CN113945282A - Infrared remote sensing satellite temperature inversion precision index distribution system and method - Google Patents
Infrared remote sensing satellite temperature inversion precision index distribution system and method Download PDFInfo
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Abstract
The invention relates to a system and a method for distributing temperature inversion accuracy indexes of an infrared remote sensing satellite, comprising the following steps of firstly, identifying influence factors of temperature inversion accuracy according to an infrared remote sensing radiation transmission principle; quantitatively analyzing the contribution of each influence factor to the temperature inversion error according to the action principle of different influence factors, namely determining the temperature inversion error caused by each influence factor; and step three, establishing a temperature inversion error model according to the result of the step two, carrying out index distribution by taking the specific temperature inversion accuracy index as a target, and decomposing to obtain control targets of different influence factors.
Description
Technical Field
The invention relates to a system and a method for distributing temperature inversion accuracy indexes of an infrared remote sensing satellite, belongs to the technical field of aerospace infrared remote sensing, and can be applied to the overall design of the infrared remote sensing satellite.
Background
The infrared remote sensing satellite can realize the full-time and non-contact temperature measurement and thermal state analysis of targets such as atmosphere, water, ground objects and the like, and is an important environment monitoring and resource detection means. The temperature inversion is an important influence link for realizing the accuracy of the infrared remote sensing data, the temperature inversion accuracy is the most important index for quantitatively evaluating the infrared remote sensing capability of the satellite, and the actual inversion effect is closely related to factors such as satellite sensor noise, calibration accuracy and atmospheric correction error. Therefore, for the design and data application of the infrared remote sensing satellite, the influence factors of the realization of the temperature inversion precision are identified and quantitatively analyzed, and then the control targets of different error items are obtained through the satellite-ground integration index decomposition, so that the realization of the task of the satellite can be ensured, and the method has practical guiding significance.
The improvement of the spatial resolution and the inversion accuracy is the development trend of the infrared remote sensing satellite. The spatial resolution of foreign infrared remote sensing satellites is improved from hundreds of meters of Landsat to 90 meters of ASTER, 20 meters of MTI and 60 meters of HyspeIRI, and military cameras even reach the meter level. The quantitative application requirements and levels are also continuously improved, the data inversion requirements are emphasized in spectrum allocation, and detection channels such as atmosphere and water vapor are added to improve the data inversion accuracy. A long-wave infrared method, in which ETM + is replaced by two long-wave infrared bands of Landsat-8, is a typical split window inversion algorithm, and atmospheric influences are removed by utilizing different atmospheric absorption effects on two adjacent channels; the MODIS, ASTER, HyspeRI and JPSS are typically multi-channel algorithms, and a plurality of thermal infrared channels are used for inverting the surface temperature and emissivity. In the aspect of satellite design, measures such as detector noise control and scaling means are adopted, and partial satellites can achieve 1K temperature inversion accuracy.
The infrared remote sensing satellite in China is represented by a resource series satellite, an environment series satellite and a high-resolution five-number satellite, and the infrared remote sensing satellite in China advances from a single spectrum section, low resolution to a multi-spectrum section, high spectrum and high resolution. However, at present, China does not have space-based high-resolution and high-quantification infrared remote sensing detection capability, and an infrared remote sensing data product with temperature inversion accuracy superior to 1K does not exist. The highest spatial resolution of the existing infrared remote sensing satellite is also in the order of tens of meters, the highest temperature inversion accuracy is only 2-3K, and due to the fact that the requirement on technical indexes is not high, the consideration factors of the existing temperature inversion accuracy index distribution method are not comprehensive enough, and the method cannot meet the development requirements of the infrared remote sensing satellite with higher resolution and higher accuracy in the future.
The method is oriented to overall design of the high-resolution and high-quantification infrared remote sensing satellite, and is characterized in that influence factors for realizing temperature inversion precision are required to be identified and quantitatively analyzed, a thermal infrared remote sensing earth surface temperature inversion precision analysis model system is established, micro error links influencing the temperature inversion precision are excavated, control targets of different error items are obtained through satellite-ground integrated index decomposition, index distribution is reasonably carried out, so that the index requirement of the 1K temperature inversion precision of the high-quantification infrared remote sensing satellite is met, and the task realization of the satellite is ensured.
Disclosure of Invention
The technical problem solved by the invention is as follows: compared with the prior art, the system and the method for distributing the temperature inversion accuracy indexes of the infrared remote sensing satellite are provided, the control targets of different error items are obtained by identifying and analyzing the temperature inversion accuracy influence factors and decomposing, and the quantification level of the satellite is improved.
The technical scheme of the invention is as follows: the method for distributing the temperature inversion accuracy indexes of the infrared remote sensing satellite comprises the following steps:
identifying influence factors of temperature inversion accuracy according to an infrared remote sensing radiation transmission principle;
quantitatively analyzing the contribution of each influence factor to the temperature inversion error according to the action principle of different influence factors, namely determining the temperature inversion error caused by each influence factor;
and step three, establishing a temperature inversion error model according to the result of the step two, carrying out index distribution by taking the specific temperature inversion accuracy index as a target, and decomposing to obtain control targets of different influence factors.
Preferably, in the first step, according to the Planck's law and the atmospheric thermal infrared radiation transmission equation, the influence factors of the entrance pupil radiance measurement error of the infrared remote sensing satellite sensor are decomposed into the influence factors of the entrance pupil radiance measurement error, the earth surface emissivity error and the atmospheric correction error of the satellite sensor; the method comprises the following steps that a sensor entrance pupil radiance measurement error is further decomposed into thermal infrared sensor measurement errors, thermal infrared camera calibration errors and channel response error influence factors; the influencing factors also comprise inversion algorithm influencing factors.
Preferably, in the second step, a formula for influencing the temperature inversion accuracy by the influencing factor is deduced according to the theoretical model and the error action principle of each influencing factor, so as to obtain a quantitative result for influencing the temperature inversion accuracy by each influencing factor.
Preferably, the measurement error of the thermal infrared sensor and the calibration error of the thermal infrared camera are represented by a noise equivalent temperature difference NETD, and a corresponding temperature inversion error is calculated according to the size of the sensor NETD; and for the channel response error, estimating a corresponding temperature inversion error according to the actual spectral response of the camera.
Preferably, the surface emissivity estimate error σ [ epsilon ]]Resulting temperature inversion error σε[LST]Is obtained by the following formula:
wherein:
Latm↑(ω)、Latm↓(omega) are each atmospheric airUplink and downlink radiation; b (lambda, T) represents the spectral radiance of the object at temperature T and wavelength lambda; c1And C2Is a physical constant, C1＝3.7418×108W·μm4·m-2，C2＝1.439×104μm·K；k1＝c1/πλ5，k2＝c2/λ；Ltoa(λ) is the satellite sensor entrance pupil radiance; tau is the atmospheric transmission rate and epsilon atmospheric emissivity.
Preferably, the temperature inversion error caused by the atmospheric correction estimation error σ ω is given by:
wherein:
Latm↑(ω)、Latm↓(omega) is the atmospheric up-going and down-going radiation respectively; b (lambda, T) represents the spectral radiance of the object at temperature T and wavelength lambda; c1And C2Is a physical constant, C1＝3.7418×108W·μm4·m-2，C2＝1.439×104μm·K；k1＝c1/πλ5，k2＝c2/λ；Ltoa(λ) is the satellite sensor entrance pupil radiance; tau is the atmospheric transmission rate, epsilon atmospheric emissivity and omega is the total atmospheric water vapor content
Preferably, the inversion algorithm results in a temperature inversion error of σALG[LST]NETD is the noise equivalent temperature difference, which is 5 × NETD.
Preferably, the temperature inversion error model is as follows:
σNETD[LST]、σCAL[LST]、σRES[LST]、σε[LST]、σω[LST]、σALG[LST]the method comprises the following steps of respectively measuring errors of a thermal infrared sensor, calibrating errors of an infrared camera, channel response errors, surface emissivity estimation errors, atmospheric correction estimation errors and temperature inversion errors caused by an inversion algorithm.
Preferably, the factors to be considered in the index distribution process in the third step include the satellite payload engineering implementation difficulty, the industrial manufacturing level, the atmospheric correction model at home and abroad and the temperature inversion algorithm precision.
An infrared remote sensing satellite temperature inversion accuracy index distribution system comprises
The temperature inversion accuracy influence factor identification module is used for establishing a radiation transmission model according to an infrared remote sensing radiation transmission principle so as to identify the influence factors of the temperature inversion accuracy; the influencing factors comprise a thermal infrared sensor measuring error, an infrared camera calibration error, a channel response error, a surface emissivity estimation error, an atmospheric correction estimation error and an inversion algorithm;
the temperature inversion error model building module is used for quantitatively analyzing the contribution of each influence factor to the temperature inversion error according to the action principle of different influence factors, namely determining the temperature inversion error caused by each influence factor; establishing a temperature inversion error model according to the quantitative analysis result;
and the temperature inversion index distribution module is used for realizing that a specific temperature inversion precision index is taken as a target, performing index distribution and decomposing to obtain control targets of different influence factors.
Preferably, the measurement error of the thermal infrared sensor and the calibration error of the thermal infrared camera are represented by a noise equivalent temperature difference NETD, and a corresponding temperature inversion error is calculated according to the size of the sensor NETD; for the channel response error, estimating a corresponding temperature inversion error according to the actual spectral response of the camera; earth surface emissivity estimation error sigma [ epsilon ]]Resulting temperature inversion error σε[LST]Is obtained by the following formula:
the temperature inversion error caused by the atmospheric correction estimation error σ [ ω ] is given by:
temperature inversion error of sigma caused by inversion algorithmALG[LST]＝5×NETD
Wherein:
Latm↑(ω)、Latm↓(omega) is the atmospheric up-going and down-going radiation respectively; b (lambda, T) represents the spectral radiance of the object at temperature T and wavelength lambda; c1And C2Is a physical constant, C1＝3.7418×108W·μm4·m-2，C2＝1.439×104μm·K；k1＝c1/πλ5，k2＝c2/λ；Ltoa(λ) is the satellite sensor entrance pupil radiance; tau is the atmospheric transmission rate, epsilon atmospheric emissivity, omega is the total water vapor content of the atmosphere, and NETD is the noise equivalent temperature difference.
Compared with the prior art, the invention has the beneficial effects that:
(1) the temperature inversion influence factors are determined, namely the temperature inversion accuracy index distribution method adopted by the invention is used for researching the temperature inversion theory of infrared remote sensing and based on the Planck's law and the atmospheric radiation transmission theory, so that the identification of the temperature inversion accuracy influence factors such as infrared detector noise, instrument calibration errors, atmospheric correction errors and the like is realized, and reference is provided for the attack and shutdown directions of satellite engineering development and ground matching system construction;
(2) establishing a temperature inversion error model, namely establishing corresponding error modules on the basis of identifying temperature inversion precision influencing factors, quantitatively analyzing the contributions of different modules to temperature inversion errors, establishing an error model system and laying a foundation for index distribution;
(3) the invention provides a direction for index control in the process of satellite engineering development by giving out different index constraints related to temperature inversion accuracy, and defines a technical path for improving the satellite inversion accuracy.
Drawings
FIG. 1 is a schematic diagram of the decomposition of temperature inversion accuracy errors of the present invention;
FIG. 2 is a block diagram of the system components of the present invention.
Detailed Description
The invention relates to an infrared remote sensing satellite temperature inversion accuracy index distribution system and a method, which mainly comprise the following three parts:
firstly, establishing a radiation transmission model according to an infrared remote sensing radiation transmission principle, and further identifying influence factors of temperature inversion accuracy;
according to the Planck's law and the atmospheric thermal infrared radiation transmission equation, the influence factors of the entrance pupil radiance measurement error of the infrared remote sensing satellite sensor are decomposed into factors such as the entrance pupil radiance measurement error, the emissivity error and the atmospheric correction error of the satellite sensor. Wherein, the sensor entrance pupil radiance measurement error can be decomposed into factors such as thermal infrared sensor measurement error, thermal infrared camera calibration error, channel response error.
Secondly, establishing a corresponding module according to the action principle of different influence factors, quantitatively analyzing the contribution of the corresponding module to the temperature inversion error, and establishing a temperature inversion error model;
and (3) aiming at different error influence factors obtained by decomposition, deducing a formula for influencing the temperature inversion accuracy according to a theoretical model and an error action principle of the influence factors, further establishing corresponding influence factor modules, and obtaining a quantitative result of each module influencing the temperature inversion accuracy under certain assumed conditions. For the measurement error of the sensor, a Noise Equivalent Temperature Difference (NETD) is adopted for representing, and a corresponding temperature inversion error is calculated according to the size of the sensor NETD; for the calibration error, because the action mechanism is similar to NETD, the corresponding temperature inversion error can be calculated by adopting the same method; for the channel response error, estimating a corresponding temperature inversion error according to the actual spectral response of the camera; for errors such as surface emissivity, atmospheric correction, inversion algorithm and the like, methods such as a table lookup method, a priori knowledge method and the like are comprehensively used for estimating the error magnitude, and then corresponding temperature inversion errors are calculated. And finally, establishing a temperature inversion error model system according to the contributions of different modules to the temperature inversion error.
And thirdly, aiming at realizing a specific temperature inversion precision index, reasonably distributing indexes and decomposing to obtain control targets of different error items aiming at each module by combining with the engineering realization difficulty.
The method is based on the satellite-ground integrated temperature inversion accuracy index, combines the satellite payload engineering realization difficulty and the industrial manufacturing level on the basis of the temperature inversion error model established in the two methods, refers to the domestic and foreign atmosphere correction model and the temperature inversion algorithm accuracy, reasonably distributes indexes of modules such as the satellite sensor NETD, the calibration error, the channel response error, the earth surface emissivity error, the atmosphere correction error and the inversion algorithm error, and decomposes to obtain control targets of different modules.
The present invention will be described in detail with reference to the accompanying drawings 1-2 and the detailed description thereof.
Temperature inversion accuracy influence factor identification
According to planck's law, any object with an absolute temperature greater than 0K will radiate energy outward in the form of electromagnetic waves. For a black body, its radiant energy can be calculated using the planck equation with the physical temperature known, namely:
wherein B (λ, T) represents the spectral radiance of the object at temperature T and wavelength λ, and has dimension W.m-2·μm-1·sr-1。C1And C2Is a physical constant (C)1＝3.7418×108W·μm4·m-2，C2＝1.439×104μ m · K). However, for most natural objects which are not black, the thermal radiation of the natural objects needs to be influenced by the emissivity epsilon (lambda) in the Planck formula, and the radiation brightness emitted by the natural objects is as follows:
if the environmental radiation is considered, the atmospheric downlink radiation flux is assumed to be Latm↓(ω), the radiance L of the target is observed on the groundgrd(λ) is the thermal radiation of the target itself plus the atmospheric downlink radiation reflected by the target, the formula is expanded as:
Lgrd(λ)＝ε(λ)B(λ,T)+(1-ε(λ))Latm↓(ω)
the target radiance observed at the sensor height via atmospheric radiance transfer is:
Ltoa(λ)＝τ(λ)Lgrd(λ)+Latm↑(ω)
where τ (λ) is the atmospheric transmission rate in the direction from the target to the sensor, Latm↑And (ω) is the heat radiation upward from the atmosphere. After combination, the radiation transmission model is obtained as follows:
Ltoa(λ)＝τ(λ)[ε(λ)B(λ,T)+(1-ε(λ))Latm↓(ω)]+Latm↑(ω)
as can be seen from the above formula, the inversion accuracy of the earth surface temperature is influenced by the entrance pupil radiance L of the satellite sensortoaMeasurement error of (lambda), emissivity error of epsilon (lambda), atmospheric correction error (including tau (lambda), Latm↓(ω)、Latm↑ω) and the like, and the influence of the inversion process.
The earth surface temperature is about 300K generally, and an infrared camera sensor with the imaging center wavelength of about 12 mu m is adopted for measuring the earth surface temperature. Therefore, the satellite sensor entrance pupil radiance measurement error mainly comes from the following aspects:
(1) measurement error of the infrared sensor. Random noise caused by dark current, video circuit and the like is included and generally represented by a sensor Noise Equivalent Temperature Difference (NETD);
(2) instrument calibration error. The infrared camera optical system and the detector response generally have response nonlinear effect and spatial nonuniformity, and are corrected by laboratory calibration in the development stage, but the calibration has certain error due to the influence of a calibration source and detection random error, and the measurement of entrance pupil radiance is influenced.
(3) Errors due to imaging spectral bandwidth. In the temperature inversion model, the output of the camera is used as the measurement result of the light energy of the central wavelength, the derivation in the model is made according to the infinitely subdivided wavelength lambda, and the transmission channel of the remote sensor is broadband in practice. Thereby causing measurement errors.
In summary, the sensor entrance pupil radiance measurement error can be decomposed into an infrared sensor measurement error, an infrared camera calibration error and a channel response error. Figure 2 shows the decomposition of the temperature inversion accuracy error.
Second, temperature inversion error model establishment
Aiming at the identified temperature inversion accuracy influencing factors, modules such as infrared sensor measurement errors, infrared camera calibration errors, channel response errors, earth surface emissivity errors, atmospheric correction errors, inversion algorithm errors and the like are set, and then the contribution of each module to the temperature inversion errors is calculated.
(1) Infrared sensor measurement error module
The infrared sensor measurement error module is used for evaluating the performance of the infrared sensor, and can generally calculate by using Noise Equivalent Temperature Difference (NETD). Analyzing radiance error sigma caused by noise equivalent temperature differenceNETD[L]。
Let k1＝c1/πλ5，k2＝c2λ, then:
temperature inversion error caused by noise equivalent temperature difference:
the atmospheric transmittance tau is mainly influenced by the atmospheric water vapor content, and the following fitting formula is provided according to the average atmospheric air in summer at the intermediate latitude:
τ＝0.978-0.13ω-0.001ω2
assuming that the central wavelength of the thermal infrared sensor is 12 mu m, T is 300K, and the content omega of atmospheric water vapor is 2g cm-2The emissivity ε is 0.98. When the noise equivalent temperature difference of the satellite is 50mK, the sigma can be calculated through the infrared sensor measurement error moduleNETD[L]Is 0.0061 W.m-2·sr-1·μm-1Further calculate the temperature inversion error sigmaNETD[LST]0.0715K.
(2) Infrared camera calibration error module
The infrared camera calibration error module is used for evaluating residual errors left after calibration, and the action mechanism of the calibration errors on the temperature inversion errors is the same as the noise equivalent errors, so the calculation method is the same.
Other assumed conditions are unchanged, and when the calibration error of the satellite infrared camera is 0.4K, the temperature inversion error sigma can be calculatedCAL[LST]It was 0.57K.
(3) Channel response error module
The derivation in the model is done for infinitely subdivided wavelengths λ, using only the central wavelength for simplified calculations, while in practice the transmission channel of the remote sensor is broadband, responding through the channelThe error module evaluates errors resulting from the simplification. For a channel response characteristic function f (λ) and a wavelength range λ1～λ2When the target temperature is T, the radiance measured by the sensor is as follows:
for different temperatures TiIn the presence of a corresponding Beff(Ti) The temperature is substituted into the Planck equation to obtain an estimated value of the temperature
From this the temperature inversion error can be calculated:
taking a certain sensor channel response function as a calculation basis, and analyzing a temperature inversion error sigma caused by a channel response error through a channel response error module when the central wavelength is 12 mu m and the T is 300KRES[LST]About 0.1K.
(4) Earth surface emissivity error module
The temperature inversion error due to the surface emissivity estimation error σ [ epsilon ] can be obtained by:
wherein:
the atmospheric uplink and downlink radiation is respectively calculated by the following formula:
Latm↑(ω)＝0.177+0.622ω+0.042ω2
Latm↓(ω)＝0.195+0.632ω+0.052ω2
other assumed conditions are unchanged, and when the earth surface emissivity estimation error is 0.02, the temperature inversion error sigma caused by the earth surface emissivity error is analyzed by an earth surface emissivity error moduleε[LST]About 0.0584K.
(5) Atmospheric correction error module
An important feature of remote sensing observation is the influence of the atmosphere on the radiation signal. Although the commonly used thermal infrared band is the atmospheric window band, the transmittance of the entire atmosphere is still less than 0.8, so atmospheric absorption must be considered. The total atmospheric water vapor content omega is a main factor causing atmospheric radiation and absorption of the thermal infrared channel, and the temperature inversion error caused by the estimation error sigma omega can be obtained by the following formula:
wherein:
other assumed conditions are unchanged, and when the estimation error of the atmospheric water vapor content is 10%, the temperature inversion error sigma caused by the estimation error of the atmospheric water vapor content is analyzed by an atmospheric correction error moduleω[LST]About 0.4K.
(6) Error module of inversion algorithm
The remote sensing inversion algorithm of pixel average temperature mainly comprises the following steps: the single channel method, the multi-temporal method and the like have wide application in the ground processing condition of the remote sensing satellite at home and abroad at present. For the currently common combination form, the algorithm amplifies random noise in the observation data by about 5 times, namely:
σALG[LST]＝5×NETD
when the NETD is 50mK, the temperature inversion error sigma caused by the inversion algorithm error is analyzed by the inversion algorithm error moduleALG[LST]About 0.25K.
(7) Temperature inversion error model
By combining the effects of the above 6 error modules, the temperature inversion error model can be described by the following formula because the modules are independent of each other:
third, temperature inversion index distribution
The temperature inversion index distribution firstly defines the satellite-ground integrated temperature inversion accuracy index, for different accuracy index requirements, the cost and the development period cost which need to be paid for meeting specific indexes are evaluated simultaneously by combining the industrial manufacturing level and the engineering realization difficulty, and on the basis, different distribution schemes can be realized by coordinating and matching parameters among modules. In clear sky and without clouds, the surface temperature T is 300K, the emissivity epsilon is 0.98, and the atmospheric water vapor content omega is 2g cm-2Under the unified typical condition that the central wavelength of the sensor is 12 mu m, different temperature inversion index distribution schemes can be obtained by taking the realization of 1K satellite-ground integrated temperature inversion accuracy as a target and coordinating and matching the relation of each module according to a temperature inversion error model system. For example, for the NETD index of the infrared sensor, the index of 0.05K can be realized at the highest industrial manufacturing level at present by combining the development conditions at home and abroad, and the index can be properly widened to 0.1-0.15K. For the calibration error index, the maximum can be 0.4K, and the maximum can be properly widened to 0.5-0.6K. The other indexes are the same. And finally, obtaining different error distribution schemes according to reasonable matching of different error modules, and guiding the design of the satellite scheme and the realization of tasks.
Table 1 error distribution scheme satisfying 1K satellite-ground integrated temperature inversion accuracy index
In conclusion, the method can well meet the design requirement of the high-quantification infrared remote sensing satellite on the temperature inversion precision index distribution in the overall design stage, can be expanded and widely applied to all remote sensing satellites with infrared spectrum band imaging capability, and has strong practicability and universality.
The parts not described in the present invention belong to the known art in the field.
Claims (11)
1. The method for distributing the temperature inversion accuracy indexes of the infrared remote sensing satellite is characterized by comprising the following steps of:
identifying influence factors of temperature inversion accuracy according to an infrared remote sensing radiation transmission principle;
quantitatively analyzing the contribution of each influence factor to the temperature inversion error according to the action principle of different influence factors, namely determining the temperature inversion error caused by each influence factor;
and step three, establishing a temperature inversion error model according to the result of the step two, carrying out index distribution by taking the specific temperature inversion accuracy index as a target, and decomposing to obtain control targets of different influence factors.
2. The method of claim 1, wherein: according to the Planck's law and the atmospheric thermal infrared radiation transmission equation, decomposing the influence factors of the entrance pupil radiance measurement error of the infrared remote sensing satellite sensor into the influence factors of the entrance pupil radiance measurement error, the earth surface emissivity error and the atmospheric correction error of the satellite sensor; the method comprises the following steps that a sensor entrance pupil radiance measurement error is further decomposed into thermal infrared sensor measurement errors, thermal infrared camera calibration errors and channel response error influence factors; the influencing factors also comprise inversion algorithm influencing factors.
3. The method according to claim 1 or 2, characterized in that: and in the second step, a formula for influencing the temperature inversion accuracy by the influencing factors is deduced according to the theoretical model and the error action principle of each influencing factor, so that a quantitative result of the influence of each influencing factor on the temperature inversion accuracy is obtained.
4. The method of claim 3, wherein: for measurement errors of the thermal infrared sensor and calibration errors of the thermal infrared camera, representing by adopting a noise equivalent temperature difference NETD, and calculating corresponding temperature inversion errors according to the size of the sensor NETD; and for the channel response error, estimating a corresponding temperature inversion error according to the actual spectral response of the camera.
5. The method of claim 2, wherein: earth surface emissivity estimation error sigma [ epsilon ]]Resulting temperature inversion error σε[LST]Is obtained by the following formula:
wherein:
Latm↑(ω)、Latm↓(omega) is the atmospheric up-going and down-going radiation respectively; b (lambda, T) represents the spectral radiance of the object at temperature T and wavelength lambda; c1And C2Is a physical constant, C1＝3.7418×108W·μm4·m-2，C2＝1.439×104μm·K；k1＝c1/πλ5，k2＝c2/λ；Ltoa(λ) is the satellite sensor entrance pupil radiance; tau is the atmospheric transmission rate and epsilon atmospheric emissivity.
6. The method of claim 2, wherein: the temperature inversion error caused by the atmospheric correction estimation error σ [ ω ] is given by:
wherein:
Latm↑(ω)、Latm↓(omega) is the atmospheric up-going and down-going radiation respectively; b (lambda, T) represents the spectral radiance of the object at temperature T and wavelength lambda; c1And C2Is a physical constant, C1＝3.7418×108W·μm4·m-2，C2＝1.439×104μm·K；k1＝c1/πλ5，k2＝c2/λ；Ltoa(λ) is the satellite sensor entrance pupil radiance; tau is the atmospheric transmission rate, epsilon atmospheric emissivity and omega is the total atmospheric water vapor content.
7. The method of claim 2, wherein: temperature inversion error of sigma caused by inversion algorithmALG[LST]NETD is the noise equivalent temperature difference, which is 5 × NETD.
8. The method of claim 1, wherein: the temperature inversion error model is as follows:
σNETD[LST]、σCAL[LST]、σRES[LST]、σε[LST]、σω[LST]、σALG[LST]the method comprises the following steps of respectively measuring errors of a thermal infrared sensor, calibrating errors of an infrared camera, channel response errors, surface emissivity estimation errors, atmospheric correction estimation errors and temperature inversion errors caused by an inversion algorithm.
9. The method of claim 8, wherein: factors to be considered in the index distribution process in the third step include the satellite payload engineering realization difficulty, the industrial manufacturing level, the atmospheric correction model at home and abroad and the temperature inversion algorithm precision.
10. The infrared remote sensing satellite temperature inversion accuracy index distribution system is characterized in that: comprises that
The temperature inversion accuracy influence factor identification module is used for establishing a radiation transmission model according to an infrared remote sensing radiation transmission principle so as to identify the influence factors of the temperature inversion accuracy; the influencing factors comprise a thermal infrared sensor measuring error, an infrared camera calibration error, a channel response error, a surface emissivity estimation error, an atmospheric correction estimation error and an inversion algorithm;
the temperature inversion error model building module is used for quantitatively analyzing the contribution of each influence factor to the temperature inversion error according to the action principle of different influence factors, namely determining the temperature inversion error caused by each influence factor; establishing a temperature inversion error model according to the quantitative analysis result;
and the temperature inversion index distribution module is used for realizing that a specific temperature inversion precision index is taken as a target, performing index distribution and decomposing to obtain control targets of different influence factors.
11. The system of claim 10, wherein: for measurement errors of the thermal infrared sensor and calibration errors of the thermal infrared camera, representing by adopting a noise equivalent temperature difference NETD, and calculating corresponding temperature inversion errors according to the size of the sensor NETD; for the channel response error, estimating a corresponding temperature inversion error according to the actual spectral response of the camera; earth surface emissivity estimation error sigma [ epsilon ]]Resulting temperature inversion error σε[LST]Is obtained by the following formula:
the temperature inversion error caused by the atmospheric correction estimation error σ [ ω ] is given by:
temperature inversion error of sigma caused by inversion algorithmALG[LST]＝5×NETD
Wherein:
Latm↑(ω)、Latm↓(omega) is the atmospheric up-going and down-going radiation respectively; b (lambda, T) represents the spectral radiance of the object at temperature T and wavelength lambda; c1And C2Is a physical constant, C1＝3.7418×108W·μm4·m-2，C2＝1.439×104μm·K；k1＝c1/πλ5，k2＝c2/λ；Ltoa(λ) is the satellite sensor entrance pupil radiance; tau is the atmospheric transmission rate, epsilon atmospheric emissivity, omega is the total water vapor content of the atmosphere, and NETD is the noise equivalent temperature difference.
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