Satellite-based atmospheric water vapor detection method and system

Abstract

The present invention provides a satellite-based atmospheric water vapor detection method and system, which relates to the field of satellite remote sensing technology, including: obtaining satellite remote sensing data and solar radiation angle data of a target area; performing correction processing according to the satellite remote sensing data and the solar radiation angle data to obtain corrected radiation data; establishing an atmospheric radiation transmission model according to the corrected radiation data to generate radiation simulation data; performing water vapor inversion processing according to the radiation simulation data to infer the vertical distribution data of water vapor in the atmosphere; performing stereo interpolation processing according to the vertical distribution data to obtain three-dimensional hierarchical water vapor monitoring data. The present invention uses a solar radiation projection model to correct the radiation error in satellite remote sensing data, solving the radiation error problem caused by ground reflection and sunshine angle; using topological analysis technology to predict the morphology of missing areas, and combining with neighborhood water vapor data to perform fitting and filling, thereby improving the accuracy and reliability of the monitoring results.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing technology, and in particular to a satellite-based atmospheric water vapor detection method and system. Background Art

[0002] At present, atmospheric water vapor detection technology is widely used in meteorological monitoring, environmental protection, climate change research and other fields. Especially with the continuous development of satellite remote sensing technology, satellite-based water vapor monitoring methods have gradually become mainstream. At present, traditional satellite water vapor detection technology mainly relies on radiation data obtained by satellite sensors, combined with simple water vapor inversion models and radiation transmission methods based on atmospheric theory for analysis. However, these methods have multiple limitations in practical applications, mainly manifested as follows: First, the existing radiation data correction technology is difficult to effectively deal with the complex errors caused by ground reflection, scattering and sunshine angle; second, the traditional water vapor inversion technology lacks an effective high-level interpolation method and cannot accurately reproduce the water vapor concentration distribution at different height levels, resulting in large errors in water vapor concentration data in some areas. In addition, the existing water vapor concentration distribution prediction method relies on simple geometric models or empirical algorithms, which makes it difficult to achieve high-precision water vapor monitoring in complex geographical environments.

[0003] Based on the above-mentioned shortcomings of the prior art, there is an urgent need for a satellite-based atmospheric water vapor detection method and system. Summary of the invention

[0004] The purpose of the present invention is to provide a satellite-based atmospheric water vapor detection method to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a satellite-based atmospheric water vapor detection method, comprising:

[0006] Acquire satellite remote sensing data and solar radiation angle data of the target area, wherein the satellite remote sensing data includes radiation data collected by at least two satellites at different orbits and sensor angles;

[0007] Correction processing is performed according to the satellite remote sensing data and the solar radiation angle data, by constructing a solar radiation projection model and correcting the radiation error caused by the sunshine angle to obtain corrected radiation data;

[0008] Establishing an atmospheric radiation transmission model based on the corrected radiation data, and generating radiation simulation data by simulating the effects of water vapor on the absorption, scattering and transmission of radiation signals during the propagation of radiation in the atmosphere;

[0009] Performing water vapor inversion processing according to the radiation simulation data, calculating the quantitative relationship between the radiation intensity and the spectral absorption characteristics of water vapor, and combining the absorption coefficient of water vapor with the radiation path length, to infer the vertical distribution data of water vapor in the atmosphere;

[0010] Stereo interpolation processing is performed based on the vertical distribution data. By calculating the rate of change of water vapor concentration at different height levels in the target area and adjusting the interpolation weight based on the height difference, the vertical and horizontal interpolation are combined to obtain three-dimensional water vapor monitoring data.

[0011] In a second aspect, the present application also provides a satellite-based atmospheric water vapor detection system, comprising:

[0012] An acquisition module is used to acquire satellite remote sensing data and solar radiation angle data of a target area, wherein the satellite remote sensing data includes radiation data collected by at least two satellites at different orbits and sensor angles;

[0013] A correction module, used for performing correction processing according to the satellite remote sensing data and the solar radiation angle data, by constructing a solar radiation projection model and correcting the radiation error caused by the sunshine angle, to obtain the corrected radiation data;

[0014] A simulation module is used to establish an atmospheric radiation transmission model according to the corrected radiation data, and generate radiation simulation data by simulating the influence of water vapor on the absorption, scattering and transmission of radiation signals during the propagation of radiation in the atmosphere;

[0015] An inversion module is used to perform water vapor inversion processing according to the radiation simulation data, calculate the quantitative relationship between the radiation intensity and the spectral absorption characteristics of water vapor, and combine the absorption coefficient of water vapor with the radiation path length to infer the vertical distribution data of water vapor in the atmosphere;

[0016] The interpolation module is used to perform stereo interpolation processing according to the vertical distribution data, calculate the change rate of water vapor concentration at different height levels in the target area, adjust the interpolation weight based on the height difference, and combine the vertical and horizontal interpolation to obtain three-dimensional water vapor monitoring data.

[0017] The beneficial effects of the present invention are:

[0018] The present invention utilizes the solar radiation projection model to correct the radiation error in satellite remote sensing data, thereby solving the radiation error problem caused by ground reflection and sunlight angle; secondly, based on the atmospheric radiation transmission model and the spectral absorption characteristics of water vapor, the vertical distribution data of water vapor is inferred to achieve accurate inversion of water vapor concentration; at the same time, a three-dimensional Kriging interpolation method is used to perform spatial interpolation processing on the water vapor concentration, thereby obtaining a high-precision water vapor concentration distribution map; finally, the present application uses topological analysis technology to predict the morphology of missing areas, and combines the neighborhood water vapor data for fitting and filling, thereby further improving the accuracy and reliability of the monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic flow chart of a satellite-based atmospheric water vapor detection method according to an embodiment of the present invention;

[0021] Figure 2 Schematic diagram of the structure of a satellite-based atmospheric water vapor detection system according to an embodiment of the present invention;

[0022] Figure 3 The figure is a schematic diagram of the structure of a satellite-based atmospheric water vapor detection device described in an embodiment of the present invention.

[0023] Markings in the figure: 800, a satellite-based atmospheric water vapor detection device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, correction module; 903, simulation module; 904, inversion module; 905, interpolation module. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. Embodiment 1:

[0026] This embodiment provides a satellite-based atmospheric water vapor detection method.

[0027] See also Figure 1 , the figure shows that the method includes steps S100 to S500.

[0028] Step S100: Acquire satellite remote sensing data and solar radiation angle data of the target area, where the satellite remote sensing data includes radiation data collected by at least two satellites at different orbits and sensor angles;

[0029] It is understandable that the accuracy of this step provides the basis for subsequent radiation data correction, radiation transfer model establishment and water vapor inversion. Satellite remote sensing data usually includes radiation intensity information from different satellites. These satellites may carry different sensors and make observations on different orbits. Appropriate sensors such as microwave radiometers (AMSU), infrared radiometers (HIRS) and visible light / infrared (MODIS) sensors are selected to obtain radiation data in different bands (microwave, infrared, visible light, etc.). Therefore, the acquired data must not only cover the spatial distribution of the target area, but also have the characteristics of temporal variation. By obtaining radiation data from at least two satellites, the errors that may be caused by single satellite observations can be eliminated, and the coverage and reliability of the data can be improved. The solar radiation angle data takes into account the influence of factors such as the solar altitude angle and the angle of incidence. The acquisition of solar radiation angle data usually depends on the accurate calculation of the relative position of the earth and the sun, as well as the calibration of geographic coordinates.

[0030] Step S200, performing correction processing according to the satellite remote sensing data and the solar radiation angle data, constructing a solar radiation projection model, and correcting the radiation error caused by the sunlight angle to obtain the corrected radiation data;

[0031] It is understandable that building a solar radiation projection model includes a detailed analysis of ground reflectivity, solar incidence angle, and its interaction with the ground surface. By comparing the changes in radiation intensity under different sunshine angles, and using mathematical methods such as regression models or least squares methods, the model can accurately map the distribution of solar radiation in space and time. This correction can not only eliminate errors caused by differences in terrain, surface type, and sun angle, but also better handle radiation deviations under different environmental conditions.

[0032] Step S300: Establish an atmospheric radiation transmission model based on the corrected radiation data, and generate radiation simulation data by simulating the influence of water vapor on the absorption, scattering and transmission of radiation signals during the propagation of radiation in the atmosphere;

[0033] It should be noted that the establishment of an atmospheric radiation transfer model involves the simulation of multiple physical processes, the most important of which are the absorption, scattering and transmission of radiation. First, the absorption characteristics of water vapor are closely related to the wavelength of the radiation signal and the optical properties of the atmosphere. The model needs to calculate the absorption effect of the radiation signal when it propagates in the atmosphere by quantitatively describing the water vapor concentration and the radiation path length. Secondly, the scattering effect of water vapor in the atmosphere cannot be ignored. During the radiation propagation process, water vapor and aerosols will scatter, changing the propagation path and intensity of the radiation. Therefore, the radiation transfer model must take into account the changes in scattering angle and scattering intensity, which is achieved by introducing a two-way scattering distribution function to simulate the influence of water vapor and aerosol on radiation. This process requires the use of mathematical integration methods, combined with different scattering intensities and angles, to accurately calculate the scattering effect of radiation. In addition, the calculation of transmittance is also an indispensable part of the model. During the propagation process, the radiation signal is affected by factors such as temperature, air pressure, and humidity. The change in transmittance needs to be quantified by simulating the radiation transmission process of different atmospheric layers. By combining the water vapor concentration data at each altitude level and the characteristics of the atmosphere, the model can simulate the transmission of radiation signals at different levels.

[0034] Step S400, performing water vapor inversion processing according to the radiation simulation data, calculating the quantitative relationship between the radiation intensity and the spectral absorption characteristics of water vapor, and combining the absorption coefficient of water vapor with the radiation path length, to infer the vertical distribution data of water vapor in the atmosphere;

[0035] Specifically, first, based on the radiation simulation data and the spectral absorption characteristics of water vapor, a quantitative relationship between radiation intensity and water vapor concentration is established. The absorption characteristics of water vapor in the atmosphere are wavelength-dependent. The radiation signal in a specific band (such as 1.4μm, 1.9μm) will be absorbed by water vapor, and the degree of absorption is proportional to the water vapor concentration. By analyzing the changes in radiation signals in different bands, regression analysis and other methods can be used to infer the spectral absorption coefficient of water vapor, thereby obtaining quantitative information on water vapor concentration. At this point, the obtained radiation data provides a key benchmark, which can help determine the absorption intensity of water vapor at different wavelengths and its corresponding concentration value. Secondly, the absorption coefficient of water vapor and the length of the radiation path are important parameters that affect the water vapor inversion results. During the propagation of the radiation signal in the atmosphere, its intensity will weaken as the path length increases, especially in areas with high water vapor concentration. By establishing a radiation propagation path model, taking into account the refraction effect of the atmosphere and the distribution of water vapor, the transmission path of the radiation signal can be calculated. These path data provide the necessary geometric information for subsequent inversion calculations, so that the changes in water vapor concentration in each layer of the atmosphere can be accurately calculated during the inversion process.

[0036] Step S500, perform stereo interpolation processing according to the vertical distribution data, calculate the change rate of water vapor concentration at different height levels in the target area, adjust the interpolation weight based on the height difference, and combine the vertical and horizontal interpolation to obtain three-dimensional water vapor monitoring data.

[0037] Before performing stereo interpolation, it is necessary to calculate the rate of change of water vapor concentration at different height levels in the target area. The core of this process is to measure or infer the gradient of water vapor concentration between different height levels. Water vapor concentration shows different trends with the change of height. Therefore, calculating the rate of change of water vapor concentration helps to understand the distribution characteristics of water vapor in the vertical direction. These gradient information provides the necessary basis for adjusting the interpolation weights and ensures the rationality of the interpolation results in the vertical direction. Next, the interpolation weights are adjusted according to the rate of change of water vapor concentration. This adjustment is based on the height difference, taking into account the changing trend of water vapor concentration between different height levels. Generally, the change of water vapor concentration at a higher level will have a greater impact on the concentration of the lower level. Therefore, the greater the height difference, the weight of the interpolation should be appropriately increased. In this way, it can be ensured that the change of water vapor concentration can be more realistically reflected in space during the interpolation process, and maintain good continuity between different height levels. Finally, the interpolation methods in the vertical and horizontal directions are combined to perform stereo interpolation processing. Interpolation in the horizontal direction usually uses classic spatial interpolation algorithms, such as Kriging, which helps to ensure the spatial uniformity of water vapor concentration in the horizontal direction. Interpolation in the vertical direction needs to take into account the change in water vapor concentration gradient and adjust the weight according to the height difference to ensure a smooth transition between different height levels. By combining these two interpolation methods, three-dimensional water vapor monitoring data is finally obtained. These data not only cover the water vapor concentration information at different height levels, but also form a continuous three-dimensional model in space where the water vapor concentration changes layer by layer.

[0038] Further, step S200 includes step S210 to step S240.

[0039] Step S210: According to the satellite remote sensing data and the solar radiation angle data, by analyzing the variation law of the ground reflectivity, combining the surface type and the spectral characteristics, a nonlinear regression method is used to calculate the quantitative relationship between the intensity of ground reflection and the solar radiation angle, and a ground reflection model is constructed;

[0040] In this step, we first need to analyze the changing pattern of ground reflectivity. Ground reflectivity reflects the reflection intensity of the ground surface to solar radiation. In order to obtain an accurate quantitative relationship, the relationship between ground reflection intensity and solar radiation angle is established by combining the surface type (such as forest, city, grassland, etc.) and the spectral characteristics of the surface (such as absorption and reflection characteristics of different bands) through nonlinear regression method. The relationship between ground reflectivity and solar radiation angle is represented by the following nonlinear regression model:

[0041] ;

[0042] in, It represents the ground reflectivity, which reflects the intensity of ground reflection at different angles; represents the incident angle of solar radiation; , , and , It is a parameter obtained by fitting through nonlinear regression method, which depends on the spectral characteristics of the surface and the law of ground reflection;

[0043] The model obtains the quantitative relationship between reflectivity and angle by analyzing the change of ground reflection intensity with the angle of the sun.

[0044] Step S220: According to the ground reflection model, by analyzing the interaction between solar radiation and the ground surface, a geometric relationship of solar radiation projected on the ground is established and the change of radiation intensity under different sunlight angles is calculated to obtain a radiation projection geometric model;

[0045] In this step, the geometric relationship of solar radiation on the ground is established, specifically how the intensity of solar radiation changes with the change of sunlight angle. By analyzing the interaction between solar radiation and the ground surface, the change of radiation intensity under different sunlight angles can be calculated, and finally the radiation projection geometric model is obtained. The relationship between the change of solar radiation intensity on the ground and the solar incidence angle and ground reflectivity is shown as follows:

[0046] ;

[0047] in, Indicates the radiation intensity on the ground; Represents the initial intensity of solar radiation. Through this formula, the distribution of radiation intensity on the ground at different sunlight angles can be calculated to form a radiation projection geometric model.

[0048] Step S230: construct a model based on the ground reflection model and the radiation projection geometric model, use a local weighted regression algorithm to simulate the change of radiation intensity under different ground features and sunshine angles, and perform radiation projection calculation in space to construct a solar radiation projection model;

[0049] In this step, the ground reflection model and the radiation projection geometric model are combined, and the local weighted regression algorithm is used to simulate the changes in radiation intensity under different ground features and sunlight angles, and the radiation projection calculation is performed in space to finally construct a solar radiation projection model.

[0050] The local weighted regression algorithm (LWR) processes the impact of different ground features and angles on radiation intensity by giving weights. The core of this step is to perform spatial projection of radiation intensity by combining multiple ground features (such as terrain, surface type) and solar radiation angles through local regression. The form of the local weighted regression model is as follows:

[0051] ;

[0052] in, Indicates location The local radiation intensity at Indicates the location of known radiation intensity data points; Indicates the serial number of the data point; Represents data points The radiation intensity at represents a weight function defined based on distance or similarity, which is a Gaussian function in this embodiment:

[0053] ;

[0054] in, Represents the parameter that controls the range of local weights.

[0055] Step S240: Correct the satellite remote sensing data based on the solar radiation projection model to obtain corrected radiation data.

[0056] Finally, based on the solar radiation projection model constructed above, the satellite remote sensing data is corrected to eliminate the errors caused by factors such as ground reflection characteristics and solar radiation angle, and finally the corrected radiation data is obtained. The corrected radiation data is expressed as:

[0057] ;

[0058] in, Indicates the corrected radiation data; Represents raw satellite remote sensing data; Represents the radiation intensity calculated by the solar radiation angle and ground reflection model; Represents the radiation intensity obtained according to the radiation projection geometric model.

[0059] Further, step S300 includes step S310 to step S340.

[0060] Step S310, constructing a model framework according to the corrected radiation data, setting the optical thickness, absorption coefficient and scattering coefficient of the atmosphere using the atmospheric characteristics of the target area, and defining coefficients based on the atmospheric components to obtain a radiation transfer equation, which includes the absorption, scattering, transmission and emission processes of radiation;

[0061] First, based on the corrected radiation data, the radiation transfer equation is constructed. The radiation transfer equation can usually be expressed as a combination of four processes: absorption, scattering, transmission, and emission. The radiation transfer equation is:

[0062] ;

[0063] in, The optical thickness is The radiation intensity at Represents the incident radiation intensity (or ground radiation intensity); represents the optical thickness; is a natural constant; It represents the absorption coefficient of radiation at different levels in the atmosphere; is an integral variable representing the cumulative optical thickness from the ground to a certain point in the atmosphere.

[0064] It can be seen that in order to obtain the radiation transfer equation, it is necessary to consider parameters such as the optical thickness, absorption coefficient and scattering coefficient of the atmosphere, which will be set based on the atmospheric characteristics of the target area.

[0065] Step S320: Based on the radiation transfer equation and in combination with the relationship between water vapor concentration and radiation path, the absorption of the radiation signal in the atmosphere is quantitatively calculated by the spectral band integration method, and the absorption coefficient in the equation is adjusted to obtain the absorption-corrected radiation transfer equation;

[0066] Next, based on the radiation transfer equation and combined with the relationship between water vapor concentration and radiation path, the absorption of the radiation signal in the atmosphere is calculated using the spectral band integration method. Absorption-corrected radiation transfer equation:

[0067] ;

[0068] in, represents the corrected absorption coefficient, which incorporates the effect of water vapor concentration on absorption.

[0069] Step S330: According to the absorption-corrected radiation transfer equation, a two-way scattering distribution function is introduced to simulate the scattering effect of water vapor and aerosol on radiation, the influence of the scattering angle on the radiation intensity is calculated, and the scattering term in the equation is adjusted to obtain a scattering-corrected radiation transfer equation;

[0070] It can be understood that this step simulates the scattering effect of water vapor and aerosol on radiation by introducing the bidirectional scattering distribution function (BIDF). The influence of scattering will cause the direction of radiation to change during propagation, so the scattering term in the radiation transfer equation needs to be adjusted. Scattering-corrected radiation transfer equation:

[0071] ;

[0072] in, and Indicates the directions of different scattering angles; represents the scattering coefficient, which indicates the scattering intensity of radiation at different levels in the atmosphere; is the bidirectional scattering distribution function, which represents the relationship between different scattering angles.

[0073] The right side of the entire equation includes two parts of scattering: the contribution of direct scattering and bidirectional scattering.

[0074] Step S340: According to the scattering-corrected radiation transfer equation, combined with the influence of different altitude levels, atmospheric temperature and pressure on transmittance, the radiation transmission process in different atmospheric layers is simulated to construct an atmospheric radiation transfer model, and radiation simulation data is calculated based on the atmospheric radiation transfer model.

[0075] Finally, based on the scattering-corrected radiation transfer equation, combined with the influence of different altitude levels, atmospheric temperature and pressure on transmittance, the radiation transmission process in different atmospheric layers is simulated, and an atmospheric radiation transfer model is constructed. The corrected formula for transmittance is:

[0076] ;

[0077] in, Indicates the depth of the atmosphere through which radiation passes Residual strength after Consider the height level and optical depth affected by atmospheric conditions (temperature, pressure).

[0078] Finally, the radiation simulation data is calculated through the atmospheric radiation transfer model:

[0079] ;

[0080] in, Indicates different heights Radiation simulation data at ; Indicates the maximum altitude of the atmosphere.

[0081] Further, step S400 includes step S410 to step S440.

[0082] Step S410: spectral absorption characteristic analysis is performed based on the radiation simulation data, and the spectral absorption coefficient of water vapor is obtained by fitting using a regression analysis method by combining the radiation intensity of different bands in the satellite remote sensing data with the absorption characteristics of water vapor in a specific band;

[0083] First, based on the radiation intensity of different bands in the radiation simulation data and satellite remote sensing data, combined with the absorption characteristics of water vapor in specific bands, regression analysis is performed to fit the spectral absorption coefficient of water vapor. The regression analysis model is:

[0084] ;

[0085] in, Indicates that water vapor has a wavelength Absorption intensity under Indicates the corresponding band in satellite remote sensing data The radiation intensity; is the regression coefficient, which indicates the sensitivity of water vapor to radiation absorption; is the bias term, which represents other influencing factors.

[0086] Through regression analysis, the spectral absorption coefficient of water vapor can be obtained, which reflects the absorption characteristics of water vapor at different wavelengths.

[0087] Step S420, performing path calculation according to the spectral absorption coefficient of water vapor, by establishing a radiation propagation path model, combining the satellite viewing angle, the incident angle of solar radiation and the refraction effect of the atmosphere, and using layer-by-layer integration to calculate the propagation path of the radiation signal in the atmosphere, to obtain a path calculation result;

[0088] This process uses the layer-by-layer integration method to calculate the propagation path of the radiation signal in the atmosphere. The path calculation formula is:

[0089] ;

[0090] in, represents the path strength of the radiated signal, Indicates the incident angle between the satellite and the sun; is the transmittance function, which indicates that radiation at different heights The degree of penetration.

[0091] Through the path calculation model, the propagation path of radiation in the atmosphere can be obtained, taking into account the influence of refraction effect and satellite viewing angle.

[0092] Step S430: Based on the path calculation result, by combining the water vapor concentration of each atmospheric layer with the corresponding path length, the radiation absorption effect is accumulated layer by layer using a recursive method to obtain the relationship between the water vapor absorption intensity and the path length at different height levels, and establish a quantitative relationship between the vertical distribution of water vapor and radiation absorption;

[0093] It should be noted that the relationship between absorption intensity and path length is:

[0094] ;

[0095] in, Indicates the height from the ground to Total water vapor absorption intensity; Indicates height Spectral absorption coefficient of water vapor at ; Indicates height The water vapor concentration at Represents the length of the radiation propagation path, taking into account the refraction effect of different atmospheric layers.

[0096] Step S440: Invert the vertical distribution of water vapor according to the quantitative relationship, and calculate the vertical distribution data of water vapor in the atmosphere by matching the radiation signal intensity with the spectral absorption characteristics of water vapor.

[0097] It can be understood that the vertical distribution inversion formula is:

[0098] ;

[0099] in, Indicates height Through this inversion formula, the radiation signal intensity is matched with the absorption characteristics of water vapor, and finally the vertical distribution data of water vapor in the atmosphere is obtained.

[0100] The above steps combine satellite remote sensing data, radiation simulation data and spectral absorption characteristics, and use layer-by-layer integration method and recursive algorithm to gradually calculate the vertical distribution of water vapor. The calculation of each step is to gradually correct and invert the water vapor concentration, and finally obtain the detailed vertical distribution of water vapor in the atmosphere. Each model and calculation in this process takes into account atmospheric characteristics and physical phenomena, such as refraction, absorption, scattering, etc., so that the final result has high accuracy and applicability.

[0101] Further, step S500 includes step S510 to step S540.

[0102] Step S510, calculating the concentration change rate according to the vertical distribution data, and obtaining the water vapor concentration change rate by calculating the gradient of water vapor concentration between different height levels in the target area;

[0103] In this step, the gradient operator is used to calculate the rate of change of water vapor concentration at different altitude levels. The core of gradient calculation is to describe the change of concentration with altitude through linear or nonlinear models. In order to describe this change more accurately, a nonlinear model can be introduced and the rate of change of concentration under different climate and atmospheric conditions can be considered. The calculation formula is:

[0104] ;

[0105] in, is the gradient of water vapor concentration, indicating the rate of change of concentration in this layer; is a regulating factor that represents the nonlinear rate at which water vapor concentration changes with altitude; The critical turning point height indicating the change of water vapor concentration; Represents the gradient operation in the height direction. This formula uses a Sigmoid function to simulate the nonlinear growth or attenuation of water vapor concentration within a specific height range, which is suitable for more complex environmental models.

[0106] Step S520, adjusting the weight according to the water vapor concentration change rate, by taking the water vapor concentration change rate as a function, adjusting the weight of each height level in the vertical interpolation process based on the elevation change, and obtaining an adjusted interpolation weight;

[0107] It should be noted that the weight adjustment should not only consider the concentration change rate, but also needs to be combined with other geographical and meteorological factors (such as terrain, air pressure, etc.). In order to further calculate accurately, this embodiment adjusts the weight by adding high-order partial derivatives to simulate the impact of height changes caused by terrain on the water vapor concentration gradient. The formula is:

[0108] ;

[0109] in, Indicates at height The interpolation weight at ; is the second-order derivative of geographic height, which represents the quadratic effect of topographic changes on the water vapor concentration gradient; It is the adjustment factor of geographical factors, indicating the influence of terrain; To prevent the constant from dividing by zero. This formula introduces the second-order derivative of the terrain to strengthen the influence of the height difference area on the water vapor concentration gradient, further improving the accuracy of water vapor monitoring.

[0110] Step S530, performing stereo interpolation processing according to the adjusted interpolation weights, and performing spatial interpolation processing by using a three-dimensional Kriging interpolation method in combination with the geographical features and water vapor concentration gradient of the target area to obtain a stereo interpolation result;

[0111] It should be noted that the Kriging interpolation method is used in stereo interpolation, but at this time, not only the change in height direction is considered, but also the spatial autocorrelation model needs to be introduced. In order to deal with the spatial heterogeneity in the atmosphere, a hybrid model combining polynomial interpolation and Kriging interpolation is used. The formula is:

[0112] ;

[0113] in, Indicates at the target point The water vapor concentration at represents the kriging weight; is a polynomial interpolation function, which represents the correction term for spatial autocorrelation and inhomogeneity; represents the polynomial interpolation coefficients; Represents the number of known points in Kriging; Represents the number of polynomial interpolation terms; Indicates This hybrid formula combines the spatial autocorrelation and inhomogeneity models, allowing the interpolation results to reflect more complex water vapor concentration variation patterns.

[0114] Step S540: Perform data fusion processing according to the stereo interpolation result, and generate stereoscopic water vapor monitoring data by integrating the interpolation data in the vertical and horizontal directions.

[0115] It is understandable that in the data fusion process, it is not only necessary to simply merge the interpolation data in the vertical and horizontal directions, but also to consider the relative importance of data in different directions. Preferably, a weighted average method is used to optimize the data fusion process by calculating the local information entropy in different directions. The specific formula is:

[0116] ;

[0117] in, represents the water vapor concentration after fusion; and Represents the weight factors in vertical and horizontal directions; is the local information entropy, reflecting the uncertainty of data at different locations; It is the information entropy adjustment factor, which controls the balance during fusion. This formula introduces information entropy, which enables more precise control of data fusion methods in all directions during the data fusion process, thereby enhancing the reliability and accuracy of monitoring data.

[0118] Further, step S530 includes step S531 to step S534.

[0119] Step S531, generating a water vapor virtual contour according to the adjusted interpolation weight, simulating the concentration change trend of water vapor by combining the geographical features, altitude and water vapor concentration gradient of the target area, constructing a continuous virtual contour in the interpolation area, and obtaining a virtual water vapor contour model;

[0120] Specifically, this step first uses geographical features (such as altitude, terrain slope, etc.) to construct a three-dimensional spatial framework. The change in water vapor concentration is not a simple linear relationship, but will be affected by complex terrain features. For example, the water vapor concentration in mountainous areas tends to decrease with increasing altitude, but due to changes in local airflow, the water vapor concentration in some areas may be higher, which requires that the particularity of local terrain be taken into account during interpolation calculations. This type of geographic data is usually obtained through an existing geographic information system (GIS). By performing elevation analysis on the topographic map of the target area, parameters such as altitude and slope are determined, which in turn affect the gradient change of water vapor concentration.

[0121] Next, by analyzing the gradient of water vapor concentration and combining it with the geographic information of the target area, the changing trend of water vapor concentration is incorporated into the model. The change in water vapor concentration in different regions is not evenly distributed. In some low-lying or humid areas, the water vapor concentration may be higher, while in plateaus or arid areas, the water vapor concentration is lower. Therefore, in this step, it is necessary to calculate the local rate of change of water vapor concentration and perform interpolation calculations based on these data to form a virtual water vapor concentration profile. The goal of this process is to smooth the local water vapor concentration data through interpolation methods to simulate a seamless and continuous water vapor concentration distribution.

[0122] Step S532: Based on the virtual water vapor contour model and in combination with water vapor concentration data at different height levels, a three-dimensional spatial intersection algorithm is used to identify overlapping areas between water vapor contours, and a weighted average of water vapor concentrations in the overlapping areas is calculated to obtain an overlapping area identification result;

[0123] It should be noted that, first of all, the use of the three-dimensional spatial intersection algorithm is to effectively identify the intersection of water vapor contours at different altitude levels. The vertical distribution of water vapor is non-uniform and is affected by airflow, temperature, pressure and other atmospheric conditions. Therefore, there are often overlapping areas in the distribution of water vapor concentrations at different altitude levels. In order to achieve this, it is first necessary to perform spatial mapping based on the water vapor concentration data in the virtual water vapor contour model, and regard the water vapor concentration as a variable in the three-dimensional coordinate space. In this process, altitude, longitude and latitude constitute the basic coordinate system of the three-dimensional space.

[0124] Next, the water vapor concentration data at different height levels are processed using a three-dimensional spatial intersection algorithm. The algorithm calculates the intersection area of ​​the water vapor concentration contours between each level in three-dimensional space to find the geometry and position of these areas. Common three-dimensional spatial intersection algorithms include voxelization methods, gridding methods, and intersection algorithms (such as ray tracing algorithms or ray methods). These algorithms can determine which spatial regions overlap by calculating the volume intersection area of ​​the water vapor concentration contours, and further extract the spatial boundaries of the overlapping regions. Once the overlapping areas are identified, a weighted average calculation can be performed. The weighted average calculation is based on the relative importance of each water vapor concentration data point, and the weight is usually determined by the water vapor concentration size, the spatial volume of the area, or other environmental factors (such as temperature, humidity, etc.). Through weighted averaging, water vapor concentration data at different height levels can be integrated to avoid the limitations of single-level data, thereby obtaining a more accurate overall water vapor concentration value.

[0125] This step can accurately identify the intersection areas of water vapor concentrations at different levels in three-dimensional space, avoiding the limitations of traditional two-dimensional analysis methods, and is particularly suitable for complex atmospheric hierarchical structures. This precise spatial intersection analysis provides a larger spatial dimension for a deeper understanding of water vapor concentration.

[0126] Step S533: perform morphological prediction on the confirmed area according to the overlapping area recognition result, analyze the topological relationship between the existing water vapor data and the contour, and perform prediction and filling based on the topological rules to obtain a water vapor concentration distribution map after filling;

[0127] It can be understood that the topological relationship mainly refers to the interconnection and relative position relationship between different points or regions in space. In the distribution of water vapor concentration, the topological relationship reflects the continuity and regularity of the change of water vapor concentration, especially between multi-level water vapor contours. By analyzing the geometric form of these contours, the trend and characteristics of the change of water vapor concentration can be found. For example, water vapor concentration often shows a certain continuity, and the parts with higher water vapor concentration in the region usually have higher concentrations in the adjacent areas. Therefore, based on this topological relationship, the water vapor concentration data of certain areas can be inferred.

[0128] Step S534: perform spatial interpolation processing according to the filled water vapor concentration distribution map, and interpolate the water vapor concentration in the entire target area by using the three-dimensional Kriging interpolation method to obtain a three-dimensional interpolation result.

[0129] It should be noted that this process first analyzes the spatial structure and change trend of water vapor concentration in the target area, combines the horizontal and vertical data, and uses the semivariance model of Kriging interpolation to quantify the spatial dependence of water vapor concentration. The three-dimensional Kriging interpolation method can accurately consider the gradient change of water vapor concentration at different height levels. By calculating the covariance between known data points, Kriging interpolation assigns the optimal interpolation weight to each data point, so that the water vapor concentration of unobserved points in the target area can be inferred by weighted average. This method not only improves the spatial prediction accuracy of water vapor concentration, but also can effectively handle the spatial distribution of water vapor concentration in areas with complex terrain and changeable climate. Especially in places with significant terrain differences such as mountainous areas or plateaus, three-dimensional interpolation can better cope with the influence of vertical gradients and geographical features, and provide more reliable water vapor concentration data. Through this interpolation process, the final generated three-dimensional water vapor concentration distribution map can provide high-precision spatial data support for atmospheric monitoring, climate research and meteorological forecasting, ensuring the accuracy and reliability of water vapor concentration.

[0130] Further, step S533 includes step S5331 to step S5334.

[0131] Step S5331: perform a topological relationship analysis between water vapor data and contours based on the overlapping area recognition result, and identify and extract the connection relationship, boundary features and topological structure between adjacent areas by constructing the topological structure of the water vapor concentration distribution map to obtain a topological analysis result;

[0132] Specifically, the purpose of topological analysis is to understand the spatial interconnection and relative position of different water vapor concentration areas, especially how adjacent areas are connected through boundaries and how the changing trend of water vapor concentration affects the interface of these areas. In order to achieve this goal, it is first necessary to define the regional boundaries in the water vapor concentration distribution map. These boundaries are not just simple physical boundaries, but are inferred through the change of water vapor concentration gradient and the distribution of water vapor sources. These boundaries can be determined by analyzing the spatial change rate, change direction and cluster distribution pattern of water vapor concentration data. Once the boundaries are identified, the topological relationship between different water vapor concentration areas can be further revealed through topological analysis algorithms, such as adjacency matrix, graph theory methods, etc. Preferably, this embodiment further understands the flow pattern of water vapor concentration between these areas by analyzing whether there is a connection between these areas, whether the connection is through adjacent boundaries or through certain water vapor concentration transition areas. Through topological analysis, the key path and possible change trend of water vapor concentration changes can be extracted, thereby providing an important basis for subsequent filling prediction, interpolation processing and water vapor concentration correction. This step not only helps to identify the physical contact points between areas, but also reveals the spatial relationship between water vapor concentrations at these points.

[0133] Step S5332: Perform feature extraction processing according to the topological analysis result, and construct an abstract map of morphological features of the missing area by identifying the boundary, connectivity and local morphological features;

[0134] It is understandable that, first of all, topological analysis has revealed the connectivity, boundary characteristics and local morphological characteristics between the regions in the water vapor concentration distribution map. Next, it is necessary to dig deeper into these structural characteristics in order to better understand and reconstruct the water vapor concentration distribution in the missing areas. In this process, by extracting boundary features, it can be clarified which areas are transition zones of water vapor concentration and which areas are stable areas with relatively gentle concentration changes. Generally, areas or boundaries with large changes in water vapor concentration will form certain morphological characteristics, such as "band-like" or "patch-like" characteristics, which will directly affect the strategy for filling in missing areas.

[0135] Secondly, the analysis of connectivity can help determine which areas are the source or sink of water vapor concentration, especially under complex terrain or climate change, where water vapor distribution often shows a certain degree of non-uniformity. By identifying these connectivity features, it is possible to predict the flow path of water vapor between different areas and determine how the missing areas should be connected to the existing areas.

[0136] The extraction of local morphological features mainly focuses on the water vapor concentration gradient and change trend in the local area, especially the sudden change or smooth transition of water vapor concentration in a small area. The change of local morphological features is usually closely related to geographical features (such as natural boundaries such as mountains and rivers) and climate characteristics (such as humidity changes, wind direction, etc.). By abstracting these local morphological features, a more detailed graphical representation can be generated to reflect the change trend of water vapor concentration in the missing area.

[0137] Therefore, the ultimate goal of feature extraction is to integrate the change trend, boundary characteristics and connectivity information of local water vapor concentration by constructing an abstract map of the morphological features of the missing area, so as to provide an accurate morphological basis for subsequent prediction and filling. This step can not only improve the understanding of the missing area, but also help to infer how the area should be consistent with the water vapor concentration change pattern of the surrounding area, making the entire water vapor concentration distribution map more realistic and coherent.

[0138] Step S5333: perform morphological fitting processing according to the morphological feature abstract graph, combine the existing water vapor data with the topological relationship, use a similarity-based matching algorithm to fit the neighborhood water vapor concentration trend, generate a fitting morphology of the missing area, and obtain a fitting result;

[0139] It is understandable that at this stage, the existing water vapor data provides the specific distribution of water vapor concentration in the target area, while the topological relationship provides the spatial and structural characteristics of the water vapor distribution. Therefore, based on this existing information, a water vapor concentration distribution that conforms to the overall pattern can be inferred in the missing area.

[0140] The specific implementation process is to compare and match the water vapor concentration trend of the missing area with the concentration trend of the surrounding known areas through a similarity matching algorithm. In this process, the algorithm analyzes the change pattern of water vapor concentration in the neighborhood and finds the area or trend that is most similar to the missing area. The definition of similarity may include factors such as spatial location, concentration gradient, and surrounding environment (such as topography and climate). Through quantitative analysis of these factors, the algorithm can fit a reasonable water vapor concentration trend so that the water vapor distribution in the missing area is consistent with the surrounding area.

[0141] The technical effect of morphological fitting is that it not only fills in the water vapor concentration data in the missing area, but also ensures that the fitted concentration distribution maintains a natural and consistent change trend in space. Through fitting, the water vapor concentration distribution in the missing area can be smoothly transitioned, while also avoiding abrupt concentration jumps, ensuring the continuity and authenticity of the water vapor distribution. This processing process combines the similarity-based algorithm with the physical characteristics of water vapor distribution, enhancing the prediction accuracy and reliability of the filled area.

[0142] Step S5334: fill in the missing area according to the fitting result to generate a filled water vapor concentration distribution map.

[0143] In practical applications, this step first generates a continuous water vapor concentration distribution map based on the fitting results. This image covers the water vapor concentration data at all height levels in the target area. By fusing the concentration trend obtained by fitting with other known concentration data in the target area, a seamless water vapor concentration image can be obtained. In this process, the algorithm automatically adjusts the concentration value of the missing area according to the fitting trend and ensures a smooth and seamless transition between the filled area and the surrounding area. In particular, the filled water vapor concentration data must not only consider spatial consistency, but also follow physically reasonable change rules. Embodiment 2:

[0144] like Figure 2 As shown, this embodiment provides a satellite-based atmospheric water vapor detection system, the system comprising:

[0145] An acquisition module 901 is used to acquire satellite remote sensing data and solar radiation angle data of a target area, where the satellite remote sensing data includes radiation data collected by at least two satellites at different orbits and sensor angles;

[0146] Correction module 902, used to perform correction processing based on satellite remote sensing data and solar radiation angle data, by constructing a solar radiation projection model and correcting the radiation error caused by the sunshine angle, to obtain corrected radiation data;

[0147] The simulation module 903 is used to establish an atmospheric radiation transmission model according to the corrected radiation data, and generate radiation simulation data by simulating the influence of water vapor on the absorption, scattering and transmission of radiation signals during the propagation of radiation in the atmosphere;

[0148] The inversion module 904 is used to perform water vapor inversion processing according to the radiation simulation data, and calculate the quantitative relationship between the radiation intensity and the spectral absorption characteristics of water vapor, and combine the absorption coefficient of water vapor with the radiation path length to infer the vertical distribution data of water vapor in the atmosphere;

[0149] The interpolation module 905 is used to perform stereo interpolation processing based on the vertical distribution data, calculate the change rate of water vapor concentration at different height levels in the target area, adjust the interpolation weight based on the height difference, and combine the vertical and horizontal interpolation to obtain three-dimensional water vapor monitoring data.

[0150] In some embodiments disclosed in this application, the correction module 902 includes:

[0151] The first correction unit is used to calculate the quantitative relationship between the intensity of ground reflection and the solar radiation angle by using a nonlinear regression method based on satellite remote sensing data and solar radiation angle data, and to construct a ground reflection model;

[0152] The second correction unit is used to establish the geometric relationship of solar radiation projected on the ground and calculate the change of radiation intensity under different sunshine angles according to the ground reflection model by analyzing the interaction between solar radiation and the ground surface, so as to obtain the radiation projection geometric model;

[0153] The third correction unit is used to perform model construction processing according to the ground reflection model and the radiation projection geometric model, use the local weighted regression algorithm to simulate the change of radiation intensity under different ground features and sunshine angles, and perform radiation projection calculation in space to construct a solar radiation projection model;

[0154] The fourth correction unit corrects the satellite remote sensing data based on the solar radiation projection model to obtain corrected radiation data.

[0155] In some embodiments disclosed in the present application, the simulation module 903 includes:

[0156] The first simulation unit is used to construct a model framework according to the corrected radiation data, set the optical thickness, absorption coefficient and scattering coefficient of the atmosphere according to the atmospheric characteristics of the target area, and define the coefficients based on the atmospheric components to obtain the radiation transfer equation, which includes the absorption, scattering, transmission and emission processes of radiation;

[0157] The second simulation unit is used to quantitatively calculate the absorption of the radiation signal in the atmosphere by using the spectral band integration method based on the radiation transfer equation and the relationship between water vapor concentration and radiation path, and adjust the absorption coefficient in the equation to obtain the absorption-corrected radiation transfer equation;

[0158] The third simulation unit is used to simulate the scattering effect of water vapor and aerosol on radiation by introducing a two-way scattering distribution function according to the absorption-corrected radiation transfer equation, calculate the influence of the scattering angle on the radiation intensity, and adjust the scattering term in the equation to obtain the scattering-corrected radiation transfer equation;

[0159] The fourth simulation unit is used to simulate the radiation transmission process in different atmospheric layers according to the scattering-corrected radiation transfer equation, combined with the influence of different altitude levels, atmospheric temperature and pressure on transmittance, to construct an atmospheric radiation transfer model, and calculate radiation simulation data based on the atmospheric radiation transfer model. Embodiment 3:

[0160] Corresponding to the above method embodiment, this embodiment also provides a satellite-based atmospheric water vapor detection device. The satellite-based atmospheric water vapor detection device described below and the satellite-based atmospheric water vapor detection method described above can refer to each other.

[0161] Figure 3 FIG. 8 is a block diagram of a satellite-based atmospheric water vapor detection device 800 according to an exemplary embodiment. Figure 3 As shown, the satellite-based atmospheric water vapor detection device 800 may include: a processor 801 and a memory 802. The satellite-based atmospheric water vapor detection device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0162] The processor 801 is used to control the overall operation of the satellite-based atmospheric water vapor detection device 800 to complete all or part of the steps in the above-mentioned satellite-based atmospheric water vapor detection method. The memory 802 is used to store various types of data to support the operation of the satellite-based atmospheric water vapor detection device 800, and these data may include, for example, instructions for any application or method operating on the satellite-based atmospheric water vapor detection device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the satellite-based atmospheric water vapor detection device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0163] In an exemplary embodiment, a satellite-based atmospheric water vapor detection device 800 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processing Device, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned satellite-based atmospheric water vapor detection method.

[0164] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned satellite-based atmospheric water vapor detection method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by a processor 801 of a satellite-based atmospheric water vapor detection device 800 to complete the above-mentioned satellite-based atmospheric water vapor detection method.

[0165] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A satellite-based atmospheric water vapor detection method, characterized in that: include: Acquire satellite remote sensing data and solar radiation angle data of the target area, wherein the satellite remote sensing data includes radiation data collected by at least two satellites at different orbits and sensor angles; Correction processing is performed according to the satellite remote sensing data and the solar radiation angle data, by constructing a solar radiation projection model and correcting the radiation error caused by the sunshine angle to obtain corrected radiation data; Establishing an atmospheric radiation transmission model based on the corrected radiation data, and generating radiation simulation data by simulating the influence of water vapor on the absorption, scattering and transmission of radiation signals during the propagation of radiation in the atmosphere; Performing water vapor inversion processing according to the radiation simulation data, calculating the quantitative relationship between the radiation intensity and the spectral absorption characteristics of water vapor, and combining the absorption coefficient of water vapor with the radiation path length, to infer the vertical distribution data of water vapor in the atmosphere; Performing stereo interpolation processing according to the vertical distribution data, by calculating the rate of change of water vapor concentration at different height levels in the target area, adjusting the interpolation weight based on the height difference, and combining the interpolation in the vertical and horizontal directions to obtain stereo water vapor monitoring data; The correction processing is performed according to the satellite remote sensing data and the solar radiation angle data, and the corrected radiation data is obtained by constructing a solar radiation projection model and correcting the ground reflection, scattering and radiation transmittance errors caused by the sunlight angle, including: According to the satellite remote sensing data and the solar radiation angle data, by analyzing the variation law of ground reflectivity, combining the surface type and spectral characteristics, a nonlinear regression method is used to calculate the quantitative relationship between the intensity of ground reflection and the solar radiation angle, and a ground reflection model is constructed; According to the ground reflection model, by analyzing the interaction between solar radiation and the ground surface, the geometric relationship of solar radiation projected on the ground is established and the change of radiation intensity under different sunlight angles is calculated to obtain the radiation projection geometric model; Model building is performed according to the ground reflection model and the radiation projection geometric model, a local weighted regression algorithm is used to simulate the change of radiation intensity under different ground features and sunshine angles, and radiation projection calculation is performed in space to construct a solar radiation projection model; The satellite remote sensing data is corrected based on the solar radiation projection model to obtain corrected radiation data.

2. The satellite-based atmospheric water vapor detection method according to claim 1, characterized in that: An atmospheric radiation transmission model is established based on the corrected radiation data, and radiation simulation data is generated by simulating the influence of water vapor on the absorption, scattering and transmission of radiation signals during the propagation of radiation in the atmosphere, including: A model framework is constructed according to the corrected radiation data, the optical thickness, absorption coefficient and scattering coefficient of the atmosphere are set using the atmospheric characteristics of the target area, and coefficients are defined based on the atmospheric components to obtain a radiation transfer equation, wherein the radiation transfer equation includes the absorption, scattering, transmission and emission processes of radiation; Based on the radiation transfer equation, combined with the relationship between water vapor concentration and radiation path, the absorption of the radiation signal in the atmosphere is quantitatively calculated by the spectral band integration method, and the absorption coefficient in the equation is adjusted to obtain the absorption-corrected radiation transfer equation; According to the absorption-corrected radiation transfer equation, a two-way scattering distribution function is introduced to simulate the scattering effect of water vapor and aerosol on radiation, the influence of the scattering angle on the radiation intensity is calculated, and the scattering term in the equation is adjusted to obtain a scattering-corrected radiation transfer equation; According to the scattering-corrected radiation transfer equation, combined with the effects of different altitude levels, atmospheric temperature and pressure on transmittance, the radiation transmission process in different atmospheric layers is simulated to construct an atmospheric radiation transfer model, and radiation simulation data is calculated based on the atmospheric radiation transfer model.

3. The satellite-based atmospheric water vapor detection method according to claim 1, characterized in that: Water vapor inversion is performed based on the radiation simulation data. By calculating the quantitative relationship between the radiation intensity and the spectral absorption characteristics of water vapor and combining the absorption coefficient of water vapor with the radiation path length, the vertical distribution data of water vapor in the atmosphere is inferred, including: Performing spectral absorption characteristic analysis based on the radiation simulation data, combining the radiation intensity of different bands in the satellite remote sensing data with the absorption characteristics of water vapor in a specific band, and using a regression analysis method to fit the spectral absorption coefficient of water vapor; The path calculation is performed according to the spectral absorption coefficient of the water vapor, by establishing a radiation propagation path model, combining the satellite viewing angle, the solar radiation incident angle and the refraction effect of the atmosphere, and using layer-by-layer integration to calculate the propagation path of the radiation signal in the atmosphere to obtain a path calculation result; According to the path calculation results, by combining the water vapor concentration of each atmospheric layer with the corresponding path length, the radiation absorption effect is accumulated layer by layer using the recursive method to obtain the relationship between the water vapor absorption intensity and the path length at different height levels, and establish a quantitative relationship between the vertical distribution of water vapor and radiation absorption; The vertical distribution of water vapor is inverted according to the quantitative relationship, and the vertical distribution data of water vapor in the atmosphere is calculated by matching the radiation signal intensity with the spectral absorption characteristics of water vapor.

4. The satellite-based atmospheric water vapor detection method according to claim 1, characterized in that: The vertical distribution data is subjected to stereo interpolation processing, by calculating the rate of change of water vapor concentration at different height levels in the target area, adjusting the interpolation weight based on the height difference, and combining the vertical and horizontal interpolation to obtain the three-dimensional water vapor monitoring data, including: Calculating the concentration change rate according to the vertical distribution data, and obtaining the water vapor concentration change rate by calculating the gradient of water vapor concentration between different height levels in the target area; The weight is adjusted according to the water vapor concentration change rate, and the weight of each height level in the vertical interpolation process is adjusted based on the elevation change by taking the water vapor concentration change rate as a function to obtain an adjusted interpolation weight; Performing stereo interpolation processing according to the adjusted interpolation weights, and performing spatial interpolation processing by using a three-dimensional Kriging interpolation method in combination with the geographical features and water vapor concentration gradient of the target area to obtain a stereo interpolation result; Data fusion processing is performed according to the stereo interpolation result, and the interpolation data in the vertical and horizontal directions are integrated to generate stereoscopic layered water vapor monitoring data.

5. The satellite-based atmospheric water vapor detection method according to claim 4, characterized in that: The three-dimensional interpolation processing is performed according to the adjusted interpolation weights, and the three-dimensional Kriging interpolation method is used to combine the geographical features and water vapor concentration gradient of the target area to perform spatial interpolation processing to obtain a three-dimensional interpolation result, including: Generate a water vapor virtual contour according to the adjusted interpolation weights, simulate the concentration change trend of water vapor by combining the geographical features, altitude and water vapor concentration gradient of the target area, construct a continuous virtual contour in the interpolation area, and obtain a virtual water vapor contour model; According to the virtual water vapor contour model, combined with water vapor concentration data at different height levels, a three-dimensional spatial intersection algorithm is used to identify overlapping areas between water vapor contours, and a weighted average of water vapor concentrations in the overlapping areas is calculated to obtain an overlapping area identification result; According to the overlapped area recognition result, the morphology of the confirmed area is predicted, and the topological relationship between the existing water vapor data and the contour is analyzed, and the prediction and filling are performed based on the topological rules to obtain the filled water vapor concentration distribution map; A spatial interpolation process is performed according to the filled water vapor concentration distribution map, and the water vapor concentration is interpolated in the entire target area by using a three-dimensional Kriging interpolation method to obtain a three-dimensional interpolation result.

6. The satellite-based atmospheric water vapor detection method according to claim 5, characterized in that: According to the overlapped area recognition result, the morphology of the confirmed area is predicted, and the topological relationship between the existing water vapor data and the contour is analyzed, and the prediction and filling are performed based on the topological rules to obtain the filled water vapor concentration distribution map, including: Performing a topological relationship analysis between water vapor data and contours according to the overlapping area recognition result, identifying and extracting the connection relationship, boundary features and topological structure between adjacent areas by constructing a topological structure of the water vapor concentration distribution map, and obtaining a topological analysis result; Performing feature extraction processing according to the topological analysis results, and constructing an abstract map of morphological features of the missing area by identifying boundaries, connectivity and local morphological features; Performing morphological fitting processing according to the morphological feature abstract graph, combining the existing water vapor data with the topological relationship, using a similarity-based matching algorithm to fit the neighborhood water vapor concentration trend, generating a fitting morphology of the missing area, and obtaining a fitting result; The missing area is filled according to the fitting result to generate a filled water vapor concentration distribution map.

7. A satellite-based atmospheric water vapor detection system, characterized in that: include: An acquisition module is used to acquire satellite remote sensing data and solar radiation angle data of a target area, wherein the satellite remote sensing data includes radiation data collected by at least two satellites at different orbits and sensor angles; A correction module, used for performing correction processing according to the satellite remote sensing data and the solar radiation angle data, by constructing a solar radiation projection model and correcting the radiation error caused by the sunshine angle, to obtain the corrected radiation data; A simulation module is used to establish an atmospheric radiation transmission model according to the corrected radiation data, and generate radiation simulation data by simulating the influence of water vapor on the absorption, scattering and transmission of radiation signals during the propagation of radiation in the atmosphere; An inversion module is used to perform water vapor inversion processing according to the radiation simulation data, calculate the quantitative relationship between the radiation intensity and the spectral absorption characteristics of water vapor, and combine the absorption coefficient of water vapor with the radiation path length to infer the vertical distribution data of water vapor in the atmosphere; An interpolation module is used to perform stereo interpolation processing according to the vertical distribution data, calculate the change rate of water vapor concentration at different height levels in the target area, adjust the interpolation weight based on the height difference, and combine the vertical and horizontal interpolation to obtain three-dimensional water vapor monitoring data; Wherein, the correction module comprises: A first correction unit is used to calculate the quantitative relationship between the intensity of ground reflection and the solar radiation angle by using a nonlinear regression method based on the satellite remote sensing data and the solar radiation angle data, by analyzing the variation law of ground reflectivity, combining the surface type and spectral characteristics, and constructing a ground reflection model; A second correction unit is used to establish a geometric relationship of solar radiation projected on the ground and calculate the change of radiation intensity under different sunshine angles according to the ground reflection model by analyzing the interaction between solar radiation and the ground surface, so as to obtain a radiation projection geometric model; A third correction unit is used to perform model construction processing according to the ground reflection model and the radiation projection geometric model, simulate the change of radiation intensity under different ground features and sunshine angles by using a local weighted regression algorithm, and perform radiation projection calculation in space to construct a solar radiation projection model; The fourth correction unit corrects the satellite remote sensing data based on the solar radiation projection model to obtain corrected radiation data.

8. The satellite-based atmospheric water vapor detection system according to claim 7, characterized in that: The simulation module comprises: A first simulation unit is used to construct a model framework according to the corrected radiation data, set the optical thickness, absorption coefficient and scattering coefficient of the atmosphere by using the atmospheric characteristics of the target area, and define coefficients based on the atmospheric components to obtain a radiation transfer equation, wherein the radiation transfer equation includes the absorption, scattering, transmission and emission processes of radiation; The second simulation unit is used to quantitatively calculate the absorption of the radiation signal in the atmosphere by using a spectral band integration method based on the radiation transfer equation and in combination with the relationship between water vapor concentration and radiation path, and adjust the absorption coefficient in the equation to obtain an absorption-corrected radiation transfer equation; A third simulation unit is used to simulate the scattering effect of water vapor and aerosol on radiation by introducing a bidirectional scattering distribution function according to the absorption-corrected radiation transfer equation, calculate the influence of the scattering angle on the radiation intensity, and adjust the scattering term in the equation to obtain a scattering-corrected radiation transfer equation; The fourth simulation unit is used to simulate the radiation transmission process in different atmospheric layers according to the scattering-corrected radiation transmission equation and the influence of different altitude levels, atmospheric temperature and pressure on transmittance, so as to construct an atmospheric radiation transmission model, and calculate radiation simulation data based on the atmospheric radiation transmission model.

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