CN114838827A - Earth surface temperature inversion channel selection method based on MERSI-II remote sensing data - Google Patents
Earth surface temperature inversion channel selection method based on MERSI-II remote sensing data Download PDFInfo
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Abstract
The invention provides a method for selecting an earth surface temperature inversion channel based on MERSI-II remote sensing data, which comprises the following steps: acquiring MERSI-II remote sensing data, taking three different land types including water bodies, town areas and natural surfaces as earth surface temperature inversion objects, and performing earth surface temperature inversion by using a universal single-channel algorithm; carrying out nonlinear fitting on the inverted surface temperature by using a time sequence analysis method; and respectively obtaining the inversion earth surface temperature and the fitting earth surface temperature of the two thermal infrared channels through the steps, carrying out regression analysis and comparison, and carrying out precision evaluation by adopting a standard decision coefficient, a deviation and a root mean square error to obtain the thermal infrared channel with high earth surface temperature inversion precision. The invention utilizes a universal single-channel algorithm to carry out surface temperature inversion on MERSI-II remote sensing data, and then carries out precision verification on the inversion result, thereby determining the inversion precision of different thermal infrared channels.
Description
Technical Field
The invention relates to the technical field of infrared remote sensing, in particular to a method for selecting a surface temperature inversion channel based on MERSI-II remote sensing data.
Background
Surface temperature lst (land Surface temperature) is a very important characteristic physical quantity characterizing Surface process changes, and is also a direct driving factor for Surface-to-atmosphere energy exchange. The time-space change information of the surface temperature has important scientific significance and application value in the fields of climate change, urban thermal environment monitoring, soil humidity estimation, agricultural drought monitoring, fire point monitoring, land utilization, land coverage and the like. The method for inverting the earth surface temperature based on the thermal infrared satellite remote sensing data has the advantages of convenience in data acquisition, wide space detection range, high inversion precision, large information acquisition amount, low cost and the like, can well make up for the limitation of acquiring the temperature by a ground station, and obtains earth surface temperature information which is more continuous in space-time scale and wider in coverage range.
Data of a satellite sensor used for inverting the earth surface temperature are mostly TM and TIRS carried on Landsat series satellites, MODIS and ASTER carried on Terra/Aqua satellites, AVHRR on NOAA satellites and MERSI on domestic Fengyun series satellites. The Medium Resolution imaging spectrometer II (MERSI-II) is a sensor carried on the second generation polar orbit satellite Fengyun third which is independently researched and developed in China, integrates the functions of a Visible and Infrared scanning Radiometer (VIRR) and a Medium Resolution spectrometer (MERSI) on the original Fengyun third satellite, comprises 25 channels, has the ground Resolution of 250m for a thermal Infrared channel 24 and a channel 25, is the first imaging instrument capable of acquiring the information of an Infrared splitting window zone with the global Resolution of 250m in the world, can be used for inverting the ground surface temperature with the high spatial Resolution, and simultaneously provides satellite meteorological information for global environment change, global climate change research, oceans, agriculture, forestry, aviation, military departments and the like. Compared with the prior MERSI sensor which only has one thermal infrared channel, the new generation MERSI-II data has more application advantages. The MERSI-II is provided with two thermal infrared channels, and the problem of uncertain precision exists when different thermal infrared channels are utilized by adopting a single-channel algorithm in the surface temperature inversion.
Disclosure of Invention
The invention aims to provide a method for selecting earth surface temperature inversion channels based on MERSI-II remote sensing data.
In order to achieve the purpose, the invention provides the following scheme:
a method for selecting a surface temperature inversion channel based on MERSI-II remote sensing data comprises the following steps:
s1, acquiring MERSI-II remote sensing data based on a MERSI-II sensor carried on the FY-3D satellite, and preprocessing the data;
s2, selecting an image with less cloud amount from the MERSI-II remote sensing data subjected to data preprocessing, taking three different land types including water bodies, town areas and natural surfaces as earth surface temperature inversion objects, and performing earth surface temperature inversion by using a universal single-channel algorithm;
s3, performing nonlinear fitting on the inverted earth surface temperature by using a time series analysis method to obtain a fitted earth surface temperature;
and S4, repeating the steps S2-S3, respectively obtaining inversion surface temperatures and fitting surface temperatures of three different types of water bodies, urban areas and natural surfaces in the two thermal infrared channels, performing regression analysis and comparison on the inversion surface temperatures and the fitting surface temperatures of the two thermal infrared channels, and performing precision evaluation by adopting standard decision coefficients, deviation and root-mean-square errors to obtain the thermal infrared channel with high surface temperature inversion precision.
Further, in step S1, the data preprocessing specifically includes: geometric correction, atmospheric correction, radiometric calibration and position clipping of research area.
Further, in step S2, selecting an image with a small cloud amount from the preprocessed MERSI-ii remote sensing data, taking three different types of water, town area, and natural surface as ground surface temperature inversion objects, and performing ground surface temperature inversion by using a universal single-channel algorithm, specifically including:
the calculation formula is as follows:
T s ＝γ[ε -1 (ψ 1 L sensor +ψ 2 )+ψ 3 ]+δ
δ＝-γL sensor +T sensor
wherein L is sensor Is the radiance of the sensor, in [ W.m ] -2 ·sr -1 ·μm -1 )]；T sensor Brightness temperature, unit K; λ is the central wavelength, in μm; epsilon is emissivity; c. C 1 、c 2 As an atmospheric parameter, c 1 ＝1.19104×10 8 μm 4 ·m - 2 .sr -1 ，c 2 ＝14387.7μm·K；ψ 1 、ψ 2 、ψ 3 Is a function related to the atmospheric water vapour content w.
Further, the emissivity epsilon is calculated by dividing the ground surface into a water body, a natural surface and a town area, wherein the emissivity of the water body is equal to a black body, namely 0.995, and the emissivity of the natural surface and the ground surface of the town area is calculated by the following formula:
natural surface emissivity:
urban area emissivity:
wherein, F V For vegetation coverage, the formula is as follows:
in the formula, NDVI represents the vegetation coverage index of the research area, NDVI min Represents the minimum value of the region of interest NDVI, NDVI max Represents the maximum value of the NDVI of the study region.
Further, the atmospheric water vapor content w is subjected to regression and weighted average calculation by utilizing the ratio of the radiance of the atmospheric water vapor waveband carried by the sensor to the radiance of the atmospheric window waveband, and the method specifically comprises the following steps:
the atmospheric water vapor content obtained by the 3 atmospheric water vapor absorption channels is as follows:
wherein R is 16 、R 17 、R 18 Respectively representing the radiance ratio of the 16 th, 17 th and 18 th channels to the 4 th channel, w 16 、w 17 、w 18 Each represents R 16 、R 17 、R 18 Regressing the calculated water vapor content;
wherein L is 16 、L 17 、L 18 、L 4 Respectively representing the radiance of 16 th, 17 th, 18 th and 4 th channels;
the atmospheric water vapor content is expressed by linear combination of 3 atmospheric water vapor absorption channels through weighting calculation, and the expression is as follows:
w＝0.208w 16 +0.433w 17 +0.359w 18 。
further, the brightness temperature T sensor The calculation formula of (a) is as follows:
in the formula, constant c 3 ＝1.19104659×10 -22 W/m 2 ，c 4 ＝1.438833×10 -2 mK; λ is the center wavelength; l is sensor The remote sensing data is obtained by converting DN value of the remote sensing data, and the conversion formula is as follows:
L sensor ＝M i Q+A i
in the formula, M i And A i Respectively a gain parameter and an offset parameter of the i channel; q is the brightness value of the image, i.e. DN value.
Further, in step S3, performing nonlinear fitting on the inverted surface temperature by using a time series analysis method to obtain a fitted surface temperature, where the specific formula is as follows:
T t ＝a+bt+ct 2
wherein T is time, a, b and c are fitting model coefficients, T t To fit the surface temperature.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a ground surface temperature inversion channel selection method based on MERSI-II remote sensing data, which relates to a technology combining single-channel ground surface temperature inversion of domestic FY-3D satellite remote sensing images and accuracy verification of inversion results of two thermal infrared channels and the like, and mainly aims at solving the problem that a single-channel algorithm is uncertain by utilizing different thermal infrared channels in ground surface temperature inversion of FY-3D MERSI-II remote sensing data, surface temperature inversion is carried out on MERSI-II remote sensing data by utilizing a universal single-channel algorithm, and accuracy verification is carried out on inversion results by utilizing two methods of temperature curve fitting and MODIS temperature product cross verification so as to solve the problem of channel selection when the single-channel algorithm is adopted in ground surface temperature inversion.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for selecting a surface temperature inversion channel based on MERSI-II remote sensing data.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method for selecting earth surface temperature inversion channels based on MERSI-II remote sensing data.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for selecting an earth surface temperature inversion channel based on MERSI-ii remote sensing data provided by the invention comprises the following steps:
s1, acquiring MERSI-II remote sensing data based on a MERSI-II sensor carried on the FY-3D satellite, and preprocessing the data; performing preprocessing operation through ENVI5.6 software, wherein the preprocessing operation process comprises the following steps: geometric correction, atmospheric correction, radiometric calibration and position cutting of a research area;
s2, selecting images with less cloud cover from the MERSI-II remote sensing data after data preprocessing, taking three different land types including water, town areas and natural surfaces as surface temperature inversion objects, and performing surface temperature inversion by using a universal single-channel algorithm;
s3, performing nonlinear fitting on the inverted surface temperature by using a time series analysis method;
and S4, repeating the steps S2-S3, respectively obtaining inversion surface temperatures and fitting surface temperatures of three different types of water bodies, urban areas and natural surfaces in the two thermal infrared channels, performing regression analysis and comparison on the inversion surface temperatures and the fitting surface temperatures of the two thermal infrared channels, and performing precision evaluation by adopting standard decision coefficients, deviation and root-mean-square errors to obtain the thermal infrared channel with high surface temperature inversion precision.
In step S2, selecting an image with a small cloud amount from the preprocessed MERSI-ii remote sensing data, taking three different types of water, town area, and natural surface as surface temperature inversion objects, and performing surface temperature inversion by using a universal single-channel algorithm, specifically including:
the calculation formula is as follows:
T s ＝γ[ε -1 (ψ 1 L sensor +ψ 2 )+ψ 3 ]+δ
δ＝-γL sensor +T sensor
wherein L is sensor Is the radiance of the sensor, in [ W.m ] -2 ·sr -1 ·μm -1 )]；T sensor Brightness temperature, unit K; λ is the central wavelength, in μm; epsilon is emissivity; c. C 1 、c 2 As an atmospheric parameter, c 1 ＝1.19104×10 8 μm 4 ·m - 2 .sr -1 ，c 2 ＝14387.7μm·K；ψ 1 、ψ 2 、ψ 3 Is a function related to the atmospheric water content w, and the formula is as follows:
ψ k ＝η kλ ω 3 +ζ kλ ω 2 +χ kλ ω+φ kλ (k＝1，2，3)
for psi 1 ：
η 1λ ＝0.00090λ 3 -0.01638λ 2 +0.04745λ+0.274365
ζ 1λ ＝0.00032λ 3 -0.06148λ 2 +1.2021λ-6.2015
χ 1λ ＝0.00986λ 3 -0.23672λ 2 +1.7133λ-3.2199
φ 1λ ＝-0.15431λ 3 +5.27572λ 2 -60.1170λ+229.3139
For psi 2 ：
η 2λ ＝-0.02883λ 3 +0.8718λ 2 -8.82712λ+29.9092
ζ 2λ ＝0.13515λ 3 -4.1171λ 2 +41.8295λ-142.2782
χ 2λ ＝-0.22765λ 3 +6.8606λ 2 -69.2577λ+233.0722
φ 2λ ＝0.41868λ 3 -14.3299λ 2 +163.6681λ-623.5300
For psi 3 ：
η 3λ ＝0.00182λ 3 -0.04519λ 2 +0.32652λ-0.60030
ζ 3λ ＝-0.00744λ 3 +0.11431λ 2 +0.17560λ-5.4588
χ 3λ ＝-0.00269λ 3 +0.31395λ 2 -5.5916λ+27.9913
φ 3λ ＝-0.07972λ 3 +2.8396λ 2 -33.6843λ+132.9798
The method for calculating the emissivity epsilon comprises the steps of dividing the earth surface into a water body, a natural surface and an urban area, wherein the emissivity of the water body is equal to a black body, namely 0.995, and the emissivity of the natural surface and the earth surface of the urban area is calculated by the following formula:
natural surface emissivity:
urban area emissivity:
wherein, F V For vegetation coverage, theoretically, the vegetation coverage of bare soil is close to 0, and the pixel vegetation coverage of all vegetation covers is close to 1. However, under the influence of the environment and other factors, the value of NDVIS always floats between 0.1 and 0.2, and the value of NDVIV also varies depending on the vegetation type and vegetation growth state. Therefore, a confidence interval of 5% -95% is selected, and the value of the vegetation coverage in the bare soil and the value of the vegetation coverage in the vegetation full-coverage pixel are represented by the 5% value and the 95% value in the cumulative percentage, so as to estimate the vegetation coverage, and the formula is represented as follows:
in the formula, NDVI represents the vegetation coverage index of the research area, NDVI min Represents the minimum value of the region of interest NDVI, NDVI max Represents the maximum value of the NDVI of the study region.
The method is characterized in that the atmospheric water vapor content w is subjected to regression and weighted average calculation by utilizing the ratio of the radiation brightness of the atmospheric water vapor wave band carried by the sensor to the radiation brightness of the atmospheric window wave band, and specifically comprises the following steps:
the atmospheric water vapor content obtained by the 3 atmospheric water vapor absorption channels is as follows:
wherein R is 16 、R 17 、R 18 Respectively representing the radiance ratio of the 16 th, 17 th and 18 th channels to the 4 th channel, w 16 、w 17 、w 18 Each represents R 16 、R 17 、R 18 Regressing the calculated water vapor content;
wherein L is 16 、L 17 、L 18 、L 4 Respectively representing the radiance of 16 th, 17 th, 18 th and 4 th channels;
the atmospheric water vapor content is expressed by linear combination of 3 atmospheric water vapor absorption channels through weighting calculation, and the expression is as follows:
w＝0.208w 16 +0.433w 17 +0.359w 18 。
the method for calculating the brightness temperature comprises various methods, and the brightness temperature T is calculated by adopting the central wavelength of the channel sensor The calculation formula of (a) is as follows:
in the formula, constant c 3 ＝1.19104659×10 -22 W/m 2 ，c 4 ＝1.438833×10 -2 mK; λ is the center wavelength; l is sensor The remote sensing data is obtained by converting DN value of the remote sensing data, and the conversion formula is as follows:
L sensor ＝M i Q+A i
in the formula, M i And A i Respectively a gain parameter and an offset parameter of the i channel; q is the brightness value of the image, i.e. DN value.
In the step S3, a time series analysis method is used to perform nonlinear fitting on the inverted surface temperature, and the specific formula is as follows:
T t ＝a+bt+ct 2
wherein T is time, a, b and c are fitting model coefficients, T t To fit the surface temperature.
The invention utilizes a time sequence analysis method to carry out nonlinear fitting on the earth surface temperature after inversion of North China plain, and the use occasion of the nonlinear fitting is the occasion with a long-term trend showing nonlinear characteristics. The idea of parameter estimation is to convert all the parameters which can be converted into linear models, and carry out parameter estimation by using a linear least square method.
In a specific embodiment, the surface temperature of the North China plain is obtained by utilizing 2020 MERSI-II remote sensing images of the North China plain and adopting a universal single-channel algorithm through extracting NDVI, emissivity and brightness temperature, and the surface temperature change conditions of the North China plain are analyzed by respectively selecting four images of 4 months, 7 months, 11 months and 2 months to represent four seasons of spring, summer, autumn and winter of the North China plain.
And selecting three different surface features of water, cities and towns and vegetation for classification in the inversion result by a method of combining supervision classification and unsupervised classification to obtain surface temperatures of 24 and 25 channels of the three surface feature types. And based on a time series analysis method, curve fitting is carried out on the inversion result by combining with statistical analysis software, and surface temperature change curves of different surface feature types of 24 and 25 channels are obtained. Determining the coefficient (R) from the standard 2 ) As an index to measure the degree of linear correlation between two variables. R 2 The closer to 1, the higher the degree of linear correlation.
Independent variables in the simulation process are days in one year, in order to fit an effective model with high correlation, equations such as unary linearity, multiple linearity, power, exponent, logarithm and the like are respectively established for fitting, and multiple tests show that the correlation is highest when various ground features are fitted by using a secondary nonlinear equation.
The earth surface temperature in 2020 year of North China plain can be calculated based on the fitting curve, and the fitting effect of 24 channels is higher than that of 25 channels, so that the fitting temperature of a 24-channel model is selected during precision verification. In order to verify the accuracy of the fitted curve model, the remote sensing data of one year in North China plain is screened again, and images with high quality and less cloud amount are selected as standard images for verification aiming at three different land types.
In order to evaluate the accuracy of the inversion result, the inversion value and the fitting value are subjected to regression analysis and comparison, and a standard determination coefficient (R) is adopted 2 ) The accuracy was evaluated by the deviation (bias) and the Root Mean Square Error (RMSE). The Bias represents a constant degree between the measured value and the true value, and the smaller the absolute deviation is, the higher the consistency degree of the measured value and the true value is; the RMSE is reasonably attributed to the measurement dispersion of the measured factors, with smaller RMSE, less fluctuation in error indicative of the inversion results. To be provided withThe arithmetic expressions corresponding to the above indexes are as follows:
in the formula, LST bias 、LST absolute bias 、LST RMSE Respectively representing the deviation, absolute deviation and root mean square error, LST, of a surface temperature verification i inv Is the inversion of the surface temperature (corresponding to T) s )，LST i ref Is a reference surface temperature (using a simulated surface temperature T) t And the surface temperature of MODIS temperature products), N is the number of verification pixels.
Analysis shows that the inversion effect of the earth surface temperature of the 24 channels of the MERSI-II remote sensing data is superior to that of the 25 channels.
In order to further verify the inversion accuracy of MERSI-II data, the invention adopts the MYD11A1 surface temperature product of MODIS to perform cross verification. The inversion results of three different ground object types have higher consistency with surface temperature products, and the R of two channels 2 Are all above 0.87, wherein the R2 of the 24-channel water body reaches 0.94; the absolute deviation of the 24 channels is between 2.77K and 3.31K, and the absolute deviation of the 25 channels is between 3.43K and 4.33K; the root mean square error of the 24 channels is between 4.03K and 4.35K, and the root mean square error of the 25 channels is between 4.87K and 5.29K, which indicates that the FY-3D MERSI-II data can replace MODIS products to carry out surface temperature inversion research.
The invention provides a ground surface temperature inversion channel selection method based on MERSI-II remote sensing data, which relates to a technology combining single-channel ground surface temperature inversion of domestic FY-3D satellite remote sensing images and accuracy verification of inversion results of two thermal infrared channels and the like, and mainly aims at solving the problem that a single-channel algorithm is uncertain by utilizing different thermal infrared channels in ground surface temperature inversion of FY-3D MERSI-II remote sensing data, surface temperature inversion is carried out on MERSI-II remote sensing data by utilizing a universal single-channel algorithm, and accuracy verification is carried out on inversion results by utilizing two methods of temperature curve fitting and MODIS temperature product cross verification so as to solve the problem of channel selection when the single-channel algorithm is adopted in ground surface temperature inversion.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (7)
1. A method for selecting a surface temperature inversion channel based on MERSI-II remote sensing data is characterized by comprising the following steps:
s1, acquiring MERSI-II remote sensing data based on a MERSI-II sensor carried on the FY-3D satellite, and preprocessing the data;
s2, selecting an image with less cloud amount from the MERSI-II remote sensing data subjected to data preprocessing, taking three different land types including water bodies, town areas and natural surfaces as earth surface temperature inversion objects, and performing earth surface temperature inversion by using a universal single-channel algorithm;
s3, performing nonlinear fitting on the inverted earth surface temperature by using a time series analysis method to obtain a fitted earth surface temperature;
and S4, repeating the steps S2-S3, respectively obtaining inversion surface temperatures and fitting surface temperatures of three different types of water bodies, urban areas and natural surfaces in the two thermal infrared channels, performing regression analysis and comparison on the inversion surface temperatures and the fitting surface temperatures of the two thermal infrared channels, and performing precision evaluation by adopting standard decision coefficients, deviation and root-mean-square errors to obtain the thermal infrared channel with high surface temperature inversion precision.
2. The earth surface temperature inversion channel selection method based on MERSI-II remote sensing data as claimed in claim 1, wherein in step S1, the data preprocessing specifically comprises: geometric correction, atmospheric correction, radiometric calibration and position clipping of research area.
3. The method for selecting an earth surface temperature inversion channel based on MERSI-II remote sensing data as claimed in claim 1, wherein in step S2, images with less cloud cover are selected from the MERSI-II remote sensing data after data preprocessing, three different land types including water, town area and natural surface are used as earth surface temperature inversion objects, and a universal single-channel algorithm is used for earth surface temperature inversion, which specifically comprises:
the calculation formula is as follows:
T s ＝γ[ε -1 (ψ 1 L sensor +ψ 2 )+ψ 3 ]+δ
δ＝-γL sensor +T sensor
wherein L is sensor Is the radiance of the sensor, in [ W.m ] -2 ·sr -1 ·μm -1 )]；T sensor Brightness temperature, unit K; λ is the central wavelength, in μm; epsilon is emissivity; c. C 1 、c 2 As an atmospheric parameter, c 1 ＝1.19104×10 8 μm 4 ·m -2 .sr -1 ，c 2 ＝14387.7μm·K；ψ 1 、ψ 2 、ψ 3 Is a function related to the atmospheric water vapour content w.
4. The method for selecting earth surface temperature inversion channel based on MERSI-II remote sensing data as claimed in claim 3, wherein the emissivity epsilon is calculated by dividing the earth surface into water, natural surface and urban area, the emissivity of water is equal to black body (0.995), and the emissivity of natural surface and urban area earth surface is calculated by the following formula:
natural surface emissivity:
urban area emissivity:
wherein, F V For vegetation coverage, the formula is as follows:
in the formula, NDVI represents the vegetation coverage index of the research area, NDVI min Represents the minimum value of the region of interest NDVI, NDVI max Represents the maximum value of the NDVI of the study region.
5. The earth surface temperature inversion channel selection method based on MERSI-II remote sensing data as claimed in claim 3, wherein the atmospheric water vapor content w is calculated by regression and weighted average using the ratio of the radiance of the atmospheric water vapor band carried by the sensor itself to the radiance of the atmospheric window band, and specifically comprises:
the atmospheric water vapor content obtained by the 3 atmospheric water vapor absorption channels is as follows:
wherein R is 16 、R 17 、R 18 Respectively representing the radiance ratio of the 16 th, 17 th and 18 th channels to the 4 th channel, w 16 、w 17 、w 18 Each represents R 16 、R 17 、R 18 Calculated by regressionThe water vapor content;
wherein L is 16 、L 17 、L 18 、L 4 Respectively representing the radiance of 16 th, 17 th, 18 th and 4 th channels;
the atmospheric water vapor content is expressed by linear combination of 3 atmospheric water vapor absorption channels through weighting calculation, and the expression is as follows:
w＝0.208w 16 +0.433w 17 +0.359w 18 。
6. the method of claim 3, wherein the brightness temperature T is a surface temperature inversion channel selection method based on MERSI-II remote sensing data sensor The calculation formula of (a) is as follows:
in the formula, constant c 3 ＝1.19104659×10 -22 W/m 2 ，c 4 ＝1.438833×10 -2 mK; λ is the center wavelength; l is sensor The remote sensing data is obtained by converting DN value of the remote sensing data, and the conversion formula is as follows:
L sensor ＝M i Q+A i
in the formula, M i And A i Respectively a gain parameter and an offset parameter of the i channel; q is the brightness value of the image, i.e. DN value.
7. The method for selecting the earth surface temperature inversion channel based on the MERSI-II remote sensing data as claimed in claim 1, wherein in the step S3, the time series analysis method is used for carrying out nonlinear fitting on the inverted earth surface temperature to obtain a fitted earth surface temperature, and the specific formula is as follows:
T t ＝a+bt+ct 2
whereinT is time, a, b, c are fitting model coefficients, T t To fit the surface temperature.
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