Method and system for estimating organic carbon content of wetland soil with different restoration years

Abstract

The invention provides a method for estimating the organic carbon content of wetland soil with different restoration years, and relates to the technical field of wetland carbon sink accounting, and the method comprises the following steps: setting a plurality of sampling points in a target research area, and collecting soil samples; preprocessing the satellite image data, determining a first input feature, determining a second input feature according to the geographic coordinates of the sampling points, and inputting the features into a regression prediction model to obtain a calculation result; generating soil organic matter content data distribution according to a calculation result; sample areas with different recovery years are divided according to land utilization history or recovery measure records by utilizing a method of replacing time with space, and in each sample area, the soil organic matter content and the total amount are counted according to soil organic matter content data distribution so as to determine the soil organic carbon content in different recovery years. According to the method, the remote sensing technology and the machine learning algorithm are utilized, rapid and lossless estimation of the organic carbon content of the wetland soil is achieved, and the workload and cost of field sampling are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wetland carbon sink accounting, and in particular to a method and system for estimating soil organic carbon content in wetlands with different restoration years. Background Art

[0002] Wetlands are huge organic carbon reservoirs in terrestrial ecosystems and an important part of the earth's carbon cycle system. Wetlands only account for 5% to 8% of the total land area, but they are one of the largest soil carbon reservoirs in the world. The organic carbon storage in wetlands is about 455 to 700 Pg (1 Pg = 1 Gt = 1 billion tons), accounting for 20% to 30% of the total carbon storage in the surface layer of the terrestrial ecosystem. Its carbon storage per unit area ranks first among all types of terrestrial ecosystems, which is three times the carbon storage per unit area of ​​forest ecosystems.

[0003] The acceleration of global warming and human development and utilization have led to the shrinkage of wetland area, the deterioration of wetland ecological environment, and the change of wetland carbon cycle process. With the proposal of my country's "dual carbon" goals, a series of important ecological restoration projects have been carried out to maintain the stability of wetland carbon sinks and restore the carbon sink function of degraded wetlands. How to monitor and calculate the carbon sink of important wetlands and evaluate the improvement of the carbon sink function of wetland ecosystems by important ecological restoration projects has become an urgent problem to be solved. However, there is currently a lack of monitoring of the dynamics of carbon sinks after the restoration of important wetlands, and the understanding of the synergistic effect of wetland ecological restoration projects and carbon fixation and sink enhancement is not comprehensive enough. It is urgent to carry out monitoring and accounting of carbon sinks in important wetland restoration areas.

[0004] Since the distribution of wetland resources is obviously regional and complex, including multiple types and dynamic changes, the current model methods and results of wetland carbon measurement are still facing many challenges. Many researchers have calculated the carbon storage and carbon sink of wetlands in different research areas in my country. Due to the different wetland types, geographical conditions and calculation methods, the results are uncertain. The main reasons are: first, the lack of long-term data. The wetland restoration process has a long time span, and the organic carbon accumulation rate of different years is difficult to continuously monitor, resulting in a lack of long-term trend data, which affects the accurate analysis of the relationship between restoration years and organic carbon storage; second, the impact of spatial heterogeneity. The wetland ecosystem has significant differences in hydrological conditions, vegetation types and soil composition. The carbon accumulation characteristics of different regions are different, making it challenging to establish a unified carbon storage estimation method in a wide area; third, the difficulty of sample acquisition. The wetland environment is complex, especially the collection of deep soil samples is limited by technology and equipment, making it difficult to obtain accurate organic carbon content data; the difference in organic carbon content in soils at different depths also interferes with accurate estimation. Therefore, how to accurately and efficiently measure the soil organic carbon content in wetland restoration areas is a hot topic and difficulty in current research. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method and system for estimating the soil organic carbon content of wetlands with different restoration years. By integrating remote sensing data, geospatial information and soil sampling data, and conducting machine learning, accurate estimation of the soil organic carbon content of wetlands with different restoration years can be achieved, thereby solving the problems of traditional methods in wetland ecosystem carbon reserve assessment, such as lack of data, time-consuming and labor-intensive, and limited accuracy.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for estimating soil organic carbon content in wetlands with different restoration years includes:

[0008] Setting a plurality of sampling points in the target study area and collecting soil samples at the sampling points;

[0009] Preprocess the satellite image data of the target study area to obtain preprocessed remote sensing image data;

[0010] Determine the first input feature according to the preprocessed remote sensing image data, and determine the second input feature according to the geographic coordinates of the sampling point; the first input feature includes: index band, radar band index, spectral band index and texture feature image; the second input feature includes the feature value of each sampling point;

[0011] Inputting the first input feature and the second input feature into the trained regression prediction model of soil organic matter content to obtain a calculation result;

[0012] generating soil organic matter content data distribution according to the calculation results;

[0013] Using the method of space instead of time, the target study area was divided into sample areas with different restoration years according to the land use history or restoration measure records. In each sample area, the soil organic matter content and total amount were statistically calculated based on the soil organic matter content data distribution to determine the soil organic carbon content in different restoration years.

[0014] Preferably, the depth of the soil sample is 0 to 30 cm.

[0015] Preferably, the satellite image data includes: Sentinel-1 and Sentinel-2 data.

[0016] Preferably, the method of preprocessing the satellite image data of the target study area includes: cloud removal and noise removal.

[0017] Preferably, determining the first input feature according to the preprocessed remote sensing image data includes:

[0018] Determine a gray level co-occurrence matrix according to the pre-processed remote sensing image data;

[0019] Perform band extraction according to the preprocessed remote sensing image data to obtain each of the index bands;

[0020] Extracting band indexes according to the preprocessed remote sensing image data to obtain the radar band index and the spectral band index;

[0021] Perform feature extraction according to the gray level co-occurrence matrix to obtain texture features;

[0022] The texture feature image is generated according to the texture feature.

[0023] Preferably, the index bands include: normalized vegetation index, enhanced vegetation index and building index.

[0024] Preferably, the regression prediction model for soil organic matter content is obtained by training a random forest regression model.

[0025] Preferably, the evaluation indicators of the random forest regression model include: determination coefficient and root mean square error.

[0026] The present invention also provides a system for estimating the organic carbon content of wetland soil with different restoration years, comprising:

[0027] A sampling module, used to set a plurality of sampling points in the target study area and collect soil samples at the sampling points;

[0028] The image preprocessing module is used to preprocess the satellite image data of the target study area to obtain preprocessed remote sensing image data;

[0029] A feature determination module, used to determine a first input feature according to the preprocessed remote sensing image data, and to determine a second input feature according to the geographic coordinates of the sampling point; the first input feature includes: an index band, a radar band index, a spectral band index and a texture feature image; the second input feature includes a feature value of each sampling point;

[0030] A machine learning module, used for inputting the first input feature and the second input feature into a trained regression prediction model of soil organic matter content to obtain a calculation result;

[0031] A data distribution generation module, used to generate soil organic matter content data distribution according to the calculation results;

[0032] The restoration estimation module is used to use the method of using space instead of time to divide the sample areas into different restoration years according to the land use history or restoration measure records of the target study area. In each sample area, the soil organic matter content and total amount are statistically calculated according to the soil organic matter content data distribution to determine the soil organic carbon content in different restoration years.

[0033] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0034] The present invention provides a method for estimating the soil organic carbon content of wetlands with different restoration years, including: setting multiple sampling points in a target study area and collecting soil samples at the sampling points; preprocessing the satellite image data of the target study area to obtain preprocessed remote sensing image data; determining a first input feature according to the preprocessed remote sensing image data, and determining a second input feature according to the geographic coordinates of the sampling point; the first input feature includes: index band, radar band index, spectral band index and texture feature image; the second input feature includes the characteristic value of each sampling point; inputting the first input feature and the second input feature into a trained regression prediction model of soil organic matter content to obtain a calculation result; generating a soil organic matter content data distribution according to the calculation result; using a method of replacing time with space, dividing sample areas with different restoration years according to the land use history or restoration measure records of the target study area, and in each sample area, statistically calculating the soil organic matter content and total amount according to the soil organic matter content data distribution to determine the soil organic carbon content in different restoration years. The present invention uses remote sensing technology and machine learning algorithms to achieve rapid and non-destructive estimation of wetland soil organic carbon content, reducing the workload and cost of field sampling. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of a SOC random forest regression model modeling process provided by an embodiment of the present invention;

[0038] Figure 3 A distribution diagram of organic carbon content provided by an embodiment of the present invention;

[0039] Figure 4A schematic diagram of 0-30cm organic carbon content in restoration plots of different years provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0041] The purpose of the present invention is to provide a method for estimating the organic carbon content of wetland soil with different restoration years. By using remote sensing technology and machine learning algorithms, a rapid and non-destructive estimation of the organic carbon content of wetland soil is achieved, reducing the workload and cost of field sampling.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a method for estimating the organic carbon content of wetland soil with different restoration years, comprising:

[0044] Step 100: setting a plurality of sampling points in the target study area and collecting soil samples at the sampling points;

[0045] Step 200: preprocessing the satellite image data of the target study area to obtain preprocessed remote sensing image data;

[0046] Step 300: determining a first input feature according to the preprocessed remote sensing image data, and determining a second input feature according to the geographic coordinates of the sampling point; the first input feature includes: an index band, a radar band index, a spectral band index, and a texture feature image; the second input feature includes a feature value of each sampling point;

[0047] Step 400: inputting the first input feature and the second input feature into the trained regression prediction model of soil organic matter content to obtain a calculation result;

[0048] Step 500: Generate soil organic matter content data distribution according to the calculation results;

[0049] Step 600: Using the method of replacing time with space, divide the sample areas into different restoration years according to the land use history or restoration measures record of the target study area. In each sample area, the soil organic matter content and total amount are statistically calculated according to the soil organic matter content data distribution to determine the soil organic carbon content in different restoration years.

[0050] Specifically, Figure 2 As shown, the present invention integrates remote sensing data, geospatial information and soil sampling data to construct a random forest regression model for machine learning, which can achieve accurate estimation of soil organic carbon content in wetlands with different restoration years, and solve the problems of traditional methods in wetland ecosystem carbon storage assessment, such as lack of data, time-consuming and labor-intensive, and limited accuracy. The specific implementation steps are as follows:

[0051] 1. Field sampling and testing: Collect soil samples of 0-30 cm in the target study area, with one sampling point required for every 16.5 square kilometers.

[0052] In the target study area, this embodiment uses a soil drill with a fixed volume (V) to collect soil samples mainly for the determination of soil organic carbon, etc. In each soil sampling square, according to 0-10cm, 10-20cm, 20-30cm. Use a soil drill with an inner diameter of ≥5cm to collect samples, and collect more than 200g of mixed samples per layer. After removing non-soil materials from the samples, they are packaged in self-sealing bags, written with sample labels, taken back to the laboratory, and air-dried to retain the soil samples of the naturally occurring layer. The number of sample collection points is required to be 1 point per 16.5 square kilometers, and the longitude and latitude of the sampling points are recorded.

[0053] 2. Satellite impact data selection and processing: Use Sentinel-1 and Sentinel-2 data, including optical images and radar images. Preprocessing steps include cloud removal and noise removal to clean up the original image data. Generate various types of index images, such as NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index) and IBI (Building Index).

[0054] This embodiment obtains satellite remote sensing data of Sentinel-1 and Sentinel-2 from relevant data sources such as the official website of the European Space Agency (ESA). Ensure that the data covers the study area and select a suitable time period to obtain representative surface information. Then, the acquired data is subjected to geometric correction, atmospheric correction, radiation correction, and terrain correction to remove the influence of the atmosphere on the surface reflectivity, etc. to improve the accuracy of the data. At the same time, the cloud detection band in the Sentinel-2 data is used to generate a cloud mask image, and the pixels blocked by the cloud layer are removed to ensure the accuracy of subsequent analysis.

[0055] 3. Extract texture feature images and determine the characteristic values ​​of sampling points: Based on the preprocessed remote sensing image data, the gray-level co-occurrence matrix (GLCM) method is used to extract various spectral indices, radar indices and texture characteristics of the wetland surface from the biophysical parameters, and the characteristic values ​​of each sampling point are obtained according to the geographic coordinates of the soil sampling points.

[0056] This embodiment selects specific bands (such as red edge bands, near infrared bands, etc.) in the Sentinel-2 data, calculates the gray level co-occurrence matrix, and extracts texture features such as contrast, energy, homogeneity, and correlation from the gray level co-occurrence matrix. Based on the texture features, a SOC texture feature image is generated. For other index band extractions, it mainly includes calculating the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and infrared band index (IBI), etc. These indices can reflect information such as vegetation growth conditions, soil moisture and surface cover, which is helpful for the prediction of SOC. Finally, radar band indexes such as radar backscatter coefficients are extracted from the Sentinel-1 data, combined with the spectral bands in the Sentinel-2 data, a multi-source data set is constructed, and the characteristic values ​​of each sampling point are obtained according to the geographic coordinates of the soil sampling point.

[0057] 4. Model construction and training: Use the random forest regression model to train and verify the measured soil organic carbon content to determine the model accuracy.

[0058] This embodiment uses SOC texture feature images, index bands such as NDVI, EVI, IBI, and spectrum and radar band indexes as input features, and uses the random forest algorithm to establish a regression prediction model for SOC content. By adjusting the parameters of the random forest (such as the number of trees, maximum depth, etc.), the model performance is optimized. Subsequently, an independent validation data set is used to verify the model, and the model parameters are adjusted according to the validation results to improve the prediction accuracy of the model. Finally, methods such as cross-validation can be used to further evaluate the stability and generalization ability of the model.

[0059] 5. Using space instead of time to obtain soil organic carbon content in different years: Based on the verified random forest regression model, the spatiotemporal distribution map of wetland soil organic carbon was generated, and the method of using space instead of time was used to determine the soil organic carbon content in different years.

[0060] This example applies the optimized random forest model to the data set of the entire study area, generates SOC content data distribution based on the model calculation results, and visualizes the data to show the spatial distribution characteristics of SOC content. Using the method of replacing time with space, the study area is divided into sample areas with different restoration years according to the land use history or restoration measures records. In each sample area, the SOC content and total amount are counted to determine the soil organic carbon content in different restoration years.

[0061] As an optional implementation method, this example conducts carbon sequestration and sink benefit assessment in the restoration area of ​​Hongze Lake Wetland National Nature Reserve in Sihong, Jiangsu Province (hereinafter referred to as Hongze Lake Reserve). In terms of tracking the changes in carbon storage in the restoration area over the years, an experiment based on the random forest regression model was conducted to estimate the organic carbon content of wetland soils with different restoration years, and the research was conducted according to the following method:

[0062] 1. Field sampling and testing: In the target study area, soil samples were collected with a fixed volume (V) soil drill, mainly for the determination of soil organic carbon, etc. In each soil sampling square, 0-10cm, 10-20cm, 20-30cm were used. A soil drill with an inner diameter of ≥5cm was used for sampling, and more than 200g of mixed samples were collected for each layer. After removing non-soil materials from the samples, they were packaged in self-sealing bags, labeled with samples, and brought back to the laboratory. The naturally occurring layer soil samples were air-dried and retained. A total of 80 points were sampled in the whole area to determine the soil organic carbon content.

[0063] 2. Satellite impact data selection and processing: Using Sentinel-1 and Sentinel-2 satellite data, various biophysical parameters including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), as well as texture feature images extracted by the Gray Level Co-occurrence Matrix (GLCM) were extracted as input features of the random forest regression model.

[0064] 3. Extract texture feature images and determine sampling point feature values:

[0065] (1) Input data: Use Sentinel-1 and Sentinel-2 data, including optical images and radar images. Preprocessing steps include cloud removal and noise removal to clean up the original image data. Generate various types of index images, such as NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index) and IBI (Building Index).

[0066] (2) Extracting eigenvalues ​​according to step (1), using the gray level co-occurrence matrix (GLCM) method to extract texture feature images from biophysical parameters, and obtaining the eigenvalues ​​of each sampling point according to the geographic coordinates of the soil sampling point.

[0067] 4. Model training: Based on field soil sampling and stratification, the organic carbon content of the soil layer 0-0.3m was used as the target value, and the random forest regression model was used for training. Through the calibration and verification process, the relevant evaluation indicators and the determination coefficient (R 2 ) reached 0.75, the root mean square error (RMSE) was 5.5, and the model simulation effect was good;

[0068] 5. Using space instead of time to obtain soil organic carbon content in different years: Based on the random forest model, the soil organic carbon content of the 0-30cm soil layer in Hongze Lake Nature Reserve was estimated ( Figure 3 ), the results showed that the highest organic carbon content was 40.97g / kg and the lowest was 9.45g / kg. More than 80% of the area had soil organic carbon content between 16g / kg and 21g / kg. The spatial vector data of the restoration area was used to submerge the soil organic carbon content result grid to obtain the average organic carbon content of the soil in different restoration years ( Figure 4 The average organic carbon content of the plots with different restoration years in Hongze Lake Nature Reserve first increased and then decreased with the increase of restoration years. The organic content of the restored plots in 2017 was the highest (26.46g / kg), followed by the plots in 2016 (24.37g / kg), and the lowest was the unrestored plot (8.99g / kg).

[0069] Corresponding to the above method, such as Figure 2 As shown, the present invention also provides a system for estimating the organic carbon content of wetland soil with different restoration years, comprising:

[0070] A sampling module, used to set a plurality of sampling points in the target study area and collect soil samples at the sampling points;

[0071] The image preprocessing module is used to preprocess the satellite image data of the target study area to obtain preprocessed remote sensing image data;

[0072] A feature determination module, used to determine a first input feature according to the preprocessed remote sensing image data, and to determine a second input feature according to the geographic coordinates of the sampling point; the first input feature includes: an index band, a radar band index, a spectral band index and a texture feature image; the second input feature includes a feature value of each sampling point;

[0073] A machine learning module, used for inputting the first input feature and the second input feature into a trained regression prediction model of soil organic matter content to obtain a calculation result;

[0074] A data distribution generation module, used to generate soil organic matter content data distribution according to the calculation results;

[0075] The restoration estimation module is used to use the method of using space instead of time to divide the sample areas into different restoration years according to the land use history or restoration measure records of the target study area. In each sample area, the soil organic matter content and total amount are statistically calculated according to the soil organic matter content data distribution to determine the soil organic carbon content in different restoration years.

[0076] The beneficial effects of the present invention are as follows:

[0077] (1) The present invention integrates Sentinel-1 and Sentinel-2 satellite data, and effectively utilizes high-resolution optical and radar data through preprocessing and cloud removal masking, thereby enhancing the information precision, richness and accuracy of soil organic matter content (SOC) prediction.

[0078] (2) The present invention constructs an efficient random forest regression model that can effectively handle a large number of nonlinear relationships and complex feature interactions, reduce the risk of overfitting, and improve the prediction accuracy and generalization ability of SOC content, thereby achieving accurate estimation of wetland soil organic carbon content.

[0079] (3) The present invention is not only applicable to the estimation of soil organic carbon content in wetlands with different restoration years, but can also be extended to the assessment of carbon reserves in other types of ecosystems.

[0080] (4) The present invention utilizes remote sensing technology and machine learning algorithms to achieve rapid and non-destructive estimation of wetland soil organic carbon content, reducing the workload and cost of field sampling.

[0081] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0082] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for estimating soil organic carbon content in wetlands with different restoration years, characterized in that: include: Setting a plurality of sampling points in the target study area and collecting soil samples at the sampling points; Preprocess the satellite image data of the target study area to obtain preprocessed remote sensing image data; Determine a first input feature based on the preprocessed remote sensing image data, and determine a second input feature based on the geographic coordinates of the sampling point; The first input feature includes: index band, radar band index, spectral band index and texture feature image; the second input feature includes feature values ​​of each sampling point; Inputting the first input feature and the second input feature into the trained regression prediction model of soil organic matter content to obtain a calculation result; generating soil organic matter content data distribution according to the calculation results; Using the method of space instead of time, the target study area was divided into sample areas with different restoration years according to the land use history or restoration measure records. In each sample area, the soil organic matter content and total amount were statistically calculated based on the soil organic matter content data distribution to determine the soil organic carbon content in different restoration years.

2. The method for estimating soil organic carbon content in wetlands with different restoration years according to claim 1, characterized in that: The depth of the soil sample is 0 to 30 cm.

3. The method for estimating soil organic carbon content in wetlands with different restoration years according to claim 1, characterized in that: The satellite image data includes: Sentinel-1 and Sentinel-2 data.

4. The method for estimating soil organic carbon content in wetlands with different restoration years according to claim 1, characterized in that: The preprocessing methods for satellite image data of the target study area include: cloud removal and noise removal.

5. The method for estimating soil organic carbon content in wetlands with different restoration years according to claim 1, characterized in that: Determining a first input feature according to the preprocessed remote sensing image data includes: Determine a gray level co-occurrence matrix according to the pre-processed remote sensing image data; Perform band extraction according to the preprocessed remote sensing image data to obtain each of the index bands; Extracting band indexes according to the preprocessed remote sensing image data to obtain the radar band index and the spectral band index; Perform feature extraction according to the gray level co-occurrence matrix to obtain texture features; The texture feature image is generated according to the texture feature.

6. The method for estimating soil organic carbon content in wetlands with different restoration years according to claim 1, characterized in that: The index bands include: normalized difference vegetation index, enhanced vegetation index and building index.

7. The method for estimating soil organic carbon content in wetlands with different restoration years according to claim 1, characterized in that: The regression prediction model of soil organic matter content is obtained by training a random forest regression model.

8. The method for estimating soil organic carbon content in wetlands with different restoration years according to claim 7, characterized in that: The evaluation indicators of the random forest regression model include: determination coefficient and root mean square error.

9. A system for estimating soil organic carbon content in wetlands with different restoration years, characterized in that: include: A sampling module, used to set a plurality of sampling points in the target study area and collect soil samples at the sampling points; The image preprocessing module is used to preprocess the satellite image data of the target study area to obtain preprocessed remote sensing image data; A feature determination module, used to determine a first input feature according to the preprocessed remote sensing image data, and to determine a second input feature according to the geographic coordinates of the sampling point; The first input feature includes: index band, radar band index, spectral band index and texture feature image; the second input feature includes feature values ​​of each sampling point; A machine learning module, used for inputting the first input feature and the second input feature into a trained regression prediction model of soil organic matter content to obtain a calculation result; A data distribution generation module, used to generate soil organic matter content data distribution according to the calculation results; The restoration estimation module is used to use the method of using space instead of time to divide the sample areas into different restoration years according to the land use history or restoration measure records of the target study area. In each sample area, the soil organic matter content and total amount are statistically calculated according to the soil organic matter content data distribution to determine the soil organic carbon content in different restoration years.

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