Wind erosion model construction method based on nested non-photosynthetic vegetation parameters

Abstract

The invention provides a wind erosion model construction method for nesting non-photosynthetic vegetation parameters, and the method comprises the steps: obtaining the non-photosynthetic vegetation coverage of a sample plot based on the non-linear function relation between the constructed spectral information and the non-photosynthetic vegetation coverage in combination with the obtained remote sensing monitoring data of the sample plot. And calculating the total vegetation coverage of the region by combining the photosynthetic vegetation coverage obtained by the MODIS-NDVI. Correcting the quantitative relation between the vegetation coverage and the windward area index based on the actually measured vegetation parameters of the sample plot, and optimizing the roughness element correction function; and aerodynamics parameters containing non-photosynthetic vegetation information are obtained through inversion by using the total vegetation coverage. The aerodynamic parameters obtained through inversion and the optimized roughness element correction function are embedded into a traditional wind erosion model, and a wind erosion model with nested non-photosynthetic vegetation parameters is constructed. The wind erosion data is actually measured through a sample plot for verification, the result shows that the model can effectively represent the regulation effect of non-photosynthetic vegetation on the wind erosion process, and the accuracy of the prediction result of the wind erosion model is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind-sand research, and particularly relates to a wind erosion model construction method based on nested non-photosynthetic vegetation parameters. BACKGROUND

[0002] Soil wind erosion process is affected by wind speed, soil moisture, topography, soil texture and vegetation coverage, etc. Among them, vegetation has a significant effect on surface roughness, wind speed profile and threshold friction wind speed, and is therefore widely considered as one of the key factors affecting the intensity of wind erosion. Vegetation includes photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV). However, since the parameters of non-photosynthetic vegetation (plant residues, dead stems, fallen leaves, etc.) are difficult to obtain, regional-scale wind erosion models only consider the coverage, windward area and roughness of photosynthetic vegetation.

[0003] Many studies have shown that non-photosynthetic vegetation has a significant inhibitory effect on wind erosion in the non-growing season. Therefore, if the regulating effect of non-photosynthetic vegetation on the wind erosion process is ignored, there will be great uncertainty in the simulation results. In the existing model framework, regional-scale wind erosion models mainly rely on normalized difference vegetation index (NDVI) and other vegetation indices based on red band (630-690 nm) and near-infrared band (770-850 nm). However, the spectral characteristic response peak of non-photosynthetic vegetation is 1300-1800 nm and 1900-2200 nm, which is significantly different from the wave band used by conventional vegetation indices. This makes it difficult for existing remote sensing methods to effectively capture the spatial distribution and dynamic changes of non-photosynthetic vegetation, resulting in large errors in the results of wind erosion models.

[0004] In recent years, with the development of remote sensing technology, high-precision monitoring of non-photosynthetic vegetation coverage has become possible, providing support for the introduction of non-photosynthetic vegetation parameters into wind erosion models. Some scholars have proposed spectral indices for characterizing non-photosynthetic vegetation based on remote sensing data such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite Program (Landsat), such as Dryness-Fraction Index (DFI) and Normalized Difference Index (NDI).

[0005] However, the current research does not apply the non-photosynthetic vegetation remote sensing monitoring technology to the development and application of the regional scale wind erosion model. Therefore, how to convert the monitored non-photosynthetic vegetation into a physical parameter in the wind erosion model, construct a wind erosion model embedded with the remote sensing monitoring parameter of the non-photosynthetic vegetation, and thus reduce the simulation error and improve the accuracy of the simulation result, is still a key problem to be solved in the field of wind and sand research. SUMMARY

[0006] The embodiment of the present application aims to provide a wind erosion model construction method based on embedded non-photosynthetic vegetation parameters, so as to improve the accuracy of the prediction result of the wind erosion model.

[0007] The embodiment of the present application provides a wind erosion model construction method based on embedded non-photosynthetic vegetation parameters, and the method comprises the following steps:

[0008] Obtain the remote sensing monitoring data of a sample plot, and establish a functional relationship with the non-photosynthetic vegetation coverage, and obtain the non-photosynthetic vegetation coverage of the sample plot based on the functional relationship;

[0009] Obtain the photosynthetic vegetation coverage of the sample plot, and obtain the total vegetation coverage based on the non-photosynthetic vegetation coverage and the photosynthetic vegetation coverage;

[0010] Based on the total vegetation coverage and the measured vegetation parameters of the sample plot, the roughness correction function is optimized, and the aerodynamic parameters containing the non-photosynthetic vegetation information are obtained by inversion;

[0011] The vegetation parameters and the aerodynamic parameters in the roughness correction function are verified based on the measured data of the sample plot;

[0012] The verified roughness correction function and the aerodynamic parameters are embedded into the wind erosion model to obtain a modified wind erosion model;

[0013] The result of the modified wind erosion model is checked based on the measured soil erosion data of the sample plot, and a wind erosion model that passes the check is obtained.

[0014] The step of obtaining the non-photosynthetic vegetation coverage of the sample plot based on the functional relationship comprises the following steps:

[0015] Obtain the spectral distribution information in the remote sensing monitoring data;

[0016] Based on the spectral distribution information and the function relationship constructed in advance based on machine learning, the non-photosynthetic vegetation coverage of the sample plot is obtained, and the function is constructed in advance by fitting the corresponding relationship between the spectral distribution and the non-photosynthetic vegetation coverage.

[0017] The step of obtaining the photosynthetic vegetation coverage of the sample plot based on the non-photosynthetic vegetation coverage and the photosynthetic vegetation coverage comprises:

[0018] The photosynthetic vegetation coverage of the sample plot is obtained based on remote sensing satellite data inversion;

[0019] The photosynthetic vegetation coverage and the non-photosynthetic vegetation coverage are superimposed to obtain the total vegetation coverage of the sample plot.

[0020] The step of optimizing the roughness element correction function based on the total vegetation coverage and the measured vegetation parameters of the sample plot and inversely obtaining the aerodynamic parameters containing non-photosynthetic vegetation information comprises:

[0021] The upwind area index and the resistance partition parameter in the shear force decomposition model are optimized based on the total vegetation coverage;

[0022] The optimized roughness element correction function is obtained based on the upwind area index and the resistance partition parameter;

[0023] A quantitative relationship between the upwind area index and the aerodynamic parameters is established to obtain the aerodynamic parameters containing non-photosynthetic vegetation information, wherein the aerodynamic parameters include the threshold friction velocity and the roughness.

[0024] The step of verifying the vegetation parameters and the aerodynamic parameters in the roughness element correction function based on the measured data of the sample plot comprises:

[0025] The measured values of the resistance partition, the threshold friction velocity and the roughness under different upwind area indexes are obtained based on the measured data of the sample plot;

[0026] The simulated values of the resistance partition, the threshold friction velocity and the roughness under different upwind area indexes are obtained based on the vegetation parameters and the aerodynamic parameters in the optimized roughness element correction function;

[0027] The process quantities including the resistance partition, the threshold friction velocity and the roughness are verified by combining the measured values and the simulated values.

[0028] The step of embedding the verified roughness element correction function and the aerodynamic parameters into the wind erosion model to obtain the modified wind erosion model comprises:

[0029] The threshold friction velocity in the wind erosion model is obtained based on the threshold friction velocity under the ideal state, the soil moisture influence function of the threshold friction velocity and the verified roughness element correction function;

[0030] The horizontal sediment transport calculation model of particles with different particle sizes is constructed based on the threshold friction velocity and the threshold wind speed.

[0031] Based on the horizontal sediment transport calculation model of particles with different particle sizes, the horizontal sediment transport flux of sand particles in a certain particle size interval is constructed.

[0032] The step of verifying the result of the corrected wind erosion model based on the measured soil wind erosion amount data of the sample plot to obtain a wind erosion model that passes the verification, comprises:

[0033] Obtaining a measured value of soil wind erosion amount based on the measured soil wind erosion amount data of the sample plot;

[0034] Based on the corrected wind erosion model, a simulated value of soil wind erosion amount in the research area is obtained;

[0035] Combining the measured value and the simulated value, the simulation result of the corrected wind erosion model is verified, and a wind erosion model that passes the verification is obtained.

[0036] The present application provides a kind of based on nesting non-photosynthetic vegetation parameter's wind erosion model construction method, by field measurement acquisition typical sample area Non-photosynthetic vegetation coverage and its corresponding hyperspectral characteristic data, establish nonlinear regression model, and utilize satellite remote sensing data, and obtain the time and space continuous non-photosynthetic vegetation coverage distribution product of region reversal.A total vegetation coverage is obtained by combining the photosynthetic vegetation coverage obtained by MODIS-NDVI.Based on sample plot measured vegetation parameter (including crown width, vegetation height and ground diameter), the quantitative relationship of vegetation coverage and wind area index and roughness is corrected, and the rough element correction function is optimized.The wind area index and roughness parameter containing non-photosynthetic vegetation information are obtained by using total vegetation coverage inversion.The aerodynamic parameter and optimized rough element correction function obtained by the above-mentioned inversion are embedded in the traditional wind erosion model, and the wind erosion model of nesting non-photosynthetic vegetation parameter is constructed.The model is verified by sample plot measured wind erosion data, and the results show that the model can effectively represent the adjustment effect of non-photosynthetic vegetation on wind erosion process, and improve the accuracy of the prediction result of wind erosion model. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0038] Figure 1 The flow chart of the wind erosion model construction method based on nesting non-photosynthetic vegetation parameter provided by the embodiments of the present application is provided.

[0039] Figure 2 The logic diagram of the construction method provided by the embodiments of the present application is provided.

[0040] Figure 3 Fig. 1 is a schematic diagram of the simulated and measured values of the resistance partition in the embodiment of the present application;

[0041] Figure 4 Fig. 2 is a schematic diagram of the simulated and measured values of the roughness in the embodiment of the present application;

[0042] Figure 5 Fig. 3 is a schematic diagram of the simulated and measured values of the threshold wind speed in the embodiment of the present application;

[0043] Figure 6 Fig. 4 is a schematic diagram of the horizontal sediment transport flux of the research area obtained by applying the wind erosion model in the embodiment of the present application;

[0044] Figure 7 Fig. 5 is a schematic diagram of the simulated and measured values of the horizontal sediment transport flux obtained in the embodiment of the present application;

[0045] Figure 8 Fig. 6 is a schematic diagram of the simulated values obtained by the improved wind erosion model, the initial model and the measured values. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings.

[0047] Please refer to Figure 1 Fig. 1 is a flowchart of the method for constructing the wind erosion model based on the nested non-photosynthetic vegetation parameters provided in the embodiment of the present application, and the detailed steps of the method for constructing the wind erosion model based on the nested non-photosynthetic vegetation parameters are introduced as follows.

[0048] S11, remote sensing monitoring data of a sample plot is obtained, a functional relationship with non-photosynthetic vegetation coverage is established, and the non-photosynthetic vegetation coverage of the sample plot is obtained based on the functional relationship.

[0049] S12, photosynthetic vegetation coverage of the sample plot is obtained, and total vegetation coverage is obtained based on the non-photosynthetic vegetation coverage and the photosynthetic vegetation coverage.

[0050] S13, a roughness element correction function is optimized based on the total vegetation coverage and the measured vegetation parameters of the sample plot, and aerodynamic parameters containing non-photosynthetic vegetation information are obtained by inversion.

[0051] S14, the vegetation parameters and the aerodynamic parameters in the roughness element correction function are verified based on the measured data of the sample plot.

[0052] S15, the verified roughness element correction function and the aerodynamic parameters are embedded into a wind erosion model to obtain a modified wind erosion model.

[0053] S16, verifying the result of the corrected wind erosion model based on the measured soil wind erosion amount data of the sample plot, to obtain a wind erosion model that passes the verification.

[0054] In this embodiment, the remote sensing monitoring data of the sample plot can be generated by a medium-resolution imaging spectrometer. By analyzing the remote sensing monitoring data, the non-photosynthetic vegetation coverage of the sample plot is obtained. Specifically, the non-photosynthetic vegetation coverage of the sample plot can be obtained based on the established functional relationship between the remote sensing monitoring data and the non-photosynthetic vegetation coverage. This step can be implemented in the following way:

[0055] Obtain the spectral distribution information in the remote sensing monitoring data; based on the spectral distribution information and the function relationship previously constructed based on machine learning, obtain the non-photosynthetic vegetation coverage of the sample plot. The function relationship is previously constructed by fitting the corresponding relationship between the spectral distribution and the non-photosynthetic vegetation coverage.

[0056] Please refer to Figure 2 In this embodiment, the function relationship between the spectral distribution and the non-photosynthetic vegetation coverage can be previously constructed using machine learning. This function relationship can be a nonlinear mapping relationship and can be embodied in the form of a machine learning model. Specifically, the corresponding spectrum and non-photosynthetic vegetation coverage can be used as samples for model training to obtain a machine learning model that has learned the corresponding relationship between the spectral information and the non-photosynthetic vegetation coverage.

[0057] The previously established function relationship between the remote sensing monitoring data and the non-photosynthetic vegetation coverage can be applied in the actual application stage. Based on the spectral distribution information in the remote sensing monitoring data of the sample plot, the spectral distribution information is substituted into the function relationship to obtain the non-photosynthetic vegetation coverage of the sample plot.

[0058] In addition, in this embodiment, the photosynthetic vegetation coverage of the sample plot can also be obtained. Specifically, the photosynthetic vegetation coverage of the sample plot can be obtained based on remote sensing satellite data inversion. The remote sensing satellite data can be a MODIS-NDVI data set, which is a global normalized vegetation index data generated by a medium-resolution spectral imaging spectrometer.

[0059] The non-photosynthetic vegetation coverage and the photosynthetic vegetation coverage of the sample plot are superimposed to obtain the total vegetation coverage of the sample plot.

[0060] In this way, the total vegetation coverage and the measured vegetation parameters of the sample plot can be used to optimize the roughness correction function and to obtain the aerodynamic parameters containing non-photosynthetic vegetation information. Specifically, this step can be implemented in the following way:

[0061] The upwind area index and the resistance partition parameter in the shear force decomposition model are optimized based on the total vegetation coverage. The roughness element correction function after optimization is obtained based on the upwind area index and the resistance partition parameter. The quantitative relationship between the upwind area index and the aerodynamic parameter is established, and the aerodynamic parameter including the non-photosynthetic vegetation information is obtained, and the aerodynamic parameter includes the friction speed and the roughness.

[0062] In the shear force decomposition model of Raupach et al., the total shear force acting on the surface τ is proportional to the square of the friction velocity u*. The total shear force can be divided into the shear force on the roughness element (τ r ) and the skin shear force of the ground surface (τ s ). The formula is as follows:

[0063] τ=ρu * 2 =τ s +τ r

[0064] In this embodiment, under the condition of considering the total vegetation coverage and the non-photosynthetic vegetation parameters (including the height, crown width and other parameters of the non-photosynthetic vegetation) of the sample plot, two important parameters representing the geometric characteristics of the roughness element, the upwind area index λ and the base area index η, are calculated by the following formula:

[0065]

[0066] The width, height and base area of each roughness element are represented by b, h and a b , n represents the number of roughness elements on the sample plot, and S represents the area of the sample plot.

[0067] Further, the ratio σ of the upwind area index and the base area index is calculated by the following formula:

[0068]

[0069] As can be seen from the above formula, τ s is the area-averaged shear force. Therefore, the average shear force τ s ' on the unit bare area is calculated by the following method:

[0070]

[0071] From the above formula, it can be obtained that:

[0072]

[0073] The resistance partition R t between the roughness element and the base surface can be obtained from the shear force ratio τ r / τ, τ s / τ and τs In this embodiment, the Raupach scheme is selected, and τ is used s The τ is expressed as:

[0074]

[0075]

[0076] In the formula, β r is the ratio of the pressure resistance coefficient C r and the friction resistance coefficient C s , C r and C s respectively represent the resistance coefficients of bare land surface and isolated roughness elements.

[0077] From the above, the roughness element correction function f λ after optimization can be expressed as:

[0078]

[0079] Wherein, m is a constant less than 1, used to explain the unevenness of surface stress.

[0080] In the above way, the roughness element correction function considering non-photosynthetic vegetation information can be obtained. In addition, the aerodynamic parameters containing non-photosynthetic vegetation information are obtained by inversion, and the aerodynamic parameters include the frictional starting wind speed, roughness, etc.

[0081] On the basis of the above, the vegetation parameters in the roughness element correction function and the aerodynamic parameters are verified based on the measured data of the sample plot. Specifically, this step can be realized by the following way:

[0082] The measured values of the resistance partition, the frictional starting wind speed and the roughness under different windward area indices are obtained based on the measured data of the sample plot; the simulated values of the resistance partition, the frictional starting wind speed and the roughness under different windward area indices are obtained based on the vegetation parameters in the optimized roughness element correction function and the aerodynamic parameters; the process quantities of the wind erosion model are checked by combining the measured values and the simulated values, and the process quantities include the resistance partition, the frictional starting wind speed and the roughness.

[0083] Wherein, Figure 3 The measured values and the simulated values of the resistance partition under different windward area indices are schematically shown in FIG. 5, and it can be seen from FIG. 5 that the distribution of the measured values is consistent with the trend of the simulated values. Figure 3 As shown in FIG. 6, it is a schematic diagram of the measured values and the simulated values of the roughness under different windward area indices, and it can be seen from FIG. 6 that the distribution of the measured values is consistent with the trend of the simulated values.

[0084] Figure 4 As shown in FIG. 6, it is a schematic diagram of the measured values and the simulated values of the roughness under different windward area indices, and it can be seen from FIG. 6 that the distribution of the measured values is consistent with the trend of the simulated values. Figure 4 ​It can be seen that the measured values are evenly distributed on both sides of the simulation values, and the trend of the measured values is consistent with that of the simulation values. In addition, as shown in Figure 5 FIG. 6 is a schematic diagram of the measured and simulated values of the threshold friction velocity under different windward area indices, wherein the trend of the measured values is consistent with that of the simulated values. Based on the verification results in Figures 3 to 5 , it can be determined that the verification of the process quantity of the wind erosion model is passed.

[0085] Based on the above verification results, the verified roughness element correction function and aerodynamic parameters are embedded into the wind erosion model to obtain a modified wind erosion model. Specifically, this step can be realized by the following way:

[0086] Based on the threshold friction velocity under ideal conditions, the soil moisture influence function of the threshold friction velocity, and the verified roughness element correction function, the threshold friction velocity in the wind erosion model is obtained; based on the threshold friction velocity and the threshold wind speed, a horizontal sand transport capacity calculation model of particles with different particle sizes is constructed; and based on the horizontal sand transport capacity calculation model of particles with different particle sizes, the horizontal sand transport flux of sand particles in a certain particle size interval is constructed.

[0087] In this embodiment, the above verified roughness element correction function is nested into the comprehensive wind erosion model (Intergrated Wind Erosion Model System, IWEMS) to simulate the horizontal sand transport flux.

[0088] Under ideal conditions, the threshold friction velocity u *t can be expressed as a function of the particle size d, so the particle influence function u s of the particle with a diameter of d *t can be written as:

[0089]

[0090] wherein p a and p p are the densities of air and sand particles respectively, g is the acceleration of gravity (9.8 m / s 2 ), and a1 and a2 are tuning parameters. Generally, the particle size range of saltation particles can be determined as 70 μm-500 μm.

[0091] In a natural environment, there are many factors affecting the threshold friction velocity, among which the most important ones are vegetation and soil moisture. The calculation formula of the threshold friction velocity is as follows:

[0092] u *t (d s ; λ, θ) = u *t (d s )f λ (λ)f ω (θ)

[0093] wherein f λ (λ) is the above-mentioned verified roughness element correction function, f ω (θ) is the soil moisture influence function of the threshold friction wind speed, θ is the soil moisture volume content, and the unit is m 3 / m 3 .

[0094] On this basis, the horizontal sediment transport calculation model of different particle sizes of particles is constructed as shown below:

[0095]

[0096] wherein A c is the erodible area ratio, which is determined by factors such as vegetation coverage and soil moisture, c0 is the Owen coefficient, u * is the threshold friction wind speed, which is determined by the following wind speed profile equation:

[0097]

[0098] wherein u z is the wind speed (measured value) at height z, k is the von Karman constant, generally 0.4, z is the height, d is the zero horizontal displacement, z0 is the roughness, and u * is the threshold friction wind speed, and the two parameters can be fitted according to the wind speed data at different heights.

[0099] According to the particle size distribution of the surface soil, the horizontal sediment transport flux of the sand particles with a particle size in a set particle size interval, such as 70 μm-500 μm, can be calculated in the following manner:

[0100]

[0101] wherein d1 and d2 are the lower limit and the upper limit of the saltation particle size respectively, and p(d s ) is the particle size distribution function of the surface soil.

[0102] After obtaining the corrected wind erosion model in the above-mentioned manner, the result of the corrected wind erosion model is verified based on the measured soil erosion data of the sample plot, and specifically, the step can be implemented in the following manner:

[0103] The measured value of the soil erosion amount is obtained based on the measured soil erosion data of the sample plot; the simulated value of the soil erosion amount of the research area is obtained based on the corrected wind erosion model; the simulated result of the corrected wind erosion model is verified in combination with the measured value and the simulated value, and the verified wind erosion model is obtained.

[0104] On the basis of the above, the wind erosion model embedded with the parameters of non-photosynthetic vegetation can be obtained. In order to ensure that the constructed wind erosion model is consistent with the actual situation, it is also necessary to verify the wind erosion model based on the measured soil wind erosion amount (saltation flux) data. Specifically, the step of verifying the result of the corrected wind erosion model can be realized by the following way:

[0105] In this embodiment, the shear force decomposition model corrected by the above-mentioned way and the wind erosion model embedded with non-photosynthetic vegetation remote sensing monitoring are used to simulate the wind erosion amount of the research area. As shown in Figure 6 , the simulation value of the soil wind erosion amount (horizontal sand transport flux) of the research area obtained after simulation based on the wind erosion model is schematically shown.

[0106] In order to verify the simulation result, the measured value of the soil wind erosion amount can also be obtained based on the measured soil wind erosion amount data to perform the verification.

[0107] In this embodiment, a set of wind erosion observation system can be constructed, including four gradient ultrasonic anemometer (used to measure the wind speed and direction at four different heights), two Sensit wind erosion flux sensors (used to measure the horizontal sand transport flux / saltation flux within 5cm and 10cm of the ground), two DustTrak aerosol monitors (used to measure the dust concentration), a set of soil temperature and humidity monitoring probe (ETM50) (used to measure the temperature and humidity of the soil), and a set of BSNE sand collector (used to measure the horizontal sand transport flux at different heights).

[0108] Based on the measured horizontal sand transport flux and the simulated horizontal sand transport flux, the schematic diagram as shown in Figure 7 can be constructed, and the results show that the simulation value and the measured value are consistent and uniformly distributed on both sides of the 1:1 line. The root mean square error value of the simulation value and the measured value of the horizontal sand transport flux is 13.31g / cm / min, and the absolute deviation of the simulation value and the measured value is 27.40%.

[0109] In addition, in order to further verify the influence of the nested non-photosynthetic vegetation on the wind erosion model, the simulation results before and after considering the parameters of non-photosynthetic vegetation are compared, as shown in Figure 8 . The results show that the root mean square errors of the nested model and the original model are 5.64 and 66.70kg / m / d respectively, and the average absolute deviations are 15.72% and 77.24% respectively. The absolute deviation of the nested model is reduced by about 61%, and the precision of the simulation result is significantly improved.

[0110] The present application embeds non-photosynthetic vegetation parameters into a wind erosion model by obtaining non-photosynthetic vegetation coverage data, combining a shear force decomposition scheme, and constructing a regional scale wind erosion model embedded with remote sensing monitoring parameters of non-photosynthetic vegetation. Specifically, the non-photosynthetic vegetation parameters obtained by remote sensing inversion are used to correct the shear force decomposition model, and the key parameters such as windward area index (λ) and threshold friction velocity (u *t ) are corrected. Then, the non-photosynthetic vegetation parameters are embedded into the wind erosion model framework, breaking the limitation of the existing model on the dependence on photosynthetic vegetation coverage.

[0111] The present application constructs a complete model of non-photosynthetic vegetation coverage inversion-shear force decomposition scheme correction-embedded non-photosynthetic vegetation parameter wind erosion model construction-horizontal sediment flux output, realizes the deep integration of surface remote sensing monitoring results and wind erosion physical model parameters, and significantly improves the estimation accuracy and regional adaptability of the wind erosion model.

[0112] In this way, the simulation accuracy and application reliability of the wind erosion model in the non-growing season, land degradation area, and arid area are improved, which has important theoretical value and practical promotion prospect.

[0113] Based on the test and verification, after introducing the non-photosynthetic vegetation parameters, the simulation error of the model for the sand lifting flux is significantly reduced. The wind erosion model embedded with non-photosynthetic vegetation parameters can capture the wind erosion inhibition effect of non-photosynthetic vegetation in winter and spring, and avoid the overestimation problem of the traditional model in the non-growing season.

[0114] The above only describes the embodiments of the present application and does not limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. The program related to the method steps can be executed on a computer, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for constructing a wind erosion model based on nested non-photosynthetic vegetation parameters, characterized in that, The method includes: Remote sensing monitoring data of the sample plots are obtained, and a functional relationship between the data and the non-photosynthetic vegetation cover is established. The non-photosynthetic vegetation cover of the sample plots is obtained based on the functional relationship. The photosynthetic vegetation cover of the sample plot is obtained, and the total vegetation cover is obtained based on the non-photosynthetic vegetation cover and the photosynthetic vegetation cover. Based on the total vegetation coverage and the measured vegetation parameters of the sample plots, the rough element correction function is optimized, and the aerodynamic parameters containing non-photosynthetic vegetation information are obtained by inversion. The vegetation parameters and aerodynamic parameters in the rough element correction function are verified based on the measured data of the sample plots. The verified rough element correction function and aerodynamic parameters are embedded into the wind erosion model to obtain the corrected wind erosion model; The results of the modified wind erosion model were verified based on the measured soil wind erosion data of the sample plots, and a wind erosion model that passed the verification was obtained.

2. The method for constructing a wind erosion model based on nested non-photosynthetic vegetation parameters according to claim 1, characterized in that, The step of obtaining the non-photosynthetic vegetation cover of the sample plot based on the functional relationship includes: Obtain the spectral distribution information from the remote sensing monitoring data; Based on the spectral distribution information and the pre-constructed functional relationship based on machine learning, the non-photosynthetic vegetation cover of the sample plot is obtained. The functional relationship is pre-constructed by fitting the correspondence between the spectral distribution and the non-photosynthetic vegetation cover.

3. The method for constructing a wind erosion model based on nested non-photosynthetic vegetation parameters according to claim 1, characterized in that, The steps of obtaining the photosynthetic vegetation cover of the sample plot and obtaining the total vegetation cover based on the non-photosynthetic vegetation cover and the photosynthetic vegetation cover include: The photosynthetic vegetation cover of the sample plot was obtained by inversion based on remote sensing satellite data. The total vegetation coverage of the sample plot is obtained by superimposing the photosynthetic vegetation coverage and the non-photosynthetic vegetation coverage.

4. The method for constructing a wind erosion model based on nested non-photosynthetic vegetation parameters according to claim 1, characterized in that, The step of optimizing the rough element correction function based on the total vegetation cover and measured vegetation parameters of the sample plots, and inverting to obtain aerodynamic parameters containing information on non-photosynthetic vegetation, includes: The windward area index and resistance zoning parameters in the shear force decomposition model are optimized based on the total vegetation coverage. Based on the windward area index and drag zoning parameters, the optimized rough element correction function is obtained. A quantitative relationship between the windward area index and aerodynamic parameters is established to obtain aerodynamic parameters including information on non-photosynthetic vegetation, such as friction-starting wind speed and roughness.

5. The method for constructing a wind erosion model based on nested non-photosynthetic vegetation parameters according to claim 1, characterized in that, The step of verifying the vegetation parameters and aerodynamic parameters in the rough element correction function based on the measured data of the sample plot includes: Based on the measured data of the sample plots, the measured values ​​of resistance zones, friction start-up wind speed, and roughness under different windward area indices were obtained. Based on the vegetation parameters and aerodynamic parameters in the optimized roughness element correction function, simulated values ​​of drag zone, friction start-up wind speed, and roughness under different windward area indices are obtained. The process quantities of the wind erosion model are verified by combining the measured and simulated values. These process quantities include resistance zone, friction start-up wind speed, and roughness.

6. The method for constructing a wind erosion model based on nested non-photosynthetic vegetation parameters according to claim 1, characterized in that, The step of embedding the verified rough element correction function and aerodynamic parameters into the wind erosion model to obtain the corrected wind erosion model includes: Based on the ideal friction-starting wind speed, the soil moisture influence function of friction-starting wind speed, and the verified rough element correction function, the friction-starting wind speed in the wind erosion model is obtained. Based on the frictional starting wind speed and frictional wind speed, a calculation model for the horizontal sand transport of particles with different particle sizes is constructed. A model for calculating the horizontal sand transport capacity of sand particles with different particle sizes is constructed to determine the horizontal sand transport flux within a specified particle size range.

7. The method for constructing a wind erosion model based on nested non-photosynthetic vegetation parameters according to claim 1, characterized in that, The step of verifying the results of the corrected wind erosion model based on the measured soil wind erosion data of the sample plot to obtain a verified wind erosion model includes: The measured values ​​of soil wind erosion were obtained based on the measured soil wind erosion data of the sample plots. Based on the modified wind erosion model, simulated values ​​of soil wind erosion in the study area were obtained; By combining the measured and simulated values, the simulation results of the corrected wind erosion model are verified, and a wind erosion model that passes the verification is obtained.

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