CN104077475B - A kind of global land table based on many algorithm integrations evapotranspires estimating system and method - Google Patents
A kind of global land table based on many algorithm integrations evapotranspires estimating system and method Download PDFInfo
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Abstract
The invention belongs to meteorological model field of measuring technique, it is related to a kind of global land table based on many algorithm integrations to evapotranspire estimating system and method.The method analyzes meteorological data and drives MODIS algoritic module, the PM remote sensing corrected evapotranspire algoritic module, the improved Priestly Taylor of algoritic module, Priestly Taylor that evapotranspire that evapotranspire to evapotranspire algoritic module, the algoritic module that evapotranspires based on empirical method again first with MODIS remotely-sensed datas and MERRA, and respective evapotranspiration is drawn respectively;Then ground is extracted from global flux website observation data by observation data extraction module according to website measured data sample quality to evapotranspire observation;Finally by Bayesian model method, the ground of the extraction evapotranspiration that observation and each algoritic module that evapotranspires calculate that evapotranspires is carried out integrated, form Bayesian model integration module.Compared with prior art, global land table of the invention evapotranspires estimating system and method has stronger stability, and applicable surface is broader, has broad application prospects.
Description
Technical field
The invention belongs to meteorological model field of measuring technique, it particularly relates to arrive a kind of based on the complete of many algorithm integrations
Ball land table evapotranspires estimating system and method.
Background technology
Currently, global water resources increasingly plaque is weary, in order to rationally utilize and distribute water resource, in the urgent need to understanding difference in depth
Evaporation water consumption situation under the conditions of vegetative coverage and Land_use change.Evapotranspiration dams evaporation capacity, transpiration amount, soil including vegetation
Earth evaporation capacity and water surface evaporation, are the main components of regional water balance and energy balance, are not only followed in water circulation and energy
There is extremely important effect during ring, and be the important tie of ecological process and hydrologic process.Land table evapotranspire be hydrosphere,
Atmospheric thermodynamics and biosphere moisture and the main process parameter of energy exchange, are Terrestrial energy circulation, water circulation and carbon cycle
In key variables.The surface evapotranspiration quantity data of specific region accurately and timely are obtained, in agricultural, the hydrology, forest and ecology etc.
Field can play highly important effect.Therefore, a kind of measurement system and method are set up, realization is accurately and rapidly obtained
Big region land table evapotranspires and estimates significant.
However, traditional assay method is not representative to Remote sensing situation interior on a large scale strong, and lay measuring point cost
It is high, it is difficult to form practical observation grid.Chinese patent (CN 200810179250.0) discloses a kind of area based on remote sensing
Evapotranspire quantity monitoring method in domain.The method is built using the visible ray/IRMSS thermal band and daily routine meteorological data of multidate
The data processing chain of land surface evaporation monitoring on regional scale is found, it is achieved thereby that the maneuverability remote sensing prison of regional evapotranspiration
Survey.Relative to the monitoring means evapotranspired is measured on traditional flux observation station, the multi-source multidimensional that remote sensing technology can provide earth's surface is more
When phase information, with large area, macroscopic view, in real time and dynamic etc. advantage, be land table evapotranspire estimation open new approach.Remote sensing is estimated
The method that calculation land table evapotranspires is a lot.Mainly there are the physical model based on Land surface energy budget, empirical statistics algorithm, Penman-
Monteith algorithms, remote sensing deltic method, data assimilation method etc..These model methods are by Mo Ning-Ao Bu Hough phases
Like theoretical key parameter (surface temperature, vegetation index etc.) and the determining that land table evapotranspires for setting up earth's surface meteorological element, remote-sensing inversion
Amount contact is evapotranspired obtaining land table, but the evapotranspiration of the estimation between algorithms of different is widely different, in addition gap reach 2 times with
On, overall accuracy is not high.And, there is very big uncertainty in the single algorithm that evapotranspires, minority is put down using simple many algorithms
Mean Method, simple average value method it is uncertain larger, had a strong impact on the computational accuracy that land table evapotranspires.
The content of the invention
In order to solve the above-mentioned technical problem, evapotranspired estimation the invention provides a kind of global land table based on many algorithm integrations
System and method.
A kind of global land table based on many algorithm integrations of the present invention estimating system that evapotranspires includes:MODIS evapotranspires calculation
Method module, the PM remote sensing corrected is evapotranspired algoritic module, and Priestly-Taylor evapotranspires algoritic module, improved Priestly-
Taylor evapotranspires algoritic module, the algoritic module that evapotranspires based on empirical method, observes data extraction module, and Bayesian model is integrated
Module;Analyzing meteorological data again first with MODIS remotely-sensed datas and MERRA drives MODIS to evapotranspire algoritic module, the PM that corrects
Remote sensing evapotranspire algoritic module, the improved Priestly-Taylor of algoritic module, Priestly-Taylor that evapotranspire evapotranspires algorithm mould
Block, the algoritic module that evapotranspires based on empirical method, draw respective evapotranspiration respectively;Then according to website measured data sample matter
Amount is extracted ground and is evapotranspired observation by observation data extraction module from global flux website observation data;Finally by Bayes's mould
Type method, the evapotranspiration that observation is evapotranspired on the ground of extraction and each algoritic module that evapotranspires is calculated is carried out integrated, forms pattra leaves
This model integrated module.
A kind of global land table based on many algorithm integrations of the present invention evapotranspires evaluation method, the specific step of methods described
Suddenly include:
1) MODIS is built to evapotranspire algoritic module:Canopy is divided into wet canopy and dry canopy, soil is divided into water saturation soil
Earth and moisture unsaturation soil, evapotranspire algorithm according to MODIS, calculate and the impedance of design earth's surface, aerodynamic resistance and side
Interlayer parameter;
2) the PM remote sensing corrected is built to evapotranspire algoritic module:Evapotranspired algorithm according to traditional Penman-Monteith, to temperature
The defect for spending restriction factor linear change is corrected;
3) Priestly-Taylor is built to evapotranspire algoritic module:According to existing Priestly-Taylor models, utilize
Ecosystem stress factors extend Priestly-Taylor coefficients, obtain Priestly-Taylor and evapotranspire algorithm;
4) improved Priestly-Taylor is built to evapotranspire algoritic module:Soil moisture is parameterized using apparent thermal inertia
Factor of evaporation amendment soil moisture restriction factor, obtains improved Priestly-Taylor and evapotranspires algorithm;
5) algoritic module that evapotranspires based on empirical method is built:Rule of thumb Evapotranspiration by Using algorithm, design is evapotranspired input, defeated
Go out and computing module;
6) carried from global flux website observation data by observation data extraction module according to website measured data sample quality
Ground is taken to evapotranspire observation;
7) analyze meteorological data actuation step 1 again using MODIS remotely-sensed datas and MERRA) MODIS evapotranspire algorithm mould
Block, step 2) the PM remote sensing corrected evapotranspires algoritic module, step 3) Priestly-Taylor evapotranspires algoritic module, step 4) change
The Priestly-Taylor for entering evapotranspires algoritic module, step 5) algoritic module that evapotranspires based on empirical method, draw respectively each
Evapotranspiration;
8) by Bayesian model method, by step 6) ground extracted evapotranspires observation and step 7) each calculation of evapotranspiring
The evapotranspiration that method module is calculated carries out integrated, formation Bayesian model integration module.
The step 1) in the MODIS algorithms that evapotranspire be:
LE=LEwet_c+LEtrans+LEsoil
In formula, LE evapotranspires for land table, LEwet_cIt is the evaporation that wet canopy evaporation process is produced, LEtransFor Vegetation canopy steams
Rise the transpiration of process generation, LEwet_soilIt is the evaporation that saturated soil moisture evaporation is produced, LEsoil_potFor unsaturated soil is potential
Evaporate the evaporation for producing, LEsoilIt is soil evaporation, Δ is the slope of saturation vapour pressure and temperature curve, and γ is wet and dry bulb constant,
VPD is the saturation vapour pressure and actual vapor pressure difference of air, and RH is relative humidity, raIt is aerodynamic resistance, rsFor earth's surface hinders
It is anti-, FcIt is vegetation coverage, FwetIt is humidity area coverage, ρ is atmospheric density, CpIt is the specific heat capacity of air, RncIt is Vegetation canopy
Shared net radiation, rhrc is canopy corresponding part aerodynamic resistance, and rvc is Ground Heat transfer impedance, PaFor big
Air pressure, RnsNet radiation shared by surface soil, rtotIt is whole aerodynamic resistance, rasIt is partial air dynamics
Impedance, β, λ and ε are coefficient;
The step 2) in temperature limiting factor correction method be:
In formula, mTIt is the temperature limiting factor, TaIt is air themperature, ToptIt is vegetation growth optimal temperature, is set to 25 DEG C;
The step 3) in the Priestly-Taylor algorithms that evapotranspire be:
In formula, fwetIt is relative surface humidity, RH is relative humidity, fwet=RH4, fgIt is green Canopy cover degrees, fTTo plant
By the temperature limiting factor, fMIt is vegetation moisture restriction factor, fSMIt is soil moisture restriction factor, G is soil heat flux.
The step 4) in soil moisture restriction factor be:
In formula, DT is air day and night temperature, DTmaxIt is maximum day and night temperature, takes 40 DEG C;
The step 5) in be based on the algorithm that evapotranspires of empirical method:
LE=Rn(a0+a1NDVI+a2Ta+a3DTaR)
In formula, DTaR is day day and night temperature, and NDVI is normalized differential vegetation index, a0, a1, a2And a3It is empirical coefficient;
The step 8) in Bayesian model integration module computing formula be:
In formula, r is represented needs evapotranspiring for estimation, and the observation evapotranspired at a time is rt, model set K, i.e.,
{f1,f2,…,fk, be estimation r all single model set, p (r | fk) it is single model fkEstimate that the probability of the r that evapotranspires is close
Degree function, p (fk|rt) it is single model fkPosterior probability, single model f can be reactedkWith the degree of observation data fit.
The step 8) in, the probability density function p of the estimated value that Bayesian model integration module is obtained (r | f1,f2,…,
fk) be exactly single model probability density function weighted average, the posterior probability sum of all single models is 1, i.e.,So, the posterior probability of each model is regarded as weight wk;
Assuming that p (r | fk) it is Gaussian Profile,It is the average value of the Gaussian Profile, σ2 KIt is variance, uses parameterThe Gaussian Profile related to g () expressions be:
p(r|fk)=g (r | θk)
The estimate for obtaining is the conditional expectation of r, cost function is optimized and is obtained weight with expectation-maximum algorithm
wk。
The step 1) by vegetation coverage FcTo distinguish the global land table available energy of vegetation and soil distribution, profit
Determine F with absorbed photosynthetically active radiation ratio FPAR products are planted in MODIS remotely-sensed datasc;
The step 2) the PM remote sensing corrected evapotranspires in algorithm, and the maximum of unified moisture restriction factor VPD is set to
2.79KPa;
The step 3) R that evapotranspire in algorithm of Priestly-TaylornsUse surface net radiation RnWith leaf area index LAI
Calculate, specially Rns=RnExp (- kLAI), k are empirical coefficient;
The step 4) improved Priestly-Taylor evapotranspire algorithm soil moisture restriction factor in, DT is to pass through
Day day and night temperature is calculated or is calculated by surface temperature;
The step 5) in empirical coefficient a0, a1, a2, and a3It is to observe data as training number by the use of global flux website
Obtained according to recurrence;
The step 6) observation data extraction module in, website measured data sample quality be by quality identification judge
Obtained from;
The step 7) leaf area index, vegetation index are chosen from MODIS remotely-sensed datas, meteorology is analyzed again from MERRA
Basic meteorologic parameter, including temperature, relative humidity are chosen in data, drives each algoritic module to calculate respective evapotranspiring
Amount.
The global land table includes forest, shrub, farmland, meadow and wetland.
Compared with the conventional method, a kind of global land table integrated based on multi-source data and many algorithms of the present invention steams
Dissipating the beneficial effect of estimating system and method is:By theory analysis, experiment test and practical application, ground is made full use of to survey
Data, remotely-sensed data and many algorithms are combined, and think that Bayesian model is relied on, existing clear and definite physical basis, and with stabilization
The advantages of property is good, simple, flexible, easily operated, the algorithm that MODIS therein can be evapotranspired, the PM remote sensing corrected evapotranspire algorithm,
Priestly-Taylor evapotranspire algorithm, improved Priestly-Taylor evapotranspire algorithm and empirical method the algorithm that evapotranspires it is comprehensive
Intersection into, there is stronger stability than the single algorithm that evapotranspires, applicable surface is broader, has broad application prospects.
Brief description of the drawings
Fig. 1 is that the global land table based on many algorithm integrations that the present invention is used evapotranspires the structural representation of estimating system;
Fig. 2 is that global land table of the present invention based on many algorithm integrations evapotranspires the flow chart of evaluation method;
Fig. 3 is global flux website measured value checking.
Specific embodiment
A kind of global land table based on many algorithm integrations of the present invention is evapotranspired with reference to specific embodiment is estimated
System and method is calculated to be described further, but protection scope of the present invention is not limited to this.
Embodiment 1
Global land table based on many algorithm integrations estimating system that evapotranspires of present invention design includes:MODIS evapotranspires algorithm mould
Block, the PM remote sensing corrected is evapotranspired algoritic module, and Priestly-Taylor evapotranspires algoritic module, improved Priestly-Taylor
Evapotranspire algoritic module, the algoritic module that evapotranspires based on empirical method, observes data extraction module, Bayesian model integration module;
Analyzing meteorological data again first with MODIS remotely-sensed datas and MERRA drives MODIS to evapotranspire algoritic module, the PM remote sensing corrected
Evapotranspire algoritic module, the improved Priestly-Taylor of algoritic module, Priestly-Taylor that evapotranspire evapotranspires algoritic module, base
In the algoritic module that evapotranspires of empirical method, respective evapotranspiration is drawn respectively;Then according to website measured data sample quality by
Observation data extraction module is extracted ground and is evapotranspired observation from global flux website observation data;Finally by Bayesian model side
Method, the evapotranspiration that observation is evapotranspired on the ground of extraction and each algoritic module that evapotranspires is calculated is carried out integrated, forms Bayes's mould
Type integration module.
The evapotranspire specific steps of evaluation method of the corresponding global land table based on many algorithm integrations include:
1) MODIS is built to evapotranspire algoritic module:Canopy is divided into wet canopy and dry canopy, soil is divided into water saturation soil
Earth and moisture unsaturation soil, evapotranspire algorithm according to MODIS, calculate and the impedance of design earth's surface, aerodynamic resistance and side
Interlayer parameter;
2) the PM remote sensing corrected is built to evapotranspire algoritic module:Evapotranspired algorithm according to traditional Penman-Monteith, to temperature
The defect for spending restriction factor linear change is corrected;
3) Priestly-Taylor is built to evapotranspire algoritic module:According to existing Priestly-Taylor models, utilize
Ecosystem stress factors extend Priestly-Taylor coefficients, obtain Priestly-Taylor and evapotranspire algorithm;
4) improved Priestly-Taylor is built to evapotranspire algoritic module:Soil moisture is parameterized using apparent thermal inertia
Factor of evaporation amendment soil moisture restriction factor, obtains improved Priestly-Taylor and evapotranspires algorithm;
5) algoritic module that evapotranspires based on empirical method is built:Rule of thumb Evapotranspiration by Using algorithm, design is evapotranspired input, defeated
Go out and computing module;
6) carried from global flux website observation data by observation data extraction module according to website measured data sample quality
Ground is taken to evapotranspire observation;
7) analyze meteorological data actuation step 1 again using MODIS remotely-sensed datas and MERRA) MODIS evapotranspire algorithm mould
Block, step 2) the PM remote sensing corrected evapotranspires algoritic module, step 3) Priestly-Taylor evapotranspires algoritic module, step 4) change
The Priestly-Taylor for entering evapotranspires algoritic module, step 5) algoritic module that evapotranspires based on empirical method, draw respectively each
Evapotranspiration;
8) by Bayesian model method, by step 6) ground extracted evapotranspires observation and step 7) each calculation of evapotranspiring
The evapotranspiration that method module is calculated carries out integrated, formation Bayesian model integration module.
The step 1) in the MODIS algorithms that evapotranspire be:
LE=LEwet_c+LEtrans+LEsoil
In formula, LE evapotranspires for land table, LEwet_cIt is the evaporation that wet canopy evaporation process is produced, LEtransFor Vegetation canopy steams
Rise the transpiration of process generation, LEwet_soilIt is the evaporation that saturated soil moisture evaporation is produced, LEsoil_potFor unsaturated soil is latent
In the evaporation that evaporation is produced, LEsoilIt is soil evaporation, Δ is the slope of saturation vapour pressure and temperature curve, and γ is that wet and dry bulb is normal
Number, VPD is the saturation vapour pressure and actual vapor pressure difference of air, and RH is relative humidity, raIt is aerodynamic resistance, rsIt is ground
Table impedance, FcIt is vegetation coverage, FwetIt is humidity area coverage, ρ is atmospheric density, CpIt is the specific heat capacity of air, RncIt is vegetation
Net radiation shared by canopy, rhrc is canopy corresponding part aerodynamic resistance, and rvc is Ground Heat transfer impedance, Pa
It is atmospheric pressure, RnsNet radiation shared by surface soil, rtotIt is whole aerodynamic resistance, rasFor partial air is dynamic
Mechanical impedance, β, λ and ε are coefficient;
The step 2) in temperature limiting factor correction method be:
In formula, mTIt is the temperature limiting factor, TaIt is air themperature, ToptIt is vegetation growth optimal temperature, is set to 25 DEG C;
The step 3) in the Priestly-Taylor algorithms that evapotranspire be:
In formula, fwetIt is relative surface humidity, RH is relative humidity, fwet=RH4, fgIt is green Canopy cover degrees, fTTo plant
By the temperature limiting factor, fMIt is vegetation moisture restriction factor, fSMIt is soil moisture restriction factor, G is soil heat flux.
The step 4) in soil moisture restriction factor be:
In formula, DT is air day and night temperature, DTmaxIt is maximum day and night temperature, takes 40 DEG C;
The step 5) in be based on the algorithm that evapotranspires of empirical method:
LE=Rn(a0+a1NDVI+a2Ta+a3DTaR)
In formula, DTaR is day day and night temperature, and NDVI is normalized differential vegetation index, a0, a1, a2And a3It is empirical coefficient;
The step 8) in Bayesian model integration module computing formula be:
In formula, r is represented needs evapotranspiring for estimation, and the observation evapotranspired at a time is rt, model set K, i.e.,
{f1,f2,…,fk, be estimation r all single model set, p (r | fk) it is single model fkEstimate that the probability of the r that evapotranspires is close
Degree function, p (fk|rt) it is single model fkPosterior probability, single model f can be reactedkWith the degree of observation data fit.
The step 8) in, the probability density function p of the estimated value that Bayesian model integration module is obtained (r | f1,f2,…,
fk) be exactly single model probability density function weighted average, the posterior probability sum of all single models is 1, i.e.,So, the posterior probability of each model is regarded as weight wk;
Assuming that p (r | fk) it is Gaussian Profile,It is the average value of the Gaussian Profile, σ2 KIt is variance, uses parameterThe Gaussian Profile related to g () expressions be:
p(r|fk)=g (r | θk)
The estimate for obtaining is the conditional expectation of r, cost function is optimized and is obtained weight with expectation-maximum algorithm
wk。
The step 1) by vegetation coverage FcTo distinguish the global land table available energy of vegetation and soil distribution, profit
Determine F with absorbed photosynthetically active radiation ratio FPAR products are planted in MODIS remotely-sensed datasc;
The step 2) the PM remote sensing corrected evapotranspires in algorithm, and the maximum of unified moisture restriction factor VPD is set to
2.79KPa;
The step 3) R that evapotranspire in algorithm of Priestly-TaylornsUse surface net radiation RnWith leaf area index LAI
Calculate, specially Rns=RnExp (- kLAI), k are empirical coefficient;
The step 4) improved Priestly-Taylor evapotranspire algorithm soil moisture restriction factor in, DT is to pass through
Day day and night temperature is calculated or is calculated by surface temperature;
The step 5) in empirical coefficient a0, a1, a2, and a3It is to observe data as training number by the use of global flux website
Obtained according to recurrence;
The step 6) observation data extraction module in, website measured data sample quality be by quality identification judge
Obtained from;
The step 7) leaf area index, vegetation index are chosen from MODIS remotely-sensed datas, meteorology is analyzed again from MERRA
Basic meteorologic parameter, including temperature, relative humidity are chosen in data, drives each algoritic module to calculate respective evapotranspiring
Amount.
When a kind of global land table based on many algorithm integrations of the present invention evapotranspires estimating system and method is used cooperatively, its
Operating procedure is specific as follows:
1st, evapotranspired algorithm (Mu et al.2011) according to MODIS, LAI, NDVI and the EVI using MODIS are (enhanced
Vegetation index) product combine analyze MERRA meteorological datas, estimation area evapotranspiration again;
2nd, evapotranspired algorithm (Yuan et al.2010) according to the PM that corrects, produced using LAI, NDVI and EVI of MODIS
Product are combined and analyze MERRA meteorological datas, estimation area evapotranspiration again;
3rd, evapotranspired algorithm (Fisher et al.2008) according to Priestly-Taylor, using LAI, NDVI of MODIS
And EVI products are combined and analyze ERRA meteorological datas, estimation area evapotranspiration again;
4th, evapotranspired algorithm (Yao et al.2013) according to improved Priestly-Taylor, using the LAI of MODIS,
NDVI and EVI products are combined and analyze ERRA meteorological datas, estimation area evapotranspiration again;
5th, evapotranspired algorithm (Wang et al.2008) according to improved empirical remote sensing, using LAI, NDVI of MODIS with
And EVI products are combined and analyze ERRA meteorological datas, estimation area evapotranspiration again;
6th, ground is extracted by Global Link discharge observation station data quality to evapotranspire observation;
7th, Bayesian model method is set up, the estimation result of separate sources is merged, obtain optimal estimation result.
R represents the variable for needing estimation, and the observation of the variable at a time is rt, model set K, i.e. { f1,f2,…,fk,
To estimate the set of all single model of r.According to breakdown law, it is necessary to be estimated the probability density function of the r that evapotranspires of calculation
It is expressed as follows by the probability density function of each model:
Wherein, p (r | fk) it is single model fkEstimate the probability density function of the r that evapotranspires, p (fk|rt) it is single model fk's
Posterior probability, single model f can be reactedkWith the degree of observation data fit.The posterior probability sum of all single models
It is 1.So, the posterior probability of each model is regarded as weight wk, this weight is exactly the parameter for needing estimation.It is expressed as:
Therefore, the estimated value for obtaining probability density function p (r | f1,f2,…,fk) it is exactly single model probability density letter
Several weighted averages, weighted value is the posterior probability of each function.Assuming that p (r | fk) it is Gaussian Profile, fkAIt is the Gaussian Profile
Average value, σ2 KIt is variance, uses parameter θK={ fkA, σ2 KThe Gaussian Profile related to g () expressions, it is as follows:
p(r|fk)=g (r | θk)
The estimated value table for obtaining is shown as the conditional expectation of r, as follows:
It will be apparent that fkAThe estimated value of single model, then the key of problem be then how the posterior probability of appraising model
wk, so as to get r probability density functions be more nearly with observation.According to bayesian theory, it is known that observe data surely, work as likelihood
When function formula (4) value is maximum, the estimated value that bayes method is obtained for true value maximum probability (Duan and Phillips,
2010).Calculate for convenience, the logarithmic function for tackling the likelihood formula is optimized:
Wherein, ∑(s,T)Represent in all spatial point s and the observation sum of all time point T, rs,tRepresent sometime point T
On the s of space, cost function is optimized with expectation-maximum algorithm (expectation-maximization, EM) algorithm
(Raftery et al.2005), single model fkAEstimated value and corresponding observation rs,tEach is calculated as training data
The posterior probability of model.
Both 8th, measurement evapotranspiration and the actual evapotranspiration of selection point position carry out correlation analysis, obtain studying in area
Relation;
9th, evaluation model evapotranspires estimation situation in the world in research area.
Checking data set is FLUXNET200, whole world flux website observation data.Global FLUXNET station datas include
200 websites, distribution Asia, Europe, North America, South America, Africa and Oceania.Across the torrid zone, subtropical zone, temperate zone, cold
With various climate zones, the various vegetation cover types such as desert, oasis, grassland, forest, savart Na, farmland and wetland are covered, it is full
Foot observation data at least one year, disclosure satisfy that the estimation requirement evapotranspired under various vegetation patterns and weather SNNP.With complete
Ball land table is research area, and the flux observation data in the research area are that 2000-2009 years section is collected, corresponding MODIS numbers
According to analyze data is also 2000-2009 years section again with MERRA.In order to the evapotranspiration for verifying model estimates effect, calculate
Obtain evapotranspiration amount carries out correlation analysis with the observation data at Global Link discharge observation station.According to each observation station of global FLUXNET
The evapotranspiration of measurement and the evapotranspiration of the integrated estimation of bayesian algorithm set up scatter diagram, its result such as Fig. 3, mean error is
2.3W/m2, coefficient correlation square (R2) it is 0.64, mean square deviation is 24.2W/m2.Result shows, model estimate value and field observation
Data have correlation higher.Visible by analyzing, Bayesian model Integrated Algorithm has very much to estimation global land table evapotranspiration
Effect.
Claims (2)
1. a kind of global land table based on many algorithm integrations evapotranspires evaluation method, it is characterised in that the specific steps of methods described
Including:
1) MODIS is built to evapotranspire algoritic module:Canopy is divided into wet canopy and dry canopy, soil be divided into water saturation soil and
Moisture unsaturation soil, evapotranspires algorithm according to MODIS, calculates and the impedance of design earth's surface, aerodynamic resistance and boundary layer
Parameter;
2) the PM remote sensing corrected is built to evapotranspire algoritic module:Evapotranspired algorithm according to traditional Penman-Monteith, temperature is limited
The defect of factor linear change processed is corrected;
3) Priestly-Taylor is built to evapotranspire algoritic module:According to existing Priestly-Taylor models, using ecology
System stress factors extend Priestly-Taylor coefficients, obtain Priestly-Taylor and evapotranspire algorithm;
4) improved Priestly-Taylor is built to evapotranspire algoritic module:Soil water evaporation is parameterized using apparent thermal inertia
Factor amendment soil moisture restriction factor, obtains improved Priestly-Taylor and evapotranspires algorithm;
5) algoritic module that evapotranspires based on empirical method is built:Rule of thumb Evapotranspiration by Using algorithm, design evapotranspire input, export and
Computing module;
6) extract ground from global flux website observation data by observation data extraction module according to website measured data sample quality
Face is evapotranspired observation;
7) analyze meteorological data actuation step 1 again using MODIS remotely-sensed datas and MERRA) MODIS evapotranspire algoritic module, step
The rapid PM remote sensing 2) corrected is evapotranspired algoritic module, step 3) Priestly-Taylor evapotranspires algoritic module, step 4) improved
Priestly-Taylor evapotranspires algoritic module, step 5) algoritic module that evapotranspires based on empirical method, respective steaming is drawn respectively
Dissipate amount;
8) by Bayesian model method, by step 6) ground extracted evapotranspires observation and step 7) each algorithm mould that evapotranspires
The evapotranspiration that block is calculated carries out integrated, formation Bayesian model integration module;
The step 1) in the MODIS algorithms that evapotranspire be:
LE=LEwet_c+LEtrans+LEsoil
In formula, LE evapotranspires for land table, LEwet_cIt is the evaporation that wet canopy evaporation process is produced, LEtransIt is Vegetation canopy transpiration process
The transpiration of generation, LEwet_soilIt is the evaporation that saturated soil moisture evaporation is produced, LEsoil_potFor unsaturated soil potential evaporation is produced
Raw evaporation, LEsoilIt is soil evaporation, Δ is the slope of saturation vapour pressure and temperature curve, and γ is wet and dry bulb constant, and VPD is
The saturation vapour pressure of air and actual vapor pressure difference, RH is relative humidity, raIt is aerodynamic resistance, rsIt is earth's surface impedance, Fc
It is vegetation coverage, FwetIt is humidity area coverage, ρ is atmospheric density, CpIt is the specific heat capacity of air, RncShared by Vegetation canopy
Net radiation, rhrc be canopy corresponding part aerodynamic resistance, rvc be Ground Heat transfer impedance, PaIt is atmospheric pressure,
RnsNet radiation shared by surface soil, rtotIt is whole aerodynamic resistance, rasIt is partial air dynamics impedance,
β, λ and ε are coefficient;
The step 2) in temperature limiting factor correction method be:
In formula, mTIt is the temperature limiting factor, TaIt is air themperature, ToptIt is vegetation growth optimal temperature, is set to 25 DEG C;
The step 3) in the Priestly-Taylor algorithms that evapotranspire be:
In formula, fwetIt is relative surface humidity, RH is relative humidity, fwet=RH4, fgIt is green Canopy cover degrees, fTIt is vegetation temperature
Degree restriction factor, fMIt is vegetation moisture restriction factor, fSMIt is soil moisture restriction factor, G is soil heat flux;
The step 4) in soil moisture restriction factor be:
In formula, DT is air day and night temperature, DTmaxIt is maximum day and night temperature, takes 40 DEG C;
The step 5) in be based on the algorithm that evapotranspires of empirical method:
LE=Rn(a0+a1NDVI+a2Ta+a3DTaR)
In formula, DTaR is day day and night temperature, and NDVI is normalized differential vegetation index, a0, a1, a2And a3It is empirical coefficient;
The step 8) in Bayesian model integration module computing formula be:
In formula, r is represented needs evapotranspiring for estimation, and the observation evapotranspired at a time is rt, model set K, i.e. { f1,
f2,…,fk, be estimation r all single model set, p (r | fk) it is single model fkEstimate the probability density letter of the r that evapotranspires
Number, p (fk|rt) it is single model fkPosterior probability, single model f can be reactedkWith the degree of observation data fit;
The step 8) in, the probability density function p of the estimated value that Bayesian model integration module is obtained (r | f1,f2,…,fk)
It is exactly the weighted average of single model probability density function, the posterior probability sum of all single models is 1, i.e.,So, the posterior probability of each model is regarded as weight wk;
Assuming that p (r | fk) it is Gaussian Profile,It is the average value of the Gaussian Profile, σ2 KIt is variance, uses parameterAnd g
() represents that the Gaussian Profile of correlation is:
p(r|fk)=g (r | θk)
The estimate for obtaining is the conditional expectation of r, cost function is optimized and is obtained weight w with expectation-maximum algorithmk;
The step 1) by vegetation coverage FcTo distinguish the global land table available energy of vegetation and soil distribution, utilize
Absorbed photosynthetically active radiation ratio FPAR products are planted in MODIS remotely-sensed datas and determines Fc;
The step 2) the PM remote sensing corrected evapotranspires in algorithm, and the maximum of unified moisture restriction factor VPD is set to
2.79KPa;
The step 3) R that evapotranspire in algorithm of Priestly-TaylornsUse surface net radiation RnCalculated with leaf area index LAI,
Specially Rns=RnExp (- kLAI), k are empirical coefficient;
The step 4) improved Priestly-Taylor evapotranspire algorithm soil moisture restriction factor in, DT is by daytime day
Night differential thermal calculation is obtained or is calculated by surface temperature;
The step 5) in empirical coefficient a0, a1, a2, and a3It is to observe data by the use of global flux website to be returned as training data
Obtain;
The step 6) observation data extraction module in, website measured data sample quality be by quality identification judge and obtain
Arrive;
The step 7) leaf area index, vegetation index are chosen from MODIS remotely-sensed datas, meteorological data is analyzed again from MERRA
It is middle to choose basic meteorologic parameter, including temperature, relative humidity, drive each algoritic module to calculate respective evapotranspiration.
2. a kind of global land table based on many algorithm integrations according to claim 1 evapotranspires evaluation method, it is characterised in that
The global land table includes forest, shrub, farmland, meadow and wetland.
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