CN111751286A - Soil moisture extraction method based on change detection algorithm - Google Patents

Soil moisture extraction method based on change detection algorithm Download PDF

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CN111751286A
CN111751286A CN202010573390.7A CN202010573390A CN111751286A CN 111751286 A CN111751286 A CN 111751286A CN 202010573390 A CN202010573390 A CN 202010573390A CN 111751286 A CN111751286 A CN 111751286A
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pixel point
water content
soil moisture
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CN111751286B (en
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陈彦
陈云坪
吴磊
姜灵海
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Beijing Shenzhen Blue Space Remote Sensing Technology Co ltd
University of Electronic Science and Technology of China
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Abstract

The invention discloses a soil moisture extraction method based on a change detection algorithm, which comprises the steps of firstly using sentinel 2 optical data and a water cloud model to remove the influence of vegetation on backscattering coefficients, then carrying out roughness normalization, namely eliminating the influence caused by surface roughness change of the same plot under different time sequences through the difference between VV polarization backscattering coefficients and VH polarization backscattering coefficients, then calculating according to the backscattering coefficients after the roughness normalization to obtain relative water content, and finally converting the relative water content into absolute water content by combining with the soil water content obtained by SMAP data to realize soil moisture inversion. The invention realizes accurate soil moisture inversion by improving the change detection algorithm.

Description

Soil moisture extraction method based on change detection algorithm
Technical Field
The invention belongs to the technical field of soil moisture extraction, and particularly relates to a soil moisture extraction method based on a change detection algorithm.
Background
Soil moisture is an important surface parameter, plays a crucial role in global surface water circulation and carbon circulation, and simultaneously influences the climatic processes of precipitation, temperature, evapotranspiration and the like, and influences the whole natural and human life at any moment. Measurements of global soil moisture help to improve understanding of the hydrological processes, ecosystem functions, and the link between the earth's water, energy and carbon cycles. The water content of the soil determines the growth of the crop. The monitoring of the water content of the farmland has great significance for the research of agricultural science such as the estimation of yield of crops, the estimation of drought, waterlogging and the like. Due to the influence of factors such as climate and terrain, the space-time distribution of surface water is extremely uneven, and drought and flood disasters often occur. The time-series ground surface water content can play a good indication role in pre-disaster warning, and meanwhile, the capability of post-disaster assessment and disaster situation research cannot be ignored. The real-time, accurate and large-area monitoring of the water content of the soil has important values for production and life, disaster prevention and control and natural science research.
The traditional soil moisture measuring method mainly comprises a time domain reflection method, a gravity method, a seed probe and a drying measuring method, and the methods rely on manual instruments to carry out field measurement, so that the time and the labor are wasted, and the real-time monitoring of the water content distribution of a large-area ground surface is difficult. The development of remote sensing technology makes it possible to monitor soil water accurately and in large area in real time. The microwave remote sensing is not affected by weather, has high sensitivity to soil moisture, and is an excellent data source for inverting the soil moisture. The sentinel No. 1 carries the microwave sensor of the C wave band, has higher time and space resolution, is free to the outside, and is very suitable for engineering application.
The change detection algorithm considers the roughness parameter of the soil as a constant value, considers the water content of the soil and the backscattering coefficient as a linear relation, and can directly calculate the relative water content through the backscattering coefficient. The occurrence of the change detection algorithm well solves the problem that a semi-empirical or empirical method constructed by theoretical model modeling cannot be well applied, and does not need to input any actually measured earth surface parameters,
however, the change detection algorithm ignores the change of the surface roughness, only obtains the relative soil moisture of the surface, and cannot directly calibrate the absolute moisture content. The roughness of the earth surface and the vegetation parameters are difficult to be completely unchanged within one year, particularly for the regions with a large number of farmlands, the roughness of the earth surface is inevitably changed to a large extent by farming activities, and the vegetation parameters are also obviously changed along with the growth of crops. Considering these regions' roughness and vegetation parameters as constant values can introduce large errors. And the relative water content needs to be converted to an absolute water content. How to utilize a change detection algorithm to accurately invert the soil moisture is a difficult problem to be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a soil moisture extraction method based on a change detection algorithm, so that the change detection algorithm is improved, and accurate soil moisture inversion is realized.
In order to achieve the above object, the soil moisture extraction method based on the change detection algorithm of the present invention comprises the following steps:
s1: acquiring serialized sentinel No. 1 microwave remote sensing data, serialized sentinel No. 2 optical data and serialized SMAP data corresponding to a region needing soil moisture extraction in a preset time period, and respectively performing the following processing:
generating an SAR image sequence according to the sentinel No. 1 microwave remote sensing data, recording the number of SAR images in the SAR image sequence as T, selecting one SAR image as a reference SAR image, registering other SAR images with the reference SAR image, and recording each SAR image after registration as
Figure BDA0002550448320000021
T ═ 1,2, …, T; then from each SAR image
Figure BDA0002550448320000022
After extracting correspondingRecording the backscattering coefficient of each pixel point in the tth SAR image as sigma to a backscattering coefficient distribution grapht(i, j), (i, j) representing coordinates of pixel points in the SAR image;
generating an optical image sequence according to the serialized sentinel No. 2 optical data, recording the number of SAR images in the optical image sequence as D, registering each optical image with a reference SAR image, and recording the optical images after registration processing as
Figure BDA0002550448320000023
d＝1,2,…,D；
Extracting a water content time sequence of the soil water extraction area from the serialized SMAP data, and acquiring a maximum value of the water content corresponding to each pixel point (i, j) from the water content time sequence according to the geographical position coordinates of each pixel point (i, j) in the SAR image
Figure BDA0002550448320000024
And minimum value
Figure BDA0002550448320000025
S2: for each SAR image
Figure BDA0002550448320000026
Searching out the optical image closest to the optical image from the optical image sequence
Figure BDA0002550448320000027
The optical image is formed
Figure BDA0002550448320000028
Combining the red light wave band and the near infrared wave band to obtain a normalized vegetation index, and recording the normalized vegetation index of the pixel point (i, j) as NDVId′(i, j) which is then converted to vegetation moisture content
Figure BDA0002550448320000029
Then the water content of the vegetation
Figure BDA00025504483200000210
Substituting the water cloud model to obtain the backscattering coefficient of each pixel point without vegetation influence
Figure BDA0002550448320000031
S3: for each SAR image
Figure BDA0002550448320000032
Obtaining the SAR image according to the backscattering coefficient distribution diagram
Figure BDA0002550448320000033
Backscattering coefficient under VV polarization of each pixel point (i, j)
Figure BDA0002550448320000034
And backscattering coefficient under VH polarization
Figure BDA0002550448320000035
Calculating the backscattering coefficient of the pixel point for roughness normalization according to the following formula
Figure BDA0002550448320000036
Figure BDA0002550448320000037
Where a is-20.35 and γ represents a predetermined cross-polarization difference σvv-σvhA reference value of (d);
s4: for each pixel point (i, j) in the SAR image, the roughness of each pixel point is normalized by T roughness values corresponding to the pixel point
Figure BDA0002550448320000038
Search out the maximum value
Figure BDA0002550448320000039
And minimum value
Figure BDA00025504483200000310
Let us reference value
Figure BDA00025504483200000311
Wet reference value
Figure BDA00025504483200000312
Each SAR image is obtained by calculation according to the following formula
Figure BDA00025504483200000313
Relative water content of soil corresponding to the middle pixel point (i, j)
Figure BDA00025504483200000314
Figure BDA00025504483200000315
S5: calculating each SAR image according to the following formula
Figure BDA00025504483200000316
The absolute water content of the soil corresponding to the middle pixel point (i, j)
Figure BDA00025504483200000317
And (3) completing soil moisture extraction:
Figure BDA00025504483200000318
the soil moisture extraction method based on the change detection algorithm comprises the steps of firstly using sentinel No. 2 optical data and a water cloud model to remove the influence of vegetation on backscattering coefficients, then carrying out roughness normalization, namely eliminating the influence caused by surface roughness change of the same plot under different time sequences through the difference between VV polarization backscattering coefficients and VH polarization backscattering coefficients, then calculating according to the backscattering coefficients after the roughness normalization to obtain relative moisture content, and finally converting the relative moisture content into absolute moisture content by combining with the soil moisture content obtained by SMAP data to realize soil moisture inversion.
According to the method, a change detection algorithm is improved, the influence of the water content of the vegetation on the backscattering coefficient is firstly removed from the original backscattering coefficient, and then roughness normalization is further introduced, so that pixel points at the same position in the SAR image have the same roughness parameter at different time, and therefore the change of the backscattering coefficient is only caused by the change of the water content of the soil, and the more accurate soil water inversion is realized.
Drawings
FIG. 1 is a flow chart of an embodiment of the soil moisture extraction method based on a change detection algorithm of the present invention;
FIG. 2 is an exemplary plot of a backscattering coefficient distribution plot;
FIG. 3 is an exemplary illustration of optical images after registration;
FIG. 4 is a graphical illustration of the relationship between cross-polarization differences and surface roughness;
FIG. 5 is a graphical representation of cross polarization difference versus VV polarization backscattering coefficient for different water cut contents;
FIG. 6 is a graph showing the relative water content of soil in Pi zones over a set period of time in this example;
fig. 7 is a diagram showing the absolute water content distribution of the soil in the Pi all areas within the set time period in the present embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
FIG. 1 is a flow chart of an embodiment of the soil moisture extraction method based on a change detection algorithm according to the present invention. As shown in FIG. 1, the soil moisture extraction method based on the change detection algorithm of the present invention comprises the following specific steps:
s101: obtaining analysis data:
acquiring serialized sentinel No. 1 microwave remote sensing data, serialized sentinel No. 2 optical data and serialized SMAP (soil Moisture Active and Passive) data corresponding to a region needing soil Moisture extraction in a preset time period, wherein the data cannot be directly used and needs to be preprocessed, and the specific method comprises the following steps:
generating an SAR (Synthetic Aperture Radar) image sequence according to serialized sentinel No. 1 microwave remote sensing data, recording the number of SAR images in the SAR image sequence as T, selecting one SAR image as a reference SAR image, registering other SAR images with the reference SAR image, and recording each SAR image after registration as
Figure BDA0002550448320000041
T is 1,2, …, T. Then from each SAR image
Figure BDA0002550448320000042
Extracting a corresponding backscattering coefficient distribution graph, and recording the backscattering coefficient of each pixel point in the tth SAR image as sigmat(i, j) and (i, j) represent the coordinates of pixel points in the SAR image. The existing extraction process of the backscattering coefficient distribution map can be realized by adopting software such as SARscape and SNAP, and the specific processes comprise filtering, radiometric calibration, geocoding and the like. In this embodiment, soil moisture extraction is performed in the urban area Pi of the city of Sichuan province, the time period is from 1 month and 9 days in 2019 to 12 months and 11 days in 2019, and the interval between SAR images is 24 days. Fig. 2 is an exemplary graph of a backscattering coefficient distribution map.
And generating an optical image sequence according to the serialized sentinel No. 2 optical data. The process can be implemented by using the existing ENVI software, and the specific process includes atmospheric correction, radiometric calibration, resampling and the like, wherein the resolution of the optical image is 10m in the embodiment. Recording the number of SAR images in the optical image sequence as D, registering each optical image with a reference SAR image, and recording the optical images after registration processing as
Figure BDA00025504483200000514
D is 1,2, …, D. The optical data of the sentinel No. 2 is greatly influenced by weather, and the generated sumThe optical image of the grid generally cannot guarantee the periodicity like the SAR image, so that the optical image of the grid cannot correspond to the SAR image one by one. Fig. 3 is an exemplary diagram of the optical image after registration.
Extracting a water content time sequence of the soil water extraction area from the serialized SMAP data, and acquiring a maximum value of the water content corresponding to each pixel point (i, j) from the water content time sequence according to the geographical position coordinates of each pixel point (i, j) in the SAR image
Figure BDA0002550448320000051
And minimum value
Figure BDA0002550448320000052
And the absolute water content is used as the calibration range of the absolute water content of the pixel point.
S102: removing vegetation influence:
in order to make the backscattering coefficient more accurate, each SAR image needs to be processed
Figure BDA0002550448320000053
The backscattering coefficient distribution diagram is used for removing vegetation influence, and the specific method is as follows:
for each SAR image
Figure BDA0002550448320000054
Searching out the optical image closest to the optical image from the optical image sequence
Figure BDA0002550448320000055
The optical image is formed
Figure BDA0002550448320000056
Combining the red light wave band and the near infrared wave band to obtain a normalized vegetation index, and recording the normalized vegetation index of the pixel point (i, j) as NDVId′(i, j) which is then converted to vegetation moisture content
Figure BDA0002550448320000057
The vegetation water content adopted in this embodiment
Figure BDA0002550448320000058
The calculation formula of (a) is as follows:
Figure BDA0002550448320000059
then the water content of the vegetation
Figure BDA00025504483200000510
Substituting the water cloud model to obtain the backscattering coefficient of each pixel point without vegetation influence
Figure BDA00025504483200000511
Backscattering coefficient in the present embodiment
Figure BDA00025504483200000512
The calculation formula of (a) is as follows:
Figure BDA00025504483200000513
where θ is the incident angle of the sentinel No. 1 radar, a and B are parameters related to the vegetation type, and in this embodiment, a is 0.0012 and B is 0.091, respectively.
S103: and (3) roughness normalization:
the research shows that the surface roughness and the backscattering coefficient have better correlation, and the backscattering coefficient is larger when the surface roughness is larger. Therefore, when soil moisture extraction is performed, it is necessary to remove the influence of the surface roughness on the backscattering coefficient, thereby improving the accuracy of soil moisture extraction. The surface roughness normalization is a process of converting backscattering coefficient values of the same land under different time sequences into corresponding backscattering coefficient values under the same roughness, so that the surface roughness under different time sequences is kept consistent, and parameters influencing the backscattering coefficients only contain water.
In order to better realize surface roughness normalization, the invention aims at the difference between cross polarization (difference between backscattering coefficients under VV polarization and VH polarization) and combined roughnessThe relationship of (2) was investigated. FIG. 4 is a graphical representation of the relationship between cross-polarization differences and surface roughness. As shown in FIG. 4, it can be found that the cross polarization difference σ is observed in the case of bare soilvv-σvhThere is a good correlation with the roughness parameter, σvvDenotes the backscattering coefficient, σ, under VV polarizationvhThe backscattering coefficient in VH polarization can be expressed by σvv-σvhTo estimate the surface roughness at each time sequence and then according to sigmavvAnd surface roughness. With avv-σvhThe roughness parameter is reduced; increased roughness parameter, σvvThe value of (a) increases. It can therefore be guessed as avv-σvhIncrease of σvvWith a decreasing trend.
To verify the above assumptions, simulations were performed using an AIEM model. Firstly, fixing the radar frequency of 5.405GHz and the incident angle of 39 DEG, and setting the absolute water content m according to the soil moisture value obtained from SMAP and the surface roughness acquired by the field experimentvValue range of (1), 0.08 < mvLess than 0.41, and the value range of the root mean square height S is 0.5cm<s<3cm and the correlation length L is fixed at 10 cm. Fig. 5 is a graphical representation of cross polarization difference versus VV polarization backscattering coefficient for different water contents. As shown in FIG. 5, σ is the water content of the watervv-σvhAnd σvvThe method shows a good negative correlation relationship, the overall change trend is not changed by the change of the water content, the change trend conforms to the form of a logarithmic function, and the relational expression of the two is obtained by fitting:
σvv＝aln(σvv-σvh)+b (3)
since the radar parameters are fixed, the a, b parameters are only related to soil moisture, and therefore the a and b values are different for different plots. The expression of a, b is obtained by least squares fitting as follows:
Figure BDA0002550448320000061
when m isvWhen a changes from-20.37 to-20.33 from 0.08 to 0.4, it can be seen that a is less affected by the moisture content, so the present invention fixes a to an average value of-20.35; b is from-27.3 to-31.7.
Because of σvv-σvhHas a one-to-one correspondence with the roughness value, and therefore, σ can be selectedvv-σvhThe roughness corresponding to γ is a standard roughness, and the value of γ can be determined as needed, and γ is 6 in this embodiment. The backscattering coefficient sigma at standard roughness can then be obtainedvv-norComprises the following steps:
σvv-nor＝aln(γ)+b (5)
the above formula, in conjunction with formula (3), yields:
σvv-nor＝σvv+a[ln(γ)-ln(σvv-σvh)](6)
since the parameter a is known and ln (γ) can be calculated, then the input parameter σvvAnd σvhThe normalized standard backscattering coefficient of the roughness parameter can be calculated.
In the context of the invention, for each SAR image
Figure BDA0002550448320000071
Obtaining the SAR image according to the backscattering coefficient distribution diagram
Figure BDA0002550448320000072
Backscattering coefficient under VV polarization of each pixel point (i, j)
Figure BDA0002550448320000073
And backscattering coefficient under VH polarization
Figure BDA0002550448320000074
Calculating the backscattering coefficient of the pixel point for roughness normalization according to the following formula
Figure BDA0002550448320000075
Figure BDA0002550448320000076
Where a is-20.35 and γ represents a preset reference value of the cross polarization difference.
S104: calculating the relative water content:
for each pixel point (i, j) in the SAR image, the roughness of each pixel point is normalized by T roughness values corresponding to the pixel point
Figure BDA0002550448320000077
Search out the maximum value
Figure BDA0002550448320000078
And minimum value
Figure BDA0002550448320000079
They are used as the wet and dry reference values σdry(i, j) and σwet(i, j), let us say the dry reference value
Figure BDA00025504483200000710
Wet reference value
Figure BDA00025504483200000711
Then each SAR image is obtained by calculation according to the following formula
Figure BDA00025504483200000712
Relative water content of soil corresponding to the middle pixel point (i, j)
Figure BDA00025504483200000713
Figure BDA00025504483200000714
Fig. 6 is a graph showing the relative water content distribution of the soil in the Pi areas within the set time period in the present example.
S105: calculating the absolute water content:
calculating each SAR image according to the following formula
Figure BDA00025504483200000715
The absolute water content of the soil corresponding to the middle pixel point (i, j)
Figure BDA00025504483200000716
And (3) completing soil moisture extraction:
Figure BDA00025504483200000717
fig. 7 is a diagram showing the absolute water content distribution of the soil in the Pi all areas within the set time period in the present embodiment.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (3)

1. A soil moisture extraction method based on a change detection algorithm is characterized by comprising the following steps:
s1: acquiring serialized sentinel No. 1 microwave remote sensing data, serialized sentinel No. 2 optical data and serialized SMAP data corresponding to a region needing soil moisture extraction in a preset time period, and respectively performing the following processing:
generating an SAR image sequence according to the sentinel No. 1 microwave remote sensing data, recording the number of SAR images in the SAR image sequence as T, selecting one SAR image as a reference SAR image, registering other SAR images with the reference SAR image, and recording each SAR image after registration as
Figure FDA0002550448310000011
Then from each SAR image
Figure FDA0002550448310000012
Extracting a corresponding backscattering coefficient distribution graph, and recording the backscattering coefficient of each pixel point in the tth SAR image as sigmat(i, j), (i, j) representing coordinates of pixel points in the SAR image;
generating an optical image sequence according to the serialized sentinel No. 2 optical data, recording the number of SAR images in the optical image sequence as S, registering each optical image with a reference SAR image, and recording the optical images after registration processing as
Figure FDA0002550448310000013
Extracting a water content time sequence of the soil water extraction area from the serialized SMAP data, and acquiring a maximum value of the water content corresponding to each pixel point (i, j) from the water content time sequence according to the geographical position coordinates of each pixel point (i, j) in the SAR image
Figure FDA0002550448310000014
And minimum value
Figure FDA0002550448310000015
S2: for each SAR image
Figure FDA0002550448310000016
Searching out the optical image closest to the optical image from the optical image sequence
Figure FDA0002550448310000017
The optical image is formed
Figure FDA0002550448310000018
Combining the red light wave band and the near infrared wave band to obtain a normalized vegetation index, and recording the normalized vegetation index of the pixel point (i, j) as NDVId′(i, j) which is then converted to vegetation moisture content
Figure FDA0002550448310000019
Then will beWater content of vegetation
Figure FDA00025504483100000110
Substituting the water cloud model to obtain the backscattering coefficient of each pixel point without vegetation influence
Figure FDA00025504483100000111
S3: for each SAR image
Figure FDA00025504483100000112
Obtaining the SAR image according to the backscattering coefficient distribution diagram
Figure FDA00025504483100000113
Backscattering coefficient under VV polarization of each pixel point (i, j)
Figure FDA00025504483100000114
And backscattering coefficient under VH polarization
Figure FDA00025504483100000115
Calculating the backscattering coefficient of the pixel point for roughness normalization according to the following formula
Figure FDA00025504483100000116
Figure FDA00025504483100000117
Where a is-20.35 and γ represents a predetermined cross-polarization difference σvv-σvhA reference value of (d);
s4: for each pixel point (i, j) in the SAR image, the roughness of each pixel point is normalized by T roughness values corresponding to the pixel point
Figure FDA00025504483100000118
Search out the maximum value
Figure FDA00025504483100000119
And minimum value
Figure FDA00025504483100000120
Let us reference value
Figure FDA0002550448310000021
Wet reference value
Figure FDA0002550448310000022
Each SAR image is obtained by calculation according to the following formula
Figure FDA0002550448310000023
Relative water content of soil corresponding to the middle pixel point (i, j)
Figure FDA0002550448310000024
Figure FDA0002550448310000025
S5: calculating each SAR image according to the following formula
Figure FDA0002550448310000026
The absolute water content of the soil corresponding to the middle pixel point (i, j)
Figure FDA0002550448310000027
And (3) completing soil moisture extraction:
Figure FDA0002550448310000028
2. the soil moisture extraction method of claim 1, wherein the vegetation water content in step S2 is
Figure FDA0002550448310000029
The calculation formula of (a) is as follows:
Figure FDA00025504483100000210
3. the soil moisture extraction method as claimed in claim 1, wherein the backscattering coefficient in step S2
Figure FDA00025504483100000211
The calculation formula of (a) is as follows:
Figure FDA00025504483100000212
where θ is the angle of incidence of the sentinel radar No. 1, and a and B are parameters related to the vegetation type.
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