CN116047500A - Vegetation coverage area soil moisture inversion method considering polarization scattering information - Google Patents
Vegetation coverage area soil moisture inversion method considering polarization scattering information Download PDFInfo
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Abstract
The invention discloses a vegetation coverage soil moisture inversion method taking polarization scattering information into consideration, which comprises the steps of extracting vegetation coverage soil moisture inversion characteristic parameters based on polarization target decomposition; analyzing and obtaining a characteristic parameter optimization combination; and constructing and solving a soil moisture inversion model integrating the polarization information and the radar back-scattering information. The method provided by the invention is characterized in that the model is constructed from a target scattering mechanism, the influence of vegetation coverage is effectively removed through decomposition and correction of polarization characteristic parameters, the optimized combination of inversion characteristic parameters is obtained by combining independent component analysis, the effective observation information of full-polarization SAR data is fully utilized, and the reliable acquisition of vegetation coverage soil moisture is realized under the condition that other auxiliary data are not needed.
Description
Technical Field
The invention relates to the technical field of soil moisture content monitoring by a synthetic aperture radar, in particular to a vegetation coverage area soil moisture content dynamic monitoring method considering multi-polarization SAR characteristics.
Background
Soil moisture is an important parameter for representing the earth surface soil state, and is widely applied to the fields of agricultural soil moisture content assessment, flood prediction, weather forecast and the like. The soil moisture content ground monitoring method has the advantage of high precision, but only can acquire the water content of the soil at discrete points, and has the defects of low updating frequency and high consumption of manpower and material resources in the dynamic monitoring of the soil moisture. The remote sensing technology is applied to the aspect of quantitative monitoring of soil moisture by virtue of the advantages of non-contact, large-scale and high-timeliness observation. In particular, the microwave remote sensing technology has all-day and all-weather observation capability, has strict theoretical basis between observed quantity and soil moisture, and provides a solid foundation for quantitative inversion of soil moisture by utilizing microwave remote sensing data.
The synthetic aperture radar (Synthetic Aperture Radar, SAR) has the advantages of high space-time resolution, multi-polarization and multi-frequency band earth observation, particularly SAR observation information is sensitive to the response of soil surface characteristics, can effectively characterize the soil surface parameter information, effectively overcomes the defects of the traditional soil moisture monitoring method, is widely researched in soil surface parameter inversion, and provides an effective technical method for large-range dynamic soil moisture monitoring. However, the inversion of the soil moisture content by radar technology is influenced by factors such as vegetation coverage and soil surface roughness, significant results are obtained in the current inversion research of bare soil moisture, and certain errors exist in the inversion analysis of the soil moisture in the vegetation coverage area, so that the influence of vegetation on radar scattering information is required to be considered in the vegetation coverage area so as to obtain scattering information representing the soil surface characteristics, and a corresponding soil moisture inversion model is established.
The different polarized radar back scattering information and the target polarized scattering information acquired by the multi-polarized SAR satellite are important components of SAR observation. The polarization scattering information can effectively represent the geometric characteristics and physical characteristics of an observation target, the polarization scattering information representing different scattering mechanisms of the target can be obtained by decomposing the polarization target for the full-polarization SAR data, and effective characteristic parameters are provided for vegetation coverage soil inversion. The radar back scattering information is sensitive to the response of the soil dielectric characteristics, and is widely applied to quantitative inversion of soil moisture as an important characteristic parameter for representing the soil surface moisture. Aiming at the influence of vegetation coverage, vegetation and soil surface characteristic parameters are respectively extracted through polarized scattering information, and the backscattering information and the polarized information which characterize the soil surface characteristics are obtained through effective elimination of the vegetation information, so that an important thought is provided for carrying out quantitative inversion on soil surface moisture by combining the polarized information and radar backscattering information.
Although the existing method for extracting the water content of the soil by utilizing SAR observation information is more, the method for effectively removing or inhibiting the vegetation coverage influence from the perspective of radar scattering mechanism is lacked facing the vegetation coverage influence, so that radar scattering information representing the soil surface characteristics is obtained. At present, single polarized scattering information or radar back scattering information is mostly adopted for soil moisture inversion research, and cooperative application of the polarized information and the radar back scattering information is lacking, so that reliability of soil moisture inversion is improved. Therefore, a method capable of effectively removing the influence of vegetation coverage on soil moisture inversion is needed in the field, the scattering characteristics of the vegetation coverage soil surface are obtained from the radar scattering mechanism angle, the optimal combination of inversion characteristic parameters is realized through the cooperative polarization scattering information and the radar back scattering information, and an accurate and reliable quantitative inversion method for the soil moisture under vegetation coverage conditions is constructed.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a vegetation coverage soil moisture inversion method taking polarization scattering information into consideration, which is based on cooperative radar back scattering information of full-polarization SAR data and polarization scattering information; firstly, carrying out incoherent target decomposition treatment on the fully polarized radar data to obtain vegetation coverage area target surface scattering, even scattering and volume scattering information; starting from a polarized scattering mechanism of vegetation and soil, considering that the vegetation is mainly characterized as volume scattering characteristics in polarization dimension, in order to effectively remove the influence of vegetation coverage on soil moisture inversion, volume scattering information acquired by target decomposition needs to be corrected, so that a normalized radar vegetation index is extracted by using multi-polarization SAR observation information, contribution components of the vegetation to the volume scattering information are described by the normalized radar vegetation index, and the volume scattering information is corrected by using the index, so that polarized scattering characteristic information for eliminating the influence of the vegetation is acquired. And extracting a multi-polarization radar backscattering coefficient representing the soil surface characteristics by using a water cloud model combined with the normalized radar vegetation index. The radar backscattering coefficient and the polarized scattering information of the scattering characteristics of the soil surface are comprehensively and effectively characterized, the independent component analysis is utilized to obtain the optimal combination of the soil inversion characteristic parameters, redundancy between the multi-polarized radar backscattering coefficient and the polarized scattering information is effectively removed, the reliability and the robustness of model construction are improved, the optimal characteristic parameters are provided for quantitative inversion of the soil moisture content of the vegetation coverage area, finally the soil moisture content of the vegetation coverage area is obtained through the multi-element nonlinear regression model, and technical support is provided for dynamic monitoring of the soil moisture content of the vegetation coverage area under the condition that the full-polarized SAR supports different climates.
The aim of the invention is achieved by the following technical scheme:
a vegetation coverage soil moisture inversion method taking polarization scattering information into consideration comprises the following steps:
step 1, extracting vegetation coverage soil moisture inversion characteristic parameters based on polarization target decomposition;
step 2, analyzing and obtaining a characteristic parameter optimization combination;
and 3, constructing and solving a soil moisture inversion model integrating the polarization information and the radar back scattering information.
One or more embodiments of the present invention may have the following advantages over the prior art:
the invention is oriented to quantitative inversion of soil moisture in a vegetation coverage area, the decomposition and optimization of vegetation and the soil scattering mechanism are carried out from the perspective of a polarized scattering mechanism, radar backscattering and polarized scattering information are fully utilized to represent vegetation and soil surface characteristics, influences of vegetation coverage on soil surface moisture inversion are eliminated through different polarized SAR observations, further radar characteristic parameter optimization combination representing soil surface characteristics is obtained, a soil moisture inversion model is constructed through simulation data and measured data, and reliable soil surface moisture information is obtained by combining multielement optimization characteristic parameter input.
Drawings
FIG. 1 is a flow chart of a vegetation cover soil moisture inversion method that takes into account polarization scatter information;
FIG. 2 is a schematic diagram of a multi-feature parametric inversion model combining polarization information and radar back-scatter information;
FIG. 3 is an exploded schematic view of a vegetation cover surface scattering mechanism.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following examples and the accompanying drawings.
As shown in fig. 1, the inversion method for the soil moisture of the vegetation coverage taking the polarization scattering information into consideration comprises the following steps:
step 1, optimizing and extracting vegetation coverage soil moisture inversion characteristic parameters based on polarization target decomposition;
step 1.1, aiming at full-polarization SAR data, acquiring polarization scattering information representing different characteristics of a target by adopting a noncoherent polarization target decomposition method
Polarized target decomposition is a method for extracting typical scattering features of fully polarized radar data, and can extract feature components characterizing different scattering mechanisms, wherein the model-based incoherent target decomposition method Freeman decomposition can decompose the fully polarized data into surface scattering, even scattering and volume scattering information. The method comprises the steps of obtaining scattering information describing the characteristics of soil and vegetation by using a polarized target decomposition method, and obtaining initial polarized characteristic parameters for soil moisture inversion from the perspective of a scattering mechanism, wherein radar scattering information of a vegetation coverage area comprises vegetation scattering, soil scattering and vegetation-soil superposition scattering, and the surface scattering mechanism decomposition situation facing the vegetation coverage area is shown in fig. 3.
The full polarization SAR data can acquire the surface backward scattering information corresponding to different polarization modes, and the full polarization scattering matrix S is composed of different polarization complex scattering coefficients S pq Composition, where p and q represent horizontal and vertical transmit/receive polarizations.
Wherein S is hh Scattering component representing horizontal emission and horizontal reception polarization mode, S hv 、S vh And S is vv Definition of (S) and S hh Similarly.
Coherence matrix T characterizing polarization characteristics 3 Constructed by Pauli basis vector k, the polarized scattering information used to describe the target:
T 3 ＝＜k·k *T ＞ (3)
in the formula, superscript x, superscript T and < > represent complex conjugate, matrix transposition and averaging process, respectively.
In order to extract the individual scattering contribution of the target ground object, the coherence matrix is processed by using a Freeman polarized target decomposition method, and is decomposed into three components, namely surface scattering, even scattering and volume scattering. Polarization scattering information characterizing the target using Freeman decomposition components:
wherein f s ，f d And f v Respectively representing the surface scattering, even scattering and bulk scattering amplitudes, and β and α respectively representing the surface scattering and even scattering parameters;
the scattering intensity of the surface, even and bulk scattering components is described by traces of a coherence matrix.
P s ＝f s (1+|β| 2 ) (5)
P d ＝f d (1+|α| 2 ) (6)
P v ＝f v (7)
Where Ps, pd and Pv represent the surface scattering, even scattering and bulk scattering characteristics of the target, respectively.
Step 1.2, extracting normalized radar vegetation index by using full-polarization radar observation information to face vegetation coverage area
Vegetation coverage is an important factor affecting SAR inversion of soil surface moisture, and for this problem, index information characterizing vegetation characteristics is extracted using fully polarized SAR data. Normalized radar vegetation (Normalized Radar Vegetation Index, NRVI) is defined for extracting vegetation scatter information of a vegetation coverage. The normalized radar vegetation index is expressed as:
NRVI＝(RVI-RVI min )/(RVI max -RVI min ) (8)
wherein RVI is radar vegetation index, RVI max And RVI min The maximum radar vegetation index and the minimum radar vegetation index extracted in the area are respectively represented, and the normalized radar vegetation index has good applicability to vegetation coverage information extraction in the area.
Based on the full-polarization SAR data, extracting radar vegetation indexes sensitive to vegetation coverage information through polarization feature combination, wherein the expression relationship is as follows:
RVI＝f(σ HH ,σ HV ,σ VH ,σ VV ) (9)
in sigma HH Sum sigma VV Representing horizontally polarized and vertically polarized radar backscatter information, σ, respectively HV Sum sigma VH Cross-polarized radar backscatter information (intensity) representing horizontal polarization transmit vertical polarization reception and vertical polarization transmit horizontal polarization reception, respectively.
NRVI may describe the vegetation coverage information for each pixel in the area, with smaller NRVI values corresponding to lower vegetation coverage and larger NRVI values corresponding to higher vegetation coverage, with increasing NRVI as vegetation coverage increases. Therefore, the full-polarization SAR image can be directly utilized to effectively extract vegetation coverage information, the requirement on other auxiliary data is avoided, and meanwhile, the aging consistency of vegetation information extraction and the consistency in space scale are ensured. And (3) analyzing the influence of vegetation on radar polarized scattering information and a backscattering coefficient by utilizing NRVI, and further correcting the radar scattering information of the vegetation coverage area to separate out vegetation contribution parts, and acquiring the polarized scattering information and the radar backscattering coefficient which characterize the soil surface characteristics, so that the method is used for inversion analysis of the vegetation coverage soil moisture.
Step 1.3, based on polarization scattering mechanism characteristics of vegetation and soil surface, vegetation scattering information is mainly characterized as volume scattering components, contribution of vegetation to the volume scattering characteristics is removed by combining with normalized radar vegetation index NRVI, a scattering mechanism for representing the polarization scattering characteristics of soil is obtained, and then the method is used for quantitative inversion analysis of soil moisture, and a processing flow is shown in a figure 2.
The normalized radar vegetation index NRVI can effectively represent vegetation scattering information, is obtained through multi-polarization radar data calculation, and can describe vegetation scattering characteristics of pixel by pixel. In the radar scattering mechanism, vegetation is mainly characterized as volume scattering information, and is represented as noise factors in soil surface moisture inversion, so that NRVI parameters are utilized to process vegetation volume scattering information, and characteristic parameters for soil moisture inversion are corrected. Defining the volume scattering information of the vegetation effect to be removed as Pv', decomposing the initial polarized target to obtain volume scattering information as Pv, correcting the volume scattering information by using NRVI, removing vegetation volume scattering contribution, and expressing corrected volume scattering characteristics as the following nonlinear conversion relation:
Pv’＝(1-NRVI)/(1+NRVI) ×Pv (10)
therefore, influences of vegetation coverage are removed through multi-polarization SAR observation information, polarization scattering information representing soil surface characteristics is extracted, and effective inversion characteristic parameters are obtained.
Through the polarization characteristic parameter optimization processing, the polarization parameter information representing the scattering characteristics of the soil surface is obtained on the basis of effectively eliminating the vegetation coverage influence.
Step 2, obtaining the feature parameter optimization combination by utilizing independent component analysis
And taking radar back scattering coefficient and polarization scattering information representing the soil surface characteristics as inversion characteristic parameter input, and extracting characteristic parameter optimization combination sensitive to soil moisture response by using an independent component analysis method.
And 2.1, eliminating influence of vegetation on the multi-polarization radar backscatter coefficients by using a normalized radar vegetation index NRVI, and obtaining the multi-polarization radar backscatter coefficients representing soil surface characteristics.
The vegetation cover radar total backscatter is described as the superposition of soil surface scatter contribution, vegetation cover soil scatter and vegetation scatter contribution, as shown in fig. 3, where part of the soil surface scatter is affected by the attenuation of the vegetation layer. The model is specifically expressed as follows:
σ o ＝NRVI×(σ veg +τ 2 σ soil )+(1-NRVI)×σ soil (11)
in sigma o Representing total backscattering information, σ, of vegetation cover surface veg Sum sigma soil Respectively representing vegetation scattering and soil surface scattering information, and τ represents vegetation subtraction coefficient. By introducing normalized radar vegetation index NRVI, the characteristics of vegetation coverage soil surface scattering, vegetation scattering and vegetation coverage soil surface scattering are fully considered, and the model has better effects on dense vegetation coverage and sparse vegetation coverageIs applicable to the (c). When the vegetation is completely covered, the model is a representation form of the traditional water cloud model on the vegetation coverage; the ground surface is bare soil, the total ground surface backscattering information is soil surface scattering information, and the radar backscattering information of the vegetation coverage area is independently decomposed through the processing, so that the radar backscattering coefficient representing the scattering characteristic of the soil surface is obtained.
The method is oriented to L-band differential interference SAR satellite full polarization data in China, different polarization observables are respectively processed in vegetation coverage areas to remove vegetation effects, and multi-polarization radar backscattering coefficients representing scattering characteristics of soil surfaces are obtained.
In sigma HHo ，σ HVo ，σ VHo Sum sigma VVo Respectively represent the total radar backscattering coefficients and sigma corresponding to different polarization modes of vegetation coverage HHveg ，σ HVveg ，σ VHveg Sum sigma VVveg Respectively representing vegetation scattering information of different polarization modes, wherein NRVI is normalized radar vegetation index; obtaining a soil surface multi-polarization radar backscattering coefficient sigma for removing vegetation influence by using a formula (12) HHsoil ，σ HVsoil ，σ VHsoil Sum sigma VVsoil It is used for constructing soil moisture inversion model.
And 2.2, taking polarized scattering information and multi-polarized radar backscattering coefficients which are subjected to vegetation influence removal as inputs, extracting characteristic parameter optimization combination sensitive to soil moisture response through independent component analysis, effectively removing redundancy between the polarized scattering information and the multi-polarized radar backscattering information, and providing effective input for construction of an inversion model.
And extracting the multi-polarization radar backscattering coefficient and polarization scattering information which are used for removing vegetation influence and characterize the soil surface characteristics by utilizing full-polarization L-band differential interference SAR satellite data. Aiming at information redundancy among multidimensional input parameters, independent component analysis is utilized to obtain a soil moisture inversion optimization characteristic parameter combination.
[Par(1), Par(i) ...]＝F(σ HHsoil , σ HVsoil , σ VHsoil , σ VVsoil , Ps, Pd, Pv') (13)
Wherein, par (1), par (i) respectively represent the optimized characteristic parameters for soil moisture inversion obtained by independent component analysis and extraction, sigma HHsoil ，σ HVsoil ，σ VHsoil Sum sigma VVsoil The soil surface multi-polarization radar backscattering coefficients for removing vegetation influence are respectively represented, and Ps, pd and Pv' respectively represent soil surface scattering, even scattering and volume scattering polarization target decomposition information subjected to vegetation removal treatment. The inversion characteristic parameter combination of the comprehensive soil surface radar backscattering coefficient and the polarization scattering information is obtained through the above processing, and important input characteristics are provided for reliable acquisition of soil moisture.
In the above processing, F represents an independent component analysis process, and is oriented to radar backscattering coefficients and polarization target decomposition information of different polarization modes for removing vegetation influence, input parameters are subjected to independent component analysis processing aiming at redundancy among multidimensional information and noise influence, characteristic parameter combinations sensitive to soil moisture response are extracted, potential noise influence and parameter redundancy information are effectively removed, independent characteristic parameter information is obtained, and effective input parameters are provided for soil moisture inversion.
And integrating the optimized characteristic parameter input of the radar backscattering information and the polarized target decomposition information, constructing a function model between the soil surface parameters and SAR characteristic parameter combination, and realizing the reliable acquisition of the soil surface moisture of the region by combining model training of measured data, wherein the overall technical flow is shown in figure 2.
And 3.1, constructing a multi-element nonlinear function model between soil surface moisture and SAR characteristic parameters by utilizing optimized characteristic parameters of comprehensive polarized scattering information and radar back scattering information.
m v ＝G[Par(1), Par(i) ...] (14)
Wherein m is v Representing the surface soil moisture content, and G represents the nonlinear conversion relationship between the radar back-scatter information and polarization information feature combination and the soil moisture.
Step 3.2, taking the on-site actually measured soil surface parameters as model driving data, and constructing a conversion relation between the optimized characteristic parameters and the soil moisture;
through the optimized extraction of the total polarization SAR data radar backscattering coefficient and the polarization scattering information, effective input parameters are provided for the construction of a soil moisture inversion model, and the function model between the multidimensional radar observation information and the soil moisture is constructed by combining the actual measurement data of the soil moisture of the sample point, so that the training and construction of the soil moisture inversion model are completed.
And 3.3, taking optimized characteristic parameters of comprehensive full-polarization SAR back scattering information and polarization information as input, and acquiring soil surface moisture information through a multi-element nonlinear driving model to realize reliable inversion of vegetation coverage soil surface moisture of the cooperative polarization scattering information and radar back scattering information.
The multi-polarization multi-dimensional radar characteristic parameters of the vegetation coverage are taken as input, and the acquisition of the soil surface moisture of the vegetation coverage is realized by combining a multi-element nonlinear model, so that effective soil moisture data products are provided for farmland soil moisture content monitoring, weather forecast, flood forecast and the like.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (10)
1. A vegetation cover soil moisture inversion method taking polarization scattering information into account, the method comprising the steps of:
step 1, extracting vegetation coverage soil moisture inversion characteristic parameters based on polarization target decomposition;
step 2, analyzing and obtaining a characteristic parameter optimization combination;
and 3, constructing and solving a soil moisture inversion model integrating the polarization information and the radar back scattering information.
2. The vegetation cover soil moisture inversion method considering polarization scattering information as claimed in claim 1, wherein the step 1 specifically comprises:
step 1.1, aiming at full polarization SAR data, adopting incoherent polarized target decomposition to acquire polarized scattering information with different surface characteristics, and carrying out polarized decomposition treatment on vegetation coverage radar echo information from the angle of a scattering mechanism, wherein the vegetation coverage radar echo information comprises vegetation body scattering information, soil surface scattering information and vegetation attenuated soil scattering information;
step 1.2, extracting a normalized radar vegetation index by using full-polarization radar observation information facing the vegetation coverage area;
step 1.3, aiming at vegetation and soil surface polarization scattering characteristic difference, acquiring polarization scattering information representing soil surface characteristics based on vegetation volume scattering characteristics and combining normalized radar vegetation index correction volume scattering characteristic parameters.
3. The vegetation coverage soil moisture inversion method considering polarization scattering information as claimed in claim 1, wherein in the step 1.1, the full polarization SAR data can obtain the surface backscattering information corresponding to different polarization modes, and the full polarization scattering matrix S is composed of different polarization complex scattering coefficients S pq Composition, where p and q represent horizontal and vertical transmit/receive polarization:
wherein S is hh Representing the level of emissionReceiving a scattering component of a polarization mode, S hv 、S vh And S is vv Definition of (S) and S hh Similarly;
coherence matrix T characterizing polarization characteristics 3 Constructed by Pauli basis vector k, the polarized scattering information used to describe the target:
T 3 ＝＜k·k *T ＞ (3)
wherein, the superscript is represented by T and < > respectively represent complex conjugation, matrix transposition and average treatment;
in order to extract the independent scattering contribution of the target ground object, a Freeman polarized target decomposition method is utilized to process the coherent matrix, and the coherent matrix is decomposed into three components, namely surface scattering, even scattering and volume scattering; polarization scattering information characterizing the target using Freeman decomposition components:
wherein f s ，f d And f v Respectively representing the surface scattering, even scattering and bulk scattering amplitudes, and β and α respectively representing the surface scattering and even scattering parameters;
describing the scattering intensity of the surface, even, and bulk scattering components with traces of a coherence matrix;
P s ＝f s (1+|β| 2 ) (5)
P d ＝f d (1+|α| 2 ) (6)
P v ＝f v (7)
where Ps, pd and Pv represent surface scattering, even scattering and bulk scattering characteristic information of the target, respectively.
4. The method for inverting the soil moisture of the vegetation coverage taking into account the polarization scattering information according to claim 2, wherein in the step 1.2, the vegetation scattering information of the vegetation coverage is extracted by defining a normalized radar vegetation, and the normalized radar vegetation index is expressed as:
NRVI＝(RVI-RVI min )/(RVI max -RVI min ) (8)
wherein RVI is radar vegetation index, RVI max And RVI min Respectively representing the maximum radar vegetation index and the minimum radar vegetation index extracted from the region;
based on the full-polarization SAR data, extracting radar vegetation indexes sensitive to vegetation coverage information through polarization feature combination, wherein the expression relationship is as follows:
RVI＝f(σ HH ,σ HV ,σ VH ,σ VV ) (9)
in sigma HH Sum sigma VV Representing horizontally polarized and vertically polarized radar backscatter information, σ, respectively HV Sum sigma VH Cross polarization radar backscatter information representing horizontal polarization transmit vertical polarization reception and vertical polarization transmit horizontal polarization reception, respectively.
5. The vegetation cover soil moisture inversion method considering polarization scattering information as claimed in claim 2, wherein in step 1.3, vegetation volume scattering information is processed by using NRVI parameters, characteristic parameters for soil moisture inversion are corrected, volume scattering information for eliminating vegetation effect is defined as Pv', volume scattering information obtained by decomposing an initial polarization target is Pv, vegetation volume scattering contribution is removed by using NRVI corrected volume scattering information, and corrected volume scattering characteristics are expressed as the following nonlinear conversion relationship:
Pv’＝(1-NRVI)/(1+NRVI)×Pv (10)
therefore, influences of vegetation coverage are removed through multi-polarization SAR observation information, polarization scattering information representing soil surface characteristics is extracted, and effective inversion characteristic parameters are obtained.
6. The vegetation cover soil moisture inversion method taking into account polarization scattering information as claimed in claim 1, wherein the step 2 comprises:
step 2.1, eliminating influence of vegetation on a multi-polarization radar backscatter coefficient by using a normalized radar vegetation index NRVI to obtain the multi-polarization radar backscatter coefficient representing soil surface characteristics;
and 2.2, taking polarized scattering information and multi-polarized radar backscattering coefficients which are subjected to vegetation influence removal as input, analyzing and extracting characteristic parameter optimization combination which is sensitive to soil moisture response, and removing redundancy between the polarized scattering information and the multi-polarized radar backscattering information.
7. The vegetation cover soil moisture inversion method considering polarization scattering information as claimed in claim 6, wherein in step 2.1, the total back scattering of the vegetation cover radar is described as a superposition of soil surface scattering contribution, soil scattering under vegetation cover and vegetation scattering contribution, wherein part of the soil surface scattering is affected by attenuation of the vegetation layer, and the soil moisture inversion model is expressed as:
σ o ＝NRVI×(σ veg +τ 2 σ soil )+(1-NRVI)×σ soil (11)
in sigma o Representing total backscattering information, σ, of vegetation cover surface veg Sum sigma soil Respectively representing vegetation scattering and soil surface scattering information, wherein tau represents a vegetation subtraction coefficient;
the method comprises the steps of carrying out processing for removing vegetation effect on different polarization observables in a vegetation coverage area respectively on L-band differential interference SAS satellite full polarization data to obtain a multi-polarization radar backscattering coefficient representing scattering characteristics of a soil surface:
σ HHo ＝NRVI×(σ HHveg +τ 2 σ HHsoil )+(1-NRVI)×σ HHsoil
σ HVo ＝NRVI×(σ HVveg +τ 2 σ HVsoil )+(1-NRVI)×σ HVsoil
σ VHo ＝NRVI×(σ VHveg +τ 2 σ VHsoil )+(1-NRVI)×σ VHsoil
σ VVo ＝NRVI×(σ VVveg +τ 2 σ VVsoil )+(1-NRVI)×σ VVsoil
in sigma HHo ，σ HVo ，σ VHo Sum sigma VVo Respectively represent the total radar backscattering coefficients and sigma corresponding to different polarization modes of vegetation coverage HHveg ，σ HVveg ，σ VHveg Sum sigma VVveg Respectively representing vegetation scattering information of different polarization modes, wherein NRVI is normalized radar vegetation index; obtaining soil surface multi-polarization radar backscattering coefficient sigma for removing vegetation influence by using the formula HHsoil ，σ HVsoil ，σ VHsoil Sum sigma VVsoil And is further used for constructing a soil moisture inversion model.
8. The method for inverting the soil moisture of the vegetation coverage taking into account the polarized scattering information according to claim 6, wherein in the step 2.2, the multi-polarized radar backscatter coefficient and the polarized scattering information which characterize the soil surface characteristics and are affected by the vegetation are extracted by using the full-polarized L-band differential interference SAR satellite data; aiming at information redundancy among multidimensional input parameters, independent component analysis is utilized to obtain a soil moisture inversion optimization characteristic parameter combination:
[Par(1),Par(i)...]＝F(σ HHsoil ,σ HVsoil ,σ VHsoil ,σ VVsoil ,Ps,Pd,Pv')
wherein, par (1), par (i) respectively represent the optimized characteristic parameters for soil moisture inversion obtained by independent component analysis and extraction, sigma HHsoil ，σ HVsoil ，σ VHsoil Sum sigma VVsoil The soil surface multi-polarization radar backscattering coefficients for removing vegetation influence are respectively represented, and Ps, pd and Pv' respectively represent soil surface scattering, even scattering and volume scattering polarization target decomposition information subjected to vegetation removal treatment.
9. The vegetation cover soil moisture inversion method taking into account polarization scattering information as claimed in claim 1, wherein the step 3 comprises:
step 3.1, constructing a multi-element nonlinear function model between soil surface moisture and SAR characteristic parameters by utilizing optimized characteristic parameters of comprehensive polarized scattering information and radar back scattering information;
step 3.2, taking the actually measured soil surface parameters as model driving data, and constructing a conversion relation between the optimized characteristic parameters and the soil moisture;
and 3.3, taking optimized characteristic parameters of the fully polarized SAR backward heat radiation information and the polarization information as input, and acquiring soil surface moisture information through a multi-element nonlinear driving model to realize reliable inversion of vegetation coverage soil surface moisture of the collaborative polarization scattering information and the radar backward scattering information.
10. The vegetation cover soil moisture inversion method considering polarization scattering information as claimed in claim 9, wherein the calculation formula of the soil moisture content in the step 3.1 is:
m v ＝G[Par(1),Par(i)...]
wherein m is v The method is characterized in that the method comprises the steps of expressing the water content of earth surface soil, G expressing the nonlinear conversion relation between radar back scattering information and polarization information characteristic combination and the soil moisture, and Par (1), and Par (i) respectively expressing optimized characteristic parameters for inversion of the soil moisture obtained through independent component analysis and extraction.
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| CN117826112B (en) * | 2024-03-05 | 2024-05-31 | 天津智云水务科技有限公司 | Soil water content inversion method based on sar |
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| CN117826112A (en) * | 2024-03-05 | 2024-04-05 | 天津智云水务科技有限公司 | Soil water content inversion method based on sar |
| CN117826112B (en) * | 2024-03-05 | 2024-05-31 | 天津智云水务科技有限公司 | Soil water content inversion method based on sar |
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