CN110057997A - A kind of forest fuel moisture content time series inverting method based on dual polarization SAR data - Google Patents
A kind of forest fuel moisture content time series inverting method based on dual polarization SAR data Download PDFInfo
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Abstract
A kind of forest fuel moisture content time series inverting method based on dual polarization SAR data, belongs to remote-sensing inversion technical field.The present invention initially sets up the semiempirical model of earth's surface totality back scattering contribution, and the semiempirical model joint then contributed using earth's surface totality back scattering under different polarization modes eliminates soil moisture parameter in model, obtains dual polarization semiempirical model；And the seasonal phenology rule demarcated based on the sample data in survey region to the dual polarization semiempirical model established, then establish look-up table based on calibrated dual polarization semiempirical model, and be aided with survey region FMC carries out the inverting of FMC.The present invention being capable of overall back scattering contribution that is true, objectively responding earth's surface, influence of the soil moisture scattering to FMC inversion result is avoided simultaneously, significantly improve the accuracy of inversion result, and it can be used for different survey region FMC invertings, universality is strong, compensates for the blank that semiempirical model inverting FMC parameter is established based on microwave remote sensing.
Description
Technical field
The invention belongs to remote-sensing inversion technical fields, and in particular to a kind of forest fuel based on dual polarization SAR data
Moisture content time series inverting method.
Background technique
Ring ecological environment is broken in forest fire, discharges greenhouse gases, and pollution atmosphere simultaneously causes casualties, countries in the world incited somebody to action
The assessment of forest fire risk and an important measure of the early warning as forest management.For the assessment of fire risk, Pyne etc.
Famous fiery Triangle Model is proposed, i.e. climatic information, terrain information and combustible information is to assess three weights of fire risk
Want index.Wherein the acquisition of climatic information and terrain information is relatively easy, and combustible information be broadly divided into fuel class, can
Combustion things load and Fuel loads (Fuel Moisture Content, FMC), wherein FMC is easy by factors such as weather
Influence to there are biggish uncertainty, therefore the key for assessing fire risk is that FMC in combustible information changes
Accurate monitoring.
The development of remote sensing technology provides effective way for the monitoring of FMC.Since vegetation is aqueous red in near-infrared and shortwave
Wave section has certain sensitivity, and optical remote sensing is the main means of inverting FMC at this stage, but due to visible light, infrared light
The limited penetration capacity of spectrum signal by cloud layer and shines upon condition and is limited, so that the precision based on optical remote sensing inverting FMC
It is highly prone to the influence of the weather conditions such as sexual intercourse mist.In contrast, microwave remote sensing have round-the-clock, round-the-clock, penetration capacity is strong
The characteristics of, it is not illuminated by the light, the influence of the weather conditions such as cloud and mist, and high sensitivity characteristic is revealed for surface water information table, in FMC
Inverting field has very big application potential.However, it is fresh few currently based on the research that microwave remote sensing carries out FMC inverting, and base
Originally the statistical relationship analyzed between backscattering coefficient and actual measurement FMC data is concentrated on and the empirical model established.These warps
The space expansibility for testing model is poor, brings very big uncertainty to estimation range FMC estimation, lacks universality.Therefore,
Need to develop a kind of universality by force and the FMC inversion method based on SAR data of high reliablity, hence it is evident that raising surface fuel contains
The precision of prediction of water rate.In recent decades, for the microwave scattering mechanism of earth's surface, domestic and foreign scholars have carried out a large amount of research,
It proposes many exposed soil scattering models with physical mechanism and vegetation scattering model is used for the inverting of Land Surface Parameters, compare experience
For statistical method, these models universality with higher and reliability.But earth's surface scattering is a sufficiently complex process,
All there is scattering in vegetation and underlying surface exposed soil, and interact.Therefore scattering tribute that is how objective, being truly reflected surface vegetation
Offering becomes this field technical problem urgently to be resolved.
In recent years, with the continuous development of synthetic aperture radar (Synthetic Aperture Radar, SAR) technology,
More and more the satellite equipped with SAR sensor goes up to the air and enters the orbit, and satellite-borne SAR is gradually from low time and space resolution ratio, list
One wave band, single polarization mode, fixed viewpoint, single mode of operation are to high time and space resolution ratio, multiband, multipolarization side
Formula, multi-angle of view, multi-operation mode transformation.This provides more fully data for the Land Surface Parameters study on monitoring based on microwave remote sensing
It supports.
Summary of the invention
Being based on microwave remote sensing inverting FMC the prior art has the influence of underlying surface exposed soil, and the present invention provides one
The forest fuel moisture content time series inverting method based on dual polarization SAR data is planted to overcome existing issue, improves earth's surface
The precision of Fuel loads estimation result.In order to solve problems in the prior art, technical solution provided by the invention is specifically such as
Under:
A kind of forest fuel moisture content time series inverting method based on dual polarization SAR data, comprising the following steps:
Step 1: data preparation；
Dual polarization SAR data and multidate FMC measured data in survey region are obtained, the dual polarization SAR data includes
First polarization mode SAR data and the second polarization mode SAR data；
Step 2: model foundation；
Linear model is scattered by coupling exposed soil and ignores the plant that rescattering is contributed between underlying surface soil and vegetable layer
By scattering water-cloud model, the semiempirical model of earth's surface totality back scattering is established, it is then total using earth's surface under different polarization modes
The semiempirical model of body back scattering carries out joint and eliminates soil moisture parameter, obtains dual polarization radar backscattering coefficient
The dual polarization semiempirical model directly related with FMC；
Step 3: model calibration；
Select the dual polarization semiempirical model that part sample establishes step 2 as training data in the sample data
It is demarcated, computation model empirical coefficient；
Step 4: parametric inversion；
Remaining data in the sample data is selected to be based on the calibrated dual polarization semiempirical of step 3 as inverting data
Model, input is according in the multidate FMC measured data that step 1 obtains determining FMC variation range and the sample data
Radar backscattering coefficients in remaining sample under the first polarization mode are simulated after obtaining the radar under the second polarization mode to scattered
Coefficient is penetrated, corresponding look-up table is established, is auxiliary, the second pole based on actual measurement with the seasonal phenology rule of FMC in survey region
Radar backscattering coefficients under change mode inverting FMC value in a lookup table.
It further, further include being pre-processed to dual polarization SAR data in the step 1.
Further, the pretreatment includes radiation calibration, spatial noise filters out, landform is corrected, projection transform, adopts again
Sample, backscattering coefficient extracts and local incidence angle correction.
Further, the dual polarization SAR data is specially dual polarization backscattering coefficient, and polarization mode can be four kinds
Polarization mode --- any two kinds of combination in HH, VV, HV, VH.
It further, further include carrying out time series to the dual polarization backscattering coefficient extracted to insert in the step 1
It is worth (cubic spline interpolation method) and smothing filtering (Savitzky-Golay smooth
Filter), to match the Radar backscattering coefficients of multidate FMC measured data.
It further include using cubic spline interpolation method (cubic as a kind of specific embodiment, in the step 1
Spline interpolation method) time series interpolation is carried out to dual polarization backscattering coefficient, then it is flat using S-G
Filter slide (Savitzky-Golay smooth filter) carries out smothing filtering to the data after interpolation, when obtaining matching more
The Radar backscattering coefficients of phase FMC measured data.
Further, the expression formula of exposed soil scattering linear model is as follows in the step 2:
In formula, C and D are the empirical coefficients of model, and mv is soil moisture.
It is because model has ignored roughness of ground surface and scatters to exposed soil that the present invention, which selects the reason of exposed soil scattering linear model,
Influence, it is believed that exposed soil contribution of scatters directly simplifies exposed soil scattering mechanism directly by the control of soil moisture.
Further, the vegetation scattering that rescattering is contributed between underlying surface soil and vegetable layer is ignored in the step 2
The expression formula of water-cloud model is as follows:
In formula, A and B are the empirical coefficient of model；V1And V2To characterize the decaying of vegetation and the parameter of scattering signatures, select
It is selected as FMC；Indicate vegetable layer to the secondary extinction coefficient of underlying surface exposed soil layer contribution of scatters；It is total for earth's surface
Body backscattering coefficient；For the contribution of scatters of exposed soil layer, calculated by formula 1.
It is because it is as a semiempirical model that the present invention, which selects vegetation scattering water-cloud model, both includes certain physics
Mechanism, and there is corresponding statistical significance, there is wide application value；And between underlying surface exposed soil and surface vegetation layer
Rescattering contribution is very weak for underlying surface exposed soil and surface vegetation layer contribution of scatters, can be ignored.
Further, earth's surface totality dual polarization backscattering coefficient and FMC are directly linked half is established in the step 2
Empirical model is specifically to first pass through formula (3) to obtain the soil moisture expression formula as shown in formula (5), then by formula (5)
It is brought directly in formula (4), eliminates this parameter of soil moisture, thus obtain dual polarization semiempirical model；Formula (3),
Formula (4), the expression formula of formula (5) are as follows:
In formula,For the earth's surface totality backscattering coefficient of the first polarization mode；Earth's surface for the second polarization mode is total
Body backscattering coefficient,Indicate that vegetable layer is to the two of underlying surface exposed soil layer contribution of scatters under the first polarization mode
Secondary attenuation coefficient；Indicate secondary declining of the vegetable layer to underlying surface exposed soil layer contribution of scatters under the second polarization mode
Subtract coefficient；A1And A2、B1And B2、C1And C2And D1And D2Experience under respectively the first polarization mode and under the second polarization mode
Coefficient.
In the semiempirical model that the present invention establishes, since soil moisture mv directly affects earth's surface totality back scattering system
Number, but lack reliable soil moisture measured data or soil moisture Satellite Product at present and can be used, thus
Cause model that can not really reflect the contribution of scatters of surface vegetation.Therefore present invention innovation proposes half based on different polarization modes
Empirical model simultaneous eliminates soil moisture, and then surface soil is avoided to scatter the influence to inverting FMC.
It is preferred that model uses backscattering coefficient (the master mould unit of linear unit in the step 2
For dB), it is therefore an objective to reduce the model complexity as brought by logarithm and index.
Further, model calibration in the step 3, i.e., according to multidate FMC measured data and dual polarization SAR data
The empirical coefficient of established semiempirical model is solved, optimal semiempirical model is obtained.
As a kind of specific embodiment, the empirical coefficient of semiempirical model is calculated using nonlinear least square method；For
Avoid the locally optimal solution of non-linear least square, preferably model calibration process repeats for 100 times (model passes through each time
The initial value for testing coefficient is randomly generated), coefficient of determination R is used for the evaluation of model calibration result2, then select R2It is maximum
Fitting coefficient participate in subsequent FMC inverting as globally optimal solution；The coefficient of determination R2Expression formula it is as follows:
In formula, μestAnd μobsRespectively represent the second polarization mode backscattering coefficient of simulation and actual measurement.
Further, after selecting LUT Method to carry out FMC inverting because of soil moisture is eliminated in the step 4
The analytic solutions that semiempirical model is difficult to directly be expressed as FMC are solved；The method that the look-up table is established is specific as follows: first
The variation range and change step of FMC parameter in look-up table are determined according to multidate FMC measured data, then by itself and actual measurement
Backscattering coefficient is input to together through optimal semiempirical model obtained by calibrating under first polarization mode, and simulation obtains the second pole
Backscattering coefficient under change mode, to establish look-up table.
Further, immediate FMC value is found in the step 4 in look-up table particular by least cost function
Carry out FMC inverting.
As a kind of specific embodiment, cost function can be difference square Ds, expression formula is as follows:
Ds=(μestr-μobsr)2 (7)
In formula, μestrAnd μobsrRespectively the second polarization mode backscattering coefficient of Mono temporal Imitating and actual measurement.
It is because looking into that the seasonal phenology rule of survey region FMC is introduced in the present invention as the reason of auxiliary information
Look for least cost function in table that may correspond to multiple FMC values.Sentenced by the seasonal phenology rule of FMC as auxiliary information
It is disconnected, influence of the look-up table ill-conditioning problem for FMC inversion result can be alleviated.
Compared with prior art, beneficial effects of the present invention are as follows:
(1) present invention provides a kind of forest fuel moisture content time series inverting method based on dual polarization SAR data,
The semiempirical model established can contribution of scatters that is true, objectively responding surface vegetation, avoid soil moisture scattering to inverting
The influence of FMC result, hence it is evident that improve the accuracy of estimation result.
(2) present invention provides a kind of forest fuel moisture content time series inverting method based on dual polarization SAR data,
Compared to the existing statistical analysis technique based on microwave remote sensing, semiempirical model proposed by the invention is due to having centainly
Physical basis, by survey region selected part data demarcate as empirical coefficient of the training data to model, energy
Enough expansions realized spatially, can be used for different survey region FMC estimations, and universality is strong.
(3) present invention provides a kind of forest fuel moisture content time series inverting method based on dual polarization SAR data,
The blank based on microwave remote sensing and semiempirical or physical model inverting FMC parameter is compensated for, for optical joint remote-sensing inversion skill
Art building Global Scale FMC monitoring framework is of great significance.
(4) present invention provides a kind of different polarization mode backscattering coefficients of joint and does not depend on other types measured data
Surface parameters inversion thinking.
Detailed description of the invention
Fig. 1 provides the flow diagram of inversion method for the present invention.
Fig. 2 provides inversion method 1 observation station timing FMC (black color dots) used in verification process for the present invention
Distribution.
Fig. 3 is pretreated Sentinel-1A time series dual polarization (VV and VH) backscattering coefficient.
Fig. 4 provides the calibration result of semiempirical model in inversion method for the present invention.
Fig. 5 is research continuous 3 years FMC measured values of website and seasonal division result.
Fig. 6 is with the inversion result of the invention for providing inversion method and obtaining FMC.
Specific embodiment
In order to enable one of ordinary skill in the art can more understand the present invention program and principle, with reference to the accompanying drawing and have
Body embodiment is described in detail:
Embodiment:
A kind of forest fuel moisture content time series inverting method based on dual polarization SAR data, as shown in Figure 1, packet
Include following steps:
Step 1: data preparation；
The present embodiment multidate FMC measured data comes from National Fuel Moisture Database, such as Fig. 2 institute
Show, the time series FMC measured data of used CNTX_McCl_TX website shares 21 phases in embodiment, from 2016
May in year, monthly measurement was primary substantially to 2 months 2018；
The present embodiment SAR data is selected as time series dual polarization (VV&VH) Sentinel-1A that European Space Agency provides free
Data；The level-one GRD product under interference wide cut mode is selected in embodiment, which is completely covered time series FMC actual measurement
Phase is total up to 46 scape image datas, SNAP and ArcGIS software is used to carry out above-mentioned processing, processing to data in the present embodiment
As a result as shown in Figure 3.
Step 2: model foundation；
By coupling exposed soil scattering linear model (such as formula (1)) and ignore secondary between underlying surface soil and vegetable layer dissipate
Vegetation scattering water-cloud model (such as formula (2)) for penetrating contribution, establishes the semiempirical model of earth's surface totality back scattering contribution, then
Joined using the semiempirical model (such as formula (3) and formula (4)) that earth's surface totality back scattering under different polarization modes is contributed
It closes and eliminates soil moisture parameter (such as formula (5)), simultaneous obtains earth's surface totality dual polarization semiempirical model after eliminating；It is described
The expression formula of formula is as follows:
In formula, C and D are the empirical coefficients of model, and mv is soil moisture；
In formula, A and B are the empirical coefficient of model；V1And V2To characterize the decaying of vegetation and the parameter of scattering signatures, select
It is selected as FMC)；Indicate vegetable layer to the secondary extinction coefficient of underlying surface exposed soil layer contribution of scatters；For earth's surface
Overall backscattering coefficient；For the contribution of scatters of exposed soil layer；
Formula (5) is brought directly in formula (4), establish contain only dual polarization backscattering coefficient (With) with
Model expression between FMC.
Step 3: model calibration；
Using all timed sample sequences 2/3rds (14) model is demarcated as training sample, using most
Small least square method carries out regression fit, and the empirical coefficient of computation model obtains dual polarization backscattering coefficient and the optimal mould of FMC
Type, regression fit result use coefficient of determination R2The goodness of fit of evaluation model calibration indicates simulation and actual measurement the pole VH
Change mode backscattering coefficient correlation, R2Calculation formula such as following formula (6) shown in:
In formula, μestAnd μobsRespectively represent simulation and actual measurement VH polarization mode backscattering coefficient.
In order to avoid the locally optimal solution of non-linear least square, model calibration process repeats 100 (moulds each time
The initial value of type empirical coefficient is randomly generated), selection repeats best (the i.e. R of the goodness of fit obtained by demarcating2Maximum value) when
Fitting coefficient participates in subsequent FMC refutation process as globally optimal solution, after acquired simulation and the VH polarization mode surveyed
It is as shown in Figure 4 to the scatterplot comparison diagram of scattering coefficient, fit equation and the coefficient of determination.
Step 4: parametric inversion；
Select all timed sample sequence residue one thirds (7) as verifying sample data, for the inverting to FMC
And verifying；Based on the calibrated dual polarization semiempirical model of step 3, input establishes FMC variation range according to 21 sample datas
Backscattering coefficient in (70%~150%, 5% step-length) and verifying sample under VV polarization mode, simulates the corresponding pole VH
Backscattering coefficient under change mode has obtained corresponding look-up table；
The present invention uses least cost function DsQuery inversion is as a result, still since look-up table inverting is usually all morbid state
, that is to say, that least cost function may correspond to multiple FMC, therefore we introduce the seasonal phenology spy of experiment website FMC
Sign carries out assisted Selection.Experiment website goes over continuous 3 years FMC measured values as shown in figure 5, finding the website by Fig. 5 analysis
FMC is relatively high in annual 4 to September (spring and summers), compares in January to March (winter) and September to December (autumn)
It is low, so in case of ill-conditioning problem, then selecting least cost function corresponding when the phase of inverting is in the high FMC period
Higher value is used as final inversion result, conversely, when the phase of inverting is in the low FMC period, in case of ill-conditioning problem,
Least cost function is then selected to correspond to lower value as final inversion result.Under the auxiliary of the seasonal phenology feature of FMC,
Using under the VH polarization mode of the verifying sample (7) of remaining one third backscattering coefficient carry out FMC inverting, estimation and
The scatterplot comparison diagram of the FMC of actual measurement is as shown in fig. 6, model inversion and actual measurement FMC coefficient of determination R2And root-mean-square error
RMSE is respectively 0.572 and 19.534%.
The embodiment of the present invention is elaborated in conjunction with attached drawing above, but the invention is not limited to above-mentioned
Specific embodiment, above-mentioned specific embodiment is only schematical, rather than restrictive, the ordinary skill people of this field
Member under the inspiration of the present invention, can also make many in the case where not departing from present inventive concept and claimed range
Deformation, these belong to protection of the invention.
Claims (10)
1. a kind of forest fuel moisture content time series inverting method based on dual polarization SAR data, which is characterized in that including
Following steps:
Step 1: data preparation；
Dual polarization SAR data and multidate FMC measured data in survey region are obtained, the dual polarization SAR data includes first
SAR data under SAR data and the second polarization mode under polarization mode；
Step 2: model foundation；
Linear model, which is scattered, by coupling exposed soil and ignores the vegetation that rescattering is contributed between underlying surface soil and vegetable layer dissipates
Jetting cloud model establishes the semiempirical model of earth's surface totality back scattering contribution, then total using earth's surface under different polarization modes
The semiempirical model of body back scattering contribution carries out joint and eliminates soil moisture parameter, and it is backward to obtain earth's surface totality dual polarization
The scattering coefficient dual polarization semiempirical model directly related with FMC；
Step 3: model calibration；
Part sample in the sample data is selected to carry out as training data to the dual polarization semiempirical model that step 2 is established
Calibration, computation model empirical coefficient；
Step 4: parametric inversion；
Remaining data in the sample data is selected to be based on the calibrated dual polarization semiempirical mould of step 3 as inverting data
Type, input are remained according in the multidate FMC measured data that step 1 obtains determining FMC variation range and the sample data
Radar backscattering coefficients under first polarization mode in remaining sample, simulation obtain radar raster-displaying system under the second polarization mode
Number, dual polarization semiempirical model establish corresponding look-up table, are auxiliary with the phenology rule of FMC in survey region, are based on the second pole
Radar backscattering coefficients inverting FMC value in a lookup table under change mode.
2. the method according to claim 1, wherein further include in the step 1 to dual polarization SAR data into
Row pretreatment.
3. the method according to claim 1, wherein the pretreatment include radiation calibration, spatial noise filter out,
Landform correction, projection transform, resampling, backscattering coefficient extracts and local incidence angle correction.
4. the method according to claim 1, wherein the dual polarization SAR data is specially after dual polarization to dissipating
Penetrate coefficient.
5. according to the method described in claim 4, it is characterized in that, further including to after the dual polarization extracted in the step 1
Time series interpolation and smothing filtering are carried out to scattering coefficient to obtain the radar raster-displaying of matching multidate FMC measured data
Coefficient.
6. the method according to claim 1, wherein exposed soil scatters the expression formula of linear model in the step 2
It is as follows:
In formula, C and D are the empirical coefficients of model, and mv is soil moisture.
7. the method according to claim 1, wherein ignore in the step 2 underlying surface soil and vegetable layer it
Between rescattering contribute vegetation scattering water-cloud model expression formula it is as follows:
In formula, A and B are the empirical coefficient of model；V1And V2To characterize the decaying of vegetation and the parameter of scattering signatures, it is selected as
FMC；Indicate vegetable layer to the secondary extinction coefficient of underlying surface exposed soil layer contribution of scatters；After earth's surface totality
To scattering coefficient；For the contribution of scatters of exposed soil layer.
8. according to the method described in claim 4, it is characterized in that, calculating semiempirical model using nonlinear least square method
Empirical coefficient；Model calibration process repeats 100 times, and the initial value of model empirical coefficient is randomly generated each time, selection
R2Maximum fitting coefficient participates in subsequent FMC inverting as globally optimal solution；The coefficient of determination R2Expression formula it is as follows:
In formula, μestAnd μobsRespectively represent the first polarization mode backscattering coefficient of Mono temporal Imitating and actual measurement.
9. according to the method described in claim 4, it is characterized in that, by searching for table inverting FMC in the step 4, specifically
FMC is searched by least cost function.
10. according to the method described in claim 4, it is characterized in that, cost function can be difference square Ds, expression formula is as follows:
Ds=(μestr-μobsr)2
In formula, μestrAnd μobsrRespectively the second polarization mode backscattering coefficient of Mono temporal Imitating and actual measurement.
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