CN117574044B - Method and system for inversion of physical temperature distribution in the permanent shadow region of the moon under secondary occlusion - Google Patents
Method and system for inversion of physical temperature distribution in the permanent shadow region of the moon under secondary occlusion Download PDFInfo
- Publication number
- CN117574044B CN117574044B CN202410052598.2A CN202410052598A CN117574044B CN 117574044 B CN117574044 B CN 117574044B CN 202410052598 A CN202410052598 A CN 202410052598A CN 117574044 B CN117574044 B CN 117574044B
- Authority
- CN
- China
- Prior art keywords
- temperature
- microwave
- brightness
- infrared
- moon
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 title claims abstract description 54
- 230000005855 radiation Effects 0.000 claims abstract description 56
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 230000004927 fusion Effects 0.000 claims description 23
- 239000002689 soil Substances 0.000 claims description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 11
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 239000004575 stone Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 4
- 239000002245 particle Substances 0.000 claims description 4
- 238000011160 research Methods 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 239000000428 dust Substances 0.000 claims description 3
- 239000011435 rock Substances 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 6
- 238000005457 optimization Methods 0.000 abstract description 3
- 238000012546 transfer Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000005259 measurement Methods 0.000 description 5
- 238000004088 simulation Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000001427 coherent effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- IXQWNVPHFNLUGD-UHFFFAOYSA-N iron titanium Chemical compound [Ti].[Fe] IXQWNVPHFNLUGD-UHFFFAOYSA-N 0.000 description 2
- 238000002310 reflectometry Methods 0.000 description 2
- 230000003746 surface roughness Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013076 target substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Probability & Statistics with Applications (AREA)
- Operations Research (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Radiation Pyrometers (AREA)
Abstract
本发明提供二次遮蔽月球永久阴影区物理温度分布反演方法及系统，属于遥感技术领域。其技术要点在于：所述方法包括：步骤一：构建二次遮蔽月球永久阴影区热传导模型，获取红外亮温数据；步骤二，构建微波辐射模型，根据红外亮温数据计算得到微波亮温；步骤三，将得到的红外亮温和微波亮温进行融合反演，得到最优的温度分布解。本发明针对二次遮蔽月球永久阴影区物理温度分布探测中的模型与反演难点问题，通过引入支持二次遮蔽效应与体散射的辐射传递模型，结合反演方法的组合与优化，提出在嫦娥微波辐射亮温、LRO红外辐射亮温、激光高度计数字高程等遥感数据下反演更精确的二次遮蔽月球永久阴影区物理温度分布的方法。
The present invention provides a method and system for inverting the physical temperature distribution of a permanent shadow area of the moon with secondary shielding, and belongs to the field of remote sensing technology. The technical key points are: the method comprises: step one: constructing a heat conduction model of the permanent shadow area of the moon with secondary shielding, and obtaining infrared brightness temperature data; step two: constructing a microwave radiation model, and calculating the microwave brightness temperature according to the infrared brightness temperature data; step three: fusing and inverting the obtained infrared brightness temperature and microwave brightness temperature to obtain the optimal temperature distribution solution. The present invention aims at the model and inversion difficulties in the detection of the physical temperature distribution of the permanent shadow area of the moon with secondary shielding, and introduces a radiation transfer model that supports the secondary shielding effect and body scattering, combined with the combination and optimization of the inversion method, and proposes a method for inverting a more accurate physical temperature distribution of the permanent shadow area of the moon with secondary shielding under remote sensing data such as Chang'e microwave radiation brightness temperature, LRO infrared radiation brightness temperature, and laser altimeter digital elevation.
Description
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to a method and a system for inverting physical temperature distribution of a secondary shielding moon permanent shadow area.
Background
The lunar water ice resources mainly exist in lunar soil of a lunar permanent shadow area with extremely low temperature, the existence form is complex, the resource collection work is carried out in a polar region in the future, firstly, the temperature distribution of the surface and the inside of the lunar permanent shadow area is required to be researched to obtain 'water trap' distribution, and then the total amount and the distribution of the water ice resources are evaluated in combination with other remote sensing data. The adoption of the heat flow probe measurement is the most direct mode for obtaining the temperature of the high-precision lunar surface, but the temperature of a lunar region, particularly a permanent shadow region, is extremely low, and direct measurement is difficult to realize. Therefore, remote sensing measurement is the main mode for detecting the physical temperature of a permanently hatched area of the moon at present, an infrared radiometer can only acquire the temperature distribution of the surface, and the remote sensing detection of the deeper temperature requires a microwave radiometer, and the number of channels of the microwave radiometer is limited, particularly the regional temperature is greatly influenced by the terrain, so that strong seasonal change is shown, and more reliable physical temperature of the lunar surface can be acquired by utilizing detection data of a plurality of detection tasks. Therefore, the study of the physical temperature distribution of the secondary shielded moon permanent shadow area necessarily requires the combination of microwave and infrared bright temperature data and the like.
In a secondary shielding moon permanent shadow area, as the temperature of the moon surface is few in-place measuring points, the common inversion method mainly comprises boundary searching, combination inversion, statistical inversion and the like. The existing temperature distribution inversion method is mostly based on a microwave passive radiation model, and is not fully applied to increment priori information brought by other detectors such as infrared radiation measurement. Temperature inversion typically defines a temperature profile function, which reduces inversion difficulty and may limit inversion results.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a inversion method and a system for physical temperature distribution of a secondary shielding moon permanent shadow area, and a method for inverting more accurate physical temperature distribution of the secondary shielding moon permanent shadow area under remote sensing data such as the back-flying to-back microwave radiation bright temperature, the LRO moon survey orbit device infrared radiation bright temperature, the laser height count word elevation and the like is explored by introducing a radiation transfer model supporting the secondary shielding effect and bulk scattering and combining with the combination and optimization of the inversion method.
According to a first aspect of the present invention, the present invention provides a method for inverting the physical temperature distribution of a permanently shaded moon region, comprising:
step one, constructing a heat conduction model of a secondary shielding moon permanent shadow area to acquire infrared brightness temperature data;
step two, constructing a microwave radiation model, and calculating to obtain microwave bright temperature according to the infrared bright temperature data;
and thirdly, performing fusion inversion on the obtained infrared brightness temperature and microwave brightness temperature to obtain an optimal temperature distribution solution.
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, the constructing the heat conduction model of the secondary shielding moon permanent shadow area specifically comprises:
firstly, obtaining lunar surface irradiance according to a VSOP87 planetary motion theory and an ELP2000-82 lunar orbit semi-analytical theory;
then, according to the situation of terrain shielding, establishing a real-time effective solar irradiance model of a moon permanent shadow area and a surrounding area thereof under secondary shielding;
and finally, calculating the adjacent area radiation received by the surface of the permanent shadow area according to the effective solar irradiance model.
Alternatively, the infrared bright temperature can be solved by the following equation:
in the method, in the process of the invention,infrared bright temperature->Is the surface temperature, ++>Is the infrared radiation of the adjacent surface element, epsilon is the surface emissivity,is the boltzmann constant.
Optionally, the constructing the microwave radiation model includes:
according to the observation position and the view field, calculating the radiation brightness temperature of the lunar dust layer, the lunar soil layer and the lunar rock layer based on the antenna caliber; meanwhile, the scattering coefficient is calculated according to the particle size and the compactness of water ice or stone substances to evaluate the bulk scattering influence, and the method is applied to calculation of reflection and transmission fields of each layer to construct a flexible reconstruction microwave radiation model.
Optionally, the calculating the microwave brightness temperature according to the infrared brightness temperature data includes:
in a microwave radiation model, lunar soil is layered, and the total received microwave brightness temperature is the sum of the radiation brightness temperatures of all layers and is expressed as:
wherein,the attenuation factor is used for representing the influence of volume scattering, roughness and the like on the brightness temperature; besides solving by adopting a traditional incoherent method, coarse surface scattering and bulk scattering are introduced, wherein the coarse surface scattering comprises Mie scattering and Rayleigh scattering, and the attenuation factor is solved; /> i Is the total lighting temperature of microwave, < >>Is the radiant brightness temperature of each layer->Upward microwave bright temperature->Is the downward microwave bright temperature.
Optionally, the microwave and infrared fusion inversion is divided into research of high-low frequency band multichannel combination inversion and research of a Markov chain Monte Carlo method based on differential evolution, and the method is used for solving the Bayesian problem of temperature posterior distribution.
Optionally, the microwave and infrared fusion inversion specifically includes the following steps:
firstly, utilizing measured data of a multi-band microwave radiometer and an infrared radiometer, removing values of brightness temperature less than 30K and greater than 400K in the measured data through data anomaly detection, fitting and interpolating, and then performing time normalization to convert time data into a unified format, so as to obtain preliminary prior parameter distribution of microwave brightness temperature and infrared brightness temperature;
and then performing high-frequency multi-channel brightness Wen Fanyan, namely inputting the temperature distribution solved by the heat conduction model into the microwave radiation model as an initial value, and solving a Bayesian problem by utilizing the actually measured high-frequency microwave brightness temperature data to obtain the optimal solution of the temperature posterior distribution in the shallow layer, namely the high fluctuation area.
Optionally, the microwave and infrared fusion inversion further includes:
and then carrying out low-frequency band multichannel brightness Wen Fanyan, taking the obtained shallow temperature distribution as a priori value, and obtaining an optimal temperature distribution solution by using a Markov chain Monte Carlo method based on differential evolution.
Alternatively, the solution of the optimal value adopts a Markov chain Monte Carlo method based on differential evolution, and the solution objective function can be expressed as follows:
where Nobs represents the number of observations, SSR represents the sum of squares of residuals, and L represents the posterior distribution.
According to a second aspect of the present invention, there is provided a physical temperature distribution inversion system for a secondarily-shaded moon permanent shading region, comprising:
the infrared bright temperature acquisition module is used for constructing a heat conduction model of a secondary shielding moon permanent shadow area to acquire infrared bright temperature data;
the microwave bright temperature acquisition module is used for constructing a microwave radiation model and calculating to obtain the microwave bright temperature according to the infrared bright temperature data;
and the fusion inversion module is used for carrying out fusion inversion on the obtained infrared brightness temperature and the obtained microwave brightness temperature to obtain an optimal temperature distribution solution.
The invention has the technical effects and advantages that:
according to the inversion method and system for physical temperature distribution of the secondary shielding moon permanent shadow area, the detection method of microwave and infrared joint remote sensing is adopted, the accuracy of a radiation model is improved around the accuracy and completeness of the model, and the accuracy of the inversion method is improved by breaking through the utilization rate of priori information. The limitation that the existing temperature distribution inversion method is difficult to be suitable for the combination detection of the microwave and the infrared is broken, the fusion of the multi-source information of the microwave and the infrared detection can be increased to obtain more inversion information, and the inversion result is converged to the global optimal point. And the inversion accuracy of the physical temperature distribution of the secondary shielded moon permanent shadow area is improved by adopting multi-source remote sensing data acquired by a plurality of moon detection plans such as goddess Chang, an infrared radiometer and the like and combining the radiation model and the inversion method, key factors influencing inversion results can be analyzed, and the practical requirements are met.
Drawings
FIG. 1 is a schematic flow chart of a method for inverting physical temperature distribution of a permanently shaded area of a secondarily shaded moon according to an embodiment of the present invention;
FIG. 2 is a flow chart of a microwave and infrared fusion inversion method provided by an embodiment of the invention;
FIG. 3 is a flow chart of simulation verification of a microwave and infrared fusion inversion method provided by an embodiment of the invention;
fig. 4 is a flowchart for calculating a physical temperature profile of a permanently shaded area of a secondarily shaded moon according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It can be understood that, based on the defects in the background technology, the embodiment of the invention provides a inversion method for physical temperature distribution of a secondary shielding moon permanent shadow area, as shown in a flow in fig. 1, which specifically comprises the following steps:
step 1: constructing a heat conduction model of a secondary shielding moon permanent shadow area to obtain infrared bright temperature data;
aiming at the heat conduction model of the moon permanent shadow area under secondary shielding, based on a one-dimensional heat conduction equation, surface roughness, multiple scattering under large-scale shielding and internal heat conduction are introduced, the application range of the heat conduction model is expanded, and then the infrared light temperature is obtained.
Further, constructing a secondary shielded moon permanent shadow thermal conduction model includes the steps of:
firstly, obtaining lunar surface irradiance according to a VSOP87 planetary motion theory and an ELP2000-82 lunar orbit semi-analytical theory;
then, according to the situation of terrain shielding, establishing a real-time effective solar irradiance model of a moon permanent shadow area and a surrounding area thereof under secondary shielding;
using this effective solar irradiance model, the near-area radiation received by the permanently shaded area surface is calculated.
The calculation of the adjacent radiation is to divide the adjacent area into grid-shaped rough surface elements by using elevation data of a laser altimeter, and then calculate the adjacent radiation quantity in a final shadow area by using ray tracing.
Since the adjacent radiation is the main thermal contribution of the permanently hatched surface, the boundary conditions of the surface can be established accordingly:
in the method, in the process of the invention,for surface thermal conductivity, ε for surface emissivity, +.>Is Boltzmann constant, & gt>For the radiation of the area in the vicinity,is the moon internal heat flow.
In addition to the lunar interior heat flow, a bottom boundary condition may be established considering that there may be lateral heat conduction inside the lunar soil, i.e., heat flow inside the illuminated area to the shadow area:
wherein,for the bottom thermal conductivity->For lateral heat conduction->Is the moon internal heat flow.
After the boundary condition is established, a finite difference method is adopted to solve. In order to evaluate the possible presence of anisotropic media, the effect of shallow tens of cm of water ice or stones is emphasized in the thermal conductivity parameters, and the effect of deeper media properties on the surface temperature is limited.
By the above improvement measures, an initial temperature profile distribution can be obtained, which will be used as input to the thermal conduction model of the permanently shaded moon under secondary shading for simulating the calculation of the microwave bright temperature in step 2.
Further, to evaluate the infrared radiation condition of the secondary shielded moon permanent shadow, the infrared bright temperature can be solved by the following equation:
in the method, in the process of the invention,infrared bright temperature->Is the surface temperature，/>Is the infrared radiation of the adjacent surface element, epsilon is the surface emissivity,is the boltzmann constant.
Step two, constructing a microwave radiation model, and calculating to obtain microwave bright temperature according to the infrared bright temperature data;
specifically, for a microwave radiation model, according to the observation position and the view field, the radiation brightness temperature of the lunar dust layer, the lunar soil layer and the lunar rock layer is calculated based on the antenna caliber. Meanwhile, substances such as water ice or stones possibly exist in a moon permanent shadow area, bulk scattering can be generated in a microwave frequency band, the scattering coefficient is calculated according to the particle size and the compactness to evaluate the bulk scattering influence, and the method is applied to calculation of reflection and transmission fields of all layers to construct a flexible reconstruction microwave radiation model.
In particular, solutions for microwave radiation processes generally have incoherent and coherent methods. Considering the bright temperature oscillation caused by the calculation of the coherent method, in the calculation of the microwave radiation model, a noncoherent method is adopted. Since the object of investigation is to secondarily shade the permanently shaded area of the moon, the problem of microwave radiation in the presence of water ice or stones needs to be considered. Based on the original dielectric constant, water ice is introduced, and a Lichtenecker formula and a MaxwellGarnett formula are adopted to calculate the mixed dielectric constant. The water ice mixed in the lunar soil may be dispersed in the lunar soil in a granular form, and thus may generate bulk scattering. Similarly, there is a phenomenon of bulk scattering in the presence of stones. Therefore, in the construction of the radiation model, in addition to the conventional incoherent method for solving, coarse surface scattering and volume scattering (including Mie scattering and Rayleigh scattering) are introduced to solve the attenuation factors in this embodiment.
It is worth to say that the key of the microwave radiation model is to accurately solve the attenuation factor and reasonably reflect the radiation characteristics of the real target substance. For volume scattering, mie scattering coefficients or Rayleigh scattering coefficients are to be added to the radiation transfer equation according to particle size; for the surface roughness, the reflectivity is solved according to the statistical characteristics of the rough surface by adopting an integral equation method, attenuation factors are obtained according to the obtained bulk scattering coefficient, reflectivity and the like, and finally the total bright temperature expression is obtained.
Finally, in a microwave radiation model, lunar soil is layered, and the total brightness temperature of microwaves received by a microwave radiometer is the sum of the brightness temperatures of all layers, and is expressed as:
wherein,the attenuation factor is used for representing the influence of volume scattering, roughness and the like on the brightness temperature; /> i Is the total lighting temperature of microwave, < >>Is the radiant brightness temperature of each layer->Upward microwave bright temperature->Is the downward microwave bright temperature.
And thirdly, performing fusion inversion on the obtained infrared brightness temperature and microwave brightness temperature to obtain an optimal temperature distribution solution.
According to the characteristics of shallow high fluctuation and deep low fluctuation of the lunar surface temperature, a microwave and infrared fusion inversion secondary shielding permanent shadow region temperature distribution method is researched, and the prior information and inversion iterative optimization method are started.
FIG. 2 shows a flow chart of a microwave and infrared fusion inversion method. On one hand, the microwave and infrared fusion inversion method is studied, and because the detection depths of different frequency bands are different, the dependence on a temperature profile function can be reduced by utilizing different layering contributions corresponding to the different frequency bands, the shallow layer is gradually iterated to the deep layer during solving, the influence of shallow layer fluctuation on deep layer temperature inversion is eliminated, the temperature distribution of a high fluctuation area and a low fluctuation area is respectively obtained, and the inversion precision is improved; on the other hand, a Markov chain Monte Carlo Method (MCMC) based on differential evolution is researched to solve the Bayesian problem of temperature posterior distribution, and the method has a good effect on solving the complex and high-dimensional posterior distribution problem and can improve the stability of inversion.
Specifically, the microwave and infrared fusion inversion specifically comprises the following steps:
firstly, utilizing measured data of a multi-band microwave radiometer and an infrared radiometer, removing values of brightness temperature smaller than 30K and larger than 400K in the measured data through data anomaly detection, fitting and interpolating, and then performing time normalization to convert time data into a unified format, namely daytime and midnight, 6 points to 18 points in the daytime and 6 points to the next day in the midnight, so as to obtain the prior parameter distribution of the preliminary microwave brightness temperature and the infrared brightness temperature. And then performing high-frequency multi-channel brightness Wen Fanyan, namely inputting the temperature distribution solved by the heat conduction model into the microwave radiation model as an initial value, and solving a Bayesian problem by utilizing the actually measured high-frequency microwave brightness temperature data to obtain the optimal solution of the temperature posterior distribution in the shallow layer, namely the high fluctuation area.
And then performing low-frequency multi-channel brightness Wen Fanyan, namely using a Bayesian network as described above, taking the obtained shallow temperature distribution as a priori value, and obtaining an optimal longitudinal temperature distribution solution by using a Markov chain Monte Carlo Method (MCMC) based on differential evolution.
The solution of the optimal value adopts a Markov chain Monte Carlo method based on differential evolution, and the solving objective function can be expressed as follows:
where Nobs represents the number of observations, SSR represents the sum of squares of residuals, and L represents the posterior distribution.
In the embodiment, the dependence on the temperature profile function during inversion can be reduced theoretically by increasing the number of channels of the multichannel microwave radiometer, and the inversion accuracy is further improved by improving the inversion optimizing solving method.
To verify the performance of the proposed method, a simulation verification experiment as in fig. 3 was designed. In simulation verification, a heat conduction and radiation model module and a microwave and infrared fusion inversion module are respectively verified. When the model is verified, the model proposed in the project is compared with the simulation brightness Wen Chayi of the traditional incoherent multilayer microwave radiation model and the traditional one-dimensional heat conduction model, and is compared with the actually measured bright temperature data and the LRO bright temperature data, so that the accuracy of simulating the bright temperature is further verified. When the microwave and infrared fusion inversion method is verified, the inversion errors of the method provided by the project and other solving algorithms such as a least square method are compared, and the inversion performance of the method provided under the assumed temperature distribution is evaluated. After comparing and verifying each module, verifying the microwave and infrared fusion inversion simulation platform, comparing with inversion of single passive radiation measurement, and verifying the advantages of the inversion method of the project through comparing inversion errors of longitudinal physical temperature distribution.
The main scientific data to be adopted in this embodiment are: four channels of goddess Chang E No. two microwave bright temperature data, iron titanium content data, LRO multichannel infrared bright temperature data and digital elevation data of a laser altimeter. For the secondary shielding moon permanent shadow area, the calculation flow of the physical temperature profile of the lunar surface is shown in fig. 4, and the construction of the dielectric constant model is derived from the iron-titanium content data. The laser altimeter provides digital elevation data for establishing a shielding model, and the LRO infrared bright temperature provides surface temperature priori information during inversion. Substituting each measured data source into a corresponding parameter model, calculating the input parameters of the inversion model, and obtaining the temperature profile of the lunar surface through repeated iterative calculation. Based on the measured temperature profile, the spatial location of the "water trap" can be obtained.
Additionally, the embodiment of the invention also provides a physical temperature distribution inversion system of a secondary shielding moon permanent shadow area, which comprises the following steps:
the infrared bright temperature acquisition module is used for constructing a heat conduction model of a secondary shielding moon permanent shadow area to acquire infrared bright temperature data;
the microwave bright temperature acquisition module is used for constructing a microwave radiation model and calculating to obtain the microwave bright temperature according to the infrared bright temperature data;
the fusion inversion module is used for carrying out fusion inversion on the obtained infrared brightness temperature and the obtained microwave brightness temperature to obtain an optimal temperature distribution solution
It can be understood that the inversion system for physical temperature distribution of the permanently shaded moon provided by the present invention corresponds to the inversion method for physical temperature distribution of the permanently shaded moon provided by the foregoing embodiments, and the relevant technical features of the inversion system for physical temperature distribution of the permanently shaded moon can refer to the relevant technical features of the inversion method for physical temperature distribution of the permanently shaded moon, which are not described herein.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The inversion method of the physical temperature distribution of the secondary shielding moon permanent shadow area is characterized by comprising the following steps of:
step one, constructing a heat conduction model of a secondary shielding moon permanent shadow area to acquire infrared brightness temperature data;
step two, constructing a microwave radiation model, and calculating to obtain microwave bright temperature according to the infrared bright temperature data; comprising the following steps:
layering lunar soil, wherein the total received microwave brightness temperature is the sum of the radiation brightness temperatures of all layers, and is expressed as:
wherein,the attenuation factor is used for representing the influence of volume scattering and roughness on the brightness temperature; besides solving by adopting a traditional incoherent method, coarse surface scattering and bulk scattering are introduced, wherein the coarse surface scattering comprises Mie scattering and Rayleigh scattering, and the attenuation factor is solved; />Is the total lighting temperature of microwave, < >>Is the radiant brightness temperature of each layer->Upward microwave bright temperature->The downward microwave brightness temperature;
thirdly, fusion inversion is carried out on the obtained infrared brightness temperature and the obtained microwave brightness temperature, and an optimal temperature distribution solution is obtained; comprising the following steps: removing values of brightness temperature less than 30K and more than 400K in measured data through data anomaly detection by utilizing measured data of a multi-band microwave radiometer and an infrared radiometer, fitting and interpolating, and then carrying out time normalization to convert time data into a unified format so as to obtain preliminary prior parameter distribution of microwave brightness temperature and infrared brightness temperature;
then, high-frequency multi-channel brightness Wen Fanyan is carried out, namely, temperature distribution solved by a heat conduction model is used as an initial value to be input into a microwave radiation model, and the actually measured high-frequency microwave brightness temperature data is utilized to carry out Bayesian problem solving to obtain the optimal solution of temperature posterior distribution in a shallow layer, namely, a high fluctuation area;
and then carrying out low-frequency band multichannel brightness Wen Fanyan, taking the obtained shallow temperature distribution as a priori value, and obtaining an optimal temperature distribution solution by using a Markov chain Monte Carlo method based on differential evolution.
2. The inversion method of physical temperature distribution of a secondary shielding moon permanent shadow area according to claim 1, wherein the constructing a heat conduction model of the secondary shielding moon permanent shadow area is specifically:
firstly, obtaining lunar surface irradiance according to a VSOP87 planetary motion theory and an ELP2000-82 lunar orbit semi-analytical theory;
then, according to the situation of terrain shielding, establishing a real-time effective solar irradiance model of a moon permanent shadow area and a surrounding area thereof under secondary shielding;
and finally, calculating the adjacent area radiation received by the surface of the permanent shadow area according to the effective solar irradiance model.
3. The inversion method of physical temperature distribution of a permanently shaded moon region according to claim 2, wherein the infrared bright temperature is solved by the following equation:
in the method, in the process of the invention,infrared bright temperature->Is the surface temperature, ++>Is the infrared radiation of the adjacent bin, ε is the surface emissivity,>is the boltzmann constant.
4. The inversion method of physical temperature distribution of a secondary shielded moon permanent shadow area according to claim 1, wherein the constructing a microwave radiation model comprises:
according to the observation position and the view field, calculating the radiation brightness temperature of the lunar dust layer, the lunar soil layer and the lunar rock layer based on the antenna caliber; meanwhile, the scattering coefficient is calculated according to the particle size and the compactness of water ice or stone substances to evaluate the bulk scattering influence, and the method is applied to calculation of reflection and transmission fields of each layer to construct a flexible reconstruction microwave radiation model.
5. The inversion method of physical temperature distribution of a secondary shielding moon permanent shadow area according to claim 1, wherein the microwave and infrared fusion inversion is divided into research of high-low frequency band multi-channel combination inversion and research of a Markov chain Monte Carlo method based on differential evolution, and the method is used for solving the Bayesian problem of temperature posterior distribution.
6. The inversion method of physical temperature distribution of a secondary shielding moon permanent shadow area according to claim 1, wherein the solution of the optimal value adopts a markov chain monte carlo method based on differential evolution, and the solving objective function can be expressed as follows:
where Nobs represents the number of observations, SSR represents the sum of squares of residuals, and L represents the posterior distribution.
7. The utility model provides a physical temperature distribution inversion system of permanently shaded area of secondary shielding moon which characterized in that includes:
the infrared bright temperature acquisition module is used for constructing a heat conduction model of a secondary shielding moon permanent shadow area to acquire infrared bright temperature data;
the microwave bright temperature acquisition module is used for constructing a microwave radiation model and calculating to obtain the microwave bright temperature according to the infrared bright temperature data; comprising the following steps: layering lunar soil, wherein the total received microwave brightness temperature is the sum of the radiation brightness temperatures of all layers, and is expressed as:
wherein,the attenuation factor is used for representing the influence of volume scattering and roughness on the brightness temperature; besides solving by adopting a traditional incoherent method, coarse surface scattering and bulk scattering are introduced, wherein the coarse surface scattering comprises Mie scattering and Rayleigh scattering, and the attenuation factor is solved; />Is the total lighting temperature of microwave, < >>Is the radiant brightness temperature of each layer->Upward microwave bright temperature->The downward microwave brightness temperature;
the fusion inversion module is used for carrying out fusion inversion on the obtained infrared brightness temperature and the obtained microwave brightness temperature to obtain an optimal temperature distribution solution; comprising the following steps: removing values of brightness temperature less than 30K and more than 400K in measured data through data anomaly detection by utilizing measured data of a multi-band microwave radiometer and an infrared radiometer, fitting and interpolating, and then carrying out time normalization to convert time data into a unified format so as to obtain preliminary prior parameter distribution of microwave brightness temperature and infrared brightness temperature; then, high-frequency multi-channel brightness Wen Fanyan is carried out, namely, temperature distribution solved by a heat conduction model is used as an initial value to be input into a microwave radiation model, and the actually measured high-frequency microwave brightness temperature data is utilized to carry out Bayesian problem solving to obtain the optimal solution of temperature posterior distribution in a shallow layer, namely, a high fluctuation area;
and then carrying out low-frequency band multichannel brightness Wen Fanyan, taking the obtained shallow temperature distribution as a priori value, and obtaining an optimal temperature distribution solution by using a Markov chain Monte Carlo method based on differential evolution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410052598.2A CN117574044B (en) | 2024-01-15 | 2024-01-15 | Method and system for inversion of physical temperature distribution in the permanent shadow region of the moon under secondary occlusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410052598.2A CN117574044B (en) | 2024-01-15 | 2024-01-15 | Method and system for inversion of physical temperature distribution in the permanent shadow region of the moon under secondary occlusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117574044A CN117574044A (en) | 2024-02-20 |
| CN117574044B true CN117574044B (en) | 2024-04-16 |
Family
ID=89864635
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410052598.2A Active CN117574044B (en) | 2024-01-15 | 2024-01-15 | Method and system for inversion of physical temperature distribution in the permanent shadow region of the moon under secondary occlusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117574044B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101738620A (en) * | 2008-11-19 | 2010-06-16 | 中国农业科学院农业资源与农业区划研究所 | Method by utilizing passive microwave remote sensing data AMSR-E (Advanced Microwave Scanning Radiometer-EOS ) to invert surface temperature |
| CN103512663A (en) * | 2013-09-13 | 2014-01-15 | 华中科技大学 | Calculation method for undulating lunar surface microwave radiation brightness temperature |
| CN106405503A (en) * | 2016-08-24 | 2017-02-15 | 华中科技大学 | Method for establishing regolith dielectric constant model |
| AU2017100037A4 (en) * | 2016-11-29 | 2017-02-23 | Macau University Of Science And Technology | Lunar Brightness Temperature Modeling Based on the Microwave Radiometer Data |
| CN115047056A (en) * | 2022-05-16 | 2022-09-13 | 吉林大学 | Lunar soil internal heat flow inversion method based on MRM data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2019262121B2 (en) * | 2018-05-02 | 2023-10-12 | Conocophillips Company | Production logging inversion based on DAS/DTS |
-
2024
- 2024-01-15 CN CN202410052598.2A patent/CN117574044B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101738620A (en) * | 2008-11-19 | 2010-06-16 | 中国农业科学院农业资源与农业区划研究所 | Method by utilizing passive microwave remote sensing data AMSR-E (Advanced Microwave Scanning Radiometer-EOS ) to invert surface temperature |
| CN103512663A (en) * | 2013-09-13 | 2014-01-15 | 华中科技大学 | Calculation method for undulating lunar surface microwave radiation brightness temperature |
| CN106405503A (en) * | 2016-08-24 | 2017-02-15 | 华中科技大学 | Method for establishing regolith dielectric constant model |
| AU2017100037A4 (en) * | 2016-11-29 | 2017-02-23 | Macau University Of Science And Technology | Lunar Brightness Temperature Modeling Based on the Microwave Radiometer Data |
| CN115047056A (en) * | 2022-05-16 | 2022-09-13 | 吉林大学 | Lunar soil internal heat flow inversion method based on MRM data |
Non-Patent Citations (2)
| Title |
|---|
| Inversion of the Lunar Subsurface Rock Abundance Using CE-2 Microwave Brightness Temperature Data;Wei Yang等;《remote sensing》;20231010;全文 * |
| 月球表面多通道辐射亮度温度的模拟与月壤厚度的反演;法文哲等;《自然科学进展》;20060131;第第16卷卷(第第1期期);第86-94页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117574044A (en) | 2024-02-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105425215B (en) | A kind of lunar soil dielectric constant inversion method of microwave band | |
| Menu et al. | On the structure of the transition disk around TW Hydrae | |
| CN106446444B (en) | Soil moisture spatial prediction method based on Bayesian maximum entropy and prior knowledge | |
| Pan et al. | Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion | |
| CN111610524B (en) | Ice cloud profile inversion method and system based on one-dimensional variational algorithm | |
| Nord et al. | Multi-frequency imaging of the galaxy cluster Abell 2163 using the Sunyaev-Zel'dovich effect | |
| Larue et al. | Simulation and assimilation of passive microwave data using a snowpack model coupled to a calibrated radiative transfer model over northeastern Canada | |
| Cai et al. | Lunar brightness temperature model based on the microwave radiometer data of Chang’E-2 | |
| Kong et al. | A coupling model for soil moisture retrieval in sparse vegetation covered areas based on microwave and optical remote sensing data | |
| Lu et al. | Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques | |
| Lan et al. | Lunar brightness temperature map and TB distribution model | |
| Zhao et al. | An air-to-soil transition model for discrete scattering-emission modelling at L-band | |
| CN117574044B (en) | Method and system for inversion of physical temperature distribution in the permanent shadow region of the moon under secondary occlusion | |
| Feng et al. | High-accuracy lunar global brightness-temperature mapping using third-order Fourier fitting and co-kriging interpolation | |
| CN106405503B (en) | A kind of method for building up of lunar soil dielectric constant model | |
| Hu et al. | Multi-sensor study of precipitable water vapor and atmospheric profiling from microwave radiometer, GNSS/MET, radiosonde, and ECMWF reanalysis in Beijing | |
| Huang et al. | Evaluation and application of on-orbit calibration of the automated vicarious calibration system | |
| van der Velde et al. | Decomposition of uncertainties between coarse MM5–Noah-simulated and fine ASAR-retrieved soil moisture over Central Tibet | |
| Tian et al. | Utilizing a new soil effective temperature scheme and archived satellite microwave brightness temperature data to estimate surface soil moisture in the Nagqu region, Tibetan Plateau of China | |
| CN115795912A (en) | Millimeter wave radar data assimilation icing prediction method and system | |
| CN111044489B (en) | Method for obtaining atmosphere refractive index height distribution profile based on multi-wavelength measurement | |
| Zheng et al. | Analyzing the Topographic Effects of the Lunar PSR on Radiometric Observations Using a Microwave Radiation Model With Coherent Surface Scattering | |
| Zhou et al. | Offshore surface evaporation duct joint inversion algorithm using measured dual-frequency sea clutter | |
| Zheng et al. | Explanations for unusual seasonal variations in Chang’E-2 microwave radiometer datasets of lunar double-shaded permanently shadowed regions | |
| Liu et al. | A comprehensive review on study methods of aerosol optical properties in different dimensions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |