CN107491724B - A kind of Spike Differentiation in Winter Wheat phase recognition methods and device - Google Patents
A kind of Spike Differentiation in Winter Wheat phase recognition methods and device Download PDFInfo
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Abstract
The embodiment of the invention provides a kind of Spike Differentiation in Winter Wheat phase recognition methods and devices, inventive method includes: according to the parameter in the winter wheat growing area to be identified being collected into, the model parameter of Wheat Simulation Model is obtained, localization Wheat Simulation Model is obtained；According to winter wheat growing area agricultural meteorological station data to be identified, the first temperature record time series of winter wheat to be identified is obtained, according to the multi-temporal remote sensing image in winter wheat growing area to be identified, obtains the second temperature record time series of winter wheat to be identified；Using data assimilation method, the first temperature record time series and the second temperature record time series are subjected to data assimilation, obtain winter wheat assimilation temperature record time series to be identified；According to assimilation temperature record time series and localization Wheat Simulation Model, the Jointing stage recognition result in winter wheat growing area to be identified is obtained.The embodiment of the present invention realizes the identification of the Spike Differentiation in Winter Wheat phase on regional scale.
Description
Technical field
The present embodiments relate to agricultural technology fields, and in particular to a kind of Spike Differentiation in Winter Wheat phase recognition methods and dress
It sets.
Background technique
A kind of index of the Crop growing stage as reflection crop growth process, for determining that it is important that potential production has
Effect.Useful information and section can be provided for the field management in each stage breeding time work by grasping Crop development process in real time
Foundation is learned, for realizing that the stable yields high receipts of grain are of great significance to.In recent years, with to plant physiological ecology basis
Theoretical further investigation, the crop growth model based on the mechanism process such as crop photosynthesis, breathing, transpiration, nutrition gradually develop
Come, crop growth model by it physiology course and kinetic mechanism, can be compared with accurate simulation crop object with " day "
For the growth and development state of crop on the single-point scale of time step, and the temperature record collection of long-term sequence is driving crop mould
One of the most important input parameter of type operation, directly determines the simulation precision of crop growth situation.
In recent years, global climate and Changes in weather are violent, abnormal and extreme climate takes place frequently, so as to cause more meteorology
Disaster occurs, and has brought tremendous economic losses to agricultural production.Spring frost is exactly Main Agricultural in China's Production of Winter Wheat
One of meteorological disaster, and the Spike Differentiation in Winter Wheat phase is the tricky time that Spring frost occurs, main cause evening is frost damage
When, young fringe subject to damage directly affects " yield forming three elements " (i.e. unit area spike number, number of grain per ear and mass of 1000 kernel).Cause
This, is recognized accurately Tillering of Winter Wheat each period, instructs work for the defence of Spring frost and realizes winter wheat
High and stable yields have great importance.
In the prior art, to the most methods using traditional field experiment of the identification of Spike Differentiation in Winter Wheat phase, have larger
Limitation.Mechanistic crop modeling WheatGrow based on winter wheat physiological and biochemical procedure can be realized with ' day ' as the time
Wheat Jointing stage simulation on the single-point scale of unit.For a long time, the temperature record of driving crop modeling operation mainly leads to
It crosses the long-term observation weather station fixed point being distributed in research area to obtain, there are following both sides defects for this method: on the one hand,
Due to the limitation of economic technology condition, studies the long-term observation weather station limited amount in area and spatial distribution is uneven, see
The temperature record collection measured is only capable of reflecting a certain range of temperature change situation near the website, can not express non-viewing
The temperature change feature of point other than website.Although can be evaluated whether the temperature value of unknown point using the method for space interpolation, by
In by landform, sample point density and distribution and interpolation method etc. influenced, the error of interpolation result is larger.Another party
Face, for crops, its growth and development and yield composition are directly influenced by agricultural microclimate locating for it, and weather station is usual
Be arranged on the spacious vacant lot in urban fringe or suburb, by urban heat land effect and underlying surface influenced, weather station temperature
There are larger differences with farmland temperature.
Therefore, how to propose a kind of scheme, can be realized the identification of Regional Fall Wheat Jointing stage, become urgently to be resolved
Problem.
Summary of the invention
For the defects in the prior art, the embodiment of the invention provides a kind of Spike Differentiation in Winter Wheat phase recognition methods and dresses
It sets.
On the one hand, the embodiment of the invention provides a kind of Spike Differentiation in Winter Wheat phase recognition methods, comprising:
According to the work of geographic factor, meteorologic parameter and winter wheat to be identified in the winter wheat growing area to be identified being collected into
Object parameter obtains the model parameter of Wheat Simulation Model, obtains localization Wheat Simulation Model；
According to the agricultural meteorological station data in the winter wheat growing area preset range to be identified, the winter to be identified is obtained
The first temperature record time series in wheat growth stage, according to the multi-temporal remote sensing shadow in the winter wheat growing area to be identified
Picture obtains the second temperature record time series in the During Growing Period of Winter Wheat to be identified；
Using data assimilation method, by the first temperature record time series and the second temperature record time series
Data assimilation is carried out, the assimilation temperature record time series in the During Growing Period of Winter Wheat to be identified is obtained；
According to the assimilation temperature record time series and the localization Wheat Simulation Model, obtain described wait know
Jointing stage recognition result in other winter wheat growing area.
Further, the agricultural meteorological station data according in the winter wheat growing area preset range to be identified, are obtained
Take the first temperature record time series in the During Growing Period of Winter Wheat to be identified, comprising:
According to the agricultural meteorological station data in the winter wheat growing area preset range to be identified, GIS-Geographic Information System is utilized
Spatial Interpolation Method carries out temperature record interpolation, obtains the first temperature record time series.
Further, described to utilize GIS-Geographic Information System Spatial Interpolation Method, temperature record interpolation is carried out, obtains described first
Temperature record time series, comprising:
Using following formula (1), temperature record interpolation is carried out:
In formula: λiFor weight；N is agricultural weather website number；diFor the reality between interpolation point and i-th of agricultural meteorological station
Ranging from；Z is the temperature estimated value of interpolation point；ZiFor the temperature measured value of i-th (i=1,2,3 ... n) a agricultural meteorological station.
Further, the multi-temporal remote sensing image according in the winter wheat growing area to be identified, obtain it is described to
Identify the second temperature record time series in During Growing Period of Winter Wheat, comprising:
The multi-temporal remote sensing image in the winter wheat growing area to be identified is obtained, according to the multi-temporal remote sensing image,
Obtain the sample Remote Sensing temperature-time sequence data of the winter wheat to be identified within a preset time at default sample point；
Obtain actual measurement temperature time of the winter wheat to be identified at the default sample point in the preset time
Sequence data；
According to the sample Remote Sensing temperature-time sequence data and the actual measurement temperature time series data, institute is obtained
State the second temperature record time series in During Growing Period of Winter Wheat to be identified.
Further, described according to the sample Remote Sensing temperature-time sequence data and the actual measurement temperature time sequence
Column data obtains the second temperature record time series in the During Growing Period of Winter Wheat to be identified, comprising:
According to the sample Remote Sensing temperature-time sequence data and the actual measurement temperature time series data, institute is established
State the Time Series Regression statistical model of winter wheat to be identified；
According to the multi-temporal remote sensing image in the winter wheat growing area to be identified, it is entire to obtain the winter wheat to be identified
Calculating Remote Sensing temperature-time sequence data in breeding time；
According to the calculating Remote Sensing temperature-time sequence data and the Time Series Regression statistical model, institute is obtained
State the second temperature record time series.
Further, described to utilize data assimilation method, by the first temperature record time series and second gas
Warm data time series carry out data assimilation, obtain the assimilation temperature record time sequence in the During Growing Period of Winter Wheat to be identified
Column, comprising:
Using Kalman filtering algorithm, by the first temperature record time series and the second temperature record time sequence
Column carry out sequence assimilation obtains the assimilation temperature record time series.
Further, described to utilize Kalman filtering algorithm, the first temperature record time series and described the are stated by described
Two temperature record time series carry out sequence assimilations obtain the assimilation temperature record time series, comprising:
First temperature record time series and the second temperature record time series are stated for described using following formula (2)
Carry out sequence assimilation obtains the assimilation temperature record time series:
In formula: Yt fFor the predicted value of temperature, XtFor predictor, i.e., the described first temperature record time series, Bt-1It is slotting
Value coefficient vector, RtFor extrapolated value BtError covariance matrix, Ct-1For Bt-1Error covariance matrix, W be dynamic noise error variance
Battle array；σtFor prediction error variance matrix,For the transposed matrix of predictor Xt, V is the error covariance matrix of observation noise；AtTo increase
Beneficial matrix,For σtInverse matrix；Yt oFor the second temperature record time series.
Further, the basis is collected into the geographic factor in winter wheat growing area to be identified, meteorologic parameter and to
The crop parameter for identifying winter wheat, obtains the model parameter of Wheat Simulation Model, comprising:
According to geographic factor, meteorologic parameter and the crop of the winter wheat to be identified ginseng in the winter wheat growing area to be identified
Number, seeks excellent method using loop iteration, obtains the model parameter of the Wheat Simulation Model.
On the other hand, the embodiment of the present invention provides a kind of device for the identification of Spike Differentiation in Winter Wheat phase, comprising:
At least one processor；And
At least one processor being connect with the processor communication, in which:
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to refer to
Order is able to carry out above-mentioned Spike Differentiation in Winter Wheat phase recognition methods.
Another aspect, the embodiment of the present invention provide a kind of non-transient computer readable storage medium, which is characterized in that described
Non-transient computer readable storage medium stores computer instruction, and it is small that the computer instruction makes the computer execute the above-mentioned winter
The recognition methods of wheat head idiophase.
Spike Differentiation in Winter Wheat phase recognition methods provided in an embodiment of the present invention is obtained to be identified by satellite remote sensing technology
Winter wheat area remote sensing images obtain the remote sensing temperature-time sequence of winter wheat to be identified, i.e. the second temperature record time series, knot
Two groups of data are utilized data assimilation by the first temperature record time series for closing the winter wheat to be identified that agricultural meteorological station obtains,
Obtain assimilation temperature record time series.Recycle localization Wheat Simulation Model, it can complete winter wheat to be identified
The identification of Spike development.Satellite remote sensing technology is utilized has that the period is short, range is wide in the variation of reflecting regional crop growth
And advantage at low cost, combined data assimilation technique carry out the data and remotely-sensed data that obtain according to agricultural meteorological station
Data assimilation realizes the identification of the Spike Differentiation in Winter Wheat phase on regional scale.
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 apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the flow diagram of Spike Differentiation in Winter Wheat phase recognition methods in the embodiment of the present invention；
Fig. 2 is the flow diagram of another Spike Differentiation in Winter Wheat phase recognition methods in the embodiment of the present invention；
Fig. 3 is the highest temperature data processed result contrast schematic diagram that sample point is preset in the embodiment of the present invention；
Fig. 4 is the lowest temperature data processed result contrast schematic diagram that sample point is preset in the embodiment of the present invention；
Fig. 5 (a)-Fig. 5 (g) is Spike Differentiation in Winter Wheat phase recognition result schematic diagram in the embodiment of the present invention；
Fig. 6 is structural schematic diagram of one of the inventive embodiments for the device of Spike Differentiation in Winter Wheat phase identification.
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 scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Fig. 1 is the flow diagram of Spike Differentiation in Winter Wheat phase recognition methods in the embodiment of the present invention, as shown in Figure 1, this hair
The Spike Differentiation in Winter Wheat phase recognition methods that bright embodiment provides includes:
Geographic factor, meteorologic parameter and the winter wheat to be identified in winter wheat growing area to be identified that S1, basis are collected into
Crop parameter, obtain the model parameter of Wheat Simulation Model, obtain localization Wheat Simulation Model；
Specifically, it is small to use Wheat Simulation Model, that is, WheatGrow crop modeling progress winter for the embodiment of the present invention
The identification model of wheat head idiophase, but Wheat Simulation Model in use, need the model is demarcated, that is, fitted
Close the localization Wheat Simulation Model of winter wheat to be identified.The embodiment of the present invention collects winter wheat growing area to be identified in advance
Interior geographic factor, meteorologic parameter and the crop parameter of winter wheat to be identified carries out wheat growth according to the parameters of acquisition
The calibration of simulation model obtains the localization Wheat Simulation Model for being suitble to winter wheat to be identified.Wherein, geographic factor includes
Longitude and latitude data；Meteorologic parameter mainly includes temperature record, including the highest temperature and lowest temperature data day by day；Crop parameter packet
Include winter wheat variety characterisitic parameter and winter wheat phenological period characteristic etc..Certainly, other needs can also be acquired as needed
Parameter information, the embodiment of the present invention is not specifically limited.
S2, according to the agricultural meteorological station data in the winter wheat growing area preset range to be identified, obtain described wait know
The first temperature record time series in other During Growing Period of Winter Wheat, it is distant according to the multidate in the winter wheat growing area to be identified
Feel image, obtains the second temperature record time series in the During Growing Period of Winter Wheat to be identified；
Specifically, it obtains after being suitble to the localization Wheat Simulation Model of winter wheat to be identified, needs to obtain to be identified
The growth data of winter wheat, to carry out the identification of Spike Differentiation in Winter Wheat phase using the localization Wheat Simulation Model.This
Inventive embodiments obtain the agricultural meteorological station data in winter wheat growing area preset range to be identified, and according to the agricultural got
The first temperature record time series in weather station data acquisition During Growing Period of Winter Wheat to be identified.Wherein, agricultural meteorological station data
The main temperature record day by day including in Winter Wheat-Growing, winter wheat growing area preset range to be identified can be according to practical need
It is configured, the agricultural meteorological station of all agricultural meteorological stations where available winter wheat growing area to be identified in municipality directly under the Central Government
Data, also can according to need the size that setting preset range makes, and the embodiment of the present invention is not specifically limited.Due to general agricultural
The website of weather station is arranged in city suburbs, and spatial distribution is uneven, and agricultural meteorological station data can not accurately represent to be identified
The temperature record of winter wheat.In order to keep the temperature record for getting winter wheat to be identified more accurate, the embodiment of the present invention is utilized
Satellite remote sensing technology obtains more in winter wheat growing area to be identified according to the latitude and longitude information of winter wheat growing area to be identified
Phase remote sensing image obtains the second temperature record time sequence in During Growing Period of Winter Wheat to be identified according to multi-temporal remote sensing image
Column.
Wherein temperature-time sequence refers to that the temperature by the winter wheat area to be identified of acquisition is temporally ranked up, obtain with
Temperature data on the basis of time.
S3, using data assimilation method, by the first temperature record time series and the second temperature record time
Sequence carries out data assimilation, obtains the assimilation temperature record time series in the During Growing Period of Winter Wheat to be identified；
Specifically, the first temperature record time series and the second temperature record time series of winter wheat to be identified are got
Afterwards, the first temperature record time series that will acquire using data assimilation method and the second temperature record time series are carried out
Data assimilation obtains the assimilation temperature record time series in During Growing Period of Winter Wheat to be identified.
S4, according to the assimilation temperature record time series and the localization Wheat Simulation Model, described in acquisition
Jointing stage recognition result in winter wheat growing area to be identified.
Specifically, it after getting the assimilation temperature record time series in During Growing Period of Winter Wheat to be identified, will acquire
Assimilation temperature record time series in During Growing Period of Winter Wheat to be identified, substitute into localization Wheat Simulation Model, obtain to
It identifies the Jointing stage recognition result in winter wheat growing area, that is, obtains the growth and development situation of winter wheat to be identified.
Wherein, the first temperature record time series and the second temperature record time series can be winter wheat plantation to be identified
The daily highest temperature and lowest temperature data in area.
Spike Differentiation in Winter Wheat phase recognition methods provided in an embodiment of the present invention is obtained to be identified by satellite remote sensing technology
Winter wheat area remote sensing images obtain the remote sensing temperature-time sequence of winter wheat to be identified, i.e. the second temperature record time series, knot
Two groups of data are utilized data assimilation by the first temperature record time series for closing the winter wheat to be identified that agricultural meteorological station obtains,
Obtain assimilation temperature record time series.Recycle localization Wheat Simulation Model, it can complete winter wheat to be identified
The identification of Spike development.Satellite remote sensing technology is utilized has that the period is short, range is wide in the variation of reflecting regional crop growth
And advantage at low cost, combined data assimilation technique carry out the data and remotely-sensed data that obtain according to agricultural meteorological station
Data assimilation realizes the identification of the Spike Differentiation in Winter Wheat phase on regional scale.
On the basis of the above embodiments, the agriculture gas according in the winter wheat growing area preset range to be identified
As data of standing, the first temperature record time series in the During Growing Period of Winter Wheat to be identified is obtained, comprising:
According to the agricultural meteorological station data in the winter wheat growing area preset range to be identified, GIS-Geographic Information System is utilized
Spatial Interpolation Method carries out temperature record interpolation, obtains the first temperature record time series.
Specifically, the agricultural meteorological station number in winter wheat growing area preset range to be identified is got in the embodiment of the present invention
According to wherein agricultural meteorological station data include temperature record.Obtain the agriculture gas in winter wheat growing area preset range to be identified
As collected temperature record of standing carries out temperature record difference, i.e., by agricultural weather using GIS-Geographic Information System Spatial Interpolation Method
Acquisition temperature record of standing is converted into the corresponding temperature data in winter wheat area to be identified, obtains the first temperature number of winter wheat to be identified
According to time series.
On the basis of the above embodiments, described to utilize GIS-Geographic Information System Spatial Interpolation Method, temperature record interpolation is carried out,
Obtain the first temperature record time series, comprising:
Using following formula (1), temperature record interpolation is carried out:
In formula: λiFor weight；N is agricultural weather website number；diFor the reality between interpolation point and i-th of agricultural meteorological station
Ranging from；Z is the temperature estimated value of interpolation point；ZiFor the temperature measured value of i-th (i=1,2,3 ... n) a agricultural meteorological station.
Pair specifically, GIS-Geographic Information System Spatial Interpolation Method is used in the embodiment of the present invention, carries out temperature record difference, i.e.,
Each collected temperature record of agricultural meteorological station generates the temperature record time day by day using inverse distance weighted interpolation method (IDW) interpolation
Sequence, specific method can carry out temperature record difference using above-mentioned formula (1).Firstly, according to interpolation point and each agriculture gas
As the measured distance between station obtains a weighted value λi, further according to the weighted value λ of acquisitioniWith the temperature of each agricultural meteorological station
Measured value successively estimates temperature estimated value Z of each agricultural meteorological station relative to interpolation point, that is, obtains the of winter wheat to be identified
One temperature record time series.Wherein, interpolation point is the position where winter wheat area to be identified, it can is existed as needed
Interpolation point is arranged in winter wheat area to be identified, and the number of interpolation point, which also can according to need, to be configured, and the embodiment of the present invention is not done
Specific to limit, the measured distance between interpolation point and each agricultural meteorological station can be obtained according to latitude and longitude coordinates.
Spike Differentiation in Winter Wheat phase recognition methods provided in an embodiment of the present invention, since general agricultural meteorological station is all disposed within city
The suburb in city, the temperature record that agricultural meteorological station obtains cannot indicate the temperature record in winter wheat area to be identified.The present invention is real
Example is applied by obtaining the agricultural meteorological station data in winter wheat preset range to be identified, using GIS-Geographic Information System space interpolation
Method carries out temperature record difference for the collected temperature record of agricultural meteorological station and is converted into the temperature in winter wheat area to be identified
Data obtain the first temperature record time series of winter wheat to be identified.Improve Spike Differentiation in Winter Wheat phase identification data used
Accuracy, further improve the accuracy of Spike Differentiation in Winter Wheat phase recognition result.
On the basis of the above embodiments, the multi-temporal remote sensing shadow according in the winter wheat growing area to be identified
Picture obtains the second temperature record time series in the During Growing Period of Winter Wheat to be identified, comprising:
The multi-temporal remote sensing image in the winter wheat growing area to be identified is obtained, according to the multi-temporal remote sensing image,
Obtain the sample Remote Sensing temperature-time sequence data of the winter wheat to be identified within a preset time at default sample point；
Obtain actual measurement temperature time of the winter wheat to be identified at the default sample point in the preset time
Sequence data；
According to the sample Remote Sensing temperature-time sequence data and the actual measurement temperature time series data, institute is obtained
State the second temperature record time series in During Growing Period of Winter Wheat to be identified.
Specifically, since agricultural meteorological station data can not accurately indicate the temperature record in winter wheat area to be identified, this hair
Bright embodiment obtains the remote sensing image data in winter wheat growing area to be identified, according to the remote sensing shadow of acquisition by remote sensing technology
As data, During Growing Period of Winter Wheat to be identified within a preset time at the default sample point in winter wheat growing area to be identified is obtained
Interior sample Remote Sensing temperature-time sequence data, that is, MODIS LST time series data.MODIS is that Terra and Aqua are defended
One of main sensors carried on star, LST are a kind of surface temperature products on MODIS, by satellite remote sensing technology, specifically
The MODIS Aqua surface temperature product MYD11A1 that multidate can be used obtains MODIS LST time series data i.e. sample
Remote Sensing temperature-time sequence data.Due to being the earth's surface temperature of winter wheat growing area to be identified by satellite remote sensing technology acquisition
Degree evidence needs to convert surface temperature data to the temperature record that can indicate winter wheat to be identified.The embodiment of the present invention is logical
It crosses and obtains the actual measurement temperature time series data of the winter wheat to be identified at default sample point within a preset time, it specifically can be with
The temperature above winter wheat to be identified is surveyed by ground, as: it can be by winter wheat growing area to be identified presets sample point
Air temperature sensor is set at top preset height such as 2m and observes acquisition actual measurement temperature time series data.Further according to acquisition
Sample Remote Sensing temperature-time sequence data and actual measurement temperature time series data, obtain in During Growing Period of Winter Wheat to be identified
Second temperature record time series.
Such as: multiple default sample points are set in winter wheat growing area to be identified in advance, are obtained by satellite remote sensing technology
The satellite remote sensing images for obtaining winter wheat kind to be identified are obtained in winter wheat growing area to be identified according to the satellite remote sensing images and are preset
The MODIS LST time series data of winter wheat to be identified within a preset time at sample point, such as obtain default sample point
When the MODIS LST of winter wheat to be identified daily 13:30 on March 12nd, 2016 to May 31 at place or so and evening 01:30 or so
Between sequence data, certain preset time, which can according to need, to be set, and the embodiment of the present invention is not especially limited.Again wait know
At default sample point in other winter wheat growing area be arranged air temperature sensor, the air temperature sensor can be set away from
Ground level is to acquire default sample point within a preset time such as by air temperature sensor: in March, 2016 at the position of 2m
Actual measurement temperature time series data to daily 13:30 on May 31 or so and evening 01:30 or so on the 12.According to what is got
MODIS LST time series data and actual measurement temperature time series data, obtain the second gas in During Growing Period of Winter Wheat to be identified
Warm data time series.
On the basis of the above embodiments, described according to the sample Remote Sensing temperature-time sequence data and the reality
Temperature time series data is surveyed, the second temperature record time series in the During Growing Period of Winter Wheat to be identified is obtained, comprising:
According to the sample Remote Sensing temperature-time sequence data and the actual measurement temperature time series data, institute is established
State the Time Series Regression statistical model of winter wheat to be identified；
According to the multi-temporal remote sensing image in the winter wheat growing area to be identified, it is entire to obtain the winter wheat to be identified
Calculating Remote Sensing temperature-time sequence data in breeding time；
According to the calculating Remote Sensing temperature-time sequence data and the Time Series Regression statistical model, institute is obtained
State the second temperature record time series.
Specifically, the sample Remote Sensing temperature-time sequence preset at sample point in planting winter wheat area to be identified is being got
After column data and actual measurement temperature time series data, to the sample Remote Sensing temperature-time sequence data and actual measurement temperature time
Sequence data carries out regression analysis, establishes the Time Series Regression statistical model of winter wheat to be identified.According to winter wheat kind to be identified
Multi-temporal remote sensing image in growing area obtains the calculating Remote Sensing temperature-time sequence in winter wheat to be identified entire breeding time
Data, by the calculating Remote Sensing temperature-time sequence data input time serial regression statistical model, it can will be to be identified
The Remote Sensing temperature inversion of winter wheat is the air themperature of winter wheat to be identified, obtains the second temperature number of winter wheat to be identified
According to time series.
Spike Differentiation in Winter Wheat phase recognition methods provided in an embodiment of the present invention obtains the winter to be identified using satellite remote sensing technology
The MODIS LST time series data of wheat, then by regression analysis, convert MODIS LST time series data to wait know
The second temperature record time series of other winter wheat.Satellite remote sensing technology has the characteristics that macroscopic view, quick and dynamic, in reflection area
There is the advantage that the period is short, range is wide and at low cost in the variation of domain crop growth.The embodiment of the present invention utilizes satellite remote sensing
Technology obtains the second temperature record time series of winter wheat to be identified, provides for the subsequent identification for carrying out the Spike Differentiation in Winter Wheat phase
Better data basis improves the accuracy of Spike Differentiation in Winter Wheat phase identification data and recognition result.
On the basis of the above embodiments, described to utilize data assimilation method, by the first temperature record time series
Data assimilation is carried out with the second temperature record time series, obtains the assimilation temperature in the During Growing Period of Winter Wheat to be identified
Data time series, comprising:
Using Kalman filtering algorithm, by the first temperature record time series and the second temperature record time sequence
Column carry out sequence assimilation obtains the assimilation temperature record time series.
Specifically, in the first temperature record time series for getting winter wheat to be identified by agricultural meteorological station, and it is logical
It crosses after satellite remote sensing technology gets the second temperature record time sequence of winter wheat to be identified, the embodiment of the present invention utilizes Kalman
Filtering algorithm, the first temperature record time series that will acquire and the sequence carry out sequence assimilation of the second temperature record time.Press
According to the corresponding relationship of time, the first temperature record time series and the second temperature record time sequence are assimilated, assimilated
Temperature record time series.
On the basis of the above embodiments, described to utilize Kalman filtering algorithm, stated for the first temperature record time for described
Sequence and the second temperature record time series carry out sequence assimilation obtain the assimilation temperature record time series, comprising:
First temperature record time series and the second temperature record time series are stated for described using following formula (2)
Carry out sequence assimilation obtains the assimilation temperature record time series:
In formula: Yt fFor the predicted value of temperature, XtFor predictor, i.e., the described first temperature record time series, Bt-1It is slotting
Value coefficient vector, RtFor extrapolated value BtError covariance matrix, Ct-1For Bt-1Error covariance matrix, W be dynamic noise error variance
Battle array；σtFor prediction error variance matrix,For predictor XtTransposed matrix, V be observation noise error covariance matrix；AtTo increase
Beneficial matrix,For σtInverse matrix；Yt oFor the second temperature record time series.
Specifically, the embodiment of the present invention is getting the first temperature record time series and the second temperature record time sequence
Afterwards, Kalman filtering (Kalman Fileter, KF) algorithm, principle is divided into prediction and updates two steps, specific to utilize card
Kalman Filtering algorithm carries out data assimilation using above-mentioned formula (2).Yt f=XtBt-1For prognostic equation, Yt fFor the predicted value of temperature,
XtFor predictor, i.e., meteorological site temperature value in the During Growing Period of Winter Wheat period to be identified, that is, when the first temperature record
Between sequence；Bt-1For interpolation coefficient vector, i.e., the weighted value λ of each meteorological site in the anti-distance weighting of temperature (IDW) interpolation algorithm；
RtFor extrapolated value BtError covariance matrix, Ct-1For Bt-1Error covariance matrix, W be dynamic noise error covariance matrix；σtIt is missed for forecast
Poor variance matrix,For predictor XtTransposed matrix, V be observation noise error covariance matrix；AtFor gain matrix,For
σtInverse matrix；Yt oFor the second temperature record time series, that is, utilize MODIS surface temperature product (LST) through regression equation meter
Temperature value above the entire breeding time wheatland of obtained winter wheat to be identified.
By the above method, after the assimilation temperature record time series for obtaining winter wheat to be identified, temperature record will be assimilated
Time series is input to localization Wheat Simulation Model, realizes the real-time monitoring of winter wheat to be identified, obtains the winter to be identified
The recognition result of wheat Jointing stage.
Satellite remote sensing technology has the characteristics that macroscopic view, quick and dynamic, has in the variation of reflecting regional crop growth
Have that the period is short, range is wide and advantage at low cost, but it is due to the limitation of the factors such as spatial resolution, is also difficult to really reflect and make
The relationship of object growth and development and meteorological factor.Data assimilation passes through coupling remote sensing technology and GIS-Geographic Information System space interpolation
Technology rationally can effectively estimate temperature record above winter wheat growing area to be identified, to drive crop in conjunction with the two advantage
The real-time monitoring of model realization crop growth situation is provided for correct cultivation step of formulating with the high and stable yields for realizing crop
Decision support.
Spike Differentiation in Winter Wheat phase recognition methods provided in an embodiment of the present invention, by acquiring winter wheat growing area pair to be identified
Two groups of data are carried out data assimilation, base using kalman filter method by the agricultural meteorological station data and satellite remote sensing date answered
Accurately identifying for the Spike Differentiation in Winter Wheat phase on regional scale is realized in the method for remote sensing and data assimilation, with traditional Spike development
Phase recognition methods is compared, easy to operate, while having saved time and manpower and material resources, is winter wheat growing way and meteorological disaster monitoring
Reference is provided with administrative decision, while providing the new thinking solved the problems, such as to improve crop modeling simulation precision.
The geographic factor in winter wheat growing area to be identified that on the basis of the above embodiments, the basis is collected into,
The crop parameter of meteorologic parameter and winter wheat to be identified, obtains the model parameter of Wheat Simulation Model, comprising:
According to geographic factor, meteorologic parameter and the crop of the winter wheat to be identified ginseng in the winter wheat growing area to be identified
Number, seeks excellent method using loop iteration, obtains the model parameter of the Wheat Simulation Model.
Specifically, in order to make Wheat Simulation Model i.e. WheatGrow crop modeling be more suitable winter wheat to be identified
Monitor environment, the embodiment of the present invention according in winter wheat growing area to be identified geographic factor, meteorologic parameter and the winter to be identified it is small
The crop parameter of wheat asks the calibration of excellent method progress model using loop iteration.It specifically can be in winter wheat growing area to be identified
The continuous 10 years highest temperatures day by day and lowest temperature data that National primary standard weather station obtains are that driving data combines the region
The phenological period data of winter wheat completes the localization calibration of model parameter, obtains the model parameter of Wheat Simulation Model, builds
It is vertical to be suitble to local localization Wheat Simulation Model.
Fig. 2 is the flow diagram of another Spike Differentiation in Winter Wheat phase recognition methods in the embodiment of the present invention, as shown in Fig. 2,
The embodiment of the present invention mainly passes through the day highest temperature data and day minimum gas that agricultural meteorological station obtains winter wheat growing area to be identified
Warm data obtain interpolation lowest temperature time series (on face) and interpolation highest temperature time sequence by inverse distance weighted interpolation method
It arranges in (on face), that is, obtains the first temperature record time series.Simultaneously.It is obtained by satellite remote sensing technology MODIS LST to be identified
The remote sensing images of winter wheat growing area, and remote sensing images are pre-processed, obtain the surface temperature time of winter wheat to be identified
Sequence data (MYD11A1 time series data) is divided into surface temperature time series on daytime (MYD11A1-day time series number
According to) and nighttime surface temperature-time sequence (MYD11A1-night time series data).Again by regression analysis, earth's surface is obtained
The regression equation of temperature-time sequence and actual measurement temperature time series data, is divided into highest temperature surface temperature time series and actual measurement
The regression equation and lowest temperature surface temperature time series of temperature time series data and returning for actual measurement temperature time series data
Return equation.The the second temperature record time series for further obtaining winter wheat to be identified is divided into the lowest temperature time above wheatland
Highest temperature time series above sequence (on face) and wheatland (on face).Using Kalman filtering algorithm, data assimilation is carried out, is obtained
The lowest temperature time series after highest temperature time series (on face) and assimilation after must assimilating (on face).By what is be obtained ahead of time
The crop parameter of winter wheat to be identified such as winter wheat variety genetic parameter, assimilation after highest temperature time series (on face) with
And the lowest temperature time series (on face) after assimilation is input to localization Wheat Simulation Model WheatGrow crop modeling
In, the Spike Differentiation in Winter Wheat recognition result on acquisition face.
Carry out the technology of the present invention is further explained embodiment with the identification of Henan Province's Shangqiu City Spike Differentiation in Winter Wheat phase below
Scheme:
Selecting ripe main breed Wenmai 6 in Shangqiu City winter wheat growing area semi-winterness is standard crop, is based on
WheatGrow crop modeling asks excellent method to solve calibration Cultivar parameter value using loop iteration.Specific steps are as follows: (1) according to Shangqiu
Each annual winter wheat phenological observation data of base station 2005-2006 to 2014-2015, determines that winter wheat universal sowing date is
October 15, date of generally emerging are October 21, and heading stage is April 17, and it is 178d that emergence to heading, which averagely lasts number of days,
In this, as emergence to the standard number of days of heading；(2) according to related literatures, temperature sensitivity TS, the life of Wenmai 6 are determined
Manage vernalization time PVT, the value range of photoperiod sensitivity PS and basic prematureness IE be respectively [1.4,1.5], [20,25],
[0.004,0.005] and [0.80,0.85], and the circulation step-length that 4 parameters are respectively set is 0.01,1,0.0001 and 0.01；
(3) based on each annual diurnal meterorological data of the above parameter and Shangqiu base station 2005-2006 to 2014-2015 and (1) step
Determining emergence date, Cultivar parameter value range and step-length, loop iteration run WheatGrow model, extract each run
Calculation average time is sought after the emergence of acquisition to the number of days of heading, and the number of days is compared with standard number of days, by difference minimum
When Cultivar parameter combination be determined as the combination of optimal Cultivar parameter, obtained the value difference of tetra- parameters of TS, PVT, PS and IE
It is 1.5,25,0.0048 and 0.80, that is, completes the single-point scale calibration of WheatGrow crop modeling, obtain localization wheat
Growth simulation model.
Based on 2015-2016 year Shangqiu City is local and periphery in totally 21 agricultural weather website Winter Wheat-Growings by
Daily temperature data obtain region daily maximum temperature and daily minimal tcmperature time sequence using inverse distance weighted interpolation method (IDW) interpolation
Column data obtains the first temperature record time series of winter wheat growing area, can specifically be carried out using above-mentioned formula (1) slotting
Value calculates.
Sample point is set up in the winter wheat contiguous plant area of the town Shangqiu City Shuan Ba, obtains wheatland by setting field sensors
It surveys temperature record and surveys temperature time series data in top.It extracts in observation period (12 days~May 31 March in 2016)
MODIS LST product daytime 13:30 or so and night 01:30 or so time series data collection, obtained respectively with field sensing
The daily maximum temperature of 2m, the lowest temperature carry out linear regression analysis and settling time serial regression statistics mould above the wheatland taken
Type.MODIS LST product MYD11A1 13:30 on daytime of winter wheat entire breeding time or so and night 01 are extracted by pixel unit:
30 or so time series data collection is calculated in winter wheat growing area using the Time Series Regression statistical model of foundation
2m daily maximum temperature and daily minimal tcmperature data above wheatland, that is, obtain the second temperature record time sequence of winter wheat growing area
Column.
Agricultural meteorological station data will be passed through) interpolation obtains region daily maximum temperature and daily minimal tcmperature time series data
(the first temperature record time series), and the daily maximum temperature and daily minimal tcmperature data (that are obtained by satellite remote sensing technology
Two temperature record time serieses), using Kalman filtering carry out sequence assimilation, can specifically be counted using above-mentioned formula (2)
According to assimilation, the assimilation temperature record time series in During Growing Period of Winter Wheat to be identified is obtained.Fig. 3 is to preset in the embodiment of the present invention
The highest temperature data processed result contrast schematic diagram of sample point, Fig. 4 are the minimum gas that sample point is preset in the embodiment of the present invention
Warm data processed result contrast schematic diagram has the data obtained by interpolation, by default sample as shown in Figure 3 and Figure 4, in figure
This is put the data that air temperature sensor is arranged at high 2m and obtains, is converted by the surface temperature data that satellite remote sensing technology obtains
Obtained by the data that obtain of temperature data, that is, LST conversion data and data assimilation.As shown in Figure 3 and Figure 4, the present invention is implemented
The assimilation temperature record time series finally chosen in example, comprehensively considered the temperature record obtained by agricultural meteorological station and
Surface temperature data are obtained by satellite remote sensing, improve the accuracy of data acquisition.
The assimilation temperature record time series of acquisition is inputted into WheatGrow crop modeling and localization wheat growth simulation
The Spike Differentiation in Winter Wheat phase recognition result of Shangqiu City can be obtained in model, moving model.Fig. 5 (a)-Fig. 5 (g) is that the present invention is implemented
Spike Differentiation in Winter Wheat phase recognition result schematic diagram in example, Jointing stage Start Date indicate that Fig. 5 (a) is with number of days after emergence
The schematic diagram of wheat list rib phase, 65d and 75d is respectively indicated after emergence 65 days and 75 days in figure, and Fig. 5 (b) is the two rib phase of wheat
Schematic diagram, 85d and 110d is respectively indicated after emergence 85 days and 110 days in figure, Fig. 5 (c) for the wheat small florescence schematic diagram, in figure
127d and 137 is respectively indicated after emergence 127 days and 137 days, and Fig. 5 (d) is the schematic diagram of wheat Pistil And Stamen idiophase, 133d in figure
It is respectively indicated with 143d after emerging 133 days and 143 days, Fig. 5 (e) is schematic diagram of the wheat medical every the phase, 137d and 151d points in figure
137 days and 151 days after Biao Shi not emerging, Fig. 5 (f) is the schematic diagram of four body idiophase of wheat, and 149d and 160d distinguishes table in figure
It shows after seedling 149 days and 160 days, Fig. 5 (g) is the schematic diagram of wheat heading stage, after 160d and 170d respectively indicates emergence in figure
160 days and 170 days.As shown in Fig. 5 (a)-Fig. 5 (g), method through the embodiment of the present invention can obtain winter wheat kind to be identified
Wheat in growing area different zones changes with time, entire breeding time variation, that is, obtain the fringe of winter wheat to be identified
Break up recognition result.
Spike Differentiation in Winter Wheat phase recognition methods provided in an embodiment of the present invention, by remote sensing technology, GIS-Geographic Information System space
Interpolation technique and crop modeling are merged by data assimilation method, and it is simple to realize the Spike Differentiation in Winter Wheat phase on regional scale
Quickly identification, improves the accuracy of Spike Differentiation in Winter Wheat phase recognition result, while having saved time and manpower and material resources, is the winter
Wheat growing way and meteorological disaster monitoring and administrative decision provide reference, while providing for raising crop modeling simulation precision new
The thinking solved the problems, such as.
Fig. 6 is structural schematic diagram of one of the inventive embodiments for the device of Spike Differentiation in Winter Wheat phase identification, such as Fig. 6
It is shown, the apparatus may include: processor (processor) 61, memory (memory) 62 and communication bus 63, wherein
Processor 61, memory 62 complete mutual communication by communication bus 63.Processor 61 can call in memory 62
Logical order, to execute following method: according in the winter wheat growing area to be identified being collected into geographic factor, meteorologic parameter and
The crop parameter of winter wheat to be identified obtains the model parameter of Wheat Simulation Model, obtains localization wheat growth simulation
Model；According to the agricultural meteorological station data in the winter wheat growing area preset range to be identified, it is small to obtain the winter to be identified
The first temperature record time series in wheat breeding time, according to the multi-temporal remote sensing shadow in the winter wheat growing area to be identified
Picture obtains the second temperature record time series in the During Growing Period of Winter Wheat to be identified；It, will be described using data assimilation method
First temperature record time series and the second temperature record time series carry out data assimilation, and it is small to obtain the winter to be identified
Assimilation temperature record time series in wheat breeding time；According to the assimilation temperature record time series and the localization wheat
Growth simulation model obtains the Jointing stage recognition result in the winter wheat growing area to be identified.
In addition, the logical order in above-mentioned memory 62 can be realized and as only by way of SFU software functional unit
Vertical product when selling or using, can store in a computer readable storage medium.Based on this understanding, this hair
Substantially the part of the part that contributes to existing technology or the technical solution can be with soft in other words for bright technical solution
The form of part product embodies, which is stored in a storage medium, including some instructions are to make
It obtains a computer equipment (can be personal computer, server or the network equipment etc.) and executes each embodiment of the present invention
The all or part of the steps of the method.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (ROM,
Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. it is various
It can store the medium of program code.
The embodiment of the present invention provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage
Medium storing computer instruction, the computer instruction make the computer execute side provided by above-mentioned each method embodiment
Method, for example, according to geographic factor, meteorologic parameter and the winter wheat to be identified in the winter wheat growing area to be identified being collected into
Crop parameter, obtain the model parameter of Wheat Simulation Model, obtain localization Wheat Simulation Model；According to described
Agricultural meteorological station data in winter wheat growing area preset range to be identified obtain the in the During Growing Period of Winter Wheat to be identified
One temperature record time series obtains described wait know according to the multi-temporal remote sensing image in the winter wheat growing area to be identified
The second temperature record time series in other During Growing Period of Winter Wheat；Using data assimilation method, when by first temperature record
Between sequence and the second temperature record time series carry out data assimilation, obtain same in the During Growing Period of Winter Wheat to be identified
Change temperature record time series；According to the assimilation temperature record time series and the localization Wheat Simulation Model,
Obtain the Jointing stage recognition result in the winter wheat growing area to be identified.
The above examples are only used to illustrate the technical scheme of the present invention, rather than its limitations；Although with reference to the foregoing embodiments
Invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each implementation
Technical solution documented by example is modified or equivalent replacement of some of the technical features；And these are modified or replace
It changes, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (5)
1. a kind of Spike Differentiation in Winter Wheat phase recognition methods characterized by comprising
According to geographic factor, meteorologic parameter and the crop of the winter wheat to be identified ginseng in the winter wheat growing area to be identified being collected into
Number obtains the model parameter of Wheat Simulation Model, obtains localization Wheat Simulation Model；
According to the agricultural meteorological station data in winter wheat growing area preset range to be identified, obtain in During Growing Period of Winter Wheat to be identified
The first temperature record time series the winter to be identified is obtained according to the multi-temporal remote sensing image in winter wheat growing area to be identified
The second temperature record time series in wheat growth stage；
Using data assimilation method, the first temperature record time series and the second temperature record time series are carried out
Data assimilation obtains the assimilation temperature record time series in During Growing Period of Winter Wheat to be identified；
According to the assimilation temperature record time series and the localization Wheat Simulation Model, winter wheat to be identified is obtained
Jointing stage recognition result in growing area；
Wherein, the agricultural meteorological station data according in winter wheat growing area preset range to be identified, it is small to obtain the winter to be identified
The first temperature record time series in wheat breeding time, comprising:
According to the agricultural meteorological station data in winter wheat growing area preset range to be identified, GIS-Geographic Information System space interpolation is utilized
Method carries out temperature record interpolation, obtains the first temperature record time series,
It is described to utilize GIS-Geographic Information System Spatial Interpolation Method, temperature record interpolation is carried out, the first temperature record time is obtained
Sequence, comprising:
Using following formula (1), temperature record interpolation is carried out:
In formula: λiFor weight；N is agricultural weather website number；diActual measurement between interpolation point and i-th of agricultural meteorological station away from
From；Z is the temperature estimated value of interpolation point；ZiFor the temperature measured value of i-th (i=1,2,3 ... n) a agricultural meteorological station；
The multi-temporal remote sensing image according in winter wheat growing area to be identified obtains in During Growing Period of Winter Wheat to be identified
Two temperature record time serieses, specifically include:
According to remote sensing image data when more in winter wheat growing area to be identified, obtain default in winter wheat growing area to be identified
Sample Remote Sensing temperature-time sequence data in During Growing Period of Winter Wheat to be identified at sample point within a preset time, then lead to
It crosses ground actual measurement and obtains winter wheat actual measurement temperature time series data to be identified, according to the sample Remote Sensing temperature-time sequence
Column data and the winter wheat to be identified survey temperature time series data, obtain the in the During Growing Period of Winter Wheat to be identified
Two temperature record time serieses；
It is described according to the sample Remote Sensing temperature-time sequence data and the actual measurement temperature time series data, obtain institute
State the second temperature record time series in During Growing Period of Winter Wheat to be identified, comprising:
According to the sample Remote Sensing temperature-time sequence data and the actual measurement temperature time series data, establish to be identified
The Time Series Regression statistical model of winter wheat；
According to the multi-temporal remote sensing image in the winter wheat growing area to be identified, obtain in winter wheat to be identified entire breeding time
Calculating Remote Sensing temperature-time sequence data；
According to the calculating Remote Sensing temperature-time sequence data and the Time Series Regression statistical model, described the is obtained
Two temperature record time serieses；
It is described to utilize data assimilation method, by the first temperature record time series and the second temperature record time series
Data assimilation is carried out, is specifically included:
First temperature record time series and the second temperature record time series progress are stated for described using following formula (2)
Sequence is assimilated, and the assimilation temperature record time series is obtained:
In formula: Yt fFor the predicted value of temperature, XtFor predictor, i.e., the described first temperature record time series, Bt-1For interpolation system
Number vector, Ct-1For Bt-1Error covariance matrix, W be dynamic noise error covariance matrix, RtFor extrapolated value BtError covariance matrix and dynamic
The sum of state noise error variance matrix；σtFor prediction error variance matrix,For predictor XtTransposed matrix, V is observation noise
Error covariance matrix；AtFor gain matrix,For σtInverse matrix；Yt oFor the second temperature record time series；CtFor Bt
Error covariance matrix.
2. the method according to claim 1, wherein described utilize data assimilation method, by first temperature
Data time series and the second temperature record time series carry out data assimilation, obtain the During Growing Period of Winter Wheat to be identified
Interior assimilation temperature record time series, comprising:
Using Kalman filtering algorithm, by the first temperature record time series and the second temperature record time series into
Row sequence is assimilated, and the assimilation temperature record time series is obtained.
3. -2 described in any item methods according to claim 1, which is characterized in that the winter wheat to be identified that the basis is collected into
The crop parameter of geographic factor, meteorologic parameter and winter wheat to be identified in growing area, obtains the mould of Wheat Simulation Model
Shape parameter, comprising:
According to geographic factor, meteorologic parameter and the crop parameter of winter wheat to be identified in the winter wheat growing area to be identified,
Excellent method is asked using loop iteration, obtains the Wheat Simulation Model.
4. a kind of device for the identification of Spike Differentiation in Winter Wheat phase characterized by comprising
At least one processor；And
At least one processor being connect with the processor communication, in which:
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to instruct energy
Enough execute method as described in any one of claims 1 to 3.
5. 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 method as described in any one of claims 1 to 3.
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