CN111751286B - Soil moisture extraction method based on change detection algorithm - Google Patents

Soil moisture extraction method based on change detection algorithm Download PDF

Info

Publication number
CN111751286B
CN111751286B CN202010573390.7A CN202010573390A CN111751286B CN 111751286 B CN111751286 B CN 111751286B CN 202010573390 A CN202010573390 A CN 202010573390A CN 111751286 B CN111751286 B CN 111751286B
Authority
CN
China
Prior art keywords
sar image
pixel point
water content
backscattering coefficient
soil moisture
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010573390.7A
Other languages
Chinese (zh)
Other versions
CN111751286A (en
Inventor
陈彦
陈云坪
吴磊
姜灵海
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Shenzhen Blue Space Remote Sensing Technology Co ltd
University of Electronic Science and Technology of China
Original Assignee
Beijing Shenzhen Blue Space Remote Sensing Technology Co ltd
University of Electronic Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Shenzhen Blue Space Remote Sensing Technology Co ltd, University of Electronic Science and Technology of China filed Critical Beijing Shenzhen Blue Space Remote Sensing Technology Co ltd
Priority to CN202010573390.7A priority Critical patent/CN111751286B/en
Publication of CN111751286A publication Critical patent/CN111751286A/en
Application granted granted Critical
Publication of CN111751286B publication Critical patent/CN111751286B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9021—SAR image post-processing techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9094—Theoretical aspects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/418—Theoretical aspects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1793—Remote sensing

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Image Processing (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

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
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 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: each frame is calculated according to the following formulaSAR image
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
Extract correspondingAnd (4) 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 2 is greatly influenced by weather and the like, and the generated qualified optical image cannot be as same as an SAR image generally, so that the periodicity of the optical image is ensured, and the optical image 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, it is necessary toFor each SAR image
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 the surface roughness normalization, the invention researches the relation between the cross polarization difference (the difference of backscattering coefficients under VV polarization and VH polarization) and the combined roughness. 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, an AIEM model was usedA simulation was performed. 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 FDA0002976561100000011
Then from each SAR image
Figure FDA0002976561100000012
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 D, registering each optical image with a reference SAR image, and recording the optical images after registration processing as
Figure FDA0002976561100000013
Extracting a water content time sequence of the soil water extraction area from the serialized SMAP data according to SThe geographical position coordinates of each pixel point (i, j) in the AR image acquire the maximum value of the corresponding moisture content of the pixel point (i, j) from the moisture content time sequence
Figure FDA0002976561100000014
And minimum value
Figure FDA0002976561100000015
S2: for each SAR image
Figure FDA0002976561100000016
Searching out the optical image closest to the optical image from the optical image sequence
Figure FDA0002976561100000017
The optical image is formed
Figure FDA0002976561100000018
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 FDA0002976561100000019
Then the water content of the vegetation
Figure FDA00029765611000000110
Substituting the water cloud model to obtain the backscattering coefficient of each pixel point without vegetation influence
Figure FDA00029765611000000111
S3: for each SAR image
Figure FDA00029765611000000112
Obtaining the SAR image according to the backscattering coefficient distribution diagram
Figure FDA00029765611000000113
Backscattering coefficient under VV polarization of each pixel point (i, j)
Figure FDA00029765611000000114
And backscattering coefficient under VH polarization
Figure FDA00029765611000000115
Calculating the backscattering coefficient of the pixel point for roughness normalization according to the following formula
Figure FDA00029765611000000116
Figure FDA00029765611000000117
Where a is-20.35 and γ represents a predetermined cross-polarization difference σvv-σvhReference value of σvvDenotes the backscattering coefficient, σ, under VV polarizationvhRepresents the backscattering coefficient under VH polarization;
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 FDA0002976561100000021
Search out the maximum value
Figure FDA0002976561100000022
And minimum value
Figure FDA0002976561100000023
Let us reference value
Figure FDA0002976561100000024
Wet reference value
Figure FDA0002976561100000025
Each SAR image is obtained by calculation according to the following formula
Figure FDA0002976561100000026
Relative water content of soil corresponding to the middle pixel point (i, j)
Figure FDA0002976561100000027
Figure FDA0002976561100000028
S5: calculating each SAR image according to the following formula
Figure FDA0002976561100000029
The absolute water content of the soil corresponding to the middle pixel point (i, j)
Figure FDA00029765611000000210
And (3) completing soil moisture extraction:
Figure FDA00029765611000000211
2. the soil moisture extraction method of claim 1, wherein the vegetation water content in step S2 is
Figure FDA00029765611000000212
The calculation formula of (a) is as follows:
Figure FDA00029765611000000213
3. the soil moisture extraction method as claimed in claim 1, wherein the backscattering coefficient in step S2
Figure FDA00029765611000000214
The calculation formula of (a) is as follows:
Figure FDA00029765611000000215
where θ is the angle of incidence of the sentinel radar No. 1, and a and B are parameters related to the vegetation type.
CN202010573390.7A 2020-06-22 2020-06-22 Soil moisture extraction method based on change detection algorithm Active CN111751286B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010573390.7A CN111751286B (en) 2020-06-22 2020-06-22 Soil moisture extraction method based on change detection algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010573390.7A CN111751286B (en) 2020-06-22 2020-06-22 Soil moisture extraction method based on change detection algorithm

Publications (2)

Publication Number Publication Date
CN111751286A CN111751286A (en) 2020-10-09
CN111751286B true CN111751286B (en) 2021-05-14

Family

ID=72674864

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010573390.7A Active CN111751286B (en) 2020-06-22 2020-06-22 Soil moisture extraction method based on change detection algorithm

Country Status (1)

Country Link
CN (1) CN111751286B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112418016A (en) * 2020-11-09 2021-02-26 中国农业大学 SAR-based irrigation information extraction method and device
CN114994087B (en) * 2022-05-27 2024-05-17 昆明理工大学 Vegetation blade water content remote sensing inversion method based on polarized SAR data

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101571594B (en) * 2009-06-11 2011-06-29 西安电子科技大学 Method for recognizing SAR target based on curvelet transform
CN104678404A (en) * 2015-02-11 2015-06-03 北京航空航天大学 Embedded real-time measurement device for soil humidity based on Beidou synchronous navigation satellite
CN105527616B (en) * 2015-10-27 2018-07-20 中国科学院遥感与数字地球研究所 A kind of vegetative coverage surface soil water content inversion method based on multipolarization RADARSAT-2 and Landsat8 data
CN105608414A (en) * 2015-12-11 2016-05-25 国网四川省电力公司电力应急中心 Surface water content distribution extracting method
CN109061639A (en) * 2018-06-28 2018-12-21 上海卫星工程研究所 High rail SAR continuously stares working system design method
CN109507669B (en) * 2018-12-26 2022-04-19 西安电子科技大学 Ground weak moving object parameter estimation method based on coherent accumulation
CN110221301A (en) * 2019-07-24 2019-09-10 中国科学院遥感与数字地球研究所 Soil Moisture Inversion method and device
CN110703244B (en) * 2019-09-05 2023-08-04 中国科学院遥感与数字地球研究所 Method and device for identifying urban water body based on remote sensing data

Also Published As

Publication number Publication date
CN111751286A (en) 2020-10-09

Similar Documents

Publication Publication Date Title
El Hajj et al. Soil moisture retrieval over irrigated grassland using X-band SAR data
Friedli et al. Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions
Huang et al. Jointly assimilating MODIS LAI and ET products into the SWAP model for winter wheat yield estimation
CN108802728B (en) Crop irrigation guiding method for assimilation of bipolar synthetic aperture radar and crop model
Wang et al. Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands
Lee et al. A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations
CN111751286B (en) Soil moisture extraction method based on change detection algorithm
CN112711989A (en) Corn straw coverage estimation method based on radar remote sensing and optical remote sensing
CN115479960B (en) Method for monitoring soil humidity in crop growth process by combining SAR (synthetic aperture radar) with optical remote sensing data
CN114387516B (en) Single-season rice SAR (synthetic aperture radar) identification method for small and medium-sized fields in complex terrain environment
CN114782834A (en) Farmland soil moisture retrieval method and system based on ratio method
CN115545311A (en) Crop yield estimation method and device, storage medium and electronic equipment
CN114065643A (en) Plant soil water content estimation method and system based on SAR and polarization decomposition
CN112418016A (en) SAR-based irrigation information extraction method and device
Ouaadi et al. C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)
JP4810604B2 (en) Paddy rice planting situation grasping system, paddy rice cultivation situation grasping method, and paddy rice cultivation situation grasping program
CN111965330B (en) Bare soil water content inversion method based on dual-polarization emissivity
El Hajj et al. Water uptake rates over olive orchards using Sentinel-1 synthetic aperture radar data
Yadav et al. Estimation of soil moisture through water cloud model using sentinel-1A SAR data
Nasirzadehdizaji et al. Application of sentinel-1 multi-temporal data for crop monitoring and mapping
CN115950838A (en) Summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on chlorophyll content
CN115641502A (en) Winter wheat drought unmanned aerial vehicle rapid monitoring and distinguishing method based on leaf area index
Cao et al. Spatial and temporal characteristics of surface soil moisture in a disturbed coal mining area of Chinese Loess Plateau
RU2705549C1 (en) Method of agrochemical survey of agricultural lands
Jing et al. Retrieving rice yield and biomass from Radarsat-2 SAR data with artificial neural network (ANN)

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant