CN112199634A - Surface component temperature multi-algorithm integration algorithm based on Bayesian model averaging method - Google Patents
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Abstract
The invention discloses a Bayesian model averaging method-based earth surface component temperature multi-algorithm integration algorithm, which comprises the following steps of: I. performing surface temperature inversion based on a split window algorithm or a temperature emissivity separation algorithm; II. Based on a data set of ground measurement and three-dimensional model DART simulation, and based on a Bayesian model averaging method, algorithm evaluation is carried out on a multi-pixel, multi-angle, multi-temporal and multi-band inversion method; III, respectively utilizing multi-pixel, multi-angle, multi-temporal and multi-band methods to invert the temperature of the earth surface components (namely vegetation and soil); and IV, performing inversion structure fusion on the weight factor of each sub-algorithm obtained through algorithm evaluation to obtain a component temperature inversion result of the integrated algorithm. According to the invention, a multi-algorithm integration framework of component temperature inversion is constructed, the uncertainty of single algorithm/single data source inversion is reduced, and the accuracy and stability of surface component temperature inversion are improved.
Description
Technical Field
The invention relates to an integrated algorithm, in particular to a multi-algorithm integrated algorithm for earth surface component temperature based on a Bayesian model averaging method.
Background
The earth surface temperature is an important input parameter in the earth surface energy balance and water circulation process, and the earth surface temperature information of a large area can be obtained by means of remote sensing. For stationary and polar satellites, the spatial resolution of the thermal infrared pixel can reach 1 kilometer or even 5 kilometers, the mixed pixels are common, and the temperature difference of components in the pixel can reach 10K. Most of the existing surface temperature inversion algorithms aim at the average temperature of the whole pixel and are difficult to reflect the temperature difference of the components in the pixel. In comparison, the physical significance of the temperature of the components in the pixels is more definite, and the accuracies of evapotranspiration estimation, drought monitoring and vegetation growth monitoring can be effectively improved.
The inversion of the component temperature can be regarded as the inverse process of surface thermal infrared radiation transmission modeling, and the component temperature inside the pixel is separated by utilizing the thermal infrared observation information of the sensor through constructing the relationship between the component temperature and the thermal infrared observation. The construction of the relationship between the component temperature and the thermal infrared observation is the basis for the inversion of the component temperature. The existing component temperature inversion algorithms include multi-angle, multi-band, multi-pixel and multi-time phase methods, etc.
Theoretical analysis and comparison of the existing component temperature inversion algorithm are important references for constructing an integrated algorithm. The multi-angle method is generally regarded as the best tool for inverting the component temperature, the temperature of the component can be inverted by utilizing thermal infrared multi-angle observation and the contribution weight of the component, but the satellite scale thermal infrared multi-angle observation data sources are few, and the spatial resolution of the pixel has difference during multi-angle observation, so that the development of the multi-angle method is limited; the multiband method is based on the characteristic that the emissivity of components and black body radiation have difference in different wave bands for inversion, but the difference between the emissivity of components and the black body radiation is small in the thermal infrared wave band, so that the multiband method has less effective information amount for component temperature inversion and is sensitive to noise, and a Bayesian optimization inversion algorithm, a genetic inversion algorithm and the like are usually needed. The multi-pixel method utilizes the difference of effective emissivity of different pixel components in the adjacent area to construct an equation set for component temperature separation, has sufficient data sources, depends on the relationship among pixels, and is not suitable for homogeneous earth surfaces; the multi-phase method is to separate the component temperatures according to the difference in the change of the component temperatures over a period of time. Both the multi-pixel and multi-temporal methods have the problem of underdetermined inversion, so that additional information quantity or constraint conditions are required to be introduced to realize inversion of component temperatures.
In summary, most of the existing researches utilize thermal infrared observation to perform temperature separation on the characteristics of a certain dimension (such as angle, wave band, time and the like), but an inversion method based on the characteristics of a single dimension cannot meet the requirements of product production on a data source, noise immunity and applicability at the same time.
Disclosure of Invention
In order to solve the defects of the technology, the invention integrates the advantages and the defects of the existing component temperature inversion method, makes up for the deficiencies, and provides a multi-algorithm integration algorithm of the earth surface component temperature based on the Bayesian model averaging method.
In order to solve the technical problems, the invention adopts the technical scheme that: the earth surface component temperature multi-algorithm integration algorithm based on the Bayesian model averaging method comprises the following steps:
I. integrating the earth surface component temperature inversion method into a unified framework, namely selecting a component temperature inversion algorithm M which has strong representativeness and complementary relation according to the advantages and the defects of different algorithmsi}；
II. Performing surface temperature inversion based on a split window or temperature emissivity separation algorithm, and performing surface component temperature inversion of a single algorithm respectively to obtain an inversion result T ═ { T ═ T }i}；
III simulation based on DART and ground measured dataAccording to a comparison algorithm MiComputing a weight matrix of an algorithm for performance under different weather conditions of different types of tables
IV, obtaining an integrated fusion result of multiple algorithms by using a weight matrix of the algorithms, and calculating an inversion result of the earth surface component temperature T based on a Bayes model average method according to the following formula:
wherein the content of the first and second substances,
as algorithm MiThe inversion result of (1), i.e. Ti，M1，M2…MnShowing the constituent temperature results of each of n different algorithms, E (T | M)1，M2…Mn) Indicating a fused result where different algorithm results are known.
Further, in step II, the single algorithm is implemented as follows:
assuming that the atmospheric effects can be completely eliminated, the surface-emitted thermal radiation can be expressed as a linear combination of the constituent thermal radiation:
wherein L issIs the emergent radiation of the top of the vegetation canopy; theta and
respectively an observation zenith angle and an azimuth angle; λ is the wavelength; t isiAnd εiTemperature and emissivity of component i, respectively; n is the number of components; f is the visual proportion of the components; epsiloni，mA multiple scattering term that is a component; b (T)iλ) is the planck function;
effective emissivity commonly referred to as composition
Representing its contribution to thermal infrared observation, and t represents the time of day, a number of observation equations can be matrixed as:
formula three of W.T
L＝[Ls(1)，Ls(2)，Ls(3)…Ls(K)]TFormula four
W＝[εe(1)，εe(2)，εe(3)…εe(K)]TFormula five
εe(i)＝[εe，1(i)，εe，2(i)，εe，3(i)…εe，N(i)]Formula six
T＝[B(1)，B(2)，B(3)…B(N)]TFormula seven
Wherein L is a thermal infrared observation vector; w is a component effective emissivity matrix; t is a component blackbody radiation vector; k is the number of observations; n is the number of components; b (i) a transmission term representing the i component; epsilone，1(i) Representing an observed i component effective emission term identified as 1; epsilone(1) Representing the effective transmit term vector for each component in the observation identified as 1;
obtaining the component temperature through Planck function inverse operation by black body radiation of known components; because the observation may correspond to different angles, different wave bands, different pixels or different moments, the above formula selects numbers to represent different observations; due to the fact that the earth surface heat radiation has differences in different dimensions, different component temperature inversion methods are developed, and the inversion results are uniformly expressed as follows:
T＝W-1l equation eight.
Further, in the step III, the algorithm weight calculation method is based on the bayesian model average theory:
hypothesis model MiPredicting the probability density function p (Tm) of the inversion result TiD) are in accordance with
Is a mean value, ω2Is normally distributed
Given sample data D, the posterior distribution of the multiple model inversion results T is represented as:
wherein, p (T | M)iD) is algorithm MiPredicting a probability density function of T; h is the conditional probability density of the normal distribution, uiRepresentation algorithm MiThe weight of (c); i represents a component; n represents the number of components; t iss，tRepresenting a component temperature result T of a space pixel s at a moment T; u. ofiAnd thetaiCan be obtained by log-likelihood estimation:
when the maximum likelihood estimate I takes the maximum value, the corresponding uiThe log-maximum likelihood estimation is implemented by an expectation-maximization algorithm for the weights of the algorithm in the Bayesian model averaging.
The invention utilizes multi-component temperature inversion algorithms, integrates through a Bayes model average method, makes the gains and the weaknesses between the algorithms, enables the inversion result to have stability and robustness, has wider applicability than a single sub-algorithm, can realize the inversion of more effective data by the gain effect of the invention compared with the integrated algorithm and an independent algorithm, and has higher precision of the inversion result.
Drawings
FIG. 1 is a block diagram of a multi-algorithm integration algorithm for surface composition temperature according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The earth surface component temperature multi-algorithm integration algorithm based on the Bayesian model averaging method as shown in FIG. 1 comprises the following steps:
I. integrating the earth surface component temperature inversion method into a unified framework, namely selecting a component temperature inversion algorithm M which has strong representativeness and complementary relation according to the advantages and the defects of different algorithmsi}；
The remote sensing observation data comprises visible light observation, thermal infrared observation, cloud mask, surface water vapor products and the like, and the data mainly used by the method is from the visible light observation, the thermal infrared observation, the cloud mask and the surface water vapor products.
II. Performing surface temperature inversion based on a split window or temperature emissivity separation algorithm, and performing surface component temperature inversion of a single algorithm respectively to obtain an inversion result T ═ { T ═ T }i}; namely, the temperature of earth surface components (namely vegetation and soil) is inverted by respectively utilizing a multi-pixel, multi-angle, multi-temporal and multi-band method;
the implementation of the single algorithm is as follows:
assuming that the atmospheric effects can be completely eliminated, the surface-emitted thermal radiation can be expressed as a linear combination of the constituent thermal radiation:
wherein L issIs the emergent radiation of the top of the vegetation canopy; theta and
respectively an observation zenith angle and an azimuth angle; λ is the wavelength; t isiAnd εiTemperature and emissivity of component i, respectively; n is the number of components; f is the visual proportion of the components; epsiloni，mA multiple scattering term that is a component; b (T)iλ) is the planck function;
effective emissivity commonly referred to as composition
Representing its contribution to thermal infrared observation, and t represents the time of day, a number of observation equations can be matrixed as:
formula three of W.T
L＝[Ls(1)，Ls(2)，Ls(3)…Ls(K)]TFormula four
W＝[εe(1)，εe(2)，εe(3)…εe(K)]TFormula five
εe(i)＝[εe，1(i)，εe，2(i)，εe，3(i)…εe，N(i)]Formula six
T＝[B(1)，B(2)，B(3)…B(N)]TFormula seven
Wherein L is a thermal infrared observation vector; w is a component effective emissivity matrix; t is a component blackbody radiation vector; k is the number of observations; n is the number of components; b (i) a transmission term representing the i component; epsilone，1(i) Representing an observed i component effective emission term identified as 1; epsilone(1) Representing the effective transmit term vector for each component in the observation identified as 1;
obtaining the component temperature through Planck function inverse operation by black body radiation of known components; because the observation may correspond to different angles, different wave bands, different pixels or different moments, the above formula selects numbers to represent different observations; due to the fact that the earth surface heat radiation has differences in different dimensions, different component temperature inversion methods are developed, and the inversion results are uniformly expressed as follows:
T＝W-1l equation eight.
III, simulation based on DART and ground measured data, and comparison algorithm MiComputing a weight matrix of an algorithm for performance under different weather conditions of different types of tables
Based on a data set of ground measurement and three-dimensional model DART simulation, and based on a Bayesian model averaging method, algorithm evaluation is carried out on a multi-pixel, multi-angle, multi-temporal and multi-band inversion method;
the calculation method of the algorithm weight is based on the Bayesian model average theory: the Bayes model average is based on Bayes theory, and the weighted average is carried out by taking the posterior probability of a single model as the weight of the single model, so as to obtain the optimal solution when a plurality of models are integrated.
Hypothesis model MiPredicting the probability density function p (Tm) of the inversion result TiD) are in accordance with
Is a mean value, ω2Is normally distributed
Given sample data D, the posterior distribution of the multiple model inversion results T is represented as:
wherein, p (T | M)iD) is algorithm MiPredicting a probability density function of T; h is the conditional probability density of the normal distribution, uiRepresentation algorithm MiThe weight of (c); i represents a component; n represents the number of components; t iss，tRepresenting a component temperature result T of a space pixel s at a moment T; u. ofiAnd thetaiCan be obtained by log-likelihood estimation:
when the maximum likelihood estimate I takes the maximum value, the corresponding uiThe log-maximum likelihood estimation is implemented by an expectation-maximization algorithm for the weights of the algorithm in the Bayesian model averaging.
IV, obtaining an integrated fusion result of multiple algorithms by using a weight matrix of the algorithms, and calculating an inversion result of the earth surface component temperature T based on a Bayes model average method according to the following formula:
wherein the content of the first and second substances,
as algorithm MiThe inversion result of (1), i.e. Ti，M1，M2…MnShowing the constituent temperature results of each of n different algorithms, E (T | M)1，M2…Mn) Indicating a fused result where different algorithm results are known.
And performing inversion structure fusion through the weight factor of each sub-algorithm obtained by algorithm evaluation to obtain a component temperature inversion result of the integrated algorithm. According to the invention, a multi-algorithm integration framework of component temperature inversion is constructed, the uncertainty of single algorithm/single data source inversion is reduced, and the accuracy and stability of surface component temperature inversion are improved.
The invention fully considers the advantages and the disadvantages of different component temperature inversion algorithms, utilizes the Bayes model averaging method, integrates the advantages of the selected sub-algorithms, gives higher weight to the algorithm with high precision, and carries out weighted averaging on the selected sub-algorithms through posterior probability, so that the inverted component temperature results have stability and robustness.
Research shows that the Bayesian model average-based integrated algorithm is more accurate than the inversion or prediction result of a single algorithm, and the method is developed mature and widely applied to the research fields of meteorology, hydrology and the like. In addition, bayesian model averaging methods can quantify the uncertainty of the inputs, model structure, and parameters. Therefore, although the existing component temperature inversion research is sufficient and has been remarkably developed, due to the problems of data source limitation, noise immunity, algorithm applicability and the like, the algorithms are good and bad, and a single algorithm is difficult to meet the production requirement at the same time. And the inversion strategy of multi-algorithm integration can utilize multi-source remote sensing data, and the algorithms make up for each other, so that the inversion result has higher stability, accuracy and robustness.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.
Claims (3)
1. The earth surface component temperature multi-algorithm integration algorithm based on the Bayesian model averaging method is characterized in that: the algorithm comprises the following steps:
I. integrating the earth surface component temperature inversion method into a unified framework, namely selecting a component temperature inversion algorithm M which has strong representativeness and complementary relation according to the advantages and the defects of different algorithmsi}；
II. Performing surface temperature inversion based on a split window or temperature emissivity separation algorithm, and performing surface component temperature inversion of a single algorithm respectively to obtain an inversion result T ═ { T ═ T }i}；
III, simulation based on DART and ground measured data, and comparison algorithm MiComputing a weight matrix of an algorithm for performance under different weather conditions of different types of tables
IV, obtaining an integrated fusion result of multiple algorithms by using a weight matrix of the algorithms, and calculating an inversion result of the earth surface component temperature T based on a Bayes model average method according to the following formula:
wherein the content of the first and second substances,
as algorithm MiThe inversion result of (1), i.e. Ti，M1，M2…MnShowing the constituent temperature results of each of n different algorithms, E (T | M)1，M2…Mn) Indicating a fused result where different algorithm results are known.
2. The Bayesian model averaging-based surface component temperature multi-algorithm integration algorithm as recited in claim 1, wherein: in step II, the single algorithm is implemented as follows:
assuming that the atmospheric effects can be completely eliminated, the surface-emitted thermal radiation can be expressed as a linear combination of the constituent thermal radiation:
wherein L issIs the emergent radiation of the top of the vegetation canopy; theta and
respectively an observation zenith angle and an azimuth angle; λ is the wavelength; t isiAnd εiTemperature and emissivity of component i, respectively; n is the number of components; f is the visual proportion of the components; epsiloni，mA multiple scattering term that is a component; b (T)iλ) is the planck function;
effective emissivity commonly referred to as composition
Representing its contribution to thermal infrared observation, and t represents time, a number of observation equations may be matrixed as:
formula three of W.T
L＝[Ls(1)，Ls(2)，Ls(3)…Ls(K)]TFormula four
W＝[εe(1)，εe(2)，εe(3)…εe(K)]TFormula five
εe(i)＝[εe，1(i)，εe，2(i)，εe，3(i)…εe，N(i)]Formula six
T＝[B(1)，B(2)，B(3)…B(N)]TFormula seven
Wherein L is a thermal infrared observation vector; w is a component effective emissivity matrix; t is a component blackbody radiation vector; k is the number of observations; n is the number of components; b (i) a transmission term representing the i component; epsilone，1(i) Representing an observed i component effective emission term identified as 1; epsilone(1) Representing the effective transmit term vector for each component in the observation identified as 1;
obtaining the component temperature through Planck function inverse operation by black body radiation of known components; because the observation may correspond to different angles, different wave bands, different pixels or different moments, the above formula selects numbers to represent different observations; due to the fact that the earth surface heat radiation has differences in different dimensions, different component temperature inversion methods are developed, and the inversion results are uniformly expressed as follows:
T＝W-1l equation eight.
3. The Bayesian model averaging-based surface component temperature multi-algorithm integration algorithm as recited in claim 2, wherein: in the step III, the calculation method of the algorithm weight is based on the Bayesian model average theory:
hypothesis model MiPredicting the probability density function p (Tm) of the inversion result TiD) are in accordance with
Is a mean value, ω2Is normally distributed
Given sample data D, the posterior distribution of the multiple model inversion results T is represented as:
wherein, p (T | M)iD) is algorithm MiPredicting a probability density function of T; h is the conditional probability density of the normal distribution, uiRepresentation algorithm MiThe weight of (c); i represents a component; n represents the number of components; t iss，tRepresenting a component temperature result T of a space pixel s at a moment T; u. ofiAnd thetaiCan be obtained by log-likelihood estimation:
when the maximum likelihood estimate I takes the maximum value, the corresponding uiThe log-maximum likelihood estimation is implemented by an expectation-maximization algorithm for the weights of the algorithm in the Bayesian model averaging.
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| CN112945390A (en) * | 2021-01-30 | 2021-06-11 | 同济大学 | Landsat image earth surface temperature inversion optimization method based on region consistency analysis |
| CN113822354A (en) * | 2021-09-17 | 2021-12-21 | 合肥工业大学 | Micro-nano probe dynamic characteristic compensation method based on Bayesian inverse calculus modeling |
| CN113822354B (en) * | 2021-09-17 | 2022-12-06 | 合肥工业大学 | Micro-nano probe dynamic characteristic compensation method based on Bayesian inverse calculus modeling |
| CN115905447A (en) * | 2022-12-23 | 2023-04-04 | 中国农业科学院农业资源与农业区划研究所 | Ground surface temperature product fusion method based on generalized triangular cap uncertainty model |
| CN115905447B (en) * | 2022-12-23 | 2023-06-30 | 中国农业科学院农业资源与农业区划研究所 | Surface temperature product fusion method based on generalized triangular cap uncertainty model |
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