CN109086524A - A kind of steady Remote Sensing temperature year variation analogy method - Google Patents
A kind of steady Remote Sensing temperature year variation analogy method Download PDFInfo
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Abstract
The present invention relates to one kind to take algorithm complexity and generalization ability Remote Sensing temperature year variation analogy method into account, uses following formulaCarry out the Remote Sensing temperature year change modeling of target area.Present invention firstly provides surface temperature years to change Unified Model normal form, and takes into account model complexity and generalization ability on this basis, realizes the precision and generalization ability Synchronous lifting of surface temperature year variation model.The method of the present invention strong applicability can change, can satisfy the needs of actual production in accurate simulation remote sensing ground temperature year.
Description
Technical field
The present invention relates to a kind of Remote Sensing temperature years for taking algorithm complexity and generalization ability into account to change analogy method, belongs to
In the research field of Remote Sensing temperature year change modeling.
Background technique
Surface temperature is the key parameters in the interaction of ground-gas and energy exchange processes, in weather, natural terrain observation and city
Vital effect is played in the research fields such as city's thermal environment monitoring.Thermal infrared remote sensing is that quick-speed large-scale obtains earth's surface temperature
The important means of degree.However, influenced due to the spaceborne discontinuous sample mode of heat sensor and by atmospheric factors such as clouds,
Sampled satellite data are caused vacancy often occur.This null value seriously limits the analysis to Remote Sensing temperature-time sequence.Cause
How this, using the surface temperature data of the time discrete of moonscope construct surface temperature continuous time variation tendency, both
It is the urgent need of the hot issue and Applied Research on Thermal Infrared Remote Sensing of thermal infrared remote sensing theoretical research.
Change (Annual Temperature Cycle, ATC) model temperature year and utilizes several discrete ground due to having
Table temperature observation value can simulate the characteristics of surface temperature year variation tendency, space-time it is seamless surface temperature production,
It is used widely in numerous applications such as the optimization of surface temperature space-time NO emissions reduction method and the monitoring of surface urban heat island space-time.For
The building of ATC model, academia have carried out a series of explorations, mainly include statistical time series analysis method, half physical model
The statistics and physics synthetic method of method and combination/do not combine auxiliary data.It is classical from the point of view of statistical time series analysis method
Harmonic function (Harmonic ANalysis of Time Series, HANTS) can rebuild surface temperature caused by cloud covers
Data vacancy, the preferable dynamic change of temporal series for simulating surface temperature, but the model complexity is higher, and generalization ability is weaker.Half
Physical Modeling can embody the change mechanism of earth's surface thermal property from Land surface energy budget equation.Have than more typical
Standard sine function and double SIN functions.This kind of sinusoidal model is compared with statistical model, and usual free parameter is less, only with a small amount of
Data point can construct model, and then predict the surface temperature changing condition in an annual cycles, and construct model
Several parameters have important value in terms of quantization.But the precision of this sinusoidal model is often limited to letter
Single functional form.Subsequent synthetic method also focuses on the collaboration statistical method such as Gaussian process Return Law, or is aided with earth's surface actual measurement
Auxiliary data improves simulation precision to the prediction of short-term earth's surface temperature fluctuation trend by improving model, and there is no in mould
Further exploration is done on type generalization ability.
The studies above has achieved fruitful progress in terms of ATC model construction, and confirms either from individually just
String function or on the basis of half physical model method couples auxiliary data to the expansion of harmonic function, and model accuracy all obtains
It improves.However there are still algorithm complexities and generalization ability to be difficult to the problem of taking into account for existing method.Therefore how to guarantee
Under the premise of precision, model generalization ability is improved to greatest extent, is needed to study and new be can be realized the same of precision and generalization ability
Walk the ATC model promoted.
Summary of the invention
It is an object of the invention to: overcome the defect of the above-mentioned prior art, proposes that one kind takes algorithm complexity and extensive into account
The Remote Sensing temperature year of ability changes analogy method.
Steady Remote Sensing temperature year proposed by the present invention changes analogy method, it is characterised in that: using formula (a) into
The Remote Sensing temperature year change modeling of row target area,
In formula, Ts(t) match value at the t days, T are indicated0For annual mean surface temperature；a1And b1Indicate that surface temperature becomes
The low-frequency component of change, a2And b2Indicate the radio-frequency component of surface temperature variation；The π of ω=2 d-1, d is 1 year number of days；γ1、
γ2、γ3、γ4For confactor, the respectively normalized differential vegetation index of target area, soil moisture, albedo and relatively wet
Degree, k1To k4It is the corresponding free parameter of each confactor respectively；ΔTairIndicate the temperature fluctuation tendency as caused by weather condition,
Calculation formula is as follows:
In formula, Tair(t) indicate that the t days target localized grounds survey per day air themperature；
Using surface temperature observation as Ts(t), surface temperature sees the time as t, parameter T0、a1、a2、b1、 b2、k1、k2、
k3And k4It is fitted and is determined by least square method, the normalized differential vegetation index γ1, soil moisture γ2And albedo γ3To defend
The observation or inversion result that star obtains, relative humidity γ4It is the observation of website.
Present invention firstly provides surface temperature years to change Unified Model normal form, and takes into account model complexity on this basis
And generalization ability, realize the precision and generalization ability Synchronous lifting of surface temperature year variation model.The method of the present invention strong applicability,
It can change in accurate simulation remote sensing ground temperature year, can satisfy the needs of actual production.
Detailed description of the invention
The present invention will be further described below with reference to the drawings.
Fig. 1 is the flow chart for obtaining Remote Sensing temperature year variation simulation model of the present invention.
Fig. 2 is to change simulation drawing in surface temperature year.
Specific embodiment
As shown in Figure 1, as follows to obtain the process of Remote Sensing temperature year variation simulation model of the present invention:
The first step changes from traditional semi physical ground temperature year variation model to statistical model, that is, expands single SIN function
To harmonic function.This step aims to solve the problem that single SIN function is difficult to portray the problem of surface temperature multi-scale variations.
The ATC model being made of single SIN function that Bechtel (2011) is proposed earliest can describe the dynamic of surface temperature in year
Variation tendency, formula are as follows:
In formula, Ts(t) match value of the model at the t days is indicated；T0, A and θ respectively indicate annual mean surface temperature, ATC vibration
Width and phase shift relative to the first point of Aries；D is 1 year total number of days.This classics ATC model is referred to as original in the present invention
ATC model (Original ATC, ATCO).But ATCO is often difficult to portray the multi-scale variations of surface temperature completely,
In view of harmonic function can make up for it this deficiency.Therefore formula (1) is redefined are as follows:
In formula, T0For annual mean surface temperature；a1And b1Respectively represent the low-frequency component of surface temperature variation, anAnd bn(n>
1) radio-frequency component of surface temperature variation is indicated；The π of ω=2 d-1, the number of days of d expression 1 year.
Second step introduces the auxiliary datas such as meteorological and surface data, constructs linear function expression between auxiliary data.This
Step, which is intended to be able to respond ground temperature by collaboration, changes the related meteorological and earth's surface factor, portrays the short-time fluctuations of ground temperature.Theoretically
N=∞ is set, and formula (2) can portray the variation of the scale of surface temperature different time, but if the earth's surface temperature of simulation in short-term
Degree floating (usually caused by weather or ground mulching change) needs a series of harmonic wave, easily causes model unstable.
Zou etc. (2018) is on the basis of ATCO by combining air themperature (surface air temperature, SAT) and normalizing
Change vegetation index (Normalized Difference Vegetation Index, NDVI), effectively portrays surface temperature
Short-time fluctuations.Therefore, referring to the thought of (2018) such as Zou, formula (2) is redefined are as follows:
In formula, T0、a1、a2、b1、b2It is identical with the meaning of parameter is corresponded in N and formula (2)；ψ indicates simultaneous Δ Tair(t) and
The function of γ (t), Δ TairIndicate the temperature fluctuation tendency as caused by weather condition, calculation formula is such as shown in (4)；Due to earth's surface
The degree of correlation of temperature and air themperature in day-Time of Day also depends on vegetative coverage situation, and therefore, the present invention is in Δ Tair
(t) a multiplier function k γ (t) is added before variable, γ (t) is that the corresponding normalization vegetation of the t days a certain certain picture elements refers to
Number, k is free parameter.
In formula, Tair(t) indicate that per day air themperature, other parameters meaning such as formula (3) institute are surveyed in the t days ground
Show.
Third step, coupled harmonics function and linear function provide the unified normal form of Remote Sensing temperature year variation model.
Zou etc. (2018) achieves certain progress in terms of quantitative description surface temperature variation as caused by weather condition, but needs
It is to be noted that the difference of LST-SAT is not merely determined by NDVI, therefore more directly or indirectly influence LST-SAT relationships
Meteorology and earth's surface variable need be considered.Therefore, by the above process, the invention proposes one surface temperature year changing pattern
Unified normal form/frame (ATCF) of type.Model normal form is as follows:
In formula, in formula, T0、a1、a2、b1、b2It is identical as the meaning of parameter is corresponded in formula (3), kmIt is free parameter, γm
For meteorological and earth's surface variable.
4th step, under the premise of taking model complexity and generalization ability into account, carry out parameter optimization.Mould is taken in building into account
The Remote Sensing temperature year of type complexity and generalization ability changes Robust model.Main includes two aspects:
(1) it is simplified harmonic function, determines the critical condition of " poor fitting " and " over-fitting ", i.e., to formula (7)In
N setting circulation (starting N=1), calculate the error E recycled each timeN, until | EN+1–EN| < 0.01, circulation terminates, at this time N
For optimal value.
(2) determine optimal auxiliary data, the auxiliary data of selection can either significant response LST-SAT relationship, while in reality
It is easy to obtain in the operation of border, i.e. calculation formula (5) ψ1γ in ()mWith Δ TairCoefficient Rm, setting auxiliary data screening
Threshold value Rm=0.2, if Rm> 0.2, then illustrate γmThe ability for responding LST-SAT relationship is stronger, which is selected to make
With, while also considering whether the acquisition of the data in practical applications facilitates.
Comprehensive (1) and (2),And ψ1() is redefined respectivelyAnd ψ2(), formula is respectively such as (6)
(7) shown in:
N is screened by round-robin algorithm in formula, is set as definite value 2, and other parameters function and formula (3) are consistent.
By selection, NDVI, soil moisture (soil moisture, SM), albedo (albedo) and phase are finally determined
To four kinds of confactors of humidity (relative humidity, RHU), the γ in formula (7) is respectively corresponded1, γ2, γ3And γ4,
k1, k2, k3And k4It is free parameter.
According to above-mentioned two aspect, have proposed surface temperature year variation model unified normal form ATCF on the basis of,
The invention proposes the Remote Sensing temperature annual variations that a kind of mixed type (hybrid) can take algorithm complexity and generalization ability into account
Change analogy method (ATCH), formula is such as shown in (8):
A kind of steady Remote Sensing temperature year of the embodiment of the present invention changes analogy method, using formula (a), i.e., above-mentioned public affairs
Formula (8) carries out the Remote Sensing temperature year change modeling of target area,
In formula, Ts(t) match value at the t days, T are indicated0For annual mean surface temperature；a1And b1Indicate that surface temperature becomes
The low-frequency component of change, a2And b2Indicate the radio-frequency component of surface temperature variation；The π of ω=2 d-1, the number of days of d expression 1 year；γ1、
γ2、γ3、γ4For confactor, the respectively normalized differential vegetation index of target area, soil moisture, albedo and relatively wet
Degree, k1To k4It is the corresponding free parameter of each confactor respectively；ΔTairIndicate the temperature fluctuation tendency as caused by weather condition,
Calculation formula is as follows:
In formula, Tair(t) indicate that the t days target localized grounds survey per day air themperature；
Using surface temperature observation as Ts(t), surface temperature sees the time as t, parameter T0、a1、a2、b1、 b2、k1、k2、
k3And k4It is fitted and is determined by least square method, the normalized differential vegetation index γ1, soil moisture γ2, albedo γ3For satellite
The observation or inversion result of acquisition, relative humidity γ4It is the observation of website.It is soft that above-mentioned confactor data pass through ENVI
Part is resampled to surface temperature the same space resolution ratio.
In the present embodiment, parameter T when being fitted is obtained using regression software 1stopt0、a1、a2、b1、b2、k1、 k2、k3And k4
Initial value, the bound of parameter is by empirically determined.
The present embodiment, the initial value and bound of parameter are as shown in the table when fitting,
Initial value and bound (unit: Kelvin) when 1 Estimating The Model Coefficients of table
After the completion of Remote Sensing temperature year change modeling, output surface temperature year variation simulation drawing (as shown in Figure 2).
In addition to the implementation, the present invention can also have other embodiments.It is all to use equivalent substitution or equivalent transformation shape
At technical solution, fall within the scope of protection required by the present invention.
Claims (5)
1. a kind of steady Remote Sensing temperature year changes analogy method, it is characterised in that: carry out target area using formula (a)
Remote Sensing temperature year change modeling,
In formula, Ts(t) match value at the t days, T are indicated0For annual mean surface temperature；a1And b1Indicate the low of surface temperature variation
Frequency ingredient, a2And b2Indicate the radio-frequency component of surface temperature variation；The π of ω=2 d-1, the number of days of d expression 1 year；γ1、γ2、
γ3、γ4For confactor, the respectively normalized differential vegetation index of target area, soil moisture, albedo and relative humidity, k1
To k4It is the corresponding free parameter of each confactor respectively；ΔTairIt indicates the temperature fluctuation tendency as caused by weather condition, calculates
Formula is as follows:
In formula, Tair(t) indicate that the t days target localized grounds survey per day air themperature；
Using surface temperature observation as Ts(t), surface temperature sees the time as t, parameter T0、a1、a2、b1、b2、k1、k2、k3And k4
It is fitted and is determined by least square method, the normalized differential vegetation index γ1, soil moisture γ2And albedo γ3For satellite acquisition
Observation or inversion result, relative humidity γ4It is the observation of website.
2. steady Remote Sensing temperature year changes analogy method according to claim 1, it is characterised in that: the auxiliary because
Subdata, which passes through, to be resampled to and surface temperature the same space resolution ratio.
3. steady Remote Sensing temperature year changes analogy method according to claim 1, it is characterised in that: soft using returning
Parameter T when part 1stopt obtains fitting0、a1、a2、b1、b2、k1、k2、k3And k4Initial value.
4. Remote Sensing temperature year changes analogy method according to claim 1, it is characterised in that: parameter is initial when fitting
Value and bound are as shown in the table,
Initial value and bound (unit: Kelvin) when 1 Estimating The Model Coefficients of table
5. steady Remote Sensing temperature year changes analogy method according to claim 1, it is characterised in that: Remote Sensing temperature
It spends after the completion of year change modeling, output surface temperature year changes simulation drawing.
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| CN110705010A (en) * | 2019-08-21 | 2020-01-17 | 南京大学 | Remote sensing-based next-day night surface heat island simulation method |
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| CN101866385A (en) * | 2010-06-24 | 2010-10-20 | 浙江大学 | Target parcel ground surface temperature simulation and optimization method |
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| CN101866385A (en) * | 2010-06-24 | 2010-10-20 | 浙江大学 | Target parcel ground surface temperature simulation and optimization method |
| CN107421644A (en) * | 2017-08-28 | 2017-12-01 | 南京大学 | The air remote sensing evaluation method of the complete surface temperature in city |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110705010A (en) * | 2019-08-21 | 2020-01-17 | 南京大学 | Remote sensing-based next-day night surface heat island simulation method |
| CN110705010B (en) * | 2019-08-21 | 2023-04-25 | 南京大学 | Method for simulating ground surface heat island at night on the basis of remote sensing |
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