Dust aerosol identification method, device and equipment, storage medium and program product

Abstract

The invention provides a dust aerosol identification method, device and equipment, a storage medium and a program product, and relates to the technical field of data processing, and the method comprises the steps: carrying out the data preprocessing of multi-observation-angle DPC data, POSP data and MCD data of a target area, carrying out the data space-time matching and sea-land data separation of the multi-observation-angle DPC data, POSP data and MCD data, and carrying out the data preprocessing of the multi-observation-angle DPC data, POSP data and MCD data; obtaining ocean area data and land area data; performing pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; and determining a dust aerosol area of the target area according to the pixel type information of each pixel at the plurality of observation angles.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a dust aerosol identification method, device, equipment, storage medium and program product. Background Art

[0002] Atmospheric aerosols are a multiphase system composed of solid or liquid particles and gaseous carriers suspended in the atmosphere. Aerosols can be broadly categorized into natural and anthropogenic sources based on their origin. Natural aerosols refer to aerosol particles produced by natural processes, such as sandstorms, soil dust, volcanic ash from volcanic eruptions, and particulate matter emitted by forest fires. Anthropogenic aerosols, on the other hand, refer to particulate matter generated in the course of human production and daily life, such as industrial emissions and transportation.

[0003] As a key component of tropospheric aerosols, dust aerosols play a vital role in the global climate system and human production and life. For the global climate system, dust aerosols directly affect the balance of the Earth-atmosphere system by absorbing solar radiation and absorbing and emitting infrared radiation, thereby changing the stability of the atmospheric system. They can also act as condensation nuclei to change the microphysical structure of clouds, thereby affecting cloud formation. For human production and life, dust aerosols not only affect atmospheric visibility and disrupt people's transportation, but also contain many inhalable particles that are harmful to the human body, such as toxic chemicals, heavy metals, and pathogens. Dust aerosol particles can trigger allergic and infectious diseases, posing a threat to human production and life.

[0004] Therefore, how to identify dust aerosols more effectively has become an urgent problem to be solved in the industry. Summary of the Invention

[0005] The present invention provides a dust aerosol identification method, device, equipment, storage medium and program product, which are used to solve the problem of how to more effectively identify dust aerosols in the prior art.

[0006] The present invention provides a dust aerosol identification method, comprising the following steps: After data preprocessing, the multi-observation angle DPC data, POSP data and MCD data of the target area are subjected to data spatiotemporal matching and sea-land data separation to obtain ocean area data and land area data; Performing pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The dust aerosol area of ​​the target area is determined according to the pixel type information of each pixel at multiple observation angles.

[0007] According to a dust aerosol identification method provided by the present invention, pixel-by-pixel analysis of the ocean area data from multiple observation angles is performed, including: Screening out the glare area data in the ocean area data to obtain the non-glare area data in the ocean area data; Filtering, from each pixel of the non-glare area data, pixels whose first reflectivity information is within a first preset reflectivity threshold range and whose first reflectivity covariance is less than the first preset covariance threshold, to obtain a first cloud pixel and a first dust pixel; From each first dust pixel, pixels having a normalized vegetation index that meets a first index range and a band linear polarization degree that falls within the first polarization degree range are selected to obtain a second dust pixel and a second cirrus cloud pixel from each first dust pixel; From each of the second dust pixels, pixels whose band linear polarization degree is within the second polarization degree range, whose dust aerosol index is greater than the first dust aerosol preset threshold, and whose non-dust aerosol index is greater than the first non-dust aerosol preset threshold are screened, thereby obtaining a third dust pixel whose pixel type information is dust type in the ocean data area at each observation angle. According to a dust aerosol identification method provided by the present invention, pixel-by-pixel analysis of the land area data at multiple observation angles is performed, including: Filtering, from each pixel in the land area data, pixels whose second reflectivity information is within a second preset reflectivity threshold range and whose second reflectivity covariance is less than the second preset reflectivity covariance threshold, to obtain a third cloud pixel and a fourth dust pixel; Filtering pixels whose normalized vegetation index is within the second index range and whose band linear polarization degree is within the second polarization degree range from the fourth dust pixel to obtain a fifth dust pixel; The fifth dust pixel in the bright surface area and the fifth dust pixel in the non-bright surface area are analyzed separately to obtain the sixth dust pixel; From the sixth dust pixel, pixels whose non-dust aerosol index is less than or equal to the second non-dust aerosol preset threshold are screened to obtain a seventh dust pixel whose pixel type information is dust type in the ocean data of each observation angle.

[0008] According to a dust aerosol identification method provided by the present invention, analyzing the fifth dust pixel in the bright surface area specifically includes: From the fifth dust pixel corresponding to the first surface classification identifier, pixels having third reflectivity information less than a preset third reflectivity threshold and pixels having a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the second surface classification identifier, pixels having a band linear polarization degree less than a third polarization degree preset threshold and pixels having a dust aerosol index greater than or equal to a second dust aerosol preset threshold are selected to obtain a sixth dust pixel of the dust type; The first surface classification identifier and the second surface classification identifier are both identifiers corresponding to bright surface areas.

[0009] According to a dust aerosol identification method provided by the present invention, analyzing the fifth dust pixel in the non-bright surface area specifically includes: From the fifth dust pixel corresponding to the third surface classification identifier, pixels having fourth reflectivity information greater than a preset fourth reflectivity threshold and a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the fourth surface classification identifier, pixels whose normalized vegetation index is within the third index range and pixels whose dust aerosol index is greater than or equal to the second dust aerosol preset threshold are screened to obtain the sixth dust pixel of the dust type.

[0010] According to a dust aerosol identification method provided by the present invention, determining the dust aerosol area of ​​the target area based on the pixel type information of each pixel at multiple observation angles includes: The pixel type information of any observation angle is a cloud pixel, which is determined to be a cloud pixel; For a first undecided pixel whose position above non-bare ground is not determined to be a cloud pixel, if the pixel type information at any observation angle is determined to be a dust pixel type, the first undecided pixel is determined to be a dust pixel; For the second undecided pixels whose pixel positions above the bare ground are not determined to be cloud pixels, the target pixels in each second undecided pixel are determined to be dust pixels; The target pixel is a second undecided pixel whose number of times of being determined as a dust pixel type at each observation angle is greater than the number of times of being determined as other pixel types.

[0011] The present invention also provides a dust aerosol identification device, comprising the following modules: a processing module for pre-processing the multi-observation angle DPC data, POSP data, and MCD data of the target area, performing data spatiotemporal matching and sea-land data separation on the multi-observation angle DPC data, POSP data, and MCD data to obtain ocean area data and land area data; an analysis module, configured to perform pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles, and obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The identification module is used to determine the dust aerosol area of ​​the target area based on the pixel type information of each pixel at multiple observation angles.

[0012] The dust aerosol identification device provided by the present invention is further used for: Screening out the glare area data in the ocean area data to obtain the non-glare area data in the ocean area data; Filtering, from each pixel of the non-glare area data, pixels whose first reflectivity information is within a first preset reflectivity threshold range and whose first reflectivity covariance is less than the first preset reflectivity covariance threshold, to obtain a first cloud pixel and a first dust pixel; From each first dust pixel, pixels having a normalized vegetation index that meets a first index range and a band linear polarization degree that falls within the first polarization degree range are selected to obtain a second dust pixel and a second cirrus cloud pixel from each first dust pixel; From each of the second dust pixels, pixels whose band linear polarization degree is within the second polarization degree range, whose dust aerosol index is greater than the first dust aerosol preset threshold, and whose non-dust aerosol index is greater than the first non-dust aerosol preset threshold are screened, thereby obtaining a third dust pixel whose pixel type information is dust type in the ocean data area at each observation angle. According to the dust aerosol identification device provided by the present invention, the device is further configured to: Filtering, from each pixel in the land area data, pixels whose second reflectivity information is within a second preset reflectivity threshold range and whose second reflectivity covariance is less than the second preset reflectivity covariance threshold, to obtain a third cloud pixel and a fourth dust pixel; Filtering pixels whose normalized vegetation index is within the second index range and whose band linear polarization degree is within the second polarization degree range from the fourth dust pixel to obtain a fifth dust pixel; The fifth dust pixel in the bright surface area and the fifth dust pixel in the non-bright surface area are analyzed separately to obtain the sixth dust pixel; From the sixth dust pixel, pixels whose non-dust aerosol index is less than or equal to the second non-dust aerosol preset threshold are screened to obtain a seventh dust pixel whose pixel type information is dust type in the ocean data at each observation angle. The dust aerosol identification device provided by the present invention is further configured to: From the fifth dust pixel corresponding to the first surface classification identifier, pixels having third reflectivity information less than a preset third reflectivity threshold and pixels having a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the second surface classification identifier, pixels having a band linear polarization degree less than a third polarization degree preset threshold and pixels having a dust aerosol index greater than or equal to a second dust aerosol preset threshold are selected to obtain a sixth dust pixel of the dust type; The first surface classification mark and the second surface classification mark are both marks corresponding to bright surface areas. The dust aerosol identification device provided by the present invention is also used for: From the fifth dust pixel corresponding to the third surface classification identifier, pixels having fourth reflectivity information greater than a preset fourth reflectivity threshold and a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the fourth surface classification identifier, pixels having a normalized vegetation index within the third index range and pixels having a dust aerosol index greater than or equal to a second dust aerosol preset threshold are selected to obtain a sixth dust pixel of the dust type. The dust aerosol identification device provided by the present invention is further configured to: The pixel type information of any observation angle is a cloud pixel, which is determined to be a cloud pixel; For a first undecided pixel whose pixel position above a non-bright surface is not determined to be a cloud pixel, if the pixel type information at any observation angle is determined to be a dust pixel type, the first undecided pixel is determined to be a dust pixel; For the second undecided pixels above the bright surface whose pixel positions are not determined to be cloud pixels, the target pixels in each second undecided pixel are determined to be dust pixels; The target pixel is a second undecided pixel whose number of times of being determined as a dust pixel type at each observation angle is greater than the number of times of being determined as other pixel types.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the dust aerosol identification method as described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the dust aerosol identification methods described above.

[0015] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described dust aerosol identification methods.

[0016] The dust aerosol identification method, device, equipment, storage medium, and program product provided by the present invention ensure data quality and consistency by preprocessing DPC data, POSP data, and MCD data from multiple observation angles, laying the foundation for subsequent analysis. Through spatiotemporal data matching and land-sea data separation, the data is divided into marine and land-region data for separate processing. This process not only improves the data's pertinence but also reduces misjudgments due to regional differences, allowing subsequent analysis to more closely focus on dust aerosol characteristics in specific environments. Pixel-by-pixel analysis of marine and land-region data utilizes information from each pixel at multiple observation angles. This multi-angle analysis method significantly enriches the understanding of each pixel's characteristics, eliminating the need for single-angle observation in dust aerosol identification, thereby effectively reducing identification errors caused by a single observation angle. By comprehensively analyzing the pixel type information for each pixel at multiple observation angles, the dust aerosol region of the target area is ultimately determined, improving identification accuracy and enhancing the scheme's adaptability to complex environments. This provides strong technical support for atmospheric environment monitoring and climate change research. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Schematic diagram of the process of the dust aerosol identification method provided by the present invention; Figure 2 A schematic diagram of the technical process provided by the present invention; Figure 3 A schematic diagram of the structure of the dust aerosol identification device provided by the present invention; Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] Figure 1 FIG. 1 is a flow chart of the dust aerosol identification method provided by the present invention, as shown in FIG. Figure 1 As shown, the method includes the following: Step 110, after data preprocessing, the multi-observation angle DPC data, POSP data and MCD data of the target area are subjected to data spatiotemporal matching and sea-land data separation to obtain ocean area data and land area data; In the solution of this application, DPC data (Directional Polarimetric Camera), DPC is a multi-angle polarization camera carried by the Gaofen-5 (GF-5) satellite, which can provide multi-spectral, multi-angle intensity and polarization data from visible light to near-infrared bands.

[0021] POSP data (Particulate Observing Scanning Polarization), POSP is a high-precision polarization scanner carried by the Gaofen-5 02 satellite, providing high-precision polarization and radiation data.

[0022] MCD data (MODIS Land Cover Type Data), MCD12Q1 is global land cover type data provided by MODIS (Moderate Resolution Imaging Spectroradiometer), providing annual land cover type distribution.

[0023] First, data preprocessing is performed on the multi-observation angle DPC data, POSP data, and MCD data of the target area, as it ensures the quality and consistency of the data and lays a solid foundation for subsequent analysis.

[0024] In the embodiment of the present application, the data preprocessing for DPC may specifically include invalid value processing. For POSP data, data preprocessing may specifically be performed by resampling, reprojection, and invalid value processing. For MCD data, data preprocessing may be performed by resampling, reprojection, and invalid value processing.

[0025] Spatiotemporal matching is a key step in remote sensing data fusion. It aligns data from different sensors in time and space, enabling them to work together and provide more comprehensive information. Specifically, temporal matching ensures that data are acquired at similar times to minimize changes in aerosol states caused by temporal differences. Spatial matching, through methods such as coordinate transformation and spatial interpolation, ensures that the spatial resolution and projection of different data sets are consistent, allowing them to accurately correspond to the same geographic location.

[0026] After completing spatiotemporal matching, the land and sea data were separated. This process utilized the land and sea identification information provided by the DPC and POSP sensors, as well as the land cover type information in the MCD data, to precisely divide the data into marine and land areas. This separation not only improved the data's relevance but also provided a clear geographical context for subsequent dust aerosol identification. This separation of marine and land area data allowed the analysis to more closely focus on the characteristics of dust aerosols in specific environments, reducing misjudgments due to regional differences and improving identification accuracy and efficiency.

[0027] More specifically, the multi-angle polarimeter captures multiple angles of data at the same pixel, while the high-precision polarimeter only captures a single angle. However, because the high-precision polarimeter and the multi-angle polarimeter are located on the same platform, their observation tracks overlap significantly. To minimize the parameter errors caused by angles, this method uses the pixel with the smallest angular distance between pixels as the matching pixel, laying the foundation for the subsequent calculation of dust identification parameters.

[0028] Multi-angle polarization imager sea and land separation, according to the sea and land identification data of the multi-angle polarization imager, the N-layer regional data is separated into ocean and land areas, where: (1) (2) (3) Among them, Flags represents the sea and land identification data of each pixel, that is, the parameter Sea_Land_Flag in the DPC data.

[0029] Step 120: performing pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; In this embodiment of the present invention, the multi-angle polarization data provided by DPC and POSP are used to analyze the reflectivity, polarization degree, and other parameters of each pixel at different angles. These parameters can provide rich information about the physical properties of the pixel, such as particle size, shape, and composition.

[0030] Calculate the physical parameters of each pixel at multiple observation angles, such as apparent reflectance, linear polarization, and observation angle. The changing trends and characteristics of these parameters can be used to distinguish different types of aerosols and surface features.

[0031] Based on the calculated physical parameters, combined with preset thresholds and identification rules, each pixel is identified as a type. For example, the Dust Aerosol Index (DAI) and Non-Dust Aerosol Index (NDAI) are used to distinguish between dust aerosols and other types of aerosols.

[0032] For ocean regions, the focus is on analyzing the reflectivity and polarization of blue light to distinguish dust aerosols from clear ocean air. For land regions, the land cover type information in the MCD data is combined to further refine the identification rules and distinguish between dust aerosols and interference factors such as bright surface.

[0033] Pixel-by-pixel analysis of multi-angle polarization data enables more accurate identification of dust aerosols and reduces misidentifications. Multi-angle observations provide richer information, helping to distinguish dust aerosols from other types of aerosols or surface features.

[0034] Pixel-by-pixel analysis can not only identify the type of pixel, but also provide detailed physical parameters of each pixel at different observation angles, providing richer data support for subsequent environmental monitoring and research.

[0035] Step 130: Determine the dust aerosol area of ​​the target area based on the pixel type information of each pixel at multiple observation angles.

[0036] In the present invention, for each pixel, its pixel type information under multiple observation angles is comprehensively considered. For example, when identifying a pixel as a cloud pixel at ≥1 angle, the pixel is treated as a cloud pixel mask.

[0037] After cloud masking, for the sky above non-bright surfaces, among the multiple angle data of the pixel position, as long as ≥1 angle data is identified as dust, the pixel is identified as dust.

[0038] After cloud masking, for pixels above bright surfaces, if the same pixel is identified as dust aerosol, clear sky, or other pixels at different angles, the location identification type is determined according to the category with the largest number of angles.

[0039] All pixels identified as dust aerosols are integrated to form a dust aerosol region. This may involve aggregating adjacent dust pixels and filtering isolated pixels to improve the spatial coherence of the identification results.

[0040] In this application, by preprocessing DPC data, POSP data and MCD data from multiple observation angles, this step ensures the quality and consistency of the data, laying the foundation for subsequent analysis. By matching the data in time and space and separating the land and sea data, the data is accurately divided into ocean area data and land area data. This process not only improves the pertinence of the data, but also reduces the misjudgment caused by regional differences, so that the subsequent analysis can focus more on the characteristics of dust aerosols in specific environments. When performing pixel-by-pixel analysis on ocean area data and land area data, the information of each pixel at multiple observation angles is utilized. This multi-angle analysis method greatly enriches the understanding of the characteristics of each pixel, so that the identification of dust aerosols is no longer limited to observation at a single angle, thereby effectively reducing the identification error caused by a single observation angle. By comprehensively analyzing the pixel type information of each pixel at multiple observation angles, the dust aerosol area of ​​the target area is finally determined, which improves the accuracy of identification and enhances the adaptability of the scheme to complex environments. It provides strong technical support for atmospheric environment monitoring and climate change research.

[0041] Optionally, performing pixel-by-pixel analysis on the ocean area data at multiple observation angles includes: Screening out the glare area data in the ocean area data to obtain the non-glare area data in the ocean area data; Filtering, from each pixel of the non-glare area data, pixels whose first reflectivity information is within a first preset reflectivity threshold range and whose first reflectivity covariance is less than the first preset reflectivity covariance threshold, to obtain a first cloud pixel and a first dust pixel; From each first dust pixel, pixels having a normalized vegetation index that meets a first index range and a band linear polarization degree that falls within the first polarization degree range are selected to obtain a second dust pixel and a second cirrus cloud pixel from each first dust pixel; From each of the second dust pixels, the pixels whose linear polarization degree of the band is in the second polarization range, whose dust aerosol index is greater than the first dust aerosol preset threshold, and whose non-dust aerosol index is greater than the first non-dust aerosol preset threshold are screened, and the third dust pixel whose pixel type information is dust type in the ocean data area of ​​each observation angle is obtained. In this application, the flare area refers to the highlight area formed by the Fresnel reflection effect of microwaves on the ocean surface when the satellite observes the sky over the ocean at a specific observation angle. The reflection characteristics of these areas are similar to those of dust aerosols, which can easily lead to misjudgment. By calculating the flare angle, the flare area data is identified and screened out, and the non-flare area data is retained for subsequent analysis.

[0042] More specifically, the degree of linear polarization (DOLP) is effective in identifying dust aerosols over ocean and land, and is also crucial for identifying cloud pixels in flare zones. A flare zone is defined as a region of the ocean surface where, when a satellite observes the ocean at a specific angle (flare angle ≤ 30°), the ocean surface micro-atoms appear brighter than the rest of the ocean due to Fresnel reflection. The following formula can be used to calculate the flare angle and identify flare zones: (4) (5) In the glare area, clean ocean pixels and dust aerosols usually exhibit similar reflectivity characteristics, so misjudgment may occur when using reflectivity for identification. Therefore, the glare area data needs to be screened out.

[0043] (6) in, Indicates flare angle information.

[0044] Then, based on the thickness of the cloud layer, clouds can be divided into thick clouds and thin clouds. Thick clouds often have a higher aerosol optical depth. At the same time, their reflectivity in the blue light band, that is, the first reflectivity, is higher than that of the surface, sea surface clean pixels and dust aerosols, which can have a strong contrast with the background. The reflectivity of dust aerosols will be between thick cloud pixels and sea surface clean pixels. Therefore, the use of blue light band reflectivity information can achieve the distinction between dust pixels and thick cloud pixels. As shown in the following formula: (7) (8) Among them, TOA443 represents the apparent reflectivity information of DPC in the 443nm band, and I443 represents the radiation intensity information of DPC in the 443nm band. represents the solar azimuth, represents the Rayleigh scattering intensity at 443 nm, is the threshold between this parameter of pixels that may be dust pixels and clear sky pixels, is the threshold between the parameter of the pixel that may be dust and the cloud pixel. TOA443-R433 is the first reflectivity information, and A first preset apparent reflectivity threshold range is formed.

[0045] Thin clouds can transmit the reflectivity information of the underlying surface to a certain extent, and the apparent reflectivity is similar to that of dust aerosols. However, the spatial distribution of dust aerosols is smoother than that of dust aerosols. Therefore, the covariance within the blue light band window can be used to filter out the thin cloud pixels that are more fragmented and reflect more rough spatial information of the underlying surface, obtaining the first cloud pixel and the first dust pixel. The following formula can be used to achieve the 3 Calculation of reflectivity covariance within the 3-window to identify thin clouds: (9) (10) in, 443nm at 3 The first reflectivity covariance within the 3-window, It is the threshold between the parameter of the pixel that may be dust and the cloud pixel, that is, the first preset reflectivity covariance threshold.

[0046] However, for relatively smooth pixels like cirrus clouds, using texture difference features can still miss some pixels. According to statistics, the NDVI value of dust aerosols is close to 0 but usually less than 0, while the NDVI value of clouds mainly ranges from -0.1 to 0.15. Furthermore, at similar optical depths, dust aerosols experience relatively stronger depolarization, resulting in a relatively lower linear polarization degree in the blue band. Therefore, collaborative identification using NDVI and DOLP490 can further enhance the identification of dust aerosol and cirrus cloud pixels, identifying a second dust pixel and a second cirrus cloud pixel within each first dust pixel.

[0047] (11) (12) (13) Because offshore pixels may occasionally have higher apparent reflectance than other dark, clean ocean pixels due to factors such as sedimentation or chlorophyll, relying on blue light band reflectance often does not provide a good screening effect for these pixels. However, due to the depolarization of aerosols, their polarization degree is often relatively reduced. Therefore, the following formula is used to distinguish between bright ocean surface pixels such as dust aerosols and nearshore turbid water bodies: (14) NDVI is the Normalized Difference Vegetation Index, DOLP490 is the degree of linear polarization in the 490nm band, Q490 and U490 are the linearly polarized light components in the 490nm band, and I490 is the radiation intensity in the 490nm band.

[0048] In order to distinguish dust pixels, thin cloud pixels and clear sky pixels, is the smaller threshold for constraining dust pixels, To constrain the higher threshold of dust pixels, and determining a first index range; is the parameter threshold between the pixels that may be dust and cloud pixels, is the parameter threshold between the pixels that may be dust and the clear sky pixels, and is used to distinguish dust pixels, offshore pixels and clear sky pixels. is the smaller threshold for constraining dust pixels, It is the upper threshold for constraining dust pixels. , for determining a first polarization degree range, , Used to determine a second polarization degree range.

[0049] The apparent reflectivity of a pixel in the atmosphere is mainly determined by atmospheric molecular scattering, aerosol absorption and scattering, and the spectral reflectance of the underlying surface. The intensity of atmospheric molecular scattering and wavelength often show the following relationship: (15) in, is the wavelength, is the Rayleigh scattering intensity at this wavelength, is the apparent reflectivity intensity of the incident light at this wavelength.

[0050] At the same incident light intensity, the Rayleigh scattering between the two bands often creates a sharp contrast, but the effect decreases exponentially with increasing wavelength. Therefore, clean ocean pixels typically have lower apparent reflectance in the blue band, resulting in an increased apparent reflectance ratio in the blue band. The sensitivity of the apparent reflectance of dust aerosols in the atmosphere to spectral band variations decreases with increasing wavelength. Therefore, dust aerosols can be identified by changes in the apparent reflectance of adjacent bands. The scattering and absorption of dust aerosols are strongly wavelength-dependent. Affected by dust particle size and composition, the scattering and absorption of dust aerosols decrease with increasing wavelength. Therefore, the apparent reflectance ratio of clean pixels over the ocean in the blue band is higher than that of dust aerosols. The following formula is used to calculate the apparent reflectance of the blue light band. After eliminating the influence of Rayleigh scattering on the apparent reflectance, the trend of the pixel apparent reflectance decreasing due to the presence of dust aerosol can be amplified. At the same time, the ratio of the two pixel parameters can be further amplified by performing a logarithmic transformation on the ratio, and this ratio is defined as the dust aerosol identification index: (16) (17) DAI is the dust aerosol index, The parameter threshold for distinguishing pixels that may be dust pixels from clear sky pixels, that is, the first dust aerosol preset threshold.

[0051] When using the Dust Aerosol Identification Index, pixels other than those indices associated with dust (such as haze) may be misidentified. Therefore, the Non-Dust Absorbing Aerosol Index (NABAI) is introduced to filter out these pixels. Dust particles are larger than those associated with haze and other particles, and therefore scatter light at 2250nm more strongly. Consequently, the ratio of the apparent reflectance of dust aerosols in the blue and near-infrared bands is typically greater. The influence of Rayleigh scattering at 2250nm is negligible. Using the following formula to calculate the Non-Dust Absorbing Aerosol Index effectively separates non-dust absorbing aerosols from dust particles.

[0052] (18) (19) NDAI is the non-dust aerosol index. The parameter threshold for distinguishing pixels that may be dust pixels from haze pixels, that is, the first non-dust aerosol preset threshold.

[0053] Through the above pixel-by-pixel analysis steps, the scheme can gradually screen and confirm the third dust pixel whose pixel type information in the ocean data area of ​​each observation angle is the dust type, reducing misjudgment and interference and improving the accuracy and reliability of recognition.

[0054] Optionally, performing pixel-by-pixel analysis on the land area data at multiple observation angles includes: From the fifth dust pixel corresponding to the first surface classification identifier, pixels having third reflectivity information less than a preset third reflectivity threshold and pixels having a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the second surface classification identifier, pixels having a band linear polarization degree less than a third polarization degree preset threshold and pixels having a dust aerosol index greater than or equal to a second dust aerosol preset threshold are selected to obtain a sixth dust pixel of the dust type; The first surface classification identifier and the second surface classification identifier are both identifiers corresponding to bright surface areas.

[0055] In the present invention, a reflectivity threshold range is set to filter out pixels whose reflectivity is within the range. 3. Reflectance covariance within the window, screen out pixels with covariance less than a preset threshold to exclude areas with large reflectance changes (such as thin clouds).

[0056] Further distinguish dust pixels from other types of pixels. Vegetated areas have significantly different NDVI and linear polarization values ​​from those of dust aerosols. A threshold range for the NDVI is set to filter out pixels whose NDVI falls within this range.

[0057] Set a threshold range for the linear polarization degree of a band to filter out pixels with linear polarization degrees within this range. Analyze the fifth dust pixel separately for bright and dark surface areas. Dust aerosol characteristics differ between bright surface areas (such as deserts and bare land) and dark surface areas (such as vegetation and water bodies), requiring separate processing.

[0058] Further confirm dust aerosol pixels and exclude other types of aerosols and interference. Non-Dust Aerosol Index (NDAI): Set an NDAI threshold and filter out pixels with an NDAI less than or equal to the threshold. By screening with the Non-Dust Aerosol Index, dust aerosol pixels are ultimately confirmed, improving identification accuracy and reliability.

[0059] More specifically, thick cloud pixels with high optical depth can be effectively screened out based on the reflectivity of the blue light band: (20) in, is the threshold between this parameter of pixels that may be dust pixels and clear sky pixels, The threshold between this parameter for pixels that may be dust and thick cloud pixels.

[0060] In addition, constraining the blue band apparent reflectance and window covariance can also effectively filter out thin cloud pixels over land: (twenty one) in, is the threshold between this parameter of pixels that may be dust pixels and clear sky pixels, is the threshold between the parameter of the pixel that may be dust and the thin cloud pixel, according to and Determine a second preset reflectivity threshold range, is a second preset reflectivity covariance threshold.

[0061] To avoid misidentification of cirrus clouds, NDVI and degree of linear polarization (DOLP) thresholds are introduced as constraints: (twenty two) is the smaller threshold for constraining dust pixels, In order to constrain the higher threshold of dust pixels, according to and Determine the second index range, is the parameter threshold between the pixels that may be dust and cloud pixels, is the parameter threshold between the pixels that may be dust and the clear sky pixels, according to and A second degree of polarization range is determined.

[0062] The fifth dust pixel in the bright surface area is then analyzed, taking into account its high reflectivity and low polarization. The fifth dust pixel in the dark surface area is also analyzed, taking into account its lower reflectivity and higher polarization. By processing the bright and dark surface areas separately, dust aerosols can be more accurately identified, reducing misidentification in bright surface areas and vegetation, and improving identification accuracy.

[0063] Optionally, analyzing the fifth dust pixel in the non-bright surface area specifically includes: From the fifth dust pixel corresponding to the third surface classification identifier, pixels having fourth reflectivity information greater than a preset fourth reflectivity threshold and a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the fourth surface classification identifier, pixels whose normalized vegetation index is within the third index range and pixels whose dust aerosol index is greater than or equal to the second dust aerosol preset threshold are screened to obtain the sixth dust pixel of the dust type.

[0064] In the present invention, the non-bright surface may include surface water bodies and surfaces of vegetation areas.

[0065] The apparent reflectance of surface water in the blue light band is usually lower than that of dust aerosols. At the same time, due to the radiation patterns of dust particles in various bands, the DAI is larger than that of water. Therefore, the following physical parameters are set for identification: (twenty three) (twenty four) (25) DAI is the dust aerosol index, The parameter threshold for distinguishing pixels that may be dusty from clear sky pixels, that is, the preset threshold of the second dust aerosol index, The parameter threshold for distinguishing pixels that may be dust pixels from water pixels, that is, the fourth reflectivity preset threshold.

[0066] Synchronous identification is performed by comprehensively utilizing the vegetation normalized index and dust aerosol index of vegetation and dust: (26) (27) (28) is the smaller threshold for constraining dust pixels, To constrain the higher threshold of dust pixels, and Used to determine the third index range, The parameter threshold for distinguishing pixels that may be dust pixels from clear sky pixels, that is, the preset threshold of the second dust aerosol index.

[0067] More specifically, analyzing the fifth dust pixel in the bright surface area includes: From the fifth dust pixel corresponding to the first surface classification identifier, pixels having third reflectivity information less than a preset third reflectivity threshold and pixels having a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the second surface classification identifier, pixels having a band linear polarization degree less than a third polarization degree preset threshold and pixels having a dust aerosol index greater than or equal to a second dust aerosol preset threshold are selected to obtain a sixth dust pixel of the dust type; The first surface classification identifier and the second surface classification identifier are both identifiers corresponding to bright surface areas.

[0068] In the embodiment of the present invention, the bright surface area may include snowy areas, bare ground, and other surfaces.

[0069] For snow and ice pixels, the apparent reflectance of dust aerosol in the blue light band is often lower than that of the ground surface. Therefore, the following thresholds are set for identification: (29) (30) The parameter threshold for distinguishing pixels that may be dust pixels from ice and snow pixels, that is, the third reflectivity preset threshold.

[0070] For bare land and other surfaces, the dust aerosol index derived from the radiation characteristics of dust aerosols in various bands and the smaller linear polarization degree caused by dust depolarization are comprehensively considered, and the following two parameters are introduced for pixel identification: (31) (32) (33) The parameter threshold for distinguishing pixels that may be dusty from clear sky pixels, that is, the preset threshold of the second dust aerosol index, The parameter threshold for distinguishing pixels that may be dust pixels from clear sky pixels, that is, the preset threshold of the third linear polarization degree.

[0071] Optionally, determining the dust aerosol area of ​​the target area according to the pixel type information of each pixel at multiple observation angles includes: The pixel type information of any observation angle is a cloud pixel, which is determined to be a cloud pixel; For a first undecided pixel whose position above non-bare ground is not determined to be a cloud pixel, if the pixel type information at any observation angle is determined to be a dust pixel type, the first undecided pixel is determined to be a dust pixel; For the second undecided pixels whose pixel positions above the bare ground are not determined to be cloud pixels, the target pixels in each second undecided pixel are determined to be dust pixels; The target pixel is a second undecided pixel whose number of times of being determined as a dust pixel type at each observation angle is greater than the number of times of being determined as other pixel types.

[0072] In the present invention, for multi-angle imaging results, identification marks of multiple angles have been obtained under the above identification rules. Finally, further confirmation of pixel type is performed according to the multi-angle identification priority rule: Cloud pixel decision: When a pixel is identified as a cloud pixel by ≥1 angle, the pixel is treated as a cloud pixel mask.

[0073] Dust pixel decision: For undecided pixels, as long as ≥1 angle data is identified as dust among the multiple angle data of the pixel position above non-bare ground, the pixel is identified as dust; for the multiple angle data of the pixel position above bare ground, the number of angles identified as dust and clear sky is compared, and the one with more angles is selected as the final type, and when the identification angles are equal, it is identified as dust aerosol.

[0074] Null value type decision: For the null value pixels that have not been decided after the above two steps, window convolution is used to determine when window 3 If there are ≥ 4 pixels in the window with defined pixels, the pixels in the window are selected as the pixel types with more pixels. If there are no defined pixels in the window, they are considered to be other pixels.

[0075] In this paper, the dust aerosol area obtained through the pixel decision process can provide high-quality data support for environmental monitoring, climate research, and disaster warning. The practicality and reliability of this method are significantly improved because it is based on comprehensive and detailed data analysis.

[0076] Figure 2 The technical process diagram provided by the present invention is as follows: Figure 2 As shown, including: Unify data resolution and projection method: Although the DPC and POSP polarization sensors are based on the same platform, their spatial resolution, projection method, and data storage format differ. To achieve temporal and spatial alignment, the spatial resolution of the DPC and POSP data was unified to the DPC sub-satellite resolution of 1.7 km, and the projection method was set to the same WGS84 latitude and longitude. The same resolution and projection unification was performed for the MCD12 data used in this method.

[0077] Spatiotemporal matching of data and auxiliary data on the same platform: This method uses POSP data as a band complement to DPC data, integrating POSP's 2250nm band data with DPC's 443nm band to construct dust identification parameters. As two polarization sensors on the same platform, DPC and POSP have a high degree of overlap in data sampling time. Therefore, this method uses only pixel longitude and latitude matching as the spatial matching criterion. Land cover data are stable over long time series, so the longitude and latitude of DPC and MCD12 data are also used as matching constraints for the auxiliary data.

[0078] Data sea and land masks and land data bright and dark surface masks: This method applies differentiated dust detection methods over oceans and land, requiring land and sea masking based on the land and sea identification from the L1 data of the DPC and POSP sensors. During severe dust events, the significant impact of dust aerosols on the radiation budget causes dust pixels to exhibit certain parameter differences relative to the background. Therefore, the physical parameters used in this method are effective in identifying severe dust events over land. However, minor dusting over brightly lit surfaces can be challenging to discern. Therefore, this method utilizes MCD12 land cover classification data to distinguish land into brightly lit and darker surfaces. A more stringent parameter threshold is used for brightly lit surfaces, while darker surfaces often provide better discrimination.

[0079] Marine dust detection: Over the ocean, dust detection primarily relies on distinguishing dust pixels from cloud pixels, clear ocean air, ocean glare, offshore sedimentary sand, and non-dust aerosols. To distinguish dust pixels from these other observed objects, this method uses the following physical parameters to set thresholds for classification: The bright characteristics of the glare area make it have spectral characteristics similar to those of dust pixels. First, the flare angle is used to divide the ocean into glare and non-glare areas, and subsequent dust detection and identification operations are performed on the non-glare areas.

[0080] Considering that the apparent reflectance of dust aerosols in the blue band is between that of thick clouds and clean pixels over the ocean, a threshold for apparent reflectance in the 443nm band was further established. Furthermore, the physical parameters of thin cloud pixels with low optical thickness are significantly affected by the underlying surface, resulting in reflectance characteristics similar to those of dust aerosol pixels. Therefore, spatial uniformity indices were used in conjunction with apparent reflectance in the 443nm band to identify pixels likely to be dust locations.

[0081] In coastal areas, due to their shallow depths, clean ocean pixels also exhibit reflectivity characteristics similar to dust in the 443nm band. However, due to the depolarization of dust aerosols, their polarization is often lower than near the coast. Therefore, using the degree of linear polarization parameter in the 490nm band (DOLP490) to set a threshold, we further screen locations identified as "possible dust areas" in the previous step.

[0082] The scattering and absorption of dust aerosols are strongly wavelength-dependent. Influenced by dust particle size, their scattering and absorption of light in the visible light band decreases with increasing wavelength. Therefore, the Dust Aerosol Index (DAI), calculated from the change in apparent reflectance in the blue light band, can be used to further identify pixels with similar scattering and absorption characteristics within the selected pixels. Although Rayleigh scattering is almost negligible in the 2250nm near-infrared band, the scattering characteristics of dust aerosol particles still exist. Because small absorptive aerosols, such as haze, are nearly transparent at this wavelength, the Non-Dust Aerosol Index (NDAI), calculated from the change in apparent reflectance in the near-infrared and blue light bands, can be used to further identify areas of dust aerosol activity.

[0083] Land dust detection: Over the ocean, dust detection mainly relies on distinguishing cloud pixels from those over land and the underlying land surfaces. This method uses the following physical parameters to set thresholds for classification: The key to identifying dust aerosols over land is distinguishing them from the underlying surface. Bright surfaces often exhibit apparent reflectance characteristics similar to those of dust aerosols. Therefore, the resampled MCD12 data are further classified into bright and dark surface regions. Bright surface regions include ice and snow (Value = 15) and bare or impervious land (Value = 13, 16). Dark surface regions include water bodies (Value = 17), vegetation (Value = 1-12, 14, indicating areas with varying degrees of vegetation cover), and other surface types in the original MCD12 data.

[0084] Thick cloud pixels often have high optical thickness, which is reflected in their physical parameters as high apparent reflectivity. The apparent reflectivity in the 865nm band (TOA865) can be used to set a threshold for identification. For thinner cloud pixel areas, such as high-altitude clouds, although the apparent reflectivity will be slightly reduced due to the influence of the underlying surface, they still have obvious grayscale texture characteristics in the spatial range. Therefore, the apparent reflectivity in the 865nm band can be used to set a threshold for identification. The variance within the 3-window (VAR865) is used to set a threshold for pixel screening. For relatively smooth clouds, such as cirrus, the characteristic of NDVI being close to 0 can be used as a criterion. Regarding altitude distribution, dust aerosols are generally distributed between the ground and 3-6 km above sea level, except for occasional high-altitude cloud formations during severe dust storms, where dust aerosols can reach altitudes of 10 km. Therefore, the apparent pressure (Papp) can be further used to distinguish between dust aerosols and cloud pixels with thin, uniform cloud layers but high altitudes.

[0085] For unlit surfaces: To identify vegetation pixels and dust aerosols above vegetated areas, the Normalized Difference Vegetation Index (NDVI) is introduced to set thresholds for aided identification. Statistically, NDVI values ​​for water bodies and snow and ice are less than 0, while those for bare land and rock are close to but usually greater than 0. NDVI values ​​in areas with vegetation are significantly greater than 0. Furthermore, given the strong wavelength dependence of dust aerosol scattering and absorption, the scattering and absorption of dust aerosols in the visible light band decreases with increasing wavelength, influenced by dust particle size. Therefore, the Dust Aerosol Index (DAI), calculated from the rate of change in apparent reflectance in the blue light band, can be used to further identify pixels with similar scattering and absorption characteristics within the selected pixels. Although Rayleigh scattering can be almost ignored in the 2250nm near-infrared band, the scattering characteristics of dust aerosol particles still exist. Since small particle absorptive aerosols such as haze are almost transparent at this wavelength, the non-dust aerosol index (NDAI) calculated from the changes in apparent reflectivity in the near-infrared and blue light bands can be used to further screen and determine the dust aerosol area.

[0086] To identify vegetation pixels and dust aerosols above water bodies and other surface pixels, the apparent reflectance of the blue light 443nm band (TOA443), the dust aerosol index (DAI), and the non-dust aerosol index (NDAI) are used for identification.

[0087] For bright surfaces: Dust aerosols over ice and snow often originate from long-range atmospheric transport, and their apparent reflectivity in the near-infrared channel is typically lower than that of cloud pixels and ice and snow pixels. Therefore, by setting an apparent reflectivity threshold in the near-infrared 865nm band, dust pixels can be screened for dust, excluding ice and snow pixels and those with high optical thickness clouds.

[0088] In areas with high brightness, such as bare land, in addition to the Dust Aerosol Index (DAI) and the Non-Dust Aerosol Index (NDAI), a polarization parameter is also required for identification. Due to the depolarization of dust aerosols, the linear polarization (DLP) of dust aerosol pixels is typically lower than that over clean land. Therefore, the DLP (Degree of Linear Polarization) in the blue band (DOLP490) is used to set a physical threshold for identification.

[0089] Dust detection at the land-sea boundary: The land-sea boundary is usually a mixed pixel. Here, the threshold is set by the apparent reflectance of the blue light band (TOA440) to divide the pixels in the land-sea boundary into two situations: "land" and "ocean". These two types of pixels are processed separately using the land / ocean aerosol identification scheme.

[0090] Multi-angle identification result decision: The multi-angle nature of multi-angle polarization imager data effectively increases the amount of remote sensing information and provides higher-dimensional information for method research. When this method performs pixel identification at each of the 17 angles, different identification results may be obtained at each angle. Comprehensively considering multiple angles at the same location can reduce data recognition errors caused by different observation angles when identifying pixels. For multiple angles at the same pixel location, the following angle identification priority rules are mainly used: Identify priorities: Cloud pixels are an important type of interfering pixels for dust aerosol identification. When a pixel is identified as a cloud pixel at ≥1 angle, the pixel is treated as a cloud pixel mask.

[0091] After cloud masking, for non-bare ground, among the multiple angle data of the pixel position, as long as ≥1 angle data is identified as dust, the pixel is identified as dust.

[0092] After cloud masking, for pixels above bare ground, if the same pixel is identified as dust aerosol, clear sky, or other pixels at different angles, the identification type of the location is determined according to the category with the largest number of angles.

[0093] Equal value priority: After cloud masking, for pixels above bare ground, when the number of angles at which the same pixel is identified as dust aerosol, clear sky, and other pixels at different angles is equal, the priority order is dust aerosol > other pixels > clear sky.

[0094] Null value discrimination: After performing angle recognition fusion according to the above rules, there may still be a situation where the pixel type cannot be identified at some pixel. For such pixels, window convolution is used to determine when the window is 3 If there are ≥ 4 pixels in the window with definitions, the pixel in the window is selected as the type of the pixel with more definitions. If there are no definitions in the window, it is considered to be other pixels.

[0095] The dust aerosol identification device provided by the present invention is described below. The dust aerosol identification device described below and the dust aerosol identification method described above can be referenced to each other.

[0096] Figure 3 The schematic diagram of the dust aerosol identification device provided by the present invention is as follows: Figure 3 Shown, including: The processing module 310 is used to perform data preprocessing on the multi-observation angle DPC data, POSP data and MCD data of the target area, and then perform data spatiotemporal matching and sea-land data separation on the multi-observation angle DPC data, POSP data and MCD data to obtain ocean area data and land area data; The analysis module 320 is configured to perform pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The identification module 330 is used to determine the dust aerosol area of ​​the target area according to the pixel type information of each pixel at multiple observation angles.

[0097] The dust aerosol identification device provided by the present invention is further used for: Screening out the glare area data in the ocean area data to obtain the non-glare area data in the ocean area data; Filtering, from each pixel of the non-glare area data, pixels whose first reflectivity information is within a first preset reflectivity threshold range and whose first reflectivity covariance is less than the first preset covariance threshold, to obtain a first cloud pixel and a first dust pixel; From each first dust pixel, pixels having a normalized vegetation index that meets a first index range and a band linear polarization degree that falls within the first polarization degree range are selected to obtain a second dust pixel and a second cirrus cloud pixel from each first dust pixel; From each of the second dust pixels, pixels whose band linear polarization degree is within the second polarization degree range, whose dust aerosol index is greater than the first dust aerosol preset threshold, and whose non-dust aerosol index is greater than the first non-dust aerosol preset threshold are screened, thereby obtaining a third dust pixel whose pixel type information is dust type in the ocean data area at each observation angle. According to the dust aerosol identification device provided by the present invention, the device is further configured to: Filtering, from each pixel in the land area data, pixels whose second reflectivity information is within a second preset reflectivity threshold range and whose second reflectivity covariance is less than the second preset reflectivity covariance threshold, to obtain a third cloud pixel and a fourth dust pixel; Filtering pixels whose normalized vegetation index is within the second index range and whose band linear polarization degree is within the second polarization degree range from the fourth dust pixel to obtain a fifth dust pixel; The fifth dust pixel in the bright surface area and the fifth dust pixel in the non-bright surface area are analyzed separately to obtain the sixth dust pixel; From the sixth dust pixel, pixels whose non-dust aerosol index is less than or equal to the second non-dust aerosol preset threshold are screened to obtain a seventh dust pixel whose pixel type information is dust type in the ocean data of each observation angle.

[0098] According to the sand and dust aerosol identification device provided by the present invention, the device is also used for: From the fifth dust pixel corresponding to the first surface classification identifier, pixels having third reflectivity information less than a preset third reflectivity threshold and pixels having a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the second surface classification identifier, pixels having a band linear polarization degree less than a third polarization degree preset threshold and pixels having a dust aerosol index greater than or equal to a second dust aerosol preset threshold are selected to obtain a sixth dust pixel of the dust type; The first surface classification identifier and the second surface classification identifier are both identifiers corresponding to bright surface areas.

[0099] The dust aerosol identification device provided by the present invention is further used for: From the fifth dust pixel corresponding to the third surface classification identifier, pixels having fourth reflectivity information greater than a preset fourth reflectivity threshold and a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the fourth surface classification identifier, pixels whose normalized vegetation index is within the third index range and pixels whose dust aerosol index is greater than or equal to the second dust aerosol preset threshold are screened to obtain the sixth dust pixel of the dust type.

[0100] The dust aerosol identification device provided by the present invention is further used for: The pixel type information of any observation angle is a cloud pixel, which is determined to be a cloud pixel; For a first undecided pixel whose position above non-bare ground is not determined to be a cloud pixel, if the pixel type information at any observation angle is determined to be a dust pixel type, the first undecided pixel is determined to be a dust pixel; For the second undecided pixels whose pixel positions above the bare ground are not determined to be cloud pixels, the target pixels in each second undecided pixel are determined to be dust pixels; The target pixel is a second undecided pixel whose number of times of being determined as a dust pixel type at each observation angle is greater than the number of times of being determined as other pixel types.

[0101] In the present invention, by preprocessing DPC data, POSP data, and MCD data from multiple observation angles, the quality and consistency of the data are ensured, laying the foundation for subsequent analysis. By matching the data in time and space and separating the land and sea data, the data are accurately divided into ocean area data and land area data. This process not only improves the pertinence of the data, but also reduces the misjudgment caused by regional differences, so that the subsequent analysis can be more focused on the characteristics of dust aerosols in specific environments. When performing pixel-by-pixel analysis on the ocean area data and land area data, the information of each pixel under multiple observation angles is utilized. This multi-angle analysis method greatly enriches the understanding of the characteristics of each pixel, so that the identification of dust aerosols is no longer limited to observation at a single angle, thereby effectively reducing the identification error caused by a single observation angle. By comprehensively analyzing the pixel type information of each pixel at multiple observation angles, the dust aerosol area of ​​the target area is finally determined, which improves the accuracy of identification and enhances the adaptability of the scheme to complex environments. It provides strong technical support for atmospheric environment monitoring and climate change research.

[0102] Figure 4 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call logic instructions in the memory 430 to execute a dust aerosol identification method, which includes: performing data preprocessing on multi-observation angle DPC data, POSP data, and MCD data of a target area, performing data spatiotemporal matching on the multi-observation angle DPC data, POSP data, and MCD data, and separating the land and sea data to obtain ocean area data and land area data; Performing pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The dust aerosol area of ​​the target area is determined according to the pixel type information of each pixel at multiple observation angles.

[0103] Furthermore, the logic instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0104] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being storable on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is capable of executing the dust aerosol identification method provided by the above methods, the method comprising: performing data preprocessing on multi-observation angle DPC data, POSP data, and MCD data of a target area, performing data spatiotemporal matching and sea-land data separation on the multi-observation angle DPC data, POSP data, and MCD data, to obtain ocean area data and land area data; Performing pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The dust aerosol area of ​​the target area is determined according to the pixel type information of each pixel at multiple observation angles.

[0105] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the dust aerosol identification method provided by the above methods, the method comprising: performing data preprocessing on multi-observation angle DPC data, POSP data, and MCD data of a target area, performing data spatiotemporal matching and sea-land data separation on the multi-observation angle DPC data, POSP data, and MCD data, to obtain ocean area data and land area data; Performing pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The dust aerosol area of ​​the target area is determined according to the pixel type information of each pixel at multiple observation angles.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0107] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A dust aerosol identification method, characterized in that: include: After data preprocessing, the multi-observation angle DPC data, POSP data and MCD data of the target area are subjected to data spatiotemporal matching and sea-land data separation to obtain ocean area data and land area data; Performing pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles to obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The dust aerosol area of ​​the target area is determined according to the pixel type information of each pixel at multiple observation angles.

2. The dust aerosol identification method according to claim 1, characterized in that Perform pixel-by-pixel analysis on the ocean area data from multiple observation angles, including: Screening out the glare area data in the ocean area data to obtain the non-glare area data in the ocean area data; Filtering, from each pixel of the non-glare area data, pixels whose first reflectivity information is within a first preset reflectivity threshold range and whose first reflectivity covariance is less than the first preset covariance threshold, to obtain a first cloud pixel and a first dust pixel; From each first dust pixel, pixels having a normalized vegetation index that meets a first index range and a band linear polarization degree that falls within the first polarization degree range are selected to obtain a second dust pixel and a second cirrus cloud pixel from each first dust pixel; From each of the second dust pixels, the pixels whose linear polarization degree of the screening band is within the second polarization range, whose dust aerosol index is greater than the first dust aerosol preset threshold, and whose non-dust aerosol index is greater than the first non-dust aerosol preset threshold are screened, and the third dust pixels whose pixel type information is dust type in the ocean data area at each observation angle are obtained.

3. The dust aerosol identification method according to claim 1, characterized in that: Perform pixel-by-pixel analysis on the land area data from multiple observation angles, including: Filtering, from each pixel in the land area data, pixels whose second reflectivity information is within a second preset reflectivity threshold range and whose second reflectivity covariance is less than the second preset covariance threshold, to obtain a third cloud pixel and a fourth dust pixel; Filtering pixels whose normalized vegetation index is within the second index range and whose band linear polarization degree is within the second polarization degree range from the fourth dust pixel to obtain a fifth dust pixel; The fifth dust pixel in the bright surface area and the fifth dust pixel in the non-bright surface area are analyzed separately to obtain the sixth dust pixel; From the sixth dust pixel, pixels whose non-dust aerosol index is less than or equal to the second non-dust aerosol preset threshold are screened to obtain a seventh dust pixel whose pixel type information is dust type in the ocean data of each observation angle.

4. The dust aerosol identification method according to claim 3, characterized in that: Analyze the fifth dust pixel in the bright surface area, specifically including: From the fifth dust pixel corresponding to the first surface classification identifier, pixels having third reflectivity information less than a preset third reflectivity threshold and pixels having a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the second surface classification identifier, pixels having a band linear polarization degree less than a third preset polarization degree threshold and pixels having a dust aerosol index greater than or equal to a second preset dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; The first surface classification identifier and the second surface classification identifier are both identifiers corresponding to bright surface areas.

5. The dust aerosol identification method according to claim 3, characterized in that: Analyze the fifth dust pixel in the non-bright surface area, specifically including: From the fifth dust pixel corresponding to the third surface classification identifier, pixels having fourth reflectivity information greater than a preset fourth reflectivity threshold and a dust aerosol index greater than or equal to a preset second dust aerosol threshold are selected to obtain a sixth dust pixel of the dust type; From the fifth dust pixel corresponding to the fourth surface classification identifier, pixels whose normalized vegetation index is within the third index range and pixels whose dust aerosol index is greater than or equal to the second dust aerosol preset threshold are screened to obtain the sixth dust pixel of the dust type.

6. The dust aerosol identification method according to claim 1, characterized in that: The step of determining the dust aerosol area of ​​the target area according to the pixel type information of each pixel at multiple observation angles includes: The pixel type information of any observation angle is a cloud pixel, which is determined to be a cloud pixel; For a first undecided pixel whose pixel position above a non-bright surface is not determined to be a cloud pixel, if the pixel type information at any observation angle is determined to be a dust pixel type, the first undecided pixel is determined to be a dust pixel; For the second undecided pixels above the bright surface whose pixel positions are not determined to be cloud pixels, the target pixels in each second undecided pixel are determined to be dust pixels; The target pixel is a second undecided pixel whose number of times of being determined as a dust pixel type at each observation angle is greater than the number of times of being determined as other pixel types.

7. A dust aerosol identification device, characterized in that: include: a processing module for pre-processing the multi-observation angle DPC data, POSP data, and MCD data of the target area, performing data spatiotemporal matching and sea-land data separation on the multi-observation angle DPC data, POSP data, and MCD data to obtain ocean area data and land area data; an analysis module, configured to perform pixel-by-pixel analysis on the ocean area data and the land area data at multiple observation angles, and obtain pixel type information of each pixel in the ocean area data and the land area data at multiple observation angles; The identification module is used to determine the dust aerosol area of ​​the target area based on the pixel type information of each pixel at multiple observation angles.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the dust aerosol identification method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the dust aerosol identification method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the dust aerosol identification method according to any one of claims 1 to 6 is implemented.

Images