CN114842325A - Ground temperature inversion method based on single-waveband medium-wave infrared satellite remote sensing data - Google Patents
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Abstract
The invention provides a ground temperature inversion method based on single-waveband medium-wave infrared satellite remote sensing data, and relates to thermal infrared remote sensing image processing and analysis. The method obtains high-resolution four-signal medium wave infrared B06 waveband data, and inverts the earth surface temperature by medium wave infrared remote sensing based on a single window algorithm; the method comprises the following steps of obtaining the atmospheric water vapor content at a corresponding moment by utilizing the re-analysis data of the NCEP, and calculating the atmospheric transmittance; calculating the earth surface specific radiance of the medium wave infrared data at the corresponding moment by using the NDVI provided by the MODIS vegetation index product data; calculating parameters C and D required in a single-window algorithm according to the ground surface emissivity and the atmospheric transmittance; and finally calculating to obtain the surface temperature. Experiments prove that the method can conveniently and accurately obtain the earth surface temperature information, provides data support for researches on hydrology, ecology, environment, biological geochemistry and the like, is particularly suitable for identifying high-temperature targets, and has good practical application value.
Description
Technical Field
The invention belongs to the technical field of thermal infrared remote sensing image processing and analysis, and particularly relates to a surface temperature inversion method based on single-spectrum mid-wave infrared satellite remote sensing data.
Background
The earth surface temperature is the comprehensive result of heat transfer and exchange between the earth surface and the underground and between the earth surface and the atmosphere under the action of solar radiation, is not only a key parameter for researching the energy cycle of the earth surface layer in the global scope, but also one of important parameter variables in a plurality of subject research fields of geophysical science, geochemistry, climatology and the like, and has important significance for researching hydrology, ecology, environment, biological geochemistry and the like. From the industrial application, the earth surface temperature data, particularly the high-temperature point information, can also be directly applied to the aspects of researching the urban heat island effect, monitoring the production state of a heat energy enterprise, monitoring forest fires and volcanic eruption and the like. Due to the time-space difference characteristic of the earth surface temperature and the diversity of human thermal energy facilities, the traditional mode of utilizing weather station information to perform spatial interpolation or utilizing weather forecast information has obvious defects for mastering the temperature information of local areas, and is difficult to meet various application requirements. The satellite remote sensing has the characteristics of large range, objectivity and dynamic property, and makes up for the defects to a certain extent. The earth surface temperature observation in a large range can be carried out through multi-platform, multi-sensor and multi-time-phase thermal infrared satellite remote sensing data. The thermal infrared remote sensing is based on a thermal infrared sensor, and obtains thermal infrared information of two atmospheric windows of 3-5 mu m medium wave infrared and 8-14 mu m long wave infrared of a target ground object to identify the ground object and quantitatively invert parameter information such as temperature. Since the effective radiation ratio of the medium wave rapidly increases as the temperature of the object increases, the absolute radiation amount also increases rapidly. Therefore, for a high-temperature object, the difference between the wave radiation intensity and the earth surface heat radiation is obvious, and the detection of a high-heat target has great advantage by utilizing the medium-wave infrared data through temperature inversion processing.
The method for inverting the earth surface temperature based on the satellite thermal infrared data mainly comprises a single-band algorithm, a multi-angle algorithm and a multi-temporal algorithm, and most of the methods are provided for foreign sensors, such as MODIS (moderate resolution imaging spectrometer), Landsat8TIRS, ASTER and the like. The multi-band, multi-angle and multi-temporal algorithms have certain requirements on the number of input infrared data bands, and a thermal infrared band atmospheric transmission equation set is solved through two or more bands, so that an atmospheric equivalent temperature variable is eliminated, and the temperature inversion accuracy is more accurate compared with that of a single-band algorithm. The single-band algorithm is suitable for extracting the earth surface temperature by only one thermal infrared remote sensing band, and compared with other inversion algorithms, the single-band algorithm needs to estimate one more atmospheric effective temperature variable, so that the inversion error is larger. Meanwhile, the existing single-waveband temperature inversion methods all need real-time earth surface specific radiance parameters of remote sensing satellites in calculation, and the parameters all need data support of red light wavebands and near infrared wavebands. Therefore, if the data of the near infrared band or the red light band has problems, the inversion processing of the single-band earth surface temperature is influenced.
Disclosure of Invention
The invention provides a ground temperature inversion method based on single-band medium wave infrared satellite remote sensing data, aiming at the problems that the single-band medium wave infrared satellite remote sensing data has more parameter data required for inverting the ground surface temperature and the acquisition difficulty is higher, and the method solves the problem of real-time parameters required to be input by a single window algorithm by processing various external data sources and meets the requirements of medium wave infrared satellite remote sensing data business engineering application.
The invention provides a ground temperature inversion method based on single-waveband medium-wave infrared satellite remote sensing data, which comprises the following steps of:
step one, performing radiometric calibration on the medium-wave infrared data, and converting the recorded original DN value into radiance.
And step two, calculating a brightness temperature value B (T) through the radiance data.
And step three, downloading MODIS vegetation index product data corresponding to the medium wave infrared data time, and calculating the ground surface emissivity according to the downloaded data. The method comprises the following specific steps:
301, downloading MODIS vegetation index MOD13A1 product data corresponding to the medium wave infrared data time as NDVI data at the time;
step 302, calculating vegetation coverage through the downloaded NDVI data, wherein the calculation formula is as follows:
wherein, F v For vegetation coverage, NDVI is the normalized vegetation index of the pixel, NDVI 0 And NDVI v NDVI values for non-vegetation cover and vegetation cover, respectively, with confidence intervals given to the NDVI statistical histogram, where NDVI values at 5% and 95% frequency are taken as NDVI 0 And NDVI v 。
Step 303, obtaining the ground surface emissivity of the typical feature by using the spectral response functions of different ranges, different table types and medium wave infrared in the ASTER spectral library, as follows:
and step 304, calculating the ground surface emissivity according to the vegetation coverage, and taking the calculated result as the ground surface emissivity at the corresponding moment of the medium-wave infrared data. The calculation formula is as follows:
ε w ＝0.9702(NDVI≤0)
ε b ＝ε v *F v +ε m (1-F v )(0≤NDVI<0.5)
ε n ＝ε v *F v +ε s (1-F v )(0.5≤NDVI<0.8)
ε w ＝0.9846(0.8≤NDVI)
wherein epsilon w 、ε b 、ε n 、ε v 、ε m 、ε s Is water body, building area, natural surface, vegetation area, building surface and bare soil in medium wave infrared spectrumSpecific emissivity of the earth's surface.
And fourthly, constructing an atmospheric water vapor content lookup table by using the global atmospheric water vapor content data provided in the NCEP (national Centers for Environmental prediction) re-analysis data. And then extracting the atmospheric water vapor content data of the phase region and the four moments of the day of the date, calculating the water vapor content data of the corresponding moment through a linear difference value, and finally calculating the atmospheric transmittance according to the relationship between the atmospheric water vapor content and the atmospheric transmittance.
And step five, calculating parameters C and D required in the single-window algorithm according to the ground surface emissivity and the atmospheric transmittance. The calculation formula is as follows:
C＝ε*τ
D＝(1-τ)*[1+(1-ε)*τ]
wherein epsilon is the earth surface specific radiance and tau is the atmospheric transmittance.
Step six, calculating the average action temperature T of the atmosphere by inquiring the acquired air temperature data a The calculation formula is as follows:
T a ＝17.9769+0.91715*T local (15 degree of tropical average air north latitude)
T a ＝16.0110+0.92621*T local (middle latitude summer atmosphere north latitude 45 degree)
T a ＝19.2704+0.91118*T local (middle latitude winter atmosphere north latitude 45 degree)
Wherein, T local Is the local air temperature data in units of K.
Step seven, calculating the earth surface temperature T through a single window algorithm S As follows:
T S ＝[a*(1-C-D)+(b*(1-C-C)+C+D)*B(T)-D*T a ]/C
wherein a and b are constants, and when the local watch temperature is 0-70 ℃, a is-68.035, and b is 0.46372.
Compared with the prior art, the invention has the advantages that:
(1) the method solves the problem of obtaining the single-waveband infrared data ground temperature inversion parameters, aims at solving the problem that the real-time parameters required by the single-window algorithm for inverting the ground surface temperature are difficult to obtain and cannot meet the business requirements of a ground processing system of the remote sensing satellite, researches and uses a plurality of external data sources to construct a normalized atmospheric transmittance and ground surface specific radiance parameter calculation model aiming at a medium-wave infrared spectrum, and realizes the calculation of the ground surface temperature by using the single-waveband infrared data through the single-window algorithm.
(2) According to the method, the ground temperature is inverted by adopting the medium wave infrared data, and the peak value of temperature rising radiation moves towards the short wave direction according to the Wien displacement law, so that compared with long wave infrared data, the medium wave infrared data is utilized for temperature inversion, and a high-heat target can be better detected and identified. The method also determines the earth surface specific radiance of the typical ground feature aiming at the medium wave infrared data so as to effectively calculate the parameters C, D required by the single window algorithm and improve the temperature inversion accuracy.
(3) The method disclosed by the invention is based on high-score four-signal medium wave infrared B06 waveband data, utilizes the NCEP (national Centers for Environmental prediction) and NDVI data to calculate the atmospheric transmittance and the ground surface specific radiance, replaces real-time parameter data to invert the ground surface temperature, solves the problem of real-time parameters required to be input by a single window algorithm, can conveniently and accurately obtain ground surface temperature information, provides data support for research of hydrology, ecology, environment, biological geochemistry and the like, meets the application requirement of a medium wave infrared satellite remote sensing data business process, and has a good practical application value.
Drawings
FIG. 1 is a flow chart of a method for inverting a surface temperature based on mid-wave infrared data according to the present invention;
FIG. 2 is a graph of the results of GF4 surface temperature inversion according to an embodiment of the present invention;
fig. 3 is a graph of GF4 surface temperature and MODIS surface temperature grading for an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail and with reference to the accompanying drawings so that those skilled in the art can understand and practice the invention.
Aiming at the problem that real-time parameters required by a single-window algorithm are difficult to obtain, the method calculates the atmospheric transmittance and the earth surface emissivity by means of the global atmospheric water vapor content data provided in the reanalysis data and the NDVI data of the MODIS based on single-band medium wave infrared remote sensing data so as to replace the real-time parameters required by the single-window algorithm, thereby inverting the earth surface temperature, and being applied to a plurality of research fields such as meteorology, geology, hydrology, ecology, agriculture, urban heat island and the like.
The ground temperature inversion method based on single-waveband medium wave infrared satellite remote sensing data is based on high-resolution fourth-order (GF4) medium wave infrared remote sensing satellite data (IRS), and utilizes the global atmospheric water vapor content data provided in the NCEP (national Centers for Environmental prediction) reanalysis data and the NDVI data of MODIS to construct the atmospheric water vapor content and the ground surface specific radiance data and realize the inversion of the ground surface temperature by a single-window algorithm, as shown in figure 1, the method comprises the following seven steps.
Step one, performing radiometric calibration on the medium-wave infrared data, and converting the recorded original DN value into radiance. The radiometric calibration calculation formula is as follows:
L＝Gain*DN+Offset
in the formula, L is the converted radiance, DN is the satellite load observed value, Gain is the scaling slope, and Offset is the absolute scaling coefficient Offset.
Step two, calculating a brightness temperature value through the radiance data, wherein the calculation formula is as follows:
in the formula, K 1 And K 2 Constant, high-resolution K for IRS of four 1 And K 2 Is constant of 1 ＝150158.9，K 2 3785.45, L is radiance. B (T) is a brightness temperature value calculated from L.
And step three, downloading MODIS vegetation index product data of a month corresponding to the time of the medium wave infrared remote sensing observation data and calculating the earth surface emissivity.
The method comprises the following specific steps:
301, downloading MODIS vegetation index MOD13A1 product data corresponding to the medium wave infrared data time, wherein the time resolution of the MODIS 13A1 product is 16 days, and because the vegetation index data mainly change obviously along with seasons, the data of a twice-month product needs to be downloaded and the average value needs to be calculated to serve as NDVI data at the time;
step 302, calculating vegetation coverage through the downloaded NDVI data, wherein the calculation formula is as follows:
in the formula, F v For vegetation coverage, NDVI is the normalized vegetation index of the pixel, NDVI 0 And NDVI v NDVI values for non-vegetation cover and vegetation cover, respectively, with confidence intervals given to the NDVI statistical histogram, where NDVI values at 5% and 95% frequency are taken as NDVI 0 And NDVI v 。
Step 303, obtaining the ground surface emissivity of the typical feature by using the spectral response functions of different ranges, different table types and medium wave infrared in the ASTER spectral library. Typical surface emissivity values for the medium wave infrared of GF4 are shown in table 1.
TABLE 1 GF4 mid-wave infrared typical ground emissivity
Table 1 shows the calculated surface emissivity for GF4 mid-wave infrared data according to the present invention.
And step 304, calculating the ground surface emissivity according to the vegetation coverage, and taking the calculated result as the ground surface emissivity at the corresponding moment of the medium-wave infrared data. The calculation formula is as follows:
ε w ＝0.9702(NDVI≤0)
ε b ＝ε v *F v +ε m (1-F v )(0≤NDVI<0.5)
ε n ＝ε v *F v +ε s (1-F v )(0.5≤NDVI<0.8)
ε w ＝0.9846(0.8≤NDVI)
in the formula, epsilon w 、ε b 、ε n 、ε v 、ε m 、ε s Specific radiance of water, building area, natural surface, vegetation area, building surface and bare soil in medium-wave infrared spectrum, F v The vegetation coverage is shown.
And fourthly, constructing an atmospheric water vapor content lookup table by using the global atmospheric water vapor content data provided in the NCEP (national Centers for Environmental prediction) re-analysis data. And then extracting the atmospheric water vapor content data of the phase region and the four moments of the day of the date, calculating the water vapor content data of the corresponding moment through a linear difference value, and finally calculating the atmospheric transmittance according to the relationship between the atmospheric water vapor content and the atmospheric transmittance.
The method comprises the following specific steps:
401, downloading the atmospheric water vapor content data of 2020 year all year in the NCEP reanalysis data, wherein the data is day data with the global range of 2.5 degrees multiplied by 2.5 degrees for 6 hours (UTC 0:00, 6:00, 12:00, 18: 00);
step 402, extracting the corresponding atmospheric water vapor content data of four moments of the day according to the date and the position of the observation of the medium wave infrared remote sensing data, then performing linear interpolation on the atmospheric water vapor content data of the four moments, and calculating the atmospheric water vapor content of the observation data at the corresponding time;
and step 403, inquiring the air temperature of the medium-wave infrared observation data area, and calculating the atmospheric transmittance tau according to the atmospheric water vapor content through estimation models at different air temperatures. The atmospheric transmittance estimation model is shown in table 2 below.
TABLE 2 atmospheric transmittance estimation model
The method utilizes the historical global atmospheric water vapor content data provided in the NCEP reanalysis data as water vapor content model data to calculate the atmospheric transmittance, and compared with the prior art, the method reduces the difficulty in data acquisition.
And step five, calculating parameters C and D required in the single-window algorithm according to the ground surface emissivity and the atmospheric transmittance. The calculation formula is as follows:
C＝ε*τ
D＝(1-τ)*[1+(1-ε)*τ]
in the formula, epsilon is the earth surface emissivity and is obtained by calculation according to the third step; τ is the atmospheric transmittance.
And step six, calculating the average atmospheric acting temperature by inquiring the acquired air temperature data. The calculation formula is as follows:
T a ＝17.9769+0.91715*T local (15 degree of tropical average air north latitude)
T a ＝16.0110+0.92621*T local (middle latitude summer atmosphere north latitude 45 degree)
T a ＝19.2704+0.91118*T local (middle latitude winter atmosphere north latitude 45 degree)
In the formula, T a Is the average temperature of action of the atmosphere, T local Is the local air temperature data in units of K.
And seventhly, calculating the earth surface temperature through a single window algorithm. The calculation formula is as follows:
T S ＝[a*(1-C-D)+(b*(1-C-C)+C+D)*B(T S )-D*T a ]/C
in the formula, T S Is the surface temperature; a. b is a constant (when the local watch temperature is 0-70 ℃, a is-68.035, b is 0.46372).
Examples
The data of the high-score fourth IRS at the junction of the provinces of Shanxi, Hebei and Henan in 2017, 9, 7, 10:18:03 are used for explanation:
firstly, the radiance is calculated by performing radiance scaling on the high-resolution four-number IRS data, wherein the radiance scaling coefficient Gain is 0.001107, and the Offset is 0.878625.
Then, the brightness temperature is calculated according to a brightness temperature calculation formula through the radiation brightness, and the K of the IRS with the height of four is calculated 1 ＝150158.9，K 2 ＝3785.45。
And thirdly, downloading data of MOD13A1 products in the second 9 th month in 2017, and calculating a mean value to generate NDVI data in the 9 th month in 2017. Giving confidence intervals to the NDVI statistical histogram, and taking the NDVI values of 5% and 95% of frequencies as the NDVI 0 And NDVI v Used for calculating vegetation coverage. And finally, calculating the earth surface specific radiance of the water body, the building area, the natural surface and the vegetation area by combining the vegetation coverage data with the earth surface specific radiance data of the typical ground object corresponding to the medium wave infrared.
And fourthly, downloading global atmospheric water vapor content data provided in the 2020 NCEP (national Centers for Environmental prediction) re-analysis data, and constructing an atmospheric water vapor content lookup table. Then, water vapor content data of the target area 2017 at four times (UTC 0:00, 6:00, 12:00, 18:00) of 9, 7 and 7 days are extracted, and water vapor content data of the target area at 10 days are calculated through linear difference values. And finally, inquiring the historical air temperature of the central point position of the region to be about 17.78 ℃, and calculating the atmospheric transmission rate through an estimation model of the atmospheric water vapor content and the transmission rate under different air temperatures.
And fifthly, calculating C, D parameters according to the earth surface emissivity and the atmospheric transmittance.
And sixthly, calculating the average atmospheric operating temperature from the historical temperature of the region obtained by query according to the linear relation between the local air temperature and the average atmospheric operating temperature.
And seventhly, inverting the earth surface temperature according to a single-window algorithm. The inversion results are shown in fig. 2.
And (4) taking the MODIS surface temperature data as standard reference data, and verifying the surface temperature data inverted by the high-resolution four-data. Because the medium wave infrared spectrum characteristics and the high-resolution fourth-order medium wave infrared band calibration coefficients are not influenced by factors such as updating, when the inversion result is verified, only the high-temperature and low-temperature distribution conditions of the inversion result and the reference data are analyzed. Therefore, before analysis, the inversion result and the MODIS temperature data need to be subjected to temperature grade division, the division method adopts a mean-standard deviation method, and the grading standard is shown in table 3, wherein: μ is the average temperature; std is the standard deviation; t is s Is a temperature value.
TABLE 3 mean-standard deviation method of geothermal grading
| Temperature grade | Temperature division standard |
| High temperature zone | T s >μ+std |
| Second high temperature zone | μ+0.5std<T s ≤μ+std |
| Middle temperature zone | μ-0.5std≤T s <μ+0.5std |
| Sub-low temperature zone | μ-std≤T s <μ-0.5std |
| Low temperature zone | T s <μ-std |
As shown in fig. 3, a is a low-temperature grade division result of the high-resolution four-signal medium-wave infrared inversion result, and b is a temperature grade division result of the MODIS ground temperature; the ratio of each grade region after grading the temperature of GF4 inversion result and MODIS ground temperature data is shown in Table 4. The combination of the graphs shows that the temperature grade distribution is integrally consistent except that the high-temperature areas and the secondary high-temperature areas in partial areas are slightly different, and the inversion result can accurately reflect the temperature difference between the areas on the whole.
TABLE 4 GF4-MODIS geothermal grade difference division ratio
The results of the above examples show that when the surface temperature is inverted by medium wave infrared remote sensing based on the single window algorithm, the surface temperature is inverted by using the global atmospheric water vapor content data provided in the NCEP reanalysis data and the atmospheric transmittance and the surface emissivity calculated by the NDVI data provided by the MODIS instead of the real-time parameter data, so that the surface temperature information can be obtained conveniently and accurately, data support is provided for research on hydrology, ecology, environment, biological geochemistry and the like, and the method has a good practical application value.
The atmospheric transmittance and the earth surface emissivity required by the single-window algorithm are calculated from historical data, remote sensing data used for inversion are only single-band medium wave infrared data, and meanwhile, the required atmospheric average temperature is also calculated by air temperature data of a data center position, so that the inversion result has certain deviation compared with the actual situation, but the deviation belongs to a receivable range.
In addition to the technical features described in the specification, the technology is known to those skilled in the art. The description of the known art is omitted. The embodiments described in the above embodiments do not represent all embodiments consistent with the present application, and various modifications or variations which may be made by those skilled in the art without inventive efforts based on the technical solution of the present invention are still within the protective scope of the present invention.
Claims (5)
1. A ground temperature inversion method based on single-waveband medium-wave infrared satellite remote sensing data is characterized by comprising the following steps:
step 1: acquiring high-resolution four-signal medium wave infrared satellite remote sensing data, carrying out radiometric calibration on the data, converting a satellite load observation value into radiance, and calculating a brightness temperature value B (T) through the radiance;
step 2, downloading MODIS vegetation index product data corresponding to the medium wave infrared remote sensing data time, taking the MODIS vegetation index product data as NDVI data under the time, and calculating the earth surface emissivity, wherein the method comprises the following steps:
(1) calculating vegetation coverage F from downloaded NDVI data v ；
(2) And (3) calculating and obtaining the ground surface emissivity of the high-resolution fourth-order medium-wave infrared typical ground object by using the spectral response functions of different ranges, different table types and medium-wave infrared in the ASTER spectral library:
the calculated surface emissivity of each typical feature in Band6 is as follows:
water body: 0.9702, respectively; bare soil: 0.7705, respectively; building: 0.8746, respectively; vegetation: 0.9846, respectively;
(3) according to vegetation coverage F v And calculating the corresponding ground surface emissivity as the ground surface emissivity of the medium-wave infrared remote sensing data at the corresponding moment as follows:
ε w ＝0.9702(NDVI≤0)
ε b ＝ε v *F v +ε m (1-F v )(0≤NDVI＜0.5)
ε n ＝ε v *F v +ε s (1-F v )(0.5≤NDVI＜0.8)
ε w ＝0.9846(0.8≤NDVI)
wherein epsilon w 、ε b 、ε n 、ε v 、ε m 、ε s The specific radiance of water, building areas, natural surfaces, vegetation areas, building surfaces and bare soil in the ground surface of a medium-wave infrared spectrum;
step 3, utilizing the global atmospheric water vapor content data provided in the NCEP reanalysis data to construct an atmospheric water vapor content lookup table; then extracting the atmospheric water vapor content data of four moments of the day corresponding to the date of the observation position of the medium wave infrared remote sensing data from the table, and calculating the atmospheric water vapor content of the observation data at the corresponding moment through a linear difference value; finally, calculating the atmospheric transmittance tau according to the relation between the atmospheric water vapor content and the atmospheric transmittance;
step 4, calculating a parameter C and a parameter D required in the single-window algorithm according to the ground surface emissivity and the atmospheric transmittance;
step 5, obtaining local air temperature data through inquiry, and calculating the average action temperature T of the atmosphere a ；
Step 6, calculating the surface temperature T by a single window algorithm S The following are:
T S ＝[a*(1-C-D)+(b*(1-C-C)+C+D)*B(T)-D*T a ]/C
wherein a and b are constants; when the local surface temperature is 0-70 ℃, a is-68.035, and b is 0.46372.
2. The method according to claim 1, wherein in step 1, the formula for calculating the brightness temperature value is as follows:
where L is the converted radiance, B (T) is a brightness temperature value calculated from the radiance L, K 1 And K 2 Are constant, are respectively K 1 ＝150158.9，K 2 ＝3785.45。
3. The method of claim 1, wherein in step 2, vegetation coverage F is calculated according to the following equation v ：
Wherein NDVI is the normalized vegetation index of the pixel, NDVI 0 And NDVI v Non-vegetation cover and vegetation cover part NDVI values, respectively; NDVI 0 And NDVI v The acquisition mode is as follows: giving confidence to NDVI statistical histogramsInterval in which the NDVI values of 5% and 95% frequency are NDVI respectively 0 And NDVI v 。
4. The method of claim 1, wherein step 3 comprises:
(1) downloading the annual atmospheric water vapor content data of 2020 year in the NCEP reanalysis data, wherein the data is day data with the global range of 2.5 degrees multiplied by 2.5 degrees by 6 hours (UTC 0:00, 6:00, 12:00, 18: 00);
(2) extracting the corresponding atmospheric water vapor content data at four moments of the day according to the date and the position observed by the medium wave infrared remote sensing data, then carrying out linear interpolation on the atmospheric water vapor content data at the four moments, and calculating the atmospheric water vapor content of the observed data at the corresponding time;
(3) and inquiring the air temperature of the medium-wave infrared observation data area, and calculating the atmospheric transmission rate according to the atmospheric water vapor content through estimation models under different air temperatures.
5. The method according to claim 1, wherein in step 5, the average temperature T of the atmosphere a The formula is as follows:
T a ＝17.9769+0.91715*T local (15 degree of tropical average air north latitude)
T a ＝16.0110+0.92621*T local (middle latitude summer atmosphere north latitude 45 degree)
T a ＝19.2704+0.91118*T local (middle latitude winter atmosphere north latitude 45 degree)
Wherein, T local Is the local air temperature data.
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