General Rayleigh scattering calculation method and system for water color remote sensing

Abstract

The invention provides a general Rayleigh scattering calculation method and system for water color remote sensing, and the method comprises the steps: forming a Rayleigh scattering data set based on observation conditions and atmospheric parameter data, and inputting the data set into a machine learning method for training, thereby obtaining a Rayleigh scattering prediction model; and finally obtaining observation conditions and atmospheric parameter data of each wave band and each pixel of the target remote sensing image, inputting the observation conditions and the atmospheric parameter data into the trained prediction model, and calculating to obtain Rayleigh scattering of each wave band of each pixel of the target remote sensing image. The method and the system disclosed by the invention are universal, do not aim at specific sensors, and are suitable for all existing and future water color remote sensors. And meanwhile, the method has the advantages of high efficiency and high precision, and errors caused by a lookup table method in a linear interpolation process are avoided.

Description

Technical Field

[0001] The invention belongs to the field of remote sensing, and in particular relates to a universal Rayleigh scattering calculation method and system for water color remote sensing. Background Art

[0002] The calculation of Rayleigh scattering radiance is a crucial step in the atmospheric correction of remote sensing images, which is usually achieved by using a single scattering algorithm or solving the radiation transfer equation. Internationally, a variety of numerical calculation methods have been developed for the problem of radiation transfer between the atmosphere and the ocean, including the discrete ordinate method, the invariant embedding method, the finite element method, the successive method, and the Monte Carlo simulation method. Based on the radiation transfer theory and numerical calculation methods, radiation transfer models such as DISORT, 6S, PolRadTran, MOMO, COART, and PCOART have been developed at home and abroad.

[0003] In water color remote sensing, since water body signals are weak and easily affected by the atmosphere, in order to extract high-precision water-leaving radiation information from remote sensing images, the error of Rayleigh scattering radiance calculation must be controlled within 1%. Therefore, the calculation of Rayleigh scattering radiance needs to be achieved by solving the accurate radiation transfer equation.

[0004] However, the solution process of the radiation transfer equation is complicated and time-consuming. In the actual atmospheric correction of water color satellite remote sensing data, in order to improve the computational efficiency, the correction of Rayleigh scattering usually adopts a lookup table method based on radiation transfer simulation, that is, for a specific water color satellite sensor, the radiation transfer equation under different solar angles and observation angles in each band is solved in advance, and a lookup table file is generated based on the calculation results. In image processing, according to the solar and observation geometry conditions of each pixel at the time of transit, the most similar pre-calculated results are found in the lookup table, and then the Rayleigh scattering radiance values ​​corresponding to each pixel in each band are obtained by linear interpolation. However, this method has certain limitations. On the one hand, the lookup table is designed for a specific remote sensor and is not universal; on the other hand, the use of the lookup table cannot avoid the errors introduced by linear interpolation, which affects the calculation accuracy. Therefore, there is currently a lack of an efficient, fast, and universal Rayleigh scattering contribution calculation method suitable for various water color satellite sensors. Summary of the invention

[0005] The present invention provides a universal Rayleigh scattering calculation method and system for water color remote sensing, so as to solve the problem that the existing lookup table method is not universal. In order to solve the above technical problems, the embodiments of the present invention disclose the following technical solutions: One aspect of the present invention provides a universal Rayleigh scattering calculation method for water color remote sensing, comprising: Inputting multiple sets of preset observation conditions and atmospheric parameter data into the radiation transfer model PCOART respectively, and calculating the corresponding Rayleigh scattering Stokes vector data, wherein the observation condition data at least includes the solar zenith angle, the relative azimuth and the observation zenith angle, and the atmospheric parameter data at least includes the Rayleigh scattering optical thickness of atmospheric molecules; Based on the observation conditions and atmospheric parameter data and the corresponding Rayleigh scattering Stokes vector data, an XGBoost machine learning model is trained to obtain a Rayleigh scattering prediction model; The observation conditions and atmospheric parameter data of the target remote sensing image are obtained and input into the Rayleigh scattering prediction model to obtain the Rayleigh scattering Stokes vector data of each pixel in the target remote sensing image.

[0006] Optionally, the step of inputting a plurality of sets of preset observation conditions and atmospheric parameter data into the radiation transfer model PCOART to calculate the corresponding Rayleigh scattering Stokes vector data includes: Taking values ​​of the atmospheric molecule Rayleigh scattering optical thickness values ​​in equal steps with a first preset step length within a first set range to obtain a set of atmospheric molecule Rayleigh scattering optical thickness values; Taking values ​​of the solar zenith angle in equal steps with a second preset step length within a second set range to obtain a solar zenith angle value set; Taking values ​​of the relative azimuth in equal steps with a third preset step length within a third setting range to obtain a relative azimuth value set; Preset the value of the observation zenith angle; Based on the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set, the relative azimuth value set and the preset observation zenith angle, different observation conditions and atmospheric parameters are combined to obtain multiple sets of different observation conditions and atmospheric parameter data; Each set of observation conditions and atmospheric parameter data is input into the radiation transfer model PCOART to obtain the corresponding Rayleigh scattering Stokes vector, which is composed of an I component, a Q component and a U component.

[0007] Optionally, the method combines different observation conditions and atmospheric parameters based on the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set, the relative azimuth value set and the preset observation zenith angle to obtain multiple sets of different observation conditions and atmospheric parameter data, including: The values ​​in the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set and the relative azimuth value set are arranged and combined with the value of the observation zenith angle to form multiple sets of observation conditions and atmospheric parameter data.

[0008] Optionally, the training of an XGBoost machine learning model based on the observation conditions and atmospheric parameter data and corresponding Rayleigh scattering Stokes vector data to obtain a Rayleigh scattering prediction model includes: All groups of observation conditions and atmospheric parameter data and corresponding Rayleigh scattering Stokes vector data are divided into training sets and test sets according to the preset ratio; The Rayleigh scattering optical thickness of atmospheric molecules, solar zenith angle, relative azimuth and observation zenith angle are used as input features, and the I component, Q component and U component of the Rayleigh scattering Stokes vector are used as output features. The training set is used to train the XGBoost machine learning model to obtain the Rayleigh scattering prediction model. The test set is used to test the trained Rayleigh scattering prediction model.

[0009] Optionally, the acquisition of observation conditions and atmospheric parameter data of the target remote sensing image and inputting them into a Rayleigh scattering prediction model to obtain Rayleigh scattering Stokes vector data of each pixel in the target remote sensing image includes: Obtain the Rayleigh scattering optical thickness values ​​of atmospheric molecules in each band in the target remote sensing image; Obtain the solar zenith angle, relative azimuth and observation zenith angle of each pixel in the target remote sensing image; The Rayleigh scattering optical thickness values ​​of atmospheric molecules in each band and the solar zenith angle, relative azimuth and observation zenith angle of each pixel are input into the Rayleigh scattering prediction model to obtain the Rayleigh scattering Stokes vector data corresponding to each pixel in the target remote sensing image in each band.

[0010] Optionally, the step of obtaining the atmospheric molecule Rayleigh scattering optical thickness values ​​of each band in the target remote sensing image includes: The equivalent atmospheric molecule Rayleigh scattering optical thickness value of each band is calculated using the following formula: Among them, band is the band of the remote sensing sensor, is the wavelength of the band, is the optical thickness of Rayleigh scattering of atmospheric molecules in the band, is the solar irradiance at the pre-acquired average sun-earth distance, obtained from the remote sensing image header file, is the band response function of the band.

[0011] Optionally, the obtaining of the solar zenith angle, relative azimuth and observation zenith angle of each pixel in the target remote sensing image includes: The nearest neighbor method is used to interpolate the solar zenith angle, observation zenith angle and relative azimuth data of the target remote sensing image according to the preset resolution to obtain the solar zenith angle, relative azimuth and observation zenith angle of each pixel.

[0012] Optional, The first setting range is from 0.001 to 0.8, and the first preset step is 0.001; The second setting range is from 0 degrees to 70 degrees, and the second preset step is 1 degree; The third setting range is from 0 degrees to 180 degrees, and the third preset step is 10 degrees; The observed zenith angle is taken as the zero of the Legendre polynomial on the interval (0,1).

[0013] Another aspect of the present invention provides a universal Rayleigh scattering calculation system for water color remote sensing, which executes the universal Rayleigh scattering calculation method for water color remote sensing provided in the aforementioned aspect.

[0014] The present invention discloses a universal Rayleigh scattering calculation method and system for water color remote sensing. First, a Rayleigh scattering data set is formed based on observation conditions and atmospheric parameter data. Then, the data set is input into a machine learning method for training to obtain a Rayleigh scattering prediction model. Finally, the observation conditions and atmospheric parameter data of each band and each pixel of the target remote sensing image are obtained, and the data are input into the trained prediction model to calculate the Rayleigh scattering of each pixel and each band of the target remote sensing image. The method and system disclosed in the present invention are universal, not for a specific sensor, and are applicable to all existing and future water color remote sensing sensors. At the same time, it has the advantages of high efficiency and high precision, and avoids the errors caused by the lookup table method in the linear interpolation process.

[0015] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the disclosure, nor is it intended to limit the scope of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present disclosure.

[0017] Figure 1 A schematic diagram of a flow chart of a universal Rayleigh scattering calculation method for water color remote sensing provided by an embodiment of the present invention; Figure 2 An implementation provided for an embodiment of the present invention Figure 1 Schematic diagram of the process of step S100; Figure 3 An implementation provided for an embodiment of the present invention Figure 1 Schematic diagram of the process of step S200; Figure 4 An implementation provided for an embodiment of the present invention Figure 1 Schematic diagram of the process of step S300. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0019] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] Figure 1 A schematic diagram of a flow chart of a universal Rayleigh scattering calculation method for water color remote sensing provided by an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps: Step S100: inputting a plurality of sets of preset observation conditions and atmospheric parameter data into the radiation transfer model PCOART respectively, and calculating and obtaining corresponding Rayleigh scattering Stokes vector data.

[0021] The observation condition data at least include the solar zenith angle, relative azimuth and observation zenith angle, and the atmospheric parameters at least include the Rayleigh scattering optical thickness of atmospheric molecules.

[0022] In one embodiment disclosed in the present invention, Figure 2 As shown, step S100 may be implemented in the following manner: Step S101: taking values ​​of the atmospheric molecule Rayleigh scattering optical thickness values ​​in equal steps with a first preset step length within a first set range to obtain a set of atmospheric molecule Rayleigh scattering optical thickness values.

[0023] The optical thickness of atmospheric molecules is one of the important parameters affecting Rayleigh scattering, reflecting the atmosphere's ability to scatter and absorb light. The larger the value, the stronger the scattering effect on light when it propagates in the atmosphere.

[0024] In a specific embodiment disclosed in the present invention, the first setting range is 0.001 to 0.8, and the first preset step size is 0.001, so as to obtain a set of equally spaced atmospheric molecular Rayleigh scattering optical thickness data, ensuring coverage from the ultraviolet band to the short-wave infrared band, while avoiding omissions or error accumulation in numerical calculations. In addition, dense step size selection helps to obtain smoother results during model calculations and more accurately capture the influence trend of atmospheric molecular Rayleigh scattering optical thickness on the scattering Stokes vector.

[0025] Step S102: taking values ​​of the solar zenith angle in equal steps with a second preset step length within a second set range to obtain a set of solar zenith angle values.

[0026] The solar zenith angle determines the angle at which sunlight enters the atmosphere, which directly affects the directionality and intensity variation of Rayleigh scattering. For a smaller solar zenith angle, sunlight is nearly vertically incident, and the path through the atmosphere is shorter, so the scattering effect is weaker; while for a larger solar zenith angle, the incident angle of sunlight is more inclined, the path through the atmosphere becomes longer, and the scattering effect is enhanced accordingly.

[0027] In a specific embodiment disclosed in the present invention, the second setting range is 0 degrees to 70 degrees, and the second preset step is 1 degree, covering different solar incidence angles from near noon to near sunset, and providing a sufficiently dense angle distribution to improve calculation accuracy.

[0028] Step S103: taking values ​​of the relative azimuth in equal steps with a third preset step length within a third set range to obtain a relative azimuth value set.

[0029] The relative azimuth describes the angular relationship between the observation direction and the incident direction of sunlight. The polarization characteristics of Rayleigh scattering will change significantly at different relative azimuths.

[0030] In a specific embodiment disclosed in the present invention, the third setting range is from 0 degrees to 180 degrees, and the third preset step length is 10 degrees, so as to ensure that the calculated data covers all possible scattering geometric relationships.

[0031] Step S104: pre-set the value of the observation zenith angle.

[0032] In a specific embodiment disclosed in the present invention, the observed zenith angle is the zero point of the Legendre polynomial in the interval (0,1).

[0033] Step S105: Based on the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set, the relative azimuth value set and the preset observation zenith angle, different observation conditions and atmospheric parameters are combined to obtain multiple groups of different observation conditions and atmospheric parameter data.

[0034] In one embodiment disclosed in the present invention, step S105 may be implemented in the following manner: The values ​​in the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set and the relative azimuth value set are arranged and combined with the value of the observation zenith angle to form multiple sets of observation conditions and atmospheric parameter data.

[0035] In order to obtain complete Rayleigh scattering characteristic data, it is necessary to fully combine the above four parameters, that is, to combine each value of the Rayleigh scattering optical thickness of atmospheric molecules with all values ​​of the solar zenith angle in turn, and then further combine it with all relative azimuth values, and finally combine it with all observation zenith angle values ​​to form all possible combinations of observation conditions.

[0036] For example, multiple loops are used to iterate over different parameter values, or matrix operations are used to generate all the combined data at once. Through the permutation and combination method, the changing trend of Rayleigh scattering under different conditions can be fully analyzed.

[0037] Step S106: input each set of observation conditions and atmospheric parameter data into the radiation transfer model PCOART to obtain the corresponding Rayleigh scattering Stokes vector.

[0038] After completing the permutations and combinations of different observation conditions and atmospheric parameters, each set of data will be sent to the PCOART radiation transfer model as input parameters to calculate the Rayleigh scattering Stokes vector under the corresponding conditions.

[0039] The Rayleigh scattering Stokes vector consists of the I component (total radiance), the Q component and the U component (two orthogonal components of linear polarization degree). Among them, the I component (light intensity) represents the total intensity of the scattered light, that is, the brightness of the light received in the observation direction, which is affected by the atmospheric optical thickness, the solar zenith angle, the relative azimuth and the observation zenith angle. When the optical thickness is large, the scattered light intensity is usually high, and under different solar zenith angles and azimuth conditions, the distribution of light intensity may be different.

[0040] The Q component (the first component of the degree of polarization) describes the degree of polarization of light in the horizontal-vertical direction. In Rayleigh scattering, when the scattering angle is close to 90°, the linear polarization degree of the scattered light is usually high, and the polarization direction is orthogonal to the scattering plane. Therefore, the value of the Q component will show different positive and negative distributions as the scattering angle changes, reflecting the polarization direction of the scattered light.

[0041] The U component (the second component of the degree of polarization) represents the degree of polarization of light in the ±45° direction. Its value depends on the rotation of the observation direction relative to the scattering plane, and together with the Q component, it determines the polarization direction of the scattered light.

[0042] In the actual calculation process, each set of observation conditions and atmospheric parameter data will affect the Stokes vector of Rayleigh scattering, and the PCOART model will calculate the corresponding I, Q, and U component values ​​based on the input parameters. By analyzing the calculation results of a large number of different parameter combinations, the polarization characteristic distribution of Rayleigh scattering under different atmospheric conditions can be obtained.

[0043] Step S200: Train an XGBoost machine learning model based on observation conditions and atmospheric parameter data and corresponding Rayleigh scattering Stokes vector data to obtain a Rayleigh scattering prediction model.

[0044] In one embodiment disclosed in the present invention, Figure 3 As shown, step S200 may be implemented in the following manner: Step S201: Divide all groups of observation conditions and atmospheric parameter data and corresponding Rayleigh scattering Stokes vector data into a training set and a test set according to a preset ratio.

[0045] For example, all groups of observation conditions and atmospheric parameter data, and the corresponding Stokes vector data can be randomly divided into a training set (80%) and a test set (20%) in a ratio of 8:2. The training set is used to train the XGBoost model to learn the characteristic relationship of Rayleigh scattering, while the test set is used to evaluate the generalization ability of the trained model on unseen data. This data division method can effectively prevent the model from overfitting and ensure that it has good prediction performance. When randomly dividing data, cross-validation and other techniques can be used to ensure the uniformity of the data so that the model can learn effective rules under different conditions.

[0046] Step S202: The Rayleigh scattering optical thickness of atmospheric molecules, the solar zenith angle, the relative azimuth and the observation zenith angle are used as input features, the I component, Q component and U component of the Rayleigh scattering Stokes vector are used as output features, and the training set is used to train the XGBoost machine learning model to obtain a Rayleigh scattering prediction model.

[0047] XGBoost is a machine learning algorithm based on the gradient boosting decision tree (GBDT) with strong nonlinear fitting capabilities and can handle high-dimensional and nonlinear data well. During the training process, the XGBoost model can learn the complex mapping relationship between input features (optical thickness of atmospheric molecule Rayleigh scattering, solar zenith angle, relative azimuth, observation zenith angle) and output variables (Stokes vector components I, Q, U), enabling the model to predict Rayleigh scattering characteristics under new observation conditions.

[0048] Step S203: using the test set to test the trained Rayleigh scattering prediction model.

[0049] The trained Rayleigh scattering prediction model is evaluated using the test set. For example, the mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R 2 ) and other indicators to measure the model's predictive ability and generalization performance. In addition, the difference between the true value and the predicted value can be compared through visualization methods (such as scatter plots and residual analysis) to determine whether the model has systematic deviations and make corresponding adjustments, such as optimizing model hyperparameters, increasing training data, or adjusting feature engineering strategies.

[0050] Step S300: Obtain observation conditions and atmospheric parameter data of the target remote sensing image and input them into a Rayleigh scattering prediction model to obtain Rayleigh scattering Stokes vector data of each pixel in the target remote sensing image.

[0051] In one embodiment disclosed in the present invention, Figure 4 As shown, step S300 can be implemented in the following manner: Step S301: Obtain the atmospheric molecule Rayleigh scattering optical thickness values ​​of each band in the target remote sensing image.

[0052] In the disclosed embodiments of the present invention, the remote sensing image acquisition by Sentinel-2 A / B satellite is taken as an example. Sentinel-2A / B satellite carries a multispectral sensor (MSI), and the response characteristics of each band are different. The equivalent atmospheric molecular Rayleigh scattering optical thickness of each band can be calculated according to the band response function provided by ESA.

[0053] Each band of a remote sensing sensor is not a single wavelength, but covers a certain spectral range, and the wavelength response within this range is different. Therefore, it is necessary to use the band response function Optical Depth of Rayleigh Scattering of Atmospheric Molecules at Different Wavelengths Perform weighted averaging to obtain the equivalent atmospheric molecular Rayleigh scattering optical thickness of each band .

[0054] The equivalent atmospheric molecule Rayleigh scattering optical thickness value of each band can be calculated using the following formula: Among them, band is the band of the remote sensing sensor, is the wavelength of the band, is the Rayleigh scattering optical thickness value of the atmospheric molecules in the band, is the solar irradiance at the pre-acquired average sun-earth distance, obtained from the remote sensing image header file, is the band response function of the band.

[0055] Step S302: Obtain the solar zenith angle, relative azimuth and observation zenith angle of each pixel in the target remote sensing image.

[0056] In one embodiment disclosed in the present invention, the solar zenith angle, observation zenith angle and relative azimuth data of the target remote sensing image are interpolated according to a preset resolution using the nearest neighbor method to obtain the solar zenith angle, relative azimuth angle and observation zenith angle of each pixel.

[0057] In remote sensing image processing, the solar zenith angle, relative azimuth and observation zenith angle are usually provided by the image metadata. In order to obtain the corresponding value of each pixel of the remote sensing image, these angle parameters need to be interpolated according to the spatial resolution of the target remote sensing image.

[0058] In this embodiment, the nearest neighbor interpolation method is used to resample the observation angle data to match the pixel resolution of the remote sensing image. The advantages of the nearest neighbor interpolation method are simple calculation, and the ability to maintain the discrete characteristics of the original data, avoiding non-physical smoothing of the angle data. After interpolation, each pixel will have accurate solar zenith angle, relative azimuth and observation zenith angle information for Rayleigh scattering calculation.

[0059] Step S303: Input the Rayleigh scattering optical thickness value of atmospheric molecules in each band and the solar zenith angle, relative azimuth and observation zenith angle of each pixel into the Rayleigh scattering prediction model to obtain the Rayleigh scattering Stokes vector data corresponding to each pixel in the target remote sensing image in each band.

[0060] After obtaining the observed geometric parameters of each pixel, they are input into the Rayleigh scattering prediction model (i.e., a machine learning model trained based on XGBoost) together with the calculated equivalent atmospheric molecular Rayleigh scattering optical thickness. For each pixel, the model will predict its corresponding Rayleigh scattering Stokes vector (I, Q, U) in each band of Sentinel-2 A / B.

[0061] The embodiment of the present invention further provides a universal Rayleigh scattering calculation system for water color remote sensing, which can execute the universal Rayleigh scattering calculation method for water color remote sensing proposed in the above embodiment.

[0062] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A universal Rayleigh scattering calculation method for water color remote sensing, characterized in that: include: Inputting multiple sets of preset observation conditions and atmospheric parameter data into the radiation transfer model PCOART respectively, and calculating the corresponding Rayleigh scattering Stokes vector data, wherein the observation condition data at least includes the solar zenith angle, the relative azimuth and the observation zenith angle, and the atmospheric parameter data at least includes the Rayleigh scattering optical thickness of atmospheric molecules; Based on the observation conditions and atmospheric parameter data and the corresponding Rayleigh scattering Stokes vector data, an XGBoost machine learning model is trained to obtain a Rayleigh scattering prediction model; The observation conditions and atmospheric parameter data of the target remote sensing image are obtained and input into the Rayleigh scattering prediction model to obtain the Rayleigh scattering Stokes vector data of each pixel in the target remote sensing image.

2. The calculation method according to claim 1, characterized in that: The method of inputting a plurality of preset observation conditions and atmospheric parameter data into the radiation transfer model PCOART to calculate the corresponding Rayleigh scattering Stokes vector data includes: Taking values ​​of the atmospheric molecule Rayleigh scattering optical thickness values ​​in equal steps with a first preset step length within a first set range to obtain a set of atmospheric molecule Rayleigh scattering optical thickness values; Taking values ​​of the solar zenith angle in equal steps with a second preset step length within a second set range to obtain a solar zenith angle value set; Taking values ​​of the relative azimuth in equal steps with a third preset step length within a third setting range to obtain a relative azimuth value set; Preset the value of the observation zenith angle; Based on the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set, the relative azimuth value set and the preset observation zenith angle, different observation conditions and atmospheric parameters are combined to obtain multiple sets of different observation conditions and atmospheric parameter data; Each set of observation conditions and atmospheric parameter data is input into the radiation transfer model PCOART to obtain the corresponding Rayleigh scattering Stokes vector, which is composed of an I component, a Q component and a U component.

3. The calculation method according to claim 2, characterized in that: The method combines different observation conditions and atmospheric parameters based on the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set, the relative azimuth value set and the preset observation zenith angle to obtain multiple groups of different observation conditions and atmospheric parameter data, including: The values ​​in the atmospheric molecule Rayleigh scattering optical thickness value set, the solar zenith angle value set and the relative azimuth value set are arranged and combined with the value of the observation zenith angle to form multiple sets of observation conditions and atmospheric parameter data.

4. The calculation method according to claim 3, characterized in that: The XGBoost machine learning model is trained based on the observation conditions and atmospheric parameter data and the corresponding Rayleigh scattering Stokes vector data to obtain a Rayleigh scattering prediction model, including: All groups of observation conditions and atmospheric parameter data and corresponding Rayleigh scattering Stokes vector data are divided into training sets and test sets according to the preset ratio; The Rayleigh scattering optical thickness of atmospheric molecules, solar zenith angle, relative azimuth and observation zenith angle are used as input features, and the I component, Q component and U component of the Rayleigh scattering Stokes vector are used as output features. The training set is used to train the XGBoost machine learning model to obtain the Rayleigh scattering prediction model. The test set is used to test the trained Rayleigh scattering prediction model.

5. The calculation method according to claim 1, characterized in that: The acquisition of observation conditions and atmospheric parameter data of the target remote sensing image and inputting them into the Rayleigh scattering prediction model to obtain Rayleigh scattering Stokes vector data of each pixel in the target remote sensing image includes: Obtain the Rayleigh scattering optical thickness values ​​of atmospheric molecules in each band in the target remote sensing image; Obtain the solar zenith angle, relative azimuth and observation zenith angle of each pixel in the target remote sensing image; The Rayleigh scattering optical thickness values ​​of atmospheric molecules in each band and the solar zenith angle, relative azimuth and observation zenith angle of each pixel are input into the Rayleigh scattering prediction model to obtain the Rayleigh scattering Stokes vector data corresponding to each pixel in the target remote sensing image in each band.

6. The calculation method according to claim 5, characterized in that: The step of obtaining the atmospheric molecule Rayleigh scattering optical thickness values ​​of each band in the target remote sensing image includes: The equivalent atmospheric molecule Rayleigh scattering optical thickness value of each band is calculated using the following formula: Among them, band is the band of the remote sensing sensor, is the wavelength of the band, is the optical thickness of Rayleigh scattering of atmospheric molecules in the band, is the solar irradiance at the pre-acquired average sun-earth distance, obtained from the remote sensing image header file, is the band response function of the band.

7. The calculation method according to claim 5, characterized in that: The step of obtaining the solar zenith angle, relative azimuth and observation zenith angle of each pixel in the target remote sensing image comprises: The nearest neighbor method is used to interpolate the solar zenith angle, observation zenith angle and relative azimuth data of the target remote sensing image according to the preset resolution to obtain the solar zenith angle, relative azimuth and observation zenith angle of each pixel.

8. The calculation method according to claim 2, characterized in that: The first setting range is from 0.001 to 0.8, and the first preset step is 0.001; The second setting range is from 0 degrees to 70 degrees, and the second preset step is 1 degree; The third setting range is from 0 degrees to 180 degrees, and the third preset step is 10 degrees; The observed zenith angle is taken as the zero of the Legendre polynomial on the interval (0,1).

9. A universal Rayleigh scattering calculation system for water color remote sensing, characterized in that: The system executes the method according to any one of claims 1-8.

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