CN114994087B - Vegetation blade water content remote sensing inversion method based on polarized SAR data - Google Patents
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Abstract
The invention discloses a vegetation leaf water content remote sensing inversion method based on polarized SAR data; the method comprises the following steps: inputting tree parameters and surface parameters into a vegetation scattering model to simulate polarized SAR data, and establishing a polarized SAR database; constructing a vegetation leaf water content microwave model based on polarized SAR data; constructing a vegetation leaf water content inversion model aiming at different vegetation types based on the vegetation leaf water content microwave model; acquiring radar SLC data of a target area, and processing the SLC data to acquire incident angle and backscattering information; substituting the incidence angle and the back scattering information into a vegetation leaf water content inversion model to perform quantitative inversion to obtain vegetation leaf water content data in a target area; according to the method, the vegetation leaf water content data can be accurately obtained by acquiring vegetation climate information and soil information of a target area and combining polarized SAR data, the vegetation leaf water content data is not affected by weather, material resources and financial resources are saved, and the method has important significance for early warning and verification of forest and grass fires.
Description
Technical Field
The invention belongs to the technical field of vegetation leaf water content monitoring, and particularly relates to a vegetation leaf water content remote sensing inversion method based on polarized SAR data.
Background
Vegetation moisture is a major factor affecting green plant photosynthesis and biomass, and many key bioelectrochemical cycle processes, including photosynthesis, evaporation and net primary productivity, are directly and intimately related. In mountain areas, a reduction in the moisture content of the forest tree increases the risk of forest fires. The vegetation burning process is influenced by the water content of the vegetation besides the organic matters in the vegetation body. On one hand, the water content of the vegetation influences the ignition point of the vegetation, the lower the water content of the vegetation is, the drier the vegetation is, and the lower the ignition point is, the easier the forest fire is caused; on the other hand, the moisture content of vegetation affects the burning rate and the amount of smoke released during the burning process, and the more moisture content of vegetation, the more thermal energy is required to evaporate the moisture, and thus the lower the rate of fire spread. Therefore, the intensive research has important research significance for accurately monitoring and diagnosing the vegetation environmental stress degree, the potential occurrence of natural fire, the effective acquisition of soil moisture and the like.
At present, in inversion of vegetation leaf water content, a vegetation index method based on multispectral data in an optical satellite is mostly adopted, a wave band sensitive to water content in multispectral data of the optical satellite can be inverted to obtain the water content of a surface vegetation canopy, the inversion method is widely applied to inversion research of the water content of the vegetation canopy, but the multispectral wave band is greatly influenced by time and weather conditions, and similar to the inversion measurement of the water content of the vegetation canopy at night or in rainy and snowy weather, the inversion measurement of the water content of the vegetation canopy cannot be completed well.
Therefore, how to effectively invert the water content of the vegetation leaf, avoid the influence of time and weather conditions, and continuously observe the water content of the vegetation leaf in all weather for a long time is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above problems, the invention provides a vegetation leaf water content remote sensing inversion method based on polarized SAR data, which at least solves the above part of technical problems, and the vegetation leaf water content can be accurately obtained by acquiring vegetation weather information and soil information of a target area and combining the polarized SAR data, so that the vegetation leaf water content is not affected by weather, and material resources and financial resources are saved.
The embodiment of the invention provides a vegetation leaf water content remote sensing inversion method based on polarized SAR data, which comprises the following steps:
s1, inputting tree parameters and surface parameters into a vegetation scattering model to simulate polarized SAR data, and establishing a polarized SAR database;
s2, constructing a vegetation leaf water content microwave model based on the polarized SAR data;
s3, constructing a vegetation leaf water content inversion model aiming at different vegetation types based on the vegetation leaf water content microwave model;
S4, acquiring radar SLC data of a target area, and processing the SLC data to acquire incident angle and backscattering information;
S5, substituting the incidence angle and the back scattering information into the vegetation blade water content inversion model to perform quantitative inversion, and obtaining vegetation blade water content data in a target area.
Further, in the step S1, the tree parameters include: tree height, vegetation water content, vegetation coverage, tree diameter and crown height;
The surface parameters include: soil moisture content, soil dielectric constant.
Further, the constructing the vegetation leaf water content microwave model comprises: constructing a single-band index model, a ratio index model and a three-band index model.
Further, the construction of the single-band exponential model is as follows:
ωc＝a1+b1*σvv (1)
ωc＝a2+b2*σhh (2)
ωc＝a3+b3*σvh (3)
Wherein ω c represents the vegetation leaf moisture content; a 1、a2、a3、b1、b2、b3 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data; σ vh is the cross-polarized backscattering coefficient.
Further, the constructed ratio index model is as follows:
wc＝a4+b4*exp[-(σhh/σvv)/c4]； (4)
wherein w c represents the vegetation leaf moisture content; a 4、b4、c4 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data.
Further, the three-band exponential model is constructed as follows:
wc＝a5+b5*exp[-(σvh/(σvv+σhh))/c5]； (5)
wherein w c represents the vegetation leaf moisture content; a 5、b5、c5 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarization backscatter coefficients;
wc＝a6+b6*exp[-(σvh/(σvv+σvh+σhh))/c6]; (6)
wherein w c represents the vegetation leaf moisture content; a 6、b6、c6 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarization backscatter coefficients;
RVI＝8*σvh/(σhh+σvv+2*σvh)； (7)
wc＝a7+b7*exp[-RVI/c7]； (8)
Wherein w c represents the vegetation leaf moisture content; a 7、b7、c7 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarized backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarized backscatter coefficients, and RVI is the radar index.
Further, in the step S3, the method specifically includes:
obtaining a microwave index with highest fitting degree as a preferable index, fitting with the water content of the vegetation leaves, and constructing a vegetation leaf water content inversion model aiming at different vegetation types;
1) Constructing a single-band model aiming at arbor and woodland vegetation;
2) Constructing a ratio vegetation index model aiming at shrub grassland vegetation;
3) And constructing a three-band radar index RVI model aiming at vegetation in the mixed area of arbor and grassland.
Further, in the step S4, the method specifically includes:
SLC data of a target area in a radar interference broad-width acquisition mode is obtained, and thermal noise removal, track file correction, noise filtering, radiometric calibration and Doppler topography correction are carried out on the SLC data to finally obtain incidence angle and backscattering information.
Compared with the prior art, the vegetation leaf water content remote sensing inversion method based on the polarized SAR data has the following beneficial effects:
1. the vegetation leaf water content data can be accurately obtained by acquiring vegetation weather information and soil information of a target area and combining polarized SAR data, the vegetation leaf water content data is not affected by weather, and material resources and financial resources are saved.
2. The method fully utilizes the strong penetrability of the polarized SAR data, so that the method can continuously observe the water content of vegetation leaves in all weather for a long time, and has important significance for early warning and verification of forest and grass fires.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
Fig. 1 is a schematic general flow chart of the water content of a vegetation leaf according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, the embodiment of the invention provides a mountain vegetation leaf water content remote sensing inversion method based on polarized SAR data, which specifically comprises the following steps:
s1, inputting tree parameters and surface parameters into a vegetation scattering model to simulate polarized SAR data, and establishing a polarized SAR database;
s2, constructing a vegetation leaf water content microwave model based on the polarized SAR data;
s3, constructing a vegetation leaf water content inversion model aiming at different vegetation types based on the vegetation leaf water content microwave model;
S4, acquiring radar SLC data of a target area, and processing the SLC data to acquire incident angle and backscattering information;
S5, substituting the incidence angle and the back scattering information into the vegetation blade water content inversion model to perform quantitative inversion, and obtaining vegetation blade water content data in a target area.
The present invention will be described in detail below by taking the application of the method to southwest mountain areas as an example; the current situation that cloud layer thickness exists in southwest mountain areas and weather conditions are greatly influenced is fully utilized, the strong penetrability of polarized SAR data is fully utilized, the method can adapt to all-weather work, and the problem that southwest mountain areas invert the water content of vegetation blades is solved.
As shown in fig. 1, various tree parameters and surface parameters required by the model are input into a vegetation scattering model (MIMICS) to simulate polarized SAR data;
1) Inputting parameters required by the model, such as tree height (H), vegetation water content (W c), vegetation coverage (F), tree diameter (d), crown height (H) and earth surface information soil water content (W s), and soil dielectric constant (e), into a vegetation scattering model (MIMICS);
2) Simulating polarized SAR data and establishing a polarized SAR database;
As shown in fig. 1, based on simulated polarized SAR data, a vegetation leaf moisture content microwave model is constructed as follows:
① Constructing a single-band index model:
ωc＝a1+b1*σvv； (1)
ωc＝a2+b2*σhh； (2)
ωc＝a3+b3*σvh； (3)
Wherein omega c represents the water content of vegetation leaves in southwest mountain areas; a 1、a2、a3、b1、b2、b3 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data; sigma vh is the cross-polarized backscattering coefficient
② Constructing a ratio index model:
wc＝a4+b4*exp[-(σhh/σvv)/c4]； (4)
wherein w c represents the water content of vegetation leaves in southwest mountain areas; a 4、b4、c4 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data;
③ Constructing a three-band index model:
wc＝a5+b5*exp[-(σvh/(σvv+σhh))/c5]； (5)
Wherein w c represents the water content of vegetation leaves in southwest mountain areas; a 5、b5、c5 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarization backscatter coefficients;
wc＝a6+b6*exp[-(σvh/(σvv+σvh+σhh))/c6]; (6)
Wherein w c represents the water content of vegetation leaves in southwest mountain areas; a 6、b6、c6 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarization backscatter coefficients;
RVI＝8*σvh/(σhh+σvv+2*σvh)； (7)
wc＝a7+b7*exp[-RVI/c7]； (8)
Wherein w c represents the water content of vegetation leaves in southwest mountain areas; a 7、b7、c7 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarized backscatter coefficients, and RVI is the radar index;
Further, based on the constructed microwave model, acquiring a microwave index with highest fitting degree as a preferable index, fitting with the water content of the vegetation leaves, and constructing a vegetation leaf water content inversion model aiming at different vegetation types;
1) Aiming at woodland and arbor equal-height trees, a single-band model is constructed by using sigma vh, and the inversion effect in a woodland is good;
2) Aiming at vegetation such as shrubs, a ratio vegetation index model is established, and the inversion effect of the ratio vegetation index model in vegetation areas such as shrubs is good;
3) Constructing a three-band radar index RVI model aiming at vegetation in a woodland and shrub mixing area, wherein the inversion effect of the three-band radar index RVI model in the vegetation type mixing area is good;
further, acquiring southwest mountain area Sentinel-1 radar backscatter data:
1) SLC data in an interference broad-width (IW) acquisition mode is ordered and downloaded from an European-air office network;
2) Carrying out thermal noise removal, track file correction, noise filtering, radiometric calibration and Doppler topography correction on the obtained data to finally obtain incident angle and backscattering information;
Further, polarized SAR data (incident angle and backscatter information) of satellite polarization; substituting the water content inversion model of the vegetation leaf to realize quantitative inversion of the water content of the vegetation leaf in southwest mountain area.
According to the vegetation leaf water content remote sensing inversion method based on the polarized SAR data, provided by the embodiment of the invention, the scattering characteristics of vegetation in southwest mountain areas are simulated by using local vegetation weather information and surface information required by a vegetation microwave scattering model (Michigan Microwave Canopy Scattering Model, MIMICS). Through carrying out sensitivity analysis on each factor, eliminating irrelevant factors, finding out the most suitable wave band and angle, analyzing the water content of vegetation leaves and simulated scattering data, and developing a simplified model for calculating the water content of the vegetation leaves in southwest mountain areas by using the polarization SAR index in the exponential relation model. The method fully utilizes the strong penetrability of the polarized SAR data, so that the method can adapt to all-weather work, is very suitable for southwest mountain areas with thick cloud layers and larger influence of weather conditions, and solves the difficult problem of southwest mountain areas in inverting the water content of vegetation blades. According to the invention, data are not required to be acquired in a specific time, and only polarization SAR data acquired by satellites above a region are required to be researched, so that the water content data of the vegetation leaves in a mountain area can be obtained quickly, destructive measurement of vegetation is omitted, material resources and financial resources are saved, and the method has important significance for early warning and verification of forest and grass fires.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (2)
1. A vegetation leaf water content remote sensing inversion method based on polarized SAR data is characterized by comprising the following steps:
s1, inputting tree parameters and surface parameters into a vegetation scattering model MIMICS to simulate polarized SAR data, and establishing a polarized SAR database;
s2, constructing a vegetation leaf water content microwave model based on the polarized SAR data;
s3, constructing a vegetation leaf water content inversion model aiming at different vegetation types based on the vegetation leaf water content microwave model;
S4, acquiring radar SLC data of a target area, and processing the SLC data to acquire incident angle and backscattering information;
s5, substituting the incidence angle and the back scattering information into the vegetation blade water content inversion model to perform quantitative inversion to obtain vegetation blade water content data in a target area;
in the step S1, the tree parameters include: tree height, vegetation water content, vegetation coverage, tree diameter and crown height; the surface parameters include: soil moisture content, soil dielectric constant;
in the step S2, the constructing a vegetation leaf water content microwave model includes: constructing a single-band index model, a ratio index model and a three-band index model; wherein:
the construction of the single-band index model is as follows:
ωc＝a1+b1*σvv (1)
ωc＝a2+b2*σhh (2)
ωc＝a3+b3*σvh (3)
wherein ω c represents the vegetation leaf moisture content; a 1、a2、a3、b1、b2、b3 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data; σ vh is the cross-polarized backscattering coefficient;
The ratio index model is constructed as follows:
wc＝a4+b4*exp[-(σhh/σvv)/c4]； (4)
wherein w c represents the vegetation leaf moisture content; a 4、b4、c4 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data;
the three-band index model is constructed as follows:
wc＝a5+b5*exp[-(σvh/(σvv+σhh))/c5]； (5)
wherein w c represents the vegetation leaf moisture content; a 5、b5、c5 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarization backscatter coefficients;
wc＝a6+b6*exp[-(σvh/(σvv+σvh+σhh))/c6]; (6)
wherein w c represents the vegetation leaf moisture content; a 6、b6、c6 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarization backscatter coefficients;
RVI＝8*σvh/(σhh+σvv+2*σvh)； (7)
wc＝a7+b7*exp[-RVI/c7]； (8)
Wherein w c represents the vegetation leaf moisture content; a 7、b7、c7 is the correlation coefficient of the model; σ vv、σhh is the vertical and horizontal polarization backscatter coefficients of the simulated polarized SAR data, σ vh is the cross polarized backscatter coefficients, and RVI is the radar index;
The step S3 specifically includes: obtaining a microwave index with highest fitting degree as a preferable index, fitting with the water content of the vegetation leaves, and constructing a vegetation leaf water content inversion model aiming at different vegetation types; wherein:
1) Constructing a single-band model aiming at arbor and woodland vegetation;
2) Constructing a ratio vegetation index model aiming at shrub grassland vegetation;
3) And constructing a three-band radar index RVI model aiming at vegetation in the mixed area of arbor and grassland.
2. The method for remotely sensing and inverting the water content of a vegetation leaf based on polarized SAR data as set forth in claim 1, wherein in the step S4, the method specifically includes:
SLC data of a target area in a radar interference broad-width acquisition mode is obtained, and thermal noise removal, track file correction, noise filtering, radiometric calibration and Doppler topography correction are carried out on the SLC data to finally obtain incidence angle and backscattering information.
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