CN110222870A - Assimilate the Regional Fall Wheat yield estimation method of satellite fluorescence data and crop growth model - Google Patents
Assimilate the Regional Fall Wheat yield estimation method of satellite fluorescence data and crop growth model Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of Regional Fall Wheat yield estimation method for assimilating satellite fluorescence data and crop growth model, comprising: sensitive model parameters analysis and model parameter calibration；In the outer layer of SIF data assimilation system, SIF satellite remote sensing date and SCOPE model are assimilated according to parameter calibration result, day accumulation GPP is obtained by the model after assimilating；In the internal layer of SIF data assimilation system, assimilate the day accumulation GPP of the resulting day accumulation GPP of the system outer layer and WOFOST modeling；Coupled Numerical data of weather forecast obtains the Prediction For Winter Wheat Production result in the region to be assessed of WOFOST model output.SIF satellite remote sensing date is introduced into process model by the embodiment of the present invention, is established from photosynthetic angle and is contacted with the mechanistic of yield, carries out region yield forecast from optimization crops photosynthesis.
Description
Technical field
The present embodiments relate to agricultural technology fields, more particularly, to a kind of assimilation satellite fluorescence data and crop
The Regional Fall Wheat yield estimation method of growth model.
Background technique
In order to which the yield to winter wheat is estimated, develop in the prior art a series of based on leaf area index, soil
Moisture, reflectivity or vegetation index are the remote sensing and mechanism process model data assimilation system for assimilating variable, in Regional Fall Wheat
Important achievement is achieved on growing way, disaster monitoring and yield forecast.But these assimilation variables in the prior art can not be from light
The angle foundation of cooperation is contacted with the mechanistic of yield, therefore, in Quantitative Monitoring winter wheat growth process and yield forecast side
There are limitations in face.
Summary of the invention
To solve the above-mentioned problems, the embodiment of the present invention provides one kind and overcomes the above problem or at least be partially solved
State the Regional Fall Wheat yield estimation method of assimilation the satellite fluorescence data and crop growth model of problem.
The embodiment of the present invention provides a kind of Regional Fall Wheat the yield by estimation side for assimilating satellite fluorescence data and crop growth model
Method selects winter wheat area accounting to be greater than setting ratio this method comprises: winter wheat planting area is divided into multiple grid
Grid is used as region to be assessed；According to environmental index data by the region division to be assessed be multiple plantation subregions, obtain kind
Plant subregion setting resolution ratio sunlight-induced chlorophyll fluorescence (Sun-Induced chlorophyll Fluorescence,
SIF) remotely-sensed data；Global sensitivity analysis is carried out to WOFOST model, is obtained to vegetation gross primary productivity (Gross
Primary Productivity, GPP), the parameter set of yield sensitivity；Global sensitivity analysis is carried out to SCOPE model, is obtained
To the sensitive parameter set of SIF, GPP；Parameter calibration is carried out to the sensitive parameter that the sensitive parameter is concentrated according to plantation subregion
It is assessed with uncertainty；In the outer layer of SIF data assimilation system, by the SCOPE model after SIF remotely-sensed data and parameter calibration into
Row assimilation obtains day accumulation GPP by the model after assimilating；In the internal layer of SIF data assimilation system, assimilate the system outer layer institute
The day of day accumulation GPP and WOFOST modeling that obtain accumulate GPP, and the day after being optimized accumulates GPP；After optimization
GPP, with weather forecast data-driven WOFOST model, the winter wheat for obtaining the region to be assessed of WOFOST model output is produced
Measure forecast result.
The Regional Fall Wheat yield estimation method of assimilation satellite fluorescence data and crop growth model provided in an embodiment of the present invention,
By the method for SIF remotely-sensed data and the assimilation of mechanism process model, SIF remotely-sensed data is introduced into process model, from photosynthetic work
Angle foundation is contacted with the mechanistic of yield, carries out region yield forecast from optimization crops photosynthesis.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described.It should be evident that the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other accompanying drawings are obtained according to these figures.
Fig. 1 is that the Regional Fall Wheat of assimilation satellite fluorescence data and crop growth model provided in an embodiment of the present invention is assessed
The flow diagram of method；
Fig. 2 is yield DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM schematic diagram in region provided in an embodiment of the present invention；
Fig. 3 is that yield provided in an embodiment of the present invention is higher than 8000kg/ha probability forecast schematic diagram.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical solution in the embodiment of the present invention is explicitly described, it is clear that described embodiment is the present invention
A part of the embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
In recent years, there are the appearance and fast development of the SIF Remote Sensing Products being closely connected, Yi Jidong with the physiology course of vegetation
Morphotype intends reaching its maturity for the process model of canopy chlorophyll fluorescence, brings new machine to remotely-sensed data assimilation yield forecast
Meet --- SIF remotely-sensed data is introduced into process model, carries out region yield forecast from optimization crops photosynthesis.
Based on this, the embodiment of the present invention provides a kind of Regional Fall Wheat for assimilating satellite fluorescence data and crop growth model
Yield estimation method, referring to Fig. 1, this method includes but is not limited to following steps:
Winter wheat planting area is divided into multiple grid by step 101, and winter wheat area accounting is selected to be greater than setting ratio
Grid be used as region to be assessed.
Specifically, before step 101, it defends on the multidate land that can get winter wheat planting area in During Growing Period of Winter Wheat
No. eight land imagers of star (Landsat 8OLI) data and sentry No. two (Sentinel 2) A/B optical datas.Based on above-mentioned
Optical data, the winter for obtaining research area's (i.e. winter wheat planting area) certain resolution (such as 30m) using decision tree classification are small
Wheat spatial distribution map.Then winter wheat spatial distribution map is divided into the grid of multiple predetermined resolutions (such as 1km), calculated every
Area ratio shared by winter wheat (i.e. winter wheat area accounting) in a grid, select area accounting be greater than setting ratio (such as
80%) grid is assessed.Wherein, the grid selected is region to be assessed.By carrying out above-mentioned processing, grid has
Higher purity, the yield by estimation result are more accurate.
Step 102, according to environmental index data by the region division to be assessed be multiple plantation subregions, obtain plantation point
The SIF remotely-sensed data of the setting resolution ratio in area.
Specifically, it is obtained behind region of assessing in a step 101, region to be assessed can be subjected to further division, obtained
Obtain multiple plantation subregions.Wherein, environmental index data is used to reflect the ambient conditions in region to be assessed, and environmental index may include gas
As condition, cropping pattern, disaster frequency, yield level etc., the embodiment of the present invention is not construed as limiting this.It therefore, can be according to wait estimate
Entirely region division to be assessed is different plantation subregions by the ambient conditions in the producing region region Yu Zhongge.Obtain region to be assessed
SIF remotely-sensed data.Wherein, SIF data and the physiology course of vegetation are closely connected, and can directly reflect the photosynthetic work of winter wheat
Use situation.
Step 103 carries out global sensitivity analysis to WOFOST model, obtains the parameter set sensitive to GPP, yield；It is right
SCOPE model carries out global sensitivity analysis, obtains the sensitive parameter set to SIF, GPP.
Wherein, WOFOST model is crop growth model, and SCOPE model can simulate canopy SIF data.It can be used
EFAST Sensitivity Analysis study WOFOST model to the global sensibility of GPP, yield and SCOPE model to SIF,
The global sensibility of GPP.The union for the parameter set for taking WOFOST model sensitive to GPP, yield is the sensitive ginseng of WOFOST model
Manifold take the union of the SCOPE model parameter set sensitive to SIF, GPP as the sensitive parameter collection of SCOPE model.
Step 104 carries out parameter calibration to the sensitive parameter that the sensitive parameter is concentrated according to plantation subregion and does not know
Property assessment.
Specifically, based on wait data such as meteorological site observation data agriculture in region of assessing, flux tower data, actual measurement SIF,
For each plantation subregion, using Markov chain Monte-Carlo (Markov Chain Monte Carlo, MCMC) method pair
Sensitive parameter carries out parameter calibration and uncertain assessment.
Step 105, in the outer layer of SIF data assimilation system, by the SCOPE model after SIF remotely-sensed data and parameter calibration
Assimilated, day accumulation GPP is obtained by the model after assimilating；In the internal layer of SIF data assimilation system, assimilate the system outer layer
The day accumulation GPP of resulting day accumulation GPP and WOFOST modeling, accumulate GPP the day after being optimized.
Specifically, the SIF data assimilation system according to the transmitting of inside and outside nested and parameter, is carried out in two steps assimilation: in outer layer,
Using the SIF data and SCOPE model obtained in four-dimensional variation (4Dvar) data assimilation method assimilation step 102, by assimilating
SCOPE model afterwards obtains day accumulation GPP and uncertainty.In internal layer, the assimilation side Ensemble Kalman Filter (EnKF) is utilized
Method assimilates the day accumulation GPP of the resulting day accumulation GPP and WOFOST simulation of outer layer, the day after being assimilated accumulates GPP.
It is defeated to obtain WOFOST model with weather forecast data-driven WOFOST model based on the GPP after optimization for step 106
The Prediction For Winter Wheat Production result in the region to be assessed out.
Wherein, weather forecast data are the meteorological datas carried out in the yield forecast period.Specifically, by the GPP after assimilation
After substituting into WOFOST model, yield forecast can be carried out with weather forecast data-driven WOFOST model, it is pre- to obtain winter wheat yields
Report result.
In this step, yield forecast mainly according to meteorological data driving carry out, but on condition that WOFOST model parameter, shape
State variable has been subjected to calibration, assimilation.Within forecast period (2 months before such as maturity period), the every GPP substituted into after assimilation of WOFOST model
It updates once, weather forecast data can be brought into and forecast.Meteorological data used by forecasting is divided into two according to the forecast moment
Part: for the data of the usable terrestrial climate data earning in a day data set generation of meteorological data before the forecast moment, and for
Meteorological data after current time in forecast period, but be divided into forecast (TIGGER meteorological dataset) in nearly 15 days and it is 15 days nearly after
Forecast (matched, found out and the most like history meteorology number of meteorological dataset with the data of analyzing again in 1979-2016 time
According to).
Since meteorological data is to input WOFOST model with aggregate form, output is yield forecast collection, and forecast may be selected
Prediction For Winter Wheat Production result of the intermediate value of set as output.In addition, can also set certain standard, such as (8000 (kg/
It ha)) is unit area output (per unit area yield) standard, according to forecast ensemble, statistics per unit area yield is higher than the probability of this standard.
The Regional Fall Wheat yield estimation method of assimilation satellite fluorescence data and crop growth model provided in an embodiment of the present invention,
By using the technique study of SIF remotely-sensed data and mechanism process model data assimilation, SIF remotely-sensed data is introduced into process mould
Type is established from photosynthetic angle and is contacted with the mechanistic of yield, is carried out region from optimization crops photosynthesis and is produced
Amount forecast.
Content based on the above embodiment, as a kind of alternative embodiment, provide it is a kind of according to environmental index data by institute
State the method that region division to be assessed is multiple plantation subregions, comprising: using agriculture gas website as node building Thiessen polygon, and according to
According to agriculture gas site record data；The corresponding Thiessen polygon of website similar in environmental index is merged according to the data of record, is obtained
To division result；Wherein, environmental index includes meteorological condition, cropping pattern, disaster frequency and yield level.
Thiessen polygon is constructed by node of agriculture gas website, and according to agriculture gas site record data, by many years environmental index
The corresponding Thiessen polygon of (environmental index includes meteorological condition, cropping pattern, disaster frequency, yield level etc.) close website is closed
And obtain division result.
Content based on the above embodiment provides a kind of setting point for obtaining plantation subregion as a kind of alternative embodiment
The method of the SIF remotely-sensed data of resolution, including but not limited to following steps: being winter wheat by the resolution setting of MODIS data
Resolution ratio used by yield forecast, i.e. setting resolution ratio, are 7km*3.5km's by the original resolution in breeding time
TROPOMI SIF remotely-sensed data NO emissions reduction is the setting resolution ratio；Wherein, the MODIS data include leaf area index LAI
Data, evapotranspiration ET data and surface temperature LST data.
In this step, the MODIS leaf area index (LAI) of setting resolution ratio (such as 1km), evapotranspiration are utilized
(EvapoTranspiration, ET) and surface temperature (Land Surface Temperature, LST) data, by breeding time
Chlorophyll fluorescence (the Sun-Induced of interior TROPOMI (monitoring instrument of No. five satellites of sentry) sunlight-induced
Chlorophyll Fluorescence, SIF) data NO emissions reduction be set resolution ratio (such as 1km) SIF data.
Content based on the above embodiment sets resolution ratio as 1km*1km as a kind of alternative embodiment；Correspondingly, it mentions
It is the method for setting the SIF data of resolution ratio for a kind of TROPOMI SIF remotely-sensed data NO emissions reduction by breeding time, including but
It is not limited to following steps:
The SIF data resampling of sunlight-induced in breeding time is the SIF number that spatial resolution is 5km*5km by step 1
According to.
Specifically, by TROPOMI SIF data, resampling is that spatial resolution is as follows in the following way
The SIF time series image of 5km*5km: if TROPOMI SIF pixel (being determined by four angle point longitude and latitude of pixel) covers certain lattice
The central point of net unit (Albers equivalent projection, size 5km*5km are projected as used in grid), then the SIF value of the pixel
By the average value for contributing to grid SIF (such as: the central point of certain grid unit is covered by 2 TROPOMI SIF pixels altogether,
The SIF value of one of pixel is 0.1, another is 0.3, then the SIF value of the grid unit is (0.1+0.3)/2=0.2).
The MODIS data re-projection of original 1km*1km is osteopetrosis equivalent projection by step 2, and by linearly gathering
Conjunction method resampling is the MODIS data of 5km*5km.
It in other words, is A Baisi by used MODIS product (LAI, ET, LST) (i.e. original MODIS data) re-projection
Equivalent projection, spatial resolution 1km*1km.The MODIS data of 5km*5km are generated by linear polymerization again.Wherein, it polymerize
When, if MODIS data deficiencies 12, it abandons polymerizeing.
Step 3, empty window when being established for the pixel of the SIF data of each 5km*5km, by when empty window in MODIS number
According to the unknown parameter in the formula of bringing into, solved in formula.
Wherein, biFor unknown parameter, i=1,2,3,4,5,6.
Wherein, by assuming that SIF data can be obtained by MODIS LAI, ET, LST data conversion, it can be obtained above formula (1).
Step 4, based on the unknown parameter solved, original MODIS data are brought into formula, obtain 5*5 resolution ratio be
The SIF data of 1km*1km.
Specifically, before step 4, the unknown parameter in above formula (1) can be solved first, solution can be in the following way:
Firstly, empty window when being established for each 5km*5km SIF pixel.Time window is set as 1 month；The setting of spatial window is divided into two
Step: the first step establishes the rectangular area (i.e. 25km*25km) of the 5*5 pixel composition centered on the pixel.Second step, from
The pixel of 11 neighbouring object pixels is selected in the rectangular area, in addition goal pels itself, it is corresponding to amount to 12 pixels
Spatial dimension constitutes the spatial window of the pixel.Then, when establishing after empty window, by when empty window in MODIS number
According to formula (1) is brought into, the residual sum of squares (RSS) with SIF data is cost function, solves public affairs using L-BFGS-B optimization algorithm
6 unknown parameters in formula (1).
After solution obtains 6 unknown parameters, the original MODIS data before polymerization are brought into the formula (1), it can be direct
Obtain the SIF data that 5*5 resolution ratio is 1km.
Content based on the above embodiment carries out global sensibility point to WOFOST model as a kind of alternative embodiment
It analyses, before the acquisition parameter set sensitive to GPP, yield, further includes: to described when the ground meteorological data in region of assessing carries out
Empty interpolation, the meteorological data day by day and the meteorological data by hour for obtaining space and time continuous distribution；By the meteorological number day by day
According to the meteorological driving data as WOFOST model, and using the meteorological data by hour as the meteorology of SCOPE model
Driving data.Specifically, temporal-spatial interpolating is carried out to ground meteorological data, obtain space and time continuous distribution by hour, it is meteorological day by day
Data, respectively as the meteorological driving data of WOFOST model and SCOPE model.
Specifically, based on sensitivity analysis as a result, determining parameter set to be calibrated；It is true according to model definition or actual conditions
The value interval of fixed each sensitive parameter, and defined parameters prior distribution is being uniformly distributed on section；It is exported according to model
Corresponding observation data (yield or leaf area index) calculate its mean value and standard deviation, and defining likelihood function is with gained mean value
With the Gaussian Profile of standard deviation；Monte Carlo is carried out from parameter prior distribution, each sampled result is brought model into and obtained
Corresponding model output, brings output into likelihood probability that likelihood function is sampled every time, connects according to Metropolis criterion
Judge whether Markov Chain restrains by sampled value, and according to Variance ratio method, is constantly carried out on the basis of preceding primary sampling new
Sampling until Markov Chain restrain, to obtain parameter posteriority sample.Finally using the intermediate value of parameter posteriority sample as mould
Shape parameter calibration result, while the root-mean-square error (RMSE) of calculating parameter sample refers in this, as probabilistic quantitative description
Mark.
Content based on the above embodiment provides a kind of in the outer of SIF data assimilation system as a kind of alternative embodiment
Layer, institute's SIF remotely-sensed data and the SCOPE model after parameter calibration are assimilated, and obtain day accumulation by the model after assimilating
GPP；In the internal layer of SIF data assimilation system, assimilate the resulting day accumulation GPP of the system outer layer and WOFOST modeling
Day accumulation GPP, the method for the day accumulation GPP after being optimized, including but not limited to following steps:
Step 1: meteorological data SCOPE model will be input to by hour per hour in the outer layer of SIF data assimilation system
Simulation fluorescence obtains simulation SIF data；The four-dimensional variation cost function of simulation SIF data and SIF remotely-sensed data is established, and
The cost function is optimized using SCE-UA algorithm, the SCOPE parameter set after being optimized is substituted into SCOPE mould
Type assimilated after SCOPE model, based on after assimilation SCOPE model obtain day accumulate GPP.
Specifically, which is divided into internal layer and outer layer.In outer layer, MCMC is demarcated into resulting sensitive ginseng
Several optimal estimation value (i.e. parameter calibration result), the necessary other parameters of model, the resulting meteorological data per hour of interpolation are defeated
Enter SCOPE model, by hour simulation fluorescence.Establish the resulting 1km resolution ratio of TROPOMI NO emissions reduction (with set resolution ratio as
Be illustrated for 1km) SIF and modeling satellite pass by the period SIF 4Dvar cost function, utilize SCE-UA
Algorithm optimizes cost function.By global optimum's parameter simulation by hour GPP, day accumulation GPP and its not is obtained with this
Certainty.
Step 2: in the internal layer of SIF data assimilation system, by the Gauss disturbance of day meteorological data and the sensitive parameter
Initial parameter collection is input to WOFOST model after calibration, generates day accumulate GPP day by day；It will be obtained by the SCOPE model after assimilating
Uncertainty be less than given threshold day accumulation GPP and WOFOST modeling day accumulate GPP assimilated, assimilated
Day afterwards accumulates GPP, this is input to WOFOST model to continue to move forwards；In system operation, if the same day is by same
The uncertainty of the resulting day accumulation GPP of SCOPE model after change is greater than the threshold value, then without assimilation, WOFOST model
Continuation is forecast forward, is assimilated again until being less than the threshold value by the uncertainty of the resulting day accumulation GPP of SCOPE model,
Day after being assimilated accumulates GPP.
Specifically, in internal layer, optimal value (the i.e. parameter mark of day meteorological data, the general sensitive parameter that MCMC is demarcated is inputted
Determine result), strong sensitive parameter (higher than the threshold value index for setting total susceptibility, the initial parameter collection of Gauss disturbance such as 0.05) with
And other parameters needed for model, WOFOST model is inputted, generates day accumulation GPP forecast collection day by day.Since SCOPE can be exported
GPP after assimilation day by day, for WOFOST model, be equivalent to has " observation " daily, so there is no " observations when assimilation
Value whether there is " this problem.The target day accumulation GPP of uncertainty smaller (being less than threshold value) only after screening SCOPE assimilation,
That is " effectively observation " carries out EnKF assimilation with the day accumulation GPP of WOFOST simulation, obtains the analytic set of day accumulation GPP and substitution
WOFOST model continues to move forwards.(it is greater than if the uncertainty of the day accumulation GPP after the assimilation of same day SCOPE model is larger
Threshold value), then without assimilation, the continuation of WOFOST model is forecast forward, until SCOPE model assimilates to obtain smaller uncertainty
The GPP of (being less than threshold value) is assimilated again.
Content based on the above embodiment obtains the described wait estimate of WOFOST model output as a kind of alternative embodiment
After the Prediction For Winter Wheat Production result in producing region domain, further includes: single-frame net operation obtains the forecast ensemble and list of each grid
The probability for being higher than specific yield threshold value is produced, winter wheat the yield by estimation space mapping is completed.In other words, single-frame net operation, output forecast collection
It closes and per unit area yield is higher than the probability of 8000 (kg/ha) (i.e. specific yield threshold value), complete yield space mapping；Referring to fig. 2 and scheme
3。
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (8)
1. a kind of Regional Fall Wheat yield estimation method for assimilating satellite fluorescence data and crop growth model characterized by comprising
Winter wheat planting area is divided into multiple grid, select winter wheat area accounting be greater than setting ratio grid as to
The yield by estimation region；
According to environmental index data by the region division to be assessed be multiple plantation subregions, obtain plantation subregion setting differentiate
The sunlight-induced chlorophyll fluorescence SIF remotely-sensed data of rate；
Global sensitivity analysis is carried out to WOFOST model, obtains the parameter set sensitive to GPP, yield；SCOPE model is carried out
Global sensitivity analysis obtains the sensitive parameter set to SIF, GPP；
Parameter calibration and uncertain assessment are carried out to the sensitive parameter that the sensitive parameter is concentrated according to plantation subregion；
In the outer layer of SIF data assimilation system, SIF remotely-sensed data and the SCOPE model after parameter calibration are assimilated, by same
SCOPE model after change obtains day accumulation GPP；In the internal layer of SIF data assimilation system, assimilate the system outer layer resulting day
The day for accumulating GPP and WOFOST modeling accumulates GPP, and the day after being assimilated accumulates GPP；
The described wait estimate of WOFOST model output is obtained with weather forecast data-driven WOFOST model based on the GPP after assimilation
The Prediction For Winter Wheat Production result in producing region domain.
2. Regional Fall Wheat yield estimation method according to claim 1, which is characterized in that described to be incited somebody to action according to environmental index data
The region division to be assessed is multiple plantation subregions, comprising:
Multiple Thiessen polygons are constructed by node of agriculture gas website；
Environmental index is established, the corresponding Thiessen polygon of agriculture gas website of close environmental index is merged, is obtained wait assess
Regional compartmentalization result.
3. Regional Fall Wheat yield estimation method according to claim 1, which is characterized in that the setting for obtaining plantation subregion
The SIF remotely-sensed data of resolution ratio, comprising:
It is resolution ratio used by Prediction For Winter Wheat Production by the resolution setting of MODIS data, that is, sets resolution ratio, will give birth to
The TROPOMI SIF remotely-sensed data NO emissions reduction that original resolution in phase is 7km*3.5km is the setting resolution ratio；Wherein,
The MODIS data include leaf area index LAI data, evapotranspiration ET data and surface temperature LST data.
4. Regional Fall Wheat yield estimation method according to claim 3, which is characterized in that the resolution ratio that sets is 1km*
TROPOMI SIF remotely-sensed data NO emissions reduction in breeding time is the SIF data for setting resolution ratio by 1km, comprising:
It is the SIF data that spatial resolution is 5km*5km by TROPOMI SIF remotely-sensed data resampling in breeding time；
It is osteopetrosis equivalent projection by the MODIS data re-projection of original 1km*1km, and is adopted again by linear polymerization method
Sample is the MODIS data of 5km*5km；
Empty window when being established for the pixel of the SIF data of each 5km*5km, by 5km*5km when being located at this in empty window
MODIS data bring into formula, solve the unknown parameter in formula；
Wherein, biFor unknown parameter to be solved, i=1,2,3,4,5,6；
Based on the unknown parameter solved, the MODIS data of original 1km*1km are brought into formula, obtain 5*5 resolution ratio
For the SIF data of 1km*1km.
5. Regional Fall Wheat yield estimation method according to claim 1, which is characterized in that carried out to WOFOST model global quick
Perceptual analysis, before obtaining the parameter set sensitive to GPP, yield, further includes:
Temporal-spatial interpolating is carried out to the ground meteorological data in the region to be assessed, obtains the meteorological number day by day of space and time continuous distribution
According to the meteorological data by hour；
Using the meteorological data day by day as the meteorological driving data of WOFOST model, and by the meteorological number by hour
According to the meteorological driving data as SCOPE model.
6. Regional Fall Wheat yield estimation method according to claim 1, which is characterized in that concentrated to the sensitive parameter quick
Feel parameter and carry out parameter calibration and uncertain assessment, comprising:
The prior distribution for determining the value interval of each sensitive parameter, and defining the sensitive parameter is uniformly dividing on section
Cloth；
Monte Carlo is carried out in this prior distribution, is brought the sampled result obtained after each sampling into model and is corresponded to
Model export result；
It brings the output result into likelihood function and obtains the likelihood probability sampled every time, receive sampling according to Metropolis criterion
Value, and judge whether Markov Chain restrains according to Variance ratio method；
New sampling is constantly carried out on the basis of preceding primary sampling until Markov Chain convergence, obtains the sensitive parameter
Posteriority sample；
Using the intermediate value of posteriority sample as the parameter calibration result.
7. Regional Fall Wheat yield estimation method according to claim 1, which is characterized in that described in SIF data assimilation system
Outer layer, institute's SIF remotely-sensed data and the SCOPE model after parameter calibration are assimilated, obtained by the SCOPE model after assimilating
Day accumulation GPP；In the internal layer of SIF data assimilation system, assimilate the resulting day accumulation GPP of the system outer layer and WOFOST model
The day of simulation accumulates GPP, and the day after being assimilated accumulates GPP, comprising:
In the outer layer of SIF data assimilation system, meteorological data SCOPE model will be input to by hour simulation fluorescence per hour,
Obtain simulation SIF data；The four-dimensional variation cost function of simulation SIF data and SIF remotely-sensed data is established, and is calculated using SCE-UA
Method optimizes the cost function, the SCOPE parameter set after being optimized, and is substituted into after SCOPE model obtains assimilation
SCOPE model, based on after assimilation SCOPE model obtain day accumulate GPP；
In the internal layer of SIF data assimilation system, by the initial parameter collection of day meteorological data and the Gauss disturbance of the sensitive parameter
It is input to WOFOST model after demarcating, generates day accumulate GPP day by day；It will be by the resulting uncertainty of SCOPE model after assimilating
Day accumulation GPP less than the day accumulation GPP and WOFOST modeling of given threshold is assimilated, the day accumulation after being assimilated
This is input to WOFOST model to continue to move forwards by GPP；In system operation, if the same day is by the SCOPE after assimilating
The uncertainty of the resulting day accumulation GPP of model is greater than the threshold value, then without assimilation, WOFOST model continues pre- forward
Report is assimilated until being less than the threshold value by the uncertainty of the resulting day accumulation GPP of SCOPE model, again after obtaining assimilation
Day accumulate GPP.
8. Regional Fall Wheat yield estimation method according to claim 1, which is characterized in that the acquisition WOFOST model output
The region to be assessed Prediction For Winter Wheat Production result after, further includes:
Single-frame net operation, the forecast ensemble and per unit area yield that obtain each grid are higher than the probability of given threshold, complete winter wheat and estimate
Produce space mapping.
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