Inversion method, device and medium for soil particle size content and its spatial distribution in mining areas

Abstract

The inversion method of the soil particle size content and its spatial distribution in the mining area provided by the present disclosure includes: obtaining multi-spectral remote sensing image data of the mining area to be tested to obtain atmospheric apparent reflectance data; sampling the local surface soil of the mining area to be tested to obtain surface soil data of multiple different particle size levels; obtaining surface reflectance data according to the atmospheric apparent reflectance data, and screening the local surface reflectance data at the sampling point; statistically analyzing the correlation between the surface soil data of each particle size level and the local surface reflectance data, respectively, to obtain the preferred surface soil data of each particle size level; one-to-one correspondence between the surface reflectance data and each preferred surface soil data, respectively constructing various soil particle size inversion models; inputting the surface reflectance data into various soil particle size inversion models, to obtain various soil particle size contents and their spatial distribution in the mining area to be tested. The present disclosure can quickly and accurately obtain the soil particle size content and its spatial distribution in a large area of ​​mining area.

Description

Technical Field

[0001] The disclosed embodiments belong to the technical field of surface ecological environment monitoring in mining areas, and particularly relate to an inversion method, device and medium for soil particle size content and spatial distribution in mining areas based on multispectral remote sensing images. Background Art

[0002] Soil texture is an important physical property of soil, and plays a key role in determining soil porosity, permeability, water holding capacity, etc. It is closely related to soil ventilation, fertilizer and water retention, and the difficulty of farming. The middle and upper reaches of the Yellow River Basin are the main production areas of China's coal industry, and large-scale mining disturbances have had a serious impact on the fragile soil ecological environment on the surface. Particle size distribution is one of the important characteristics of reclaimed soil in arid and semi-arid mining areas in western China, and is closely related to the protection and management of the ecological environment in the Yellow River Basin. At the same time, soil texture conditions are an important basis for formulating soil utilization, management and improvement measures. Monitoring the changes in soil particle size distribution during mine reclamation is a key indicator for evaluating the success or failure of land reclamation in mining areas. Therefore, long-term, effective and rapid monitoring methods for soil particle size content and distribution information are of vital practical significance for the ecological restoration and topsoil reconstruction management evaluation of arid and semi-arid mining areas.

[0003] At present, the methods for determining soil particle size mainly include dry screening method, pipette method, hydrometer method, etc. These methods have their own characteristics and are suitable for analyzing soil particles in different particle size ranges, but they are time-consuming and labor-intensive, and have limited scope of application. They cannot quickly obtain soil particle size content and its spatial distribution information in a large scale range. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the embodiments of the present invention provide an inversion method, device and medium for the soil particle size content and its spatial distribution in a mining area, which can be convenient, fast, efficient, reusable and can accurately obtain the content and spatial distribution characteristics of different particle sizes of surface soil in a large area of ​​a mining area, and is particularly suitable for arid and semi-arid mining areas.

[0006] In order to achieve the above objectives, the present disclosure adopts the following technical solutions:

[0007] The first aspect of the present disclosure provides an inversion method for soil particle size content and spatial distribution in a mining area, comprising:

[0008] Obtain multispectral remote sensing image data R0 of the mining area to be tested, and obtain atmospheric apparent reflectance data R0a based on it;

[0009] Take local surface soil samples from the mining area to be tested and analyze the samples to obtain surface soil data of N different particle sizes. The surface soil data of the i-th particle size level is recorded as Di, i = 1, 2, ..., N. The surface soil data Di consists of several arrays, each of which includes the soil content of the i-th particle size level at the sampling point, the sampling point location and the sampling soil thickness;

[0010] Perform atmospheric correction on the atmospheric apparent reflectance data R0a to obtain the surface reflectance data R0b, and filter out the surface reflectance at each sampling point from the surface reflectance data R0b to obtain the local surface reflectance data R0c;

[0011] The correlation between the surface soil data Di of each particle size level and the local surface reflectance data R0c was statistically analyzed respectively, and the sampling soil thickness with the highest correlation was defined as the optimal inversion soil thickness range. The arrays within the optimal inversion soil thickness range were screened out from the surface soil data Di of each particle size level to obtain the preferred surface soil data Dia respectively;

[0012] The surface reflectance data R0c is matched with each preferred surface soil data Dia one by one, and each type of soil particle size inversion model Si is constructed respectively;

[0013] The surface reflectivity data R0b is input into various soil particle size inversion models Si to obtain the surface soil data Dib of the mining area to be tested, which includes the content of various soil particle sizes and their spatial distribution information in the mining area to be tested.

[0014] In some embodiments, after obtaining the multispectral remote sensing image data R0 of the mining area to be measured, image preprocessing is first performed on it, including image cropping and radiation calibration, and then the DN value of each data point in the preprocessed multispectral remote sensing image data is converted into atmospheric apparent reflectance, so as to obtain the atmospheric apparent reflectance data R0a.

[0015] In some embodiments, the local surface soil sampling and sample analysis of the mining area to be tested to obtain surface soil data of N different particle size levels specifically includes:

[0016] Sampling locations were dispersed in the study area, and the surface soil at each sampling location was sampled in layers. A soil auger was used to collect soil samples of equal weight at each sampling point, and the sampling time, location, sample number, sample soil thickness, and latitude and longitude coordinates of each sampling location were recorded;

[0017] All collected samples were sieved using a standard 2 mm screening method, and each sample was divided into N particle size levels, obtaining the soil contents of N different particle size levels, which together with the sampling point location and sampling soil thickness of each sample constituted the surface soil data Di, i = 1, 2, ..., N.

[0018] In some embodiments, the 6SV atmospheric correction model is used when performing atmospheric correction on the atmospheric apparent reflectance data R0a.

[0019] In some embodiments, the preferred surface soil data Dia is obtained according to the following steps:

[0020] The correlation between the different soil particle size contents at each sampling soil thickness in the surface soil data Di and the surface reflectance data R0c was analyzed, and the sampling soil thickness with the highest correlation was defined as the optimal inversion soil thickness range. Then, the array within the optimal inversion soil thickness range was screened from the surface soil data Di to obtain the preferred surface soil data Dia.

[0021] In some embodiments, the structures of the various soil particle size inversion models Si constructed are the same or different, and the methods used to construct the various soil particle size inversion models Si include multiple linear regression, partial least squares regression and support vector machine method.

[0022] In some embodiments, when constructing various types of soil particle size inversion models Si, multiple candidate soil particle size inversion models are constructed for the preferred surface sand soil data Dia of each particle size level, and statistical indicators are used to screen out the candidate soil particle size inversion model with the highest inversion accuracy from the multiple candidate soil particle size inversion models as the soil particle size inversion model Si for the corresponding particle size level.

[0023] In some embodiments, when testing samples, the particle size levels of the surface soil of the mining area to be tested are divided into N=3 types, namely sand, silt and clay.

[0024] The second aspect of the present disclosure provides an inversion device for soil particle size content and spatial distribution in a mining area, comprising:

[0025] The first module is configured to obtain multi-spectral remote sensing image data R0 of the mining area to be tested, and obtain atmospheric apparent reflectance data R0a based thereon;

[0026] The second module is configured to obtain N surface soil data of different particle sizes by sampling and analyzing the local surface soil in the mining area to be tested, and record the surface soil data of the i-th particle size level as Di, i = 1, 2, ..., N, and the surface soil data Di of the i-th particle size level is composed of several arrays, each of which includes the soil content of the i-th particle size level at the sampling point, the sampling point location and the sampling soil thickness;

[0027] The third module is configured to perform atmospheric correction on the atmospheric apparent reflectance data R0a to obtain surface reflectance data R0b, and filter out the surface reflectance at each sampling point from the surface reflectance data R0b to obtain local surface reflectance data R0c;

[0028] The fourth module is configured to perform statistical analysis on the correlation between the surface soil data Di of each particle size level and the local surface reflectance data R0c, define the sampling soil thickness with the highest correlation as the optimal inversion soil thickness range, and screen out the array within the optimal inversion soil thickness range from the surface soil data Di of each particle size level to obtain the preferred surface soil data Dia respectively;

[0029] The fifth module is configured to correspond the local surface reflectance data R0c to each preferred surface soil data Dia one by one, and construct various soil particle size inversion models Si respectively;

[0030] The sixth module is configured to input the surface reflectance data R0b into various soil particle size inversion models Si respectively to obtain the surface soil data Dib of the mining area to be tested, which includes the content of various soil particle sizes and their spatial distribution information in the mining area to be tested.

[0031] A third aspect of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the inversion method described in any embodiment of the first aspect of the present disclosure.

[0032] The inversion method, device and medium for the particle size content and spatial distribution of soil in a mining area provided by the embodiments of the present disclosure have the following characteristics and beneficial effects:

[0033] This disclosure aims at the technical problems existing in the existing related technologies, and combines the current remote sensing image processing methods, atmospheric correction methods, modeling prediction and calculation methods to propose an inversion method, device and medium for the soil particle size content and its spatial distribution in mining areas. This disclosure can be effectively applied to the monitoring of changes in soil particle size distribution in arid and semi-arid mining areas, and provide data support for timely and accurate dynamic monitoring of topsoil information in mining areas. It has practical significance for the evaluation of ecological restoration and management in mining areas, and is applicable to the field of ecological environment monitoring in arid and semi-arid mining areas in western China. Specifically:

[0034] The 6SV atmospheric correction method used in the embodiment of the present disclosure can simulate continuous and orderly scattering, calculate radiation polarization, and then solve the radiation transfer equation on the basis of accurately simulating the satellite and plane observation altitudes and reasonably considering factors such as anisotropy, non-uniform surface, and non-Lambertian body. It can more effectively eliminate the influence of Rayleigh scattering and aerosols. Combined with auxiliary meteorological data with higher temporal and spatial resolution such as atmospheric pressure, water vapor, and ozone calculated daily by MODIS, the calculated surface reflectance data is used for modeling and analysis of soil particle size content with higher accuracy and better applicability. Compared with other methods, it can more truly reflect the surface reflectance characteristics in areas with large surface undulations and microscopic surface unevenness such as mines. The inversion method for the soil particle size content and its spatial distribution in arid and semi-arid mining areas provided by the embodiments of the present disclosure uses a 6SV-SVM inversion model to calculate the content of various soil particle sizes. The 6SV-SVM model shows excellent classification and generalization capabilities in processing small samples and nonlinearity. It is based on statistical theory and does not require an excessively large data set. It uses a support vector machine modeling method based on a radial basis kernel function to increase the dimension of low-dimensional eigenvalues ​​and construct a linear decision function in a high-dimensional space to achieve regression prediction of nonlinear problems. It is more suitable for predicting soil properties based on surface reflectance spectral characteristics, and the prediction accuracy is more accurate. The present disclosure can be used in the ecological environment monitoring of mines in arid and semi-arid areas, and can provide an effective technical means for dynamically monitoring soil texture change information for mine ecological restoration and soil reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 An overall flow chart of an inversion method for soil particle size content and spatial distribution in a mining area provided in an embodiment of the present disclosure;

[0036] Figure 2 This is an example diagram of the multispectral remote sensing image data R0 selected in the inversion method provided in the embodiment of the present disclosure, wherein a, b, c, and d are respectively an image overview map of the study area, an altitude change map, an NDVI distribution map, and a schematic diagram of the sampling point locations;

[0037] Figure 3 An example diagram of soil particle size analysis data of sampling points obtained in the inversion method provided in the embodiment of the present disclosure, (a), (b), and (c) are soil particle size content data of sampling soil thickness of 0-20cm, 20cm-40cm, and 40cm-60cm, respectively;

[0038] Figure 4 This is an example diagram of the correlation between the three kinds of particle size contents of different sampling soil thicknesses and the local surface reflectance data R0c in the inversion method provided in the embodiment of the present disclosure, which is a data diagram of the correlation between the sand, silt, and clay contents and the local surface reflectance data R0c from top to bottom;

[0039] Figure 5 An example diagram of selecting the optimal modeling method by comparing and analyzing the inversion accuracy through modeling in the inversion method provided in the embodiment of the present disclosure, (a), (b), and (c) are the MLR model, the PLSR model, and the SVM model, respectively, and (Ⅰ), (Ⅱ), and (Ⅲ) are the contents of sand, silt, and clay, respectively, reflecting the fitting effect of the predicted value and the true value;

[0040] Figure 6 This is an example diagram of the inversion results for calculating the three types of soil particle size contents and their spatial distribution in the mining area to be tested provided in the embodiment of the present disclosure, where a, b, and c are the inversion results of the sand, silt, and clay contents, respectively.

[0041] Figure 7 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the third aspect of the present disclosure. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] On the contrary, the present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application as defined by the claims. Further, in order to make the public have a better understanding of the present application, some specific details are described in detail in the detailed description of the present application below. Those skilled in the art can fully understand the present application without the description of these details.

[0044] See also Figure 1 The inversion method of the soil particle size content and its spatial distribution in the mining area provided by the first aspect of the present disclosure includes the following steps:

[0045] Step S1, obtaining multispectral remote sensing image data R0 of the mining area to be tested, and obtaining atmospheric apparent reflectance data R0a based thereon;

[0046] Step S2, sampling and analyzing the local surface soil of the mining area to be tested, obtaining surface soil data of N different particle size levels, recording the surface soil data of the i-th particle size level as Di, i=1, 2, ..., N, and the surface soil data Di of the i-th particle size level is composed of several arrays, each array including the soil content of the i-th particle size level at the sampling point, the sampling point location and the sampled soil thickness;

[0047] Step S3, performing atmospheric correction on the atmospheric apparent reflectance data R0a obtained in step S1 to obtain surface reflectance data R0b, and filtering the surface reflectance at each sampling point from the surface reflectance data R0b to obtain local surface reflectance data R0c;

[0048] Step S4, statistically analyzing the correlation between the surface soil data Di of each particle size level obtained in step S2 and the local surface reflectance data R0c, defining the sampling soil thickness with the highest correlation as the optimal inversion soil thickness range, and selecting arrays within the optimal inversion soil thickness range from the surface soil data Di of each particle size level to obtain the preferred surface soil data Dia respectively;

[0049] Step S5, the local surface reflectance data R0c is matched with each preferred surface soil data Dia one by one, and each type of soil particle size inversion model Si is constructed;

[0050] Step S6: Input the surface reflectance data R0b into various soil particle size inversion models Si respectively to obtain the surface soil data Dib of the mining area to be tested, which includes the content of various soil particle sizes and their spatial distribution information in the mining area to be tested.

[0051] In some embodiments, the mining area to be tested is an arid or semi-arid open-pit mining area, that is, an open-pit mining area with an annual rainfall of less than 400 mm. The climatic conditions and mining disturbance factors of the arid and semi-arid open-pit mining area greatly reduce the influence of factors such as soil moisture and vegetation coverage on the soil texture of the reflectance spectrum inversion, effectively improving the model inversion accuracy.

[0052] In some embodiments, in step S1, a Landsat-8 remote sensing image of the mining area to be tested is selected as the multispectral remote sensing image data R0, which can be obtained by any of the following two methods:

[0053] (1) Search and download satellite remote sensing images of the mining area to be measured through the USGS official website and the Geospatial Data Cloud website;

[0054] (2) Query and search the remote sensing images of the mining area to be measured through cloud computing platforms such as GEE and PIE, and download them.

[0055] Further, in step S1, after obtaining the multispectral remote sensing image data R0 of the mining area to be tested, the multispectral remote sensing image data R0 is first preprocessed, including image clipping and radiation calibration. The above preprocessing operation can be performed by ENVI software, and the DN (Digital Number) value of each data point in the preprocessed multispectral remote sensing image data is converted into the atmospheric surface reflectivity, so as to obtain the atmospheric apparent reflectivity data R0a, wherein the calculation formula used to convert the DN value into the atmospheric surface reflectivity is:

[0056] ρ=DN·gain+offset (1)

[0057] Where ρ represents the atmospheric surface reflectance; DN value is the pixel brightness value of the multispectral remote sensing image, which is directly stored in the remote sensing image file; gain is the gain, and offset is the offset value. The two parameters are directly read by ENVI software from the metadata in the remote sensing image header file.

[0058] In some embodiments, step S2 specifically includes the following steps:

[0059] Step S21, at the dispersed sampling positions in the mining area to be tested, the surface soil at each sampling position is sampled in layers. In this embodiment, each sampling position is divided into 3 layers and the sampling is equally spaced. The sampling thickness of each layer is 20 cm, and the sampling soil thickness corresponding to each layer is: 0-20 cm, 20 cm-40 cm, 40 cm-60 cm, respectively. A soil drill is used to collect soil samples at each sampling point. About 1 kg of samples are collected from each layer and put into a sample bag. The sampling time, location, sample number, and sampling soil thickness are recorded, and the longitude and latitude coordinates of each sampling position are recorded using a global positioning system (GPS) receiver.

[0060] Step S22: All collected samples are sieved using a 2 mm standard screening method, and each surface soil sample is divided into N particle size classes, and the soil content of N different particle size classes is obtained. According to the soil content and the corresponding sampling point location (the sampling point location is represented by the latitude and longitude coordinates recorded in step S21) and the sampled soil thickness, surface soil data Di of each particle size class is obtained, i=1, 2, ..., N. In this embodiment, N=3 is set, and all collected samples are sieved according to three classification standards: sand (0.05-2.0 mm), silt (0.002-0.05 mm), and clay (<0.002 mm), and then the surface sand soil data D1, silt soil data D2, and clay soil data D3 are obtained.

[0061] In some embodiments, in step S3, the model used when atmospheric apparent reflectance data R0a is atmospherically corrected is the 6SV atmospheric correction model. More specifically, the 6SV atmospheric correction module in the RSD (Remote Sensing Desktop) software is used in this embodiment. The RSD software includes the atmospheric correction program (LaSRC) designed for Landsat8 by the United States Geological Survey (USGS) based on the 6SV2.1 model. The auxiliary meteorological data is calculated daily for LaSRC by the USGS through the Moderate-resolution Imaging Spectroradiometer (MODIS). Meteorological data such as atmospheric pressure, water vapor and ozone content, and aerosol optical thickness have higher temporal and spatial resolutions. The auxiliary meteorological data of the imaging day is called to perform atmospheric correction on the atmospheric apparent reflectance data R0a, thereby obtaining the surface reflectance data R0b. Subsequently, the surface reflectance at each sampling point in the surface reflectance data R0b is screened out, and the local surface reflectance data R0c is constituted by it.

[0062] In some embodiments, step S4 specifically includes the following steps:

[0063] The correlation between the different soil particle size content and the surface reflectance data R0c at each sampling soil thickness in the surface soil data Di was analyzed. When the correlation between the surface soil data Di of each particle size level and the local surface reflectance data R0c was statistically analyzed, the Pearson correlation coefficient was used to measure the correlation between the surface soil data Di of each particle size level and the local surface reflectance data R0c. Its value is between [-1,1]. When the calculated value is closer to ±1, it indicates that the linear correlation between the two is more significant. After calculating the correlation coefficient between the particle size content data of different sampling soil thickness ranges in the surface soil data Di of each particle size level and the local surface reflectance data R0c, the sampling soil thickness with the highest correlation coefficient is defined as the optimal inversion soil thickness range. The array within the optimal inversion soil thickness range is screened out from the surface soil data Di of each particle size level, and the preferred surface soil data Dia, i = 1, 2, ..., N are obtained respectively. In this embodiment, it is assumed that N=3, and through correlation statistical analysis, the optimal inversion soil thickness range is obtained to be 0-40 cm, and the preferred surface sand soil data D1a, the preferred silt soil data D2a and the preferred clay soil data D3a are obtained.

[0064] In some embodiments, in step S5, the structures of various soil particle size inversion models Si constructed are the same or different, and the methods for constructing various soil particle size inversion models Si include multiple linear regression (MLR), partial least squares regression (PLSR) and support vector machine (SVM) methods.

[0065] Further, in order to improve the inversion accuracy of the method of this embodiment, in step S5, for each preferred surface sand soil data Dia of each particle size level, multiple candidate soil particle size inversion models are respectively constructed, and then the candidate soil particle size inversion model with the highest inversion accuracy is screened out from the multiple candidate soil particle size inversion models using statistical indicators as the soil particle size inversion model Si of the corresponding particle size level. In this embodiment, the MLR, PLSR and SVM methods are used to respectively construct candidate soil particle size inversion models and screen out the optimal candidate soil particle size inversion models as various types of soil particle size inversion models Si. The specific steps are as follows:

[0066] Step S51: Construct multiple candidate soil particle size inversion models

[0067] a. Candidate soil particle size inversion models constructed using MLR or PLSR methods

[0068] Define all the surface reflectance data of each band in the local surface reflectance data R0c as the independent variable x j , j = 1, 2, ..., J, J is the total number of bands contained in the multispectral remote sensing image data R0 of the mining area to be tested. In this embodiment, J = 7, j is the band number, and the content of N different types of soil particle sizes is the dependent variable y i ,i=1,2,…,N。 Use MLR or PLSR method to establish x j With y i The regression equation between is as follows:

[0069]

[0070] Among them, k ij and b i is the coefficient of the regression equation.

[0071] b. Candidate soil particle size inversion model based on SVM machine learning method

[0072] N candidate soil particle size inversion models with different soil particle size contents were established respectively, and the two main functions svmtrain and svmpredict were used one by one. 70% of the data were used for training and 30% of the data were used for testing. The other main parameters were set as follows: SVM type: epsilon-SVR; kernel type: Radial Basis Function; γ: 2.8; cost: 1; ε: 0.05.

[0073] Step S52: Optimization of soil particle size inversion model

[0074] Using R2 The prediction accuracy of each candidate soil particle size inversion model constructed in step S51 is verified by using statistical values ​​such as R-Square and MSE (Mean Squared Error). These verification indicators can be calculated according to the following formula:

[0075]

[0076] In the formula, y m and are the true value and predicted value of the soil particle size content corresponding to the mth sample in the preferred surface sand soil data Dia of the i-th particle size level in the input model, is the average value of the true value of the soil particle size content corresponding to the preferred surface sand soil data Dia, and M is the total number of samples in the preferred surface sand soil data Dia of the i-th particle size level input into the model. 2 The calculation range is (0,1), and the closer it is to 1, the better the model effect. MSE describes the degree of discreteness of the sample. The closer its value is to 0, the better the quality of the model and the higher the prediction accuracy. Then, the verification indicators of various candidate soil particle size inversion models are compared and analyzed, and various types of soil particle size inversion models Si with the highest inversion accuracy are screened out, i=1,2,…,N. In this embodiment, it is assumed that N=3, that is, the sand particle inversion model S1, silt particle inversion model S2 and clay particle inversion model S3 with the highest inversion accuracy are screened out. This embodiment constructs various soil particle size inversion models, and selects the SVM modeling method with the highest inversion accuracy to construct various soil particle size inversion models Si, thereby ensuring the accuracy of the model inversion of the soil particle size content in the mining area to be tested.

[0077] In some embodiments, in step S6, the surface reflectivity data R0b is input into various soil particle size inversion models Si. Since the surface reflectivity data R0b contains the location information of the mining area to be tested, the model inversion calculation obtains the surface soil data Dib of the mining area to be tested, i=1,2,…,N, and the surface soil data Dib of the mining area to be tested includes the content of various soil particle sizes and their spatial distribution information of the mining area to be tested. In this embodiment, it is assumed that N=3, that is, the surface reflectivity data R0b is respectively input into the sand inversion model S1, the silt inversion model S2 and the clay inversion model S3, and the surface sand soil data D1b, silt soil data D2b and clay soil data D3b of the mining area to be tested are calculated, that is, the content of sand, silt and clay soil particle sizes and their spatial distribution information of the mining area to be tested are obtained.

[0078] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure are further described in detail in combination with the accompanying drawings and embodiments. In this embodiment, Wuhai City, Inner Mongolia Autonomous Region, China is taken as the study area, and the content of different soil particle sizes and their spatial distribution information in the study area are obtained based on the inversion of Landsat8 OLI multispectral image data. The following specific embodiments do not constitute a limitation of the present disclosure.

[0079] The inversion method of soil particle size content and spatial distribution in arid and semi-arid mining areas of this embodiment includes the following steps:

[0080] Step S1: Obtain multispectral remote sensing image data R0 of the study area and perform preprocessing to obtain atmospheric apparent reflectance data R0a, specifically including:

[0081] The Landsat8 OLI image R0 (acquired on July 31, 2021, row: 129, column: 33) with a time close to the field sampling was selected. The image data was downloaded free of charge from the USGS (United States Geological Survey) website. After obtaining the multispectral remote sensing image data R0 of the study area, ENVI5.3 was used for image preprocessing, including image cropping and radiometric calibration. The DN (Digital Number) value of each data point in the preprocessed multispectral remote sensing image data was converted into the atmospheric surface reflectance by formula (1), thereby obtaining the atmospheric apparent reflectance data R0a.

[0082] The example of multispectral remote sensing image data R0 selected in this embodiment is shown in FIG. Figure 2 As shown, a, b, c, and d are the image overview map of the study area, the altitude change map, the NDVI distribution map, and the schematic diagram of the sampling point locations, respectively.

[0083] Step S2: Sampling and analyzing local surface soil in the study area to obtain surface sandy soil data D1, silt soil data D2 and clay soil data D3, specifically including:

[0084] Field sampling was carried out on July 22, 2021. A total of 45 surface soil samples were collected at 15 locations near Wuhai Lake, with 3 layers at each location (sampling soil thickness: 0-20cm, 20cm-40cm, 40cm-60cm). A manual threaded soil drill was used to collect soil samples at each sampling point. About 1kg of samples were collected from each layer and put into sample bags. The sampling time, location, sample number, and sampling depth were recorded, and the longitude and latitude coordinates of each sampling location were recorded using a global positioning system (GPS) receiver. All collected samples were brought back to the laboratory, and the air-dried surface soil samples were screened using a 2mm standard screening technique in accordance with the Chinese Soil Particle Size Classification Standard (1987 "China Soil"), and then the soil particle data was analyzed. The measured soil particle analysis data were integrated into three categories: sand (0.05-2.0 mm), silt (0.002-0.05 mm), and clay (<0.002 mm), and the surface sand soil data D1, silt soil data D2, and clay soil data D3 were obtained.

[0085] The sample point soil particle size analysis data obtained in this embodiment is shown in the figure below: Figure 3 As shown, (a), (b), and (c) are the soil particle size content data for sampling soils with thicknesses of 0-20 cm, 20 cm-40 cm, and 40 cm-60 cm, respectively.

[0086] Step S3: The model used for atmospheric correction of the atmospheric apparent reflectance data R0a is the 6SV atmospheric correction model. More specifically, the 6SV atmospheric correction module in the RSD (Remote Sensing Desktop) software is used in this embodiment. The RSD software includes the atmospheric correction program (LaSRC) designed for Landsat8 by the United States Geological Survey (USGS) based on the 6SV2.1 model. The auxiliary meteorological data is calculated daily for LaSRC by the USGS through the Moderate-resolution Imaging Spectroradiometer (MODIS). Meteorological data such as atmospheric pressure, water vapor and ozone content, and aerosol optical thickness have higher temporal and spatial resolutions. The auxiliary meteorological data of the imaging day is called to perform atmospheric correction on the atmospheric apparent reflectance data R0a, thereby obtaining the surface reflectance data R0b. Subsequently, the surface reflectance at each sampling point in the surface reflectance data R0b is screened out, and the local surface reflectance data R0c is formed by it.

[0087] Step S4: Statistically analyze the correlation between the surface soil data D1, D2 and D3 of each particle size level and the local surface reflectance data R0c, and select the preferred surface sand soil data D1a, the preferred silt soil data D2a and the preferred clay soil data D3a within the optimal inversion soil thickness range, specifically including:

[0088] The Pearson correlation coefficient is used to measure the correlation between the surface sand soil data D1, silt soil data D2 and clay soil data D3 and the local surface reflectance data R0c. Its value is between -1 and 1. The specific calculation formula is as follows:

[0089]

[0090] In the formula, X q Indicates the soil particle size content at a certain sampling point, Y q represents the surface reflectance data of a certain sampling point, q represents the number of sampling points, is the average content of soil particle size at all sampling points, is the average surface reflectance of all sampling points. r represents the Pearson correlation coefficient. If its value is less than 0, it means that the surface reflectance and soil particle size content are negatively correlated. Conversely, if it is greater than 0, it means that the two are positively correlated. When the calculated value r is closer to ±1, the linear correlation between the two is more significant.

[0091] After calculating the correlation coefficient between the particle content data of different sampling soil thickness ranges (0-20cm, 0-40cm and 0-60cm) in the surface soil data D1, D2 and D3 of each particle size level and the local surface reflectance data R0c, the sampling soil thickness (0-40cm) with the highest correlation is defined as the optimal inversion soil thickness range, and the array within the optimal inversion soil thickness range (0-40cm) is screened out from the surface soil data D1, D2 and D3 of each particle size level, and the preferred surface sand soil data D1a, the preferred silt soil data D2a and the preferred clay soil data D3a are obtained respectively.

[0092] The example diagram of the correlation between the three kinds of particle size contents of different soil thicknesses and the local surface reflectivity data R0c analyzed in this embodiment is as follows: Figure 4 As shown in the figure, (a), (b), and (c) are the correlation data diagrams between the sand, silt, and clay content and the local surface reflectance data R0c, respectively. The horizontal axis Band in the figure represents the band, B1~B7 corresponds to band 1 to band 7 respectively, and the vertical axis Pearson Correlation is the calculated correlation coefficient.

[0093] Step S5: Using the local surface reflectivity data R0c and the preferred surface soil data D1a, D2a and D3a, respectively, construct the sand inversion model S1, the silt inversion model S2 and the clay inversion model S3, specifically including:

[0094] The local surface reflectance data R0c is matched one by one with each preferred surface soil data D1a, D2a and D3a, and candidate soil particle size inversion models based on multiple linear regression (MLR), partial least squares regression (PLSR) and support vector machine (SVM) are respectively constructed. The specific construction process is detailed in the above step S51;

[0095] Using R calculated according to formula (3) and formula (4) 2 The prediction accuracy of the candidate soil particle size inversion models was verified by R-Square and MSE (Mean Squared Error). The modeling method with the highest inversion accuracy was screened out as support vector machine (SVM). That is, the sand inversion model S1, silt inversion model S2 and clay inversion model S3 of the study area were constructed using support vector machine (SVM).

[0096] The example diagram of the modeling comparison analysis and inversion accuracy screening of the optimal modeling method in this embodiment is as follows Figure 5 As shown in the figure, (a), (b), and (c) are the MLR model, PLSR model, and SVM model, respectively; (Ⅰ), (Ⅱ), and (Ⅲ) are the contents of sand, silt, and clay, respectively, reflecting the fitting effect between the predicted value and the true value.

[0097] Step S6: Input the surface reflectivity data R0b into various soil particle size inversion models S1, S2 and S3. Since the surface reflectivity data R0b contains the location information of the mining area to be tested, the surface sand soil data D1b, silt soil data D2b and clay soil data D3b of the mining area to be tested are calculated, that is, the content of sand, silt and clay soil particle sizes in the mining area to be tested and their spatial distribution information are included.

[0098] The example of the inversion result of calculating the content and distribution of three kinds of soil particle sizes in the mining area to be tested in this embodiment is shown in the figure below: Figure 6 As shown, a, b, and c are the inversion results of sand, silt, and clay contents, respectively.

[0099] The inversion method of soil particle size content and its spatial distribution information in arid and semi-arid mining areas provided by the disclosed embodiment uses multispectral remote sensing image data, combined with small sample data of local field soil sampling, to invert soil thickness and modeling methods for the soil particle size content as a measurement indicator, and inverts the soil particle size content and spatial distribution information of the study area. This method can be applied to the ecological restoration and topsoil reconstruction management evaluation of arid and semi-arid mining areas to achieve long-term, effective and rapid monitoring of soil particle size content and its temporal and spatial distribution laws.

[0100] The second aspect of the present disclosure provides an inversion device for soil particle size content and its spatial distribution, comprising:

[0101] The first module is configured to obtain multi-spectral remote sensing image data R0 of the mining area to be tested, and obtain atmospheric apparent reflectance data R0a based thereon;

[0102] The second module is configured to obtain N surface soil data of different particle sizes by sampling and analyzing the local surface soil in the mining area to be tested, and record the surface soil data of the i-th particle size level as Di, i = 1, 2, ..., N, and the surface soil data Di of the i-th particle size level is composed of several arrays, each of which includes the soil content of the i-th particle size level at the sampling point, the sampling point location and the sampling soil thickness;

[0103] The third module is configured to perform atmospheric correction on the atmospheric apparent reflectance data R0a to obtain surface reflectance data R0b, and filter out the surface reflectance at each sampling point from the surface reflectance data R0b to obtain local surface reflectance data R0c;

[0104] The fourth module is configured to perform statistical analysis on the correlation between the surface soil data Di of each particle size level and the local surface reflectance data R0c, define the sampling soil thickness with the highest correlation as the optimal inversion soil thickness range, and screen out the array within the optimal inversion soil thickness range from the surface soil data Di of each particle size level to obtain the preferred surface soil data Dia respectively;

[0105] The fifth module is configured to correspond the local surface reflectance data R0c to each preferred surface soil data Dia one by one, and construct various soil particle size inversion models Si respectively;

[0106] The sixth module is configured to input the surface reflectance data R0b into various soil particle size inversion models Si respectively to obtain the surface soil data Dib of the mining area to be tested, which includes the content of various soil particle sizes and their spatial distribution information in the mining area to be tested.

[0107] It should be noted that the above-mentioned explanation of the embodiment of the inversion method of the soil particle size content and its spatial distribution in a mining area is also applicable to the inversion device of the soil particle size content and its spatial distribution in a mining area in this embodiment, and will not be repeated here.

[0108] In order to implement the above embodiment, the embodiment of the present disclosure also proposes a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to execute the inversion method of the soil particle size content and its spatial distribution in the mining area of ​​the above embodiment.

[0109] Reference below Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. It should be noted that the electronic device in the embodiments of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, servers, etc. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0110] like Figure 7 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 108 to a random access memory (RAM) 103. In the RAM 103, various programs and data required for the operation of the electronic device are also stored. The processing device 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0111] Typically, the following devices may be connected to the I / O interface 105: an input device 106 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, etc.; an output device 107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 108 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 109. The communication device 109 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 7 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0112] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 109, or installed from the storage device 108, or installed from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0113] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0114] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0115] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the inversion method of the soil particle size content and its spatial distribution in the mining area.

[0116] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, python, and conventional procedural programming languages, such as "C-" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] 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 application. 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, without contradiction.

[0118] In addition, 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 the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0119] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0120] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0121] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0122] A person of ordinary skill in the art may understand that all or part of the steps carried out in the above-mentioned embodiment method may be completed by instructing the relevant hardware through a program, and the developed program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0123] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0124] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for inverting the particle size content and spatial distribution of soil in a mining area, characterized in that: include: Obtain multispectral remote sensing image data R0 of the mining area to be tested, and obtain atmospheric apparent reflectance data R0a based on it; Take local surface soil samples from the mining area to be tested and analyze the samples to obtain surface soil data of N different particle sizes. The surface soil data of the i-th particle size level is recorded as Di, i = 1, 2, ..., N. The surface soil data Di consists of several arrays, each of which includes the soil content of the i-th particle size level at the sampling point, the sampling point location and the sampling soil thickness; Perform atmospheric correction on the atmospheric apparent reflectance data R0a to obtain the surface reflectance data R0b, and filter out the surface reflectance at each sampling point from the surface reflectance data R0b to obtain the local surface reflectance data R0c; The correlation between the surface soil data Di of each particle size level and the local surface reflectance data R0c was statistically analyzed respectively, and the sampling soil thickness with the highest correlation was defined as the optimal inversion soil thickness range. The arrays within the optimal inversion soil thickness range were screened out from the surface soil data Di of each particle size level to obtain the preferred surface soil data Dia respectively; The surface reflectance data R0c is matched with each preferred surface soil data Dia one by one, and each type of soil particle size inversion model Si is constructed respectively; The surface reflectivity data R0b is input into various soil particle size inversion models Si to obtain the surface soil data Dib of the mining area to be tested, which includes the content of various soil particle sizes and their spatial distribution information in the mining area to be tested.

2. The inversion method according to claim 1, characterized in that: After obtaining the multispectral remote sensing image data R0 of the mining area to be measured, the image is first preprocessed, including image cropping and radiation calibration, and then the DN value of each data point in the preprocessed multispectral remote sensing image data is converted into the atmospheric apparent reflectance, so as to obtain the atmospheric apparent reflectance data R0a.

3. The inversion method according to claim 1, characterized in that: The local surface soil sampling and sample analysis of the mining area to be tested obtains surface soil data of N different particle size levels, specifically including: Sampling locations were dispersed in the study area, and the surface soil at each sampling location was sampled in layers. A soil auger was used to collect soil samples of equal weight at each sampling point, and the sampling time, location, sample number, sample soil thickness, and latitude and longitude coordinates of each sampling location were recorded; All collected samples were sieved using a standard 2 mm screening method, and each sample was divided into N particle size levels, obtaining the soil contents of N different particle size levels, which together with the sampling point location and sampling soil thickness of each sample constituted the surface soil data Di, i = 1, 2, ..., N.

4. The inversion method according to claim 1, characterized in that: The 6SV atmospheric correction model is used to perform atmospheric correction on the atmospheric apparent reflectance data R0a.

5. The inversion method according to claim 1, characterized in that: The preferred surface soil data Dia is obtained by following the steps below: The correlation between the different soil particle size contents at each sampling soil thickness in the surface soil data Di and the surface reflectance data R0c was analyzed, and the sampling soil thickness with the highest correlation was defined as the optimal inversion soil thickness range. Then, the array within the optimal inversion soil thickness range was screened from the surface soil data Di to obtain the preferred surface soil data Dia.

6. The inversion method according to claim 1, characterized in that: The structures of various soil particle size inversion models Si constructed are the same or different. The methods used to construct various soil particle size inversion models Si include multiple linear regression method, partial least squares regression method and support vector machine method.

7. The inversion method according to claim 1, characterized in that: When constructing various soil particle size inversion models Si, multiple candidate soil particle size inversion models are constructed for the preferred surface sand soil data Dia of each particle size level. Statistical indicators are used to screen out the candidate soil particle size inversion model with the highest inversion accuracy from the multiple candidate soil particle size inversion models as the soil particle size inversion model Si for the corresponding particle size level.

8. The inversion method according to any one of claims 1 to 7, characterized in that: When testing samples, the particle size levels of the surface soil in the mining area to be tested are divided into N=3 types, namely sand, silt and clay.

9. An inversion device for soil particle size content and spatial distribution in mining areas, characterized in that: include: The first module is configured to obtain multi-spectral remote sensing image data R0 of the mining area to be tested, and obtain atmospheric apparent reflectance data R0a based thereon; The second module is configured to obtain N surface soil data of different particle sizes by sampling and analyzing the local surface soil in the mining area to be tested, and record the surface soil data of the i-th particle size level as Di, i = 1, 2, ..., N, and the surface soil data Di of the i-th particle size level is composed of several arrays, each of which includes the soil content of the i-th particle size level at the sampling point, the sampling point location and the sampling soil thickness; The third module is configured to perform atmospheric correction on the atmospheric apparent reflectance data R0a to obtain surface reflectance data R0b, and filter out the surface reflectance at each sampling point from the surface reflectance data R0b to obtain local surface reflectance data R0c; The fourth module is configured to perform statistical analysis on the correlation between the surface soil data Di of each particle size level and the local surface reflectance data R0c, define the sampling soil thickness with the highest correlation as the optimal inversion soil thickness range, and screen out the array within the optimal inversion soil thickness range from the surface soil data Di of each particle size level to obtain the preferred surface soil data Dia respectively; The fifth module is configured to correspond the local surface reflectance data R0c to each preferred surface soil data Dia one by one, and construct various soil particle size inversion models Si respectively; The sixth module is configured to input the surface reflectance data R0b into various soil particle size inversion models Si respectively to obtain the surface soil data Dib of the mining area to be tested, which includes the content of various soil particle sizes and their spatial distribution information in the mining area to be tested.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the inversion method according to any one of claims 1 to 8.

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