CN108802728B - Crop irrigation guiding method for assimilation of bipolar synthetic aperture radar and crop model - Google Patents

Crop irrigation guiding method for assimilation of bipolar synthetic aperture radar and crop model Download PDF

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CN108802728B
CN108802728B CN201810403545.5A CN201810403545A CN108802728B CN 108802728 B CN108802728 B CN 108802728B CN 201810403545 A CN201810403545 A CN 201810403545A CN 108802728 B CN108802728 B CN 108802728B
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黄健熙
李俐
卓文
苏伟
刘峻明
刘哲
张超
朱德海
张晓东
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Abstract

The invention belongs to the field of agricultural remote sensing, and relates to a crop irrigation guidance method for assimilation of a dual-polarization synthetic aperture radar and a crop model, which comprises the following specific steps: collecting satellite data of the dual-polarization synthetic aperture radar and preprocessing the satellite data, and inverting the soil moisture SM by adopting an AIEM (advanced information technology) model and a MIMICS (simulation-integrated simulation system) model; calibrating a SWAP model of the crops in the research area; assimilating the obtained remote sensing inversion SM and the obtained SWAP model simulation SM by using an ensemble Kalman filtering algorithm, and optimizing the SWAP model; and (4) obtaining irrigation requirements on the basis of the soil water percentage content output by the optimized SWAP model one by one crop grid, obtaining a regional crop irrigation system chart, and guiding crop production. The method disclosed by the invention integrates the advantages of SAR remote sensing data and a crop model, effectively solves the problem of satellite remote sensing data loss under a cloud and rain condition by adopting the SAR remote sensing data, greatly increases the time resolution of available data, and effectively improves the soil moisture estimation precision of regional crops.

Description

Crop irrigation guiding method for assimilation of bipolar synthetic aperture radar and crop model
Technical Field
The invention belongs to the field of agricultural remote sensing, and particularly relates to a crop irrigation guidance method for assimilation of a dual-polarized synthetic aperture radar and a crop model.
Background
The traditional crop irrigation guiding method mainly comprises a statistical investigation method, a forecasting method based on a crop model, an agricultural weather forecasting method and the like. These methods, due to their inherent limitations, have difficulty in achieving a high accuracy estimate of the amount of irrigation needed for regional crops. The estimation method based on the satellite remote sensing technology has the advantage of being unique in estimation of the irrigation quantity needed by regional crops by virtue of the characteristics of spatial continuity and temporal dynamics. Meanwhile, the remote sensing technology is combined with a crop growth model based on the mechanism processes of crop photosynthesis, respiration, transpiration, nutrition and the like, so that the purpose of guiding irrigation quantity in a region with high precision can be achieved. The data assimilation method can combine the advantages of the crop growth model on point and remote sensing observation, and becomes a hotspot of research on agricultural quantitative remote sensing at home and abroad in recent years.
However, optical remote sensing is greatly restricted by weather factors, and radar remote sensing has the characteristic of being less affected by cloud and fog compared with optical remote sensing data, and can be monitored all day long, so that continuous and long-time observation data can be obtained in the growing season of crops, and the method is very helpful for monitoring the growth of crops and guiding irrigation. The radar remote sensing and the crop model are combined by using a data assimilation method, so that the defect of the crop model in the aspect of regional crop water demand estimation can be overcome, the limitation that common optical remote sensing data are greatly restricted by weather factors such as cloud and fog can be overcome, and the method can be suitable for guiding and researching crop irrigation in a regional range.
Disclosure of Invention
In order to solve the following problems in the prior art: the invention provides a crop irrigation guidance method for assimilation of a dual-polarized synthetic aperture radar and a crop model, and aims to assimilate the remote sensing data of the dual-polarized synthetic aperture radar with high timeliness in a large range and accurately simulate a mechanism model for crop growth so as to accurately guide irrigation in the large range.
The invention provides a crop irrigation guidance method for assimilation of a dual-polarization synthetic aperture radar and a crop model, which comprises the following specific steps of:
s1, collecting satellite data of the dual-polarized synthetic aperture radar in the growth period of the crop to be detected in the research area, and preprocessing the satellite data to obtain VH and VV dual-polarized backscattering coefficients of a time sequence C waveband (C band), namely dual-polarized SAR data;
s2, after crop emergence or green turning, inverting the soil moisture SM by adopting an AIEM (improved integral equation Model) and an MIMICS (Michigan Microwave Canopy Scattering Model) based on dual-polarization SAR data to obtain a remote sensing inversion SM;
s3, collecting weather, crop, soil and crop management parameters in the research area and taking the weather, crop, soil and crop management parameters as input parameters, calibrating a SWAP model (a model simulated by a soil-moisture-atmosphere-crop system) of the crop in the research area to obtain a SWAP model simulation SM;
s4, taking the SM as an assimilation variable, assimilating the remote sensing inversion SM obtained in the S2 and the SWAP model simulation SM obtained in the S3 by using a Kalman filter (Kalman filter) algorithm, and optimizing the SWAP model;
s5, judging whether the water content of the soil is close to a water stress critical early warning value or not according to the percentage content of the soil water output by the optimized SWAP model and an irrigation upper limit and an irrigation lower limit, and giving out whether irrigation is needed or not, irrigation date and irrigation quantity;
and S6, operating the steps S4-S5 one by one on the crop grids, simulating a crop irrigation system distribution diagram of an output area, and guiding crop production.
In step S1, the satellite data of the dual-polarization synthetic aperture radar is preferably Sentinel 1 (Sentinel-1) satellite data, which is slc (single look complete) data of a Sentinel 1 (Sentinel-1) satellite, and the data is complex data including amplitude and phase information.
The preprocessing in step S1 refers to multi-view processing, terrain correction, speckle noise filtering, and the like.
Step S2, the soil moisture SM is inverted by adopting the AIEM model and the MIMICS model, and the inversion method comprises the following steps: describing pq polarization earth surface backscattering coefficients by adopting an AIEM model under the condition of vegetation sparsity (p and q respectively represent a transmitting polarization mode and a receiving polarization mode and can be respectively horizontally polarized H or vertically polarized V, so that different polarization combinations are formed, pq is a unified representation of uncertain polarization modes, HV polarization is a signal received by certain H polarization transmitting V polarization, and VV polarization and VH polarization are commonly adopted for used sentinel data dual polarization in an equation (3), so that the polarization modes are definitely given); in the case of dense implants covered, the backscattering is determined by MIMICS model.
The pq polarization earth surface backscattering coefficient is described by adopting an AIEM model under the condition of vegetation sparsity, and the calculation is carried out by adopting a formula (1):
Figure BDA0001646300010000031
wherein the content of the first and second substances,
Figure BDA0001646300010000032
expressing the roughness of the soil by taking the total backward scattering quantity and s as the root-mean-square height of the farmland, and determining the value according to ground survey experimental data; k 2 pi/lambda 2 pi f/c, the SAR signal wavenumber, kx、ky、kz、ksx、ksy、kszRespectively determining the SAR signal wave number, the radar incidence angle and the pitch angle; w(n)() Is the nth order fourier transform of the surface autocorrelation function.
In the case of thick plants covered, the backscattering is determined by the MIMICS model and calculated by equation (2):
Figure BDA0001646300010000033
wherein the content of the first and second substances,
Figure BDA0001646300010000034
respectively, the total backscattering, the direct backscattering of the surface soil (determined by the AIEM model of equation (1)), the direct backscattering of the crop canopy, the dihedral scattering of the crop stalk and the surface, and the scattering after the interaction of the corn interior with the ground, a is the surface scattering attenuation coefficient brought by the crop layer, determined from the above field measurement scatterometer data and the simulation analysis data values.
And S2, inverting the soil moisture SM, and performing iterative optimization by using a Support Vector Regression (SVR) algorithm to obtain the soil moisture SM.
Step S2, the soil moisture SM is inverted by adopting the AIEM model and the MIMICS model, the AIEM model and the MIMICS model are selected by the cost function, the dual-polarization backscattering coefficient difference between simulation and observation is calculated according to the formula (3):
Figure BDA0001646300010000041
wherein σ0VV、
Figure BDA0001646300010000042
Respectively representing the actually observed VV polarization backscattering coefficient value and the VV polarization backscattering coefficient value simulated by AIEM and MIMICS models; in the same way, σ0VH、
Figure BDA0001646300010000043
Observed backscattering values and simulated values representing VH polarization, respectively; a is a weighting coefficient of VH polarization influence considering the growth period of crops; when in use
Figure BDA0001646300010000044
Below a certain value, e.g.<And returning to the soil moisture SM value at 0.01 dB.
In step S3, weather, crop, soil and crop management parameters in the research area are collected and used as input parameters to calibrate the SWAP model of the crop in the research area, and an Inverse Distance Weight (IDW) interpolation algorithm is required to be used for the weather parameters and the accumulated temperature parameters required by the crop model to complete regional parameter calibration.
Wherein, in step S4, assimilating is performed, and calculation is performed according to the classic ensemble kalman formulas (4), (5), and (6):
Bt＝HAt+vt (4)
Figure BDA0001646300010000045
Figure BDA0001646300010000046
Atrepresenting a LAI (leaf area index) state variable set in the crop model at the time t;
Figure BDA0001646300010000047
a forecast set representing the LAI; b istAn observation data set at time t; the optimal estimation set of the state at the moment is
Figure BDA0001646300010000048
The average value of (1) is the optimal estimation value of the state at the moment; h is an observation operator; m is a state transformation equation; v. oftIs the measurement noise; w is atIs a process error; ktIs the kalman gain, representing the weight of the observed data.
Wherein, in step S4, assimilation is performed, KtThe calculation method of (2) is calculated according to the following equations (7) to (12):
Figure BDA0001646300010000049
Figure BDA00016463000100000410
Figure BDA0001646300010000051
Figure BDA0001646300010000052
Figure BDA0001646300010000053
Figure BDA0001646300010000054
wherein, N represents the size of the set,
Figure BDA0001646300010000055
representing a forecast state of the model ith set member at time t,
Figure BDA0001646300010000056
to represent
Figure BDA0001646300010000057
Mean value of bi,tRepresenting the ith member of the observation set at time t,
Figure BDA0001646300010000058
denotes btThe average value of (a) of (b),
Figure BDA0001646300010000059
is the variance, R, of the prediction set representing time ttAnd H is an observation operator, and other symbols represent intermediate variables in the calculation process.
In step S5, the irrigation upper limit is the saturated water content of soil, and the irrigation lower limit is 50% -60% of the field water holding rate.
Wherein the crops are dry-land crops, and preferably any one of dry-land crops such as winter wheat, corn and the like.
The invention also provides application of the bipolar synthetic aperture radar and a crop irrigation guidance method for crop model assimilation in guidance of crop production.
Compared with the prior art, the invention has the beneficial effects that:
the method disclosed by the invention integrates the advantages of SAR remote sensing data and a crop model, and optimizes the simulation process of the model by assimilating the soil SM inverted by the SAR remote sensing data into the SWAP crop growth model, so that the accurate soil moisture content of winter wheat is estimated, and the irrigation system is optimized. By adopting the SAR remote sensing data, the influence of severe weather factors such as cloud and fog is effectively overcome, the time resolution of available data is greatly increased, and the soil moisture estimation precision of regional winter wheat is effectively improved.
Drawings
Fig. 1 is a schematic flow chart of a crop irrigation guidance method for implementing dual-polarization synthetic aperture radar and crop model assimilation on winter wheat in embodiment 1 of the present invention.
Fig. 2 is a crop irrigation guidance map of 29-month-3-2017 winter wheat obtained by implementing a crop irrigation guidance method of dual-polarization synthetic aperture radar and crop model assimilation on to winter wheat in embodiment 1 of the present invention.
Detailed Description
The following describes in further detail specific embodiments of the present invention with reference to examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Example 1
The schematic flow chart of the crop irrigation guidance method for assimilation of the dual-polarized synthetic aperture radar and the crop model aiming at the winter wheat estimation is shown in the attached figure 1.
The three provinces of Hebei, Henan and Shandong were selected as the study areas between the east longitude 110 ° 12 '-122 ° 42' and the north latitude 31 ° 17 '-42 ° 41'. The total area of a research area is 50.9 kilo-square kilometers, the terrain is mainly plain, cultivated land occupies more than 40 percent of the total area, and the method belongs to seasonal climates in warm temperature zones, the annual sunshine duration is 2300h, and the annual average precipitation is about 650 mm.
S1, collecting satellite data of the dual-polarized synthetic aperture radar in the growing period of winter wheat to be detected in a research area, and preprocessing the satellite data to obtain VH and VV dual-polarized backscattering coefficients of a time sequence C waveband (C band), namely dual-polarized SAR data;
the preprocessing refers to multi-view processing, terrain correction, speckle noise filtering and the like.
S2, after the winter wheat is turned green, inverting the soil moisture SM by adopting an AIEM (advanced information technology) model and an MIMICS (simulation analytical learning system) model based on dual-polarization SAR data to obtain a remote sensing inversion SM;
step S2, the soil moisture SM is inverted by adopting the AIEM model and the MIMICS model, and the inversion method comprises the following steps: describing pq polarization earth surface backscattering coefficients by adopting an AIEM model under the condition of vegetation sparsity; in the case of dense implants covered, the backscattering is determined by MIMICS model.
The pq polarization earth surface backscattering coefficient is described by adopting an AIEM model under the condition of vegetation sparsity, and the calculation is carried out by adopting a formula (1):
Figure BDA0001646300010000061
wherein the content of the first and second substances,
Figure BDA0001646300010000062
expressing the roughness of the soil by taking the total backward scattering quantity and s as the root-mean-square height of the farmland, and determining the value according to ground survey experimental data; k 2 pi/lambda 2 pi f/c, the SAR signal wavenumber, kx、ky、kz、ksx、ksy、kszRespectively determining the SAR signal wave number, the radar incidence angle and the pitch angle; w(n)() Is the nth order fourier transform of the surface autocorrelation function.
In the case of thick plants covered, the backscattering is determined by the MIMICS model and calculated by equation (2):
Figure BDA0001646300010000071
wherein the content of the first and second substances,
Figure BDA0001646300010000072
respectively, the total backscattering, the direct backscattering of the surface soil (determined by the AIEM model of equation (1)), the direct backscattering of the crop canopy, the dihedral scattering of the crop stalk and the surface, and the scattering after the interaction of the corn interior with the ground, a is the surface scattering attenuation coefficient brought by the crop layer, determined from the above field measurement scatterometer data and the simulation analysis data values.
And S2, inverting the soil moisture SM, and performing iterative optimization by using a Support Vector Regression (SVR) algorithm to obtain the soil moisture SM.
Step S2, the soil moisture SM is inverted by adopting the AIEM model and the MIMICS model, the AIEM model and the MIMICS model are selected by the cost function, the dual-polarization backscattering coefficient difference between simulation and observation is calculated according to the formula (3):
Figure BDA0001646300010000073
wherein σ0VV、
Figure BDA0001646300010000074
Respectively representing the actually observed VV polarization backscattering coefficient value and the VV polarization backscattering coefficient value simulated by AIEM and MIMICS models; in the same way, σ0VH、
Figure BDA0001646300010000075
Observed backscattering values and simulated values representing VH polarization, respectively; a is a weighting coefficient of VH polarization influence considering the growth period of crops; when in use
Figure BDA0001646300010000076
Below a certain value, e.g.<And returning to the soil moisture SM value at 0.01 dB.
S3, collecting weather, crop, soil and crop management parameters in the research area and using the weather, crop, soil and crop management parameters as input parameters, and calibrating a SWAP model of winter wheat in the research area to obtain a SWAP model simulation SM;
the SWAP model of the crops in the research area is calibrated, and the regional calibration of the parameters is completed by adopting an Inverse Distance Weight (IDW) interpolation algorithm for meteorological parameters and accumulated temperature parameters required by the crop model.
S4, assimilating the remote sensing inversion SM obtained in the S2 and the SWAP model simulation SM obtained in the S3 by using the SM as an assimilation variable through an ensemble Kalman filtering algorithm, and optimizing the SWAP model;
the calculation is performed according to the classic ensemble kalman formulas (4), (5) and (6):
Bt＝HAt+vt (4)
Figure BDA0001646300010000081
Figure BDA0001646300010000082
Atrepresenting a LAI (leaf area index) state variable set in the crop model at the time t;
Figure BDA0001646300010000083
a forecast set representing the LAI; b istAn observation data set at time t; the optimal estimation set of the state at the moment is
Figure BDA0001646300010000084
The average value of (1) is the optimal estimation value of the state at the moment; h is an observation operator; m is a state transformation equation; v. oftIs the measurement noise; w is atIs a process error; ktIs the kalman gain, representing the weight of the observed data.
KtThe calculation method of (2) is calculated according to the following equations (7) to (12):
Figure BDA0001646300010000085
Figure BDA0001646300010000086
Figure BDA0001646300010000087
Figure BDA0001646300010000088
Figure BDA0001646300010000089
Figure BDA00016463000100000810
wherein, N represents the size of the set,
Figure BDA00016463000100000811
representing a forecast state of the model ith set member at time t,
Figure BDA00016463000100000812
to represent
Figure BDA00016463000100000813
Mean value of bi，tRepresenting the ith member of the observation set at time t,
Figure BDA00016463000100000814
denotes btThe average value of (a) of (b),
Figure BDA00016463000100000815
is the variance, R, of the prediction set representing time ttAnd H is an observation operator, and other symbols represent intermediate variables in the calculation process.
S5, judging whether the water content of the soil is close to a water stress critical early warning value or not according to the percentage content of the soil water output by the optimized SWAP model and an irrigation upper limit and an irrigation lower limit, and giving out whether irrigation is needed or not, irrigation date and irrigation quantity;
the irrigation upper limit is the saturated water content of soil, and the irrigation lower limit is 50-60% of the field water holding rate.
And S6, operating the steps S4-S5 one by one on the winter wheat grids, simulating the irrigation system distribution diagram of the winter wheat in the output area, and guiding the production of the winter wheat.
The obtained 2017, 3 and 29 winter wheat irrigation guidance map is shown in figure 2.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (11)

1. A crop irrigation guidance method for assimilation of a dual-polarized synthetic aperture radar and a crop model is characterized by comprising the following specific steps:
s1, collecting satellite data of the dual-polarized synthetic aperture radar in the growth period of the crop to be detected in the research area, and preprocessing the satellite data to obtain VH and VV dual-polarized backscattering coefficients of a time sequence C wave band, namely dual-polarized SAR data;
s2, after crop emergence or green turning, inverting the soil moisture SM by adopting an AIEM model and a MIMICS model based on dual-polarization SAR data to obtain a remote sensing inversion SM;
s3, collecting meteorological, crop, soil and crop management parameters in the research area and using the meteorological, crop, soil and crop management parameters as input parameters, and calibrating a SWAP model of the crops in the research area to obtain a SWAP model simulation SM;
s4, assimilating the remote sensing inversion SM obtained in the S2 and the SWAP model simulation SM obtained in the S3 by using the SM as an assimilation variable through an ensemble Kalman filtering algorithm, and optimizing the SWAP model;
s5, judging whether the water content of the soil is close to a water stress critical early warning value or not according to the percentage content of the soil water output by the optimized SWAP model and an irrigation upper limit and an irrigation lower limit, and giving out whether irrigation is needed or not, irrigation date and irrigation quantity;
and S6, operating the steps S4-S5 one by one on the crop grids, simulating a crop irrigation system distribution diagram of an output area, and guiding crop production.
2. The method of claim 1, wherein the preprocessing in step S1 includes multi-view processing, terrain correction, and speckle noise filtering.
3. The method according to claim 1, wherein the step S2 of inverting the soil moisture SM using the AIEM model and the MIMICS model is: adopting an AIEM model under the condition of vegetation sparsity; in the case of vegetation bloom, the surface reflection utilizes the AIEM model, and the crop coverage portion total scatter is determined by the MIMICS model.
4. The method of claim 3, wherein the step S2 is implemented by inverting the soil moisture SM and performing an iterative optimization using a support vector regression algorithm to obtain the soil moisture SM.
5. The method according to claim 4, wherein the soil moisture SM is inverted using the AIEM model and the MIMICS model at S2, the cost function selects the AIEM and the MIMICS model, and the difference between the simulated and observed dual polarization backscattering coefficients is calculated according to formula (1):
Figure FDA0002969859470000021
wherein σ0VV、
Figure FDA0002969859470000022
Respectively representing the actually observed VV polarization backscattering coefficient value and the VV polarization backscattering coefficient value simulated by AIEM and MIMICS models; in the same way, σ0VH、
Figure FDA0002969859470000023
Observed backscattering values and simulated values representing VH polarization, respectively; a is a weighting coefficient of VH polarization influence considering the growth period of crops; when in use
Figure FDA0002969859470000024
And returning the soil moisture SM value when the water content is lower than the specific value.
6. The method of claim 5, wherein the particular value is 0.01 dB.
7. The method as claimed in claim 1, wherein step S3 is implemented by collecting weather, crop, soil and crop management parameters in the research area as input parameters, calibrating the SWAP model of the crops in the research area, and performing a regional parameter calibration by applying an inverse distance weighted interpolation algorithm to the weather parameters and the accumulated temperature parameters required by the crop model.
8. The method of claim 1, wherein assimilating in step S4 is performed according to the classical collective kalman formula (2) (3) (4):
Bt＝HAt+vt (2)
Figure FDA0002969859470000025
Figure FDA0002969859470000026
Atrepresenting an LAI state variable set in the crop model at the time t;
Figure FDA0002969859470000027
a forecast set representing the LAI; b istAn observation data set at time t; the optimal estimation set of the state at the moment is
Figure FDA0002969859470000028
The average value of (1) is the optimal estimation value of the state at the moment; h is an observation operator; m is a state transformation equation; v. oftIs the measurement noise; w is atIs a process error; ktIs the kalman gain, representing the weight of the observed data;
then, calculation is performed according to the following formulas (5) to (10):
Figure FDA0002969859470000029
Figure FDA0002969859470000031
Figure FDA0002969859470000032
Figure FDA0002969859470000033
Figure FDA0002969859470000034
Figure FDA0002969859470000035
wherein, N represents the size of the set,
Figure FDA0002969859470000036
representing a forecast status of the ith set member of the model at time t, bi,tRepresenting the ith member of the observation set at time t,
Figure FDA0002969859470000037
is the variance, R, of the prediction set representing time ttAnd H is an observation operator, and other symbols represent intermediate variables in the calculation process.
9. The method of claim 1, wherein the irrigation upper limit of step S5 is a soil saturation moisture content and the irrigation lower limit is 50% -60% of a field moisture retention.
10. The method of any one of claims 1 to 9, wherein the crop is any one of winter wheat and corn.
11. Use of the bipolar synthetic aperture radar according to any one of claims 1 to 10 in a method for crop irrigation guidance for assimilation with crop models for guidance of crop production.
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