Atmospheric correction method and device for radiation signal of coastal water area under solar zenith angle

Abstract

The invention provides an atmospheric correction method and device for a solar zenith angle lower coastal water area radiation signal. The method provided by the invention comprises the following steps: generating noon remote sensing reflectivity satellite data based on observation data of a coastal water area by utilizing an ultraviolet-near infrared atmospheric correction combination model; utilizing a maximum cross correlation method to obtain adjusted noon remote sensing reflectivity satellite data based on the spatial resolution and the time resolution of the data; calculating a Rayleigh correction radiance based on a Rayleigh scattering lookup table corresponding to the observation data, and matching the Rayleigh correction radiance with the adjusted noon remote sensing reflectivity satellite data based on time information and position information to obtain a training set; and inputting the to-be-corrected coastal water area radiation signal into the model trained based on the training set, and determining the off-water radiance of the radiation signal. According to the method and the device provided by the invention, atmospheric correction of a coastal water area radiation signal at a high solar zenith angle is realized for a turbid coastal water area.

Description

Technical Field

[0001] The present application relates to the field of remote sensing and atmospheric correction technology, and in particular to a method and device for atmospheric correction of radiation signals of coastal waters at the solar zenith angle. Background Art

[0002] Since gases and aerosols in the atmosphere will scatter and absorb the radiation signals received by the sensor, the information of the target object in the image will be disturbed and distorted. Through atmospheric correction, the influence of these atmospheric effects can be removed or reduced, and the radiation values ​​observed by the sensor can be converted into the true reflectivity of the ground object. This can ensure that the remote sensing data reflects the surface characteristics more accurately, thereby improving the reliability and accuracy of applications such as land cover, water quality monitoring and ecosystem analysis. Especially in water remote sensing, accurate atmospheric correction is of great significance for distinguishing water bodies and suspended matter and monitoring water quality changes.

[0003] At present, the radiation signal is usually observed by geostationary ocean color sensors, which observe once every hour from dawn to dusk, and inevitably encounter the challenge of extremely high solar zenith angles in the morning and evening. Especially in winter, more than 50% of the data at the central observation location of the Global Ocean Observing Indicator-II are collected at high solar zenith angles (greater than 70 degrees), and there are more observations at high latitudes under extreme solar zenith angles. This high solar zenith angle observation of the geostationary satellite ocean color sensor greatly affects the reflectivity of the top of the atmosphere. It is worth noting that with the increase of the solar zenith angle, the atmospheric path radiance increases due to the contribution of Rayleigh scattering and aerosol scattering, and the specular reflection of the sea surface also increases when the amount of light penetrating the water body decreases rapidly. Therefore, the backscattered signal carrying water body information will decrease. Due to the weak illumination at high solar zenith angles, the optical path of Rayleigh scattering and aerosol scattering is extended, and the bidirectional reflection effect is enhanced. In addition, due to the increased influence of the earth's curvature, the traditional atmospheric correction mode is not very suitable for high solar zenith angle observations.

[0004] The atmospheric correction model used in the traditional near-infrared iterative scheme has inaccurate estimates of aerosol radiance at high solar zenith angles (SZA), resulting in a large loss of effective products from geostationary satellite ocean color sensors when observing at dawn or dusk. To address this problem, an atmospheric correction model was developed that is applicable to the open ocean observed by the first geostationary satellite Ocean Color Imager (GOCI) at high solar zenith angles. The model is built based on a stable open ocean dataset. Due to the large dynamic changes in water properties and the lack of reliable measurements during the midday period, the model is not applicable to turbid coastal areas and is not very suitable for coastal waters. Therefore, there is an urgent need for a method to achieve atmospheric correction of coastal water radiation signals at high solar zenith angles for turbid coastal waters. Summary of the invention

[0005] In view of this, the present application provides an atmospheric correction method and device for coastal water radiation signals at a solar zenith angle, so as to achieve atmospheric correction of coastal water radiation signals at a high solar zenith angle for turbid coastal waters.

[0006] Specifically, the present application is implemented through the following technical solutions: The first aspect of the present application provides an atmospheric correction method for coastal waters radiation signal under the solar zenith angle, the method comprising: Using an ultraviolet-near infrared atmospheric correction combined model, noon remote sensing reflectance satellite data are generated based on the observation data of coastal waters; the noon remote sensing reflectance satellite data represent the water-leaving radiance of the radiation signal in the coastal waters; Using a maximum cross-correlation method, based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data, pixels in the noon remote sensing reflectivity satellite data with a flow velocity exceeding a flow velocity threshold are deleted to obtain adjusted noon remote sensing reflectivity satellite data; Determine Rayleigh scattering radiance based on the Rayleigh scattering lookup table corresponding to the observation data, calculate Rayleigh corrected radiance based on the Rayleigh scattering radiance, and match the Rayleigh corrected radiance with the adjusted noon remote sensing reflectance satellite data based on time information and location information to obtain a training set; The ultraviolet-near-infrared atmospheric correction combined model is trained based on the training set, the Rayleigh-corrected radiance corresponding to the coastal water radiation signal to be corrected is input into the trained ultraviolet-near-infrared atmospheric correction combined model, and the water-leaving radiance of the radiation signal is determined based on the output noon remote sensing reflectance satellite data.

[0007] The second aspect of the present application provides an atmospheric correction device for coastal waters radiation signals under the solar zenith angle, the device comprising a generation module, an adjustment module, a matching module and a determination module; wherein, The generating module is used to generate noon remote sensing reflectance satellite data based on the observation data of the coastal waters by using the ultraviolet-near infrared atmospheric correction combined model; the noon remote sensing reflectance satellite data represents the water-leaving radiance of the radiation signal in the coastal waters; The adjustment module is used to delete pixels in the noon remote sensing reflectivity satellite data whose flow velocity exceeds a flow velocity threshold value based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data by using a maximum cross correlation method to obtain adjusted noon remote sensing reflectivity satellite data; The matching module is used to determine the Rayleigh scattering radiance based on the Rayleigh scattering lookup table corresponding to the observation data, calculate the Rayleigh corrected radiance based on the Rayleigh scattering radiance, and match the Rayleigh corrected radiance with the adjusted noon remote sensing reflectance satellite data based on time information and location information to obtain a training set; The determination module is used to train the ultraviolet-near infrared atmospheric correction combined model based on the training set, input the Rayleigh corrected radiance corresponding to the coastal water radiation signal to be corrected into the trained ultraviolet-near infrared atmospheric correction combined model, and determine the water-leaving radiance of the radiation signal based on the output noon remote sensing reflectance satellite data.

[0008] The atmospheric correction method and device for the radiation signal of coastal waters under the solar zenith angle provided by the present application, in the first aspect, by combining the near-infrared atmospheric correction model and the ultraviolet atmospheric correction model, the observation data of the coastal waters are processed based on the ultraviolet-near infrared atmospheric correction combined model to generate noon remote sensing reflectivity satellite data. Since the coastal waters include both clear waters and turbid waters, this not only takes advantage of the near-infrared atmospheric correction model to better consider the influence of suspended matter and other particulate matter in the water body on the radiation signal when processing turbid waters, but also improves the accuracy of the correction; and also takes advantage of the ultraviolet atmospheric correction model being suitable for the correction of clear waters, which can effectively process the optical properties of the water body and ensure the accurate reflectivity under the condition of small light absorption. By combining the two models, various water body conditions can be processed, the accuracy of noon remote sensing reflectivity satellite data can be improved, and the adaptability is strong. The most suitable model can be selected for processing according to the characteristics of the actual observation data, so as to obtain more reliable noon remote sensing reflectivity satellite data. In addition, this ultraviolet-near infrared atmospheric correction combined model can be applied to different types of waters, not limited to specific clear or turbid waters, so that it has a wider applicability. Secondly, the flow velocity of each pixel is calculated based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data, and pixels with flow velocities exceeding the flow velocity threshold are eliminated. This can effectively filter out large flow pixels and ensure that within the temporal resolution of the noon remote sensing reflectivity satellite data, pixels with slower flow velocities remain in the original pixel blocks, thereby avoiding rapid changes in water bodies caused by strong water flows. By eliminating pixels with faster flow velocities, the impact of instantaneous changes caused by water flows on the analysis of observation data is reduced, thereby improving the stability of the data. Pixels with slower flow velocities usually represent a more stable state of the water body, which helps to more accurately reflect the optical properties and other relevant information of the water body. In addition, the flow velocity threshold can be flexibly adjusted according to different water characteristics to adapt to a variety of environmental conditions and application requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1A flow chart of Embodiment 1 of the atmospheric correction method for coastal waters radiation signal at the solar zenith angle provided by the present application; Figure 2 This is a schematic structural diagram of Embodiment 1 of the atmospheric correction device for coastal water radiation signals at the solar zenith angle provided in the present application. DETAILED DESCRIPTION

[0010] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0011] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0012] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0013] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0014] Figure 1 This is a flow chart of the first embodiment of the atmospheric correction method for the coastal waters radiation signal under the solar zenith angle provided by this application. Figure 1 , the method provided in this embodiment may include: S101. Generate noon remote sensing reflectance satellite data based on observation data of coastal waters using an ultraviolet-near infrared atmospheric correction combined model; the noon remote sensing reflectance satellite data characterizes the water-leaving radiance of the radiation signal in the coastal waters.

[0015] Specifically, the ultraviolet-near infrared atmospheric correction combined model is used to perform atmospheric correction on the radiation signal of the coastal waters, and it determines the water-leaving emissivity of the radiation signal in the coastal waters based on the observation data of the coastal waters. Among them, the water-leaving emissivity removes the water body reflection characteristics affected by the atmosphere, and can more accurately reflect the spectral characteristics of the water body. The ultraviolet-near infrared atmospheric correction combined model includes an input layer, a first hidden layer, a second hidden layer and an output layer connected in sequence. Among them, the activation function of the first hidden layer and the second hidden layer is a hyperbolic tangent activation function.

[0016] Furthermore. Coastal waters are usually turbid waters. The observation data of coastal waters include water body reflectance, pigment concentration, suspended matter concentration, water temperature data, meteorological related data, etc. Noon remote sensing reflectance satellite data refers to the remote sensing reflectance satellite data at noon. At noon, the sun is near the zenith, the solar zenith angle is usually small (close to 0°), and the incident angle of sunlight is close to vertical, which can minimize the shadow effect and the impact of light changes, provide more stable observation conditions, and the solar radiation intensity at noon is usually high, which can increase the signal strength of the reflectance and reduce the impact of atmospheric scattering on the reflectance. Therefore, the noon remote sensing reflectance satellite data is closer to the real radiation signal of the water body itself, and can better represent the water-leaving radiance of the radiation signal in coastal waters.

[0017] In a specific implementation, the method of using the ultraviolet-near infrared atmospheric correction combined model to generate noon remote sensing reflectance satellite data based on the observation data of coastal waters includes: (1) The input layer receives the observation data, preprocesses the observation data, and passes the preprocessed observation data to the first hidden layer; the preprocessed observation data includes observation angle information and Rayleigh-corrected radiances of multiple bands.

[0018] Specifically, the preprocessed observation data includes observation angle information (solar zenith angle, observation zenith angle, etc.) and Rayleigh-corrected radiances of multiple bands.

[0019] In specific implementation, the input layer receives observation data of coastal waters detected by remote sensing instruments (usually including radiation signals of water bodies and related observation information), extracts observation angle information from the observation data, and uses the Rayleigh scattering correction algorithm to correct the original radiation signal, removes the influence caused by atmospheric scattering, and obtains Rayleigh-corrected radiances of multiple bands. Finally, the extracted observation angle information and Rayleigh-corrected radiance are standardized or normalized, and the preprocessed observation data are passed to the first hidden layer.

[0020] (2) Each neuron in the first hidden layer performs weighted summation on the preprocessed observation data to extract features of the preprocessed observation data, and each neuron outputs first features of different scales, which are passed to the second hidden layer. The first features represent the correlation between the observation angle information and the Rayleigh-corrected radiance.

[0021] Specifically, the first hidden layer includes multiple neurons, each neuron is used to extract the first feature of the observed data at a scale, and the first feature extracted by each neuron is different. The first feature characterizes the correlation between the observation angle information and the Rayleigh-corrected radiance in the observed data, reflecting the data characteristics under different observation conditions.

[0022] In the specific implementation, each neuron in the first hidden layer performs weighted summation on the preprocessed observation data based on the neuron weight (the neuron weight of each neuron is obtained through training and learning, and the neuron weights of different neurons are different), and applies the hyperbolic tangent activation function to perform nonlinear changes on the result of the weighted summation, introduce nonlinear features, and obtain the first features of different scales.

[0023] (3) Each neuron in the second hidden layer performs weighted summation and transformation on the first features of different scales output by the first hidden layer, and each neuron in the second hidden layer outputs a second feature, and the scales of the second features output by each neuron are different. The second feature is passed to the output layer; the second feature characterizes the correlation between the observation angle information and the Rayleigh-corrected radiance, and the data accuracy of the second feature is higher than that of the first feature.

[0024] Specifically, the second hidden layer is similar to the first hidden layer, and the second hidden layer also includes multiple neurons, each neuron is used to extract the second feature at a deeper level of the first feature, and the size of the second feature extracted by each neuron is different. The second feature also characterizes the correlation between the observation angle information and the Rayleigh-corrected radiance in the observation data. Compared with the first feature, the second feature has higher data accuracy and more deeply reflects the data characteristics under different observation conditions.

[0025] In specific implementation, each neuron in the second hidden layer performs weighted summation of the first features of different scales output by the first hidden layer based on the neuron weight, applies a hyperbolic tangent activation function to perform nonlinear changes on the result of the weighted summation, introduces nonlinear features, and obtains second features of different scales.

[0026] (4) The output layer performs weighted summation on each second feature and obtains the noon remote sensing reflectance satellite data output by each neuron through nonlinear transformation; the number of the neurons is the same as the number of the bands.

[0027] Specifically, the number of neurons in the output layer is the same as the number of bands contained in the input observation data, that is, each neuron corresponds to the reflectance output of one band, and the output layer can process information of multiple bands at the same time.

[0028] In specific implementation, multiple neurons in the output layer perform weighted summation on the second features of each scale respectively, and apply nonlinear transformation (such as softmax transformation or sigmoid transformation) to convert the summation result into noon remote sensing reflectance satellite data, so as to ensure that the output noon remote sensing reflectance satellite data is within a reasonable range, and obtain the noon remote sensing reflectance satellite data output by multiple neurons.

[0029] Optionally, the ultraviolet-near infrared atmospheric correction combined model includes a near infrared atmospheric correction model and an ultraviolet atmospheric correction model. The near infrared atmospheric correction model adopts the dark pixel assumption, that is, the water-offset emissivity of the near infrared band is zero, resulting in lower accuracy in highly turbid waters. The ultraviolet atmospheric correction model adopts the dark pixel assumption of the ultraviolet band, that is, the water-offset emissivity of the near ultraviolet band is zero, which is suitable for turbid waters but not for clear ocean waters. Since in this application, coastal waters include both clear waters and turbid waters, the near infrared atmospheric correction model and the ultraviolet atmospheric correction model are combined to generate noon remote sensing reflectance satellite data.

[0030] The method of using the ultraviolet-near infrared atmospheric correction combined model to generate noon remote sensing reflectance satellite data based on the observation data of coastal waters includes: using the near infrared atmospheric correction model to calculate the noon remote sensing reflectance satellite data of the first band based on the observation data of the first band, and obtaining the noon remote sensing reflectance satellite data of the second band based on the noon remote sensing reflectance satellite data of the first band; the wavelength of the second band is greater than that of the first band; the proportion of clear water in the noon remote sensing reflectance satellite data of the second band is greater than that of turbid water; using the ultraviolet atmospheric correction model to calculate the noon remote sensing reflectance satellite data of the third band; the wavelength of the third band is less than that of the first band; the proportion of turbid water in the noon remote sensing reflectance satellite data of the third band is greater than that of clear water; fitting the first band, the second band, and the third band by a logistic regression method, determining the weight of the first band, the weight of the second band, and the weight of the third band, and weighted fusion of the noon remote sensing reflectance satellite data based on the weights to obtain the noon remote sensing reflectance satellite data.

[0031] Specifically, the first waveband, the second waveband, and the third waveband are set according to actual needs, and are not limited in this embodiment. It should be noted that the wavelength of the third waveband is less than the wavelength of the first waveband and less than the wavelength of the second waveband. The first waveband and the second waveband are usually near-infrared wavebands, and the third waveband is usually ultraviolet wavebands.

[0032] In the specific implementation, the observation data of the first band is used, and the near-infrared atmospheric correction model is applied to calculate the noon remote sensing reflectance satellite data of the first band. Since the near-infrared band is sensitive to the optical properties of the water body, the atmospheric influence can be effectively removed. Based on the noon remote sensing reflectance satellite data of the first band, the noon remote sensing reflectance satellite data of the second band is derived using an appropriate algorithm (for example, the color ratio method or model fitting) (realized by the correlation of spectral features). Further, the ultraviolet atmospheric correction model is used to process the observation data and calculate the noon remote sensing reflectance satellite data of the third band. Since the third band is an ultraviolet band, the relationship between its spectral characteristics and the water body and atmospheric components may be different, and the data of the ultraviolet band can provide different spectral information. The noon remote sensing reflectance satellite data of the first band, the second band and the third band are fitted using the logistic regression method to determine the weight of each band. Based on the determined weights, the noon remote sensing reflectance satellite data of the first band, the second band and the third band are weighted fused to obtain the final noon remote sensing reflectance satellite data. This weighted fusion better integrates information from different bands and improves the overall quality and reliability of the data.

[0033] S102, using a maximum cross correlation method, based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data, deleting pixels in the noon remote sensing reflectivity satellite data whose flow velocity exceeds a flow velocity threshold, to obtain adjusted noon remote sensing reflectivity satellite data.

[0034] Specifically, the flow rate threshold is set according to actual needs, and is not limited in this embodiment. For example, in one embodiment, the flow rate threshold is . The velocity threshold is determined based on the spatial resolution of the noon remote sensing reflectivity satellite data, which ranges from 250 meters for the secondary satellite point to 500 meters for the entire scene. This velocity threshold effectively filters out pixels with large flow rates, ensuring that pixels with slower flow rates remain in the original pixel blocks within the 1-hour time interval of the noon remote sensing reflectivity satellite data, thus avoiding rapid changes in the water body caused by strong water flow.

[0035] In a specific implementation, the maximum cross-correlation method is used to delete pixels in the noon remote sensing reflectivity satellite data whose flow velocity exceeds a flow velocity threshold based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data, including: determining a time interval based on the temporal resolution of the noon remote sensing reflectivity satellite data; spatially aligning the noon remote sensing reflectivity satellite data of adjacent time intervals; calculating the cross-correlation corresponding to pixels at the same position in the noon remote sensing reflectivity satellite data of the adjacent time intervals, and determining an actual distance based on a pixel displacement corresponding to a maximum value and the spatial resolution of the noon remote sensing reflectivity satellite data; and determining the flow velocity of each pixel based on the actual distance and the time interval.

[0036] Specifically, the adjacent time intervals (e.g., 1 hour) are determined according to the temporal resolution of the noon remote sensing reflectivity satellite data (e.g., the frequency of data acquisition). For all the noon remote sensing reflectivity satellite data, the noon remote sensing reflectivity satellite data of adjacent time intervals are spatially aligned (using image registration technology to correct the noon remote sensing reflectivity satellite data at different times so that the images of the same water body area at different times can be accurately aligned), ensuring that the noon remote sensing reflectivity satellite data acquired at adjacent time intervals correspond to the same position in space. Furthermore, for the pixels at the same position in the noon remote sensing reflectivity satellite data of adjacent time intervals, their cross-correlation is calculated (cross-correlation can quantify the similarity between two images), and the maximum cross-correlation value is determined, and the pixel displacement corresponding to the maximum cross-correlation value is determined as the actual pixel displacement. The product of the actual pixel displacement and the spatial resolution of the noon remote sensing reflectivity satellite data is determined as the actual distance, and the flow velocity of each pixel is calculated based on the quotient of the actual distance and the determined time interval.

[0037] It should be noted that the spatial resolution of noon remote sensing reflectivity satellite data is not an exact value, but changes dynamically within a variable range.

[0038] Optionally, the method also includes: determining the sensor type based on the characteristics of the coastal waters and the working band and characteristics of the sensor; determining the range of spatial resolution based on the spatial resolution of each working band of the sensor and external observation conditions; verifying the spatial resolution based on the resolvability of different features in the observation data, and adjusting the range of spatial resolution according to the verification results.

[0039] In the specific implementation, the characteristics of coastal waters (optical characteristics, turbidity, phytoplankton distribution, etc.) are considered to select appropriate sensors. For example, for water body monitoring, sensors with visible light, near infrared and ultraviolet bands may be selected to conduct a comprehensive analysis of water quality and biological characteristics. Based on the sensitivity, dynamic range, spectral resolution and other parameters of the selected sensor, further select suitable sensors. Based on the spatial resolution of each working band of the sensor, its applicability is analyzed, that is, the spatial resolution affects whether the water body and the surrounding environment can be clearly distinguished, and whether small water body features (such as floating objects and algae distribution) can be monitored. External factors such as atmospheric conditions, light intensity and marine environment are considered, which will affect the observation effect of the sensor. For example, in turbid water bodies, higher spatial resolution may be required to improve target detection capabilities. Further, according to the spectral characteristics and reflectance characteristics of different features (such as water bodies, floating objects, bottom topography, etc.), a resolution analysis is performed. The detection effect of different spatial resolutions on different water body features is tested. According to the detection results, if it is found that a specific feature cannot be effectively identified at the current spatial resolution, the resolution range needs to be adjusted. For example, if higher resolution is required to detect specific water quality changes, the sensor parameters need to be adjusted or a different sensor needs to be selected.

[0040] S103, determining the Rayleigh scattering radiance based on the Rayleigh scattering lookup table corresponding to the observation data, calculating the Rayleigh corrected radiance based on the Rayleigh scattering radiance, and matching the Rayleigh corrected radiance with the adjusted noon remote sensing reflectance satellite data based on time information and location information to obtain a training set.

[0041] Specifically, the Rayleigh scattering lookup table records the Rayleigh scattering radiance under different observation data, and different observation data corresponds to different time information and location information. The Rayleigh-corrected radiance is calculated based on the Rayleigh scattering radiance, and the Rayleigh-corrected radiance is equal to the total radiance minus the Rayleigh scattering radiance, the white-hat radiance, and the flash radiance. In this application, the ultraviolet-near-infrared atmospheric correction combined model performs atmospheric correction on the radiation signal of coastal waters based on the Rayleigh-corrected radiance. Therefore, the Rayleigh-corrected radiance must first be calculated based on the Rayleigh scattering radiance (the white-hat radiance and the flash radiance can be calculated according to standard procedures).

[0042] In a specific implementation, the process of determining the Rayleigh scattering lookup table includes: (1) Based on the observation data, calculating the Rayleigh optical thickness of each band in the observation data.

[0043] In specific implementation, the Rayleigh optical thickness of each band can be calculated according to the following formula: ; Among them, the For band The Rayleigh optical thickness of is the spectral response function; is the average extraterrestrial solar irradiance; For band The Rayleigh optical thickness.

[0044] (2) The Rayleigh atmosphere is divided into different altitude layers based on its physical properties, and the relevant parameters of each altitude layer are determined based on the optical properties, radiation transmission principles and climatic and environmental factors of the altitude layer.

[0045] Specifically, the physical properties of the Rayleigh atmosphere are different at different altitudes. In this step, the Rayleigh atmosphere is divided based on the physical properties of the Rayleigh atmosphere at different altitudes to obtain different altitude layers. The relevant parameters of each altitude layer (including observation zenith angle, solar zenith angle, wind speed, etc.) are determined according to the chemical characteristics, radiation transmission principles, and climate and environmental factors of each altitude layer.

[0046] As an optional embodiment, the Rayleigh atmosphere is divided based on the physical properties of the Rayleigh atmosphere to obtain different altitude layers, which specifically includes: determining multiple altitude thresholds according to the atmospheric mechanism, and performing a first division of the Rayleigh atmosphere according to the multiple altitude thresholds to obtain a first altitude division result; determining the boundary positions of each altitude layer in the first altitude division result as positions to be optimized; obtaining the climatic and environmental factors of each position point of the position to be optimized, and establishing an environmental factor distribution map of the boundary area; determining the radiation transmission influence margin of each point at the boundary position based on the environmental factor distribution map, adjusting the height of the corresponding point based on the influence margin, completing the optimization of the first altitude division result, and obtaining different altitude layers.

[0047] Specifically, the impact margin refers to the degree of influence of environmental factors (such as temperature, humidity, etc.) on radiation propagation.

[0048] In the specific implementation, firstly, based on the physical characteristics of the Rayleigh atmosphere (such as the law of change of atmospheric pressure, temperature, gas density, etc. with altitude), the appropriate altitude threshold is determined through known atmospheric data (such as the International Standard Atmosphere Model) or experimental data. Each altitude threshold represents an important physical change point, such as a temperature inversion layer or a key position of air pressure change. Further, according to the determined multiple altitude thresholds, the first division is performed to divide the Rayleigh atmosphere into multiple different altitude layers. The range of each altitude layer is defined by the upper and lower altitude thresholds, and the climate, air pressure, temperature and other environmental conditions of each layer can be considered relatively uniform within the same layer. In the first division result, each altitude layer has clear upper and lower boundaries. Then, these upper and lower boundaries are determined as the positions to be optimized. For each position to be optimized, the climate and environmental factors near the position are obtained, including temperature, air pressure, humidity, wind speed, cloud density, etc. The obtained climate and environmental factor data are visualized and drawn into an environmental factor distribution map. The distribution map shows how the climate characteristics at different positions (i.e., the boundary positions to be optimized) change, and indicates the potential impact of radiation transmission at each position. The environmental factor distribution map may include a temperature distribution map, an air pressure distribution map, a humidity distribution map, etc. Based on the environmental factor distribution map, a radiation transfer simulation is performed to calculate the radiation transfer influence margin at each position to be optimized. The radiation transfer intensity and influence of each position to be optimized are evaluated through a radiation transfer model (such as the radiation transfer equation RTT). A position with a large influence margin means that the environmental factors at this position have a greater influence on the radiation transfer, and the height layer boundary of this position may need to be adjusted. According to the calculated radiation transfer influence margin, adjust the height of the position to be optimized. If the environmental factors at a certain position have a greater influence on the radiation transfer, consider adjusting the height of the position upward or downward to optimize the radiation transfer effect. For example, if factors such as temperature and humidity have a greater influence on the radiation transmission at a certain boundary position, the height of the position can be adjusted to reduce the interference of the radiation transmission. By evaluating the influence margin, the height of each position to be optimized is gradually adjusted.

[0049] The method provided in this embodiment divides and optimizes the altitude layer based on the physical properties of the Rayleigh atmosphere. By introducing multiple altitude thresholds and optimizing on this basis, the understanding and modeling of the Rayleigh atmosphere structure can be significantly improved. The initial division is based on the mechanism of the atmosphere. By determining multiple altitude thresholds, the Rayleigh atmosphere is divided for the first time to form a preliminary altitude layer structure. However, the physical properties of the Rayleigh atmosphere are very complex, and simple preliminary divisions often cannot fully reflect the subtle differences in radiation transmission, climate change, etc. at different altitudes. Therefore, this preliminary division may not provide sufficient reference for accurate calculation. By optimizing the boundary positions of each altitude layer, the actual climate conditions can be better combined with the physical properties of the atmosphere, avoiding the possible neglect of environmental factors or overly simple assumptions in traditional methods. Specifically, by analyzing the climate and environmental factors at the boundary position and establishing an environmental factor distribution map, the influence margin of radiation transmission between different altitude layers can be accurately revealed. The concept of this margin means that in some areas, due to the influence of factors such as climate, humidity, and air pressure, the behavior of radiation transmission may change significantly. The optimized altitude layer division adjusts the height of each position according to the distribution of these environmental factors, thereby achieving an accurate description of the radiation transmission characteristics of different altitude layers. First, it improves the accuracy of atmospheric division, making the modeling of the atmosphere more in line with the actual situation, thereby improving the accuracy of the simulation results. For example, in applications such as satellite communications and remote sensing, the accuracy of the radiation transmission model directly affects the effect of signal transmission and the quality of data. Refined altitude layer division can effectively reduce the errors caused by inaccurate division and improve the reliability of transmission prediction. Secondly, this optimization method can flexibly respond to different climatic conditions in practical applications and enhance the adaptability of the model, especially in complex climatic environments. It can adapt to sudden meteorological changes by adjusting the boundary positions of each altitude layer in real time, thereby improving the ability to predict the characteristics of the atmosphere under extreme weather conditions.

[0050] For example, the Rayleigh atmosphere is divided into 32 altitude layers up to 100 km altitude, with each layer increasing by 1 km below 25 km, each layer increasing by 5 km from 25 to 50 km, and two upper layers (50 to 70 km and 70 to 100 km). The optical thickness of each altitude layer is proportional to the pressure curve of the US Standard Atmospheric Model. According to the chemical characteristics, radiation transfer principles and climate environmental factors of each altitude layer, the observation zenith angle (OZA) is set to 0 to 84.2 degrees, with an increment of about 0.5 degrees. The solar zenith angle (SZA) is set to 0 to 88 degrees, with an increment of 2 degrees. The wind speed is set from 0 milliseconds. -1 to 30 ms -1 , in increments of 5 milliseconds -1 .

[0051] (3) Inputting the Rayleigh optical thickness and related parameters into the ocean-atmosphere coupled radiation transfer model to construct a Rayleigh scattering lookup table.

[0052] In specific implementation, the ocean-atmosphere coupled radiation transfer model expands the vector radiation transfer equation through Fourier series to obtain an azimuth-independent equation; discretizes the solar zenith angle in the azimuth-independent equation, calculates the radiation transfer at each solar zenith angle, and obtains a vector radiation transfer matrix equation; solves the vector radiation transfer matrix equation using the doubling method to obtain the distribution of the vector radiation signal; Based on the reflection and refraction characteristics of light on the ocean-atmosphere interface and interference factors, the distribution of the vector radiation signal is adjusted, and a Rayleigh scattering lookup table is constructed based on the distribution.

[0053] Optionally, after inputting the Rayleigh optical thickness and related parameters into the ocean-atmosphere coupled radiation transfer model to construct a Rayleigh scattering lookup table, the method further includes: inputting the Rayleigh optical thickness and related parameters into a radiation transfer model based on a Monte Carlo method to construct a target Rayleigh scattering lookup table; comparing the difference between the Rayleigh scattering radiance of the Rayleigh scattering lookup table and the actual Rayleigh scattering radiance under different observation data in the target Rayleigh scattering lookup table, and adjusting the Rayleigh scattering lookup table based on the difference.

[0054] Specifically, a radiation transfer model based on the Monte Carlo method determines the scattering characteristics and reflection characteristics of photons based on the input Rayleigh optical thickness and related parameters; the photons are emitted from a detector, and the scattering of the photons in the atmosphere and the reflection on the sea surface are simulated based on the scattering characteristics and reflection characteristics of the photons; the relevant parameters of the scattering of the photons in the atmosphere are calculated based on a scattering phase matrix, and when the photons reach the sea surface, the relevant parameters of the reflection of the photons in the target direction are calculated based on the Fresnel formula; the target direction is consistent with the direction of the sun; and a target Rayleigh scattering lookup table is constructed based on the relevant parameters of the scattering of the photons in the atmosphere and the relevant parameters of the reflection of the photons in the target direction.

[0055] Furthermore, the differences between the Rayleigh scattering radiance of the Rayleigh scattering lookup table constructed based on the ocean-atmosphere coupled radiation transfer model and the actual Rayleigh scattering radiance under different observation data and the difference between the Rayleigh scattering radiance of the Rayleigh scattering lookup table constructed based on the radiation transfer model of the Monte Carlo method and the actual Rayleigh scattering radiance under different observation data are calculated respectively, and the differences between the two are compared, and the one with the smaller difference is selected as the Rayleigh scattering lookup table, and the Rayleigh scattering lookup table is adjusted based on the comparison result (the difference of the Rayleigh scattering lookup table constructed based on the ocean-atmosphere coupled radiation transfer model is smaller).

[0056] Further, the Rayleigh scattering radiance corresponding to the observed data is obtained based on the constructed Rayleigh scattering lookup table, and the Rayleigh corrected radiance is calculated based on the Rayleigh scattering radiance (the Rayleigh scattering radiance, white cap radiance and flash radiance are subtracted from the total radiance). After obtaining the Rayleigh corrected radiance and the adjusted noon remote sensing reflectance satellite data, before matching the two based on time information and location information, it is necessary to ensure that the change in the optical properties of the water body is minimal. In this case, the noon remote sensing reflectance satellite data must be uniform in space to eliminate the front area and stray clouds, and uniform in time to eliminate pixels with significant inherent changes in optical properties due to algae blooms or strong currents. Spatially, the spatial uniformity is evaluated by using the coefficient of variation within the pixel block, and the coefficient of variation is calculated based on the value of each pixel, the total number of pixels and the average value, and pixels with a coefficient of variation exceeding 0.15 are eliminated. In time, the coefficient of variation is calculated for a specified number of observations, and pixels with a coefficient of variation exceeding 0.15 are eliminated. After adjusting the spatial and temporal consistency of the noon remote sensing reflectivity satellite data, the adjusted noon remote sensing reflectivity satellite data is matched with the Rayleigh corrected radiance according to the time information (the time information of the two differs by a preset time), and the coefficient of variation is calculated for the matched data. Data with a coefficient of variation exceeding 0.15 are eliminated to obtain the training set (the input of the training set is the Rayleigh corrected radiance, and the output of the training set is the noon remote sensing reflectivity satellite data).

[0057] For example, the preset time is 3 hours. In this step, the Rayleigh corrected radiance at 07:15 is corresponded to the noon remote sensing reflectance satellite data at 10:15, the Rayleigh corrected radiance at 08:15 is corresponded to the noon remote sensing reflectance satellite data at 11:55, and the Rayleigh corrected radiance at 16:15 is corresponded to the noon remote sensing reflectance satellite data at 13:15.

[0058] S104, training the ultraviolet-near infrared atmospheric correction combined model based on the training set, inputting the Rayleigh-corrected radiance corresponding to the coastal waters radiation signal to be corrected into the trained ultraviolet-near infrared atmospheric correction combined model, and determining the water-leaving radiance of the radiation signal based on the output noon remote sensing reflectance satellite data.

[0059] In the specific implementation, the training set is divided into a training data set and a test data set according to a preset ratio. For example, 70% of the training set is used as a training data set to train the ultraviolet-near infrared atmospheric correction combination model, and 30% of the training set is used as a test data set to verify the performance of the trained ultraviolet-near infrared atmospheric correction combination model. In addition, during the training process, k-fold cross validation is used to prevent the trained model from overfitting. After obtaining the trained ultraviolet-near infrared atmospheric correction combination model, the corresponding Rayleigh scattering radiance and total radiance are determined based on the coastal waters radiation signal to be corrected, and then the corresponding Rayleigh corrected radiance is determined, and the Rayleigh corrected radiance corresponding to the coastal waters radiation signal to be corrected is input into the trained ultraviolet-near infrared atmospheric correction combination model, and the model outputs the corresponding noon remote sensing reflectance satellite data, that is, the water-leaving radiance of the coastal waters radiation signal to be corrected.

[0060] The method provided in this embodiment, in the first aspect, combines the near-infrared atmospheric correction model and the ultraviolet atmospheric correction model, processes the observation data of the coastal waters based on the ultraviolet-near-infrared atmospheric correction combined model, and generates noon remote sensing reflectance satellite data. Since the coastal waters include both clear waters and turbid waters, this not only takes advantage of the near-infrared atmospheric correction model in better considering the influence of suspended matter and other particulate matter in the water body on the radiation signal when processing turbid waters, but also improves the accuracy of the correction; and also takes advantage of the ultraviolet atmospheric correction model being suitable for the correction of clear waters, being able to effectively process the optical properties of the water body, and ensuring the advantage of obtaining accurate reflectance under conditions of small light absorption. By combining the two models, various water body conditions can be processed, the accuracy of noon remote sensing reflectance satellite data can be improved, and the adaptability is strong. The most suitable model can be selected for processing according to the characteristics of the actual observation data, so as to obtain more reliable noon remote sensing reflectance satellite data. In addition, this ultraviolet-near-infrared atmospheric correction combined model can be applied to different types of waters, not limited to specific clear or turbid waters, so that it has a wider range of applicability. Secondly, the flow velocity of each pixel is calculated based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data, and pixels with flow velocities exceeding the flow velocity threshold are eliminated. This can effectively filter out large flow pixels and ensure that within the temporal resolution of the noon remote sensing reflectivity satellite data, pixels with slower flow velocities remain in the original pixel blocks, thereby avoiding rapid changes in water bodies caused by strong water flows. By eliminating pixels with faster flow velocities, the impact of instantaneous changes caused by water flows on the analysis of observation data is reduced, thereby improving the stability of the data. Pixels with slower flow velocities usually represent a more stable state of the water body, which helps to more accurately reflect the optical properties and other relevant information of the water body. In addition, the flow velocity threshold can be flexibly adjusted according to different water characteristics to adapt to a variety of environmental conditions and application requirements. Thirdly, when constructing the Rayleigh scattering lookup table, the ocean-atmosphere coupled radiation transfer model and the radiation transfer model based on the Monte Carlo method were used at the same time. First, an initial Rayleigh scattering lookup table was constructed based on the ocean-atmosphere coupled radiation transfer model, and then a target Rayleigh scattering lookup table for cross-validation was constructed based on the radiation transfer model of the Monte Carlo method. The initial Rayleigh scattering lookup table was continuously optimized through the target Rayleigh scattering lookup table, so that the final Rayleigh scattering lookup table can more clearly reflect the relationship between the Rayleigh scattering radiance and the observed data. In the subsequent determination of the Rayleigh corrected radiance based on the Rayleigh scattering radiance and the construction of the training set to train the ultraviolet-near-infrared atmospheric correction combined model, the performance of the ultraviolet-near-infrared atmospheric correction combined model was improved, and the accuracy of the atmospheric correction of the radiation signal in coastal waters was improved.

[0061] Corresponding to the aforementioned embodiment of the atmospheric correction method for the radiation signal of the coastal waters at the solar zenith angle, the present application also provides an embodiment of the atmospheric correction device for the radiation signal of the coastal waters at the solar zenith angle.

[0062] Figure 2 This is a schematic diagram of the structure of the first embodiment of the atmospheric correction device for the radiation signal of coastal waters under the solar zenith angle provided by this application. Figure 2 The device provided in this embodiment includes a generating module 210, an adjusting module 220, a matching module 230 and a determining module 240; wherein, The generating module 210 is used to generate noon remote sensing reflectance satellite data based on the observation data of the coastal waters by using the ultraviolet-near infrared atmospheric correction combined model; the noon remote sensing reflectance satellite data represents the water-leaving radiance of the radiation signal in the coastal waters; The adjustment module 220 is used to delete pixels in the noon remote sensing reflectivity satellite data whose flow velocity exceeds a flow velocity threshold value based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data by using a maximum cross correlation method to obtain adjusted noon remote sensing reflectivity satellite data; The matching module 230 is used to determine the Rayleigh scattered radiance based on the Rayleigh scattering lookup table corresponding to the observed data, calculate the Rayleigh corrected radiance based on the Rayleigh scattered radiance, and match the Rayleigh corrected radiance with the adjusted noon remote sensing reflectance satellite data based on time information and location information to obtain a training set; The determination module 240 is used to train the ultraviolet-near infrared atmospheric correction combined model based on the training set, input the Rayleigh corrected radiance corresponding to the coastal water radiation signal to be corrected into the trained ultraviolet-near infrared atmospheric correction combined model, and determine the water-leaving radiance of the radiation signal based on the output noon remote sensing reflectance satellite data.

[0063] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0064] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0065] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0066] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for atmospheric correction of coastal waters radiation signals under solar zenith angle, characterized in that: The method comprises: Using an ultraviolet-near infrared atmospheric correction combined model, noon remote sensing reflectance satellite data are generated based on the observation data of coastal waters; the noon remote sensing reflectance satellite data represent the water-leaving radiance of the radiation signal in the coastal waters; Using a maximum cross-correlation method, based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data, pixels in the noon remote sensing reflectivity satellite data with a flow velocity exceeding a flow velocity threshold are deleted to obtain adjusted noon remote sensing reflectivity satellite data; Determine Rayleigh scattering radiance based on the Rayleigh scattering lookup table corresponding to the observation data, calculate Rayleigh corrected radiance based on the Rayleigh scattering radiance, and match the Rayleigh corrected radiance with the adjusted noon remote sensing reflectance satellite data based on time information and location information to obtain a training set; The ultraviolet-near-infrared atmospheric correction combined model is trained based on the training set, the Rayleigh-corrected radiance corresponding to the coastal water radiation signal to be corrected is input into the trained ultraviolet-near-infrared atmospheric correction combined model, and the water-leaving radiance of the radiation signal is determined based on the output noon remote sensing reflectance satellite data.

2. The method according to claim 1, characterized in that The ultraviolet-near infrared atmospheric correction combined model comprises an input layer, a first hidden layer, a second hidden layer and an output layer connected in sequence; the method of using the ultraviolet-near infrared atmospheric correction combined model to generate noon remote sensing reflectivity satellite data based on the observation data of coastal waters comprises: The input layer receives the observation data, preprocesses the observation data, and passes the preprocessed observation data to the first hidden layer; the preprocessed observation data includes observation angle information and Rayleigh-corrected radiances of multiple bands; Each neuron in the first hidden layer performs weighted summation on the preprocessed observation data to extract features of the preprocessed observation data, and each neuron outputs first features of different scales, which are transmitted to the second hidden layer, wherein the first features represent the correlation between the observation angle information and the Rayleigh-corrected radiance; Each neuron in the second hidden layer performs weighted summation and transformation on the first features of different scales output by the first hidden layer, and each neuron in the second hidden layer outputs a second feature, and the scales of the second features output by each neuron are different, and the second feature is transmitted to the output layer; the second feature represents the correlation between the observation angle information and the Rayleigh-corrected radiance, and the data accuracy of the second feature is higher than that of the first feature; The output layer performs weighted summation on each second feature, and obtains the noon remote sensing reflectance satellite data output by each neuron through nonlinear transformation; the number of the neurons is the same as the number of the bands.

3. The method according to claim 1, characterized in that The method of using the maximum cross correlation method to delete pixels in the noon remote sensing reflectivity satellite data whose flow velocity exceeds a flow velocity threshold based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data includes: Determining a time interval based on the temporal resolution of the noon remote sensing reflectivity satellite data; Spatial alignment of noon remote sensing reflectance satellite data at adjacent time intervals; Calculate the cross-correlation corresponding to the pixels at the same position in the noon remote sensing reflectivity satellite data of the adjacent time intervals, and determine the actual distance based on the pixel displacement corresponding to the maximum value and the spatial resolution of the noon remote sensing reflectivity satellite data; The flow velocity of each pixel is determined based on the actual distance and the time interval.

4. The method according to claim 1, characterized in that The method further comprises: Determining the sensor type based on the characteristics of the coastal waters and the operating band and characteristics of the sensor; Determining the range of spatial resolution based on the spatial resolution of each working band of the sensor and external observation conditions; The spatial resolution is verified based on the resolution of different features in the observed data, and the range of the spatial resolution is adjusted according to the verification result.

5. The method according to claim 1, characterized in that The process of determining the Rayleigh scattering lookup table includes: Based on the observation data, calculating the Rayleigh optical thickness of each band in the observation data; The Rayleigh atmosphere is divided based on its physical properties to obtain different altitude layers, and relevant parameters of each altitude layer are determined based on the optical properties, radiation transmission principles and climate and environmental factors of the altitude layer; The Rayleigh optical thickness and related parameters are input into an ocean-atmosphere coupled radiation transfer model to construct a Rayleigh scattering lookup table.

6. The method according to claim 5, characterized in that After inputting the Rayleigh optical thickness and related parameters into the ocean-atmosphere coupled radiation transfer model to construct a Rayleigh scattering lookup table, the method further includes: Inputting the Rayleigh optical thickness and related parameters into a radiation transfer model based on the Monte Carlo method to construct a target Rayleigh scattering lookup table; The Rayleigh scattering lookup table and the target Rayleigh scattering lookup table are compared to determine the difference between the Rayleigh scattering radiance under different observation data and the actual Rayleigh scattering radiance, and the Rayleigh scattering lookup table is adjusted based on the difference.

7. The method according to claim 1, characterized in that The ultraviolet-near infrared atmospheric correction combined model includes a near infrared atmospheric correction model and an ultraviolet atmospheric correction model; the method of using the ultraviolet-near infrared atmospheric correction combined model to generate noon remote sensing reflectivity satellite data based on observation data of coastal waters includes: The near-infrared atmospheric correction model is used to calculate the noon remote sensing reflectance satellite data of the first band based on the observation data of the first band, and the noon remote sensing reflectance satellite data of the second band is obtained based on the noon remote sensing reflectance satellite data of the first band; the wavelength of the second band is greater than that of the first band; and the proportion of clear water in the noon remote sensing reflectance satellite data of the second band is greater than that of turbid water; The ultraviolet atmosphere correction model is used to calculate the noon remote sensing reflectance satellite data of the third band; the wavelength of the third band is shorter than that of the first band; the proportion of turbid water areas in the noon remote sensing reflectance satellite data of the third band is greater than that of clear water areas; The first band, the second band, and the third band are fitted by a logistic regression method to determine the weight of the first band, the weight of the second band, and the weight of the third band. The noon remote sensing reflectivity satellite data are weighted fused based on the weights to obtain the noon remote sensing reflectivity satellite data.

8. The method according to claim 5, characterized in that The step of inputting the Rayleigh optical thickness and related parameters into an ocean-atmosphere coupled radiation transfer model to construct a Rayleigh scattering lookup table comprises: The ocean-atmosphere coupled radiative transfer model obtains the azimuth-independent equation by expanding the vector radiative transfer equation through Fourier series; Discretizing the solar zenith angle in the azimuth-independent equation, calculating the radiation transfer at each solar zenith angle, and obtaining a vector radiation transfer matrix equation; Solving the vector radiation transfer matrix equation using the doubling method to obtain the distribution of the vector radiation signal; Based on the reflection and refraction characteristics of light on the ocean-atmosphere interface and interference factors, the distribution of the vector radiation signal is adjusted, and a Rayleigh scattering lookup table is constructed based on the distribution.

9. The method according to claim 6, characterized in that The step of inputting the Rayleigh optical thickness and related parameters into a radiation transfer model based on a Monte Carlo method to construct a target Rayleigh scattering lookup table includes: The radiative transfer model based on the Monte Carlo method determines the scattering and reflection characteristics of photons based on the input Rayleigh optical thickness and related parameters; The photons are emitted from the detector, and the scattering of the photons in the atmosphere and the reflection of the photons on the sea surface are simulated based on the scattering characteristics and reflection characteristics of the photons; Calculating relevant parameters of the photon scattering in the atmosphere based on the scattering phase matrix, and calculating relevant parameters of the photon reflection in the target direction based on the Fresnel formula when the photon reaches the sea surface; the target direction is consistent with the direction of the sun; A target Rayleigh scattering lookup table is constructed based on the relevant parameters of the photon scattering in the atmosphere and the relevant parameters of the photon reflection in the target direction.

10. An atmospheric correction device for the radiation signal of coastal waters under the solar zenith angle, characterized in that: The device includes a generating module, an adjusting module, a matching module and a determining module; wherein, The generating module is used to generate noon remote sensing reflectance satellite data based on the observation data of the coastal waters by using the ultraviolet-near infrared atmospheric correction combined model; the noon remote sensing reflectance satellite data represents the water-leaving radiance of the radiation signal in the coastal waters; The adjustment module is used to delete pixels in the noon remote sensing reflectivity satellite data whose flow velocity exceeds a flow velocity threshold value based on the spatial resolution and temporal resolution of the noon remote sensing reflectivity satellite data by using a maximum cross correlation method to obtain adjusted noon remote sensing reflectivity satellite data; The matching module is used to determine the Rayleigh scattering radiance based on the Rayleigh scattering lookup table corresponding to the observation data, calculate the Rayleigh corrected radiance based on the Rayleigh scattering radiance, and match the Rayleigh corrected radiance with the adjusted noon remote sensing reflectance satellite data based on time information and location information to obtain a training set; The determination module is used to train the ultraviolet-near infrared atmospheric correction combined model based on the training set, input the Rayleigh corrected radiance corresponding to the coastal water radiation signal to be corrected into the trained ultraviolet-near infrared atmospheric correction combined model, and determine the water-leaving radiance of the radiation signal based on the output noon remote sensing reflectance satellite data.

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