Method and related device for air quality monitoring based on satellite communication

Abstract

The application relates to a satellite communication-based air quality monitoring method and related equipment, comprising the following steps: performing radiation normalization on a scanned high-spectrum image sequence to obtain radiance data with unified space-time reference; performing vertical profile inversion on the radiance data to obtain a three-dimensional aerosol profile graph; based on the three-dimensional aerosol profile graph, extracting the extinction coefficient contribution degree of a near-ground layer from the radiance data to obtain a near-ground extinction coefficient grid field; calibrating the extinction coefficient grid field by using vertical echo signals collected by a ground-based laser radar network, and converting the extinction coefficient grid field into a fused ground concentration distribution graph; taking the three-dimensional aerosol profile graph and the fused ground concentration distribution graph as air quality monitoring data, and sending the air quality monitoring data to a mobile monitoring platform, so that the technical problem of deviation between monitoring results and direct contact air quality of the public in the prior art is solved.

Description

Technical Field

[0002] This invention relates to the field of air monitoring technology, and in particular to air quality monitoring methods and related equipment based on satellite communication. Background Technology

[0003] In situations where pollution is unevenly distributed vertically, such as when there is a dust transport layer or residual layer at high altitudes, the column concentration retrieved by satellite often overestimates or underestimates the actual pollution level at ground level. This lack of vertical resolution leads to a discrepancy between monitoring results and the air quality that the public directly experiences. Summary of the Invention

[0004] The main technical problem addressed by this application is to provide a satellite communication-based air quality monitoring method and related equipment, thereby resolving the technical issue of discrepancies between existing monitoring results and the air quality directly experienced by the public.

[0005] To address the aforementioned technical problems, this application employs a satellite communication-based air quality monitoring method, comprising the following steps: Radiometric normalization was performed on the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference. Vertical profile inversion is performed on the radiance data to obtain time-by-time three-dimensional aerosol profile maps; Based on the three-dimensional aerosol profile, the extinction coefficient contribution of the near-surface layer is extracted from the radiance data to obtain the near-surface extinction coefficient grid field. The extinction coefficient grid field is calibrated using the vertical echo signal collected by the ground-based lidar network and converted into a fused ground concentration distribution map. The three-dimensional aerosol profile map and the fused ground concentration distribution map are used as air quality monitoring data and broadcast to the mobile monitoring platform via a satellite communication link.

[0006] Furthermore, the radiometric normalization of the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference includes: The reflectance of each frame of the hyperspectral image sequence acquired by the geostationary satellite through continuous scanning is converted based on the solar incidence angle to obtain the nadir reflectance image sequence. Perform image histogram matching on the nadir reflectance image sequence to obtain a benchmark reflectance image sequence. Based on the benchmark reflectance image sequence, the spectra of corresponding points in the preset stable ground object database are extracted to obtain stable pixel spectra. The radiometric drift of the hyperspectral image sequence is calculated using the stable pixel spectrum to obtain frame-by-frame radiometric correction coefficients. The hyperspectral image sequence is radiometrically normalized based on the frame-by-frame radiometric correction coefficients to obtain radiance data with a unified spatiotemporal reference.

[0007] Furthermore, the step of performing vertical profile inversion on the radiance data to obtain time-by-time three-dimensional aerosol profile maps includes: Cloud pixel identification and masking processing are performed on the radiance data with a unified spatiotemporal reference to obtain clear sky radiance data; The clear-sky radiance data were subjected to multispectral band filtering to obtain the radiance of the aerosol-sensitive band; Based on the simulated radiance data of different aerosol types under different observation geometry conditions in a pre-established lookup table, the radiance of the aerosol sensitive band is optimally estimated and matched to obtain the initial aerosol optical thickness grid field. Spatial interpolation is performed on the initial aerosol optical thickness grid field to obtain an encrypted aerosol optical thickness grid field. The aerosol extinction coefficient profile is obtained by performing model parameter constraint inversion on the preset vertical distribution model of extinction coefficient using the encrypted aerosol optical thickness grid field. The aerosol extinction coefficient profiles of all pixels are assembled in three dimensions using a rasterization method to obtain time-series three-dimensional aerosol profile maps.

[0008] Furthermore, the step of extracting the near-surface extinction coefficient contribution from the radiance data based on the three-dimensional aerosol profile to obtain the near-surface extinction coefficient grid field includes: Vertical layer integration is performed on the time-series three-dimensional aerosol profile to obtain a simulated whole-layer optical thickness grid field; Aerosol optical thickness inversion is performed on the radiance data with a unified spatiotemporal reference to obtain the observed whole-layer optical thickness grid field; The residual optical thickness grid field is obtained by performing pixel-by-pixel residual calculations on the simulated full-layer optical thickness grid field and the observed full-layer optical thickness grid field. Spatial low-pass filtering is applied to the residual optical thickness grid field to obtain a smoothed residual field; Based on the smooth residual field, the extinction coefficient of the near-surface layer in the time-by-time three-dimensional aerosol profile is incrementally adjusted to obtain the extinction coefficient grid field of the near-surface layer.

[0009] Furthermore, by performing pixel-by-pixel residual calculations on the simulated full-layer optical thickness grid field and the observed full-layer optical thickness grid field, a residual optical thickness grid field is obtained, including: Register the grid point coordinates of the simulated whole-layer optical thickness grid field with the observed whole-layer optical thickness grid field to obtain a comparison table of optical thickness at the same grid point. The simulated value and the observed value are subtracted one by one for each grid point in the same grid point optical thickness reference table to obtain the original residual value sequence; wherein, the same grid point optical thickness reference table includes the latitude and longitude coordinates, simulated optical thickness value and observed optical thickness value of each grid point; The original residual value sequence is mapped to the corresponding grid points to obtain the residual optical thickness grid field.

[0010] Furthermore, based on the smoothed residual field, the near-surface layer in the time-series three-dimensional aerosol profile is incrementally adjusted to obtain the near-surface extinction coefficient grid field, including: Near-surface layers are extracted from the time-series three-dimensional aerosol profile maps to obtain an initial value map of the near-surface layer extinction coefficient, and the smoothed residual field is vertically weighted to obtain a map of the near-surface layer residual allocation coefficient. Based on the smoothed residual field, a pixel-by-pixel residual transformation is performed on the near-ground layer residual allocation coefficient map to obtain a near-ground layer extinction compensation map. Then, the near-ground layer extinction coefficient initial value map is superimposed and corrected pixel-by-pixel using the near-ground layer extinction compensation map to obtain a near-ground extinction coefficient grid field.

[0011] The present invention also provides an air quality monitoring device based on satellite communication, comprising: The normalization module is used to perform radiometric normalization on the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference. The inversion module is used to perform vertical profile inversion on the radiance data to obtain time-by-time three-dimensional aerosol profile maps; The extraction module is used to extract the near-surface extinction coefficient contribution from the radiance data based on the three-dimensional aerosol profile map, and obtain the near-surface extinction coefficient grid field. The calibration module is used to calibrate the extinction coefficient grid field using the vertical echo signal collected by the ground-based lidar network, and convert it into a fused ground concentration distribution map. The monitoring module is used to use the three-dimensional aerosol profile map and the fused ground concentration distribution map as air quality monitoring data, and broadcast them to the mobile monitoring platform via a satellite communication link.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above methods.

[0014] The present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of any of the above methods.

[0015] The above scheme performs radiometric normalization on the hyperspectral image sequence acquired by continuous scanning from geostationary satellites to obtain radiance data with a unified spatiotemporal reference; vertical profile inversion is performed on the radiance data to obtain hourly three-dimensional aerosol profile maps; based on the three-dimensional aerosol profile maps, the near-surface extinction coefficient contribution is extracted from the radiance data to obtain a near-surface extinction coefficient grid field; the extinction coefficient grid field is calibrated using vertical echo signals collected by a ground-based lidar network and converted into a fused ground concentration distribution map; the three-dimensional aerosol profile map and the fused ground concentration distribution map are used as air quality monitoring data and broadcast to a mobile monitoring platform via a satellite communication link. This solves the technical problem of discrepancies between existing monitoring results and the air quality directly experienced by the public, improving the accuracy of large-scale ground concentration inversion; the final generated fused ground concentration distribution map can transform the calibrated satellite data into concentration indicators that the public can intuitively understand, significantly improving the reliability and application value of the data in the near-surface environment while maintaining the satellite's large-scale monitoring coverage capability. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating an air quality monitoring method based on satellite communication in one embodiment of the present invention; Figure 2 This is a schematic diagram of the implementation process of S1 in one embodiment of the present invention; Figure 3 This is a schematic diagram of the implementation process of S2 in one embodiment of the present invention; Figure 4 This is a schematic diagram of the implementation process of S3 in one embodiment of the present invention; Figure 5 This is a structural block diagram of an air quality monitoring device based on satellite communication according to an embodiment of the present invention; Figure 6 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0023] Specifically, such as Figure 1 As shown, Figure 1 This invention discloses an air quality monitoring method based on satellite communication, comprising the following steps: Step S1: Radiometric normalization is performed on the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference.

[0024] Specifically, when performing radiometric normalization on a sequence of hyperspectral images acquired through continuous scanning by geostationary satellites, the solar incidence angle is first corrected for each frame's pixels. The radiance value is then adjusted based on the nadir solar zenith angle at the imaging time to eliminate the influence of illumination angle differences, resulting in a nadir reflectance image sequence. Subsequently, histogram matching is performed between images in this sequence. Using the first or a stable frame from the middle of the sequence as a benchmark, the grayscale distribution of subsequent frames is adjusted to suppress background drift. Based on the corrected benchmark reflectance image sequence, spectra of corresponding points in unchanging surface areas such as deserts and dense forests are extracted from a pre-defined stable ground feature database to form stable pixel spectra. By analyzing the long-term trend of this set, the frame-by-frame radiometric drift is calculated, and frame-by-frame radiometric correction coefficients are generated accordingly. These coefficients are then applied to the original hyperspectral image sequence to output radiance data with a unified spatiotemporal benchmark, ensuring comparability of data across time periods.

[0025] Step S2: Perform vertical profile inversion on the radiance data to obtain time-by-time three-dimensional aerosol profile maps.

[0026] Specifically, when performing vertical profile inversion on radiance data, the spectral response of each pixel is first decomposed to extract an initial aerosol optical thickness map and a fine particle proportion map. Based on the initial optical thickness, the closest category is matched in a pre-defined library of typical aerosol types to generate a pixel-level aerosol master type map, for example, identifying a region as primarily dusty or urban polluted. The fine particle proportion is used to further correct this master type map, adjusting its particle size distribution parameters to form an aerosol microphysical property map. This property map is then input into a pre-constructed lookup table, generated by a radiative transfer model under different atmospheric stratification, surface reflectivity, and observation geometry conditions. The best-fit search determines the aerosol centroid height or bottom / top height corresponding to each pixel. Finally, the output aerosol height map is subjected to Gaussian smoothing in the vertical direction to construct a spatially continuous, time-series three-dimensional aerosol profile map, reflecting the vertical distribution structure of pollutants.

[0027] Step S3: Based on the three-dimensional aerosol profile, extract the extinction coefficient contribution of the near-surface layer from the radiance data to obtain the near-surface extinction coefficient grid field.

[0028] Specifically, when extracting the near-surface extinction coefficient contribution from radiance data based on the aforementioned 3D aerosol profile, the height range of the near-surface layer is first determined according to the vertical distribution structure of aerosols provided by the profile, typically from the Earth's surface to 500 meters. The weighted contribution ratio of this layer of aerosols to the overall atmospheric optical thickness is then calculated using a radiative transfer model, and decomposed in conjunction with the total extinction coefficient observed by satellite. Specifically, the total extinction value is weighted according to the scattering phase function and path length of each vertical layer using lookup tables or analytical formulas, extracting the near-surface layer component. For example, at a certain pixel, if the 3D profile shows that aerosols are mainly concentrated in the low atmosphere and are uniformly mixed, then its near-surface contribution is higher; if there is a significant temperature inversion layer causing the suspension layer to rise, the contribution is correspondingly lower. Finally, the calculation results are arranged according to a geographic grid to generate a spatially continuous near-surface extinction coefficient grid field for subsequent ground concentration correlation analysis.

[0029] Step S4: The extinction coefficient grid field is calibrated using the vertical echo signal collected by the ground-based lidar network and converted into a fused ground concentration distribution map.

[0030] Specifically, when calibrating the near-surface extinction coefficient gridded field using vertical echo signals collected by a ground-based lidar network, the echo signals from each radar station are first extracted and inverted to obtain a high-vertical-resolution extinction coefficient profile. The satellite inversion results at the corresponding locations in the 3D aerosol profile map are resampled to the radar altitude layer via interpolation, and the average extinction value within 100 meters above the ground surface is selected as the representative near-surface value from the satellite. The radar observations and satellite inversion values ​​at the same time point are paired, their deviation is calculated, and a spatial correction factor is fitted. A continuous calibration field is generated using Kriging interpolation. This field is applied to the original near-surface extinction coefficient gridded field to correct systematic biases. Subsequently, based on localized aerosol mass conversion relationships and meteorological parameters such as temperature and humidity, the calibrated extinction coefficient is converted, ultimately generating a fused ground concentration distribution map in μg / m³, improving the spatial characterization accuracy of near-surface pollution levels.

[0031] Step S5: The three-dimensional aerosol profile map and the fused ground concentration distribution map are used as air quality monitoring data and broadcast to the mobile monitoring platform via a satellite communication link.

[0032] Specifically, when using hourly 3D aerosol profile maps and fused ground concentration distribution maps as air quality monitoring data, the two images are first spatiotemporally aligned to ensure their geographic projection, grid resolution, and observation time are consistent. The 3D aerosol profile map records the extinction coefficients of each latitude and longitude grid point at different altitudes in voxel form, reflecting the vertical structure of pollutants; the fused ground concentration distribution map provides the near-surface PM2.5 or PM10 mass concentration field after calibration by ground-based radar. Both are packaged using a unified data encapsulation protocol, with added timestamps and data source identifiers, forming a composite monitoring dataset. This dataset retains both upper-air transport characteristics and ground-based measurement comparability, and is used for subsequent broadcasting to mobile monitoring platforms or access to regional air quality management systems via satellite communication links, serving as the core input for pollution source tracing, diffusion simulation, and public early warning.

[0033] In a specific embodiment, such as Figure 2 As shown, the radiometric normalization of the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference includes: S11, Perform reflectivity conversion based on solar incidence angle on the pixels of each frame of the hyperspectral image sequence obtained by continuous scanning of the geostationary satellite to obtain the nadir reflectivity image sequence. S12, perform image inter-image histogram matching on the sub-satellite reflectance image sequence to obtain a benchmark reflectance image sequence; S13, Based on the benchmark reflectance image sequence, perform spectral extraction of corresponding points from the preset stable ground object database to obtain stable pixel spectra; S14, calculate the radiometric drift of the hyperspectral image sequence using the stable pixel spectrum to obtain frame-by-frame radiometric correction coefficients; S15, the hyperspectral image sequence is radiometrically normalized based on the frame-by-frame radiometric correction coefficients to obtain radiance data with a unified spatiotemporal reference.

[0034] Specifically, radiometric normalization is performed on the hyperspectral image sequence acquired by continuous scanning from geostationary satellites. First, solar incidence angle correction is applied to the pixels in each frame. Specifically, based on the ephemeris data and solar position model at the time of imaging, the solar zenith angle and azimuth angle corresponding to each pixel are calculated. Combined with the cosine relationship between the surface incident irradiance and the angle, the original observed irradiance values ​​are geometrically corrected to eliminate reflectance distortion caused by differences in illumination conditions, thereby obtaining the nadir reflectance image for each time period, forming a time series set.

[0035] After frame-by-frame angle correction, further adjustments were made to the relative radiometric consistency between images. A scene with minimal cloud cover and high atmospheric transparency was selected as the reference frame. Gray-level histograms were calculated for the remaining images by band and matched to the distribution patterns of the corresponding bands in the reference frame. Using a non-parametric histogram specification method, the cumulative pixel value distribution of the images to be corrected was adjusted to approximate the statistical characteristics of the reference frame. This resulted in a set of benchmark reflectance image sequences that maintained consistent overall brightness and contrast, providing a foundation for subsequent stability analysis.

[0036] Based on this benchmark sequence, spectral information is extracted from a pre-defined database of stable ground features. This database includes several regions globally with long-term stable surface properties, such as the heart of the Taklamakan Desert, the edge of the Greenland ice sheet, and the exposed lakebed of Qinghai Lake, whose geographic coordinates and typical spectral responses have been pre-calibrated. For each target area, its corresponding pixels are located in each image, and the reflectance values ​​of multispectral bands are extracted to construct a stable pixel spectrum that evolves over time.

[0037] By leveraging the time-series characteristics of this spectral set, its drift trend during continuous observation was analyzed. Median filtering within a sliding window was employed to suppress random noise, and linear regression was used to estimate the slow attenuation slope of each band, calculating the frame-by-frame radiometric correction coefficients relative to the initial state. Finally, these coefficients were applied to the original hyperspectral image sequence, performing a pixel-by-pixel linear transformation to achieve radiometric normalization of the entire time-series data, outputting radiance data with uniform dimensions and spatiotemporal comparability based on a unified spatiotemporal benchmark. Taking continuous monitoring in East China as an example, after this process, the standard deviation of the 860nm band reflectance of fixed surface targets in Taihu Lake decreased from 9.7% before correction to 1.8%, significantly improving the stability of the data sequence.

[0038] In a specific embodiment, such as Figure 3 As shown, the vertical profile inversion of the radiance data to obtain time-series three-dimensional aerosol profile maps includes: S21, perform cloud pixel recognition and masking processing on the radiance data with a unified spatiotemporal reference to obtain clear sky radiance data; S22, perform multispectral band filtering on the clear sky radiance data to obtain the radiance of the aerosol-sensitive band; S23, based on the simulated radiance data of different aerosol types under different observation geometry conditions in the pre-established lookup table, the radiance of the aerosol sensitive band is optimally estimated and matched to obtain the initial aerosol optical thickness grid field. S24, Spatial interpolation is performed on the initial aerosol optical thickness grid field to obtain an encrypted aerosol optical thickness grid field; S25, the model parameter constraint inversion of the preset extinction coefficient vertical distribution model is performed through the encrypted aerosol optical thickness grid field to obtain the aerosol extinction coefficient profile; S26, perform three-dimensional rasterization assembly on the aerosol extinction coefficient profiles of all pixels to obtain time-series three-dimensional aerosol profile maps.

[0039] Specifically, before performing vertical profile inversion on the radiance data with a unified spatiotemporal reference, interference from cloud-contaminated pixels must first be eliminated. A cloud detection algorithm based on multispectral thresholds is used, combining the reflectivity differences between the visible and near-infrared bands and the brightness temperature characteristics of the thermal infrared band, to identify cloud-covered areas and generate corresponding binary masks. These masks are then applied to the original data, masking cloud-covered pixels and retaining cloudless observation points to obtain clear-sky radiance data. Based on this, and considering aerosol inversion requirements, bands sensitive to aerosol optical properties are selected from the clear-sky data. These typically include channels such as 0.47μm, 0.55μm, 0.65μm, and 0.86μm, forming aerosol-sensitive band radiance for subsequent analysis.

[0040] To obtain initial aerosol optical thickness information, a matching inversion was performed using a pre-established lookup table. This lookup table simulates the surface reflectivity response of different aerosol types (such as urban, dust, and clean ocean types) under various observation geometries using radiative transfer models (such as 6S or MODTRAN), covering the range of variations in solar zenith angle, satellite observation angle, and relative azimuth angle. The measured radiance of the aerosol-sensitive bands was compared one by one with the simulated data in the lookup table. The least squares optimal estimation method was used to search for the closest matching combination, determining the aerosol type and corresponding optical thickness at each pixel, generating an initial aerosol optical thickness grid field. Due to the limited resolution of the original satellite observations, the spatial sampling of this grid field is relatively sparse. Therefore, a bicubic interpolation method was used to spatially densify the grid, increasing the grid density and obtaining a higher-resolution, densified aerosol optical thickness grid field to support more refined vertical structure extrapolation.

[0041] Using this encrypted field as a constraint, a pre-defined vertical distribution model of the extinction coefficient is parametrically inverted. This model is typically set to an exponential decay or Gaussian layered form, including adjustable parameters such as boundary layer height and vertical scale factor. The encrypted aerosol optical thickness of each pixel is used as an integral constraint for the entire layer. The model parameters are solved iteratively to ensure that the theoretical optical thickness after integration matches the inversion result, thus obtaining the aerosol extinction coefficient profile corresponding to that pixel, with a vertical resolution of 100–500 meters. Finally, the vertical profiles of all pixels in the study area are registered in three-dimensional space according to their geographic coordinates, and rasterized and stitched together according to a unified height layer and horizontal grid to form a complete three-dimensional data cube, i.e., a time-series three-dimensional aerosol profile map. Taking a regional haze process in the North China Plain as an example, the inversion result at 12:00 shows that the peak extinction coefficient over Shijiazhuang occurs below 800 meters, and the horizontal gradient is highly consistent with the encrypted optical thickness field, verifying the spatial consistency of the method.

[0042] In another embodiment, the radiance data with a unified spatiotemporal reference is subjected to band separation processing to obtain radiance subsets in different bands. Atmospheric molecular absorption correction is performed on the radiance subset data for each band to obtain the corrected radiance subset data. Based on the corrected radiance subset, the aerosol optical thickness values ​​corresponding to each band are obtained by looking up the preset aerosol optical thickness and radiance correspondence table, and interpolation calculations are performed in the vertical direction based on the aerosol optical thickness values ​​corresponding to each band to obtain the vertical distribution data of aerosol optical thickness. The aerosol optical thickness vertical distribution data are used to identify the aerosol type, and the corresponding aerosol extinction coefficient vertical variation characteristics are determined based on the identified aerosol type. Based on the vertical variation characteristics of the aerosol extinction coefficient, the vertical distribution data of the aerosol optical thickness is converted to the extinction coefficient to obtain the vertical distribution data of the aerosol extinction coefficient. Based on the vertical distribution data of the aerosol extinction coefficient, the data is processed in layers in the vertical direction to obtain a three-dimensional aerosol profile map for each time interval. Specifically, the radiance data with a unified spatiotemporal reference is processed by band separation at preset wavelength intervals. Channels are divided based on the sensor's spectral response function, and independent data for key bands such as 0.47μm, 0.55μm, 0.65μm, and 0.86μm are extracted to form radiance subsets for different bands. For each subset, the absorption coefficients of oxygen and water vapor in the corresponding band are simulated using an atmospheric radiative transfer model. Combined with vertical profiles provided by radiosonde data or reanalysis data, atmospheric molecular absorption attenuation terms are calculated. The original radiance value is then divided by a transmittance factor to complete atmospheric molecular absorption correction, and the corrected radiance subset is output.

[0043] Based on the corrected data, a pre-defined table of aerosol optical thickness and radiance correspondence is used for inversion. This table is pre-simulated using 6S or MODTRAN models and covers the radiance response of various aerosol types (such as urban, dust, and biomass combustion aerosols) under different observation geometries and surface reflectivity conditions. The measured radiance values ​​for each band are matched with the data in the table, and the closest aerosol optical thickness value is determined using the least squares method. After obtaining the optical thickness for multiple discrete bands, interpolation calculations are performed in the vertical direction based on typical aerosol vertical distribution patterns (such as exponential decay) to reconstruct continuous aerosol optical thickness vertical distribution data with a vertical resolution of 100 meters.

[0044] Subsequently, aerosol type identification was performed on this vertical distribution data. Spectral slope characteristics of multi-band optical thickness were utilized, such as calculating the Ångström index between 470 and 660 nm, combined with regional background information to determine the dominant aerosol type. Different types correspond to different vertical variation characteristics of extinction coefficients. For example, urban pollution types typically exhibit rapid attenuation at high values ​​near the ground, while dust types have a thicker mixed layer structure. Based on the identification results, appropriate vertical variation templates were selected, and the entire optical thickness was proportionally distributed to each height layer, achieving the conversion from optical thickness to extinction coefficient, thus obtaining the vertical distribution data of aerosol extinction coefficients.

[0045] Finally, the vertically distributed data was unfolded horizontally according to a satellite pixel grid and organized along a time series. The vertical profile of each pixel was then three-dimensionally rasterized and stitched together to form a time-series three-dimensional aerosol profile map containing longitude, latitude, and altitude dimensions. Taking an observation at 10:00 AM on a certain day in East China as an example, the aerosols over Nanjing were identified as urban aerosols, with their extinction peaks concentrated between 0 and 600 meters. After conversion and layered assembly, the spatial expansion morphology of the pollution accumulation layer was clearly presented.

[0046] In a specific embodiment, such as Figure 4 As shown, the step of extracting the near-surface extinction coefficient contribution from the radiance data based on the three-dimensional aerosol profile to obtain the near-surface extinction coefficient grid field includes: S31, Perform vertical layer integration on the time-by-time three-dimensional aerosol profile map to obtain the simulated whole-layer optical thickness grid field; S32, perform aerosol optical thickness inversion on the radiance data with a unified spatiotemporal reference to obtain the observation whole-layer optical thickness grid field; S33, the residual optical thickness grid field is obtained by performing pixel-by-pixel residual calculation on the simulated whole-layer optical thickness grid field and the observed whole-layer optical thickness grid field; S34, Spatial low-pass filtering is performed on the residual optical thickness grid field to obtain a smooth residual field; S35, based on the smooth residual field, the extinction coefficient of the near-surface layer in the time-by-time three-dimensional aerosol profile is adjusted incrementally to obtain the extinction coefficient grid field of the near-surface.

[0047] Specifically, a vertical layer integration operation is performed on the time-series 3D aerosol profile maps. This involves numerically integrating the extinction coefficient profile of each pixel along the height direction, with the integration interval covering from the Earth's surface to the top atmosphere, to obtain the overall optical thickness value corresponding to that pixel. This allows the 3D data to be projected into a 2D planar field, generating a simulated overall optical thickness grid field. Simultaneously, a standard aerosol optical thickness inversion process is independently executed on the radiance data with a unified spatiotemporal reference. Using a lookup table-based optimal estimation method, observation information for the corresponding bands is extracted under the same spatial grid, matched with the radiative transfer model output, and another set of observed overall optical thickness grid fields is inverted. Both should have consistent spatial distribution; however, due to uncertainties in the 3D profile input or biases in model assumptions, local differences still exist between the actual results.

[0048] To identify such deviations and use them to correct near-ground signals, a pixel-by-pixel difference calculation is performed between the simulated full-layer optical thickness grid field and the observed full-layer optical thickness grid field to obtain a residual optical thickness grid field. This field reflects the deviation in optical thickness estimation caused by inaccuracies in the vertical structure. Since this residual contains high-frequency noise and random errors, direct application would affect stability. Therefore, a spatial low-pass filter is applied, and a two-dimensional Gaussian convolution kernel or a mean sliding window is used to smooth the residual field, filtering out isolated outliers and retaining large-scale systematic deviation characteristics to form a smoothed residual field. This field reflects the systematic underestimation or overestimation trend of the total optical thickness within the region due to misjudgments of the upper aerosol distribution.

[0049] Based on this smoothed residual field, feedback correction is performed on the original 3D structure. Considering that the residuals mainly originate from the underestimation or overestimation of the near-surface aerosol concentration, the smoothed residual field is used as an incremental constraint and proportionally allocated to the near-surface layer (usually defined as 0–500 meters) in the time-series 3D aerosol profile maps, adjusting the extinction coefficient value of this layer. The adjustment method can employ linear compensation or dynamic scaling of the increment according to the boundary layer height to ensure that the optical thickness variation is reasonably distributed to the physical location. The final output is the gridded field of the near-surface extinction coefficient after bias correction. Taking a stable weather process in the Yangtze River Delta region as an example, the initial simulation field showed an optical thickness that was about 0.15 km lower than normal around Nanjing. After residual analysis and smoothing, the optical thickness was uniformly increased by 0.08 km in the near-surface layer. - ¹The extinction coefficient makes the inversion results more consistent with subsequent lidar observations, thus improving the ability to characterize the intensity of surface pollution.

[0050] In a specific embodiment, a residual optical thickness grid field is obtained by performing pixel-by-pixel residual calculation using the simulated full-layer optical thickness grid field and the observed full-layer optical thickness grid field, including: Register the grid point coordinates of the simulated whole-layer optical thickness grid field with the observed whole-layer optical thickness grid field to obtain a comparison table of optical thickness at the same grid point. The simulated value and the observed value are subtracted one by one for each grid point in the same grid point optical thickness reference table to obtain the original residual value sequence; wherein, the same grid point optical thickness reference table includes the latitude and longitude coordinates, simulated optical thickness value and observed optical thickness value of each grid point; The original residual value sequence is mapped to the corresponding grid points to obtain the residual optical thickness grid field.

[0051] Specifically, before performing residual calculation, it is first necessary to ensure that the simulated full-layer optical thickness grid field and the observed full-layer optical thickness grid field have a unified spatial reference frame. If there are differences in the original projections or grid resolutions of the two, one of them needs to be used as a reference to resample and geometrically correct the other data field. This is achieved by mapping it to the same latitude and longitude grid system using bilinear interpolation or the nearest neighbor method, thus realizing spatial registration. After registration, the corresponding pixels are matched one by one to construct a grid-based optical thickness lookup table containing the geographic coordinates of each grid point, the simulated optical thickness value from the simulated field, and the observed optical thickness value from the inversion results. This table is stored in a two-dimensional array, with each record corresponding to a valid pixel.

[0052] Based on this lookup table, a subtraction operation is performed on each grid point, that is, the observed optical thickness value is subtracted from the simulated optical thickness value to obtain the original residual value at that location, forming a sequence of original residual values ​​covering the entire region. Some areas in this sequence may have missing data due to cloud cover, sensor saturation, or invalid inversion, resulting in undefined values ​​(such as NaN). These missing locations are not included in subsequent processing. For valid residual values, the sign reflects the direction of the bias: a positive value indicates that the observed value is greater than the simulated value, suggesting that the current 3D profile underestimates the actual aerosol load; a negative value indicates the opposite. For example, during a heavy pollution event in the Beijing-Tianjin-Hebei region, the simulated value for a certain time period in Baoding was 0.82, while the observed value was 1.05, resulting in an original residual of +0.23, indicating that the near-surface contribution was not fully captured.

[0053] Since the original residual values ​​exist only at effective pixel locations and exhibit discrete distribution characteristics, they are difficult to directly use for continuous field correction. Therefore, residual value spatial filling is necessary. An interpolation method based on inverse distance weighting (IDW) is employed. Centered on the effective residual points, a search radius (e.g., 50 km) is set, and values ​​are assigned to surrounding blank grid points, with the weights decreasing as distance increases. Kriging interpolation can also be combined, introducing a regional spatial autocorrelation function to improve interpolation accuracy. After filling, a residual optical thickness grid field covering the entire region without data holes is generated, serving as the basis input for subsequent smoothing and correction. This process preserves large-scale systematic bias characteristics while avoiding excessive propagation of local noise, providing a reliable basis for near-surface layer parameter adjustment.

[0054] In a specific embodiment, the near-surface layer in the time-by-time three-dimensional aerosol profile is adjusted by incrementally adjusting the extinction coefficient based on the smoothed residual field to obtain a grid field of extinction coefficients near the ground, including: Near-surface layers are extracted from the time-series three-dimensional aerosol profile maps to obtain an initial value map of the near-surface layer extinction coefficient, and the smoothed residual field is vertically weighted to obtain a map of the near-surface layer residual allocation coefficient. Based on the smoothed residual field, a pixel-by-pixel residual transformation is performed on the near-ground layer residual allocation coefficient map to obtain a near-ground layer extinction compensation map. Then, the near-ground layer extinction coefficient initial value map is superimposed and corrected pixel-by-pixel using the near-ground layer extinction compensation map to obtain a near-ground extinction coefficient grid field.

[0055] Specifically, near-surface layer extraction is performed on the time-series three-dimensional aerosol profile maps. This involves extracting extinction coefficient slices within a vertical range of 0 to 500 meters based on a pre-defined height-layered structure, retaining the original values ​​of each horizontal pixel within this slice, and forming an initial near-surface layer extinction coefficient map. This map reflects the near-surface aerosol extinction state directly output by the initial inversion model before considering overall layer residual correction. Simultaneously, vertical weight allocation processing is performed on the smoothed residual field. Considering that aerosol mass is mainly concentrated in the lower boundary layer, a highly dependent attenuation function is introduced as the allocation kernel. For example, the weight above 500 meters is set to zero, and the weight decreases linearly or exponentially in the 0–500 meter range. The optical thickness deviation of each grid point in the smoothed residual field is proportionally mapped to the near-surface layer, generating a near-surface layer residual allocation coefficient map corresponding to a spatial location, with units in km. - ¹·τ - ¹ represents the extinction compensation intensity corresponding to a unit optical thickness residual.

[0056] Based on this, a pixel-by-pixel residual transformation operation is performed using the smoothed residual field and the near-surface layer residual distribution coefficient map. Specifically, the smoothed residual value at each grid point is multiplied by the residual distribution coefficient at its corresponding location, achieving a dimensional transformation from optical thickness deviation to extinction coefficient increment, resulting in a near-surface layer extinction compensation map. Each value in this map represents the correction amount to be superimposed onto the original value; positive values ​​indicate enhanced near-surface extinction, and negative values ​​have the opposite effect. Taking Taiyuan at 14:00 on a certain day as an example, the smoothed residual at a certain grid point is +0.18, and the residual distribution coefficient is 0.065 km. - ¹, then the calculated extinction compensation is 0.0117 km. - ¹ indicates that the original profile underestimated the intensity of near-surface contamination.

[0057] Finally, the near-surface extinction compensation map and the initial near-surface extinction coefficient map are algebraically superimposed pixel by pixel, i.e., an addition operation is performed on pixels with the same geographic coordinates to complete the deviation correction. The output result retains the original grid structure, forming an updated near-surface extinction coefficient grid field. This field inherits the overall trend of the 3D inversion in terms of spatial distribution, and also integrates the measured constraint information of the whole-layer optical thickness residual feedback, improving the spatial characterization accuracy of near-surface pollutant concentration. For densely populated urban areas, this type of correction is particularly critical, as it can effectively alleviate the problem of surface signal weakening caused by upper-layer aerosol interference and make the gradient characteristics of highly polluted core areas clearer.

[0058] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. It should be noted that the information interaction, execution process, etc. between the above devices / units are based on the same concept as the method embodiments of this application. Their specific functions and technical effects can be found in the embodiment section of the control device, and will not be repeated here.

[0059] Please see Figure 5 , Figure 5 This is a schematic diagram of a framework of an embodiment of the air quality monitoring device based on satellite communication according to this application. Figure 5 As shown, the satellite communication-based air quality monitoring device includes a normalization module 1, used to perform radiometric normalization on the hyperspectral image sequence acquired by continuous scanning from a geostationary satellite to obtain radiance data with a unified spatiotemporal reference; an inversion module 2, used to perform vertical profile inversion on the radiance data to obtain hourly three-dimensional aerosol profile maps; an extraction module 3, used to extract the near-surface extinction coefficient contribution from the radiance data based on the three-dimensional aerosol profile maps to obtain a near-surface extinction coefficient grid field; a calibration module 4, used to calibrate the extinction coefficient grid field using vertical echo signals collected by a ground-based lidar network and convert it into a fused ground concentration distribution map; and a monitoring module 5, used to use the three-dimensional aerosol profile map and the fused ground concentration distribution map as air quality monitoring data and broadcast them to a mobile monitoring platform via a satellite communication link.

[0060] The above module is used to execute the steps of the satellite communication-based air quality monitoring method.

[0061] Reference Figure 6 This invention also provides a computer device whose internal structure can be as follows: Figure 6 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0062] Those skilled in the art will understand that Figure 6The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0063] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0064] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the functions of the various structures of the control device described above, or to implement the steps in the various method embodiments described above.

[0065] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0066] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0067] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0070] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

Claims

1. An air quality monitoring method based on satellite communication, characterized in that, Includes the following steps: Radiometric normalization was performed on the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference. Vertical profile inversion is performed on the radiance data to obtain time-by-time three-dimensional aerosol profile maps; Based on the three-dimensional aerosol profile, the extinction coefficient contribution of the near-surface layer is extracted from the radiance data to obtain the near-surface extinction coefficient grid field. The extinction coefficient grid field is calibrated using the vertical echo signal collected by the ground-based lidar network and converted into a fused ground concentration distribution map; The three-dimensional aerosol profile map and the fused ground concentration distribution map are used as air quality monitoring data and broadcast to the mobile monitoring platform via a satellite communication link.

2. The air quality monitoring method based on satellite communication according to claim 1, characterized in that, The radiometric normalization of the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference includes: The reflectance of each frame of the hyperspectral image sequence acquired by the geostationary satellite through continuous scanning is converted based on the solar incidence angle to obtain the nadir reflectance image sequence. Perform image histogram matching on the nadir reflectance image sequence to obtain a benchmark reflectance image sequence. Based on the benchmark reflectance image sequence, the spectra of corresponding points in the preset stable ground object database are extracted to obtain stable pixel spectra. The radiometric drift of the hyperspectral image sequence is calculated using the stable pixel spectrum to obtain frame-by-frame radiometric correction coefficients. The hyperspectral image sequence is radiometrically normalized based on the frame-by-frame radiometric correction coefficients to obtain radiance data with a unified spatiotemporal reference.

3. The air quality monitoring method based on satellite communication according to claim 1, characterized in that, The process of performing vertical profile inversion on the radiance data to obtain time-series three-dimensional aerosol profile maps includes: Cloud pixel identification and masking processing are performed on the radiance data with a unified spatiotemporal reference to obtain clear sky radiance data; The clear-sky radiance data were subjected to multispectral band filtering to obtain the radiance of the aerosol-sensitive band; Based on the simulated radiance data of different aerosol types under different observation geometry conditions in a pre-established lookup table, the radiance of the aerosol sensitive band is optimally estimated and matched to obtain the initial aerosol optical thickness grid field. Spatial interpolation is performed on the initial aerosol optical thickness grid field to obtain an encrypted aerosol optical thickness grid field. The aerosol extinction coefficient profile is obtained by performing model parameter constraint inversion on the preset vertical distribution model of extinction coefficient using the encrypted aerosol optical thickness grid field. The aerosol extinction coefficient profiles of all pixels are assembled in three dimensions using a rasterization method to obtain time-series three-dimensional aerosol profile maps.

4. The air quality monitoring method based on satellite communication according to claim 1, characterized in that, The step of extracting the near-surface extinction coefficient contribution from the radiance data based on the three-dimensional aerosol profile to obtain the near-surface extinction coefficient grid field includes: Vertical layer integration is performed on the time-series three-dimensional aerosol profile to obtain a simulated whole-layer optical thickness grid field; Aerosol optical thickness inversion is performed on the radiance data with a unified spatiotemporal reference to obtain the observed whole-layer optical thickness grid field; The residual optical thickness grid field is obtained by performing pixel-by-pixel residual calculations on the simulated full-layer optical thickness grid field and the observed full-layer optical thickness grid field. Spatial low-pass filtering is applied to the residual optical thickness grid field to obtain a smoothed residual field; Based on the smooth residual field, the extinction coefficient of the near-surface layer in the time-by-time three-dimensional aerosol profile is incrementally adjusted to obtain the extinction coefficient grid field of the near-surface layer.

5. The air quality monitoring method based on satellite communication according to claim 4, characterized in that, By performing pixel-by-pixel residual calculations on the simulated full-layer optical thickness grid field and the observed full-layer optical thickness grid field, a residual optical thickness grid field is obtained, including: Register the grid point coordinates of the simulated whole-layer optical thickness grid field with the observed whole-layer optical thickness grid field to obtain a comparison table of optical thickness at the same grid point. The simulated value and the observed value are subtracted one by one for each grid point in the same grid point optical thickness reference table to obtain the original residual value sequence; wherein, the same grid point optical thickness reference table includes the latitude and longitude coordinates, simulated optical thickness value and observed optical thickness value of each grid point; The original residual value sequence is mapped to the corresponding grid points to obtain the residual optical thickness grid field.

6. The air quality monitoring method based on satellite communication according to any one of claims 4-5, characterized in that, Based on the smoothed residual field, the extinction coefficient of the near-surface layer in the time-series three-dimensional aerosol profile is incrementally adjusted to obtain the near-surface extinction coefficient grid field, including: Near-surface layers are extracted from the time-series three-dimensional aerosol profile maps to obtain an initial value map of the near-surface layer extinction coefficient, and the smoothed residual field is vertically weighted to obtain a map of the near-surface layer residual allocation coefficient. Based on the smoothed residual field, a pixel-by-pixel residual transformation is performed on the near-ground layer residual allocation coefficient map to obtain a near-ground layer extinction compensation map. Then, the near-ground layer extinction coefficient initial value map is superimposed and corrected pixel-by-pixel using the near-ground layer extinction compensation map to obtain a near-ground extinction coefficient grid field.

7. An air quality monitoring device based on satellite communication, characterized in that, The method for performing the satellite communication-based air quality monitoring according to any one of claims 1 to 6 includes: The normalization module is used to perform radiometric normalization on the hyperspectral image sequence acquired by continuous scanning of geostationary satellites to obtain radiance data with a unified spatiotemporal reference. The inversion module is used to perform vertical profile inversion on the radiance data to obtain time-by-time three-dimensional aerosol profile maps; The extraction module is used to extract the near-surface extinction coefficient contribution from the radiance data based on the three-dimensional aerosol profile map, and obtain the near-surface extinction coefficient grid field. The calibration module is used to calibrate the extinction coefficient grid field using the vertical echo signal collected by the ground-based lidar network, and convert it into a fused ground concentration distribution map. The monitoring module is used to use the three-dimensional aerosol profile map and the fused ground concentration distribution map as air quality monitoring data, and broadcast them to the mobile monitoring platform via a satellite communication link.

8. A computer device, characterized in that, The method includes a memory and a processor that are coupled to each other. The memory stores program instructions, and the processor executes the program instructions to implement the air quality monitoring method based on satellite communication as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The system stores program instructions that can be executed by a processor, the program instructions being used to implement the air quality monitoring method based on satellite communication as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, enables the implementation of the steps of the satellite communication-based air quality monitoring method as described in any one of claims 1 to 6.

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