CN115618174A - Soil humidity inversion method based on pixel scale surface roughness spectrum parameters - Google Patents

Soil humidity inversion method based on pixel scale surface roughness spectrum parameters Download PDF

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CN115618174A
CN115618174A CN202211304708.7A CN202211304708A CN115618174A CN 115618174 A CN115618174 A CN 115618174A CN 202211304708 A CN202211304708 A CN 202211304708A CN 115618174 A CN115618174 A CN 115618174A
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CN115618174B (en
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汪左
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Abstract

The invention discloses a soil humidity inversion method based on pixel scale surface roughness spectrum parameters, which comprises the following steps: constructing vv polarization backscattering component based on roughness spectrum parameter p
Figure DDA0003905318060000011
With the soil moisture M v Combined roughness Z s The relation model I of (1); constructing vh polarization backscattering component based on pixel incident angle theta
Figure DDA0003905318060000012
With the soil moisture M v Combined roughness Z s The relation model II of (1); construction based on vv-polarized backscatter components
Figure DDA0003905318060000013
vh polarized backscatter component
Figure DDA0003905318060000014
And an inversion model of a roughness spectrum parameter p of the pixel incident angle theta; scattering component of soil surface observed by radar
Figure DDA0003905318060000015
And inputting the corresponding pixel incident angle theta into the relation model I, the relation model II and the inversion model of the roughness spectrum parameter p, and inverting the soil humidity of the corresponding position of the pixel. The soil humidity inversion is carried out based on the roughness spectrum parameter p of the pixel scale, the spatial heterogeneity and the incidence angle influence of the roughness spectrum parameter p are fully considered, and the precision and the adaptability of the soil humidity inversion model are further improved.

Description

Soil humidity inversion method based on pixel scale surface roughness spectrum parameters
Technical Field
The invention belongs to the technical field of remote sensing, and particularly relates to a soil humidity inversion method based on pixel scale earth surface roughness spectrum parameters.
Background
Constructing vv, vh polarized backscattering components
Figure BDA0003905318040000011
Incident angle theta with pixel element and soil humidity M v Combined roughness Z s Such as the equations (1) and (2), the combined roughness Z is eliminated in a simultaneous manner s Thereby obtaining the soil humidity M v 。
Figure BDA0003905318040000012
Figure BDA0003905318040000013
In the formula (I), the compound is shown in the specification,
Figure BDA0003905318040000014
and
Figure BDA0003905318040000015
the soil surface scattering components under vv polarization and vh polarization respectively; a. The vv 、B vv 、C vv 、D vv 、A vh 、B vh 、C vh 、D vh Are all related to the angle of incidence theta.
The influence of the roughness spectrum parameter p value on the backscattering coefficient is huge, the backscattering coefficient belongs to a strong sensitive parameter, the p value has spatial heterogeneity, and the existing soil humidity inversion model generally takes a uniform p value in a regional scale range, and the influence of the spatial heterogeneity and the incident angle of the roughness spectrum parameter p is not considered, so that the precision and the adaptability of the soil humidity inversion model are reduced, and the soil humidity high-precision inversion with refined scale cannot be realized.
Disclosure of Invention
The invention provides a soil humidity inversion method based on pixel scale surface roughness spectrum parameters, and aims to solve the problems.
The invention is realized in such a way that a soil humidity inversion method based on pixel scale surface roughness spectrum parameters specifically comprises the following steps:
s1, constructing a vv polarization backscattering component based on a roughness spectrum parameter p
Figure BDA0003905318040000021
With the soil moisture M v Combined roughness Z s The relation model I of (1);
s2, constructing a vh polarization backscattering component based on the pixel incident angle theta
Figure BDA0003905318040000022
With the soil moisture M v Combined roughness Z s The relation model II of (1);
s3, constructing backward scattering components based on vv polarization
Figure BDA0003905318040000023
vh polarized backscatter component
Figure BDA0003905318040000024
And an inversion model of a roughness spectrum parameter p of the pixel incident angle theta;
s4, observing the scattering component of the soil surface observed by the radar
Figure BDA0003905318040000025
And inputting the corresponding pixel incident angle theta into the relation model I, the relation model II and the inversion model of the roughness spectrum parameter p, and inverting the soil humidity of the bare soil and the vegetation covered ground surface.
Further, the relation model i is as follows:
Figure BDA0003905318040000026
wherein A is vv (θ,p)、B vv (theta, p) and C vv (theta, p) is a coefficient related to a roughness spectrum parameter p and a pixel incident angle theta;
or is;
Figure BDA0003905318040000027
wherein A is vv (p)、B vv (p) and C vv (p) is a coefficient related to the roughness spectrum parameter p.
Further, A vv (θ,p)、B vv (theta, p) and C vv The expression of (θ, p) is specifically as follows:
A vv (θ,p)＝r 1 ·sin 3 θ·p+s 1 ·sin 2 θ+t 1 ·sinθ+u 1
B vv (θ,p)＝r 2 ·ln(p)+s 2 ·sin 2 θ+t 2 ·sinθ+u 2
C vv (θ,p)＝r 3 ·p 3 ·sin 2 θ+s 3 ·p 2 ·sinθ+t 3 ·p+u 3
in the formula, r 1 、s 1 、t 1 、u 1 、r 2 、s 2 、t 2 、u 2 、r 3 、s 3 、t 3 、u 3 Respectively, are various coefficients.
Further, coefficient A vv (p)、B vv (p) and C vv The determination method of (p) is specifically as follows:
for coefficient A under different pixel incident angles theta vv (p)、B vv (p) and C vv (p) performing nonlinear regression on the relationship between the (p) and the roughness spectrum parameter p to obtain the coefficient A under different pixel incident angles theta vv (p)、B vv (p) and C vv (p) a relationship with the roughness spectrum parameter p.
Further, coefficient A vv (p)、B vv (p) and C vv (p) and the roughness spectrum parameter p, asThe following:
A vv (p)＝a 1 ·p 3 +b 1 ·p 2 +c 1 ·p+d 1
B vv (p)＝a 2 ·p 3 +b 2 ·p 2 +c 2 ·p+d 2
C vv (p)＝a 3 ·p 4 +b 3 ·p 3 +c 3 ·p 2 +d 3 ·p+e 3
in the formula, a 1 、b 1 、c 1 、d 1 、a 2 、b 2 、c 2 、d 2 、a 3 、b 3 、c 3 、d 3 、e 3 The coefficients are respectively the coefficients of each fitting formula, the pixel incident angles theta are different, and the values of the coefficients are different.
Further, the roughness spectrum parameter p is inverted by the inverse model based on the roughness spectrum parameter p, and the inverse model of the roughness spectrum parameter p is specifically as follows:
Figure BDA0003905318040000031
in the formula, a 4 、b 4 、c 4 、d 4 Are fitting coefficients.
Further, the method for constructing the inverse model of the roughness spectrum parameter p is as follows:
(1) Acquiring soil humidity measured values of a plurality of ground sample points through satellite-ground synchronous observation, and backscattering coefficients and pixel incident angles theta of radar satellite synchronous observation of the soil humidity measured values;
(2) The scattering component of the soil surface obtained by synchronous observation
Figure BDA0003905318040000032
And
Figure BDA0003905318040000033
inputting each pixel incident angle theta into soil humidity inversion models under different p values to obtain soil humidity values under different p values；
(3) Matching the soil humidity values inverted under different p values with the soil humidity measured values of the corresponding pixels, and finding the p value corresponding to the inverted value closest to the measured values, namely the optimal p value, or performing linear interpolation on 2 p values most matched with the measured values to obtain the optimal p value.
(4) For the optimum p value and
Figure BDA0003905318040000034
carrying out regression analysis on the sine value sin theta of the pixel incident angle to obtain an inversion model of a roughness spectrum parameter p;
further, the expression of the relationship model ii is specifically as follows:
Figure BDA0003905318040000041
wherein, C vh (theta) is a coefficient related to the incident angle theta of the picture element, A vh 、B vh Respectively fitting coefficients. Further, C vh The expression of (θ) is specifically as follows:
C vh (θ)＝a 5 ·cos 2 θ+b 5 ·cosθ+c 5
in the formula, a 5 、b 5 、c 5 Are fitting coefficients.
The soil humidity inversion model consists of a relation model I, a relation model II and an inversion model of a roughness spectrum parameter p.
Furthermore, if the earth surface is bare earth, the backward scattering coefficient observed by the radar is the scattering component of the earth surface
Figure BDA0003905318040000042
And
Figure BDA0003905318040000043
if the earth surface is covered by the plants, the backscattering coefficient observed by the radar is the backscattering coefficient of the canopy
Figure BDA0003905318040000044
And
Figure BDA0003905318040000045
the surface body is subjected to surface body scattering separation, and the scattering component of the soil surface can be obtained
Figure BDA0003905318040000046
And
Figure BDA0003905318040000047
according to the soil humidity inversion method, the soil humidity inversion is carried out based on the roughness spectrum parameter p of the pixel scale, the spatial heterogeneity and the incidence angle influence of the roughness spectrum parameter p are fully considered, the precision and the adaptability of a soil humidity inversion model are further improved, and the soil humidity inversion requirement of the refined scale is met.
Drawings
FIG. 1 shows the vv polarization backscattering coefficient provided by an embodiment of the invention
Figure BDA0003905318040000048
With respect to the roughness spectrum parameter p, wherein (a) is the vv polarization backscattering coefficient at different values of the roughness spectrum parameter p
Figure BDA0003905318040000049
A variation curve along with the incident angle theta of the pixel; (b) For vv polarization backscattering coefficient under different roughness spectrum parameter p values
Figure BDA00039053180400000410
A change curve with soil moisture;
FIG. 2 shows vh polarization backscattering coefficients at different pixel incident angles θ according to an embodiment of the present invention
Figure BDA00039053180400000411
And combined roughness Z s The relationship of (a); wherein the vh polarization backscattering coefficient when (a) is theta =20 DEG
Figure BDA00039053180400000412
And combined roughness Z s The relationship of (1); (b) Polarization backscattering coefficient of vh at theta =30 DEG
Figure BDA00039053180400000413
And combined roughness Z s The relationship of (1); (c) Polarization of vh backscatter coefficient at θ =40 °
Figure BDA00039053180400000414
And combined roughness Z s The relationship of (a); (d) Polarization backscattering coefficient of vh at θ =50 °
Figure BDA00039053180400000415
And combined roughness Z s The relationship of (1);
FIG. 3 shows vh polarization backscattering coefficients at different pixel incident angles θ according to an embodiment of the present invention
Figure BDA0003905318040000051
With the soil moisture M v The relationship of (1);
FIG. 4 is a flow chart of a roughness spectrum parameter p-value inversion model modeling provided by an embodiment of the invention;
FIG. 5 illustrates the p-value inversion model accuracy provided by an embodiment of the present invention;
fig. 6 is a soil moisture inversion accuracy verification result diagram provided by the embodiment of the invention.
Detailed Description
The following detailed description of the embodiments of the present invention is provided to help those skilled in the art to more fully, accurately and deeply understand the inventive concept and technical solution of the present invention by describing the embodiments with reference to the accompanying drawings.
The influence of the p value of the roughness spectrum parameter on the backscattering coefficient is huge, and the p value belongs to a strong sensitive parameter (as can be seen from figure 1). As can be seen from fig. 1 (a), under the condition that the soil humidity, the root mean square height and the correlation length are the same, the backscattering coefficient corresponding to the same incident angle is gradually reduced along with the increase of the p value, the larger the incident angle is, the larger the reduction amplitude is, when p is increased from 1.2 to 3, the backscattering coefficient difference at the incident angle of 33 degrees is 13.12db, and the difference at the incident angle of 44 degrees can reach 18.64dB. As can be seen from fig. 1 (b), when the incident angle, the root mean square height, and the correlation length are the same, the difference in soil moisture for the same backscattering coefficient is large if p is different.
Therefore, in view of the above problems, the present invention proposes: (1) A soil moisture inversion model considering a roughness spectrum parameter p; (2) Aiming at the problem that the influence of spatial heterogeneity and incidence angle of a unified p value of a regional scale is neglected, a roughness spectrum parameter p inversion model of a pixel scale is established; (3) And (3) combining the step (1) and the step (2), completely constructing a soil humidity inversion model based on pixel scale earth surface roughness spectrum parameters.
(1) Soil humidity inversion model considering roughness spectrum parameter p
The analysis of an Oh model simulation database discovers that the vh polarization backscattering coefficient is under the condition of different pixel incident angles theta
Figure BDA0003905318040000061
With soil humidity M v Surface combined roughness Z s The relationship of (a) only appears as an up-down translation (see fig. 2 and 3), only the intercept of the curve changes. Thus constructing one
Figure BDA0003905318040000062
And M v And Z s As shown in equation 3:
Figure BDA0003905318040000063
in the formula, coefficient A vh 、B vh Coefficient C independent of angle of incidence theta vh As a function of the angle of incidence theta. Obtained by least squares fitting:
A vh ＝3.040，B vh ＝3.255，C vh (θ)＝-7.2358cos 2 θ+23.633cosθ-20.08 (4)
the influence of the roughness spectrum parameter p value on the backscattering coefficient is huge, and the method belongs to a strong sensitive parameter (shown by figure 1)Known). In that
Figure BDA0003905318040000064
And M v And Z s In the relation (a) and a roughness spectrum parameter p are added and considered, and a relation (b) which considers the roughness spectrum parameter p is constructed
Figure BDA0003905318040000065
And M v And Z s See formula (5):
Figure BDA0003905318040000066
in the formula, A vv 、B vv 、C vv The incidence angle theta, which can be obtained by radar imaging, is correlated with the roughness spectral parameter p. Carrying out nonlinear regression on 3 coefficients in the formula (5) by using data of different incidence angles and different roughness spectrum parameters p in a backscattering simulation database to obtain a coefficient calculation formula:
A vv (θ,p)＝0.044sin 3 θ·p-0.080sin 2 θ+0.031sinθ+2.579 (6)
R 2 ＝0.996，RMSE＝0.0004，MRE＝0.014％
B vv (θ,p)＝4.722ln(p)-7.643sin 2 θ+9.243sinθ-0.511 (7)
R 2 ＝0.995，RMSE＝0.0930，MRE＝1.486％
C vv (θ,p)＝0.698p 3 ·sin 2 θ-8.085p 2 ·sinθ+14.079p-6.445 (8)
R 2 ＝0.987，RMSE＝0.6925，MRE＝26.311％
fitting accuracy R of fitting formula of each coefficient 2 Very high, all above 0.98, but still with some error, especially C vv The Root Mean Square Error (RMSE) was 0.6925, and the average relative error (MRE) reached 26.311%.
To further improve the fitting accuracy of the improved vv polarization relationship model coefficients, it is feasible to fit the coefficients in view of the incident angle θThe image is obtained through image preprocessing and belongs to known parameters, so that the method is based on a formula (5), the incidence angle theta is fixed one by one, and the coefficient A is adjusted vv 、B vv 、C vv And the roughness spectrum parameter p are subjected to nonlinear regression again to obtain a new model (formula 9) considering the relationship between the vv polarization backscattering coefficient of the roughness spectrum parameter p and the soil humidity and combined roughness and a new A vv 、B vv 、C vv Fitting the equations (10), (11), (12)) and A at different angles of incidence vv 、B vv 、C vv Coefficient lookup tables (table 1, table 2, table 3).
Figure BDA0003905318040000071
A vv (p)＝a 1 ·p 3 +b 1 ·p 2 +c 1 ·p+d 1 (10)
B vv (p)＝a 2 ·p 3 +b 2 ·p 2 +c 2 ·p+d 2 (11)
C vv (p)＝a 3 ·p 4 +b 3 ·p 3 +c 3 ·p 2 +d 3 ·p+e 3 (12)
In the formula, a 1 、b 1 、c 1 、d 1 、a 2 、b 2 、c 2 、d 2 、a 3 、b 3 、c 3 、d 3 、e 3 The coefficients of the fitting formulas can be found from table 1, table 2 and table 3.
As can be seen from tables 1, 2 and 3, the coefficient A of formula (9) at different incident angles vv The fitting accuracy of (2) is closer to that of the formula (5). For coefficient B vv And C vv The accuracy of equation (9) is much higher than equation (5). Therefore, the formula (9) is selected as the relation model under vv polarization, and when the method is actually applied, the corresponding coefficient can be found from the coefficient lookup table (table 1, table 2 and table 3) according to the specific incidence angle of the radar image。
In summary, by combining the formula (9) and the formula (3) and combining the coefficient fitting formulas (the formula (10), the formula (11), the formula (12) and the formula (4)) and the coefficient lookup tables (the tables 1, 2 and 3), a soil moisture inversion model considering the roughness spectrum parameter p is created. But in the case of the model, however,
Figure BDA0003905318040000072
and
Figure BDA0003905318040000073
for known observed values, soil moisture M v Surface combined roughness Z for the final unknown parameters to be inverted s And the roughness spectrum parameter p is an unknown parameter. Two simultaneous equations with 3 unknown parameters still cannot solve the soil humidity M v It is further desirable to provide a roughness profile parameter, pbo, to eliminate the combined roughness, Z s And further inversing soil humidity M v 。
TABLE 1 different angles of incidence A vv Calculating each coefficient of formula and fitting precision
Figure BDA0003905318040000081
TABLE 2 different angles of incidence B vv Calculating each coefficient of formula and fitting precision
Figure BDA0003905318040000091
TABLE 3 different angles of incidence C vv Calculating each coefficient of formula and fitting precision
Figure BDA0003905318040000101
(2) Inversion model of roughness spectrum parameter p of pixel scale
Aiming at the problem that the influence of spatial heterogeneity and incidence angle of a unified p value of the area scale is neglected, a roughness spectrum parameter p inversion model of the pixel scale is established. Namely, for each pixel in the research area, the roughness spectrum parameter p value which is most matched with the surface condition of the pixel is found. The specific establishment method is shown in fig. 4 and described as follows:
(1) And performing satellite-ground synchronous observation to obtain the soil humidity measured values of a plurality of ground sample points and the backscattering coefficients and incidence angles of radar satellite synchronous observation. The greater the number of sample points the better, the 646 sample pixels are used in the present invention for modeling herein.
(2) The scattering component of the soil surface obtained by synchronous observation
Figure BDA0003905318040000111
And
Figure BDA0003905318040000112
(if the earth's surface is bare, the backward scattering coefficient observed by radar is directly the soil surface scattering component
Figure BDA0003905318040000113
And
Figure BDA0003905318040000114
if the earth surface is covered by the plants, the backscattering coefficient observed by the radar is the backscattering coefficient of the canopy
Figure BDA0003905318040000115
And
Figure BDA0003905318040000116
(
Figure BDA0003905318040000117
representing the vv polarization canopy backscatter coefficient,
Figure BDA0003905318040000118
representing the backscattering coefficient of the vh polarization canopy), and performing surface body scattering separation on the vh polarization canopy by using a water cloud model to obtain the soil surface scattering component
Figure BDA0003905318040000119
And
Figure BDA00039053180400001110
) And inputting the soil humidity inversion model considering the roughness spectrum parameter p under the assumption of different p values according to the incidence angle theta of each pixel element to obtain the soil humidity inversion value under the assumption of different p values.
(3) And matching the soil humidity inversion values under the different p values with the soil humidity measured values of the corresponding pixels, and finding the p value corresponding to the inversion value closest to the measured values, namely the optimal p value. In the actual matching process, since the assumed p-values are discrete, linear interpolation can be performed on the 2 p-values that best match the measured values, so as to obtain a more accurate optimal p-value. In this way, the optimal p-value is obtained for all sample pels (646).
(4) For the optimum p value and
Figure BDA00039053180400001111
and the correlation analysis is carried out on the sine value sin theta of the pixel incident angle, and the result (Table 4) shows that the p value is related to the sine value sin theta
Figure BDA00039053180400001112
And sin theta are all significantly related at the 0.01 level, and the correlation coefficients are 0.137, 0.665 and-0.539 respectively. Thus, using the 646 pel samples, the optimal p-value and is regressed
Figure BDA00039053180400001113
And sin theta, an inversion model of the roughness spectrum parameter p of the pixel scale is established, and the expression of the inversion model is shown in a formula (13). After the inversion model is established, pixel scale p value inversion can be realized without ground observation data.
TABLE 4 correlation coefficient between p value and sine value of scattering component and incidence angle of soil surface
Figure BDA0003905318040000121
Note: * Indicates significant correlation at the 0.01 level.
The roughness spectrum parameter p inversion model is specifically as follows:
Figure BDA0003905318040000122
R 2 ＝0.875，RMSE＝0.0828，MAE＝0.0638
the scatter diagram of the p value and the optimal p value of the model inversion is shown in figure 5, and as can be seen, the p value inversion model has high fitting precision, R 2 0.875 for RMSE, 0.0828 for RMSE, 0.0638 for MAE
(3) Soil humidity inversion model based on pixel scale surface roughness spectrum parameters
The formula (9), the formula (3) and the formula (13) are combined, and the coefficient fitting formula (10), the formula (11), the formula (12) and the formula (4)) and the coefficient lookup table (table 1, table 2 and table 3) are combined, so that the soil humidity inversion model based on the pixel scale surface roughness spectrum parameters is completely constructed.
Scattering component of soil surface observed by radar
Figure BDA0003905318040000123
And
Figure BDA0003905318040000124
(if the earth's surface is bare, the backward scattering coefficient observed by radar is directly the soil surface scattering component
Figure BDA0003905318040000125
And
Figure BDA0003905318040000126
if the earth surface is planted and covered, the backscattering coefficient observed by the radar is the backscattering coefficient of the canopy
Figure BDA0003905318040000127
And
Figure BDA0003905318040000128
(
Figure BDA0003905318040000129
representing the vv polarization canopy backscatter coefficient,
Figure BDA00039053180400001210
representing the backscattering coefficient of the vh polarization canopy), and performing surface body scattering separation on the vh polarization canopy by using a water cloud model to obtain the soil surface scattering component
Figure BDA00039053180400001211
And
Figure BDA00039053180400001212
surface body scattering separation is conventional operation, no repeated description is needed), and the pixel incident angle theta is input into the model, so that the combination roughness Z can be eliminated s So as to obtain the soil humidity M by inversion v 。
For the constructed soil humidity inversion model based on the pixel scale surface roughness spectrum parameters, inversion result precision verification is performed by using soil humidity measured value samples (total 213 sample pixels) outside the modeling, and the verification result is shown in fig. 6. It can be seen that the scattering points of the inversion value and the measured value are distributed on two sides of the line 1.
The invention has been described by way of example, and it is to be understood that its specific implementation is not limited to the details of construction and arrangement shown, but is within the scope of the invention.

Claims (10)

1. A soil humidity inversion method based on pixel scale surface roughness spectrum parameters is characterized by comprising the following steps:
s1, constructing vv polarization backscattering components based on roughness spectrum parameters p
Figure FDA0003905318030000011
With the soil moisture M v Combined roughness Z s The relation model I of (1);
s2, constructing a vh polarization backscattering component based on the pixel incident angle theta
Figure FDA0003905318030000012
With the soil moisture M v Combined roughness Z s The relation model II of (1);
s3, constructing backward scattering components based on vv polarization
Figure FDA0003905318030000013
vh polarized backscatter component
Figure FDA0003905318030000014
And an inversion model of the roughness spectrum parameter p of the pixel incident angle theta;
s4, observing the scattering component of the soil surface observed by the radar
Figure FDA0003905318030000015
And inputting the corresponding pixel incidence angle theta into the relation model I, the relation model II and the inversion model of the roughness spectrum parameter p, and inverting the soil humidity of the bare soil and the vegetation covered ground surface.
2. The pixel scale earth surface roughness spectrum parameter-based soil humidity inversion method of claim 1, wherein the relation model I is as follows:
Figure FDA0003905318030000016
wherein A is vv (θ,p)、B vv (theta, p) and C vv (theta, p) is a coefficient related to a roughness spectrum parameter p and a pixel incident angle theta;
or the following steps:
Figure FDA0003905318030000017
wherein A is vv (p)、B vv (p) and C vv (p) is a coefficient related to the roughness spectrum parameter p.
3. The pixel-scale earth surface roughness spectrum parameter-based soil humidity inversion method of claim 2, wherein A is vv (θ,p)、B vv (theta, p) and C vv The expression of (θ, p) is specifically as follows:
A vv (θ,p)＝r 1 ·sin 3 θ·p+s 1 ·sin 2 θ+t 1 ·sinθ+u 1
B vv (θ,p)＝r 2 ·ln(p)+s 2 ·sin 2 θ+t 2 ·sinθ+u 2
C vv (θ,p)＝r 3 ·p 3 ·sin 2 θ+s 3 ·p 2 ·sinθ+t 3 ·p+u 3
in the formula, r 1 、s 1 、t 1 、u 1 、r 2 、s 2 、t 2 、u 2 、r 3 、s 3 、t 3 、u 3 Respectively, are various coefficients.
4. The pixel-scale earth surface roughness spectrum parameter-based soil humidity inversion method of claim 2, wherein the coefficient A is vv (p)、B vv (p) and C vv The determination method of (p) is specifically as follows:
respectively at different pixel incident angles theta to coefficient A vv (p)、B vv (p) and C vv (p) performing nonlinear regression on the relationship between the (p) and the roughness spectrum parameter p to obtain the coefficient A under different pixel incident angles theta vv (p)、B vv (p) and C vv (p) a relationship with the roughness spectrum parameter p.
5. The pixel-scale earth surface roughness spectrum parameter-based soil humidity inversion method of claim 4, wherein the coefficient A is vv (p)、B vv (p) and C vv (p) the relationship between p and the roughness spectrum parameter p is as follows:
A vv (p)＝a 1 ·p 3 +b 1 ·p 2 +c 1 ·p+d 1
B vv (p)＝a 2 ·p 3 +b 2 ·p 2 +c 2 ·p+d 2
C vv (p)＝a 3 ·p 4 +b 3 ·p 3 +c 3 ·p 2 +d 3 ·p+e 3
in the formula, a 1 、b 1 、c 1 、d 1 、a 2 、b 2 、c 2 、d 2 、a 3 、b 3 、c 3 、d 3 、e 3 The coefficients are respectively the coefficients of each fitting formula, the pixel incident angles theta are different, and the values of the coefficients are different.
6. The soil humidity inversion method based on pixel scale earth surface roughness spectrum parameters as claimed in claim 1, wherein the roughness spectrum parameter p is inverted by an inversion model based on the roughness spectrum parameter p, and the inversion model of the roughness spectrum parameter p is specifically as follows:
Figure FDA0003905318030000021
in the formula, a 4 、b 4 、c 4 、d 4 Are fitting coefficients.
7. The pixel scale earth surface roughness spectrum parameter-based soil humidity inversion method according to claim 6, wherein the inversion model construction method of the roughness spectrum parameter p is as follows:
(1) Acquiring soil humidity measured values of a plurality of ground sample points through satellite-ground synchronous observation, and backward scattering coefficients and pixel incident angles theta of radar satellite synchronous observation of the soil humidity measured values;
(2) The scattering component of the soil surface obtained by synchronous observation
Figure FDA0003905318030000031
And
Figure FDA0003905318030000032
inputting the pixel incident angle theta into soil humidity inversion models under different p values to obtain soil humidity values under different p values;
(3) Matching the soil humidity values inverted under different p values with the soil humidity measured values of the corresponding pixels, and finding the p value corresponding to the inverted value closest to the measured values, namely the optimal p value, or performing linear interpolation on 2 p values most matched with the measured values to obtain the optimal p value.
(4) For the optimum p value and
Figure FDA0003905318030000033
and carrying out regression analysis on the sine value sin theta of the pixel incident angle to obtain an inversion model of the roughness spectrum parameter p.
8. The soil humidity inversion method based on the pixel scale surface roughness spectrum parameters as claimed in claim 1, wherein the expression of the relation model II is as follows:
Figure FDA0003905318030000034
wherein, C vh (theta) is a coefficient related to the incident angle theta of the picture element, A vh 、B vh Respectively fitting coefficients.
9. The pixel-scale earth surface roughness spectrum parameter-based soil humidity inversion method of claim 8, wherein C is vh The expression of (θ) is specifically as follows:
C vh (θ)＝a 5 ·cos 2 θ+b 5 ·cosθ+c 5
in the formula, a 5 、b 5 、c 5 Are fitting coefficients.
The soil humidity inversion model consists of a relation model I, a relation model II and an inversion model of a roughness spectrum parameter p.
10. The method of claim 1, wherein if the earth's surface is bare, the radar-observed backscattering coefficient is the soil surface scattering component
Figure FDA0003905318030000035
And
Figure FDA0003905318030000036
if the earth surface is covered by the plants, the backscattering coefficient observed by the radar is the backscattering coefficient of the canopy
Figure FDA0003905318030000037
And
Figure FDA0003905318030000038
the surface body is subjected to surface body scattering separation, and the scattering component of the soil surface can be obtained
Figure FDA0003905318030000039
And
Figure FDA00039053180300000310
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