CN108985588A - A kind of crop yield prediction remote sensing estimation method, device and system - Google Patents
A kind of crop yield prediction remote sensing estimation method, device and system Download PDFInfo
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Abstract
The present invention provides a kind of crop yield prediction remote sensing estimation method, device and system, which comprises is based on crop remotely-sensed data, identifies Crop Group；Vegetation index time series based on the Crop Group, sunshine parameter, meteorologic parameter and crop estimate the net productivity of crop first；Crop yield amount is estimated in conjunction with crop growth environment information and plant growth physiological and biochemical procedure parameter based on the net productivity of the crop first.The present invention can more effectively play the effect of remotely-sensed data, make full use of the growth mechanism of crop, it is ensured that precision, efficiency and the stability of crop yield estimation in more large scale.
Description
Technical field
The present invention relates to field of agricultural production technologies, more particularly, to a kind of crop yield prediction remote sensing estimation method, dress
It sets and system.
Background technique
Crop yield is the final goal of farmland cultivation, and the direct finger of assessment conduit for farmland drainage and peasant's benefited intensity
Mark.Real-time estimation is carried out to crop yield on region or more large scale, foodstuff preservation and grain transaction etc. can be provided
Guidance；And the yield of a certain region Different Crop is grasped, be conducive to analyze the ecological region planting suitability degree of crop, and then right
Farming strategy provides support.
The formation of crop yield is a complicated process, and centre is related to physiological and biochemical procedures much more very, meteorological condition,
Edaphic condition, field management, phenology information and farming season information etc. are all that per unit area yield estimates factor in need of consideration.Eighties of last century 60 years
Since generation, with the development of remote sensing technology, Rapid development stage is formally stepped into per unit area yield estimation, and more than 50 years later, is made
Object Yield Estimation Model, which experienced, counts on the yield estimation system based on Physio-ecological mechanism by simple parameter.
Crop growth simulation model is also referred to as crop modeling, i.e., by simulation process of crop growth, obtains the biomass of crop
Cumulant, the water of crop yield and farmland ecosystem, carbon, nitrogen and energy recycle stream momentum.Crop growth simulation at present
Model has become the powerful of calculating crop-producing power and the agricultural ecological factor with World-wide influence power, estimates in crop
Production, crop breeding, field management etc., which have to compare, to be widely applied.
Crop yield appraising model based on remote sensing technology can be by empirical model, semiempirical mechanism model with simplified summary
It is two kinds of main implementations of mechanism model based on the efficiency of light energy utilization and based on plant growth to the development process of mechanism model,
And both modes are in grid cell size enterprising row crop per unit area yield estimation, be respectively present excessively simplify crop physiology process and
The problem of parameter calibration cost prohibitive.
Summary of the invention
In order to overcome the above problem or at least be partially solved the above problem, it is distant that the present invention provides a kind of crop yield prediction
Feel evaluation method, device and system, more effectively playing the effect of remotely-sensed data, make full use of the growth mechanism of crop,
Ensure in more large scale to precision, efficiency and the stability of crop yield estimation.
In a first aspect, the present invention provides a kind of crop yield prediction remote sensing estimation method, comprising: it is based on crop remotely-sensed data,
Identify Crop Group；Vegetation index time series based on the Crop Group, sunshine parameter, meteorologic parameter and crop, estimation
The net productivity of crop first；Based on the net productivity of the crop first, in conjunction with crop growth environment information and plant growth physiology
Biochemical process parameter estimates crop yield amount.
Wherein, the step of estimation first net productivity of crop further comprises: especially by utilize the efficiency of light energy utilization
MODEL C ASA estimates the net productivity of the crop first.
Wherein, the vegetation index time series based on the Crop Group, sunshine parameter, meteorologic parameter and crop,
The step of estimating the first net productivity of crop further comprises: based on sunshine duration information, the astronomical spoke in the sunshine parameter
Information and temperature difference per day information are penetrated, calculates total solar radiation, and the vegetation index time series based on the crop, calculating is photosynthetic to be had
Imitate radiation absorption ratio；Based on the total solar radiation and the photosynthetically active radiation absorptance, photosynthetically active radiation is calculated；It is based on
Temperature information, rainfall data and the solar radiation information of the meteorologic parameter, the temperature/water stress factor, and it is based on institute
Temperature/water stress factor is stated, the efficiency of light energy utilization is calculated；Based on the Crop Group, the photosynthetically active radiation and the light
Energy utilization rate estimates the net productivity of the crop first using light use efficiency model CASA.
Wherein, described to be based on the net productivity of the crop first, in conjunction with crop growth environment information and plant growth physiology
The step of biochemical process parameter, estimation crop yield amount further comprises: based on the crop growth environment information, initialization is set
Ordered goods growing environment, and carry out the Soil Moisture Simulation and Crop evapotranspiration mould of the corresponding process of crop growth of the Crop Group
It is quasi-, obtain the plant growth physiological and biochemical procedure parameter including crop TSUM parameter and Crop stage DVS；Based on the work
The net productivity of object first and the plant growth physiological and biochemical procedure parameter, estimate the crop yield amount.
Further, it is based on the crop growth environment information described, the step of initializing set crop growth environment
Before, the method also includes: be based on the Crop Group and the crop growth environment information, construct the Crop Group pair
The WOFOST model answered；Correspondingly, described be based on the net productivity of the crop first, in conjunction with crop growth environment information and crop
The step of growth characters biochemical process parameter, estimation crop yield amount, further comprises: respectively in Dry Matter, water stress
With three levels of nutrient stress, Model coupling is carried out to the light use efficiency model CASA and the WOFOST model, is obtained
Coupling model；Based on the Crop Group, the sunshine parameter, the meteorologic parameter and the vegetation index time sequence of the crop
Column, using the coupling model, estimate the crop yield amount.
Wherein, described the step of being based on crop remotely-sensed data, identifying Crop Group, further comprises: distant based on the crop
Feel data, sequentially carries out the pre- place that radiation calibration, atmospheric correction, geometric correction, ortho-rectification, image mosaic and image are cut
Reason, obtains effective image data；Based on effective image data, sequentially carry out at Image Segmentation processing and artificial vector quantization
Reason obtains target field boundary；The spectral reflectivity feature of image data, vegetation in the target field bounds is extracted to refer to
Number feature and image texture sign, and it is based on the spectral reflectivity feature, the vegetation index feature and the image texture
Sign identifies the Crop Group using the sorting algorithm based on supervision.
Further, after the acquisition coupling model the step of, the method also includes: using method of document,
Farm data collection, Field observation and remote-sensing inversion mode, it is raw to the meteorologic parameter of input model, the plant growth
Manage biochemical process parameter and soil parameters, the corresponding parameter calibration for carrying out farm scale, plot scale and grid cell size.
Second aspect, the present invention provide a kind of crop yield prediction remote sensing appraising device, comprising: at least one processor, extremely
A few processor, communication interface and bus；The memory, the processor and the communication interface are complete by the bus
At mutual communication, the communication interface is obtained for the estimation device and mode input parameter or is stored between equipment
Information transmission；The computer program that can be run on the processor is stored in the memory, the processor executes institute
When stating computer program, crop yield prediction remote sensing estimation method as described above is realized.
The third aspect, the present invention provide a kind of non-transient computer readable storage medium, and the non-transient computer is readable
Storage medium stores computer instruction, and the computer instruction makes the computer execute crop yield prediction remote sensing as described above
Evaluation method.
Fourth aspect, the present invention provide a kind of crop yield prediction remote sensing appraising system characterized by comprising institute as above
The crop yield prediction remote sensing appraising device stated and the mode input parameter obtain or storage equipment.
A kind of crop yield prediction remote sensing estimation method, device and system provided by the invention, according to crop remotely-sensed data pair
Crop Group is identified, and comprehensively considers the plant growth efficiency of light energy utilization and plant growth Physiology and biochemistry mistake on this basis
Journey can more effectively play the effect of remotely-sensed data, make full use of the growth mechanism of crop, it is ensured that work in more large scale
Precision, efficiency and the stability of object per unit area yield estimation.
Detailed description of the invention
Fig. 1 is a kind of flow chart of crop yield prediction remote sensing estimation method of the embodiment of the present invention；
Fig. 2 is to be located in advance according to progress crop remotely-sensed data in a kind of crop yield prediction remote sensing estimation method of the embodiment of the present invention
The flow chart of reason；
Fig. 3 is the flow chart of another kind of embodiment of the present invention crop yield prediction remote sensing estimation method；
Fig. 4 is a kind of structural block diagram of crop yield prediction remote sensing appraising device of the embodiment of the present invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached in the embodiment of the present invention
Figure, is clearly and completely described the technical solution in the present invention, it is clear that described embodiment is one of the invention
Divide embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making
Every other embodiment obtained, shall fall within the protection scope of the present invention under the premise of creative work.
As the one aspect of the embodiment of the present invention, the present embodiment provides a kind of crop yield prediction remote sensing estimation method, ginsengs
Fig. 1 is examined, is a kind of flow chart of crop yield prediction remote sensing estimation method of the embodiment of the present invention, comprising:
S1 is based on crop remotely-sensed data, identifies Crop Group；
S2, the vegetation index time series based on Crop Group, sunshine parameter, meteorologic parameter and crop, estimation crop the
One net productivity；
S3 is based on the net productivity of crop first, joins in conjunction with crop growth environment information and plant growth physiological and biochemical procedure
Number estimates crop yield amount.
It is understood that the embodiment of the present invention is based on crop remotely-sensed data, to the remotely-sensed data
Reason analysis, in obtaining remotely-sensed data on the basis of Crop Group, by calculating crop under the conditions of current all kinds of environmental parameters
The first net productivity (Net Primary Productivity, NPP), finally the yield of crop is accurately estimated.
Specifically, in step sl, first to pre-process to the crop remotely-sensed data of acquisition, acquisition can more express crop
Effective image data of feature, and Classification and Identification processing is carried out to effective image data, it is corresponding to obtain current remotely-sensed data
Crop Group.In one of them embodiment, it is above-mentioned be based on crop remotely-sensed data, identify Crop Group the step of further wrap
It includes:
Based on crop remotely-sensed data, radiation calibration, atmospheric correction, geometric correction, ortho-rectification, image mosaic are sequentially carried out
The pretreatment cut with image, obtains effective image data；
Based on effective image data, Image Segmentation processing and artificial vectorized process are sequentially carried out, target field side is obtained
Boundary；
Extract spectral reflectivity feature, vegetation index feature and the image texture of image data in target field bounds
Sign, and it is based on spectral reflectivity feature, vegetation index feature and image texture sign, using the sorting algorithm based on supervision,
Identify Crop Group.
It is understood that the present embodiment is needed in advance to the plot crop remotely-sensed data of yield to be evaluated and relevant
Environmental parameter etc. is collected.For example, obtaining the GF-1WFV of crop by the data station of access high score No.1 satellite (GF-1)
Remotely-sensed data obtains meteorologic parameter by Meteorological Center database, while obtaining the geospatial information and root where plot
According to the statistical yearbook data etc. of historgraphic data recording.
High score No.1 satellite GF-1 therein is run using sun-synchronous orbit, and orbit altitude 645km, inclination angle is
98.0506 °, camera can survey 25 ° of pendulum.Available 16 meters of GF-1 WFV camera multispectral color image (blue, green, red, near-infrareds
4 wave bands).GF-1 satellite is loaded with tetra- WFV sensors of WFV1, WFV2, WFV3 and WFV4, and the breadth of imaging is up to 800 public affairs
In or so.Wherein the parameter of WFV camera is as shown in table 1, is GF-1 WFV data parameters table.
Table 1, GF-1 WFV data parameters table
The GF-1 WFV data obtained above are 1 grade of products, need to do the level one data radiation calibration, geometric correction etc.
Pretreatment process, to obtain the effective image data that can more express crop feature.Specifically as shown in Fig. 2, for according to of the invention real
Apply the progress pretreated flow chart of crop remotely-sensed data in a kind of crop yield prediction remote sensing estimation method of example, comprising: radiation calibration,
Atmospheric correction, geometric correction, ortho-rectification, image mosaic and image cutting etc..
Wherein, radiation calibration refers to spoke in the digital quantization output valve DN value and its corresponding visual field for establish remote sensor
Penetrate the quantitative relationship between brightness value.GF-1 calibration coefficient is downloaded in China Resource Satellite Applied Center website, utilizes following public affairs
The channel observation (DN value) of each load of GF-1WFV is converted to apparent spoke brightness value by formula:
Lε(λε)=GainDN+Bias
In formula, Lε(λε) indicating apparent spoke brightness after conversion, Gain indicates that calibration slope, DN indicate satellite load observation,
Bias indicates calibration intercept.
During atmospheric correction, it is contemplated that all radiation energies for remote sensing are intended to through Earth'S atmosphere, and
Interaction (scattering, absorption etc.) occurs with atmosphere, the attenuation of atmosphere is selective the light of different wave length, is
The true reflectivity of atural object is obtained, the treatment process that weakening atmospheric conditions influences remotely-sensed data is known as atmospheric correction.For example, adopting
With FLAASH (the Fast Line-of-sigh Atmospheric Analysis of Spectral in ENVI software
Hypercubes) module carries out atmospheric correction.FLAASH is by optical imagery research institute-US Air gas where wave spectrum scientific research
Dynamic Test Chamber supports the lower atmospheric correction module developed, and uses MODTRAN4+ radiative transfer model, can be according to research area
ARDC model atmosphere ARDC and aerosol type carry out Reflectivity for Growing Season meter.
For original remote sensing image usually there is geometry deformity, these geometric distortions have plenty of the posture due to satellite, track
And caused by the movement and the external factor such as shape of the earth, have plenty of due to remote sensor structural behaviour itself and scanning mirror not
Caused by the internal factors such as the registration imbalance between regular motion, detector sampling delay, the configuration of detector, wave band.Geometry school
Positive purpose is exactly deformation of image caused by correcting these factors, and geometric correction includes common geometric correction and ortho-rectification.
Ortho-rectification is to carry out the correction of deformation of image to each pixel of image by landform altitude model (DEM), make remote sensing shadow
Requirement as more meeting orthogonal projection.
On the basis of above-mentioned data processing, geometric accurate correction is carried out, such as by the correction of actual measurement control point
On the basis of OLI image, geometric accurate correction is carried out using quadratic polynomial method, makes to study image used with Geometrical consistency.
Finally, carrying out re-projection, cutting.To guarantee all image projections having the same used in the embodiment of the present invention
Information needs to carry out re-projection processing to image, then cuts by research area's range to image, obtains effective image number
According to.
On the basis of obtaining effective image data according to above-mentioned processing, due to the presence of shift of crops, need to annual
Crop-planting type identified, crop identification mainly use GF-2 data, the basic datas such as land use pattern basis
It is upper using towards field classification method, effective identification in cotton planting plot in realization area.
Specifically, by Image Segmentation and artificial vector quantization mode, being obtained first based on above-mentioned effective image data
Area's field data boundary is studied, i.e. acquisition target field boundary.Then, the light of image data in target field bounds is extracted
The characteristic of division such as Reflectivity, vegetation index feature and image texture sign are composed, and choose MLC (maximum likelihood), SVM (branch
Hold vector machine) or the supervised classification methods such as NN (neural network), classify to the characteristic image of field grade, studies each classification spy
The influence of sign and classification method to crop remote sensing recognition result, identifies Crop Group.
Specifically, in step s 2, calculating the first net productivity NPP of crop.Particularly for different crops
Classification, the yield obtained under equal conditions may be different, therefore, comprehensively consider crop factor itself, including Crop Group and work
The vegetation index time series of object and crop growth environment condition element, including sunshine parameter, meteorologic parameter etc., are made
The calculating of the net productivity of object first.It is understood that vegetation index time series therein can be by according to the time
The analysis for the multiframe crop remotely-sensed data that sequence obtains and processing gained.
Specifically, in step s3, under the premise of the first net productivity of crop is calculated according to above-mentioned steps, needing
It is further calculated according to the net productivity of crop first to obtain crop yield corresponding to Crop Group.Specifically first have to pair
Soil moisture information, Crop evapotranspiration information and plant growth information initialize and are set in the growing environment information of plant growth
It is fixed, and plant growth physiological and biochemical procedure parameter is determined on this basis, it is then based on the net productivity of crop first and crop is raw
Long physiological and biochemical procedure parameter carries out Comprehensive Assessment calculating, obtains the estimation result to crop yield.
A kind of crop yield prediction remote sensing estimation method provided in an embodiment of the present invention, according to crop remotely-sensed data to making species
It is not identified, and comprehensively considers the plant growth efficiency of light energy utilization and plant growth physiological and biochemical procedure on this basis, it can
The effect for more effectively playing remotely-sensed data, makes full use of the growth mechanism of crop, it is ensured that crop yield in more large scale
Precision, efficiency and the stability of estimation.
The net productivity of crop first is estimated especially by light use efficiency model CASA is utilized according to above-described embodiment.Tool
Body, the vegetation index time series based on Crop Group, sunshine parameter, meteorologic parameter and crop, the life only of estimation crop first
The step of force of labor, further comprises:
Based on sunshine duration information, astronomical radiation information and the temperature difference per day information in sunshine parameter, total solar radiation is calculated,
And the vegetation index time series based on crop, calculate photosynthetically active radiation absorptance；
Based on total solar radiation and photosynthetically active radiation absorptance, photosynthetically active radiation is calculated；
Temperature information, rainfall data and solar radiation information based on meteorologic parameter, the temperature/water stress factor,
And it is based on temperature/water stress factor, calculate the efficiency of light energy utilization；
Crop is estimated using light use efficiency model CASA based on Crop Group, photosynthetically active radiation and the efficiency of light energy utilization
First net productivity.
It is understood that needing first when carrying out the estimation of the net productivity of crop first using CASA model to crop
Photosynthetically active radiation APAR and the efficiency of light energy utilization in growth course are respectively calculated.Specifically comprehensively consider sunshine parameter, gas
As parameter and crop vegetation index time series, the two indexs are solved respectively.
Wherein when calculating photosynthetically active radiation, on the one hand based on sunshine duration information, astronomical radiation information and temperature difference per day
Information calculates total solar radiation SQL, on the other hand according to crop vegetation index time sequence using light use efficiency model CASA
Then on this basis column count photosynthetically active radiation absorptance FRAR calculates the photosynthetically active radiation in process of crop growth
APAR。
Wherein when calculating the efficiency of light energy utilization, the meteorological data based on acquisition is carried out.Such as in one embodiment of the invention
Temperature data, rainfall data and solar radiation data are used, which downloads in China Meteorological data network.The number that will have been downloaded
It puts in order according in text document as input data.Comprehensive analysis and processing are carried out to these meteorologic parameter data later, really
Temperature/water stress factor of ordered goods growth, and on this basis, calculate the efficiency of light energy utilization.
Then, after photosynthetically active radiation and the efficiency of light energy utilization for determining plant growth according to above-mentioned steps, light is utilized
Energy utilization rate MODEL C ASA carries out comprehensive analysis to Crop Group, photosynthetically active radiation and the efficiency of light energy utilization, calculates, obtains and make
The net productivity of object first.
According to above-described embodiment, it is based on the net productivity of crop first, it is raw in conjunction with crop growth environment information and plant growth
The step of managing biochemical process parameter, estimating crop yield amount further comprises: based on crop growth environment information, initializing set
Crop growth environment, Soil Moisture Simulation and the Crop evapotranspiration simulation of the corresponding process of crop growth of row crop classification of going forward side by side, is obtained
Take the plant growth physiological and biochemical procedure parameter including crop TSUM parameter and Crop stage DVS；It is net based on crop first
Productivity and plant growth physiological and biochemical procedure parameter estimate crop yield amount.
It is understood that the yield of crop and crop itself and the growing environment of crop are closely bound up, wherein in basis
Above-described embodiment is calculated on the basis of the net productivity of crop first, also needs to comprehensively consider the soil moisture of plant growth, make
The initial informations such as object evapotranspiration.Especially by crop growth environment soil moisture, Crop transpirstion and crop growthing state
Initialization carries out process of crop growth simulation, and that crop includes crop TSUM parameter and Crop stage DVS in acquisition plot
Plant growth physiological and biochemical procedure parameter, and combine the net production of the crop first obtained according to above-described embodiment on this basis
Power estimates corresponding crop yield.
On the basis of the above embodiments, the evaluation method further include:
Based on Crop Group and crop growth environment information, the corresponding WOFOST model of Crop Group is constructed；
Respectively in three Dry Matter, water stress and nutrient stress levels, to light use efficiency model CASA and
WOFOST model carries out Model coupling, obtains coupling model；
Vegetation index time series based on Crop Group, sunshine parameter, meteorologic parameter and crop, using coupling model,
Estimate crop yield amount.
It is understood that the present embodiment passes through couple light energy utilization rate model (Carnegie-Ames-Stanford
Approach, CASA) and crop modeling (World Food Studies, WOFOST) solve the problems, such as it is of the existing technology.Coupling
It closes CASA model and WOFOST model obtains CASA-WOFOST per unit area yield appraising model, the fortune of CASA model can be combined
The mechanism advantage of speed and WOFOST model is calculated, while can effectively play the effect of remotely-sensed data, it is ensured that is enterprising in large scale
The precision and stability of row per unit area yield estimation, can achieve 85-90% to the estimation precision of staple food crop.
Specifically, CASA model is the main body frame of coupling model, by the simulation day by day to the net productivity of crop first,
It obtains dry matter accumulation amount (biomass), then crop yield is obtained by harvest index, while the correlation of WOFOST model is added
Ability of the module raises CASA model in terms of per unit area yield estimation.
(Nutrient Absorption and benefit mainly specifically are acted on to Dry Matter, transpiration (soil water circulation) and nutrient stress
With) three processes are coupled.Coupling is divided into three-level, and the first order is to couple Dry Matter process, increases CASA mould
The analog capability that type distributes dry matter in different growing to storage organ, to be avoided unnecessary instead of harvest index
Error；The second level is the coupling to water stress, promotes its adaptibility to response to extreme weather conditions, and the third level is coerced to nutrient
Urgent coupling makes CASA model have simulation crop to the absorption of Available N-P-K and the simulation utilized by increasing nutrient module
Ability.Finally to work on the basis of CASA-WOFOST per unit area yield estimates coupling model parameter calibration and remotely-sensed data assimilation is realized
Object growth is simulated, and by considering that nutrient stress acts on obtaining final per unit area yield estimation result.
It should be understood that since the coupled modes of above-mentioned back two-stage are still related to more parameter to be calibrated,
Only corresponding module is called when being abnormal stress, to guarantee model running speed and cost.
A kind of crop yield prediction remote sensing estimation method provided in an embodiment of the present invention, by coupling obtained CASA-
WOFOST per unit area yield appraising model can combine the arithmetic speed of CASA model and the mechanism advantage of WOFOST model, can
The effect for more effectively playing remotely-sensed data, makes full use of the growth mechanism of crop, it is ensured that crop yield in more large scale
Precision, efficiency and the stability of estimation.
Further, after the step of obtaining coupling model, this method further include: use method of document, farm number
Meteorologic parameter, plant growth physiological and biochemical procedure parameter according to the mode of collection, Field observation and remote-sensing inversion, to input model
And soil parameters, the corresponding parameter calibration for carrying out farm scale, plot scale and grid cell size.
It is understood that carrying out what parameters sensitivity analysis and ground observation data set obtained according to above-described embodiment
On the basis of, carry out the calibration of coupling model parameter.Model calibration is mainly carried out by four kinds of modes: method of document, farm number
According to collection, Field observation and remote-sensing inversion.
Specifically when carrying out model parameter calibration, it is broadly divided into following several scales and carries out: the meteorological number in addition to temperature
It is demarcated on the scale of farm according to, most of crop parameter and soil parameters；Partial soil nutrient parameter is in plot scale
Upper calibration；Part core parameter (Crop growing stage, leaf area index etc.) is demarcated in grid cell size.Main calibrating parameters
Including meteorologic parameter, crop parameter and soil parameters etc..
To further illustrate technical solution of the present invention, following preferred process flow is provided, but not to guarantor of the invention
Shield range is limited.
It is the flow chart of another kind of embodiment of the present invention crop yield prediction remote sensing estimation method with reference to Fig. 3, wherein this method
Operation be regarded as include data acquisition and pretreatment, based on crop timing remotely-sensed data Crop Group identification, CASA mould
Type estimates crop NPP and merges the calculating process of the several process flows of WOFOST model estimated crops.
Wherein in data acquisition phase, to include crop timing remotely-sensed data, geographical spatial data, unified yearbook data,
The data of meteorological data and sun parameter etc. are collected.Then suitable to crop timing remotely-sensed data in data preprocessing phase
The secondary pretreatment for carrying out radiation calibration, atmospheric correction, geometric correction, ortho-rectification, image mosaic and image cutting etc., acquisition have
The crop image data of effect.
Then in the Crop Group remote sensing recognition stage, be based respectively on effective crop image data, carry out characteristic of division extraction,
Crop Information is extracted and precision evaluation/statistics.Namely based on effective crop image data, when being extracted, and carried out by image NDVI
Sequence NDVI is smoothly reconstructed, acquisition time sequence NDVI.Partial data in access time sequence NDVI is inputted as training sample,
It is based on effective crop image data simultaneously, explains the true classification for obtaining crop by visual observation.Using the true classification of crop as sample
This label, by training sample input and sample label input Crop Group identification model together, to Crop Group identification model into
Row training, is counted by precision evaluation, obtains ideal Crop Group identification model.Recycle the ideal Crop Group identification
Model carries out Crop Group identification to image feature to be identified.
The crop NPP stage is estimated in CASA model, using light use efficiency model CASA, according to sunshine parameter, including such as
Sunshine duration, astronomical radiation this day temperature difference etc., obtain total solar radiation, while photosynthetic according to vegetation index time series acquisition
Net long wave radiation absorptance determines photosynthetic effective spoke of sunshine condition further according to total solar radiation and photosynthetically active radiation absorptance
Penetrate APAR.On the other hand, according to meteorological data, including such as temperature, rainfall, solar radiation, obtain temperature/water stress because
Son, and the efficiency of light energy utilization is determined based on this.It is finally based on photosynthetically active radiation APAR and the efficiency of light energy utilization, is obtained corresponding to crop
The first net productivity NPP.
Finally in the fusion WOFOST model estimated crops stage, first to the Soil Moisture Simulation module of model, work
Object pop-in upgrades and Crop evapotranspiration module carry out initiation parameter setting respectively.On this basis, it is determined using WOFOST model
Crop TSUM information, Crop stage DVS parameter and storage organ distribution coefficient.Finally it is based on these informations parameter and crop
The first net productivity NPP, estimated crops.
As the other side of the embodiment of the present invention, the present embodiment provides a kind of crop yield prediction remote sensing appraising device,
It is a kind of structural block diagram of crop yield prediction remote sensing appraising device of the embodiment of the present invention with reference to Fig. 4, comprising: at least one storage
Device 1, at least one processor 2, communication interface 3 and bus 4.
Wherein, memory 1, processor 2 and communication interface 3 complete mutual communication by bus 4, and communication interface 3 is used
Information between the acquisition or storage equipment of estimation device and mode input relevant parameter is transmitted；Being stored in memory 1 can
The computer program run on processor 2 when processor 2 executes the computer program, is realized as described in above-described embodiment
Crop yield prediction remote sensing estimation method.
It is to be understood that including at least memory 1, processor 2, communication in the crop yield prediction remote sensing appraising device
Interface 3 and bus 4, and memory 1, processor 2 and communication interface 3 form mutual communication connection by bus 4, and can
Complete mutual communication.
Communication interface 3 realizes the acquisition or storage equipment of crop yield prediction remote sensing appraising device and mode input relevant parameter
Between communication connection, and achievable mutual information transmission such as realized by communication interface 3 to crop remotely-sensed data, sunshine
The acquisition etc. of parameter and meteorologic parameter etc..
When crop yield prediction remote sensing appraising device is run, processor 2 calls the program instruction in memory 1, on executing
State method provided by each method embodiment, for example, be based on crop remotely-sensed data, sequentially carry out radiation calibration, atmosphere school
Just, the pretreatment that geometric correction, ortho-rectification, image mosaic and image are cut, obtains effective image data；And it is based on crop
The vegetation index time series of classification, sunshine parameter, meteorologic parameter and crop, estimation net productivity of crop first etc..
In another embodiment of the present invention, a kind of non-transient computer readable storage medium, the non-transient calculating are provided
Machine readable storage medium storing program for executing stores computer instruction, and the computer instruction executes the computer as described in above-described embodiment
Crop yield prediction remote sensing estimation method.
It is to be understood that the logical order in above-mentioned memory 1 can be realized simultaneously by way of SFU software functional unit
When sold or used as an independent product, it can store in a computer readable storage medium.Alternatively, realizing above-mentioned
This can be accomplished by hardware associated with program instructions for all or part of the steps of embodiment of the method, and program above-mentioned can store
In a computer-readable storage medium, which when being executed, executes step including the steps of the foregoing method embodiments；And it is aforementioned
Storage medium include: the various media that can store program code such as USB flash disk, mobile hard disk, ROM, RAM, magnetic or disk.
The embodiment of crop yield prediction remote sensing appraising device described above is only schematical, wherein as separation
The unit of part description may or may not be physically separated, and both can be located in one place, or can also be with
It is distributed on heterogeneous networks unit.Some or all of the modules therein can be selected to realize the present embodiment according to actual needs
The purpose of scheme.Those of ordinary skill in the art are without paying creative labor, it can understand and implement.
By the description of embodiment of above, those skilled in the art is it will be clearly understood that each embodiment can borrow
Help software that the mode of required general hardware platform is added to realize, naturally it is also possible to pass through hardware.Based on this understanding, above-mentioned
Substantially the part that contributes to existing technology can be embodied in the form of software products technical solution in other words, the meter
Calculation machine software product may be stored in a computer readable storage medium, such as USB flash disk, mobile hard disk, ROM, RAM, magnetic disk or light
Disk etc., including some instructions, with so that a computer equipment (such as personal computer, server or network equipment etc.)
Execute method described in certain parts of above-mentioned each method embodiment or embodiment of the method.
A kind of crop yield prediction remote sensing appraising device provided in an embodiment of the present invention and a kind of non-transient computer is readable deposits
Storage media identifies Crop Group according to crop remotely-sensed data, and comprehensively considers plant growth luminous energy benefit on this basis
With rate and plant growth physiological and biochemical procedure, the effect of remotely-sensed data can be more effectively played, the growth of crop is made full use of
Mechanism, it is ensured that precision, efficiency and the stability of crop yield estimation in more large scale.
As the another aspect of the embodiment of the present invention, the present embodiment provides a kind of crop yield prediction remote sensing appraising system,
The system includes crop yield prediction remote sensing appraising device as described in above-described embodiment and the mode input for communicating connection
The acquisition or storage equipment of relevant parameter.Mode input relevant parameter therein includes crop remotely-sensed data, sunshine parameter, meteorology
Parameter and crop growth environment information etc..
The system utilizes the efficiency of light energy utilization and plant growth for being based on crop remote sensing images in the above embodiments
The estimation of crop yield amount is realized in the fusion of journey.Therefore, retouching in crop yield prediction remote sensing appraising in the above embodiments
It states and defines, can be used for the understanding of each execution module in the embodiment of the present invention.
It is understood that can be by hardware processor (hardware processor) come real in the embodiment of the present invention
Existing related function module.
A kind of crop yield prediction remote sensing appraising system provided in an embodiment of the present invention, by the way that estimation dress as described above is arranged
It sets and corresponding parameter obtains or storage facilities, Crop Group is identified according to crop remotely-sensed data, and is basic herein
On comprehensively consider the plant growth efficiency of light energy utilization and plant growth physiological and biochemical procedure, can more effectively play remotely-sensed data
Effect, makes full use of the growth mechanism of crop, it is ensured that precision, efficiency and the stabilization of crop yield estimation in more large scale
Property.
In addition, those skilled in the art are it should be understood that in application documents of the invention, term " includes ",
"comprising" or any other variant thereof is intended to cover non-exclusive inclusion so that include a series of elements process,
Method, article or equipment not only include those elements, but also including other elements that are not explicitly listed, or are also wrapped
It includes as elements inherent to such a process, method, article, or device.In the absence of more restrictions, by sentence " including
One ... " limit element, it is not excluded that there is also another in the process, method, article or apparatus that includes the element
Outer identical element.
In specification of the invention, numerous specific details are set forth.It should be understood, however, that the embodiment of the present invention can
To practice without these specific details.In some instances, well known method, structure and skill is not been shown in detail
Art, so as not to obscure the understanding of this specification.It is similar, it should be appreciated that disclose to simplify the present invention and help to manage
One or more of the various inventive aspects is solved, in the above description of the exemplary embodiment of the present invention, of the invention is each
A feature is grouped together into a single embodiment, figure, or description thereof sometimes.
However, the disclosed method should not be interpreted as reflecting the following intention: i.e. the claimed invention requirement
Features more more than feature expressly recited in each claim.More precisely, as claims are reflected
Like that, inventive aspect is all features less than single embodiment disclosed above.Therefore, it then follows the power of specific embodiment
Thus sharp claim is expressly incorporated in the specific embodiment, wherein each claim itself is as independent reality of the invention
Apply example.
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, and those skilled in the art is it is understood that it still can be right
Technical solution documented by foregoing embodiments is modified or equivalent replacement of some of the technical features；And this
It modifies or replaces, the spirit and model of technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (10)
1. a kind of crop yield prediction remote sensing estimation method characterized by comprising
Based on crop remotely-sensed data, Crop Group is identified；
Vegetation index time series based on the Crop Group, sunshine parameter, meteorologic parameter and crop, estimation crop first are net
Productivity；
Based on the net productivity of the crop first, in conjunction with crop growth environment information and plant growth physiological and biochemical procedure parameter,
Estimate crop yield amount.
2. the method according to claim 1, wherein the step of estimation first net productivity of crop, is further
It include: to estimate the net productivity of the crop first especially by light use efficiency model CASA is utilized.
3. according to the method described in claim 2, it is characterized in that, described based on the Crop Group, sunshine parameter, meteorological ginseng
The step of several and crop vegetation index time series, estimation the first net productivity of crop, further comprises:
Based on sunshine duration information, astronomical radiation information and the temperature difference per day information in the sunshine parameter, total solar radiation is calculated,
And the vegetation index time series based on the crop, calculate photosynthetically active radiation absorptance；
Based on the total solar radiation and the photosynthetically active radiation absorptance, photosynthetically active radiation is calculated；
Temperature information, rainfall data and solar radiation information based on the meteorologic parameter, the temperature/water stress factor,
And it is based on the temperature/water stress factor, calculate the efficiency of light energy utilization；
Based on the Crop Group, the photosynthetically active radiation and the efficiency of light energy utilization, using light use efficiency model CASA,
Estimate the net productivity of the crop first.
4. according to the method described in claim 2, it is characterized in that, described be based on the net productivity of the crop first, in conjunction with work
The step of object growing environment information and plant growth physiological and biochemical procedure parameter, estimation crop yield amount, further comprises:
Based on the crop growth environment information, initializing set crop growth environment, and it is corresponding to carry out the Crop Group
The Soil Moisture Simulation and Crop evapotranspiration of process of crop growth are simulated, and obtaining includes crop TSUM parameter and Crop stage
The plant growth physiological and biochemical procedure parameter of DVS；
Based on the net productivity of the crop first and the plant growth physiological and biochemical procedure parameter, the crop yield is estimated
Amount.
5. according to the method described in claim 4, it is characterized in that, being based on the crop growth environment information described, initially
Before the step of changing setting crop growth environment, further includes:
Based on the Crop Group and the crop growth environment information, the corresponding WOFOST model of the Crop Group is constructed；
Correspondingly, described be based on the net productivity of the crop first, it is raw in conjunction with crop growth environment information and plant growth physiology
The step of changing procedure parameter, estimating crop yield amount further comprises:
Respectively in three Dry Matter, water stress and nutrient stress levels, to the light use efficiency model CASA and institute
It states WOFOST model and carries out Model coupling, obtain coupling model；
Vegetation index time series based on the Crop Group, the sunshine parameter, the meteorologic parameter and the crop, benefit
With the coupling model, the crop yield amount is estimated.
6. identifying Crop Group the method according to claim 1, wherein described be based on crop remotely-sensed data
Step further comprises:
Based on the crop remotely-sensed data, radiation calibration, atmospheric correction, geometric correction, ortho-rectification, image mosaic are sequentially carried out
The pretreatment cut with image, obtains effective image data；
Based on effective image data, Image Segmentation processing and artificial vectorized process are sequentially carried out, target field side is obtained
Boundary；
Extract the spectral reflectivity feature, vegetation index feature and image texture of image data in the target field bounds
Sign, and it is based on the spectral reflectivity feature, the vegetation index feature and the image texture sign, using based on supervision
Sorting algorithm, identify the Crop Group.
7. according to the method described in claim 5, it is characterized in that, after the acquisition coupling model the step of, further includes:
By the way of method of document, farm data collection, Field observation and remote-sensing inversion, to the meteorology of input model
Parameter, the plant growth physiological and biochemical procedure parameter and soil parameters are corresponding to carry out farm scale, plot scale and pixel ruler
The parameter calibration of degree.
8. a kind of crop yield prediction remote sensing appraising device characterized by comprising at least one processor, at least one processing
Device, communication interface and bus；
The memory, the processor and the communication interface complete mutual communication, the communication by the bus
The information that interface obtained or stored between equipment for the estimation device and mode input parameter is transmitted；
The computer program that can be run on the processor is stored in the memory, the processor executes the calculating
When machine program, the method as described in any in claim 1 to 7 is realized.
9. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute the method as described in any in claim 1 to 7.
10. a kind of crop yield prediction remote sensing appraising system characterized by comprising estimation device as claimed in claim 8 with
And the mode input parameter obtains or storage equipment.
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