Quantitative extraction method and system of long-wave infrared emissivity based on VIIRS remote sensing data

Abstract

The present invention provides a method and system for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data. The method comprises: step 1, preprocessing a remote sensing image and extracting data; step 2, simulating atmospheric parameters of the remote sensing image; step 3, determining coefficients of a bidirectional reflectivity estimation model, and calling the model to calculate the bidirectional reflectivity of the VIIRS image; step 4, setting a maximum emissivity estimate, calculating the surface temperature of the ground in all long-wave infrared bands, and taking the maximum surface temperature as the initial value of the surface temperature; step 5, constructing a cost function model based on the initial temperature and the difference between the maximum and minimum emissivity spectra of the emissivity, and iteratively obtaining a local optimal solution for the surface temperature; step 6, if the difference between the local optimal solution and the initial temperature is greater than a threshold, taking the temperature as the initial temperature and repeating steps 5 to 6; otherwise, the iteration converges, and the emissivity of the VIIRS image is calculated according to the infrared radiation transmission equation.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing image processing, and in particular relates to a method and system for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data. Background Art

[0002] Surface emissivity, as the direct driving force for the exchange of long-wave radiation and latent heat flux between the surface and the atmosphere, is an important parameter describing the surface thermal radiation properties and surface spectral information. It can reflect important attributes such as land feature type and surface temperature. However, surface emissivity has certain spatial and temporal heterogeneity, and measurement methods based on stations or local areas are difficult to meet diverse application needs. Satellite remote sensing's long time series and large spatial range observation characteristics make it the main technical means to obtain surface emissivity data. Obtaining high-frequency and large-scale spatiotemporal variation information on surface emissivity provides key data support for many research fields such as global-scale meteorological forecasting, climate change, agricultural and forestry monitoring, and urban thermal environment.

[0003] The VIIRS remote sensor aboard NASA's SNPP satellite has five medium-resolution bands in the long-wave infrared (LWIR) (M12, M13, M14, M15, and M16). The M12 and M13 bands lie within the 3-5 micron mid-infrared atmospheric window, while the M14, M15, and M16 bands lie within the 8-14 micron thermal infrared atmospheric window. The mid-infrared band, closer to shortwave wavelengths, is more susceptible to solar radiation, while the thermal infrared band, with its longer wavelengths, is virtually unaffected by solar radiation. Therefore, during daytime conditions, the radiation transmission processes in the M12-M13 and M14-M16 bands are completely different. This makes the inversion methods for LWIR emissivity independent and separate, easily leading to incomplete extraction of ground object emissivity information and inconsistent scales. Summary of the Invention

[0004] The present invention is carried out to solve the above-mentioned problems, and its purpose is to provide a method and system for quantitative extraction of long-wave infrared emissivity based on VIIRS remote sensing data, which overcomes the difference in radiation transmission of the solar radiation source to the two infrared windows, realizes the continuous and synchronous inversion of all long-wave infrared emissivities under daytime conditions, and maintains the temporal continuity and spectral integrity of the surface emissivity data.

[0005] In order to achieve the above purpose, the present invention adopts the following scheme:

[0006] <Method>

[0007] like Figure 1 As shown, the present invention provides a method for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data, which is characterized by comprising the following steps:

[0008] Step 1: Preprocess the VIIRS remote sensing image to extract radiance data and observation geometry parameters;

[0009] Step 2: Use the atmospheric radiation transfer model to simulate the atmospheric parameters of the VIIRS remote sensing image;

[0010] Step 3: The coefficients of the bidirectional reflectance estimation model are determined by integrating the differences in solar zenith angle and solar direct irradiance between two adjacent channels. The bidirectional reflectance of the VIIRS image is calculated using the model.

[0011] Step 4: Set the maximum emissivity estimate, calculate the surface temperature of the ground in all long-wave infrared bands, and use the maximum surface temperature as the initial surface temperature.

[0012] Step 5: construct a cost function model based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and iterate to find the local optimal solution of the surface temperature;

[0013] In step 6, if the difference between the local optimal solution of the surface temperature and the initial temperature is greater than the threshold, the temperature has not converged. This temperature is used as the initial temperature and steps 5 to 6 are repeated. If the difference between the local optimal solution of the surface temperature and the initial temperature is less than the threshold, the iteration converges and the emissivity of the VIIRS image is calculated according to the infrared radiation transfer equation.

[0014] Preferably, the method for quantitative extraction of long-wave infrared emissivity based on VIIRS remote sensing data provided by the present invention may also have the following characteristics: in step 5, a cost function model is constructed based on the difference between the initial temperature and the maximum and minimum emissivity spectra, and a local optimal solution for the surface temperature is iteratively obtained;

[0015] Substitute T0 into the following formula (5-1) to calculate the initial emissivity;

[0016]

[0017] Reconstruct the relative firing rate ε′ that conforms to the MMD empirical model based on the initial firing rate i :

[0018]

[0019] Where, is the mean emissivity of all bands, MMD is the range of the relative emissivity spectrum, and the cost function model is constructed:

[0020]

[0021] Where ‖·‖2 represents the second norm. The temperature value is gradually changed by the Newton iteration method to solve the local optimal solution temperature T when the cost function E takes the minimum value.

[0022] Preferably, the method for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data provided by the present invention may also have the following characteristics: in step 6, if the difference between the local optimal solution of temperature and the initial temperature is less than a threshold, the iteration converges, and the temperature is the inverted surface temperature, and the corresponding emissivity is the inverted emissivity; if the temperature does not converge, the temperature is used as the initial temperature, and steps 5 to 6 are repeated;

[0023] Calculate the difference between the local optimal solution temperature T and the initial temperature T0:

[0024] |T-T0| <threshold (6)

[0025] In the formula, T is the local optimal solution temperature, T0 is the initial temperature, and threshold is the threshold. When the above formula is satisfied, the iteration converges. The local optimal solution temperature T is the surface temperature. The relative emissivity ε′ calculated by formula (5-2) is i That is the inverted emissivity; when the above formula is not satisfied and the iteration does not converge, let T0 = T and repeat steps 5 to 6.

[0026] Preferably, the quantitative extraction method of long-wave infrared emissivity based on VIIRS remote sensing data provided by the present invention may also have the following characteristics: in step 1, the zenith radiance of the M12-M16 band is extracted from the VNP02MOD product of the VIIRS data by using the calibration coefficient and projection conversion, and the solar zenith angle, solar azimuth angle, sensor zenith angle and sensor azimuth geometric parameters of the image are extracted from the VNP03MOD product; in step 2, according to the time and longitude and latitude of the VIIRS image, the NCEP-FNL atmospheric profile data is downloaded, and the MODTRAN atmospheric radiation transfer software is driven to calculate the atmospheric parameters of band i required for inversion in combination with the observation geometric information, including the atmospheric transmittance τ i , atmospheric upward radiation R atm-i ↑, upward solar radiation scattered by the atmosphere Atmospheric upward radiation Downward solar radiation scattered by the atmosphere and direct solar irradiance

[0027] <system>

[0028] Furthermore, the present invention also provides a long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data, which can automatically implement the above-mentioned <method>, characterized in that it includes:

[0029] The preprocessing unit preprocesses VIIRS remote sensing images and extracts radiance data and observation geometry parameters;

[0030] The simulation department uses the atmospheric radiation transfer model to simulate the atmospheric parameters of VIIRS remote sensing images;

[0031] The bidirectional reflectance calculation unit combines the solar zenith angle and solar direct irradiance differences between two adjacent channels to determine the coefficients of the bidirectional reflectance estimation model, and uses this model to calculate the bidirectional reflectance of the VIIRS image.

[0032] The initial surface temperature calculation unit sets the maximum emissivity estimate and calculates the apparent surface temperature of the ground in all long-wave infrared bands, taking the maximum apparent surface temperature as the initial surface temperature value.

[0033] The model building part constructs a cost function model based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and iteratively finds the local optimal solution for the surface temperature;

[0034] In the emissivity calculation unit, if the difference between the local optimal solution of the surface temperature and the initial temperature is greater than a threshold, the temperature has not converged, and the temperature is used as the initial temperature, and steps 5 to 6 are repeated; if the difference between the local optimal solution of the surface temperature and the initial temperature is less than a threshold, the iteration converges, and the emissivity of the VIIRS image is calculated according to the infrared radiation transfer equation; and

[0035] The control unit is connected to the pre-processing unit, the simulation unit, the bidirectional reflectivity calculation unit, the surface initial temperature calculation unit, the model construction unit, and the emissivity calculation unit to control their operations.

[0036] Preferably, the long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data provided by the present invention may further include: an input display unit, which is communicatively connected to the control unit and is used to allow the user to input operation instructions and perform corresponding display.

[0037] Preferably, the long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data provided by the present invention may also have the following features: in the model construction unit, a cost function model is constructed based on the initial temperature and the difference between the maximum and minimum emissivity spectra of the emissivity, and the local optimal solution of the surface temperature is iteratively obtained; T0 is substituted into the following formula (5-1) to calculate the initial emissivity:

[0038]

[0039] Reconstruct the relative firing rate ε′ that conforms to the MMD empirical model based on the initial firing rate i :

[0040]

[0041] Where, is the mean emissivity of all bands, MMD is the range of the relative emissivity spectrum, and the cost function model is constructed:

[0042]

[0043] Where ‖·‖2 represents the second norm. The temperature value is gradually changed by the Newton iteration method to solve the local optimal solution temperature T when the cost function E takes the minimum value.

[0044] Preferably, the long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data provided by the present invention may also have the following characteristics: in the emissivity calculation unit, the following formula is used to determine whether convergence occurs:

[0045] |T-T0| <threshold (6)

[0046] In the formula, T is the local optimal solution temperature, T0 is the initial temperature, and threshold is the threshold. When the above formula is satisfied, the iteration converges. The local optimal solution temperature T is the surface temperature. The relative emissivity ε′ calculated by formula (5-2) is i That is the inverted emissivity; when the above formula is not satisfied and the iteration does not converge, let T0 = T and repeat steps 5 to 6.

[0047] Functions and effects of the invention

[0048] 1. The present invention fully considers the differences in mid-infrared and thermal infrared radiation transmission caused by solar radiation, realizes the synchronous inversion of all long-wave infrared emissivity during daytime, maintains the temporal continuity and spectral integrity of the surface emissivity data, and provides new support for constructing full-spectrum spectral information of the surface.

[0049] 2. Compared with the traditional nighttime emissivity separation algorithm, the present invention extends the inversion conditions of long-wave infrared emissivity to all day long, while making full use of the respective advantages and characteristics of mid-infrared and thermal infrared data, thereby improving the inversion accuracy of surface emissivity to a certain extent.

[0050] 3. The method of the present invention is mainly based on the long-wave infrared image of the remote sensor itself, and does not rely on a priori products such as surface reflectivity and surface cover type. It reduces the amount of input data, saves calculation time, and has the characteristics of simple and easy operation.

[0051] 4. This invention expands the wavelength range and time span covered by existing emissivity data products, and can provide an important data source for weather forecasting, surface thermal environment monitoring and target recognition technology.

[0052] In summary, unlike the existing technology in which mid-infrared and thermal infrared are calculated separately, or only mid-infrared and thermal infrared at night are calculated, the present invention uses mid-infrared and thermal infrared daytime data at the same time to obtain continuous and reliable results, overcomes the difference in radiation transmission between the mid-infrared atmospheric window and the thermal infrared atmospheric window, realizes the synchronous inversion of all long-wave infrared emissivity under daytime conditions, reduces the dependence on surface auxiliary data, greatly shortens the calculation time, ensures the temporal and spectral continuity of surface emissivity data, and provides a reference for constructing all-day and full-spectrum spectral information of the surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data according to the present invention;

[0054] Figure 2 The graphs of six atmospheric parameters calculated by the embodiment of the present invention include (a) atmospheric transmittance τ, (b) atmospheric upward radiation R atm ↑, (c) atmospheric downward radiation R atm ↓, (d) upward solar radiation scattered by the atmosphere (e) Downward solar radiation scattered by the atmosphere (f) Direct solar radiation R s ;

[0055] Figure 3 These are emissivity images of five long-wave infrared bands extracted according to an embodiment of the present invention, including (a) emissivity of the M12 band, (b) emissivity of the M13 band, (c) emissivity of the M14 band, (d) emissivity of the M15 band, and (e) emissivity of the M16 band. DETAILED DESCRIPTION

[0056] The specific implementation scheme of the method and system for quantitative extraction of long-wave infrared emissivity based on VIIRS remote sensing data according to the present invention is described in detail below with reference to the accompanying drawings.

[0057] <Example>

[0058] like Figure 1 As shown, the quantitative extraction method of long-wave infrared emissivity based on VIIRS remote sensing data adopted in this embodiment includes the following steps:

[0059] Step 1: Preprocess the VIIRS remote sensing image to extract radiance data and observation geometry parameters.

[0060] In this embodiment, the calibration coefficient and projection conversion are used to extract the zenith radiance of the M12-M16 band from the VNP02MOD product of the VIIRS data, and then the geometric parameters of the image, such as the solar zenith angle, solar azimuth angle, sensor zenith angle, and sensor azimuth angle, are extracted from the VNP03MOD product.

[0061] Step 2: Use the atmospheric radiation transfer model to simulate the atmospheric parameters of the VIIRS remote sensing image.

[0062] In this embodiment, Figure 2 As shown in the figure, according to the time and longitude of VIIRS image imaging, the NCEP-FNL atmospheric profile data provided by the National Centers for Environmental Prediction of the United States is downloaded, and combined with the observation geometry information, the MODTRAN atmospheric radiation transfer software is driven to calculate the atmospheric parameters of band i required for inversion, including the atmospheric transmittance τ i , atmospheric upward radiation Upward solar radiation scattered by the atmosphere Atmospheric upward radiation Downward solar radiation scattered by the atmosphere and direct solar irradiance

[0063] Step 3: Determine the coefficients of the bidirectional reflectance estimation model by integrating the differences in solar zenith angle and solar direct irradiance between two adjacent channels, and use the model to calculate the bidirectional reflectance of the VIIRS image.

[0064] The coefficients of the bidirectional reflectance estimation model are determined by combining the differences in solar zenith angle and direct solar irradiance between two adjacent channels. This model is then used to calculate the bidirectional reflectance of the VIIRS image. This includes: the radiation information received by the sensor is affected by atmospheric attenuation and path radiation, and atmospheric correction is used to convert the radiance at the top of the atmosphere into surface brightness temperature, as follows:

[0065]

[0066] Where L i is the zenith radiance of band i, T g-i is the surface brightness temperature of band i, and B represents the Planck function.

[0067] The coefficients a1, a2, and a3 of the bidirectional reflectivity estimation model are derived from the cosine value of the solar zenith angle SZA, as shown in the following formula:

[0068]

[0069] The surface brightness temperature without solar radiation can be estimated from the surface brightness temperature of the M12 and M13 bands using the following formula:

[0070]

[0071] Then calculate the surface bidirectional reflectivity of the M12 band as follows (3-4)

[0072]

[0073] In the formula, the wavelengths of the M12 band and the M13 band are similar, and it is assumed that their surface bidirectional reflectivity is consistent. In addition, the M14, M15, and M16 bands are located in the thermal infrared atmospheric window, where the direct solar radiation is very weak and the reflection effect of direct solar radiation is not considered.

[0074] Step 4: Set the maximum emissivity estimate, calculate the surface temperature of the ground in all long-wave infrared bands, and use the maximum surface temperature as the initial surface temperature.

[0075] In this embodiment, the maximum emissivity estimate ε is set max =0.97, calculate the apparent surface temperature of all bands, and take the maximum apparent temperature as the initial surface temperature T0, as shown in the following formula (4):

[0076]

[0077] Where B -1 is the inverse function of Planck function, Lg i It is the surface radiation without the influence of direct solar radiation, and max means the maximum value.

[0078] Step 5: Construct a cost function model based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and iterate to find the local optimal solution of the surface temperature.

[0079] Substitute T0 into the following formula (5-1) to calculate the initial emissivity:

[0080]

[0081] Reconstruct the relative firing rate ε′ that conforms to the MMD empirical model based on the initial firing rate i , as follows:

[0082]

[0083] Where, is the mean emissivity of all bands, MMD is the range of the relative emissivity spectrum, and the cost function model is constructed:

[0084]

[0085] Where ‖·‖2 represents the second norm. The temperature value is gradually changed by the Newton iteration method to find the local optimal solution temperature T when the cost function E takes the minimum value.

[0086] In step 6, if the difference between the local optimal solution for surface temperature and the initial temperature is greater than a threshold, the temperature has not converged. This temperature is used as the initial temperature and steps 5 to 6 are repeated. If the difference between the local optimal solution for surface temperature and the initial temperature is less than a threshold, the iteration has converged and the emissivity of the VIIRS image is calculated according to the infrared radiation transfer equation.

[0087] In this embodiment, the following formula is used to determine whether convergence occurs:

[0088] |T-T0| <threshold (6)

[0089] Where T is the local optimal solution temperature, T0 is the initial temperature, and threshold is the threshold. When the above equation is satisfied, the iteration converges, and the emissivity calculated by equation (5-2) is the inverted emissivity. If the above equation is not satisfied, the iteration does not converge, and steps 5 to 6 are repeated with T0 = T.

[0090] Figure 3 This is the last iteration of this embodiment. The inversion results of the five long-wave infrared bands (involving mid-infrared and thermal infrared bands) are calculated and verified with the actual situation. The inversion results are consistent with the actual situation, indicating that the present invention can effectively realize the continuous synchronous inversion of all long-wave infrared emissivity under daytime conditions, and can continuously and accurately extract the emissivity, maintaining the temporal continuity and spectral integrity of the surface emissivity data.

[0091] Furthermore, in this embodiment, a long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data is provided, which can automatically implement the above method of the present invention. The system includes a preprocessing unit, a simulation unit, a bidirectional reflectivity calculation unit, a surface initial temperature calculation unit, a model building unit, an emissivity calculation unit, an input display unit, and a control unit.

[0092] The preprocessing section performs the steps described in step 1 above to preprocess the VIIRS remote sensing images and extract radiance data and observation geometry parameters.

[0093] The simulation part performs the steps described in step 2 above and uses the atmospheric radiation transfer model to simulate the atmospheric parameters of the VIIRS remote sensing image.

[0094] The bidirectional reflectance calculation unit performs the steps described in step 3 above, comprehensively considers the difference in solar zenith angle and direct solar irradiance between two adjacent channels, determines the coefficients of the bidirectional reflectance estimation model, and calls the model to calculate the bidirectional reflectance of the VIIRS image.

[0095] The initial surface temperature calculation unit performs the steps described in step 4 above, sets the maximum emissivity estimate, calculates the surface surface temperature in all long-wave infrared bands, and uses the maximum surface surface temperature as the initial surface temperature value.

[0096] The model building unit executes the content described in step 5 above, constructs a cost function model based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and iteratively finds the local optimal solution for the surface temperature;

[0097] The emissivity calculation unit executes the contents described in step 6 above. If the difference between the local optimal solution of the surface temperature and the initial temperature is greater than the threshold, the temperature has not converged. This temperature is used as the initial temperature and steps 5 and 6 are repeated. If the difference between the local optimal solution of the surface temperature and the initial temperature is less than the threshold, the iteration converges and the emissivity of the VIIRS image is calculated according to the infrared radiation transfer equation.

[0098] The input and display unit is used to allow the user to input operation instructions and display them accordingly.

[0099] The control unit is communicatively connected with the pre-processing unit, the simulation unit, the bidirectional reflectivity calculation unit, the surface initial temperature calculation unit, the model building unit, the emissivity calculation unit, and the input and display unit to control their operations.

[0100] The above embodiments are merely illustrative of the technical solutions of the present invention. The method and system for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data involved in the present invention are not limited solely to the contents described in the above embodiments, but are subject to the scope defined by the claims. Any modifications, supplements, or equivalent substitutions made by those skilled in the art based on this embodiment are within the scope of protection claimed by the claims of the present invention.

Claims

1. A quantitative extraction method of long-wave infrared emissivity based on VIIRS remote sensing data, characterized in that: The following steps are involved: Step 1: Preprocess the VIIRS remote sensing image to extract radiance data and observation geometry parameters; Step 2: Use the atmospheric radiation transfer model to simulate the atmospheric parameters of the VIIRS remote sensing image; Step 3: The coefficients of the bidirectional reflectance estimation model are determined by integrating the differences in solar zenith angle and solar direct irradiance between two adjacent channels. The bidirectional reflectance of the VIIRS image is calculated using the model. Step 4: Set the maximum emissivity estimate, calculate the surface temperature of the ground in all long-wave infrared bands, and use the maximum surface temperature as the initial surface temperature. Step 5: construct a cost function model based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and iterate to find the local optimal solution of the surface temperature; In step 6, if the difference between the local optimal solution of the surface temperature and the initial temperature is greater than the threshold, the temperature has not converged. This temperature is used as the initial temperature and steps 5 to 6 are repeated. If the difference between the local optimal solution of the surface temperature and the initial temperature is less than the threshold, the iteration converges and the emissivity of the VIIRS image is calculated according to the infrared radiation transfer equation.

2. The method for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data according to claim 1, characterized in that: in, In step 5, the cost function model is constructed based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and the local optimal solution of the surface temperature is iteratively obtained; Substitute T0 into the following formula (5-1) to calculate the initial emissivity; Reconstruct the relative firing rate ε′ that conforms to the MMD empirical model based on the initial firing rate i : Where, is the mean emissivity of all bands, MMD is the range of the relative emissivity spectrum, and the cost function model is constructed: Where ‖·‖2 represents the second norm. The temperature value is gradually changed by the Newton iteration method to solve the local optimal solution temperature T when the cost function E takes the minimum value.

3. The method for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data according to claim 2, characterized in that: in, In step 6, the following formula is used to determine whether convergence has occurred: |T-T0| <threshold (6) In the formula, T is the local optimal solution temperature, T0 is the initial temperature, and threshold is the threshold. When the above formula is satisfied, the iteration converges. The local optimal solution temperature T is the surface temperature. The relative emissivity ε′ calculated by formula (5-2) is i That is the inverted emissivity; when the above formula is not satisfied and the iteration does not converge, let T0 = T and repeat steps 5 to 6.

4. The method for quantitatively extracting long-wave infrared emissivity based on VIIRS remote sensing data according to claim 1, characterized in that: in, In step 1, the zenith radiance of the M12-M16 band is extracted from the VNP02MOD product of the VIIRS data using the calibration coefficient and projection conversion, and the solar zenith angle, solar azimuth angle, sensor zenith angle, and sensor azimuth geometric parameters of the image are extracted from the VNP03MOD product. In step 2, according to the time and longitude and latitude of the VIIRS image, the NCEP-FNL atmospheric profile data is downloaded, and the MODTRAN atmospheric radiation transfer software is driven to calculate the atmospheric parameters of band i required for inversion, including the atmospheric transmittance τ, in combination with the observation geometry information. i , atmospheric upward radiation Upward solar radiation scattered by the atmosphere Atmospheric upward radiation Downward solar radiation scattered by the atmosphere and direct solar irradiance 5. A quantitative extraction system for long-wave infrared emissivity based on VIIRS remote sensing data, characterized by: include: The preprocessing unit preprocesses VIIRS remote sensing images and extracts radiance data and observation geometry parameters; The simulation department uses the atmospheric radiation transfer model to simulate the atmospheric parameters of VIIRS remote sensing images; The bidirectional reflectance calculation unit combines the solar zenith angle and solar direct irradiance differences between two adjacent channels to determine the coefficients of the bidirectional reflectance estimation model, and uses this model to calculate the bidirectional reflectance of the VIIRS image. The initial surface temperature calculation unit sets the maximum emissivity estimate and calculates the apparent surface temperature of the ground in all long-wave infrared bands, taking the maximum apparent surface temperature as the initial surface temperature value. The model building part constructs a cost function model based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and iteratively finds the local optimal solution for the surface temperature; In the emissivity calculation unit, if the difference between the local optimal solution of the surface temperature and the initial temperature is greater than a threshold, the temperature has not converged, and the temperature is used as the initial temperature, and steps 5 to 6 are repeated; if the difference between the local optimal solution of the surface temperature and the initial temperature is less than a threshold, the iteration converges, and the emissivity of the VIIRS image is calculated according to the infrared radiation transfer equation; and The control unit is connected to the pre-processing unit, the simulation unit, the bidirectional reflectivity calculation unit, the surface initial temperature calculation unit, the model construction unit, and the emissivity calculation unit to control their operations.

6. The long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data according to claim 5, characterized in that: Also includes: The input and display unit is connected to the control unit for communication, and is used to allow the user to input operation instructions and display them accordingly.

7. The long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data according to claim 5, characterized in that: in, In the model construction part, the cost function model is constructed based on the initial temperature and the difference between the maximum and minimum emissivity spectra, and the local optimal solution of the surface temperature is iteratively obtained; T0 is substituted into the following formula (5-1) to calculate the initial emissivity: Reconstruct the relative firing rate ε′ that conforms to the MMD empirical model based on the initial firing rate i : Where, is the mean emissivity of all bands, MMD is the range of the relative emissivity spectrum, and the cost function model is constructed: Where ‖·‖2 represents the second norm. The temperature value is gradually changed by the Newton iteration method to solve the local optimal solution temperature T when the cost function E takes the minimum value.

8. The long-wave infrared emissivity quantitative extraction system based on VIIRS remote sensing data according to claim 7, characterized in that: in, In the emissivity calculation unit, the following formula is used to determine whether convergence occurs: |T-T0| <threshold (6) In the formula, T is the local optimal solution temperature, T0 is the initial temperature, and threshold is the threshold. When the above formula is satisfied, the iteration converges. The local optimal solution temperature T is the surface temperature. The relative emissivity ε′ calculated by formula (5-2) is i That is the inverted emissivity; when the above formula is not satisfied and the iteration does not converge, let T0 = T and repeat steps 5 to 6.

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