CN114065643A - Plant soil water content estimation method and system based on SAR and polarization decomposition - Google Patents
Plant soil water content estimation method and system based on SAR and polarization decomposition Download PDFInfo
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Abstract
The invention discloses a method and a system for estimating the water content of plant soil based on SAR and polarization decomposition, which comprises the steps of firstly obtaining SAR image data and preprocessing the SAR image data; extracting a coherent matrix T3; calculating a surface scattering matrix TG and a backscattering coefficient which represent the surface scattering components; calculating a surface backscattering coefficient by utilizing a soil moisture estimation data set through a training set; simulating a surface backscattering coefficient; obtaining the soil water content of each sampling point by using a lookup table and a minimum cost function strategy; and an effective roughness parameter; and finally estimating the soil water content of the plants. According to the method, the water content of the plant soil is estimated through SAR and polarization decomposition, the effective roughness parameter is used for representing the soil roughness of the plant growing area, the solution of the water content of the soil is simplified, the actual measurement roughness parameter is not relied on, meanwhile, the backscattering component of the earth surface is obtained through polarization decomposition, the influence of vegetation scattering contribution is avoided, and the accuracy of estimating the water content of the soil is improved.
Description
Technical Field
The invention relates to the technical field of remote sensing image data processing, in particular to a method and a system for estimating the water content of plant soil based on SAR and polarization decomposition.
Background
Soil moisture is an important component of surface ecological water circulation and controls surface runoff and surface water evaporation. In agricultural production, soil moisture is an important source of moisture absorbed by crops, is an important factor influencing soil fertility, and is also an important condition for crop survival. The volume water content of the soil refers to the ratio of the volume of water in the soil to the total volume of the soil. The traditional soil water content monitoring method is time-consuming and labor-consuming, only information of a limited point position can be obtained, large-area soil water monitoring cannot be achieved, and an effective way is provided for soil water content estimation due to the appearance of a remote sensing technology. The quality of optical remote sensing data is often influenced by weather conditions such as cloud and fog, and in contrast, microwave remote sensing has strong penetrating power and can realize all-weather monitoring of ground objects.
In addition, the surface microwave radiation is very sensitive to the change of soil moisture, so the microwave remote sensing is gradually the main means for monitoring the soil moisture, wherein the Synthetic Aperture Radar (SAR) has higher spatial resolution, can provide multi-angle and multi-polarization data, and has become the main means for actively monitoring the soil moisture by the microwave remote sensing. Wheat is the grain crop with the largest sowing area, the largest output and the widest distribution in the world, so that the accurate and reliable acquisition of the soil moisture information of the wheat planting area has important guiding significance for agricultural management.
Currently, many scholars construct soil water content inversion models based on SAR, and the research mainly focuses on the relationship between the soil water content and SAR backscattering coefficients. Common models are empirical or semi-empirical models, such as the Oh model, the Dubois model; physical models, such as IEM models; machine learning models such as support vector machines, random forests, artificial neural networks, and the like. For most physical models or empirical/semi-empirical models, the model construction usually requires many additional input parameters, such as root mean square height and correlation length, representing roughness parameters, but these roughness parameters are difficult to measure and are subject to human interference, especially in heavily vegetated areas, leading to uncertainty in soil moisture estimation.
Furthermore, in vegetation covered areas, such as wheat growing areas, the presence of wheat will produce backscattering and attenuate backscattering from the surface, so that the observed backscattering coefficient contains multiple scattering components, making estimation of soil moisture under vegetation coverage difficult.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for estimating water content in plant soil based on SAR and polarization decomposition, in which the method utilizes polarization information provided by SAR and adopts polarization decomposition technique to decompose a full polarization signal into scattering components representing different scattering components, thereby eliminating scattering contribution of vegetation and further estimating water content in soil.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides a plant soil water content estimation method based on SAR and polarization decomposition, which comprises the following steps:
acquiring and preprocessing SAR image data, wherein the image data comprises a plant and soil image;
extracting a coherent matrix T3 according to the image data;
calculating a surface scattering matrix TG representing the surface scattering components according to the coherent matrix T3;
calculating the backscattering coefficients of VV polarization and HH polarization of the earth surface according to the surface scattering matrix TG;
constructing a soil moisture estimation data set, and determining a training set and a testing set;
calculating the surface backscattering coefficient through a training set;
constructing a surface scattering model, and simulating a surface backscattering coefficient through the surface scattering model;
constructing a lookup table of the earth surface scattering model;
based on a minimum cost function strategy, inverting from the lookup table to obtain the soil water content of each sampling point;
calculating an effective roughness parameter at the current sampling date;
and constructing a lookup table of the earth surface scattering model according to the effective roughness parameters, and estimating the soil water content of the earth surface covered by the plant by combining the observed backscattering coefficient and the polarization decomposition method.
Further, the training set is determined by obtaining measured soil moisture content data.
Further, the calculation of the effective roughness parameter at the current sampling date is performed according to the following steps:
calculating the estimation precision of the current sampling date under the roughness condition;
and traversing all the given roughness parameter values, repeating the cycle, and taking the roughness parameter with the estimation precision meeting the preset condition as the effective roughness parameter at the current sampling date.
Further, the method also comprises the following steps:
the evaluation and the precision verification of the soil water content of the test set are as follows:
and for each sampling date, constructing a lookup table of the earth surface scattering model by using the effective roughness parameters, obtaining the estimated soil water content on the test set on the basis of the surface backscattering coefficient obtained by polarization decomposition and the backscattering coefficient simulated by the earth surface scattering model on the test set in combination with a minimum cost function strategy, comparing the estimated soil water content with the actually measured soil water content of the test set, carrying out precision verification and judging the estimation precision.
Further, the method also comprises the following steps:
regional estimation and mapping of the water content of plant soil are specifically as follows:
and for each sampling date, constructing a backscattering coefficient lookup table of a CIEM (common information element) model or a Dubois model by using the effective roughness parameters, traversing all pixels of the plant area in the image data, estimating the soil water content information of each pixel by using a soil water content estimation method, and finally completing the soil water content estimation of the plant area and carrying out soil water content area mapping.
Further, the calculation of the backscattering coefficient is performed according to the following steps:
extracting a coherent matrix T3 of each sampling point from the image data according to the longitude and latitude information of the sampling point;
calculating a polarization coherence matrix TG corresponding to surface scattering from the coherence matrix T3 by using different volume scattering matrices;
based on a non-negative eigenvalue decomposition method and a conversion relation between a polarization coherent matrix and a backscattering coefficient, calculating the backscattering coefficient corresponding to the surface scattering component according to the following formula:
wherein TG represents a polarization coherence matrix;
TGijis the ith row and jth column element of TG;
further, the determination of the effective roughness parameter is performed according to the following steps:
for different sampling dates, constructing a surface scattering model based on different roughness parameters to obtain a backscattering coefficient lookup table of VV polarization and HH polarization:
under the conditions of different roughness parameters and different soil water contents, simulating the backscattering coefficients of VV polarization and HH polarization of the bare earth surface;
on the training set, obtaining an effective surface backscattering coefficient and a surface backscattering coefficient simulated by a surface scattering model according to polarization decomposition, and obtaining the soil water content of each sampling point from a lookup table by inversion according to a minimum cost function strategy shown by the following formula:
wherein the content of the first and second substances,
backscattering coefficients respectively representing VV polarization and HH polarization simulated by the earth surface scattering model;
when the F value is the minimum value, taking the soil water content corresponding to the current lookup table element as the estimated soil water content of the sampling point;
calculating the root mean square error between the actually measured soil water content and the estimated soil water content under the roughness condition, and taking the root mean square error as the estimation precision of the current sampling date;
and traversing all the given roughness parameter values, repeating the cycle, and taking the roughness parameter with the precision meeting the requirement as the effective roughness parameter at the sampling date.
The invention provides a plant soil water content estimation system based on SAR and polarization decomposition, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method of any one of claims 1-7.
The invention has the beneficial effects that:
compared with the traditional SAR-based soil moisture estimation model, the SAR-and polarization-decomposition-based plant soil moisture estimation method uses the effective roughness parameter to represent the soil roughness of the plant growing region, reduces the unknown parameters of the earth surface scattering model, simplifies the solution of the soil moisture content, does not depend on the actually measured roughness parameter, and uses the polarization decomposition method to decompose the observed backscattering signals into scattering components representing different components, thereby obtaining the backscattering components of the earth surface, avoiding the influence of vegetation scattering contribution and improving the accuracy of soil moisture estimation.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a schematic flow diagram of the overall process of the present invention.
FIG. 2 is a schematic diagram of a research area of a Middling farm in an embodiment of the present invention, wherein the selected research area is a wheat growing area in the southeast of Ontario, Canada.
FIG. 3 is a graph showing the accuracy of estimation on a test set using two different surface scattering models in an embodiment of the present invention
Fig. 4 is a regional plot of soil water content for wheat growing areas at different sampling dates obtained in an embodiment of the present invention.
Detailed Description
The present invention is further described with reference to the following drawings and specific examples so that those skilled in the art can better understand the present invention and can practice the present invention, but the examples are not intended to limit the present invention.
Example 1
As shown in fig. 1, the method for estimating the water content of plant soil based on SAR and polarization decomposition provided by this embodiment includes the following steps:
step 1: preprocessing an SAR image, and extracting a T3 matrix; t3 denotes a coherence matrix.
For radar targets, the scattering properties can be fully characterized by a polarization scattering matrix S,
wherein VV, VH, HH represent the backscattering coefficients of VV polarization, VH polarization, HH polarization observed by radar, respectively, and the unit is Intensity (Intensity).
If the polarization scattering matrix is vectorized on the basis of Pauli basis, its scattering matrix vector kp is expressed as,
where T denotes a matrix transpose. The polarization coherence matrix T3 corresponding to the n-view processed scattering vector kp can be expressed as:
wherein denotes a matrix conjugate;
step 2: a surface scattering matrix TG characterizing the earth surface scattering components is calculated from the T3 matrix based on the vertical volume scattering matrix (TVv) and the horizontal volume scattering matrix (TVh), and the backscattering coefficients of VV polarization and HH polarization of the earth surface are further calculated based on the TG.
Based on the polarization decomposition method of the three-component mechanism (Freeman decomposition), the T3 matrix can be expressed in the form,
wherein T isijRow i and column j elements representing the coherence matrix T3; TG, TD and TV are polarization coherent matrixes corresponding to surface scattering, secondary scattering and bulk scattering respectively; beta is the normalized difference of H-polarized and V-polarized Bragg scattering; alpha is the normalized difference of Fresnel scattering of H-polarized and V-polarized earth-vegetation stems; f. ofs、fdThe intensity coefficients of the surface scattering component and the secondary scattering component are respectively; "+" denotes the conjugate transpose.
The vertical volume scattering matrix (TVv) and the horizontal volume scattering matrix (TVh) proposed by Yamaguchi are selected as the volume scattering matrix,
wherein, VijRow i and column j elements representing a volume scattering matrix; f. ofvRepresenting the intensity coefficient of the bulk scatter component.
Through the analysis of the scattering mechanism components, the proportion of the secondary scattering component TD is negligible, and T3 can be expressed as T3 ═ TG + TV, so TG can be expressed as: TG is expressed as a band unknown parameter f, T3-TVvBased on a non-negative eigenvalue decomposition method (NNED), the minimum f when the minimum characteristic of TG is not less than 0 is obtainedvAs coefficient of bulk scattering intensity fv。
After solving a coherent matrix TG corresponding to the surface scattering, calculating a backscattering coefficient corresponding to the surface scattering component based on a conversion relation between the polarized coherent matrix and the backscattering coefficient as shown in the following formula:
wherein TG represents a polarization coherence matrix; TG (gamma-ray) in a single phaseijIs the ith row and jth column element of TG;
calculating a backscattering coefficient corresponding to a surface scattering component of VV polarization and an HH polarization mode based on a polarization decomposition method, wherein the unit is Intensity (Intensity); selecting one obtained by calculation based on vertical body scattering matrix
Selecting the back scattering coefficient obtained by calculation based on a horizontal volume scattering matrix as the back scattering coefficient of the surface in the VV polarization mode
The surface backscatter coefficient is expressed as the HH polarization mode.
And step 3: calculated surface backscattering coefficient
And constructing a soil moisture estimation data set together with the actually measured soil moisture. The data of each day is divided into training data and testing data according to a certain proportion.
And 4, step 4: for a certain sampling date, on training data, a look-up table of a surface scattering model (CIEM or Dubois) is constructed based on different roughness parameters (root mean square height): and the simulated VV polarization of the bare earth surface and the backscattering coefficient of HH polarization under different root mean square heights under different soil water contents.
And then calculating according to polarization decomposition to obtain a surface backscattering coefficient and a surface backscattering coefficient obtained by simulating a scattering model, and inverting from the lookup table to obtain the soil water content of each sampling point based on a minimum cost function strategy.
Wherein the content of the first and second substances,
backscattering coefficient values respectively representing VV polarization and HH polarization simulated by the earth surface scattering model; and when the F is minimum, taking the soil moisture content corresponding to the simulated backscattering coefficient as the estimated soil moisture content value of the sampling point. Then, under the roughness condition, the Root Mean Square Error (RMSE) between the estimated soil moisture content and the actually measured soil moisture content on the current sampling date is calculated as the estimation accuracy on the current date.
Wherein the content of the first and second substances,
and yiRespectively representing the estimated soil moisture content and the measured soil moisture content of the ith sampling point.
And traversing all the given roughness parameter values, repeating the step, and taking the roughness parameter with the highest estimation precision as the effective roughness parameter at the sampling date.
And 5: for a certain sampling date, a lookup table of a surface scattering model (CIEM or Dubois) is constructed by using effective roughness parameters, all pixels of a plant coverage area of the SAR image are traversed to obtain an observed total backscattering coefficient, then surface backscattering coefficients of all pixels in the area are obtained based on the steps 1 and 2, and finally soil water content of each pixel in the plant area is obtained through inversion according to a minimum cost function strategy.
The plant provided by the embodiment can be a farmland for planting wheat, and can also be plants for planting rice, potatoes, flowers and plants, trees and the like.
Example 2
The method for estimating the soil water content of the wheat farmland based on SAR and polarization decomposition provided by the embodiment is further described as follows:
data acquisition: the remote sensing data adopted by the invention is RADARSAT-2 full-polarization SAR backscattering coefficient data, the actual measurement data comprises soil water content information of a wheat planting research area, the remote sensing data and the actual measurement data are acquired on the same date, eight dates in the growth period of the wheat are covered, and the specific date is as shown in figure 4.
The experimental procedure is shown in FIG. 1 and is described in detail below.
The method comprises the following steps: preprocessing the remote sensing image: the method mainly comprises radiometric calibration, coherent matrix extraction T3, polarization filtering, terrain correction and projection conversion.
Step two: solving the surface backscattering coefficient based on a polarization decomposition method:
extracting a coherent matrix T3 of each sampling point from a T3 image according to longitude and latitude information of the sampling point, then calculating a polarized coherent matrix (TG) corresponding to surface scattering from the coherent matrix T3 by using different volume scattering matrixes (a vertical volume scattering matrix and a horizontal volume scattering matrix), and then calculating a backscattering coefficient corresponding to a surface scattering component based on a non-negative eigenvalue decomposition method (NNED) and a conversion relation between the polarized coherent matrix and the backscattering coefficient.
Wherein TG represents a polarization coherence matrix;
TGijis the ith row and jth column element of TG;
in step two, the matrix is obtained by using a scattering matrix based on a vertical body
As effective VV surface scattering component of wheat research area, obtained based on horizontal volume scattering matrix
As an effective HH surface scattering component in wheat research.
Step three: and (3) constructing a soil moisture estimation data set:
and (3) constructing a soil moisture estimation data set by actually measuring soil moisture of the sampling points and the effective surface scattering components (VV, HH) extracted in the second step, and dividing the data into 7 for each sampling date: 3, training set and testing set;
step four: estimating soil water content and determining effective roughness parameters in a training set:
for different sampling dates, constructing a surface scattering model (a CIEM model and a Dubois model) based on different roughness parameters (root mean square height) to obtain a backscattering coefficient lookup table of VV polarization and HH polarization: and simulating the backscattering coefficients of VV polarization and HH polarization of the bare earth surface under different roughness parameters and different soil water contents. On the training set, an effective surface backscattering coefficient and a surface backscattering coefficient simulated by a surface scattering model are obtained according to polarization decomposition, and the soil water content of each sampling point is obtained through inversion from a lookup table based on a minimum cost function strategy shown in the specification.
Wherein the content of the first and second substances,
representing the VV polarization backscattering coefficient value of the earth surface scattering model simulation;
values of HH polarization backscattering coefficients representing a surface scattering model simulation;
and when the F value is the minimum value, taking the soil water content corresponding to the current lookup table element as the estimated soil water content of the sampling point. Then, the Root Mean Square Error (RMSE) between the measured soil moisture content and the estimated soil moisture content under the roughness condition is calculated as the estimation accuracy of the current sampling date. And traversing all given roughness parameter values, repeating the step, and taking the roughness parameter with the highest precision as an effective roughness parameter at the sampling date.
Step five: the evaluation and the precision verification of the soil water content of the test set are as follows:
and for each sampling date, constructing a lookup table of a surface scattering model (CIEM model or Dubois model) by using the effective roughness parameters obtained in the fourth step, obtaining the estimated soil moisture content on the test set on the basis of the surface backscattering coefficient obtained by polarization decomposition and the backscattering coefficient simulated by the surface scattering model on the test set in combination with a minimum cost function strategy, comparing the obtained soil moisture content with the actually measured soil moisture content of the test set, and verifying the precision (as shown in figure 3) so as to judge the effectiveness and the estimation precision of the method.
Step six: regional estimation and mapping of water content of wheat farmland soil
For each sampling date, a backscattering coefficient lookup table of a surface scattering model (a CIEM model or a Dubois model) is constructed by using the effective roughness parameters, then all pixels of a wheat planting area in the RADARSAT-2 image are traversed, soil water content information of each pixel is estimated by using the soil water content estimation method in the steps, and finally, soil water content estimation of a wheat farmland area is completed and soil water content area mapping is carried out (as shown in figure 4).
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (8)
1. The plant soil water content estimation method based on SAR and polarization decomposition is characterized by comprising the following steps: the method comprises the following steps:
acquiring and preprocessing SAR image data, wherein the image data comprises a plant and soil image;
extracting a coherent matrix T3 according to the image data;
calculating a surface scattering matrix TG representing the surface scattering components according to the coherent matrix T3;
calculating the backscattering coefficients of VV polarization and HH polarization of the earth surface according to the surface scattering matrix TG;
constructing a soil moisture estimation data set, and determining a training set and a testing set;
calculating the surface backscattering coefficient through a training set;
constructing a surface scattering model, and simulating a surface backscattering coefficient through the surface scattering model;
constructing a lookup table of the earth surface scattering model;
based on a minimum cost function strategy, inverting from the lookup table to obtain the soil water content of each sampling point;
calculating an effective roughness parameter at the current sampling date;
and constructing a lookup table of the earth surface scattering model according to the effective roughness parameters, and estimating the soil water content of the earth surface covered by the plant by combining the observed backscattering coefficient and the polarization decomposition method.
2. The SAR and polarization decomposition based plant soil water content estimation method of claim 1, characterized in that: the training set is determined by obtaining measured soil moisture content data.
3. The SAR and polarization decomposition based plant soil water content estimation method of claim 1, characterized in that: the calculation of the effective roughness parameter at the current sampling date is carried out according to the following steps:
calculating the estimation precision of the current sampling date under the roughness condition;
and traversing all the given roughness parameter values, repeating the cycle, and taking the roughness parameter with the estimation precision meeting the preset condition as the effective roughness parameter at the current sampling date.
4. The SAR and polarization decomposition based plant soil water content estimation method of claim 1, characterized in that: further comprising the steps of:
the evaluation and the precision verification of the soil water content of the test set are as follows:
and for each sampling date, constructing a lookup table of the earth surface scattering model by using the effective roughness parameters, obtaining the estimated soil water content on the test set on the basis of the surface backscattering coefficient obtained by polarization decomposition and the backscattering coefficient simulated by the earth surface scattering model on the test set in combination with a minimum cost function strategy, comparing the estimated soil water content with the actually measured soil water content of the test set, carrying out precision verification and judging the estimation precision.
5. The SAR and polarization decomposition based plant soil water content estimation method of claim 1, characterized in that: further comprising the steps of:
regional estimation and mapping of the water content of plant soil are specifically as follows:
and for each sampling date, constructing a backscattering coefficient lookup table of a CIEM (common information element) model or a Dubois model by using the effective roughness parameters, traversing all pixels of the plant area in the image data, estimating the soil water content information of each pixel by using a soil water content estimation method, and finally completing the soil water content estimation of the plant area and carrying out soil water content area mapping.
6. The SAR and polarization decomposition based plant soil water content estimation method of claim 1, characterized in that: the calculation of the backscattering coefficient is carried out according to the following steps:
extracting a coherent matrix T3 of each sampling point from the image data according to the longitude and latitude information of the sampling point;
calculating a polarization coherence matrix TG corresponding to surface scattering from the coherence matrix T3 by using different volume scattering matrices;
based on a non-negative eigenvalue decomposition method and a conversion relation between a polarization coherent matrix and a backscattering coefficient, calculating the backscattering coefficient corresponding to the surface scattering component according to the following formula:
wherein TG represents a polarization coherence matrix;
TGijis the ith row and jth column element of TG;
7. The SAR and polarization decomposition based plant soil water content estimation method of claim 1, characterized in that: the effective roughness parameter is determined according to the following steps:
for different sampling dates, constructing a surface scattering model based on different roughness parameters to obtain a backscattering coefficient lookup table of VV polarization and HH polarization:
under the conditions of different roughness parameters and different soil water contents, simulating the backscattering coefficients of VV polarization and HH polarization of the bare earth surface;
on the training set, obtaining an effective surface backscattering coefficient and a surface backscattering coefficient simulated by a surface scattering model according to polarization decomposition, and obtaining the soil water content of each sampling point from a lookup table by inversion according to a minimum cost function strategy shown by the following formula:
wherein the content of the first and second substances,
backscattering coefficients respectively representing VV polarization and HH polarization simulated by the earth surface scattering model;
when the F value is the minimum value, taking the soil water content corresponding to the current lookup table element as the estimated soil water content of the sampling point;
calculating the root mean square error between the actually measured soil water content and the estimated soil water content under the roughness condition, and taking the root mean square error as the estimation precision of the current sampling date;
and traversing all the given roughness parameter values, repeating the cycle, and taking the roughness parameter with the precision meeting the requirement as the effective roughness parameter at the sampling date.
8. A plant soil water content estimation system based on SAR and polarization decomposition, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the program.
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