CN108509836A - Crop yield estimation method based on double-polarized synthetic aperture radar and crop model data assimilation - Google Patents
Crop yield estimation method based on double-polarized synthetic aperture radar and crop model data assimilation Download PDFInfo
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Abstract
the invention belongs to the field of agricultural remote sensing, and relates to a crop yield estimation method for assimilation of dual-polarization synthetic aperture radar and crop model data.
Description
Technical field
The invention belongs to agricultural remote sensing fields, and in particular to a kind of dual polarization synthetic aperture radar and crop modeling data are same
The agricultural output assessment method of change.
Background technology
Traditional Crop Estimation Method mainly has method of statistical survey, forecasting procedure and agricultural based on crop modeling
Method of meteorological forecast etc..These methods are estimated since its intrinsic limitation is all difficult to realize area crops yield high-precision.And
The characteristics of estimating and measuring method based on satellite remote sensing technology, dynamic upper by the spatially continuous and time, estimate in area crops yield
There is advantageous advantage in survey.Meanwhile by remote sensing technology and based on mechanism processes such as crop photosynthesis, breathing, transpiration, nutrition
Crop growth model combination can achieve the purpose that region high-precision assess.Data assimilation method can combine plant growth mould
Type is putting the upper, advantage of remote sensing observations on the whole, becomes the hot spot of the research of recent domestic agricultural quantitative remote sensing.
However optical remote sensing is restricted larger by weather conditions, and radar remote sensing is compared to optical remote sensing data, have by
The small feature of Influence of cloud, can round-the-clock, round-the-clock be monitored, therefore can be obtained continuously in Growing Season of Crops, it is long
The observation data of time, it is very helpful to crop condition monitoring, the yield by estimation.Using data assimilation method by radar remote sensing and crop
Model is combined the deficiency that can not only make up crop modeling in terms of area crops Granule weight, and can overcome normal light
It learns remotely-sensed data and is restricted larger limitation by weather conditions such as clouds and mists, the agricultural output assessment that can be suitable for regional extent is studied.
Invention content
It is existing in the prior art as follows to solve the problems, such as：" how by the dual polarization synthetic aperture of a wide range of high-timeliness
Radar remote sensing data and the mechanism model of accurate simulation plant growth are assimilated, and then can be a wide range of interior accurately to making
Estimated for yield ", the present invention provides a kind of agricultural output assessment of dual polarization synthetic aperture radar and crop modeling data assimilation
Method.
The present invention provides a kind of agricultural output assessment method of dual polarization synthetic aperture radar and crop modeling data assimilation, specifically
Steps are as follows：
The satellite data of dual polarization synthetic aperture radar, is pre-processed in S1, collection research area Crop growing stage to be measured
Obtain VH the and VV dual polarization backscattering coefficients of time series C-band (C band), i.e. dual polarization SAR data；
S2, to dual polarization SAR data carry out polarization decomposing, analyze different scattering component characteristics and with LAI value correlativities；
The LAI inverse model inverting LAI of the highest scattering component composition of relations of S3, choice accuracy, obtain remote sensing observations
LAI gives the uncertainty of assessment observation；
S4, the meteorology in collection research area, crop, soil and crop management parameter and as input parameter, calibration
WOFOST (the world food studies) model for studying area crop obtains obtaining WOFOST simulations LAI；
S5, take LAI as assimilation variable, remote sensing observations LAI and WOFOST simulation LAI is carried out using particle filter algorithm same
Change, the tracks Crop growing stage LAI after being optimized；
S6, one by one crop grid run the step of S5, are driven again using the tracks Crop growing stage LAI after optimization
WOFOST models, the crop yield in simulation output region carry out space mapping, instruct crop production.
The satellite data of dual polarization synthetic aperture radar described in step S1, preferably No. 1 (Sentinel-1) satellite of sentry
Data are SLC (single look complex) data of No. 1 (Sentinel-1) satellite of sentry, and data are complex data, packet
Include amplitude and phase information.
It refers to orbital data self-correcting, removal thermal noise, tiltedly conversion, haplopia that pretreatment is carried out described in step S1
Complex data generates polarization scattering matrix S, radiation calibration, topographical correction and coherent speckle noise and filters out.
Wherein, removal thermal noise is eliminated the thermal noise that sensor is brought using bilinear interpolation and is influenced.
Wherein, coherent speckle noise is filtered out to be filtered using Lee and be realized.
Dual polarization SAR data described in step S1, defining target vector collision matrix is：
In formula, k is target vector, SVV、SVHComponent has respectively represented dissipating for dual polarization data VV, VH linear polarization state
Penetrate information.
Polarization decomposing is carried out to dual polarization SAR data described in step S2, polarization decomposing process calculates covariance matrix first：The atural object of nature can generally keep the symmetrical of heading
Property, it can be assumed that co polarized component and cross polar component are uncorrelated, have：Then
Wherein, ＜ ...>Indicate to assume random scattering media it is each to homogeneity in the case of spatial statistics it is average.T indicates to turn
Set matrix.| ... | complex amplitude is sought in expression.
The method that Freeman-Durden is decomposed is used for reference, the polarization decomposing of dual polarization SAR data can be by covariance matrix
Or coherence matrix resolves into two kinds of ingredients：The volume scattering ingredient obtained by a series of Vegetation canopy director；By single order
The single scattering ingredient and rescattering ingredient that Prague (Bragg) surface scattering and dihedral angle reflect.
Polarization decomposing is carried out to dual polarization SAR data described in step S2, decomposable process is as follows：
F in formula (2)vCorrespond to volume scattering component (corresponding volume scattering ingredient above), fs+dCorrespond to odd times scattering and idol
The secondary coefficient component of scattering (the single scattering ingredient that the corresponding scattering of single order bragg surfaces and dihedral angle above are reflected
With rescattering ingredient)；
Thus, it is possible to release the power of various composition：
Ps+d=fs+d (3)
PV=4fv/3 (4)
W=Ps+d+PV=| SHV|2+|SVV|2 (5)
Parameter P in formulas+dIndicate that odd times scattering and even scatter the power of coefficient component, parameter PVIndicate that body dissipates
The power of component is penetrated, parameter w is then the total scattering power of two scattering components.
The LAI inverse model inverting LAI of the highest scattering component composition of relations of step S3 choice accuracies, distinguish first
Choose polarization indexPV/W、Ps+dThen/W utilizes measured data, establishes LAI to the variation sensibility of LAI values
With PV/w、Ps+dMultivariate regression models between/w：
LAI=f (PV/w,Ps+d/W) (6)
To realize area crops LAI invertings.
The WOFOST models of calibration research area crop, need to needed for meteorologic parameter and crop modeling in the step S4
Accumulated temperature parameter, using anti-distance weighting (IDW) interpolation algorithm, completes parameter regionization calibration on the basis of meteorological site.
Remote sensing observations LAI and WOFOST simulation LAI is assimilated using particle filter algorithm in the step S5, with public affairs
Formula (7) (8) (9) is calculated：
Indicate the modeling state variable of i-th of particle at k moment；Indicate i-th of particle at k+1 moment
Modeling state variable；M is nonlinear operator, i.e. WOFOST models；ukFor model-driven parameter；When indicating k+1
Carve the observation state variable of i-th of particle；H is Observation Operators, and ε is observation noise；xk+1For k+1 moment optimal estimation values；For the weight of each particle after normalization resampling；N indicates particle number；
In formula,Indicate particle weights of importance；Expression observation is yk+1When likelihood probability it is close
Degree, i.e.,It is y in the case of generationk+1The probability of generation；Similarly,It indicatesIn the case of generationOccur
Probability；Indicate importance sampling function；kiAttach most importance to downsampling factor；Expression takes
Integer part.
Wherein, the crop is preferably corn.
The present invention also provides the agricultural output assessment methods of the dual polarization synthetic aperture radar and crop modeling data assimilation to exist
Instruct the application in production estimation.
Compared with prior art, the present invention having the beneficial effect that：
The method of the present invention has merged SAR remotely-sensed datas and the advantage of crop modeling, takes full advantage of multipolarization SAR data
The abundant information of offer recycles particle filter to assimilate LAI to WOFOST moulds using dual polarized signals resolution model inverting LAI
In type, the problem of overcoming corn growth stage optical remote sensing shortage of data, the crop yield mould of crop modeling can be not only improved
The tracks LAI in quasi- precision optimizing Crop growing stage, additionally it is possible to crop yield is estimated on regional scale.
Description of the drawings
Fig. 1 is the work that the embodiment of the present invention 1 implements corn dual polarization synthetic aperture radar and crop modeling data assimilation
The flow diagram of object yield estimation method；
Fig. 2 is that 1 dual polarization synthetic aperture radar of embodiment and the agricultural output assessment method of crop modeling data assimilation are estimated
Corn yield result figure.
Specific implementation mode
With reference to embodiment, the specific implementation mode of Ben Fanming is described in further detail.Following embodiment is used for
Illustrate the present invention, but is not limited to the scope of the present invention.
Embodiment 1
With the agricultural output assessment method of dual polarization synthetic aperture radar of the present invention and crop modeling data assimilation for corn into
The flow diagram of row the yield by estimation is referring to attached drawing 1.
Sentinel-1 data in S1 collection research area corn growth stage carry out the pretreatments such as landform correction, obtain the time
The backscattering coefficient of two kinds of polarization (VH, VV) of the C band of sequence, i.e. dual polarization SAR data；
Select Hebei province's Hengshui City as survey region, which is located in 115 ° 10 ' -116 ° 34 ' of east longitude, 37 ° of north latitude
Between 03 ' -38 ° 23 '.Area gross area 8815km2 is studied, for landform based on Plain, arable land accounts for 60% of the gross area or more, belongs to warm
Temperate zone semi-moist monsoon climate, year sunshine time 2400-3100h, mean annual precipitation 300-800mm.
The time series Sentinel-1 for choosing Hebei province Hengshui City summer corn key developmental stages June to October in 2017 is defended
Star remotely-sensed data.Meanwhile the pretreatment of image is carried out, obtain the backward of two kinds of polarization (VH, VV) of the C band of time series
Scattering coefficient.
Main pre-treatment step further includes with orbital data self-correcting, removal thermal noise, tiltedly conversion, haplopia complex data
Polarization scattering matrix S, radiation calibration, topographical correction and coherent speckle noise is generated to filter out.Wherein, removal thermal noise uses bilinearity
Interpolation method, which eliminates the thermal noise that sensor is brought, to be influenced.Coherent speckle noise is filtered out to be filtered using Lee and be realized.For dual polarization SAR,
Defining target vector collision matrix is：
In formula, k is target vector, SVV,SVHComponent has respectively represented dissipating for dual polarization data VV, VH linear polarization state
Penetrate information.
S2 carries out (the i.e. polarization of dual polarization SAR data point of breeding time VH and VV dual polarization backscattering coefficient polarization decomposing
Solution), analyze different scattering component characteristics and with LAI value correlativities；
Polarization decomposing process calculates covariance matrix first： It is natural
The atural object on boundary can generally keep the symmetry of heading, it can be assumed that co polarized component and cross polar component not phase
It closes, has： Then
Wherein, ＜ ...>Indicate to assume random scattering media it is each to homogeneity in the case of spatial statistics it is average.T indicates to turn
Set matrix.| ... | complex amplitude is sought in expression.
The method that Freeman-Durden is decomposed is used for reference, the polarization decomposing of dual polarization SAR data can be by covariance matrix
Or coherence matrix resolves into two kinds of ingredients：The volume scattering ingredient obtained by a series of Vegetation canopy director；By single order
The single scattering ingredient and rescattering ingredient that Bragg surface scatterings and dihedral angle reflect.Decomposable process is as follows：
F in formulavCorrespond to volume scattering component, fs+dIt corresponds to odd times scattering and even scatters coefficient component.
Thus, it is possible to release the power of various composition：
Ps+d=fs+d (3)
PV=4fv/3 (4)
W=Ps+d+PV=| SHV|2+|SVV|2 (5)
Parameter Ps+dIndicate that odd times scattering and even scatter the power of coefficient component, parameter PVIndicate volume scattering point
The power of amount, parameter w are then the total scattering power of two scattering components.
The LAI inverse models of the highest scattering component composition of relations of S3 choice accuracies, inverting LAI give assessment observation
It is uncertain；
Choose polarization index respectively firstPV/w、Ps+dThen/w utilizes the variation sensibility of LAI values
Measured data establishes LAI and PV/w、Ps+d/ w, between multivariate regression models：
LAI=f (PV/w,Ps+d/w) (6)
To realize LAI invertings.
Meteorology, crop, soil and crop management parameter in S4 collection research area and as input parameter, calibration is ground
Study carefully the WOFOST models of area's corn
Obtain following data：According to the external envelope range in research area choose 21 National Meteorologicals observation station 2017 by
The data of 6 meteorological elements such as daily maximum temperature, the lowest temperature, sunshine time, vapour pressure, wind speed, precipitation；Out of research area
Agricultural weather website obtains soil parameters, crop parameter and the phenology data of acquisition；Obtain the control parameters such as longitude and latitude, elevation；
The yield of Summer Corn data of agricultural weather data and 17 years each counties of Hebei province's Hengshui City.
The long-term sequence image and field investigation data interpretation in research on utilization area obtain summer corn planting area, generate 1
Milimeter grid calculates summer corn in grid cell and plants percentage, and given threshold rejects summer corn planting proportion and is less than 20%
Unit.Meanwhile WOFOST models are demarcated using the meteorology of collection, crop, soil and management parameters in each agriculture gas website.
To the yield output of model and meteorological data unified metric, and according to tentatively demarcating as a result, by meteorological data and crop parameter
It carries out anti-distance weighting interpolation and generates 1 kilometer of raster data per pixel.
S5, take LAI as assimilation variable, remote sensing observations LAI and WOFOST simulation LAI is carried out using particle filter algorithm same
Change, optimizes the tracks LAI in corn growth stage；
During model running, assimilated if the same day there are remote sensing observations data.Assimilation method indicates as follows：
Indicate the modeling state variable of i-th of particle at k moment；Indicate i-th of particle at k+1 moment
Modeling state variable；M is nonlinear operator, i.e. WOFOST models；ukFor model-driven parameter；When indicating k+1
Carve the observation state variable of i-th of particle；H is Observation Operators, and ε is observation noise；xk+1For k+1 moment optimal estimation values；For the weight of each particle after normalization resampling；N indicates particle number；
In formula,Indicate particle weights of importance；Expression observation is yk+1When likelihood probability it is close
Degree, i.e.,It is y in the case of generationk+1The probability of generation；Similarly,It indicatesIn the case of generationOccur
Probability；Indicate importance sampling function；kiAttach most importance to downsampling factor；Expression takes
Integer part.
The basic process of assimilation is that population is set as 300 first, in forecast period, is made an uproar at random with given Gaussian Profile
The initial LAI x of acoustic disturbance movable modelk, obtain the primary group at kth momentIn the more new stage, mould is utilized
Type predicted state, remote sensing observations and initial importance probability density calculate the weights of importance of each particle by formula (14)After each resampling,Specific selection is done to the initial importance probability density function of particle, that is, and it is false
Surely the situation of prediction particle deviation observation meets normal distribution, so calculating prediction grain with the probability density function of normal distribution
The weighted value of son：
In formula, Rk+1For the observation error covariance matrix at+1 moment of kth.Then when obtaining kth+1 by formula (13)
The state estimation at quarter.To optimize the tracks LAI in corn growth stage.
The step of S6, one by one corn grid, operation S5, drives WOFOST models, simulation defeated again using the LAI after optimization
Go out the corn yield in region, carries out space mapping.
The corn that the present embodiment dual polarization synthetic aperture radar and the agricultural output assessment method of crop modeling data assimilation are estimated
Yield result figure is shown in Fig. 2.
The method of the present invention has merged SAR remotely-sensed datas and the advantage of crop modeling, takes full advantage of multipolarization SAR data
The abundant information of offer recycles particle filter to assimilate LAI to WOFOST moulds using dual polarized signals resolution model inverting LAI
In type, the problem of overcoming corn growth stage optical remote sensing shortage of data, the crop yield mould of crop modeling can be not only improved
The tracks LAI in quasi- precision optimizing Crop growing stage, additionally it is possible to crop yield is estimated on regional scale.
Although above the present invention is described in detail with a general description of the specific embodiments,
On the basis of the present invention, it can be made some modifications or improvements, this will be apparent to those skilled in the art.Cause
This, these modifications or improvements, belong to the scope of protection of present invention without departing from theon the basis of the spirit of the present invention.
Claims (10)
1. a kind of agricultural output assessment method of dual polarization synthetic aperture radar and crop modeling data assimilation, which is characterized in that specific
Steps are as follows：
The satellite data of dual polarization synthetic aperture radar, carries out pretreatment acquisition in S1, collection research area Crop growing stage to be measured
VH the and VV dual polarization backscattering coefficients of time series C-band, i.e. dual polarization SAR data；
S2, to dual polarization SAR data carry out polarization decomposing, analyze different scattering component characteristics and with LAI value correlativities；
The LAI inverse model inverting LAI of the highest scattering component composition of relations of S3, choice accuracy, obtain remote sensing observations LAI, give
Accepted opinion estimates the uncertainty of observation；
S4, the meteorology in collection research area, crop, soil and crop management parameter and as input parameter, calibration research
The WOFOST models of area crop obtain obtaining WOFOST simulations LAI；
S5, take LAI as assimilation variable, remote sensing observations LAI and WOFOST simulation LAI assimilated using particle filter algorithm,
The tracks Crop growing stage LAI after being optimized；
S6, one by one crop grid run the step of S5, and WOFOST moulds are driven again using the tracks Crop growing stage LAI after optimization
Type, the crop yield in simulation output region carry out space mapping, instruct crop production.
2. the method as described in claim 1, which is characterized in that it refers to using orbital data to carry out pretreatment described in step S1
Self-correcting, removal thermal noise, tiltedly conversion, haplopia complex data generate polarization scattering matrix S, radiation calibration, topographical correction and phase
Dry spot noise filtering.
3. method as claimed in claim 2, which is characterized in that removal thermal noise eliminates sensor strip using bilinear interpolation
The thermal noise come influences.
4. method as claimed in claim 2, which is characterized in that coherent speckle noise is filtered out to be filtered using Lee and be realized.
5. method as claimed in claim 2, which is characterized in that dual polarization SAR data described in step S1 defines target vector
Collision matrix is：
In formula, k is target vector, SVV、SVHComponent has respectively represented the scattering letter of dual polarization data VV, VH linear polarization state
Breath.
6. method as claimed in claim 5, which is characterized in that polarization decomposing is carried out to dual polarization SAR data described in step S2,
Decomposable process is as follows：
F in formula (2)vCorrespond to volume scattering component, fs+dIt corresponds to odd times scattering and even scatters coefficient component；
Thus, it is possible to release the power of various composition：
Ps+d=fs+d (3)
PV=4fv/3 (4)
W=Ps+d+PV=| SHV|2+|SVV|2 (5)
Parameter P in formulas+dIndicate that odd times scattering and even scatter the power of coefficient component, parameter PVIndicate volume scattering component
Power, parameter w then be two scattering components total scattering power.
7. method as claimed in claim 6, which is characterized in that the highest scattering component relationship group of step S3 choice accuracies
The LAI inverse model inverting LAI of conjunction, choose polarization index respectively firstPV/w、Ps+dVariations of/the w to LAI values
Then sensibility utilizes measured data, establishes LAI and PV/w、Ps+dMultivariate regression models between/w：
LAI=f (PV/w,Ps+d/w) (6)
To realize area crops LAI invertings.
8. the method for claim 7, which is characterized in that the WOFOST moulds of calibration research area crop in the step S4
Type is needed to the accumulated temperature parameter needed for meteorologic parameter and crop modeling, on the basis of meteorological site, using anti-distance weighting interpolation
Algorithm completes parameter regionization calibration.
9. method as claimed in claim 8, which is characterized in that using particle filter algorithm to remote sensing observations in the step S5
LAI and WOFOST simulations LAI is assimilated, and is calculated with formula (7) (8) (9)：
Indicate the modeling state variable of i-th of particle at k moment；Indicate the mould of i-th of particle at k+1 moment
Pattern intends state variable；M is nonlinear operator, i.e. WOFOST models；ukFor model-driven parameter；Indicate the k+1 moment i-th
The observation state variable of a particle；H is Observation Operators, and ε is observation noise；xk+1For k+1 moment optimal estimation values；
For the weight of each particle after normalization resampling；N indicates particle number；
In formula,Indicate particle weights of importance；Expression observation is yk+1When likelihood probability density, i.e.,It is y in the case of generationk+1The probability of generation；Similarly,It indicatesIn the case of generationThe probability of generation；Indicate importance sampling function；kiAttach most importance to downsampling factor；Expression takesInteger
Part.
10. the agricultural output assessment side of any one of claim 1-9 the dual polarization synthetic aperture radar and crop modeling data assimilation
Application of the method in instructing production estimation.
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