Salinization dynamic monitoring method based on water-salt migration characteristics and related device

Abstract

The invention discloses a salinization dynamic monitoring method based on water-salt migration characteristics and a related device, and relates to the technical field of agricultural informationization, the method comprises the following steps: processing microwave remote sensing data into characteristic data, and using a soil salt content inversion model to obtain remote sensing inversion soil salt content, and assimilating microwave remote sensing data by using a water-salt migration model in combination with daily evaporation capacity, soil water content and underground water data to obtain soil salt content changing along with time in a target period. The microwave remote sensing data and the water-salt migration model are combined, the defects of the water-salt migration model in the aspect of soil salinization monitoring can be overcome, the limitation that common optical remote sensing data are greatly restricted by weather factors such as cloud and mist can be overcome, the method can be suitable for soil salinization monitoring in a regional range, and the application prospect is wide. The dynamic change of the salt content of the saline soil can be accurately monitored in a large range for a long time.

Description

Technical Field

[0001] The present application relates to the field of agricultural information technology, and in particular to a dynamic monitoring method for salinization based on water-salt migration characteristics and related devices. Background Art

[0002] Soil salinization not only causes ecological problems such as land desertification, but also threatens agricultural production such as grain yield and quality. At present, the study of soil water and salt movement process and migration mechanism is still the core issue of soil salinization research. Rapid and accurate acquisition of soil salinization information is of great significance to protecting the ecological environment and promoting the sustainable development of agricultural production.

[0003] The traditional method of obtaining soil salinization information is the investigation and research of salinized soil environment. This method requires field soil sampling and laboratory testing, which is time-consuming and labor-intensive. Moreover, the obtained salinization results can only be distributed in a point-like manner, which is poorly representative and cannot achieve dynamic monitoring of large areas of land. Summary of the invention

[0004] The purpose of this application is to provide a dynamic salinization monitoring method and related devices based on water-salt migration characteristics, which can realize efficient and accurate monitoring of the dynamic changes of soil salt content over a large range and for a long time.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for dynamic monitoring of salinization based on water-salt migration characteristics, comprising:

[0007] Acquire meteorological data, soil moisture remote sensing image data and microwave remote sensing data in the target period in the study area, wherein the soil moisture remote sensing image data and the microwave remote sensing data are time series data with the same acquisition time;

[0008] Calculate daily evaporation according to the meteorological data;

[0009] Obtaining soil moisture content according to the soil moisture content remote sensing image data;

[0010] Obtaining characteristic data of soil salinity based on the microwave remote sensing data;

[0011] According to the characteristic data, a soil salt content inversion model is used to obtain remote sensing inversion soil salt content, wherein the soil salt content inversion model is a regression model with remote sensing inversion soil salt content as a dependent variable and the characteristic data as an independent variable;

[0012] The water-salt transport model is calibrated based on the daily evaporation, the soil moisture content, the remotely sensed inverted soil salinity and the groundwater data in the target period in the study area, and the calibrated water-salt transport model is used to assimilate the microwave remote sensing data to obtain the soil salinity that varies with time and space in the target period.

[0013] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic monitoring method for salinization based on water-salt migration characteristics as described in the first aspect above.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the dynamic monitoring method for salinization based on water-salt migration characteristics described in the first aspect above.

[0015] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the dynamic monitoring method for salinization based on water-salt migration characteristics described in the first aspect above.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] The present application provides a method and related device for dynamic monitoring of salinization based on water-salt migration characteristics, the method comprising processing microwave remote sensing data into characteristic data, and using a soil salt content inversion model to obtain remote sensing inversion of soil salt content, and combining daily evaporation, soil water content and groundwater data, and using a water-salt migration model to assimilate microwave remote sensing data to obtain soil salt content that changes with time and space during a target period. The present application combines microwave remote sensing data with a water-salt migration model, which can not only make up for the shortcomings of the water-salt migration model in soil salinization monitoring, but also overcome the limitations of ordinary optical remote sensing data that are greatly restricted by weather factors such as clouds and fog, and can be applied to soil salinization monitoring within a regional scope, realizing accurate monitoring of the dynamic changes in salt content of salt soils over a large range and for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1This is an application environment diagram of a dynamic salinization monitoring method based on water-salt migration characteristics in Example 1 of the present application;

[0020] Figure 2 A schematic flow chart of a method for dynamic monitoring of salinization based on water-salt migration characteristics provided in Example 1 of the present application;

[0021] Figure 3 This is a schematic diagram of a process for dynamically monitoring salinity in a specific study area using a dynamic monitoring method for salinization based on water-salt migration characteristics in Example 1 of the present application;

[0022] Figure 4 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0025] Example 1

[0026] The dynamic monitoring method of salinization based on water-salt migration characteristics provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the meteorological data, soil moisture remote sensing image data and microwave remote sensing data in the target period in the study area to the server 104, and the server 104 calculates the daily evaporation according to the meteorological data; obtains the soil moisture according to the soil moisture remote sensing image data; obtains the characteristic data of the soil salt content according to the microwave remote sensing data; obtains the remote sensing inversion soil salt content according to the characteristic data using the soil salt content inversion model; calibrates the water-salt migration model according to the daily evaporation, the soil moisture, the remote sensing inversion soil salt content and the groundwater data in the target period in the study area, and uses the calibrated water-salt migration model to assimilate the microwave remote sensing data to obtain the soil salt content that changes with time and space in the target period. The server 104 can feed back the soil salt content that changes with time during the target period to the terminal 102. In addition, in some embodiments, the dynamic monitoring method for salinization based on water-salt migration characteristics can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly use the dynamic monitoring method for salinization based on water-salt migration characteristics to process the meteorological data, soil moisture remote sensing image data, and microwave remote sensing data in the target period in the study area. The server 104 can also obtain the meteorological data, soil moisture remote sensing image data, and microwave remote sensing data in the target period in the study area from the data storage system, and use the dynamic monitoring method for salinization based on water-salt migration characteristics to process them.

[0027] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0028] In an exemplary embodiment, Figure 2 As shown, a dynamic monitoring method for salinization based on water-salt migration characteristics is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 208. Among them:

[0029] Step 201, obtaining meteorological data, soil moisture remote sensing image data and synthetic aperture radar remote sensing data in a target period in a study area, wherein the soil moisture remote sensing image data and the microwave remote sensing data are time series data with the same acquisition time.

[0030] Step 202, calculating the daily evaporation according to the meteorological data.

[0031] Step 203, obtaining soil moisture content according to the soil moisture remote sensing image data.

[0032] Step 204: obtaining characteristic data of soil salinity based on the microwave remote sensing data.

[0033] Step 205, based on the characteristic data, using a soil salinity inversion model, obtain remote sensing inversion of soil salinity, wherein the soil salinity inversion model is a regression model with remote sensing inversion of soil salinity as a dependent variable and characteristic data as an independent variable.

[0034] Step 206, calibrate the water-salt transport model according to the daily evaporation, the soil moisture content, the remotely sensed inverted soil salinity and the groundwater data in the target period in the study area, and use the calibrated water-salt transport model to assimilate the microwave remote sensing data to obtain the soil salinity that varies with time and space in the target period.

[0035] Optical remote sensing images are easily affected by weather and data acquisition cannot be guaranteed. Microwave remote sensing, due to its advantage of all-day and all-weather data acquisition and its sensitivity to soil complex dielectric constant, can avoid the defect of "optical remote sensing images are easily affected by weather and data acquisition cannot be guaranteed", providing a new opportunity for remote sensing monitoring of soil salinization. Therefore, this embodiment uses the assimilation method to combine microwave remote sensing (microwave remote sensing technology can effectively realize dynamic monitoring over a large range by virtue of its spatial continuity and temporal dynamics) and the water-salt transport model (adjusting the key parameters in the ecological process model through the assimilation algorithm can better predict soil properties), which can not only make up for the shortcomings of the water-salt transport model in soil salinization monitoring, but also overcome the limitations of ordinary optical remote sensing data that are greatly restricted by weather factors such as clouds and fog, and can be applied to soil salinization monitoring within the region.

[0036] The following combination Figure 3 , the specific process of the above-mentioned dynamic monitoring method of salinization based on water-salt migration characteristics in this embodiment is explained with a specific example.

[0037] Da'an City, Jilin Province was selected as the study area. The area is located in the middle of the Songnen Plain with a flat terrain. The geographical coordinates are between 123°08′45″~124°21′56″ east longitude and 44°57′00″~45°45′51″ north latitude. The total area is 4,897 square kilometers, with an altitude between 99 meters and 161 meters. It has a temperate monsoon climate, an average annual temperature of 4.3℃, and an annual rainfall of 600-800 mm.

[0038] 1) Collect meteorological data, soil moisture remote sensing image data and microwave remote sensing data of Da'an City from 2022 to 2023.

[0039] Meteorological data include: rainfall, total radiation, temperature, humidity, wind direction, wind speed, air pressure, light intensity, and water surface evaporation data, which are used for subsequent calculation of daily evaporation.

[0040] After spatial interpolation of meteorological data, the daily evaporation (ETc) was calculated using the Penman-Monteith formula. The calculation formula is:

[0041]

[0042] Where Δ is the slope of the steam pressure curve, R n represents the net radiation surface, G is the soil heat flux density, T is the daily average temperature, is the wind speed at a height of two meters, v s is the saturated vapor pressure, v a is the actual vapor pressure and γ is the humidity constant.

[0043] The spatial interpolation method in this embodiment is Kriging interpolation, and the calculation formula is:

[0044]

[0045] Among them, Z(S0) is the estimated value of point S0, N0 is the number of known data points of meteorological data, and Z(S i ) is point S i The known value at x For points S0 and S i The Euclidean distance function (variogram) value between Z(S i ) is used to calculate the weight of .

[0046] This embodiment adopts the above-mentioned process of processing meteorological data to obtain the daily evaporation of Da'an City.

[0047] 2) In this embodiment, the soil moisture remote sensing image data of Da'an City from 2022 to 2023 is the time series SMAP satellite secondary soil moisture image data. With 48 days as a time series, the soil moisture content θ is obtained by projecting the time series SMAP satellite secondary soil moisture image data.

[0048] Input the time series SMAP satellite secondary soil moisture image data into the Build GLT tool of ENVI software, build a geographic information lookup table (GLT), and then use the Georeference from GLT tool to set the projection mode of the geographic information lookup table (GLT) to the Mercator projection (UTM), which is the projection coordinate of the time series SMAP satellite secondary soil moisture data.

[0049] The soil moisture content θ corresponding to each projection coordinate is obtained based on the projection coordinate.

[0050] The above-mentioned time series SMAP satellite secondary soil moisture image data is stored in .HDF5 format with a spatial resolution of 9km.

[0051] The surface soil moisture data is obtained by performing data correction, quality control, and projection definition on the obtained SMAP satellite secondary soil moisture product data to obtain the soil moisture content θ.

[0052] 3) In this embodiment, the microwave remote sensing data of Da'an City from 2022 to 2023 is synthetic aperture radar remote sensing data, wherein the synthetic aperture radar remote sensing data is time series Sentinel-1SLC image data, and the backscattering coefficient is obtained after preprocessing such as orbit correction, radiation calibration, coherent speckle removal, multi-look processing, and terrain correction.

[0053] Orbit correction: Input the precise orbit file downloaded from the official website of Sentinel-1 into the Radar-Apply OrbitFile tool of the SANP software to perform orbit correction on the Sentinel-1SLC image data. The corrected data should meet the requirement that the deviation between the actual geographic location and the corresponding pixel position in the image is within 10 meters.

[0054] Radiation calibration: Radiation calibration is to convert the received backscatter signal (gray value DN) into the backscatter coefficient σ 0 :

[0055]

[0056] Where A is the calibration parameter, which is obtained from the Sentinel-1SLC image metadata file.

[0057] Speckle removal: Use Lee filtering to reduce the noise of Sentinel-1SLC images.

[0058] Multi-view processing: To enhance the image radiation resolution, i.e. the scattering intensity information, ensure that radar data from different viewing angles have the same azimuth and range parameters for data registration and fusion.

[0059] Terrain correction: Terrain correction is performed using the SRTM30 DEM digital elevation model to make the geometric representation of Sentinel-1 images closer to the actual surface conditions.

[0060] The Sentinel-1SLC image data storage format is .SAFE, and the spatial resolution is 10km.

[0061] The synthetic aperture radar remote sensing data of this embodiment is dual-polarization synthetic aperture radar remote sensing data. Therefore, after preprocessing the synthetic aperture radar remote sensing data, the C-band VV and VH dual-polarization backscattering coefficients are obtained.

[0062] According to the VH polarization backscattering coefficient and the VV polarization backscattering coefficient, the sensitive characteristics of soil salt content are determined. 8 backscattering characteristic parameters, i.e. soil salinity sensitive characteristics.

[0063] A feature selection algorithm is used to screen important features from the soil salt content sensitive features, and the important features are used as characteristic data of soil salt content.

[0064] The feature selection algorithm in this embodiment is the Boruta algorithm. The Boruta algorithm is used to select important features that can achieve the highest accuracy in soil salinity inversion. The specific process includes:

[0065] a) The constructed eight backscattering features are used as the original data set;

[0066] b) Randomly perturb all features in the original data set to generate a perturbed data set;

[0067] c) Use the original dataset and the perturbed dataset to train multiple random forest models respectively;

[0068] d) Use the random forest model to calculate the importance score of each feature;

[0069] e) Compare the importance score of each feature in the original training set with the importance score in the perturbation training set. If the importance score of a feature in the original training set is higher than that in the perturbation training set, the feature is considered important.

[0070] The preprocessed SMAP satellite secondary soil moisture image data was resampled using bilinear interpolation, and its spatial resolution was 10 km, which was consistent with the spatial resolution of Sentinel-1SLC image data, facilitating subsequent experiments.

[0071] The bilinear interpolation method described above includes:

[0072] The bilinear interpolation algorithm estimates the pixel value of the desired position using the values ​​of the four pixels that are closest to the desired position. For a new grid point (X+a, Y+b), that is, the desired position point, where X and Y are non-negative integers, and a and b are floating point numbers in the interval [0, 1], the value of this grid point (X+a, Y+b) can be determined by the values ​​of the four grid points (X, Y), (X+1, Y), (X, Y+1), and (X+1, Y+1) in the original grid:

[0073] (X+a,Y+b)=(1-a)(1-b)(X,Y)+(1-a)b(X,Y+1)+a(1-b)(X+1,Y)+ab(X+1,Y+1).

[0074] 4) Based on the sample data of soil salinity measured value SS, a multiple linear regression model was established with remote sensing inversion of soil salinity as the dependent variable and important features as the independent variable. Based on the soil salinity inversion model, the remote sensing inversion of soil salinity M was obtained. ss .

[0075] 5) According to the soil water content migration principle and soil salt content migration principle, determine the parameter set that needs to be calibrated for the water and salt migration model (HYDRUS-1D model), which includes θ r ,θ s ,α,n,l,K s , B, D P , D q ; Based on the soil water content θ of the soil sample points in the study area and the soil salt content M inverted by remote sensing ss , daily evaporation and groundwater data, and calibrated the parameter set in the HYDRUS-1D model using a parameter localization algorithm to obtain the first calibration parameter set and the sample soil salinity corresponding to the first calibration parameter set.

[0076] The parameter localization algorithm in this embodiment adopts the multiple data assimilation ensemble smoother algorithm, namely, the ES-MDA method.

[0077] The HYDRUS-1D model consists of two parts: the moisture module and the solute transport module. The upper and lower boundaries, initial conditions and key parameters of the moisture module and the solute transport module need to be determined before the model can be started.

[0078] The upper and lower boundaries are: in the moisture module, the calculated daily evaporation is used as the upper boundary, and the variable pressure head data collected in the groundwater data is used as the lower boundary; in the solute transport module, the upper and lower boundaries are both Na + Concentration boundary; initial conditions are soil moisture content θ and soil salt content M at the initial moment ss .

[0079] Principle of soil moisture migration:

[0080]

[0081] in, represents the sign of the partial derivative; θ is the soil moisture content, m 3 / m 3 ; t is time; δ is negative water pressure head, obtained by field measurement, m; θ(δ) represents the soil moisture content under the given negative water pressure head δ; z is the vertical depth, m, with downward being the positive direction; K(δ) is the unsaturated soil hydraulic conductivity; represents the rate of change of soil moisture content θ with time t; represents the partial derivative of vertical depth z; It represents the changing trend of the negative water pressure head δ along the vertical depth Z direction.

[0082] The Van-Genuchten model is used to simulate the soil moisture characteristic curve θ(δ) and the unsaturated hydraulic conductivity K(δ):

[0083]

[0084] Among them, θ r Residual moisture refers to the minimum soil moisture content that cannot flow in the soil. 3 / m 3 );θ s It indicates the maximum soil moisture content when the soil is fully saturated, that is, the saturated moisture content (m 3 / m 3 );θ e is the relative saturation of soil; θ(δ) represents the soil water content associated with the negative water pressure head δ; θ(δ)-θ r represents the soil moisture content that can flow freely in the soil under a given negative water pressure head δ; θ s -θ r It refers to the soil moisture content that can flow freely when the soil is fully saturated, excluding the residual moisture content; K s is the saturated hydraulic conductivity, m / h; α, n, l are empirical parameters, where m = 1-1 / n, representing the uniformity of soil pore distribution, α is a parameter related to soil physical properties; l is an empirical fitting parameter; The power function form of soil relative saturation can effectively describe the nonlinear relationship between soil relative saturation and negative water pressure head δ.

[0085] Soil salt transport principle of HYDRUS-1D model:

[0086]

[0087] in, represents the sign of the partial derivative; C is the solute concentration, i.e., Na + Concentration (g·m -3 ), obtained from field measurements; t is time; It represents the rate of change of solute concentration C with time t; x is the horizontal coordinate, rightward is positive, m; z is the vertical coordinate, downward is positive, m; D p is the lateral diffusion coefficient, m 2 / h; D q is the longitudinal diffusion coefficient, m 2 / h; represents the osmotic diffusion of solute concentration C in the horizontal direction x; It represents the osmotic diffusion of solute concentration C in the vertical direction z; B is the water flux (m / h); Represents the adsorption or degradation of solute concentration C in the vertical direction.

[0088] According to the soil moisture migration principle and soil salt migration principle, the calibration parameter set P is determined as: P(j) = [θ r ,θ s ,α,n,l,K s , B, D P , D q ] T , j=1,2,3…9.

[0089] The process of calibrating the parameter set P using the ES-MDA method includes:

[0090] S1. Determine the number of iterations (M) and the corresponding expansion coefficient ω each time i , where ω i satisfy

[0091]

[0092] S2. Define the sampling time S days, and the measured values ​​of soil salt content obtained within the time interval d days as the observation value set Observation, where S is an integer greater than 1, d is an integer greater than or equal to 1, and j = S / d = 9:

[0093] Observation(j)=[SS1,SS2…SS j ]T .

[0094] S3. Use the empirical parameters of the HYDRUS-1D model as initial parameters to form an initial calibration parameter set:

[0095] Initial(j)=[θ r _init,θ s _init, α_init, n_init, l_init, K s _init, B_init, D P _init, D q _init] T .

[0096] S4. Define the prior probability distribution of each parameter in the initial calibration parameter set Initial, such as normal distribution, to obtain the initial sample set Sample:

[0097] Sample(j)=[θ r _prior1,θ s _prior1, α_prior1, n_prior1, l_prior1, K s _prior1, B_prior1, D P _prior1, D q _prior1] T .

[0098] S5. Input soil moisture θ and remote sensing inversion soil salt content M into the HYDRUS-1D model ss , daily evaporation and groundwater data, and all parameters in the set Sample, the set of simulated values ​​of initial soil salinity (i.e., initial simulated soil salinity) of the HYDRUS-1D model for simulation time S and time interval d is obtained:

[0099]

[0100] S6. Use the update function to update the sample set Sample. The expression of the update function is:

[0101]

[0102] Sample i+1,j =Sample i,j +CMD(C DD +R) -1 (duc(j)-Prediction(i,j);

[0103] Where duc is the added expansion coefficient ω iThe observed value after, duc(j) is a column vector, indicating that the first row and the jth column add the expansion coefficient ω i Observaiton(j) is also a column vector, which indicates the measured value of soil salinity corresponding to the first row and the jth column; β is the random error introduced by the correction and obeys the Gaussian distribution; Sample i+1,j Indicates the updated value of the jth column in the i+1th row. If i=1, j=1, then Sample i+1,j Indicates that in the first row of the initial sample set Sample(j), the first column is θ r After mathematical operation based on _prior1, we get the second row and first column, i.e. θ r The updated value θ r _prior2; R is the autocovariance matrix of the error between the observed value Observation and the simulated value Prediction; C MD is the covariance matrix between the observed value Observation and the simulated value Prediction, C DD is the autocovariance matrix of the simulated value Prediction; Prediction(i,j) represents the set of simulated values ​​of soil salinity after S iterations. When i=1, it is the initial set of simulated values ​​of soil salinity Prediction(j).

[0104] S7, repeat steps S5 and S6, complete S iterations, and obtain the soil salinity simulation value set Prediction (including the soil salinity output in each iteration) as follows:

[0105]

[0106] The sample set Sample (including the calibration parameter set for each iteration) is:

[0107]

[0108] S7. Select the calibration parameter set that minimizes the Ψ value (i.e., the error variance of the difference between the soil salinity calculated in each iteration and the measured value of the soil salinity) from the sample set Sample as the first calibration parameter set of the HYDRUS-1D model:

[0109]

[0110] In this embodiment, the moisture module of the HYDRUS-1D model uses the atmospheric boundary in the 2022 meteorological data of Da'an City as the upper boundary and the variable pressure head as the lower boundary.

[0111] In the solute transport module, the upper and lower boundaries are the soil Na +Concentration boundary.

[0112] The initial conditions are the soil moisture content θ and soil salt content M in January 2022. ss .

[0113] The soil salt content H simulated by the model is obtained ss Generate the soil salt content change set of Da'an City in 2022 for the sample set Result.

[0114] 6) Using a particle swarm assimilation algorithm to assimilate the simulated soil salinity output by the calibrated water-salt transport model and the remote sensing inverted soil salinity to obtain the assimilated soil salinity; stopping the assimilation when the cost function value is the smallest, and taking the water-salt transport model calibrated at the current moment as the optimal water-salt transport model, wherein the cost function is a function constructed based on the assimilated soil salinity and the remote sensing inverted soil salinity; obtaining the final soil salinity that changes with time during the target period based on the optimal water-salt transport model.

[0115] This embodiment uses a particle filter algorithm to remotely invert the soil salt content M ss and soil salinity simulated by HYDRUS-1D model ss Assimilate.

[0116] The expression of the particle filter algorithm is:

[0117]

[0118] Where r is a non-zero natural number; t represents the time; Indicates that the rth particle at time t corresponds to H ss State variables; Indicates that the rth particle at time t+1 corresponds to H ss state variable; τ is a nonlinear operator, i.e., HYDRUS-1D model; u t is the driving model parameter; Indicates that the rth particle at time t+1 corresponds to M ss state variable; Λ is the observation operator, i.e. the soil salinity inversion model; ε is the observation noise; A represents the optimal estimated soil salt content at time t+1 ss ; is the weight of each particle after normalized resampling; N represents the number of particles.

[0119] The calculation process is:

[0120]

[0121] set up Obey For a normal distribution with mean and σ as standard deviation, we have:

[0122]

[0123] in, Represents the particle importance weight; is the measured soil salt content SS1 at time t+1; express In case of occurrence The probability of occurrence, that is, the rth particle at time t+1 The corresponding soil salt content H ss Under , the probability of observing the actual soil salinity SS1; express In case of occurrence The probability of occurrence, that is, from time t The corresponding soil salt content H ss Change to time t+1 The corresponding soil salt content H ss probability; represents the importance sampling function; f r is the resampling coefficient; Indicates taking The integer part of .

[0124] During the assimilation process, when the cost function Ω is the smallest, the assimilation is stopped and the θ in HYDRUS-1D is r ,θ s ,α,n,l,K s , B, D P , D q The values ​​are taken as the optimal parameters of the HYDRUS-1D model, thereby determining the optimal HYDRUS-1D model.

[0125] The expression of the cost function is:

[0126]

[0127] Among them, n represents the number of samples of remote sensing inversion of soil salinity SS1; is the mean value of soil salinity retrieved from remote sensing; is the mean soil salt content after assimilation.

[0128] 7) Based on the optimal HYDRUS-1D model, input the remote sensing soil salinity M of the 2023 time series ss , soil moisture content θ, meteorological data and groundwater data, the soil salt content change curve of Da'an City in 2023 is obtained, and dynamic monitoring of soil salinization is realized.

[0129] This embodiment combines dual-polarization synthetic aperture radar remote sensing data with a water-salt migration model to achieve continuous time, continuous space, and large-scale dynamic monitoring of soil salt content.

[0130] The present application also provides an application scenario, which applies the above-mentioned dynamic monitoring method of salinization based on water-salt migration characteristics. Specifically: The dynamic monitoring method of salinization based on water-salt migration characteristics provided in this embodiment can be applied to saline soil management and prevention scenarios in agricultural production. Saline soil management and prevention scenarios in agricultural production include soil quality monitoring, irrigation management, and salinization prevention and control. The dynamic monitoring method of salinization based on water-salt migration characteristics provided in the embodiment belongs to the dynamic monitoring link in salinization prevention and control. The dynamic monitoring method of salinization based on water-salt migration characteristics of this embodiment can obtain information on changes in soil salt content in continuous time and continuous space, provide scientific data support and decision-making basis for agricultural management, and help improve the management efficiency of saline soil and reduce the negative impact of salinization on agricultural production.

[0131] Example 2

[0132] This embodiment provides a computer device, which may be a server or a terminal. Its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store any data involved in the dynamic monitoring method of salinization based on water-salt migration characteristics. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the dynamic monitoring method of salinization based on water-salt migration characteristics in Example 1 is implemented.

[0133] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] Example 3

[0135] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the dynamic monitoring method of salinization based on water-salt migration characteristics in Example 1 is implemented.

[0136] Example 4

[0137] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for dynamic monitoring of salinization based on water-salt migration characteristics in Embodiment 1 is implemented.

[0138] Example 5

[0139] This embodiment provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for dynamic monitoring of salinization based on water-salt migration characteristics in Embodiment 1 is implemented.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0141] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0142] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0143] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A dynamic monitoring method for salinization based on water-salt migration characteristics, characterized in that: The dynamic monitoring method of salinization based on water-salt migration characteristics includes: Acquire meteorological data, soil moisture remote sensing image data and microwave remote sensing data in the target period in the study area, wherein the soil moisture remote sensing image data and the microwave remote sensing data are time series data with the same acquisition time; Calculate daily evaporation according to the meteorological data; Obtaining soil moisture content according to the soil moisture content remote sensing image data; Obtaining characteristic data of soil salinity based on the microwave remote sensing data; According to the characteristic data, a soil salt content inversion model is used to obtain remote sensing inversion soil salt content, wherein the soil salt content inversion model is a regression model with remote sensing inversion soil salt content as a dependent variable and the characteristic data as an independent variable; The water-salt transport model is calibrated based on the daily evaporation, the soil moisture content, the remotely sensed inverted soil salinity and the groundwater data in the target period in the study area, and the calibrated water-salt transport model is used to assimilate the microwave remote sensing data to obtain the soil salinity that varies with time and space in the target period.

2. The method for dynamic monitoring of salinization based on water-salt migration characteristics according to claim 1 is characterized in that: The microwave remote sensing data is dual-polarization synthetic aperture radar remote sensing data; According to the microwave remote sensing data, characteristic data of soil salinity is obtained, including: According to the dual-polarization synthetic aperture radar remote sensing data, a VH polarization backscattering coefficient and a VV polarization backscattering coefficient are obtained respectively; Determining a sensitive characteristic of soil salinity according to the VH polarization backscattering coefficient and the VV polarization backscattering coefficient; Using a feature selection algorithm to select important features from the soil salt content sensitive features; The important features are used as characteristic data of soil salinity.

3. The method for dynamic monitoring of salinization based on water-salt migration characteristics according to claim 1 is characterized in that: The water-salt transport model is calibrated according to the daily evaporation, the soil water content, the remote sensing inversion soil salinity and the groundwater data in the target period in the study area, specifically including: Based on the soil water content migration principle and soil salt content migration principle, determine the parameter set that needs to be calibrated in the water and salt migration model; According to the daily evaporation, the soil moisture content, the remote sensing inverted soil salinity and the groundwater data in the target period in the study area, the parameter set is calibrated using a parameter localization algorithm to obtain a first calibration parameter set and a water-salt migration model corresponding to the first calibration parameter set.

4. The method for dynamic monitoring of salinization based on water-salt migration characteristics according to claim 1 is characterized in that: The microwave remote sensing data is assimilated using a calibrated water-salt transport model to obtain soil salinity that changes over time during the target period, specifically including: The simulated soil salinity output by the calibrated water-salt transport model and the remote sensing inverted soil salinity are assimilated using a particle swarm assimilation algorithm to obtain the assimilated soil salinity; When the cost function value is the minimum, the assimilation is stopped, and the water-salt transport model calibrated at the current moment is used as the optimal water-salt transport model, wherein the cost function is a function constructed according to the soil salinity after assimilation and the soil salinity inverted by remote sensing; The soil salt content that changes with time during the final target period is obtained according to the optimal water-salt transport model.

5. The method for dynamic monitoring of salinization based on water-salt migration characteristics according to claim 3 is characterized in that: The expression of soil moisture migration principle is: in, is the sign of the partial derivative; θ is the soil moisture content; t is the time; δ is the negative water pressure head; θ(δ) is the soil moisture content under the given negative water pressure head δ; z is the vertical depth of the soil; K(δ) is the unsaturated soil hydraulic conductivity; represents the rate of change of soil moisture content θ with time t; represents the partial derivative of vertical depth z; It represents the variation trend of negative water pressure head δ along the vertical depth z direction; The expression of soil salt migration principle is: in, is the sign of the partial derivative; C is the solute concentration; t is the time; represents the rate of change of solute concentration C with time t; x is the horizontal coordinate; z is the vertical coordinate; D p is the lateral diffusion coefficient, D q is the longitudinal diffusion coefficient; represents the osmotic diffusion of solute concentration C in the horizontal direction x; represents the osmotic diffusion of solute concentration C in the vertical direction z; B is the water flux; Represents the adsorption or degradation of solute concentration C in the vertical direction.

6. The method for dynamic monitoring of salinization based on water-salt migration characteristics according to claim 3 is characterized in that: According to the daily evaporation, the soil moisture content, the remote sensing inversion soil salinity and the groundwater data in the target period in the study area, the parameter set is calibrated using a parameter localization algorithm to obtain a first calibration parameter set and a water-salt migration model corresponding to the first calibration parameter set, specifically including: Calibrate the parameter set based on experience to obtain an initial calibration parameter set; According to the initial calibration parameter set, the daily evaporation, the soil moisture content, the remote sensing inversion soil salinity and the groundwater data in the target period in the study area, the initial simulated soil salinity is obtained using the initial water-salt transport model, wherein the initial water-salt transport model is the water-salt transport model corresponding to the initial calibration parameter set; The initial calibration parameter set is updated according to an update function to obtain an updated calibration parameter set, wherein the update function is a function constructed according to the actual value of soil salinity, the expansion coefficient, the initial simulated soil salinity, a first autocovariance matrix, a second autocovariance matrix, a covariance matrix and the initial calibration parameter set, the first autocovariance matrix is ​​the autocovariance matrix of the error between the actual value of soil salinity and the initial simulated soil salinity, the second autocovariance matrix is ​​the autocovariance matrix of the initial simulated soil salinity, and the covariance matrix is ​​the covariance matrix between the actual value of soil salinity and the initial simulated soil salinity; The updated calibration parameter set is used as a new initial calibration parameter set, and the process returns to step "obtaining an initial simulated soil salinity using an initial water-salt transport model according to the initial calibration parameter set, the daily evaporation, the soil moisture content, the remote sensing inversion soil salinity, and the groundwater data in the target period in the study area"; When the number of iterations is reached, the initial calibration parameter set, each initial water-salt transport model and each initial simulated soil salinity of each iteration are obtained; Calculating the error variance between each of the initial simulated soil salinity values ​​and the actual measured value of the soil salinity; The initial calibration parameter set and the initial water-salt migration model corresponding to the minimum error variance are selected as the final first calibration parameter set and the water-salt migration model corresponding to the first calibration parameter set, respectively.

7. The method for dynamic monitoring of salinization based on water-salt migration characteristics according to claim 6 is characterized in that: The expression of the update function is: Sample i+1,j =Sample i,j +C MD (C DD +R) -1 (duc(j)-Prediction(i,j); Where duc(j) is the expansion coefficient ω added to the first row and jth column i Observation(j) is the measured value of soil salt content corresponding to the first row and the jth column; β is the random error; Sample i+1,j is the parameter update value of the i+1th row and jth column; R is the first autocovariance matrix; C MD is the covariance matrix; C DD is the second autocovariance matrix; Prediction(i,j) is the initial simulated soil salinity in the first row and jth column.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic monitoring method for salinization based on water-salt migration characteristics as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the dynamic monitoring method for salinization based on water-salt migration characteristics described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the dynamic monitoring method for salinization based on water-salt migration characteristics described in any one of claims 1 to 7 is implemented.

Images