Ultraviolet absorption characteristic-based wildfire smog remote sensing identification and emission coefficient estimation method

Abstract

The invention relates to a wildfire smog remote sensing recognition and emission coefficient estimation method based on ultraviolet absorption characteristics, which utilizes the unique absorption characteristics of smog to ultraviolet band radiation to distinguish the smog from cloud and other confusion targets, and on the basis, combines information such as wildfire radiation power measured by satellite fire data to estimate the emission coefficient of the wildfire smog. And carrying out quantitative estimation on the wildfire radiation energy, the wildfire smog particulate matter emission amount and the emission coefficient. Compared with a traditional optical observation means, the method is suitable for the situation that an optical remote sensing image is prone to cloud coverage interference, and cloud interference information can be recognized and removed at the same time; compared with a traditional particulate matter emission estimation method based on aerosol optical thickness, the method has the advantages that the recognition precision of high-concentration smog haze is improved, and the estimation precision is further improved; the method can meet the requirements of wildfire smog business monitoring, and improves the working efficiency of wildfire smog emission observation and estimation.

Description

Technical Field

[0001] The invention relates to the technical field of satellite remote sensing wildfire haze monitoring, and in particular to a wildfire haze remote sensing identification and emission coefficient estimation method based on ultraviolet absorption characteristics. Background Art

[0002] Wildfire haze is one of the important objects of atmospheric environmental monitoring. It not only directly destroys the original local ecosystem and threatens the safety of neighboring communities, but also releases a large amount of carbonaceous particulate matter and harmful gases into the atmosphere, which has a negative impact on public health and transportation. In this process, satellite remote sensing technology has become an important means of accurately monitoring and quantitatively evaluating wildfire haze disasters due to its advantages of high spatial resolution, timeliness, multi-spectrum, and large scale.

[0003] Remote sensing of wildfire haze faces complex composition, transmission process and monitoring objects: (1) The composition of wildfire haze is complex. Wildfire burning not only emits a large amount of greenhouse and harmful gases (such as CO2, SO2, CH4, etc.) into the atmosphere, but also forms carbon particles with varying degrees of charring, which constitute the main solid component of haze. (2) The transmission process of wildfire haze is complex. Wildfire haze particles can be transported to the open ocean through large-scale atmospheric circulation systems and enter the ocean through dry and wet deposition processes, which has an important impact on the large-scale diffusion and transmission of haze. (3) The monitoring objects of wildfire haze are complex. Wildfire haze and different types of clouds (cirrus, cumulus) have similar spectral responses and visual characteristics in remote sensing images. In addition, wildfire haze faces complex underlying surface backgrounds (seawater, bare land, vegetation, etc.) during large-scale transmission.

[0004] Theoretical research on wildfire smoke and haze optical remote sensing has made great progress in recent years. The accumulation of theoretical and applied research in this field shows that: (1) There is a wealth of satellite data available for application, such as China's Haiyang-1C / D satellite (HY-1C / D) and the United States Geostationary Operational Environmental Satellite-R Series (GOES-R), which provide a wealth of satellite remote sensing images and fire product data. (2) The detection and classification targets (wildfire smoke and haze, clouds, etc.) have different optical processes (such as reflection, scattering, absorption, etc.) for incident light in the ultraviolet characteristic band, which will produce different optical response characteristics. Among them, wildfire smoke and haze show significant absorption characteristics, which provides a theoretical basis for optical remote sensing identification, classification and quantitative estimation. (3) The mechanism of wildfire smoke and haze emission estimation is clear. The particulate matter emission is estimated based on the optical remote sensing absorption characteristics, and the fire radiation energy release is estimated based on the satellite fire products. The regression analysis of the two is used to estimate the smoke and haze particulate matter emission coefficient. The application characteristics and advantages of optical remote sensing technology in this field have gradually become clear, and the relevant basic theoretical framework has gradually been improved, fully demonstrating its ability and potential for wildfire haze monitoring and identification and quantitative estimation of emissions. Summary of the invention

[0005] The technical problem to be solved by the present invention is: in view of the fact that optical remote sensing identification of wildfire haze is easily interfered by highly heterogeneous background and various cloud targets, based on the strong absorption characteristics of wildfire haze on incident light in the ultraviolet band, the emission of haze particles is quantitatively estimated, which is of great significance for wildfire haze pollution monitoring.

[0006] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0007] The wildfire smoke haze remote sensing identification and emission coefficient estimation method based on ultraviolet absorption characteristics includes the following steps:

[0008] Step 1: Data preprocessing

[0009] Preprocess the optical remote sensing image data and convert the optical band data into apparent reflectance ρ TOA Data, the optical bands include ultraviolet bands, visible light bands and near infrared bands; preprocessing the satellite fire data to construct a fire radiation power FRP (fire radiative power) time series curve;

[0010] Step 2: Identify the area of ​​interest

[0011] For areas where wildfires are known or suspected to have occurred, the apparent reflectance image ρ after preprocessing in step 1 TOAAreas of interest that may contain wildfire haze are circled above;

[0012] Step 3: UV-Visible spectrum set construction

[0013] Detect the apparent reflectivity ρ of the research target, typical interference target and background seawater TOA Spectral curves, construct a typical target spectrum set, the research target is wildfire haze, and the interference targets include cirrus and cumulus clouds;

[0014] Step 4: Feature band detection

[0015] Select multiple typical spectral data of typical targets as training samples, the typical spectral data refers to the characteristic spectral data obtained by the spectrum determined as the designated target through the above steps 1-3, the typical targets include wildfire haze, cirrus, cumulus and sea water; take the average value of the characteristic spectral data of the training samples of each target, and calculate the contrast C between the spectral characteristic data according to the following formula:

[0016]

[0017] In the formula, ρ TOA,UV is the apparent reflectance value of the typical target characteristic spectrum in the ultraviolet band, ρ TOA,VNIR is the apparent reflectance value of the typical target characteristic spectrum in the visible light-near infrared band; the band combination with the largest contrast C is selected for wildfire haze extraction based on ultraviolet absorption characteristics. The final selected band combination is: ultraviolet band and red light band combination;

[0018] Step 5: Build a wildfire haze recognition algorithm based on absorption features

[0019] Based on the contrast calculation results, the ultraviolet smoke index UVSI (ultraviolet smoke index) is constructed for the identification and extraction of smoke in optical remote sensing images. The formula is as follows:

[0020]

[0021] In the formula, ρ TOA,UV and ρ TOA,RED are the apparent reflectances of the pixels to be detected in the remote sensing image of the area of ​​interest in the ultraviolet band and the red light band, respectively, and K is the semi-quantitative correction coefficient;

[0022] Step 6: Wildfire smoke detection and target classification for images

[0023] Wildfire haze pixel extraction is based on the UVSI index. The specific steps are as follows:

[0024] 1) For the apparent reflectance image ρ TOA The near infrared band grayscale image ρTOA,NIR , use the threshold ε1 for threshold segmentation to obtain the background seawater pixels and the pixels to be segmented. The specific formula is as follows:

[0025]

[0026] In the water_mask image, pixels with a value of 1 represent seawater, and pixels with a value of 0 represent non-seawater;

[0027] 2) Use the Water_mask image as a mask to the apparent reflectance image ρ TOA Operation is used to remove seawater pixels. For the apparent reflectance image ρ after removing seawater pixels TOA , the ultraviolet haze index UVSI image is calculated using the ultraviolet band and the red light band, and the threshold ε2 and threshold ε3 are used for threshold segmentation to obtain wildfire haze pixels and cloud pixels. The specific formula is as follows:

[0028]

[0029] In the smoke_mask image, pixels with a value of 0 represent non-wildfire smoke haze, and pixels with a value of 1 represent wildfire smoke haze, which is the wildfire smoke haze pixel extraction result and is used to calculate the haze particle emission TPM (total particulate matter) of the wildfire smoke haze pixel;

[0030] Step 7: Wildfire radiant energy time and space matching

[0031] Taking the local time t1 as the starting time and the optical remote sensing image imaging time t2 as the ending time, the fire radiation power FRP of the specified satellite fire product in the specified time period is integrated to obtain the corresponding wildfire radiation energy FRE (fire radiative energy). Combined with the spatial coordinates, the time-space matching of the smoke and haze particle emission TPM obtained based on the optical remote sensing image and the wildfire radiation energy FRE obtained based on the satellite fire product is completed.

[0032] Step 8: Regression analysis of smoke and haze particle emissions and fire radiation energy

[0033] The haze particle emission-fire radiation energy regression analysis was performed on the satellite fire products and UVSI index images with time and space matching to extract the haze particle emission coefficient C e , the specific steps are as follows:

[0034] 1) For a given wildfire sample, calculate the smoke particulate matter emissions TPM based on the UVSI index value of the smoke extraction result;

[0035] 2) According to the time-space matching results, the statistical relationship scatter points between the TPM value and the FRE value of the corresponding fire point are obtained;

[0036] 3) Use the least squares method to perform linear fitting on the scattered points, build the TPM-FRE regression model, and obtain the particle emission coefficient C of the wildfire haze sample. e , the particulate matter emission coefficient C e Used for estimating haze particulate matter emissions.

[0037] The present invention has the following beneficial effects:

[0038] The present invention aims at the strong absorption characteristics of wildfire haze on incident light in the ultraviolet band, designs the ultraviolet haze index to identify and extract the smoke haze, and quantitatively estimates the particle emission of wildfire haze in combination with satellite fire products. The actual effect shows that the present invention can reduce the time, manpower and material cost of wildfire haze monitoring, identification and emission estimation, improve the identification and extraction efficiency, and ensure the accuracy of quantitative estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the process of the present invention.

[0040] Figure 2 This is the HY-1C / D true color synthetic remote sensing image of wildfire haze.

[0041] Figure 3 Apparent reflectance spectra of haze, different types of clouds, and background seawater.

[0042] Figure 4 Schematic diagram of the contrast index for training-recognition of the present invention.

[0043] Figure 5 A schematic diagram of setting the target detection threshold of the present invention.

[0044] Figure 6 This is a diagram of the haze identification result of the present invention.

[0045] Figure 7 Schematic diagram of the spatiotemporal matching of wildfire haze samples.

[0046] Figure 8 This is the extraction result diagram of haze particulate matter emissions. DETAILED DESCRIPTION

[0047] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0048] This embodiment is applied to the HY-1C / D satellite multispectral image data and the quasi-synchronous GOES-R satellite fire products from September 6 to 11, 2020. The HY-1C / D satellite multispectral image data contains 10 optical bands of 355nm, 385nm, 412nm, 443nm, 490nm, 520nm, 565nm, 670nm, 750nm, and 865nm, with a pixel spatial resolution of 1100m. GOES-R satellite fire products

[0049] like Figure 1 FIG. 1 is a flow chart of a method for identifying and estimating wildfire smoke haze based on ultraviolet absorption characteristics according to an embodiment of the present invention, which includes the following steps:

[0050] Step 1: Data preprocessing

[0051] Preprocess the HY-1C / D satellite multispectral remote sensing image data, including geometric correction, radiation calibration and atmospheric correction, to obtain the apparent reflectance image ρ TOA ; Preprocess the GOES-R satellite fire data, including fire radiation power FRP calibration, and construct daily FRP time series curves.

[0052] Step 2: Identify the area of ​​interest

[0053] The region of interest selected in this embodiment is North America. For areas where wildfire haze events are known or suspected to occur, the apparent reflectance image ρ after preprocessing in step 1 is used to calculate the vector file of the study area. TOA The area of ​​interest that may contain wildfire haze is circled. In this embodiment, the area of ​​interest is circled as follows: Figure 2 Figure 2 shows the study area of ​​HY-1C / D from September 6 to 11, 2020. TOA True color composite image. Figure 2 As shown in the figure, wildfire smoke and clouds appear as similar visual features in the image, which poses certain challenges to the identification and extraction of wildfire smoke.

[0054] Step 3: UV-Visible spectrum set construction

[0055] The apparent reflectivity ρ of the research target (wildfire haze), typical interference targets (cirrus, cumulus) and background seawater is detected. TOA The spectral curve is averaged, such as Figure 3 As shown in the figure, a spectrum set is constructed. Compared with the background seawater and two cloud targets, wildfire smoke haze has a clear spectral feature that the visible light signal is stronger than the ultraviolet signal, indicating that it has absorption characteristics in the ultraviolet band and scattering characteristics in the visible light band. The above spectral characteristics provide a theoretical basis for designing an exponential model method for smoke haze identification and extraction.

[0056] Step 4: Feature band detection

[0057] In this embodiment, all targets except wildfire haze are regarded as interference and background targets. The purpose of characteristic band detection is to suppress other target signals while enhancing wildfire haze signals. Multiple typical spectral data of typical targets (haze, cirrus, cumulus, seawater) are selected as training samples, and the characteristic spectral data of the training samples of each target are averaged. The contrast index C is used to quantitatively evaluate the band combination effect, and the difference and ratio between the band combinations are calculated respectively. The calculation formula of contrast C is as follows:

[0058]

[0059] In the formula, ρ TOA,UV is the apparent reflectance value of the typical target characteristic spectrum in the ultraviolet band (355nm, 385nm), ρ TOA,VNIR is the apparent reflectance value of the typical target characteristic spectrum in the visible light-near infrared band (412nm-865nm). The calculation results are as follows Figure 4 As shown in the figure, the contrast C of the combination of the 355nm ultraviolet band and the 670nm red light band is the largest. The above band combination is used to construct a wildfire haze recognition algorithm based on absorption characteristics.

[0060] Step 5: Build a wildfire haze recognition algorithm based on absorption features

[0061] Based on the contrast calculation results, the ultraviolet haze index UVSI is constructed for the identification and extraction of haze in optical remote sensing images. Considering that the total air column particle concentration and the near-surface particle concentration are affected by the vertical distribution difference and the change of relative humidity, they show a nonlinear correlation. Therefore, this embodiment uses an exponential model to construct this nonlinear relationship, which has good sensitivity for the identification and estimation of wildfire haze. The formula is as follows:

[0062]

[0063] In the formula, ρ TOA,355nm and ρ TOA,670nm are the apparent reflectances of the pixels to be detected in the remote sensing image of the area of ​​interest in the 355nm and 670nm bands, respectively; K is a semi-quantitative correction coefficient, which is set to 1 in this embodiment.

[0064] Step 6: Wildfire smoke detection and target classification for images

[0065] Wildfire haze pixel extraction is based on the UVSI index. The specific steps are as follows: Figure 5 As shown:

[0066] 1) The background seawater has strong absorption characteristics in the near-infrared band, so for the near-infrared band grayscale image ρ TOA,865nm, set the threshold ε1 = 0.05 for ρ TOA,865nm The image is segmented by threshold and the seawater pixel extraction result is obtained. The formula is as follows:

[0067]

[0068] In the water_mask image, pixels with a value of 1 represent seawater, and pixels with a value of 0 represent non-seawater.

[0069] 2) Use the Water_mask image as a mask to the apparent reflectance image ρ TOA Operation is used to remove seawater pixels. For the apparent reflectance image ρ after removing seawater pixels TOA , using the 355nm ultraviolet band and the 670nm red light band, the UVSI index image is calculated. The image contains wildfire smoke and cloud pixels. Wildfire smoke has absorption characteristics in the ultraviolet band and scattering characteristics in the visible light band; clouds show scattering characteristics in both the ultraviolet and red light bands. Therefore, for the UVSI image and the ultraviolet band grayscale image ρ TOA,355nm , set the thresholds ε2 = 0.3 and ε3 = 0.3 for threshold segmentation, and obtain the wildfire haze pixel extraction results. The formula is as follows:

[0070]

[0071] In the smoke_mask image, pixels with a value of 0 represent non-wildfire smoke, and pixels with a value of 1 represent wildfire smoke, which is the result of wildfire smoke pixel extraction.

[0072] The results of wildfire haze pixel extraction are as follows Figure 6 As shown. The UVSI value can not only be used to extract wildfire haze pixels, but also to characterize the concentration of wildfire haze. The two are positively correlated, that is, the higher the UVSI value, the greater the haze concentration.

[0073] Step 7: Wildfire radiant energy time and space matching

[0074] This example combines HY-1C / D satellite multispectral image data and quasi-synchronous GOES-R satellite fire products to estimate wildfire haze emissions. The HY-1C / D polar-orbiting satellite only images at the time of transit, while the GOES-R geostationary satellite observes around the clock with a sampling interval of 5 minutes. Therefore, the two data need to be matched in time and space. The matching diagram is shown in the figure below: Figure 7 shown.

[0075] The integration start time is selected as 6:00 local time, which is usually the time of the day when the burning activity is the lowest; the end time is selected as the afternoon transit sampling time of the HY-1C / D satellite local time, which is usually around 13:30 local time. The discrete fire radiation power FRP observed by the GOES-R geostationary orbit satellite during this period is accumulated and multiplied by the minimum sampling interval, and the theoretical value of fire radiation energy FRE is obtained by integration. The FRE calculation formula is as follows:

[0076]

[0077] Where △t is the minimum sampling interval, i.e. 5 minutes; t1 is the integration start time, i.e. 6:00 local time; t2 is the integration end time, i.e. the afternoon transit sampling time of the HY-1C / D satellite local time.

[0078] Step 8: Regression analysis of smoke and haze particle emissions and fire radiation energy

[0079] This embodiment is based on Figure 6 The wildfire haze extraction results of HY-1C / D satellite UVSI index images were obtained. After time-space matching, the haze particle emission-fire radiation energy regression analysis was performed on the GOES-R satellite fire products and HY-1C / D satellite UVSI index images to extract the haze particle emission coefficient (C e ), the specific steps are as follows:

[0080] 1) Based on the UVSI index image of HY-1C / D satellite, the emission of haze particulate matter TPM is estimated using the following formula:

[0081]

[0082] In the formula, i is the valid pixel number, β e is the mass extinction coefficient of haze aerosol, and its value is 4.6m 2 ·g -1 .

[0083] 2) According to the time-space matching results, the statistical relationship scatter points between the TPM value and the FRE value of the corresponding fire point are obtained;

[0084] 3) For the matched TPM values ​​and FRE values, the least squares method is used to perform linear fitting on the scattered points to construct a TPM-FRE regression model with a fixed slope of 0. The particle emission coefficient C of the wildfire haze sample can be obtained. e (Unit: g·MJ -1 ), the fitting formula is as follows:

[0085] TPM=C e ×FRE

[0086] In the formula, the slope of the regression model is the particle emission coefficient C e (Unit: g·MJ -1 ).

[0087] In this embodiment, the fitting results are as follows: Figure 8 As shown in the figure, the TPM-FRE estimation model based on the HY-1C / D satellite multispectral image data and the quasi-synchronous GOES-R satellite fire products is:

[0088] y=34.37×x

[0089] Where y is TPM (unit: g), x is FRE (unit: MJ), and the slope 34.37 is the haze particle emission coefficient C e , Indication: The wildfire combustible type corresponding to this embodiment, every 1MJ of wildfire radiation energy will release 34.37g of haze particles.

[0090] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for remote sensing identification of wildfire smoke and haze and estimation of emission coefficient based on ultraviolet absorption characteristics, comprising the following steps: Step 1: Data preprocessing Preprocess the optical remote sensing image data and convert the optical band data into apparent reflectance ρ TOA Data, the optical bands include ultraviolet band, visible light band and near infrared band; preprocessing the satellite fire data to construct a fire radiation power FRP time series curve; Step 2: Identify the area of ​​interest For areas where wildfires are known or suspected to have occurred, the apparent reflectance image ρ after preprocessing in step 1 TOA Areas of interest that may contain wildfire haze are circled above; Step 3: UV-Visible spectrum set construction Detect the apparent reflectivity ρ of the research target, typical interference target and background seawater TOA Spectral curves, construct a typical target spectrum set, the research target is wildfire haze, and the interference targets include cirrus and cumulus clouds; Step 4: Feature band detection Select multiple typical spectral data of typical targets as training samples, the typical spectral data refers to the characteristic spectral data obtained by the spectrum determined as the designated target through the above steps 1-3, the typical targets include wildfire haze, cirrus, cumulus and sea water; take the average value of the characteristic spectral data of the training samples of each target, and calculate the contrast C between the spectral characteristic data according to the following formula: In the formula, ρ TOA,UV is the apparent reflectance value of the typical target characteristic spectrum in the ultraviolet band, ρ TOA,VNIR is the apparent reflectance value of the typical target characteristic spectrum in the visible light-near infrared band; the band combination with the largest contrast C is selected for wildfire haze extraction based on ultraviolet absorption characteristics. The final selected band combination is: ultraviolet band and red light band combination; Step 5: Build a wildfire haze recognition algorithm based on absorption features Based on the contrast calculation results, the ultraviolet haze index UVSI is constructed for the identification and extraction of haze in optical remote sensing images. The formula is as follows: In the formula, ρ TOA,UV and ρ TOA,RED are the apparent reflectances of the pixels to be detected in the remote sensing image of the area of ​​interest in the ultraviolet band and the red light band, respectively, and K is the semi-quantitative correction coefficient; Step 6: Wildfire smoke detection and target classification for images Wildfire haze pixel extraction is based on the UVSI index. The specific steps are as follows: 1) For the apparent reflectance image ρ TOA The near infrared band grayscale image ρ TOA,NIR , use the threshold ε1 for threshold segmentation to obtain the background seawater pixels and the pixels to be segmented. The specific formula is as follows: In the water_mask image, pixels with a value of 1 represent seawater, and pixels with a value of 0 represent non-seawater; 2) Use the Water_mask image as a mask to the apparent reflectance image ρ TOA Operation is used to remove seawater pixels. For the apparent reflectance image ρ after removing seawater pixels TOA , the ultraviolet haze index UVSI image is calculated using the ultraviolet band and the red light band, and the threshold ε2 and threshold ε3 are used for threshold segmentation to obtain wildfire haze pixels and cloud pixels. The specific formula is as follows: The pixels with a value of 0 in the smoke_mask image represent non-wildfire smoke haze, and the pixels with a value of 1 represent wildfire smoke haze, which is the wildfire smoke haze pixel extraction result and is used to calculate the haze particle emission TPM of the wildfire smoke haze pixel; Step 7: Wildfire radiant energy time and space matching Taking the local time t1 as the starting time and the optical remote sensing image imaging time t2 as the ending time, the fire radiation power FRP of the specified satellite fire product in the specified time period is integrated to obtain the corresponding wildfire radiation energy FRE. Combined with the spatial coordinates, the time-space matching of the smoke and haze particle emission TPM obtained based on the optical remote sensing image and the wildfire radiation energy FRE obtained based on the satellite fire product is completed. Step 8: Regression analysis of smoke and haze particle emissions and fire radiation energy The haze particle emission-fire radiation energy regression analysis was performed on the satellite fire products and UVSI index images with time and space matching to extract the haze particle emission coefficient C e , the specific steps are as follows: 1) For a given wildfire sample, calculate the smoke particulate matter emissions TPM based on the UVSI index value of the smoke extraction result; 2) According to the time-space matching results, the statistical relationship scatter points between the TPM value and the FRE value of the corresponding fire point are obtained; 3) Use the least squares method to perform linear fitting on the scattered points, build the TPM-FRE regression model, and obtain the particle emission coefficient C of the wildfire haze sample. e , the particulate matter emission coefficient C e Used for estimating haze particulate matter emissions.

2. The method for remote sensing identification of wildfire smoke and haze and estimation of emission coefficient based on ultraviolet absorption characteristics according to claim 1 is characterized in that: In step 1, the optical remote sensing image has a wavelength band of 355-865 nm, and the preprocessing includes geometric correction, radiation calibration, atmospheric correction and smoothing filtering; the satellite fire product includes the name of the wildfire event and the corresponding event and location, and the preprocessing is to construct an FRP time series curve for each wildfire event.

3. The method for remote sensing identification of wildfire smoke and haze and estimation of emission coefficient based on ultraviolet absorption characteristics according to claim 1, characterized in that: In step 2, the area of ​​interest is divided based on the location of the fire provided by the satellite fire product.

4. The method for remote sensing identification of wildfire smoke and haze and estimation of emission coefficient based on ultraviolet absorption characteristics according to claim 1 is characterized in that: In step 6, threshold ε1=0.05, threshold ε2=0.3, and threshold ε3=0.

3.

5. The method for remote sensing identification of wildfire smoke and haze and estimation of emission coefficient based on ultraviolet absorption characteristics according to claim 1, characterized in that: In step 7, the satellite fire product wildfire radiant energy FRE calculation formula is as follows: Where FRP is the fire radiation power data provided by satellite fire products, t1 is the integration start time, t2 is the optical remote sensing image imaging time, △t is the minimum sampling time, and t1 is 6:00 local time.

6. The method for remote sensing identification of wildfire smoke and haze and estimation of emission coefficient based on ultraviolet absorption characteristics according to claim 1, characterized in that: In step 8, the calculation formula for the haze particulate matter emission TPM is as follows: In the formula, i is the valid pixel number, β e is the mass extinction coefficient of haze aerosol, and its value is 4.6m 2 ·g -1 .

7. The method for remote sensing identification of wildfire smoke and haze and estimation of emission coefficient based on ultraviolet absorption characteristics according to claim 1 is characterized in that: In step 8, the regression of TPM value and FRE value adopts least square linear fitting, with the intercept fixed to 0, and the fitting formula is as follows: TPM=C e ×FRE In the formula, the slope of the regression model is the particle emission coefficient C e .

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