climate condition data of different dimensions, analyzing the change relationship between the carbon pool historical data and the climate condition data to determine the environmental dimension dependency; According to the environmental dimension dependency and in combination with the overall change trend of the carbon pool historical data, the climate condition anomaly is obtained; According to the climate condition anomaly, combined with the least square fitting coefficient in the SG algorithm, the denoising fitting coefficient of the SG algorithm is determined; Adding the denoising fitting coefficients to the least squares fitting result matrix of the SG algorithm to obtain a denoising optimization result; According to the denoising optimization results, data without noise influence and retaining original characteristics are obtained to evaluate the loss of soil erosion carbon pool; The carbon pool historical data include soil organic carbon content, and the climate condition data include temperature and precipitation; Under the climate condition data of different dimensions, analyzing the change relationship between the carbon pool historical data and the climate condition data to determine the environmental dimension dependency includes: Analyzing a first relationship between the soil organic carbon content change corresponding to two adjacent time points and the corresponding time temperature change; Analyzing a second relationship between the change in soil organic carbon content corresponding to two adjacent time points and the change in precipitation at the corresponding time; Determining the environmental dimension dependency according to the first relationship and the second relationship; The quantitative formula of the environmental dimension dependency is: In the formula, Indicates time The following is the environmental dimension dependence corresponding to the soil organic carbon content data; Indicated in Soil organic carbon content value at time; Indicated in Soil organic carbon content value at time; Indicated in Temperature value at the moment; Indicated in Temperature value at the moment; Indicated in Precipitation value at the moment; Indicated in Precipitation value at time; represents a symbolic function; represents the weight coefficient; Indicates the number of neighborhood points involved in the calculation, represents the normalization function; According to the environmental dimension dependency and combined with the overall change trend of the carbon pool historical data, the climate condition anomaly is obtained, including: Analyzing a third relationship between the difference in soil organic carbon content corresponding to two adjacent time points and the average value of the difference in soil organic carbon content corresponding to all two adjacent time points in different seasons; Calculate the inverse of the mean of multiple neighborhood data of the environmental dimension dependency, and combine with the third relationship to obtain the climate condition anomaly; The quantitative formula for the anomaly of climate conditions is: In the formula, Indicates time The amount of climate change corresponding to the soil organic carbon content data under the Indicates time The following is the environmental dimension dependence corresponding to the soil organic carbon content data; Indicates time Soil organic carbon content and time under The difference between the soil organic carbon content values ​​under Indicates time Soil organic carbon content and time under The difference between the soil organic carbon content values ​​under Indicates the total amount of data involved in calculating seasonal changes; Indicates a constant The exponent of the base.

2. The method for calculating soil erosion carbon pool loss according to claim 1, characterized in that: Said The value range is [0.2~0.45].

3. The method for calculating soil erosion carbon pool loss according to claim 1, characterized in that: The quantitative formula of the denoising fitting coefficient is: In the formula, Indicates the updated time The soil organic carbon content under the condition is involved in the calculation of the data coefficients when the least squares fitting is used; Indicates at time The soil organic carbon content under the condition is involved in the calculation of the data coefficients when the least squares fitting is used; Indicates time The amount of climate change corresponding to the soil organic carbon content data under the Indicates increment; Indicates a constant The exponent of the base.

4. The method for calculating soil erosion carbon pool loss according to claim 1, characterized in that: According to the denoising optimization results, data without noise influence and retaining original characteristics are obtained to evaluate the loss of soil erosion carbon pool, including: In the denoising optimization results, soil organic carbon content data after denoising at relevant time points are collected; Based on the soil organic carbon content data after the denoising process, the soil carbon pool storage is estimated by using the initial soil organic carbon content data as a baseline; The soil carbon pool storage at different time points is compared to estimate the soil erosion carbon pool loss between different time points.

5. The method for calculating soil erosion carbon pool loss according to claim 4, characterized in that: The estimation formula for soil carbon pool storage is: 。

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