Method and system for measuring soil moisture under crop coverage, medium and electronic equipment

Abstract

The invention provides a method and a system for measuring soil moisture under crop coverage, a medium and electronic equipment. The soil moisture measurement method comprises the following steps: acquiring a microwave remote sensing image and field actual measurement data of a farmland to be monitored; preprocessing the microwave remote sensing image to obtain a farmland backscattering coefficient image; building a crop microwave scattering model based on the field measured data, and obtaining a farmland backscattering coefficient; and based on the farmland backscattering coefficient image, optimizing the farmland backscattering coefficient by using a Bayesian algorithm to obtain a farmland soil moisture estimation result. According to the method provided by the invention, the accuracy of soil moisture measurement under crop coverage can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of soil moisture measurement, and relates to a soil moisture measurement method, system, medium and electronic equipment under crop cover. Background Art

[0002] Soil moisture controls the process of vegetation transpiration and water distribution, plays a key role in the exchange of energy and water between the surface and the atmosphere, and is one of the most important parameters in agricultural applications. Therefore, accurate estimation of soil moisture is crucial for agricultural irrigation scheduling, drought monitoring, and yield prediction. The spatiotemporal estimation of land surface soil moisture has always been a challenge in the field of remote sensing. Therefore, how to provide a soft measurement method for soil moisture under crop cover has become a technical problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0003] The purpose of the present application is to provide a soil moisture measurement method, system, medium and electronic equipment under crop cover, so as to improve the accuracy of soil moisture measurement under crop cover.

[0004] In a first aspect, the present application provides a method for measuring soil moisture under crop cover, the measurement method comprising: obtaining microwave remote sensing images and field measured data of the farmland to be monitored; preprocessing the microwave remote sensing images to obtain farmland backscattering coefficient images; constructing a crop microwave scattering model based on the field measured data to obtain the farmland backscattering coefficient; based on the farmland backscattering coefficient image, using a Bayesian algorithm to optimize the farmland backscattering coefficient to obtain the farmland soil moisture estimation result.

[0005] In an implementation of the first aspect, the process of constructing a crop microwave scattering model based on the field measured data includes: obtaining the soil complex dielectric constant and the plant complex dielectric constant based on the field measured data; fitting the geometric probability distribution function of each crop component; and constructing the crop microwave scattering model according to the fitted geometric probability distribution function of each crop component, the soil complex dielectric constant, and the plant complex dielectric constant.

[0006] In an implementation of the first aspect, the process of obtaining the soil complex dielectric constant based on the field measured data includes: obtaining the soil complex dielectric constant according to the field measured data and the frequency of a satellite radar beam.

[0007] In an implementation of the first aspect, the backscattering intensity of the crop includes the backscattering intensity of each component of the crop and the backscattering intensity of the surface under the crop canopy cover, and the process of obtaining the backscattering intensity of the crop according to the crop microwave scattering model includes: obtaining the backscattering intensity of each component of the crop according to the crop microwave scattering model; and obtaining the surface backscattering intensity under the crop canopy cover using an advanced integral equation model based on the soil complex dielectric constant.

[0008] In an implementation of the first aspect, the backscatter intensity of each crop component includes volume scattering of each crop component and secondary scattering between each crop component and the ground surface.

[0009] In an implementation of the first aspect, the backscattering intensity of each component of the crop further includes: the backscattering intensity of the ear, the backscattering intensity of the stalk, and the backscattering intensity of the leaf.

[0010] In an implementation of the first aspect, based on the farmland backscatter coefficient image, the farmland backscatter coefficient is optimized using the Bayesian algorithm, and the process of obtaining the farmland soil moisture estimation result includes: optimizing the farmland backscatter coefficient using the Bayesian algorithm to obtain the optimal soil volume moisture content; traversing the farmland area of ​​the farmland backscatter coefficient image to obtain the farmland soil moisture estimation result.

[0011] In the second aspect, the present application provides a soil moisture measurement system under crop cover, the measurement system comprising: an acquisition module, used to acquire microwave remote sensing images and field measured data of the farmland to be monitored; a preprocessing module, used to preprocess the microwave remote sensing images and obtain farmland backscattering coefficient images; a construction module, used to construct a crop microwave scattering model based on the field measured data and obtain the farmland backscattering coefficient; an optimization module, used to optimize the farmland backscattering coefficient based on the farmland backscattering coefficient image using a Bayesian algorithm to obtain the farmland soil moisture estimation result.

[0012] In a third aspect, the present application provides an electronic device, comprising: a memory on which a computer program is stored; and a processor, communicatively connected to the memory, for executing the computer program to implement the above-mentioned soil moisture measurement method under crop cover.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned soil moisture measurement method under crop cover.

[0014] As described above, the soil moisture measurement method, system, medium and electronic device under crop cover described in the present application have the following beneficial effects:

[0015] The soil moisture measurement method under crop cover of the embodiment of the present application can process the microwave remote sensing image and field measured data of the monitored farmland, further obtain the farmland backscatter coefficient image and farmland backscatter coefficient, and optimize the farmland backscatter coefficient using the Bayesian algorithm to obtain the farmland soil moisture estimation result. This method can improve the accuracy of soil moisture measurement under crop cover. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a process schematic diagram of the method for measuring soil moisture under crop cover described in an embodiment of the present application.

[0017] Figure 2 Shown is a schematic diagram of the process of constructing a crop microwave scattering model as described in an embodiment of the present application.

[0018] Figure 3 Shown is a schematic diagram of the process of obtaining the backscatter intensity of the crop described in an embodiment of the present application.

[0019] Figure 4 Shown is a schematic diagram of the process of optimizing soil moisture content to obtain the minimum simulation error of farmland backscattering coefficient as described in an embodiment of the present application.

[0020] Figure 5 Shown is a schematic diagram of the structure of the soil moisture measurement system under crop cover described in an embodiment of the present application.

[0021] Figure 6 Shown is a schematic diagram of the structure of an electronic device described in an embodiment of the present application.

[0022] Component number description

[0023] 1 Soil moisture measurement system

[0024] 11 Get Module

[0025] 12 Preprocessing Module

[0026] 13 Building Blocks

[0027] 14 Optimization Module

[0028] 2 Electronic devices

[0029] 21 Memory

[0030] 22 Processor

[0031] Steps S11 to S14

[0032] Steps S21 to S22

[0033] Steps S31 to S33

[0034] Steps S41 to S42 DETAILED DESCRIPTION

[0035] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0036] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0037] Soil moisture controls the process of vegetation transpiration and water distribution, plays a key role in the exchange of energy and water between the surface and the atmosphere, and is one of the most important parameters in agricultural applications. Therefore, accurate estimation of soil moisture is crucial for agricultural irrigation scheduling, drought monitoring, and yield prediction. The spatiotemporal estimation of land surface soil moisture has always been a challenge in the field of remote sensing. Synthetic aperture radar (SAR) has a high spatial resolution and can capture subtle surface spatial information characteristics. Its signal is highly sensitive to the surface dielectric constant, and the soil dielectric properties are directly related to the size of soil moisture. Therefore, active microwave remote sensing has great potential in the field of soil moisture inversion. However, due to the mixed scattering mechanism of microwaves, the complex interaction with ground objects, and the susceptibility to the influence of soil surface roughness and crop cover, accurate inversion of soil moisture based on SAR has also become an open problem that needs to be solved urgently.

[0038] The existing soil moisture inversion algorithms under crop cover based on active microwave remote sensing can be divided into empirical model methods, semi-empirical model methods, theoretical model methods and machine learning methods. Among them, the empirical model method for soil moisture estimation is to assume that the roughness remains unchanged, based on a large amount of remote sensing data and the measured data of the corresponding stations, use the scattering characteristics of microwaves to perform statistical correlation analysis between the backscattering coefficient and the observed soil moisture, establish a fitting function model between the two, and realize the inversion estimation of farmland soil moisture. However, the empirical model is generally only valid for specified data sets and experimental conditions (such as specific radar beam incident frequency, incident direction and surface roughness, etc.), and its reliability is subject to the quality of experimental data matched in time and space, and its transferability is poor. The semi-empirical model inversion method is a compromise between the empirical model and the theoretical model. It is based on certain statistical laws and reflects the scattering mechanism to a certain extent. It has clear physical meanings and small calculations and complexity, and can obtain better accuracy. However, the semi-empirical model commonly used for soil moisture inversion in vegetation-covered areas is mainly the water content model (WCM), which has a simple description of the vegetation layer and ignores the influence of the vegetation canopy, stalks, stems and leaves on the total backscattering. It is only applicable to the surface conditions with known vegetation coverage and surface roughness or relatively stable surface conditions. The mechanism model soil moisture inversion method is realized by integrating the forward mechanism scattering model and the inversion algorithm. The commonly used physical model is the Michigan microwave canopy scattering model (MIMICS), which makes up for the shortcomings of the water cloud model and divides the vegetation into the canopy layer, the trunk layer and the soil layer, and calculates the backscattering coefficient between each medium layer and the surface respectively. The inversion algorithms combined with the physical model include: lookup table method, optimization algorithm and machine learning method. Common soil moisture machine learning inversion methods include support vector regression (SVR) and random forest regression (RFR). Based on the nonlinear relationship between soil moisture and scattering coefficient, the machine learning inversion algorithm uses artificial intelligence methods as a tool to establish an inversion method for this nonlinear relationship based on measured moisture data and corresponding radar satellite data. However, this method ignores the potential connection between vegetation biophysical parameters.

[0039] In the field of soil moisture estimation under crop cover, the theoretical model inversion method can physically interpret the electromagnetic wave transmission process in a theoretical sense and has better universality. However, this method still has the following problems during use: (1) Existing models such as the MIMICS model can simulate the impact of tall vegetation on microwave backscattering more realistically, but it is difficult to apply to the inversion of surface soil moisture in short crop areas where there is no obvious difference between the stems and canopy of vegetation. (2) The mechanism simulation itself has ideal assumptions, assuming that the surface reflection under vegetation cover is mirror reflection, which cannot objectively reflect the surface conditions.

[0040] Therefore, how to provide a soft measurement method for soil moisture under crop cover to solve the defects of existing technologies such as difficulty in applying to short crop areas and imperfect expression of surface reflectance has become a technical problem that needs to be urgently solved by technical personnel in this field.

[0041] At least to address the above problems, the following embodiments of the present application provide a method for measuring soil moisture under crop cover. The technical solution in the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present application.

[0042] Figure 1 The process diagram of the method for measuring soil moisture under crop cover in one embodiment of the present application is shown. Figure 1 As shown, the measuring method comprises:

[0043] S11, obtaining microwave remote sensing images and field measurement data of the farmland to be monitored.

[0044] Among them, the field measured data include crop ear size (length, diameter and weight moisture content), leaf size (leaf length, leaf width, leaf thickness, leaf inclination and weight moisture content), straw size (length, diameter and weight moisture content), number of leaves per plant, crop height and number density, soil volume moisture content, but the application is not limited to this.

[0045] The microwave remote sensing image is a C-band microwave remote sensing image of the farmland area to be detected. Radar remote sensing data includes four polarization modes, namely HH, VV, HV and VH. Horizontal polarization (H) means that when the satellite transmits a signal to the ground, the vibration direction of its radio wave is horizontal. Vertical polarization (V) means that when the satellite transmits a signal to the ground, the vibration direction of its radio wave is vertical. Radar systems using H and V linear polarization usually use a pair of symbols to represent the transmit and receive polarizations, so they can have the following channels—HH, VV, HV, VH. Radar remote sensing systems commonly use four polarization modes: (1) HH-for horizontal transmission and horizontal reception. (2) VV-for vertical transmission and vertical reception. (3) HV-for horizontal transmission and vertical reception. (4) VH-for vertical transmission and horizontal reception.

[0046] S12, preprocessing the microwave remote sensing image to obtain a farmland backscattering coefficient image.

[0047] Exemplarily, the preprocessing of the microwave remote sensing image mainly includes radiation calibration, multi-view processing, coherent speckle filtering, terrain correction and decibel processing. Among them, radiation calibration is to establish a quantitative relationship between the quantized value (DN) of the sensor's electrical signal output and its corresponding radiation brightness value with a specific physical meaning, eliminate the system error introduced in the data acquisition process, and obtain the backscatter coefficient (Sigma) of the ground object. Multi-view processing is to convert the data stored in the form of slant range into the form of ground range, and can effectively suppress coherent speckle noise in the distance direction domain to improve the image quality. Coherent speckle filtering refers to suppressing the speckle noise generated by the coherent superposition of electromagnetic wave echo signals through a filtering algorithm, thereby smoothing the internal pixels, retaining edge information and highlighting the characteristics of the ground object. Terrain correction uses DEM data to eliminate or correct the geometric errors of SAR (Synthetic aperture radar) images and assign geographic codes to the images, and masks the images according to vector files to obtain crop planting areas. Decibel conversion refers to logarithmic transformation of radar backscatter coefficients.

[0048] S13, constructing a crop microwave scattering model based on the field measured data to obtain the farmland backscattering coefficient.

[0049] S14, based on the farmland backscatter coefficient image, the farmland backscatter coefficient is optimized using a Bayesian algorithm to obtain a farmland soil moisture estimation result.

[0050] According to the above description, the soil moisture measurement method under crop cover of the embodiment of the present application can process the microwave remote sensing image and field measured data of the monitored farmland, further obtain the farmland backscatter coefficient image and farmland backscatter coefficient, and use the Bayesian algorithm to optimize the farmland backscatter coefficient to obtain the farmland soil moisture estimation result. This method can improve the accuracy of soil moisture measurement under crop cover.

[0051] Figure 2 The diagram shows a process of constructing a crop microwave scattering model in one embodiment of the present application. Figure 2 As shown, the process of constructing a crop microwave scattering model based on the field measured data includes:

[0052] S21, obtaining the soil complex dielectric constant and the plant complex dielectric constant based on the field measured data.

[0053] For example, the ear, leaf and stalk of the crop plant are compared to a dielectric mixture composed of plant dry matter, free water and bound water. The formula for calculating the complex dielectric constant of the plant using the Ulaby double dispersion model is expressed as follows:

[0054] ε v =ε′ v -jε″ v

[0055] in,

[0056] ε′ v =ε r +v fw ·ε′ w +v bw ·ε′ b

[0057] ε″ v =v fw ·ε″ w +v bw ·ε″ b

[0058] In the formula, ε r is the non-dispersive residual part of the dielectric constant of the implanted bead; ε' b and ε″ b are the real and imaginary parts of the bound water respectively; ε' w and ε″ w are the real and imaginary parts of the free water part, respectively; v bw represents the volume fraction of the vegetation body that is bound to water; v fw Represents the volume fraction of vegetation body bound to free water.

[0059] The calculation formulas of the above parameters are as follows:

[0060]

[0061]

[0062] σ i ≈0.17S-0.0013S 2

[0063]

[0064] v fw =m g (0.55m g -0.076)

[0065] Where, f is the frequency of the satellite radar beam, which is 5.405 GHz; S is the salt content of the vegetation (usually 15); m gis the weight water content of vegetation, obtained from field measured data.

[0066] Exemplarily, soil is a mixed dielectric material composed of air, solid soil, free water and bound water. The formula for calculating the complex dielectric constant of soil according to the Dobson soil dielectric model is expressed as follows:

[0067] ε soil =ε′ soil -jε″ soil

[0068] in,

[0069]

[0070]

[0071] In the formula, ε′ soil , ε″ soil Respectively represent the real and imaginary parts of the soil complex dielectric constant; m v is the volumetric water content of the soil; α is the empirical determination coefficient, which is generally taken as 0.65; ρ s It is the specific density of soil solid particles, usually 2.66g / cm 3 ρ b is the soil bulk density; s is the dielectric constant of the soil solid, and the calculation formula is as follows:

[0072] ε s =(1.01+0.44ρ s ) 2 -0.062

[0073] The soil type shape factors β1 and β2 are functions of the soil sand content percentage S and the soil clay content percentage C. The calculation formula is as follows:

[0074] β1=1.2748-0.519S-0.152C

[0075] β2=1.33797-0.603S-0.166C

[0076] ε' w and ε' w ' are the real and imaginary parts of the relative dielectric constant of pure water, respectively. The calculation formula is as follows:

[0077]

[0078] Where f is the frequency of the satellite radar beam, which is 5.405 GHz; T is the soil moisture (℃), which is obtained from field measured data; ε0 is the dielectric constant of free space, which is 8.854×10-12 ; ε w∞ is the upper limit of the dielectric constant of pure water, which is 4.9; σ eff is the effective conductivity, and the calculation formula is as follows:

[0079] σ eff =-1.645+1.939ρ b -2.25622S+1.594C, f>1300

[0080] σ eff =0.0467+0.2204ρ b -0.4111S+0.6614C,300≤f≤1300

[0081] Among them, ε w0 Refers to the static dielectric constant of pure water, τ w represents the relaxation time constant of pure water. Both are functions of temperature T. The calculation formula is as follows:

[0082] ε w0 (T)=88.045-0.4147T+6.295×10 -4 T 2 +1.075×10 -5 T 3

[0083]

[0084] S22, fitting the geometric probability distribution function of each crop component.

[0085] Exemplarily, fitting the geometric probability distribution function of each component of the crop includes standardizing the probability distribution function of the ear, leaf, and stalk of the crop plant, which is specifically manifested as: simplifying the stalk and ear of the crop into a long cylinder. Affected by the natural environment and crop varieties, the plant stalk will tilt to varying degrees, and the inclination angle (β) is used to represent the angle between the cylinder axis and the vertical direction in the clockwise direction. Since the crop ear and stalk basically vary within 0° to 60°, trigonometric functions are used to simulate the geometric probability distribution function of the ear and stalk. The leaves of the crop are simplified into an elliptical disk shape, and the angle between the normal vector of the elliptical disk and the vertical direction in the clockwise direction, that is, the normal angle (β), is used to describe the distribution morphology of the leaves, which is a complementary angle (α) with the leaf insertion angle, that is, β = 90°-α. Based on the leaf insertion angle data obtained by field measurements, a polynomial curve is used to fit the probability distribution function of the leaves.

[0086] S23, constructing the crop microwave scattering model according to the fitted geometric probability distribution function of each component of the crop, the soil complex dielectric constant, and the plant complex dielectric constant.

[0087] Figure 3 The diagram shows a process of obtaining the backscatter intensity of the crop in one embodiment of the present application. Figure 3 As shown, the backscattering intensity of the crop includes the backscattering intensity of each component of the crop and the backscattering intensity of the ground surface covered by the crop canopy. The process of obtaining the backscattering intensity of the crop according to the crop microwave scattering model includes:

[0088] S31, obtaining the backscattering intensity of each crop component according to the crop microwave scattering model. The backscattering intensity of each crop component includes the volume scattering of each crop component and the secondary scattering of each crop component and the ground surface. The crop components are, for example, ears, leaves, and stalks.

[0089] Exemplarily, the calculation process of the backscatter intensity of the spike includes:

[0090] According to the growth status of crops in the field, the influence of ear components on the backscattering intensity of the field is calculated from the earing stage. The volume scattering of the ear is the superposition of the sub-scattering intensities of all ear components. According to the radiation transfer equation, its first-order approximate analytical expression is expressed as follows:

[0091]

[0092] in, (p, q are H or V polarization) refers to the scattering matrix per unit volume of the spike; L1 corresponds to the polarization attenuation factor of the spike layer; k1 is the extinction coefficient of the spike layer; θ i is the angle of incidence of the satellite radar beam.

[0093] When calculating the interaction between scatterers in different scattering layers and the soil surface, the surface is simplified to a slightly rough soil surface according to the radiation transfer equation theory, so the calculation formula for the secondary scattering intensity between the ear and the surface is expressed as follows:

[0094]

[0095] in,

[0096]

[0097] Where k is the wave number, and the formula is: k = 2πf / 0.2998, where f is the frequency of the radar satellite, which is 5.405 GHz; s is the root mean square height of the ground, which is 0.5 cm; R pp and R qq Refers to the Fresnel reflectivity of the surface p, q polarization, the formula is as follows:

[0098] R pp =|R p (θ i )|2

[0099] In the formula, R p (θ i ) is the Fresnel reflection coefficient, and the formula is as follows:

[0100]

[0101] is the scattering phase matrix of the spike per unit volume, and is given by:

[0102]

[0103] Among them, N ear Refers to the number density of ears per unit volume, obtained from measured data; is the scattering amplitude tensor matrix of a single ear in the ear layer, which is related to the polarization tensor and the demagnetization factor. The overall average value of the scattering amplitude matrix of the wheat ear is The calculation formula is as follows:

[0104]

[0105] pdf ear (α, β, γ) = pdf ear (α)·pdf ear (β)·pdf ear (γ)

[0106] In the formula, the symbol <> represents the statistical average of the scattering amplitude tensor of all spike components; pdf ear (α, β, γ) is the probability distribution function of the spike direction angle. The spike is approximated as a cylinder, which has symmetry, so γ = 0, pdf ear (γ) = 1; in the actual farmland environment, the α of the ear is approximately uniformly distributed, and β is simulated by trigonometric functions.

[0107] Exemplarily, the calculation process of the backscattering intensity of the leaf includes:

[0108] The leaves of the crop are assumed to be long and thin elliptical disks, and their volume scattering and secondary scattering are the sum of the cumulative contributions of all leaves in the ear layer and stalk layer, which can be expressed as follows:

[0109] σ leaf =σ leaf1 +σ leaf2

[0110]

[0111] in,

[0112]

[0113] Among them, k2 refers to the extinction coefficient of the stalk layer; and They refer to the scattering phase matrix of leaves per unit volume in the ear layer and stalk layer respectively, and the formula is as follows:

[0114]

[0115] Among them, N leaf It is the number density of blades per unit volume in the medium layer where the blades are located, obtained from measured data; is the scattering phase matrix of the blade. is the overall average of the leaf scattering amplitude matrix, and the formula is as follows:

[0116]

[0117] pdf leaf (α, β, γ) = pdf leaf (α)·pdf leaf (β)·pdf leaf (γ)

[0118] Among them, pdf leaf (α, β, γ) is the geometric probability distribution function of the blade direction angle. In the natural environment, the α and γ of the blade are approximately uniformly distributed, and β is fitted using a polynomial function.

[0119] Exemplarily, the calculation process of the backscatter intensity of the stalk includes:

[0120] The stalk is simplified into a slender cylinder, and the calculation formulas for its volume scattering and secondary scattering are expressed as follows:

[0121]

[0122]

[0123] in,

[0124]

[0125] The calculation formula of the scattering intensity per unit volume is as follows:

[0126]

[0127] Among them, N stem Refers to the number density of stalks per unit volume, obtained from measured data; is the scattering amplitude tensor matrix of a single stalk in the stalk medium layer. The overall average calculation formula of the stalk scattering amplitude matrix is ​​expressed as follows:

[0128]

[0129] pdf stem (α, β, γ) = pdf stem (α)·pdf stem (β)·pdf stem (γ)

[0130] In the formula, pdf stem (α, β, γ) is the probability distribution function of the stalk’s direction angle. The stalk is approximated as a cylinder, which has symmetry. Therefore, γ = 0, pdf stem (γ) = 1; in the natural environment, the α of the stalk is approximately uniformly distributed, and β is fitted using a trigonometric function.

[0131] L np (n=1,2) is the polarization attenuation factor of the first or second dielectric layer, and the calculation formula is as follows:

[0132]

[0133] Where p refers to the polarization mode of H or V; H n is the height of the nth dielectric layer; θ i is the incident angle of the radar beam; k np It represents the extinction coefficient of the p-polarization of the first or second dielectric layer. The calculation formula is as follows:

[0134]

[0135] Among them, M n The nth layer has a total of M n In the farmland microwave scattering model, both dielectric layers contain two types of scatterers, so M1 = M2 = 2; Refers to the number density of the mth type of scatterer in the nth layer; (pp=HH, VV) refers to the forward scattering amplitude vector matrix of the mth scatterer.

[0136] S32, according to the soil complex dielectric constant, using an advanced integral equation (AIEM, Advanced IEM) model to obtain the surface backscattering intensity under the crop canopy cover.

[0137] For example, the magnitude of the backscatter intensity of the ground surface is mainly related to the dielectric properties of the soil, the surface roughness and the attenuation of the plant canopy. The surface roughness and geometry can be represented by the correlation length, the root mean square height and the surface autocorrelation function. According to the radiation transfer theory, the calculation formula for the surface backscatter intensity under the vegetation cover is as follows:

[0138]

[0139] in, is the soil surface scattering matrix, which can be calculated according to the advanced integral equation model as follows:

[0140]

[0141] In the formula,

[0142]

[0143]

[0144] R p (T) = R p (θ)+[R p (0)-R p (θ)]·γ p

[0145]

[0146] Where p or q refers to H or V polarization; s refers to the root mean square height of the soil surface, which is 0.5 cm; k refers to the free wave number in the surface space. k z = kcosθ i , k sz = kcosθ s ,θ i =θ s =θ, f qp is the Kirchhoff coefficient, is the supplementary field coefficient, R p (T) is the improved Fresnel coefficient, Represents the normalized ratio, by introducing the transition function γ p , can change continuously with the change of surface roughness, radar beam incident frequency, and incident direction, and has a wider range of applications. (n) (K) is the n-th order Fourier transform of the spectral density function, and the formula is as follows:

[0147]

[0148] Where l is the relevant length, which is 6 cm; b p The calculation is done by the following formula:

[0149]

[0150] In the formula, when p = 1.5, W (n) (K) is the exponential autocorrelation function; when p = ∞, W (n)(K) is the form of Gaussian autocorrelation function; when the power p is between 1.5 and ∞, the surface roughness function is a transition from exponential function to Gaussian function.

[0151] In one embodiment of the present application, the process of obtaining the backscattering coefficient of the farmland according to the backscattering intensity of the crop includes: adding the backscattering intensity of each component of the plant and the backscattering intensity of the ground surface to obtain the overall backscattering coefficient of the farmland. According to the first-order radiation transfer equation, the overall backscattering coefficient of the farmland is the linear superposition of the volume scattering of each component of the plant ear, leaf, and stalk, the surface scattering, and the secondary scattering between each layer of scatterers and the underlying surface. The calculation formula is as follows:

[0152]

[0153] Figure 4 The diagram shows a process of optimizing soil moisture content to obtain the minimum observation error of farmland backscatter coefficient in one embodiment of the present application. Figure 4 As shown, based on the farmland backscatter coefficient image, the farmland backscatter coefficient is optimized using the Bayesian algorithm to obtain the farmland soil moisture estimation result, including:

[0154] S41, optimizing the farmland backscattering coefficient using the Bayesian algorithm to obtain the optimal soil volume moisture content.

[0155] For example, the soil moisture inversion mainly uses the Bayesian optimization method (BO) to optimize the input parameter soil volume water content in the CMSM (Crop microwave scattering model). The Bayesian optimization method is based on the Bayesian theorem. It establishes the probability distribution of the proxy model through the Gaussian process to fit the true objective function, determines the next set of hyperparameters to be sampled, and continuously updates the prior probability distribution of the objective function according to the parameter information of the previous cycle until the global optimal solution is obtained.

[0156] First, based on the statistical analysis of field measured data, the parameter soil volumetric water content m v The value interval of is set to [0.05, 0.35], and the objective function f(x) is calculated as follows:

[0157]

[0158] Among them, σ SAR-pp (pp refers to HH, VV and VH polarization) represents the backscatter coefficient of different polarizations of SAR image (i.e. farmland backscatter coefficient image), σ CMSM-ppRefers to the backscattering coefficients of different polarizations obtained through CMSM simulation. The root mean square error is selected as the objective function. The smaller f(x), the better the inversion result. The Bayesian theorem formula used is as follows:

[0159]

[0160] Where, f is the objective function f(x); D t ={(x1,y1),(x2,y2),...,(x t ,y t )} represents the observed set, x is the soil moisture content m v , x t represents the decision vector, y t =f(x t )+ε t represents the observed value, ε t represents the observation error; P(D t |f) represents the likelihood distribution of y; P(f) represents the prior probability distribution of f, that is, the assumption about the state of the target function; P(D t ) represents the marginal likelihood distribution of marginalized f, which is mainly used to optimize hyperparameters; P(f|D t ) represents the posterior probability distribution of f, which is used to describe the confidence of the unknown target function after the prior is corrected based on the observed data.

[0161] Assume that the observed quantity D t ={(x1,y1),(x2,y2),...,(x t ,y t )} obeys Gaussian distribution GP(μ(x),σ(x) 2 ), where μ(x), σ(x) 2 are the mean and variance respectively, and the optimal output parameters are Set the maximum number of iterations to 500 and the stop condition to f(x)≤0.5dB. Using Gaussian process, we can get Then, by optimizing the acquisition function, we can get the x to be sampled at the next moment. t+1 , that is The obtained sample points are input into the CMSM model to obtain When the maximum number of iterations is reached or the stopping condition is met (i.e. the observation error is minimized), the iteration ends. The final output result is the optimal hyperparameter, i.e. soil moisture content x t and the corresponding observed soil moisture content That is, the root mean square error of the backscatter coefficient of the pixel.

[0162] S42, traversing the farmland area in the farmland backscatter coefficient image to obtain the farmland soil moisture estimation result.

[0163] Traverse each grid of the farmland backscatter coefficient image, use the S41 method to optimize the soil moisture content in each grid, obtain the soil moisture content corresponding to the minimum simulation error of the farmland backscatter coefficient of each grid, and summarize the soil moisture content of each grid to obtain the farmland soil moisture estimation result.

[0164] In summary, the soil moisture measurement method under crop cover provided by the present application fully considers the physical structure of dwarf crops and achieves a good simulation of the entire growth cycle of the plant canopy. The transition function is used to calculate the surface reflection under vegetation cover, which can more realistically reflect the surface conditions of different roughness. The Bayesian algorithm is used to optimize the farmland backscattering coefficient of the CMSM model, realizing real-time soft measurement of farmland soil moisture in the field of soil moisture inversion with higher accuracy.

[0165] The protection scope of the soil moisture measurement method under crop cover described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0166] An embodiment of the present application also provides a soil moisture measurement system under crop cover, which can implement the soil moisture measurement method under crop cover described in the present application. However, the implementation device of the soil moisture measurement method under crop cover described in the present application includes but is not limited to the structure of the soil moisture measurement system under crop cover listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0167] Figure 5 The structure diagram of the soil moisture measurement system under crop cover in one embodiment of the present application is shown. Figure 5 As shown, the measurement system 1 includes: an acquisition module 11, a preprocessing module 12, a construction module 13, and an optimization module 14. Among them, the acquisition module 11 is used to obtain the microwave remote sensing image and field measured data of the farmland to be monitored. The preprocessing module 12 is used to preprocess the microwave remote sensing image to obtain the farmland backscattering coefficient image. The construction module 13 is used to construct a crop microwave scattering model based on the field measured data to obtain the farmland backscattering coefficient. The optimization module 14 is used to optimize the farmland backscattering coefficient based on the farmland backscattering coefficient image using the Bayesian algorithm to obtain the farmland soil moisture estimation result.

[0168] It should be noted that Figure 5 The modules in the soil moisture measurement system 1 under crop cover are shown as follows Figure 1 The steps in the method for measuring soil moisture under crop cover correspond to each other and will not be described in detail here.

[0169] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0170] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0171] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0172] The present application embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor, the soil moisture measurement method under crop cover provided by the present application embodiment is implemented. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state hard disk (SSD)), etc.

[0173] The embodiment of the present application may also provide an electronic device. Figure 6 The diagram shows a schematic diagram of the structure of an electronic device 2 in an embodiment of the present application. Figure 6 As shown, in this embodiment, the electronic device 2 includes a memory 21 and a processor 22 .

[0174] The memory 21 is used to store computer programs. In some possible implementations, the memory 21 may include: ROM, RAM, a disk, a USB flash drive, a memory card, or an optical disk, etc., various media that can store program codes.

[0175] In the embodiment of the present application, the memory 21 may include a computer system readable medium in the form of a volatile memory, such as a RAM and / or a cache memory. The electronic device 2 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 21 may include at least one program product having a set (e.g., at least one) of program modules, which are configured to perform the functions of the various embodiments of the present application.

[0176] The processor 22 is connected to the memory 21 and is used to execute the computer program stored in the memory 21 so that the electronic device 2 can perform the soil moisture measurement method under crop cover.

[0177] Exemplarily, the processor 22 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. In other embodiments, the processor 22 may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0178] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0179] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for measuring soil moisture under crop cover, characterized in that: The measuring method comprises: Obtain microwave remote sensing images and field measurement data of the farmland to be monitored; Preprocessing the microwave remote sensing image to obtain a farmland backscatter coefficient image; Building a crop microwave scattering model based on the field measured data to obtain the farmland backscattering coefficient; Based on the farmland backscatter coefficient image, the farmland backscatter coefficient is optimized using a Bayesian algorithm to obtain a farmland soil moisture estimation result.

2. The measuring method according to claim 1, characterized in that: The process of constructing a crop microwave scattering model based on the field measured data includes: Obtaining the soil complex dielectric constant and the plant complex dielectric constant based on the field measured data; Fitting the geometric probability distribution function of each crop component; The crop microwave scattering model is constructed according to the fitted geometric probability distribution function of each component of the crop, the soil complex dielectric constant, and the plant complex dielectric constant.

3. The method according to claim 2, characterized in that The process of obtaining the soil complex dielectric constant based on the field measured data includes: obtaining the soil complex dielectric constant according to the field measured data and the frequency of the satellite radar beam.

4. The measuring method according to claim 2, characterized in that: The backscattering intensity of the crop includes the backscattering intensity of each component of the crop and the backscattering intensity of the ground surface covered by the crop canopy. The process of obtaining the backscattering intensity of the crop according to the crop microwave scattering model includes: Acquiring the backscattering intensity of each component of the crop according to the crop microwave scattering model; According to the soil complex dielectric constant, an advanced integral equation model is used to obtain the surface backscattering intensity under the crop canopy cover.

5. The method according to claim 4, characterized in that The backscattering intensity of each crop component includes the volume scattering of each crop component and the secondary scattering of each crop component and the ground surface.

6. The method according to claim 4, characterized in that The backscattering intensity of each component of the crop also includes: the backscattering intensity of the ear, the backscattering intensity of the stalk, and the backscattering intensity of the leaf.

7. The method according to claim 1, characterized in that Based on the farmland backscatter coefficient image, the process of optimizing the farmland backscatter coefficient using the Bayesian algorithm to obtain the farmland soil moisture estimation result includes: The Bayesian algorithm is used to optimize the farmland backscattering coefficient to obtain the optimal soil volume moisture content; The farmland area of ​​the farmland backscatter coefficient image is traversed to obtain the farmland soil moisture estimation result.

8. A soil moisture measurement system under crop cover, characterized in that: The measuring system comprises: The acquisition module is used to obtain microwave remote sensing images and field measurement data of the farmland to be monitored; A preprocessing module, used for preprocessing the microwave remote sensing image to obtain a farmland backscattering coefficient image; A construction module is used to construct a crop microwave scattering model based on the field measured data to obtain the farmland backscattering coefficient; The optimization module is used to optimize the farmland backscatter coefficient based on the farmland backscatter coefficient image using a Bayesian algorithm to obtain the farmland soil moisture estimation result.

9. An electronic device, characterized in that: The electronic device comprises: a memory having a computer program stored thereon; A processor is communicatively connected to the memory and is used to execute the computer program to implement the soil moisture measurement method under crop cover according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the soil moisture measurement method under crop cover according to any one of claims 1 to 7 is implemented.

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