Remote sensing monitoring methods, devices, equipment, and media for sulfur dioxide in the atmosphere

Abstract

This application discloses a remote sensing monitoring method, device, equipment, and medium for sulfur dioxide in the atmosphere, relating to the field of remote sensing detection technology. The method includes: indexing high-resolution satellite remote sensing spectra, pre-processed spatiotemporal data, and wavelength data from a target dataset obtained through spatiotemporal analysis of an initial dataset; spatially splitting and pixel labeling the spectral data of the CCD row and column range corresponding to the area to be inverted based on the spatiotemporal data, and processing multiple partitioned data blocks in parallel; spectral splitting of the observed spectra of each partitioned data block based on the wavelength data, and inputting the resulting multiple sub-band data blocks into a radiative transfer model in parallel to obtain the simulated spectrum corresponding to each sub-band data block; fitting and integrating the simulated and observed spectra, and correcting the initial inversion result of the sulfur dioxide concentration to obtain the target inversion result. This method can improve the computational efficiency of sulfur dioxide inversion.

Description

Technical Field

[0001] This application relates to the field of remote sensing detection technology, and in particular to a remote sensing monitoring method, device, equipment, and medium for sulfur dioxide in the atmosphere. Background Technology

[0002] In the field of atmospheric environmental monitoring, remote sensing technology is becoming an indispensable monitoring method. Remote sensing technology provides information about the Earth's surface and atmosphere by detecting and analyzing electromagnetic waves reflected or radiated from the Earth's surface and atmosphere. In particular, satellite remote sensing technology based on passive observation is widely used in atmospheric environmental monitoring due to its wide coverage, rapid information acquisition capabilities, and lack of terrain limitations. Sulfur dioxide (SO2) is one of the six major typical pollutants, making its monitoring a crucial part of environmental monitoring. Early remote sensing payloads suffered from limitations in instrument performance and observation techniques, resulting in less than ideal spatiotemporal resolution and accuracy. With technological advancements, the most advanced spaceborne sensors now offer significantly improved resolution and signal-to-noise ratios, providing unprecedented atmospheric monitoring capabilities, but also placing higher demands on data processing capabilities.

[0003] Currently, atmospheric sulfur dioxide retrieval algorithms mainly rely on Beer-Lambert's law combined with radiative transfer models, including nonlinear fitting methods. While nonlinear fitting algorithms can provide more accurate retrieval results, they require multiple spectral simulations, leading to low computational efficiency, especially when dealing with large-scale data, where their processing speed cannot meet operational needs. Therefore, improving the computational efficiency of sulfur dioxide retrieval is an urgent problem to be solved. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a remote sensing monitoring method, device, equipment, and medium for sulfur dioxide in the atmosphere, which can invert the concentration of sulfur dioxide in the atmosphere through a segmentation strategy and a parallel strategy based on the spatiotemporal dimension and the spectral wavelength dimension, thereby improving the computational efficiency of sulfur dioxide inversion.

[0005] In a first aspect, embodiments of this application provide a remote sensing monitoring method for sulfur dioxide in the atmosphere, comprising:

[0006] Spatiotemporal analysis is performed on the initial dataset obtained from monitoring to obtain the target dataset;

[0007] Based on the time parameters, high-resolution satellite remote sensing spectra, preprocessed spatiotemporal data, and wavelength data are obtained from the target dataset.

[0008] Based on the latitude and longitude information of the high-resolution satellite remote sensing spectrum, determine the CCD row and column range corresponding to the area to be inverted;

[0009] Based on the preprocessed spatiotemporal data, the spectral data of the CCD row and column range corresponding to the region to be inverted are subjected to spatial dimension splitting and pixel labeling to obtain multiple partitioned data blocks with different partitions.

[0010] Parallel data processing and spectral preprocessing are performed on multiple partitioned data blocks. Based on the wavelength dimension data, the observed spectrum of each partitioned data block is split into multiple sub-band data blocks.

[0011] The multiple sub-band data blocks are input into the radiative transfer model in parallel, and the simulated parallel calculation is performed based on the simulated spectral parameters to obtain the simulated spectrum corresponding to each sub-band data block.

[0012] The simulated spectra and observed spectra are fitted and integrated to obtain the initial inversion results of sulfur dioxide concentration.

[0013] The initial inversion results are sequentially subjected to stripe background correction and dimensional background correction to obtain the corrected target inversion results of sulfur dioxide concentration.

[0014] Secondly, embodiments of this application provide a remote sensing monitoring device for sulfur dioxide in the atmosphere, including at least one processor and a memory for communicatively connecting to the at least one processor; the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the remote sensing monitoring method for sulfur dioxide in the atmosphere as described in any of the embodiments of the first aspect.

[0015] Thirdly, embodiments of this application provide an electronic device, including a remote sensing monitoring device for sulfur dioxide in the atmosphere as described in the second aspect embodiment.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform a remote sensing monitoring method for sulfur dioxide in the atmosphere as described in any of the embodiments of the first aspect.

[0017] This application's embodiments include: First, using a remote sensing monitoring device for sulfur dioxide in the atmosphere, after performing spatiotemporal analysis on the initial dataset obtained from the monitoring to obtain a target dataset, the following steps are taken: First, high-resolution satellite remote sensing spectra, pre-processed spatiotemporal dimension data, and wavelength dimension data are indexed from the target dataset based on time parameters; second, the CCD row and column range corresponding to the area to be inverted is determined based on the latitude and longitude information of the high-resolution satellite remote sensing spectra; then, based on the pre-processed spatiotemporal dimension data, spatial dimension splitting and pixel labeling processing are performed on the spectral data of the CCD row and column range corresponding to the area to be inverted to obtain multiple partitioned data blocks of different partitions; finally, parallel processing is performed on the multiple partitioned data blocks. According to the data processing and spectral preprocessing, the observed spectra of each partition data block are split into multiple sub-band data blocks based on wavelength dimension data. Then, the multiple sub-band data blocks are input in parallel into the radiative transfer model, and simulated parallel calculations are performed based on simulated spectral parameters to obtain the simulated spectrum corresponding to each sub-band data block. Next, fitting and data integration processing are performed on each simulated spectrum and the observed spectrum to obtain the initial inversion result of sulfur dioxide concentration. Finally, the initial inversion result is subjected to stripe background correction and dimensional background correction processing in sequence to obtain the corrected target inversion result of sulfur dioxide concentration. By retrieving the sulfur dioxide concentration in the atmosphere in parallel, the efficiency of sulfur dioxide inversion calculation is improved. In other words, the embodiments of this application improve the efficiency of sulfur dioxide inversion calculation by using a segmentation strategy based on spatiotemporal dimension and spectral wavelength dimension and a parallel strategy to invert the sulfur dioxide concentration in the atmosphere. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of the steps for remote sensing monitoring of sulfur dioxide in the atmosphere provided in one embodiment of this application;

[0019] Figure 2 yes Figure 1 A detailed flowchart of step S180 is shown below;

[0020] Figure 3 This is a schematic diagram of the hardware structure of a remote sensing monitoring device for sulfur dioxide in the atmosphere provided in one embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0022] It should be noted that although a logical order is shown in the flowcharts in this application, in some cases, the steps shown or described may be performed in a different order than that shown in the flowcharts. In the description of this application, "several" means one or more, and "more" means two or more. The terms "first" and "second" are used only to distinguish technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or implicitly indicating the order in which the technical features are indicated.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0024] First, let me explain some of the terms used in this application:

[0025] Tikhonov regularization, also known as ridge regression, is a regularization technique widely used in statistics, machine learning, and solving ill-posed problems. It improves the generalization ability of a model by adding a regularization term to the objective function and helps stabilize coefficient estimation, especially on datasets where features are highly correlated.

[0026] The Gauss-Newton iterative method is an iterative algorithm for solving nonlinear least squares problems; it is primarily used for parameter estimation problems where the model is a nonlinear function of the parameters, and there is a discrepancy between the observed data and the model predictions. The Gauss-Newton method minimizes the sum of squared residuals between the observed data and the model predictions through iterative approximation.

[0027] This application discloses a remote sensing monitoring method, device, electronic equipment, and computer-readable storage medium for sulfur dioxide in the atmosphere, relating to the field of remote sensing detection technology. The method includes: indexing high-resolution satellite remote sensing spectra, pre-processed spatiotemporal data, and wavelength data from a target dataset obtained by spatiotemporal analysis of an initial dataset; spatially splitting and pixel labeling the spectral data of the CCD row and column range corresponding to the area to be inverted based on the spatiotemporal data, and processing multiple partitioned data blocks in parallel; spectrally splitting the observed spectra of each partitioned data block based on the wavelength data, and inputting the resulting multiple sub-band data blocks into a radiative transfer model in parallel to obtain the simulated spectrum corresponding to each sub-band data block; fitting and integrating the simulated and observed spectra, and correcting the initial inversion result of the sulfur dioxide concentration to obtain the target inversion result. This method can improve the computational efficiency of sulfur dioxide inversion.

[0028] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0029] Firstly, such as Figure 1 As shown, the remote sensing monitoring method for sulfur dioxide in the atmosphere may include, but is not limited to, steps S110 to S180.

[0030] Step S110: Perform spatiotemporal analysis on the initial dataset obtained from monitoring to obtain the target dataset.

[0031] Step S120: Based on the time parameters, index the high-resolution satellite remote sensing spectrum, the preprocessed spatiotemporal data, and the wavelength data from the target dataset.

[0032] Step S130: Determine the CCD row and column range corresponding to the area to be inverted based on the latitude and longitude information of the high-resolution satellite remote sensing spectrum.

[0033] Step S140: Based on the preprocessed spatiotemporal data, perform spatial dimension splitting and pixel labeling on the spectral data of the CCD row and column range corresponding to the region to be inverted, to obtain multiple partitioned data blocks of different partitions.

[0034] Step S150: Perform parallel data processing and spectral preprocessing on multiple partitioned data blocks. Based on wavelength dimension data, perform spectral dimension splitting on the observed spectrum of each partitioned data block to obtain multiple sub-band data blocks.

[0035] Step S160: Input multiple sub-band data blocks into the radiative transfer model in parallel, perform parallel simulation calculations based on the simulated spectral parameters, and obtain the simulated spectrum corresponding to each sub-band data block.

[0036] Step S170: Fit and integrate the simulated and observed spectra to obtain the initial inversion results of sulfur dioxide concentration.

[0037] Step S180: Perform stripe background correction and dimensional background correction on the initial inversion results in sequence to obtain the corrected target inversion results of sulfur dioxide concentration.

[0038] The time parameter in step S120 refers to a selected month or several months; thus, the target dataset within the time parameter is selected. For example, the target dataset for October, or the target dataset within half a year.

[0039] Through steps S110 to S180, by utilizing a remote sensing monitoring device for sulfur dioxide in the atmosphere, after performing spatiotemporal analysis on the initial dataset obtained from the monitoring to obtain the target dataset, firstly, high-resolution satellite remote sensing spectra, preprocessed spatiotemporal dimension data, and wavelength dimension data are indexed from the target dataset according to time parameters; secondly, the CCD row and column range corresponding to the area to be inverted is determined based on the latitude and longitude information of the high-resolution satellite remote sensing spectra; then, based on the preprocessed spatiotemporal dimension data, spatial dimension splitting and pixel labeling processing are performed on the spectral data of the CCD row and column range corresponding to the area to be inverted, resulting in multiple partitioned data blocks of different zones; finally, the multiple partitioned data blocks are processed... Parallel data processing and spectral preprocessing are employed. Based on wavelength-dimensional data, the observed spectra of each partition data block are split into multiple sub-band data blocks. These sub-band data blocks are then input in parallel into a radiative transfer model, and simulated parallel computation is performed based on simulated spectral parameters to obtain the simulated spectrum corresponding to each sub-band data block. Next, fitting and data integration processing are performed on each simulated spectrum and the observed spectrum to obtain the initial inversion result of sulfur dioxide concentration. Finally, the initial inversion result is sequentially subjected to fringe background correction and dimensional background correction processing to obtain the corrected target inversion result of sulfur dioxide concentration. By inverting atmospheric sulfur dioxide concentration in parallel, the computational efficiency of sulfur dioxide inversion is improved. In other words, this embodiment of the application improves the computational efficiency of sulfur dioxide inversion by using a segmentation strategy based on spatiotemporal dimensions and a parallel strategy based on spectral wavelength dimensions to invert atmospheric sulfur dioxide concentration.

[0040] It should be noted that satellite-acquired spectral data has a three-dimensional structure, also known as a data cube, comprising two spatial dimensions and a single spectral dimension. Traditional serial processing methods are inadequate for the characteristics of satellite spectral data, particularly its spatial and spectral dimensions, limiting its practical application. Traditional serial computing methods involve tasks being executed sequentially in a fixed order, with each task waiting for the previous one to complete before starting. This approach is linear, with only one processing unit executing tasks. When processing large-scale spectral simulations, this often requires significant computational resources and is time-consuming. In contrast, the embodiments of this application employ parallel computing, using multiple processors simultaneously to execute multiple tasks or different parts of a single task, significantly improving processing speed and computational power. Furthermore, based on the principle of optimization estimation algorithms and combined with the spatial and spectral characteristics of satellite spectral data, a parallel computing strategy has been developed; this improves processing speed and spectral simulation time at the algorithmic level, thereby supporting operational data analysis.

[0041] According to some embodiments of this application, step S110 is further described. Specifically, the initial dataset includes: an initial hyperspectral dataset and initial inversion auxiliary data; the target dataset includes: a hyperspectral dataset divided by time series, spatiotemporal data divided by time series, and wavelength data divided by band interval; step S110: perform spatiotemporal analysis processing on the initial dataset obtained from monitoring to obtain the target dataset, including but not limited to steps S111 to S115.

[0042] Step S111: Monitor and acquire the initial dataset, which includes: the initial hyperspectral dataset and the initial inversion auxiliary data; wherein, the inversion auxiliary data includes: spatiotemporal dimension data and wavelength dimension data.

[0043] Step S112: Perform spatiotemporal analysis, data indexing, and data preprocessing on the initial spatiotemporal data to obtain spatiotemporal data divided according to time sequence.

[0044] Step S113: Perform spatiotemporal analysis and data indexing on the initial hyperspectral dataset based on time attributes to obtain a hyperspectral dataset divided according to time sequence.

[0045] Step S114: Perform band interval division and data indexing on the initial wavelength dimension data to obtain wavelength dimension data divided by band intervals.

[0046] Step S115: Obtain the target dataset based on the hyperspectral dataset divided by time sequence, the spatiotemporal dimension data divided by time sequence, and the wavelength dimension data divided by band interval.

[0047] The inversion auxiliary data and its acquisition method in step S111 are further explained. In the process of inverting atmospheric sulfur dioxide based on satellite remote sensing, inversion auxiliary data is required; this data includes spatiotemporal data and wavelength-dimensional data. The spatiotemporal data includes atmospheric prior profile parameters, meteorological data, and cloud height, cloud cover, and cloud pressure data. The wavelength-dimensional data includes gas absorption cross-section data and surface reflectivity parameters.

[0048] Specifically, the atmospheric a priori profile parameters include: sulfur dioxide a priori profile, ozone a priori profile, BrO a priori profile, and HCHO a priori profile. It should be noted that in the process of retrieving the concentration of sulfur dioxide in the atmosphere, in addition to sulfur dioxide, other light-absorbing gases such as ozone, BrO, and HCHO need to be considered as interfering gases. The specific method for obtaining atmospheric prior profile parameters is as follows: the GEOS-Chem model can simulate daily global profile data of sulfur dioxide, BrO, and HCHO to obtain the three input prior profiles required in this application: sulfur dioxide prior profile, BrO prior profile, and HCHO prior profile; among which, the latitude and longitude spatial resolution of the sulfur dioxide prior profile, BrO prior profile, and HCHO prior profile is 2.5°×2.5°; in addition, the ozone prior profile is obtained by using the validated AURA MLS ozone product (MLS203.004); this ozone prior profile contains vertical stratification information sampled in different latitude zones, and the latitudinal resolution of the ozone prior profile is approximately 5°.

[0049] Specifically, the meteorological data includes: pressure data between the surface and the tropopause, surface temperature data, and temperature profiles. The meteorological data was obtained through the Daily Reanalysis Dataset (FNL) jointly released by NCEP and NCAR; the normalized meteorological data has a latitude and longitude spatial resolution of 1° × 1°.

[0050] Specifically, the cloud height, cloud amount, and cloud pressure data are obtained by: acquiring cloud height, cloud amount, and cloud pressure data through secondary cloud product data of the observed spectrum; the cloud height, cloud amount, and cloud pressure data are used as fixed values ​​for input.

[0051] Specifically, the gas absorption cross-section data includes: sulfur dioxide absorption cross-section, ozone absorption cross-section, BrO absorption cross-section, and HCHO absorption cross-section. It is understood that the intensity of the gas absorption cross-section varies with wavelength. The specific method for obtaining the gas absorption cross-section data is as follows: First, initial absorption cross-sections for sulfur dioxide, ozone, BrO, and HCHO are obtained using existing measurement data. Second, since ozone concentration has a significant impact on sulfur dioxide concentration, the absorption cross-sections for sulfur dioxide and ozone also need to consider their variation with temperature. Therefore, the final sulfur dioxide and ozone absorption cross-sections need to be determined by combining the temperature profiles in the meteorological data. Specifically, the sulfur dioxide absorption cross-section considers standard absorption cross-sections at five temperatures: 203K, 223K, 243K, 273K, and 293K; the ozone absorption cross-section considers standard absorption cross-sections at four temperatures: 218K, 228K, 243K, and 295K.

[0052] Specifically, the surface reflectance parameter is the ratio of the ground-observed spectrum to the solar-observed spectrum at 347 nm.

[0053] This application establishes a data segmentation strategy and a data spatiotemporal indexing strategy based on the spatiotemporal characteristics of parameters through steps S112 to S113.

[0054] Specifically, the atmospheric sulfur dioxide concentration retrieval method based on satellite observation provided in this application requires data preprocessing and spatiotemporal analysis segmentation based on the necessary prior atmospheric profile parameters and meteorological data. Further explanation is provided regarding the spatiotemporal analysis segmentation, data indexing, and data preprocessing performed on the initial spatiotemporal data in step S112.

[0055] The spatiotemporal analysis and segmentation of the initial spatiotemporal data specifically includes: considering the relatively small monthly variations in sulfur dioxide, HCHO, and BrO, the monthly average profile data from the GEO-Chem simulation results are used as the input for the atmospheric prior profile parameters; while considering the large daily variations in ozone and meteorological data, daily AURAMLS ozone products and daily FNL meteorological data are still used. In other words, the prior profiles for sulfur dioxide, BrO, and HCHO use the monthly average profile data from the GEO-Chem simulation results; the prior profile for ozone uses the daily average profile data; and the meteorological data uses daily meteorological data.

[0056] The data preprocessing for the initial spatiotemporal data specifically includes: Since different data sets have varying vertical resolutions, preprocessing is required to interpolate all data to be processed onto the same standard vertical distribution. The standard vertical distribution is set to 53 layers. Assuming the Earth's surface pressure is 1 standard atmosphere, the sampling step size is approximately 0.015 standard atmospheres within the range of 1.0–0.6 standard atmospheres; and approximately 0.02 standard atmospheres within the range of 0.6–0 standard atmospheres. When preprocessing the atmospheric prior profile parameters, the volume ratio data of the gas vertical distribution is first converted into vertical column concentration data. Using the principle that the total vertical column concentration remains constant across all altitude layers, this data is linearly interpolated into the standard vertical distribution. When preprocessing meteorological data, the meteorological field data in the standard vertical distribution can be directly obtained through linear interpolation.

[0057] The data indexing process for the initial spatiotemporal dimension data specifically includes: labeling the data to facilitate indexing.

[0058] When establishing a spatiotemporal segmentation strategy for data, it is necessary to consider the multidimensional characteristics of the data, including the differences between different CCD instruments in satellite spectral data, prior gas profiles, and the spatiotemporal distribution of meteorological data. Further explanation of step S113, which involves spatiotemporal analysis, segmentation, and data indexing of the initial hyperspectral dataset, specifically includes: segmenting and organizing the dataset according to its temporal attributes based on monthly distribution, and creating an index for each month's data to facilitate rapid location and access.

[0059] To address the characteristics of hyperspectral satellite data and enable spectral parallelism of band-related parameters during the inversion process, a spectral dimension segmentation strategy is required. Further explanation of step S114, which involves band interval division and data indexing of the initial wavelength-dimensional data, includes: First, for the absorption characteristics of sulfur dioxide, an inversion window of 311.0–326.0 nm is selected. Second, the bands indicated by the inversion window are divided into several sub-bands, ensuring a certain overlap between any two adjacent bands to guarantee data continuity and reliability. Finally, for the input wavelength-dimensional data (specifically, gas absorption cross-section data) that is independent of the interval distribution but related to wavelength, the gas absorption cross-section data is divided according to the sub-band intervals obtained from the inversion window, and a data index is established. Specifically, the gas absorption cross-sections of sulfur dioxide and ozone need to be interpolated from the standard absorption cross-section to the temperature of each vertical layer in each pixel to obtain temperature-dependent sulfur dioxide and ozone absorption cross-sections.

[0060] Through steps S111 to S115, the target dataset can be obtained based on spatiotemporal analysis, laying the data foundation for subsequent inversion calculations to obtain the target inversion results of sulfur dioxide concentration in the atmosphere.

[0061] Further explanation of step S130: Determining the CCD row and column range corresponding to the area to be inverted based on the latitude and longitude information of the high-resolution satellite remote sensing spectrum specifically includes: For a specific area that needs to be inverted, the latitude and longitude information of the satellite observation spectrum is used to accurately locate the row and column range of the area to be inverted in the CCD instrument data; wherein, the row information of the row and column range reflects the latitude change, and the column information of the row and column range reflects the longitude change.

[0062] Further explanation of step S140: Based on the spatiotemporal dimension data after data preprocessing, the spectral data of the CCD row and column range corresponding to the region to be inverted is subjected to spatial dimension splitting and pixel labeling processing to obtain multiple partitioned data blocks with different partitions.

[0063] The spatial dimension splitting process includes: considering the spatial resolution characteristics of atmospheric prior profile parameters, the spectral data of the CCD row and column range corresponding to the observed region to be inverted is segmented according to the spatial distribution rate of atmospheric prior profile parameters simulated by GEOS-Chem (2.5°×2.5°). When the region to be segmented is mainly distributed along the latitude, it can be segmented according to column elements to reduce the preprocessing time of the observed spectra of different column pixels.

[0064] Pixel labeling processing includes: determining the row and column range of the area to be inverted in the CCD instrument data, and then labeling the spectral data using cloud height, cloud amount, and cloud pressure data, where cloud amount is the percentage of cloud within a pixel. Pixels with cloud amount greater than 30% are labeled as invalid data, and pixels with cloud amount less than 30% are labeled as data to be calculated.

[0065] Further explanation of step S150: Parallel data processing and spectral preprocessing are performed on multiple partitioned data blocks. Based on wavelength dimension data, the observed spectrum of each partitioned data block is split into multiple sub-band data blocks.

[0066] Step S150 involves parallel data processing and spectral preprocessing of multiple partitioned data blocks using multiple working nodes. It can be understood that step S140 divides the spectral data of the CCD row and column range corresponding to the region to be inverted into multiple sub-regions to obtain multiple partitioned data blocks. To enable parallel processing of the spectral dimensions in the subsequent step S160, the number of partitioned data blocks should be much larger than the number of working nodes. As shown in the screenshot, when M partitioned data blocks need to be processed in parallel, the main program will allocate N partitioned data blocks to N working nodes in parallel, and the remaining (MN) partitioned data blocks will be allocated to the working nodes in a cyclical manner, that is, they will be allocated one by one to the working nodes that finish processing first. During the data processing of each partitioned data block, invalid pixels marked as invalid within the partitioned data block will be skipped, and spectral preprocessing will not be performed on invalid pixels; conversely, valid pixels marked as valid within the partitioned data block will undergo spectral preprocessing, resulting in partitioned data blocks that include valid pixels after spectral preprocessing. Specifically, spectral preprocessing includes spectral wavelength calibration and spectral intensity calibration using simulated spectral data.

[0067] Further explanation of step S160: Multiple sub-band data blocks are input into the radiative transfer model in parallel. Simulated parallel computation is performed based on the simulated spectral parameters to obtain the simulated spectrum corresponding to each sub-band data block. During the simulated parallel computation process, the radiative transfer model needs to combine band information and other simulated spectral parameters to simulate the observed spectrum, thus obtaining the simulated spectrum. Specifically, the simulated spectral parameters include: inversion auxiliary data and a preset simulated spectral sampling rate. The inversion auxiliary data used here includes, but is not limited to: geometric information parameters, atmospheric condition parameters, cloud height, cloud cover, and cloud pressure data, and reflectance parameters. Geometric information parameters include: latitude and longitude, altitude, solar zenith angle, and observation celestial focus, derived from observed spectral information; atmospheric condition parameters include: atmospheric prior profile parameters simulated by the GEOS-Chem model and meteorological data from NCEP reanalysis data; cloud height, cloud cover, and cloud pressure data are derived from secondary cloud product data of the observed spectrum; reflectance parameters are derived from the ratio of the corresponding ground-observed spectrum to the solar-observed spectrum at 347 nm. The simulated spectral sampling rate is 0.2 nm, and the simulated spectrum is also a normalized spectrum.

[0068] According to some embodiments of this application, step S170 is further described, in which the simulated spectra and observed spectra are fitted and integrated to obtain the initial inversion result of sulfur dioxide concentration, including but not limited to steps S171 to S172.

[0069] Step S171: Perform linear interpolation and optimization estimation on a simulated spectrum and an observed spectrum, and determine the sulfur dioxide concentration parameter that minimizes the difference between the simulated spectrum and the observed spectrum as the sub-inversion result within a sub-band interval;

[0070] Step S172: Integrate the sub-inversion results within multiple sub-band intervals to obtain the initial inversion results within the region to be inverted.

[0071] Step S171 specifically involves: using linear interpolation to interpolate the simulated spectrum to the wavelength distribution of the observed spectrum, thereby ensuring the consistency of the simulated spectrum and the observed spectrum in wavelength distribution.

[0072] Step S172 specifically involves: fitting the observed spectrum and the simulated spectrum within the inversion window, and then inputting the atmospheric sulfur dioxide concentration of the obtained pixel back into the simulated spectral parameters. The Tikhonov regularization technique is used and embedded into the Gauss-Newton iterative method. The final sulfur dioxide concentration is obtained by minimizing the difference between the observed spectrum and the simulated spectrum through an optimization estimation method.

[0073] Due to parallel processing, after fitting each simulated spectrum and observed spectrum, multiple partitioned inversion results of sulfur dioxide concentration are obtained; these are then merged according to the row and column numbers of the original spectra to obtain the initial inversion results of sulfur dioxide concentration for the complete region.

[0074] Steps S171 to S172 can quickly yield the initial inversion results within the region to be inverted.

[0075] According to some embodiments of this application, such as Figure 2 As shown, step S180 is further explained by performing stripe background correction and dimensional background correction on the initial inversion result in sequence to obtain the corrected target inversion result of sulfur dioxide concentration, including but not limited to steps S181 to S184.

[0076] Step S181: Based on the preset formula for calculating the striped background correction value, perform the first calculation process on the initial inversion result to obtain the striped background correction value;

[0077] Step S182: Subtract the stripe background correction value from the initial inversion result to obtain the first inversion correction data of sulfur dioxide concentration after stripe background correction;

[0078] Step S183: Based on the preset latitude background correction value calculation formula, perform a second calculation process on the first inversion correction data to obtain the latitude background correction value;

[0079] Step S184: Subtract the latitude background correction value from the first inversion correction data to obtain the target inversion result after latitude background correction.

[0080] According to some embodiments of this application, the formula for calculating the striped background correction value is as follows:

[0081] V t,k =MEDIAN[V k ];

[0082] Among them, V k This represents the sulfur dioxide concentration distribution in the k-th column across all latitudes from -10° to 10°; MEDIAN represents the median function; V t,k This represents the stripe background correction value for the sulfur dioxide concentration distribution in the kth column.

[0083] According to some embodiments of this application, the formula for calculating the latitude background correction value is as follows:

[0084] V t,l =MEDIAN[V l ];

[0085] Among them, V lV represents the sulfur dioxide concentration in the l-th latitude band after stripe background correction; MEDIAN represents the median function; V t,l This represents the latitude background correction value for the obtained l-th latitude zone.

[0086] Steps S181 to S184 yield a more accurate target inversion result after correction, improving the accuracy of sulfur dioxide concentration inversion calculation.

[0087] As an example, this application illustrates the stripe background correction processing and dimensional background correction processing in its remote sensing monitoring method for sulfur dioxide in the atmosphere.

[0088] First, a clear, cloudless Pacific region corresponding to the observed spectra over a one-month period is selected to determine the row and column range of the area to be inverted in the CCD instrument data. The selected latitude range needs to cover from -80° to 80°. Based on cloud cover data, satellite data blocks are marked as invalid and valid pixels, ensuring that the selected valid pixels cover all column pixels. Initial inversion results for sulfur dioxide concentration within this complete clean background region are obtained through parallel computation.

[0089] Next, for the sulfur dioxide concentration distribution results in the initial inversion results within the latitude range of -10° to 10°, the median sulfur dioxide concentration of each column is calculated to obtain the sulfur dioxide concentration sequence varying along the column pixels, which is the stripe background correction data (stripe background correction value) of sulfur dioxide concentration. The calculation formula is V. t,k =MEDIAN[V k ]; where V k This represents the sulfur dioxide concentration distribution in the k-th column across all latitudes from -10° to 10°; MEDIAN represents the median function; V t,k This represents the stripe background correction value for the sulfur dioxide concentration distribution in the kth column.

[0090] Then, within a range of -10° to 10°, the initial inversion results are subtracted for the stripe background correction value along each column to obtain the first inversion-corrected sulfur dioxide concentration data after stripe background correction. The calculation formula is: V i,k ′=V i,k -V t,k Among them, V t,k V represents the stripe background correction value for the sulfur dioxide concentration distribution in the k-th column; i,k V represents the initial inversion result of sulfur dioxide concentration in the i-th row and j-th column. i,k ′ represents the first inversion correction data of sulfur dioxide concentration after stripe background correction.

[0091] Next, within the -80° to 80° range of the first inversion correction data, the range is divided into different latitudinal zones with a step size of 2.5°. The median of the first inversion correction data within each latitudinal zone is calculated to obtain the sulfur dioxide concentration sequence varying along the latitude, i.e., the latitudinal background correction data of sulfur dioxide (i.e., the latitudinal background correction value). The calculation formula is: V t,l =MEDIAN[V l ]; where V l V represents the sulfur dioxide concentration in the l-th latitude band after stripe background correction; MEDIAN represents the median function; V t,l This represents the latitude background correction value for the obtained l-th latitude zone.

[0092] Finally, based on each dimensional band, the first inversion correction data of sulfur dioxide concentration after stripe background correction is subtracted from the latitudinal background correction value to obtain the target inversion result; the calculation formula is: V i,k " = V i,k ′-V t,l Among them, V i,k ′ represents the first inversion correction data of sulfur dioxide concentration in the i-th row and j-th column after stripe background correction, V t,l V represents the latitude background correction value for the l-th row. i,k "" indicates the target inversion result of the final corrected sulfur dioxide concentration.

[0093] According to some embodiments of this application, after step S180, i.e. after obtaining the target inversion result of the corrected sulfur dioxide concentration, the method further includes step S190: performing parallel normalization processing on the target inversion result of the corrected sulfur dioxide concentration to obtain a regular gridded regional sulfur dioxide concentration level 3 data product.

[0094] Step S190 specifically includes: dividing the area to be inverted into several regular grids according to the required resolution to obtain gridded pixels; processing each gridded pixel using an asynchronous parallel computing method; during the parallel computing process, the calculation results of each gridded pixel are temporarily stored in the corresponding grid data structure to ensure the continuity and stability of the calculation process, and finally output to a file. When processing each gridded pixel, it is necessary to screen the sulfur dioxide concentration data of the original pixels to filter out those pixels with large fitting residuals; for the screened data, a weighted average method is used based on pixel weights; wherein, the pixel weight is determined based on the reciprocal of the square of the distance from the pixel center to the grid pixel center, so as to more accurately reflect the contribution of different pixels to the final result.

[0095] like Figure 3As shown, the present invention also provides a remote sensing monitoring device for sulfur dioxide in the atmosphere, comprising:

[0096] The processor 301 can be implemented using a general-purpose central processing unit, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0097] The memory 302 can be implemented as a read-only memory, static storage device, dynamic storage device, or random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301 to execute the remote sensing method for sulfur dioxide in the atmosphere according to the embodiments of this application.

[0098] Input / output interface 303 is used to implement information input and output;

[0099] The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0100] Bus 305 transmits information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304);

[0101] The processor 301, memory 302, input / output interface 303, and communication interface 304 are connected to each other within the device via bus 305.

[0102] This application also provides an electronic device, including the remote sensing monitoring device for sulfur dioxide in the atmosphere as described above.

[0103] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described remote sensing monitoring method for sulfur dioxide in the atmosphere.

[0104] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0106] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by this application.

Claims

1. A method for remote sensing monitoring of sulfur dioxide in the atmosphere, characterized in that, The method comprises the following steps: spatiotemporal analysis of the initial data set obtained by monitoring to obtain a target data set; indexing of high-resolution satellite remote sensing spectra, spatiotemporal data and wavelength data after data preprocessing from the target data set according to a time parameter; determination of the CCD row and column range corresponding to the area to be retrieved according to the latitude and longitude information of the high-resolution satellite remote sensing spectra; spatial dimension splitting processing and pixel marking processing of the spectral data of the CCD row and column range corresponding to the area to be retrieved according to the spatiotemporal data after data preprocessing to obtain a plurality of partition data blocks of different partitions; the spatial dimension splitting processing comprises: dividing the spectral data of the CCD row and column range corresponding to the area to be retrieved according to the spatial distribution rate 2.5°×2.5° of the simulated atmospheric prior profile parameter, wherein when the area to be divided is mainly distributed along the latitude, the division is performed according to the column pixel; parallel data processing and spectral preprocessing of the plurality of partition data blocks, spectral dimension splitting processing of the observed spectrum of each partition data block based on the wavelength data to obtain a plurality of sub-band data blocks; inputting the plurality of sub-band data blocks into a radiative transfer model in parallel, simulating and performing parallel calculation processing based on the simulated spectral parameters to obtain a simulated spectrum corresponding to each sub-band data block; fitting processing and data integration processing of each simulated spectrum and the observed spectrum to obtain an initial retrieval result of sulfur dioxide concentration; stripe background correction processing and dimension background correction processing of the initial retrieval result in sequence to obtain a target retrieval result of sulfur dioxide concentration after correction; wherein the initial data set comprises an initial hyperspectral data set and initial retrieval auxiliary data; the target data set comprises a hyperspectral data set divided by time sequence, spatiotemporal data divided by time sequence and wavelength data divided by wavelength interval; the spatiotemporal analysis of the initial data set obtained by monitoring to obtain a target data set comprises: monitoring and obtaining the initial data set, which comprises the initial hyperspectral data set and the initial retrieval auxiliary data; wherein the retrieval auxiliary data comprises spatiotemporal data and wavelength data; spatiotemporal analysis cutting processing, data indexing processing and data preprocessing of the initial spatiotemporal data to obtain spatiotemporal data divided by time sequence; time attribute-based spatiotemporal analysis cutting processing and data indexing processing of the initial hyperspectral data set to obtain a hyperspectral data set divided by time sequence; wavelength interval division processing and data indexing processing of the initial wavelength data to obtain wavelength data divided by wavelength interval; the target data set is obtained according to the hyperspectral data set divided by time sequence, the spatiotemporal data divided by time sequence and the wavelength data divided by wavelength interval.

2. The method for remote sensing monitoring of sulfur dioxide in the atmosphere according to claim 1, characterized in that, the fitting processing and data integration processing of each simulated spectrum and the observed spectrum to obtain an initial retrieval result of sulfur dioxide concentration comprises: Linear interpolation and optimal estimation are performed on the simulated spectrum and the observed spectrum, and a sulfur dioxide concentration parameter that minimizes the difference between the simulated spectrum and the observed spectrum is determined as a sub-inversion result in a sub-waveband interval; The sub-inversion results in multiple sub-waveband intervals are integrated to obtain an initial inversion result in the to-be-inverted region.

3. The method for remote sensing monitoring of sulfur dioxide in the atmosphere according to claim 1, characterized in that, The initial inversion result is subjected to stripe background correction and dimension background correction in sequence to obtain a target inversion result of the corrected sulfur dioxide concentration, including: Based on a preset stripe background correction value calculation formula, first calculation processing is performed on the initial inversion result to obtain a stripe background correction value; The initial inversion result is subtracted by the stripe background correction value to obtain first inversion correction data of the sulfur dioxide concentration after stripe background correction; Based on a preset latitude background correction value calculation formula, second calculation processing is performed on the first inversion correction data to obtain a latitude background correction value; The first inversion correction data is subtracted by the latitude background correction value to obtain the target inversion result after latitude background correction.

4. The method for remote sensing monitoring of sulfur dioxide in the atmosphere according to claim 3, characterized in that, The stripe background correction value calculation formula is: ； wherein, represents the sulfur dioxide concentration profile of the kth column in all the latitude profiles -10° to 10° of sulfur dioxide concentration; MEDIAN represents the function to take the median; represents the stripe background corrected value of the kth column of sulfur dioxide concentration profile obtained.

5. The method for remote sensing monitoring of sulfur dioxide in the atmosphere according to claim 3, characterized in that, The latitude background correction value calculation formula is: ； wherein represents the concentration of sulfur dioxide in the lth latitude band after the stripe background correction; MEDIAN represents the function of taking the median; represents the latitude background correction value of the lth latitude band obtained.

6. The method for remote sensing monitoring of sulfur dioxide in the atmosphere according to claim 1, characterized in that, After the target inversion result of the corrected sulfur dioxide concentration is obtained, the method further includes: The target inversion result of the corrected sulfur dioxide concentration is subjected to normalization processing in parallel to obtain a regular gridded regional sulfur dioxide concentration three-level data product.

7. A device for remote sensing of sulfur dioxide in the atmosphere, characterized in that, The device includes at least one processor and a memory connected in communication with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the remote sensing monitoring method of sulfur dioxide in the atmosphere according to any one of claims 1 to 6.

8. An electronic device, comprising: The device includes the remote sensing monitoring device of sulfur dioxide in the atmosphere according to claim 7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions for causing a computer to execute the remote sensing monitoring method of sulfur dioxide in the atmosphere according to any one of claims 1 to 6.

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