CN108733952B - Three-dimensional characterization method for spatial variability of soil water content based on sequential simulation - Google Patents
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Abstract
The invention discloses a three-dimensional characterization method for spatial variability of soil water content based on sequential simulation, which comprises the steps of selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting random sampling points in each grid; measuring soil water content data of all random sampling points, and performing normality test on the soil water content data of the random sampling points; carrying out multi-layer process treatment on each grid to obtain multi-layer fine grids; simulating the multilayer fine grid by adopting a sequential simulation method, wherein the simulation comprises random walking, local search and condition estimation, and obtaining three-dimensional surface data of the soil water content; and alternately expressing the three-dimensional surface data of the soil water content by using the GRID and TIN data formats to form a three-dimensional model of the soil water content. The invention obtains the continuous surface of the soil water content based on the sequential simulation method, and three-dimensional expression of the soil water content is carried out by using GRID or TIN format on the basis of the continuous surface, thereby overcoming the smoothing effect of the Kriging method.
Description
Technical Field
The invention relates to a three-dimensional characterization method for spatial variability of soil water content based on sequential simulation.
Background
Soil water is the foundation for survival of plants in an ecological system and is the most active part in water circulation of a drainage basin, and the reasonable distribution and the efficient utilization of plant growth, ecological environment and water resources are influenced. The soil moisture variable deeply influences the physical properties of soil and the transmission and migration of soil nutrients, and plays an important role in the growth of crops, water-saving irrigation and the like. Therefore, special attention should be paid to soil moisture conservation in the crop growth process to improve the soil environment for crop growth, and according to the soil moisture content and the crop water demand of different growth periods of crops, precise irrigation is implemented, water resources are saved, and the utilization efficiency of water resources is improved. During actual research, it is often not possible to measure the soil moisture content of each sampling point within the area of interest one by one. Generally, a plurality of discrete sample points are selected for measurement, the value of the water content of soil at an unsampled point is obtained through a mathematical model, a seamless point-to-surface expansion surface is obtained, and the sampling points can be selected randomly, selected in layers or selected regularly.
The method for expanding the discrete points to the surface is widely researched at home and abroad. The grid surface is generated by the point data spatial interpolation expansion, and the common methods include global polynomial interpolation, inverse distance weight, radial basis function, and modified schild method, Kriging, natural neighborhood method, spline function method, and the like. Each method has certain premise hypothesis when prediction estimation is carried out according to regional variables to be modeled and the conditions of sampling points, but no matter which method is adopted, the more the number of the sampling points is, the more uniform the distribution of the sampling points is, and the better the effect of point-to-surface interpolation is. In recent years, the combination of the geography statistics and the classical statistics with Kriging as the core gradually becomes a feasible and large-scale application method for describing and analyzing regional variables, the method is widely applied in various fields, the related contents are wide, the method is not only used for carrying out sampling point soil water content, soil basic physicochemical properties, soil temperature, biological properties and heavy metal pollution, but also used for carrying out crop productivity simulation and prediction, crop evapotranspiration, crop growth and development stages and crop growth property simulation, and the method is widely applied in the economic and social fields of farmland water conservancy, agricultural output value spatialization and the like. The regional variable point-to-surface extension research covers different scales, such as farm scale, farmland scale, small scale, county scale, and global scale. The assumption of the Kriging method is that the distance and direction between sample points can reflect certain spatial correlation and use them to account for spatial variation, and Kriging estimates the value of each point by fitting a certain mathematical function to a particular point or all points within a given search radius. The method realizes the expansion and expression of point data to surface data, but the method is easy to generate a smoothing effect, smoothes the maximum value appearing in the estimation process, and reduces the estimation precision of the non-sampling point.
In conventional applications, the emphasis of uncertainty model building estimation is to determine the mean and variance, which is obviously an incomplete description of the uncertainty of the random variables. A more complete description of the statistical uncertainty at a point requires an estimate of the probability distribution of the modeled variable. In practice, estimating the probability distribution relies on a sample set or other known information (e.g., empirical collective information, knowledge and historical data such as soil type during agricultural cultivation, field grading, management measures, and farmers' comprehensive empirical measures for improving yield) near the estimation points. The point-to-surface representation of how to quantify aggregate information for a sample is itself a very complex scientific problem. The Kriging-based geostatistical method is combined with a spatial analysis technology to perform spatial interpolation calculation, and although the spatial expansion of the regionalized variable is realized, the estimation of the spatial plane has a smoothing effect. Around the precision of the regional variable space estimation, researchers have made various attempts, some researchers use multi-point statistics to perform the spatial estimation and prediction of the regional variable, and the multi-point statistics simulation replaces the variation function by training images of multiple points, so that the spatial distribution structure of the research target is more effectively reflected. Other researchers put forward an equine chain random domain theory and a transition probability function (Transiogram) theory, construct a theoretical framework of statistical equichain on the basis of the theory, and put forward a linear interpolation method and a mathematical model simulation method of a joint simulation test transition probability function graph.
A typical example of the Kriging interpolation is that a group of sampling points is used to generate a continuous surface of a regional variable, each sampling point has various soil moisture content values, the laboratory determination of the soil moisture content can be performed by collecting undisturbed soil in the field, the soil moisture content information can be intelligently collected by an agricultural internet of things system, a measuring instrument can be used for determining the soil moisture content by using a Time Domain reflectometer (TDR, hereinafter the same) instrument, and the moisture content values of other points in the region can be obtained by the Kriging interpolation. Geostatistics is an important mathematical analysis tool for a continuous distribution mode of regional variable space variation such as soil water content, and the research on continuous surface expansion of the regional variable space of geostatistical has made a great progress by taking the Kriging method and the co-Kriging of the variety thereof and indicating Kriging as the core. However, due to the fact that the geography statistics with the core of the Kriging method inevitably has a smoothing effect, the estimation and prediction of regional variables such as soil water content and the like deviate from the reality to different degrees, and the smoothing effect of the regional variable estimation result under the random sampling condition cannot be solved by the Kriging method. Deep analysis shows that smoothing effect still inevitably exists in geostatistical taking Kriging method interpolation as a core, and estimated values cannot reflect the real change characteristics of regional variables in space. Many researchers try to correct the smoothing effect, and some researchers wish to introduce random parameters or multi-model fusion to perform regional variable space-time variation research, such as introducing random parameters into a deterministic model to perform research on influence of spatial variation of saturation hydraulic conductivity of surface soil on field scale on farmland moisture leakage. The main component analysis and the general Kriging are combined to carry out the spatial variation research of the soil water content, and the research result shows that the combination of the randomness parameters and the multiple models is improved on the quantitative expression of the regional variable spatial variation mode, but the introduction of the randomness parameters needs certain precondition to be significant, thereby undoubtedly increasing the research challenge.
In summary, in the prior art, an effective solution is still lacking for the problems that the smoothing effect generated by the Kriging and its derivation method on the point-to-surface estimation cannot reproduce the corrected spatial correlation relationship, and the like.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a soil water content space variability three-dimensional characterization method based on sequential simulation, continuous surfaces of the soil water content are obtained based on the sequential simulation method, the GRID or TIN format is used for three-dimensional expression of the soil water content on the basis of the continuous surfaces, and the smoothing effect is overcome.
The technical scheme adopted by the invention is as follows:
the invention provides a three-dimensional characterization method for spatial variability of soil water content based on sequential simulation, which comprises the following steps:
step 1: selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting a random sampling point in each grid;
step 2: measuring soil water content data of all random sampling points, and performing normality test on the soil water content data of the random sampling points;
and step 3: carrying out multi-layer process treatment on the grid of each random sampling point to obtain a plurality of layers of fine grids;
and 4, step 4: processing the multilayer fine grids by adopting a sequential simulation method, wherein the processing comprises random walking, local searching and condition estimation, and obtaining three-dimensional surface data of the soil water content;
and 5: and alternately expressing the three-dimensional surface data of the soil water content by using the GRID and TIN data formats to form a three-dimensional model of the soil water content.
Further, in step 2, the method for measuring the soil moisture content data of all the random sampling points comprises the following steps:
obtaining soil samples on the surface layer of soil on all random sampling points, and measuring soil water content data of the soil samples; or,
measuring soil water content data of all random sampling points through a curing instrument; or,
and measuring the soil water content data of all random sampling points through an agricultural Internet of things system.
Further, in step 2, the step of performing normality test on the soil water content data of the random sampling point includes:
and (3) performing a normality test on the soil water content data of the random sampling points, if the soil water content data of the random sampling points is normally distributed data, executing the step 3, and if the soil water content data of the random sampling points is not normally distributed data, performing data conversion, including logarithm, sine or cosine.
Further, the step of performing a multi-layer process on the grid of each random sampling point to obtain a multi-layer fine grid includes:
sampling the grid of each random sampling point by adopting a diagonal sampling method, a quincunx sampling method, a checkerboard sampling method or an S-shaped sampling method to obtain a plurality of sampling points;
and rasterizing the sampling points according to the set size of the grid unit to obtain a multi-layer fine grid.
Further, the step 3 further includes screening a variation function model, where the variation function model includes an exponential model, a gaussian model, or a spherical model.
Further, the processing of the multilayer fine grid by the sequential simulation method comprises random walking, local search and condition estimation, and the step of obtaining the continuous surface of the soil water content comprises the following steps:
selecting a random position X from the fine grid, and determining all nearest neighbor positions located in a set search radius range of the position X;
based on the variation function model, acquiring a predicted value of X and an estimated standard deviation positioned at X as linear weighted combination of N selection points;
screening a random variable by utilizing the cumulative normal distribution of the average value M and the standard deviation SD of the random position X predicted value of the soil water content on the N point, and taking the random variable as the estimation of X;
then, another random position X is selected from the fine grid, random variables of random points of the fine grid are obtained according to the method, and the steps are sequentially circulated until random variables of all random points of the multi-layer fine grid are obtained;
and obtaining three-dimensional surface data of the soil water content according to the random variable data of all the random points.
Further, the GRID data format is a data format which represents three-dimensional distribution of soil water content in a regular array, and each data in the data format represents an attribute characteristic of the soil water content.
Further, the TIN data format is a high-low variation value of soil moisture content.
A second object of the present invention is to provide a computer device for three-dimensional characterization of spatial variability of soil moisture content, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the following steps, including:
selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting a random sampling point for each grid;
measuring soil water content data of all random sampling points, and performing normality test on the soil water content data of the random sampling points;
carrying out multi-layer process treatment on the grid of each random sampling point to obtain a plurality of layers of fine grids;
processing the multilayer fine grids by adopting a sequential simulation method, wherein the processing comprises random walking, local searching and condition estimation, and obtaining three-dimensional surface data of the soil water content;
and alternately expressing the three-dimensional surface data of the soil water content by using the GRID and TIN data formats to form a three-dimensional model of the soil water content.
A third object of the present invention is to provide a computer-readable storage medium having stored thereon a computer program for three-dimensional characterization of spatial variability of soil moisture content, characterized in that the program, when executed by a processor, implements the steps of:
selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting a random sampling point for each grid;
measuring soil water content data of all random sampling points, and performing normality test on the soil water content data of the random sampling points;
carrying out multi-layer process treatment on the grid of each random sampling point to obtain a plurality of layers of fine grids;
processing the multilayer fine grids by adopting a sequential simulation method, wherein the processing comprises random walking, local searching and condition estimation, and obtaining three-dimensional surface data of the soil water content;
and alternately expressing the three-dimensional surface data of the soil water content by using the GRID and TIN data formats to form a three-dimensional model of the soil water content.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention adopts a sequential simulation method to obtain the continuous surface of the soil water content, 3D expression of the soil water content is carried out by applying GRID or TIN format on the basis of the continuous surface, the continuous surface of the soil water content can be correctly and effectively expressed, and the continuous surface is stored in different data formats, 3D surfaces under different data can be continuously generated, a 3D series chart of dynamic change of the soil water content is formed, the series 3D chart reflects and reproduces the difference of the soil water content in the farmland, a basic basis is provided for accurate irrigation and irrigation according to needs, variable irrigation can be carried out pertinently, water can be saved, and economic benefit can be improved;
(2) the method can effectively solve the problem of anisotropy of the water content of the soil by adopting a sequential simulation method, overcomes the smoothing effect of the Kriging method, expresses the specific spatial pattern quantized by a variation function or a histogram through series random simulation reality, improves the estimation lightness, and avoids estimation distortion caused by the smoothing effect.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of a three-dimensional characterization method for spatial variability of soil water content based on sequential simulation;
FIG. 2 is a schematic view of a soil moisture content spherical model;
FIG. 3 is a three-dimensional model of soil moisture content.
Detailed Description
The invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As introduced by the background art, the method of Kriging and the like in the prior art has low estimation precision, has a smoothing effect and is easy to cause estimation distortion, and in order to solve the technical problem, the application provides a three-dimensional characterization method of spatial variability of soil water content based on sequential simulation.
As shown in fig. 1, an embodiment of the present invention provides a three-dimensional characterization method for spatial variability of soil water content based on sequential simulation, including the following steps:
step 1: selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting a random sampling point for each grid.
The number of grids is determined according to different research purposes.
Step 2: and measuring the soil water content of all random sampling points, and performing normality analysis and inspection on the soil water content data of the random sampling points.
The method comprises the steps of obtaining soil water content through obtaining a soil sample on a random sampling point through experiments, or measuring the soil water content of the random sampling point through solidification instruments such as TDR (time dependent reflection) instruments, or completing information acquisition of the soil water content of the random sampling point through an agricultural Internet of things system.
And then, performing a normality test on the soil water content data of the random sampling points, if the soil water content data of the random sampling points is normally distributed data, executing the step 3, if the soil water content data of the random sampling points is not normally distributed data, performing data conversion, wherein the model for performing the data conversion comprises logarithm taking, sine taking, cosine taking and the like. At the beginning of the calculation, the point data normality test of the soil water content is completed.
And step 3: and carrying out multi-layer process treatment on the grids involved by the random sampling points.
In order to enhance the improvement of the long-range effect and the short-range variability, the process of multiple layers is carried out on the grid related to the random sampling point, and a finer grid is gradually generated from a relatively coarse grid to finally form a fine grid.
The multi-layer processing process is carried out on the grid of each random sampling point, and relates to two aspects, one is a sampling mode, the sampling mode has multiple kinds, and the sampling methods under different conditions are different and respectively are as follows: 1. diagonal sampling method: the method is suitable for irrigating plots by sewage, and sampling is carried out at each equally divided central point of a diagonal line; 2. quincunx sampling method: the method is suitable for plots with small area, flat terrain and uniform soil; 3. a checkerboard sampling method: the method is suitable for plots with medium areas, flat terrain, basically complete terrain and less uniform soil; 4. sigmoid or serpentine sampling: the method is suitable for the plots with small areas, uneven terrains and uneven soil, and more sampling points are required to be taken; 5. the invention relates to a grid sampling method, wherein black points in a three-dimensional graph of the invention are grid sampling; the other aspect refers to the rasterization processing of the sampling points, and the rasterization processing saves time because the data structure has a typical matrix structure; the grid Cell Size (Cell Size), also called the analysis resolution, can be set artificially for research purposes, e.g. when performing fusion analysis with satellite images, it is generally consistent with the resolution of satellite images, e.g. the resolution of 25 m × 25 m of remote sensing TM images; the spatial analysis of the raster data is performed on a per raster unit basis, with too large a raster unit resulting in less accuracy of the analysis results, and too small a unit resulting in a large amount of data and a reduced computation speed. It is necessary to select an appropriate cell size.
The invention relates to a multi-layer processing process of grids of random sampling points, namely a process of setting proper grids and subdividing the grids according to different research targets. The finer the multilayering of the grid, the more computationally expensive. Therefore, the realization process of the grid multi-layer process processing of the random sampling points is not complicated, and the key is to set the size of the grid unit according to the research purpose, then select the grid number and execute the computer automatic processing.
And screening a variation function model, wherein the screening variation model is mainly subjected to computer automatic processing and operation according to curve fitting. The variation function model mainly comprises an exponential model, a Gaussian model or a spherical model and the like. The soil moisture content spherical model is shown in figure 2.
And 4, step 4: and realizing sequential simulation of the soil water content of random sampling points based on random walk, local search and condition estimation to form a seamless surface.
Sequential simulation integrates various information by randomly modeling soil water content attributes, fuses the relevance and uncertainty of the information into a model, and emphasizes the overall probability characteristics of the probability model as a function and a result. In the sequential simulation process, the simulation value conditions of the actual measurement points need to be converted into actual measurement values, so that the simulation values of the actual measurement points are equal to the actual measurement values, and the method is suitable for quantitatively depicting the heterogeneity and uncertainty of the water content of the soil. With the increase of the simulation times, the description of the distribution of the soil parameter values in the whole simulation area is more detailed through sequential simulation, the ratio of the lump metal value to the base station value is gradually increased, and the variation process gradually approaches to the measured data.
The concrete implementation mode for realizing the sequential simulation of the soil water content of the random sampling points to form the seamless surface based on the random walk, the local search and the condition estimation comprises the following steps:
aiming at a random position X in the fine grid, determining all nearest neighbor positions which are positioned in the X position and are within a defined search radius range; the selection of the nearest neighbor position is exactly required by the autocorrelation characteristic of the regional variation of the soil moisture content, and the autocorrelation refers to the correlation characteristic presented by the soil moisture content along with the change of the distance.
And 3, providing a predicted value at X and an estimated standard deviation at X by using the variation function model obtained in the step 3, taking the predicted value and the estimated standard deviation as linear weighted combination of N selected points, and screening a random value of a random variable by using the cumulative normal distribution of the average value M and the standard deviation SD to be used as the estimation of X. The linear weighted combination as N selection points can be considered as an index, and the average value M is the average of the predicted values of the random position X of the soil moisture content over the N points.
And then randomly walking to another grid position which is not visited, and continuously determining random variables according to the definition of the random position of the fine grid and the variation graph model until all nodes of the fine grid are visited. The process is repeated for the multi-layer fine grid, each result and input data points are used as source data point values to conduct random walk simulation, the risk of false images is reduced when the grid is searched by a random walk method in the repeated process, and the stability of model operation is achieved. For regional variables such as soil water content, stability is one of targets in the process of participating in simulation and modeling of grid space data, and the model of stability promotes gradual popularization of model application. In the random walk search process, there are two types of stationarity: one is that the mean is stationary, which assumes that the mean is invariant and independent of position; the other is second order stationarity related to covariance function and intrinsic stationarity related to half-variance function. The second order stationary assumes that the covariance of any two points with the same distance and direction is the same, the covariance being related only to the values of these two points and not to their positions.
Stochastic simulation can generate numerous implementations, each of which exhibits the same pattern but a different representation. In the univariate distribution model, the uncertainty is counted by the series of results of the random variables. Similarly, the output of a series of stochastic simulations characterizes the uncertainty. In the stochastic process, there is a specific correlation rule with which predictions can be made and uncertainty of the prediction result can be estimated, the pattern of uncertainty being represented in the form of a random probability by means of sequential modeling. For stationary implications, the assumption of stationary implications of random sample point data shows that variances of any two points in the same distance and direction are the same, and the stationary assumption is necessary for stability of a variation function model of random sample points and determination of the type of the variation function, or the stationary assumption is a precondition for establishing the variation model.
The process of obtaining three-dimensional stereo surface data from point data comprises linear weighting, design and search of adjacent position radius range, and verification of statistical indexes such as average value and standard deviation, and the executed result precision avoids estimation distortion caused by Kriging 'smoothing' effect and improves estimation precision.
Gaussian sequence sequential simulation (Gaussian sequential simulation) is used as a basic simulation method, the smooth effect of the Kriging method is overcome, the simulation needs to define a joint probability model of all grid point attribute values, and joint distribution is defined as
F(z1,z2,z3,…,zN)＝Pr(Z(u1)≤z1,…,Z(uN)≤zN)
u1,u2,u3,…,uNZ(u1)u1
Wherein z is1,z2,z3,…,zNIs a point u1,u2,u3,…,uNThe measured value of (c); pr is the probability; z (u)1) Is u1Generating a sample point from the distribution takes into account the spatial correlation between all points.
The main idea of the sequential Gaussian simulation condition method is to sequentially calculate the condition cumulative distribution function of each grid node along a random path and obtain a simulation value from the condition cumulative distribution function. The method is characterized in that the randomness and the structurality of the regional variable space distribution surrounding the soil water content are reproduced, the fluctuation and the discreteness of the variable space information are reproduced, and each time a simulation value is obtained, the simulation value, the original data and the previously obtained simulation data are used as condition data to enter the simulation of the next point, so that the condition data set can be continuously expanded along with the progress of the simulation. The spatial distribution trend of the sequential simulation results is consistent with the original data. Further analysis confirmed that the simulated and measured values were equal at the measured points.
The invention adopts a sequential simulation method to effectively treat the anisotropy problem of the soil water content, and expresses the specific spatial pattern quantified by a variation function or a histogram through series random simulation reality. While the Kriging method has the disadvantage of estimating the attribute values of the unsampled points independently without considering the correlation between the points and the unknown points which have been estimated previously, obviously, the Kriging method cannot reproduce the corrected spatial correlation, which is also the reason of the smoothness of the result.
And 5: and visualizing the 3D surface of the soil by using a GRID or TIN data format.
And alternately expressing the three-dimensional surface data of the soil water content by using the GRID and TIN data formats to form a three-dimensional model of the soil water content. As shown in fig. 3, a three-dimensional model diagram of soil water content is shown, wherein a sampling point of a GRID pattern at a black point, the soil water content represented by GRID is arranged at the lower part, and the soil water content represented by TIN format is arranged at the upper part, so that the three-dimensional change of the soil water content can be represented more vividly and clearly.
Tin (triangulated irregular network) is a triangular method that uses the data format to express the 3D morphology of soil water content primarily because the data model has data on irregularly distributed points in storage space and accessibility to other parameters such as grade and fine irrigation terrain parameters, and in addition the data format allows rapid determination of discontinuities in the 3D surface such as extremely steep soil water content, extremely high water content or extremely low sampling points. However, the data format also has some problems: one is that there are many different triangles that may be generated from the same set of points, and there are many different triangle algorithms that may generate a large number of inconsistent, "shard" triangles, each of which consumes a significant amount of computing time than the decomposition of a regular space set of points. And secondly, the data layers of two irregular grids are difficult to be superposed, and not to mention the deep information generated after the information of the soil nutrient content and the soil water content is superposed. The GIRD data format avoids the generation of fragment triangles, and has the advantages of performing superposition and interaction with other GRID-format layers, performing deep analysis on spatial data and mining valuable variable information, such as soil water content GRID format information, regional crop yield GRID format information and soil nutrient content GRID format information … …. The GRID format and TIN format may therefore take advantage of each format for storage of soil moisture content data for different development purposes.
Kriging is a local weighted average interpolation method, which estimates the soil moisture content value in the sampling point range according to the soil moisture content data of the sampling point, although the method has smoothing effect, the method still has strong reference significance for the prediction and estimation expression of the soil moisture content, and the array or hierarchy of the data format is less time consuming than the TIN calculation for the same soil moisture content data. In addition, the GRID data model GRID is also applied to a base fertilizer fertilization prescription chart and an accurate irrigation chart in the whole growth period of crops, the fertilization amount is judged by using whether the GRID fertilization amount is less than or greater than 0, and the irrigation time and the irrigation water amount can be effectively adjusted by using map algebra operation in GRID expression.
The method is characterized in that a continuous surface of the soil water content is obtained based on a sequential simulation method, and 3D expression of the soil water content is carried out by using a GRID or TIN format on the basis of the continuous surface, wherein the 3D expression is mainly obtained through the following steps:
it is first clear that raster data (GRID) and irregular Triangulation (TIN) data can be translated into each other, depending on the purpose of the study.
GRID raster data presents a simple and intuitive spatial structure, also called a GRID structure (raster or GRID cell) or a pixel structure (pixel), and refers to the division of the earth's surface into an array of uniformly sized and closely adjacent GRIDs, each GRID being a pixel or pixel, defined by a row and column number, and containing a code representing the type or magnitude of the attribute of the pixel, or simply a pointer to its record of the soil moisture content attribute. Thus, a grid data structure is an organization of data representing the distribution of spatial features or phenomena in a regular array, with each datum in the organization representing a non-geometric attribute characteristic of a feature or phenomenon.
The formation of the TIN data format is created from a variety of vector data sources. The vector data of points, lines and polygon elements are used as a data source for creating TINs. For the purposes of the present invention, it is understood that the Z value is the value of the change in soil moisture content.
The GRID and TIN formats are easy to process in the process of geographic information processing, and the GRID and TIN formats are mainly obtained by setting a proper GRID size according to a digitalized map format, obtaining the required GRID number, and adding some simple code lines or packaging codes with the two processing formats to form a plug-in.
The three-dimensional characterization method for spatial variability of soil water content based on sequential simulation provided by the embodiment of the invention aims at the soil water content basis of sampling points, no matter whether the internet of things system intelligently acquires soil water content data, sample point soil water content data monitored by TDR, or soil water content data obtained by field soil laboratory tests, the sequential simulation technology can correctly and effectively express the continuous surface of soil water content, realize estimation and prediction with higher point-to-surface precision, and store the continuous surface in different data formats such as TIN (triangulated irregular identification) or GRID (generalized identification). According to the invention, the 3D series diagram of the dynamic change of the soil water content can be reflected and reappeared according to the difference of the soil water content in the area range, so that a basic basis is provided for accurate irrigation and irrigation according to needs, variable irrigation can be performed in a targeted manner, water can be saved, and economic benefits can be improved.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (8)
1. A three-dimensional characterization method for spatial variability of soil water content based on sequential simulation is characterized by comprising the following steps:
step 1: selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting a random sampling point in each grid;
step 2: measuring soil water content data of all random sampling points, and performing normality test on the soil water content data of the random sampling points;
and 3, step 3: carrying out multi-layer process treatment on the grid of each random sampling point to obtain a plurality of layers of fine grids;
and 4, step 4: processing the multilayer fine grids by a sequential simulation method, including random walking, local search and condition estimation, to obtain three-dimensional surface data of the soil water content;
and 5: alternately expressing three-dimensional surface data of the soil water content by using GRID and TIN data formats to form a three-dimensional model of the soil water content;
the step of carrying out multi-layer process processing on the grid of each random sampling point to obtain a multi-layer fine grid comprises the following steps:
sampling the grid of each random sampling point by adopting a diagonal sampling method, a quincunx sampling method, a checkerboard sampling method or an S-shaped sampling method to obtain a plurality of sampling points;
performing rasterization processing on the sampling points according to the size of the set grid unit to obtain a multi-layer fine grid;
the method comprises the following steps of processing the multilayer fine grid by adopting a sequential simulation method, wherein the processing comprises random walking, local searching and condition estimation, and the step of obtaining the continuous surface of the soil water content comprises the following steps:
selecting a random position X from the fine grid, and determining all nearest neighbor positions located in a set search radius range of the position X;
based on the variation function model, acquiring a predicted value of X and an estimated standard deviation positioned at X as linear weighted combination of N selection points;
screening a random variable by utilizing the accumulated normal distribution of the average value M and the standard deviation SD of the predicted value X of the random position of the soil water content on the point N, and taking the random variable as the estimation of X;
then, another random position X is selected from the fine grid, random variables of random points of the fine grid are obtained according to the method, and the steps are sequentially circulated until random variables of all random points of the multi-layer fine grid are obtained;
and obtaining three-dimensional surface data of the soil water content according to the random variable data of all the random points.
2. The three-dimensional characterization method of spatial variability of soil moisture content based on sequential simulation as claimed in claim 1, wherein in the step 2, the method for measuring the soil moisture content data of all random sampling points comprises:
acquiring soil samples on the surface layer of soil on all random sampling points, and measuring soil water content data of the soil samples; or,
measuring soil water content data of all random sampling points through a curing instrument; or,
and measuring the soil water content data of all random sampling points through an agricultural Internet of things system.
3. The method for three-dimensional characterization of spatial variability of soil water content based on sequential simulation as claimed in claim 1, wherein in step 2, the step of performing normality test on soil water content data of random sampling points comprises:
and (3) performing normality test on the soil water content data of the random sampling points, if the soil water content data of the random sampling points are normally distributed data, executing the step 3, and if the soil water content data of the random sampling points are not normally distributed data, performing data conversion, including logarithm, sine or cosine extraction.
4. The method for three-dimensional characterization of spatial variability of soil water content based on sequential simulation according to claim 1, wherein the step 3 further comprises screening a variation function model, the variation function model comprising an exponential model, a gaussian model or a spherical model.
5. The method for three-dimensional characterization of spatial variability of soil moisture content based on sequential simulation of claim 1, wherein the GRID data format is a data format representing a three-dimensional distribution of soil moisture content in a regular array, each data in the data format representing an attribute characteristic of soil moisture content.
6. The soil moisture content spatial variability three-dimensional characterization method based on sequential simulation according to claim 1, wherein the TIN data format is a variation value of soil moisture content.
7. A computer device for three-dimensional characterization of spatial variability of soil moisture content, comprising a memory, a processor and a computer program stored on the memory and executed on the processor, wherein the processor executes the program to perform the steps comprising:
selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting a random sampling point for each grid;
measuring soil water content data of all random sampling points, and performing normality test on the soil water content data of the random sampling points;
carrying out multi-layer process treatment on the grid of each random sampling point to obtain a plurality of layers of fine grids;
processing the multilayer fine grids by adopting a sequential simulation method, wherein the processing comprises random walking, local searching and condition estimation, and obtaining three-dimensional surface data of the soil water content;
alternately expressing three-dimensional surface data of the soil water content by using GRID and TIN data formats to form a three-dimensional model of the soil water content;
the step of carrying out multi-layer process processing on the grid of each random sampling point to obtain a multi-layer fine grid comprises the following steps:
sampling the grid of each random sampling point by adopting a diagonal sampling method, a quincunx sampling method, a checkerboard sampling method or an S-shaped sampling method to obtain a plurality of sampling points;
performing rasterization processing on the sampling points according to the set size of the grid unit to obtain a multi-layer fine grid;
the method comprises the following steps of processing the multilayer fine grid by adopting a sequential simulation method, wherein the processing comprises random walking, local searching and condition estimation, and the step of obtaining the continuous surface of the soil water content comprises the following steps:
selecting a random position X from the fine grid, and determining all nearest neighbor positions in a set search radius range of the position X;
based on the variation function model, acquiring a predicted value of X and an estimated standard deviation positioned at X as linear weighted combination of N selection points;
screening a random variable by utilizing the cumulative normal distribution of the average value M and the standard deviation SD of the random position X predicted value of the soil water content on the N point, and taking the random variable as the estimation of X;
then, another random position X is selected from the fine grid, random variables of random points of the fine grid are obtained according to the method, and the steps are sequentially circulated until random variables of all random points of the multi-layer fine grid are obtained;
and obtaining three-dimensional surface data of the soil water content according to the random variable data of all the random points.
8. A computer-readable storage medium having stored thereon a computer program for three-dimensional characterization of spatial variability of soil moisture content, the program when executed by a processor implementing the steps of:
selecting an exemplary soil block, dividing the soil block into a plurality of grids, and setting a random sampling point for each grid;
measuring soil water content data of all random sampling points, and performing normality test on the soil water content data of the random sampling points;
carrying out multi-layer process treatment on the grid of each random sampling point to obtain multi-layer fine grids;
processing the multilayer fine grids by a sequential simulation method, including random walking, local search and condition estimation, to obtain three-dimensional surface data of the soil water content;
alternately expressing three-dimensional surface data of the soil water content by using GRID and TIN data formats to form a three-dimensional model of the soil water content;
the step of carrying out multi-layer process processing on the grid of each random sampling point to obtain a multi-layer fine grid comprises the following steps:
sampling the grid of each random sampling point by adopting a diagonal sampling method, a quincunx sampling method, a checkerboard sampling method or an S-shaped sampling method to obtain a plurality of sampling points;
performing rasterization processing on the sampling points according to the size of the set grid unit to obtain a multi-layer fine grid;
the method for processing the multilayer fine grids by adopting the sequential simulation method comprises the steps of random walking, local search and condition estimation, and the step of obtaining the continuous surface of the soil water content comprises the following steps:
selecting a random position X from the fine grid, and determining all nearest neighbor positions located in a set search radius range of the position X;
based on the variation function model, acquiring a predicted value of X and an estimated standard deviation positioned at X as linear weighted combination of N selection points;
screening a random variable by utilizing the accumulated normal distribution of the average value M and the standard deviation SD of the predicted value X of the random position of the soil water content on the point N, and taking the random variable as the estimation of X;
then, another random position X is selected from the fine grid, random variables of random points of the fine grid are obtained according to the method, and the steps are sequentially circulated until random variables of all random points of the multi-layer fine grid are obtained;
and obtaining three-dimensional surface data of the soil water content according to the random variable data of all the random points.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102306229A (en) * | 2011-05-30 | 2012-01-04 | 中国水利水电科学研究院 | Method for representing spatial variability of soil moisture content on the basis of multi-fractal parameter |
| CN102542051A (en) * | 2011-12-29 | 2012-07-04 | 武汉大学 | Design method for multi-target cooperative sampling scheme of randomly-distributed geographic elements |
| CN105243435A (en) * | 2015-09-15 | 2016-01-13 | 中国科学院南京土壤研究所 | Deep learning cellular automaton model-based soil moisture content prediction method |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102306229A (en) * | 2011-05-30 | 2012-01-04 | 中国水利水电科学研究院 | Method for representing spatial variability of soil moisture content on the basis of multi-fractal parameter |
| CN102542051A (en) * | 2011-12-29 | 2012-07-04 | 武汉大学 | Design method for multi-target cooperative sampling scheme of randomly-distributed geographic elements |
| CN105243435A (en) * | 2015-09-15 | 2016-01-13 | 中国科学院南京土壤研究所 | Deep learning cellular automaton model-based soil moisture content prediction method |
Non-Patent Citations (4)
| Title |
|---|
| 3D Geostatistical Modeling and Uncertainty Analysis in a Carbonate Reservoir, SW Iran;Mohammad Reza Kamali;《Journal of Geological Research》;20130131(第1期);第1-7页 * |
| Kriging插值和序贯高斯条件模拟的原理比较及在土壤空间变异研究中的案例分析;赵彦锋等;《土壤学报》;20110715(第04期);第186-192页 * |
| Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content;Masoomeh Delbari 等;《CATENA》;20091115;第79卷(第2期);第163-169页 * |
| 沿海滩涂土壤盐分空间分布的三维随机模拟与不确定性评价;姚荣江等;《农业工程学报》;20101130(第11期);第101-107页 * |
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