Sea fog detection method and device based on satellite-borne laser radar

Abstract

The invention provides a sea fog detection method and device based on a satellite-borne laser radar, and belongs to the field of atmosphere remote sensing monitoring. The method comprises the following steps: setting a multi-parameter joint threshold value for cloud-aerosol distinguishing; based on a set multi-parameter combined threshold value, primary distinguishing of cloud-aerosol is completed, and the number of cloud layers within the specified height from the sea surface is screened out from the cloud-aerosol range; in combination with the cloud base height and the scattering characteristic parameters of the cloud layer, the area meeting the scattering characteristic criterion is calibrated as a sea fog layer; calculating continuity characteristics of sea fog levels in vertical and horizontal distribution, and identifying signal interruption points and abnormal distribution areas; and carrying out interpolation completion on scattering characteristic data of a signal interruption point region, eliminating an abnormal distribution region, re-correcting hierarchical characteristics of vertical and horizontal distribution, and outputting an optimized sea fog hierarchical distribution result. According to the sea fog detection method and device based on the satellite-borne laser radar provided by the invention, detection of vertical distribution of sea fog can be accurately and effectively realized.

Description

Technical Field

[0001] The present application relates to the field of atmospheric remote sensing monitoring technology, and in particular to a method and device for detecting sea fog based on a satellite-borne laser radar. Background Art

[0002] Sea fog is a low-level atmospheric phenomenon formed by condensation or bloom of water vapor. It is usually formed by the accumulation of a large number of water droplets or ice crystals (or both), which can reduce horizontal visibility to less than 1 km. Sea fog is common in oceans or coastal areas and has a serious impact on air, sea and land transportation. At the same time, it also brings great inconvenience to fields such as communications, fishing and maritime military activities. Therefore, the formation mechanism, evolution process and temporal and spatial distribution characteristics of sea fog have always been important topics in the field of meteorological research.

[0003] Due to the sparseness of meteorological stations in ocean areas, traditional observation methods can hardly meet the demand for obtaining large-scale, high-precision sea fog data. In addition, the existing sea fog distribution detection algorithms mainly detect the coverage of sea fog and rely on multi-channel spectral data from satellite-borne passive remote sensing, but do not fully utilize the vertical structure detection capabilities of satellite-borne active remote sensing. As a result, the sea fog distribution detection results are limited to a two-dimensional plane and lack detailed data on vertical profiles, which to a certain extent limits the precision and accuracy of sea fog monitoring. Summary of the invention

[0004] In view of this, the present application provides a sea fog detection method and device based on satellite-borne laser radar, which can accurately and effectively detect the vertical distribution of sea fog.

[0005] Specifically, the present application is implemented through the following technical solutions:

[0006] The first aspect of the present application provides a sea fog detection method based on a space-borne laser radar, the method comprising:

[0007] Calculating statistical distribution characteristics of scattering characteristic parameter values ​​according to the scattering characteristics, and setting a multi-parameter joint threshold for cloud-aerosol differentiation according to the statistical characteristic distribution and an existing scattering characteristic database;

[0008] Based on the set multi-parameter joint threshold, the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data are analyzed to complete the preliminary distinction between clouds and aerosols, and the cloud layers within the specified height from the sea surface are screened out from the cloud-aerosol range;

[0009] The vertical structure information of the atmospheric layer is obtained according to the spaceborne laser radar, and the area meeting the scattering characteristic criterion is calibrated as the sea fog layer in combination with the cloud base height and scattering characteristic parameters of the cloud layer;

[0010] Calculating the continuity characteristics of the sea fog layer in vertical and horizontal distribution, judging the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixel points in the vertical direction, and identifying the signal interruption points and abnormal distribution areas in combination with the spatial topological characteristics of the sea fog layer in the horizontal distribution;

[0011] The scattering characteristic data in the signal interruption point area are interpolated and supplemented, the abnormal distribution area is eliminated, and the hierarchical characteristics of the vertical and horizontal distribution are recalibrated to output the optimized sea fog hierarchical distribution results.

[0012] A second aspect of the present application provides a sea fog detection device based on a space-borne laser radar, the device comprising a setting module, a differentiation module, a calibration module, an identification module and an output module;

[0013] The setting module is used to calculate the statistical distribution characteristics of the scattering characteristic parameter value according to the scattering characteristics, and to set the multi-parameter joint threshold for cloud-aerosol differentiation according to the statistical characteristic distribution and an existing scattering characteristic database;

[0014] The differentiation module is used to analyze the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data based on a set multi-parameter joint threshold, complete the preliminary cloud-aerosol differentiation, and screen out the cloud layers within a specified height from the sea surface from the cloud-aerosol range;

[0015] The calibration module is used to obtain vertical structure information of the atmospheric layer according to the satellite-borne laser radar, and to calibrate the area that meets the scattering characteristic criterion as the sea fog layer in combination with the cloud base height and scattering characteristic parameters of the cloud layer;

[0016] The identification module is used to calculate the continuity characteristics of the sea fog layer in vertical and horizontal distribution, judge the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixel points in the vertical direction, and identify the signal interruption point and abnormal distribution area in combination with the spatial topological characteristics of the sea fog layer in the horizontal distribution;

[0017] The output module is used to interpolate and complete the scattering characteristic data of the signal interruption point area, eliminate the abnormal distribution area, and recalibrate the hierarchical characteristics of the vertical and horizontal distribution, and output the optimized sea fog hierarchical distribution result.

[0018] The sea fog detection method and device based on satellite-borne laser radar provided in the present application calculates the statistical distribution characteristics of the scattering characteristic parameter values ​​and sets a multi-parameter joint threshold. Compared with the traditional method that relies on only a single or fewer parameters for judgment, it takes into account the combined influence of multiple scattering characteristics, greatly improves the accuracy of distinction, and reduces the possibility of misjudgment. By using the vertical structure information of the satellite-borne laser radar, on the basis of cloud-aerosol distinction, the cloud layer within a specified height from the sea surface is further screened out, and combined with the cloud bottom height and scattering characteristic parameters for judgment, the area that meets the scattering characteristic criteria can be accurately calibrated as the sea fog layer. In this way, the advantages of laser radar in vertical detection are utilized, and the defects of traditional observation methods that are difficult to obtain atmospheric vertical structure information are overcome, making the identification of sea fog layers more accurate and reliable. In addition, by calculating the continuity characteristics of the sea fog layers in the vertical and horizontal directions, identifying the signal interruption points and abnormal distribution areas, interpolating and completing the interruption point areas and eliminating the abnormal areas, and finally recalibrating the layer characteristics and outputting the optimized sea fog layer distribution results, it can effectively solve the problems of sea fog layer discontinuity and misidentification caused by factors such as detection signal weakening and data noise, and improve the integrity and accuracy of the sea fog distribution results. At the same time, this operation helps to remove misidentified sea fog layers and ensure the physical and geographical rationality of all identified layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of Example 1 of a sea fog detection method based on a satellite-borne laser radar provided in this application;

[0020] Figure 2 This is a schematic diagram of identifying signal discontinuity points and abnormal distribution areas based on continuity features shown in the present application;

[0021] Figure 3 This is a verification result diagram of the space-borne laser radar sea fog recognition result shown in this application;

[0022] Figure 4 This is a schematic structural diagram of Example 2 of a sea fog detection device based on a satellite-borne laser radar provided in this application. DETAILED DESCRIPTION

[0023] 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.

[0024] 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.

[0025] 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".

[0026] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0027] Embodiment 1:

[0028] Figure 1 This is a flow chart of the first embodiment of the sea fog detection method based on satellite-borne laser radar provided in this application. Figure 1 , the method provided in this embodiment may include:

[0029] S101. Calculate statistical distribution characteristics of scattering characteristic parameter values ​​according to scattering characteristics, and set a multi-parameter joint threshold for cloud-aerosol differentiation according to the statistical characteristic distribution and an existing scattering characteristic database.

[0030] It should be noted that scattering characteristics refer to a series of characteristics exhibited by particles in the atmosphere (such as aerosols, cloud droplets, ice crystals, etc.) when scattering lasers or other electromagnetic waves. In the case of sea fog detection based on spaceborne lidar, the main scattering characteristic parameters involved include scattering ratio, attenuated backscattering coefficient, and backscattering coefficient. The scattering ratio is the ratio of the intensity of light of a certain wavelength scattered in the atmosphere to the total reflected light intensity of the wavelength. It can reflect the differences in scattering characteristics of different particles in the atmosphere and help to distinguish different atmospheric components. For example, it can be used to distinguish sea fog from other atmospheric layers (such as low clouds or aerosols) in sea fog detection. The attenuated backscattering coefficient describes the attenuation of the energy of a laser or other electromagnetic beam during propagation due to scattering and absorption by atmospheric particles. It takes into account the number and properties (such as particle size and shape) of scattering particles in the atmosphere and the loss of light energy. It can accurately reflect the actual detected signal intensity and is crucial for analyzing the distribution and concentration of micro-matter in the atmosphere. The backscattering coefficient indicates the degree to which particles within a unit volume scatter incident light. It reflects the scattering ability of atmospheric particles from another perspective. It is interrelated with the scattering ratio and attenuated backscattering coefficient, and together they constitute an important basis for judging the atmospheric composition and the presence or absence of sea fog.

[0031] Optionally, to ensure accurate identification of sea fog layers, multi-level data can be obtained from the spaceborne lidar (ACDL) remote sensing observation system. Specifically, it can include the attenuation backscattering coefficient of the first-level 532nm wavelength channel and the second-level 532nm wavelength backscattering coefficient, scattering ratio, radar ratio, depolarization ratio and extinction coefficient. These parameters help to distinguish different atmospheric layers (such as clouds, aerosols and sea fog) and provide support for the accuracy of sea fog identification.

[0032] In a specific implementation, the multi-parameter joint threshold for cloud-aerosol differentiation is set, including:

[0033] (1) The scattering ratio, attenuated backscattering coefficient and backscattering coefficient are extracted from the satellite-borne lidar observation data as scattering characteristic parameters.

[0034] During operation, the satellite-borne laser radar will continuously observe the atmosphere and collect a large amount of echo data. Through special signal processing algorithms and data analysis modules, key scattering characteristic parameters such as scattering ratio, attenuated backscattering coefficient and backscattering coefficient are extracted from these raw observation data. These algorithms and modules are usually based on optical principles and radar signal processing technology, and can accurately identify and separate scattering signals of different wavelengths, intensities and angles, and then calculate the corresponding parameter values. For example, for the calculation of the scattering ratio, it is necessary to screen and calculate the required values ​​from the complex signal data based on the ratio of the scattering intensity of light of a specific wavelength in the atmosphere to the total reflected light intensity. For the attenuated backscattering coefficient, it is calculated based on the initial laser energy, propagation distance and received backscattering energy, combined with relevant theories and radar equations (the attenuated backscattering coefficient is related to the energy attenuation and backscattering of the laser when it propagates in the atmosphere. When the laser propagates in the atmosphere, its energy will be attenuated due to the scattering and absorption of atmospheric particles). The backscattering coefficient is calculated based on the product of the scattering cross section and the particle number density (the backscattering coefficient indicates the degree to which particles within a unit volume scatter incident light).

[0035] (2) Perform statistical analysis on the extracted scattering characteristic parameters and calculate the mean, standard deviation and data distribution range of each scattering characteristic parameter.

[0036] The extracted scattering characteristic parameters can be summarized and organized using statistical analysis software or corresponding tools. Specifically, when calculating the mean, all parameter values ​​are added and divided by the total number of data points to obtain the average level of these parameters over the entire observation period and spatial range. The standard deviation is calculated through a series of mathematical formulas to measure the degree of dispersion of the parameter value relative to the mean and reflect the fluctuation of the data. For the data distribution range, the coverage interval on the numerical axis is determined by finding the maximum and minimum values ​​of the parameter value.

[0037] (3) Based on the existing cloud and aerosol scattering characteristic criteria, the extracted scattering characteristic parameters are compared with the cloud and aerosol distribution range to determine the scattering ratio, attenuated backscattering coefficient, and the characteristics of the backscattering coefficient in different atmospheric components. The existing scattering characteristic database is combined to form a distinction rule.

[0038] It should be noted that the researchers have previously collected and sorted out a large amount of information on the scattering characteristics of clouds and aerosols, which may come from previous experimental studies, summaries of field observation data, and derivations of theoretical models. The extracted scattering characteristic parameters are compared one by one with the known distribution ranges of clouds and aerosols. For example, if it is known that the scattering ratio of clouds is usually within a certain numerical range, and the scattering ratio of aerosols is in another range, by comparing the range in which the currently extracted scattering ratio parameter value is located, a preliminary judgment is made as to the possibility that it belongs to clouds or aerosols. At the same time, combined with the existing scattering characteristic database, which stores a variety of scattering characteristic data and related analysis results of clouds and aerosols under different meteorological conditions and geographical locations, the atmospheric component characteristics corresponding to the current parameters are further analyzed and determined, such as the differences in scattering characteristics of clouds and aerosols in specific ocean areas and specific seasons, thereby forming more accurate and detailed distinction rules.

[0039] (4) According to the statistical distribution characteristics of the scattering ratio, the attenuated backscattering coefficient and the backscattering coefficient and the distinction rule, a multi-parameter joint threshold is set.

[0040] Combined with the previous description, for example, if it is found that the mean value of the scattering ratio of clouds is higher and the standard deviation is smaller, while the mean value of the scattering ratio of aerosols is lower and the standard deviation is larger, and considering the different distribution patterns of the attenuated backscattering coefficient and the backscattering coefficient in clouds and aerosols, a reasonable scattering ratio threshold range and a matching threshold combination of the attenuated backscattering coefficient and the backscattering coefficient are set, so as to effectively distinguish the cloud layer and the aerosol layer from the lidar profile.

[0041] Specifically, the set multi-parameter joint threshold is derived from a large number of historical observation results. According to the long-term observation data of ground-based lidar, the multi-parameter joint threshold is as follows:

[0042] SR>10

[0043] ATB≥0.02km -1 sr -1

[0044] β≥0.03km -1 sr -1

[0045] Where SR is the scattering ratio at 532 nm, ATB is the attenuated backscattering coefficient at 532 nm, and β is the backscattering coefficient at 532 nm.

[0046] S102. Based on the set multi-parameter joint threshold, the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data are analyzed to complete the preliminary distinction between clouds and aerosols, and cloud layers within a specified height from the sea surface are screened out from the cloud-aerosol range.

[0047] It should be noted that based on the set multi-parameter joint threshold, the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data are analyzed to complete the preliminary distinction between clouds and aerosols, including:

[0048] (1) Extract the scattering characteristic parameters of each vertical resolution unit, and match the scattering characteristic parameters of each unit with the set multi-parameter joint threshold one by one.

[0049] It should be noted that the detection data of the space-borne lidar is presented in the form of a profile. In the software system, each vertical resolution unit is operated through a specific data extraction program. These programs accurately calculate the scattering characteristic parameters such as scattering ratio, attenuated backscattering coefficient and backscattering coefficient from a large amount of original echo data for each unit based on a pre-set algorithm. Then, the scattering characteristic parameters extracted from each unit are compared one by one with the previously set multi-parameter joint threshold. This comparison process requires judging each parameter according to the set threshold range. Specifically, if the set scattering ratio threshold range is greater than a certain value, the attenuated backscattering coefficient is in another specific interval, and the backscattering coefficient also has a corresponding value range, then only when the three parameters of a vertical resolution unit meet their respective threshold conditions at the same time, will it be judged as meeting the characteristics of the cloud and then marked as a cloud area; conversely, if any parameter does not meet the threshold requirements, it will be marked as an aerosol area. For example, taking the scattering ratio threshold as an example, if the set threshold is greater than 10, this condition is met when the scattering ratio calculation result of a unit is 12; a similar one-by-one comparison is also performed for the attenuated backscattering coefficient and the backscattering coefficient. Only when all parameters meet their corresponding threshold ranges will the unit be judged to belong to the cloud area, otherwise it will be marked as an aerosol area.

[0050] (2) If the scattering characteristic parameters of the cell meet the joint threshold, it is marked as a cloud area; if not, it is marked as an aerosol area.

[0051] For details, please refer to the description of step (1) above, which will not be repeated here.

[0052] (3) The noise points and outlier data in the spaceborne lidar profile data are filtered using a neighborhood smoothing algorithm to remove abnormal data and output the cloud-aerosol distribution results after preliminary differentiation.

[0053] It should be noted that the satellite-borne lidar will inevitably be disturbed by various factors during the data collection process, resulting in noise points and outlier data. These abnormal data may affect the accuracy of cloud-aerosol distinction. In order to eliminate this influence, the neighborhood smoothing algorithm is used for filtering. The principle of this algorithm is based on the spatial correlation of the data. For each data point, the values ​​of the neighboring data points within a certain range around it are considered, and the noise points and outlier data are corrected or eliminated through a certain weighted average or other smoothing calculation method.

[0054] Specifically, for the identification of noise points and outlier data, the statistical characteristics of the scattering characteristic parameters within a certain neighborhood range (for example, a 3x3 or 5x5 unit matrix) around each vertical resolution unit, such as the mean and standard deviation, can be calculated. If the deviation of the parameter value of a unit from the neighborhood mean exceeds a certain multiple of the standard deviation (such as 3 times the standard deviation), it is preliminarily determined to be a noise point or outlier data. Then, a neighborhood smoothing algorithm is used for filtering. Common neighborhood smoothing algorithms such as the simple average method, for a unit determined to be abnormal, average the scattering characteristic parameters of the normal units in its neighborhood, and replace the parameter value of the abnormal unit with the average value; or use the weighted average method, according to the distance between the neighborhood unit and the abnormal unit, assign different weights for weighted average calculation, the closer the distance, the greater the weight, so as to correct the abnormal data to make it more consistent with the actual situation of the surrounding environment.

[0055] It should also be noted that the scattering characteristic parameters of each vertical resolution unit in the spaceborne laser radar profile data are analyzed, including: obtaining the spaceborne laser radar profile data; determining the minimum vertical resolution unit according to the data distribution characteristics of the profile data, the minimum vertical resolution unit being a rectangular area; dividing the profile data using the minimum vertical resolution unit as a unit; judging the area to which each unit belongs based on a multi-parameter joint threshold value, and obtaining a preliminary partitioning conclusion of the profile data; determining the center point of each unit, and correcting the preliminary partitioning conclusion with the partitioning result between adjacent units that are horizontally distributed and whose center point height difference is lower than the threshold, and obtaining a preliminary cloud-aerosol distinction result.

[0056] Specifically, in the process of sea fog detection based on space-borne laser radar, the processing of profile data is crucial. First, the acquired space-borne laser radar profile data presents certain spatial distribution characteristics, and its overall shape is rectangular. In order to analyze the data more finely, it is necessary to determine the minimum vertical resolution unit according to the data distribution characteristics of the profile data, which is a rectangular area. This process is to divide a large rectangular canvas into many small rectangular grids, and each small rectangle is a minimum vertical resolution unit. After completing the unit division, the profile data is finely divided based on these minimum vertical resolution units. Then, the area to which each unit belongs is judged based on the pre-set multi-parameter joint threshold, so as to obtain the preliminary partition conclusion of the profile data. However, there may be certain errors in the preliminary partition conclusion. At this time, the center point of each unit is further determined, and the adjacent units with horizontal distribution and center point height difference below the threshold are focused on. Due to the complexity of the atmospheric environment and the volatility of the data, the unit partition results on the same horizontal line may fluctuate. By analyzing the partition results of these adjacent units, suspected areas with large changes in partition results can be found.

[0057] Finally, for these suspected areas, the method of fitting the partition change trend is used for correction. Specifically, based on the reliable partition results of the suspected area's adjacent areas and the existing change trends, mathematical models and algorithms are used to predict the reasonable partition results of the suspected area, and use them as the corrected partition results. For example, if the adjacent areas are mostly judged as aerosol areas and there is a trend of gradual transition, when the preliminary judgment results of the suspected area do not match them, they are re-judged and corrected in combination with trend analysis, so as to obtain a more accurate preliminary cloud-aerosol distinction result.

[0058] Furthermore, after completing the preliminary distinction between clouds and aerosols, the cloud layers within the specified height from the sea surface will be screened out from the cloud-aerosol range. In specific implementation, based on the long-term research and understanding of the distribution characteristics of sea fog near sea level, sea fog usually exists within a certain height range from sea level. Researchers will set a suitable vertical height range value (for example, 100 meters to 1 kilometer from sea level, and the specific value is determined according to actual research and application scenarios) based on a large number of past field observations, statistical analysis and related theoretical research results.

[0059] Then, within the cloud-aerosol range that has been distinguished previously, data is screened according to the set height range, retaining only cloud layer-related data within the height range and excluding cloud data outside this range. This will initially narrow the target range and focus on cloud layer areas that are more likely to be sea fog.

[0060] It should also be noted that when screening cloud layers within a specified height range above the sea surface, the accurate cloud base height can be obtained through the satellite-borne lidar, and its distance from the sea surface can be calculated as the basis for distinguishing sea fog from stratus clouds. Specifically, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) method points out that the vertical feature mask (VFM) data of CALIOP may misidentify sea fog as sea surface, but because ACDL is also equipped with an altimetry payload and has real-time measurement data of surface elevation, this problem will not exist when using ACDL to obtain sea fog layers.

[0061] S103, obtaining vertical structure information of the atmospheric layer according to the satellite-borne laser radar, combining the cloud base height and scattering characteristic parameters of the cloud layer, and calibrating the area that meets the scattering characteristic criterion as the sea fog layer.

[0062] It should be noted that the areas that meet the scattering characteristic criteria are marked as sea fog levels, including:

[0063] (1) Obtain the vertical distribution information of the atmospheric layers from the spaceborne lidar profile data, including the signal intensity changes of the scattering characteristic parameters, the cloud base height, and the distribution characteristics of the cloud layers.

[0064] It should be noted that the space-borne laser radar emits laser pulses into the atmosphere and receives signals scattered back by atmospheric particles at different altitudes. Based on the laser radar's echo signal processing mechanism, it is possible to analyze how the signal intensity changes with altitude, that is, how the signal intensity of the scattering characteristic parameters changes. As the altitude increases, the concentration and size of particles in different atmospheric layers are different, which causes the intensity of the scattered laser signal to change accordingly. By analyzing these echo intensities, we can obtain a curve of how the signal intensity changes with altitude.

[0065] At the same time, the cloud base height can be identified through specific algorithms. For example, when the echo signal intensity suddenly increases to a certain threshold, and then maintains a relatively stable high intensity within a certain height range, it can be determined that this is the height of the cloud base. Specifically, the calculation of the cloud base height includes: the satellite-borne laser radar continuously emits laser pulses to the atmosphere, receives signals scattered back by atmospheric particles at different heights, and records the time information and signal strength information corresponding to each echo signal; monitors and analyzes the received echo signal intensity in real time, marks the signal intensity value and the corresponding time point when the echo signal intensity begins to show an increasing trend; monitors the subsequent echo signal intensity, and records the height information when the echo signal intensity reaches a pre-set threshold and maintains a high intensity state within a specified height range; the starting height at which the echo signal maintains a high intensity is determined as the cloud base height. In specific implementation, accurate calculations can be performed using the radar ranging principle through information such as the emission time and reception time of the laser pulse and the speed of light. In addition, it is also possible to analyze the distribution characteristics of cloud layers in the vertical direction, such as cloud thickness, the interval between clouds, etc. This information together constitutes the vertical distribution information of the atmospheric layer, providing basic data support for the subsequent screening of sea fog layers.

[0066] (2) extracting scattering characteristic parameters of cloud layers within a specified height from the sea surface from the cloud-aerosol range, including scattering ratio, attenuated backscattering coefficient and backscattering coefficient, and performing statistical analysis on the vertical distribution characteristics of the scattering characteristic parameters.

[0067] For the cloud layer data within the specified height, the corresponding data extraction method can be used to accurately obtain the key scattering characteristic parameters such as scattering ratio, attenuated backscattering coefficient and backscattering coefficient (the values ​​of these parameters in the vertical direction will change with the height and have their own distribution rules). Then, the vertical distribution characteristics of these parameters are studied using statistical analysis methods. For example, the mean and standard deviation of the scattering ratio at different heights are calculated, and its change trend with height is analyzed to see whether it is increasing, decreasing or fluctuating, etc. Through such statistical analysis, the specific performance of these cloud layers in terms of scattering characteristics can be further understood.

[0068] (3) Based on the cloud base height information detected by the satellite-borne laser radar, determine whether there is a low cloud layer within the vertical height range. If the scattering characteristics of the low cloud layer coincide with the aerosol area, the low cloud layer is determined to be a sea fog layer.

[0069] It should be noted that, based on the cloud base height information detected by the satellite-borne laser radar, check whether there is a low cloud layer within the set vertical height range. For the existing low cloud layer, analyze its scattering characteristic parameters (scattering ratio, attenuated backscattering coefficient, backscattering coefficient). When it is found that these scattering characteristics of the low cloud layer coincide with or are very similar to the scattering characteristics of the aerosol area previously determined, based on the understanding of the formation mechanism of sea fog and its relationship with aerosols and low clouds, in this special case, it can be determined that the low cloud layer is a sea fog layer. Among them, compared with aerosols, sea fog has stronger backscattering, smaller laser radar ratio, stronger attenuation of laser radar signals, larger scattering ratio, and clearer layer edges. The main difference between sea fog and low-level clouds is that low-level clouds are not directly connected to the surface, while the bottom of sea fog is in direct contact with the ocean surface. Through such feature overlap judgment, the possible location of sea fog can be identified in a complex atmospheric environment.

[0070] (4) The scattering characteristic parameters of the screened area are compared with the set scattering characteristic criteria one by one to confirm whether the multi-parameter joint threshold conditions of the sea fog characteristics are met.

[0071] The scattering characteristic parameters of the selected areas (including the low cloud layer that may be sea fog and other cloud layer areas within the specified height) are compared parameter by parameter with the pre-set multi-parameter joint threshold conditions of sea fog characteristics. For example, the multi-parameter joint threshold of sea fog characteristics stipulates that the scattering ratio must be in a certain range and the attenuated backscattering coefficient must also meet the corresponding range.

[0072] Only when all the scattering characteristic parameters of the area meet these set sea fog characteristic threshold conditions, it is more likely to be a sea fog layer. Such comparison operation further improves the accuracy of determining the sea fog layer and avoids the occurrence of misjudgment.

[0073] (5) Calibrate the aerosol areas that meet the scattering characteristic criteria, record the vertical distribution positions and corresponding scattering characteristic parameters of the aerosol areas that meet the scattering characteristic criteria, and complete the preliminary identification of the sea fog layers.

[0074] In this way, the preliminary identification of the sea fog layers is completed, and the subsequent processes can further perform analysis, optimization and other operations based on these preliminary identified sea fog layer data to obtain more accurate and complete sea fog distribution results.

[0075] S104, calculating the continuity characteristics of the sea fog layer in vertical and horizontal distribution, judging the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixel points in the vertical direction, and identifying the signal interruption points and abnormal distribution areas in combination with the spatial topological characteristics of the sea fog layer in the horizontal distribution.

[0076] Specifically, identify signal interruption points and abnormal distribution areas, including:

[0077] (1) Extracting the scattering characteristic parameters of the calibrated sea fog layer from the satellite-borne laser radar data, and recording the vertical and horizontal distribution positions of the scattering characteristic parameters.

[0078] It should be noted that from the massive data obtained by the satellite-borne laser radar, data extraction operations are specifically performed on the part that has been previously calibrated as the sea fog layer, and the corresponding data extraction algorithms and programs are used to accurately separate the scattering characteristic parameters (key parameters such as scattering ratio, attenuated backscattering coefficient, and backscattering coefficient). Furthermore, in order to analyze the spatial changes of these parameters later, it is also necessary to record their corresponding vertical and horizontal distribution positions. For example, the height value of each pixel point from the sea level is recorded in the vertical direction, and its latitude and longitude coordinates or relative coordinate position in the radar scanning plane are recorded in the horizontal direction. These position information corresponds to the scattering characteristic parameters one by one.

[0079] (2) performing differential calculation on the scattering characteristic parameters of adjacent pixel points in the vertical direction, analyzing the variation trend of the scattering characteristic parameters, and determining whether the variation of the scattering characteristic parameters is continuous; if the variation of the scattering characteristic parameters exceeds a preset continuity threshold, it is determined to be a discontinuity point in the vertical direction.

[0080] Specifically, in the vertical direction, the scattering characteristic parameters of adjacent pixels are differentially calculated. For example, for two adjacent pixels in a vertical column, their scattering ratio difference, attenuated backscattering coefficient difference, and backscattering coefficient difference are calculated. Through this differential calculation, it is possible to intuitively see how these parameters change in the vertical direction, and then analyze their changing trends.

[0081] Next, the changes in these parameters are compared with the preset continuity threshold. This preset continuity threshold is a reasonable range value determined based on a large amount of past experimental data, theoretical analysis, and the understanding of the actual physical characteristics of sea fog. For example, if the absolute value of the difference in the scattering ratio between adjacent pixels is stipulated not to exceed a certain value (such as 0.5), when the actual calculation finds that the difference in the scattering ratio of adjacent pixels exceeds this value, it means that the scattering characteristic parameters are discontinuous in the vertical direction. At this time, the position is determined to be a discontinuity point in the vertical direction. Among them, these discontinuity points may be caused by atmospheric interference on the detection signal, instrument accuracy limitations, and other reasons.

[0082] (3) Based on the spatial connectivity of the calibrated sea fog layer in the horizontal distribution, the topological analysis method is used to detect the boundary shape and distribution connectivity of the sea fog layer, identify isolated distribution areas and discontinuous areas, and calibrate them as horizontal discontinuity points.

[0083] It should be noted that topological analysis focuses on the geometric properties of the graph (which can be compared to the distribution shape of the sea fog layer on the horizontal plane) that remain unchanged under continuous deformation, such as connectivity and closure. Through relevant topological analysis algorithms, the boundary shape of the sea fog layer is detected to see whether it is complete and continuous, and the distribution connectivity between different areas of the sea fog layer on the horizontal plane is analyzed. For example, if it is found that there is an isolated small area in the horizontal direction of the sea fog layer that has no connection with the surrounding sea fog layer, or there are some areas that should be continuous but are discontinuous, then these isolated distribution areas and discontinuous areas will be marked as horizontal breakpoints.

[0084] (4) Based on the vertical and horizontal breakpoints and the distribution patterns of sea fog layers, identify the abnormal distribution areas caused by noise and errors.

[0085] Figure 2 Please refer to the schematic diagram of the present application for identifying signal interruption points and abnormal distribution areas based on continuity features. Figure 2 , Figure 2 Figure a in the figure is the attenuated backscatter coefficient identification result diagram. Figure 2 Figure b is the scattering ratio identification result diagram. Figure 2 Figure c in the figure is the backscatter coefficient identification result diagram. Figure 2 Figure d in the figure is the result of sea fog identification. The distribution law of sea fog layers is usually summarized based on meteorological principles and past observation experience, such as the approximate horizontal and vertical range of sea fog under certain meteorological conditions, the degree of continuity, and the relationship with the surrounding atmospheric environment. By comparing the actual detected sea fog distribution with discontinuity points with these laws, it is possible to determine which areas are not in line with normal distribution, which may be abnormal distribution areas caused by noise interference or measurement errors (data deviation caused by instrument accuracy, calibration and other issues).

[0086] S105, interpolating and completing the scattering characteristic data of the signal interruption point area, eliminating the abnormal distribution area, and recalibrating the hierarchical characteristics of the vertical and horizontal distribution, and outputting the optimized sea fog hierarchical distribution result.

[0087] It should be noted that the interpolation and completion of the scattering characteristic data in the signal interruption point area includes:

[0088] (1) For vertical breakpoints, the scattering characteristic parameters of adjacent units are extracted and the missing data are supplemented using linear interpolation.

[0089] It should be noted that after the vertical break point is determined, it is necessary to check the situation of its adjacent units, which include units such as pixels located above and below the break point in the vertical direction. After extracting the scattering characteristic parameters of the adjacent units, the linear interpolation method can be used to fill in the missing data. Among them, linear interpolation is based on a simple linear relationship assumption, that is, it is assumed that the change of the scattering characteristic parameters between two adjacent units is linear. For example, it is known that the scattering ratio of the adjacent unit above the break point is A, the scattering ratio of the adjacent unit below is B, the vertical distance from the break point to the upper unit is d1, and the vertical distance from the break point to the lower unit is d2 (the total distance is d1+d2), then the linear interpolation formula (interpolation result = A+(BA)*(d1 / (d1+d2))) can be used to calculate the scattering ratio value to be supplemented at the break point position.

[0090] (2) For horizontal discontinuity points, a polynomial fitting algorithm is used to complete the scattering characteristic parameters of the discontinuity area based on the trend characteristics of the sea fog layer in the horizontal distribution.

[0091] It should be noted that by analyzing the changing pattern of the scattering characteristic parameters of the sea fog layer in the horizontal direction, for example, by checking the parameter value changes of adjacent horizontal pixel points within a certain range, it can be determined whether it presents a linear change, a quadratic function change, or a more complex curve change trend, etc., and then using statistical analysis methods, the parameter values ​​of multiple sampling points in the horizontal direction are fitted to try to determine the approximate type of change trend. Based on the determined trend characteristics, a polynomial fitting algorithm is used to complete the scattering characteristic parameters of the interrupted area. For example, if the analysis shows that the scattering ratio of the sea fog layer in the horizontal direction roughly conforms to the quadratic function change trend, then a quadratic polynomial function (such as y=ax 2 +bx+c), and then use the scattering ratio values ​​of the known and continuous pixel points on both sides of the horizontal interruption point as sample data, and determine the values ​​of the coefficients a, b, and c in the polynomial through fitting algorithms such as the least squares method, so as to obtain a complete fitting function. This function can be used to calculate the scattering ratio values ​​that should be supplemented at each position in the horizontal interruption point area, and similar polynomial fitting operations are also performed on parameters such as the attenuated backscattering coefficient and the backscattering coefficient to complete the data in the horizontal interruption area and make the distribution of sea fog in the horizontal direction more coherent.

[0092] (3) Perform continuity check on the completed data. If the difference between the completed data and the neighborhood scattering characteristic parameters is greater than the specified threshold, readjust the interpolation result.

[0093] After completing the interpolation of the vertical and horizontal breakpoint areas, the completed data needs to be checked for continuity. For each completed data point, calculate the difference between it and the neighborhood scattering characteristic parameters. For example, for a data point whose scattering ratio value has just been completed, calculate the absolute value of the scattering ratio difference between it and several adjacent normal pixels (pixels that have been verified to meet continuity). Then compare this difference with a pre-specified threshold, which is a reasonable value determined based on the understanding of the parameter variation range under normal distribution of sea fog, as well as past experience, experimental analysis, etc.

[0094] If it is found that the difference between the completed data points and the neighborhood scattering characteristic parameters is greater than the specified threshold, it means that the result of this interpolation completion may not meet the actual continuity requirements of sea fog distribution, then it is necessary to readjust the interpolation result. Specifically, the interpolation method used, the selected neighborhood data range and other factors can be adjusted, and the interpolation calculation can be performed again until the completed data passes the continuity check.

[0095] It should be noted that the abnormal distribution areas are excluded, including:

[0096] (1) Perform continuity analysis on the calibrated sea fog layer data, extract the areas that do not meet the continuity characteristics in the vertical and horizontal distribution, and mark them as abnormal areas.

[0097] Specifically, in the vertical direction, we can check whether the scattering characteristic parameters of each pixel point change continuously along the height direction according to a reasonable change law, such as whether there are sudden jumps, breakpoints that do not conform to the trend, etc. In the horizontal direction, we can observe whether the distribution of the sea fog layer in the plane is continuous, and whether there are isolated areas that are inconsistent with the overall distribution. Those areas that do not conform to the continuity characteristics in the vertical and horizontal distribution are extracted and clearly marked as abnormal areas.

[0098] (2) extracting scattering characteristic parameters of the abnormal area. If the scattering characteristic parameter value of the abnormal area deviates from the statistical distribution characteristics of the calibrated sea fog layer, the abnormal area is determined to be a to-be-deleted area.

[0099] It should be noted that the scattering characteristic parameter values ​​of the abnormal area are compared with the statistical distribution characteristics of the normal sea fog layer calibrated before. If it is found that the scattering characteristic parameter values ​​of the abnormal area are obviously deviated from the statistical distribution characteristics of the normal sea fog layer, for example, its scattering ratio value is far beyond the reasonable value range of the normal sea fog scattering ratio, and this deviation is not caused by reasonable factors such as normal local changes in sea fog, then the abnormal area can be determined to be deleted (these abnormal areas may be caused by noise, errors and misjudgments). Among them, the statistical distribution characteristics of the normal sea fog layer are obtained by statistical analysis of a large amount of accurate sea fog observation data, including the mean, standard deviation and common value range of each scattering characteristic parameter.

[0100] Combined with the above description, specifically, extract scattering characteristic parameters from the calibrated abnormal area data, calculate the difference between each scattering characteristic parameter and the mean of the scattering characteristic parameter corresponding to the normal sea fog level, and divide it by the standard deviation of the scattering characteristic parameter of the normal sea fog level to obtain a standardized deviation value; set a standardized deviation threshold, and when the scattering characteristic parameters in an area all meet the standardized deviation threshold, determine the area as a candidate abnormal area; analyze the changes in the scattering characteristic parameters in the candidate abnormal area and the preset area, and determine the cause of the abnormality based on the change trend and the relevant records in the data collection process; when the cause of the abnormality is noise, detection error and misjudgment, determine the area as an abnormal area and mark it as an area to be deleted. It should also be noted that if the cause of the abnormality cannot be clearly determined, the area is further manually analyzed or verified using a more complex algorithm until it is determined whether it is an abnormal area.

[0101] (3) Eliminate the areas judged to be abnormal and readjust the calibrated sea fog level range.

[0102] Once certain areas are determined to be abnormal areas, during the data processing process, the data of these areas are directly removed from the calibrated sea fog layer data, so that they no longer participate in subsequent analysis and result display, etc., to avoid these inaccurate data from misleading the final sea fog layer distribution results. And because the abnormal areas are removed, the originally calibrated sea fog layer range needs to be readjusted. According to the remaining data after the removal and the surrounding sea fog areas that meet the continuity characteristics, the reasonable distribution range of the sea fog layer in the vertical and horizontal directions is re-determined to ensure that the final output of the sea fog layer distribution results can truly and accurately reflect the distribution state of sea fog in the actual atmosphere.

[0103] It should also be noted that the output optimized sea fog layer distribution results include:

[0104] (1) Generate a vertical profile based on the completed and optimized sea fog layer distribution data.

[0105] Professional drawing software or the drawing function of the data analysis platform can be used to draw the distribution of sea fog layers at different heights into an intuitive vertical profile with the height as the vertical coordinate (usually the height from the sea level) and the horizontal position or related identification parameters as the horizontal coordinate (for example, the horizontal coordinate position along the radar scanning line, etc.). Such a profile can clearly show the thickness, layered structure and boundary of the sea fog with the surrounding atmospheric environment in the vertical direction.

[0106] (2) Obtain geographic coordinate information and extract horizontal distribution information of sea fog layers to generate a sea fog horizontal distribution map.

[0107] Using geographic information system (GIS) related software or tools with geographic coordinate mapping function, with longitude and latitude as the coordinate system, the distribution information of sea fog on the horizontal plane is drawn into a sea fog horizontal distribution map. In the map, you can intuitively see the coverage of sea fog, shape, and density of sea fog in different areas.

[0108] (3) Integrate the vertical profile and horizontal distribution diagram to form a three-dimensional distribution result.

[0109] The data contained in the vertical profile and the sea fog horizontal distribution map generated earlier are merged, and the vertical sea fog layer information and the horizontal distribution are correlated and integrated in three-dimensional space. Using three-dimensional modeling software or professional meteorological data visualization tools, with the longitude and latitude in geographic coordinates as the horizontal coordinates, the height from the sea level as the vertical coordinates, and the relevant characteristics of sea fog (such as scattering characteristic parameters, which can be represented by different colors, transparency, etc.) to build a three-dimensional model to form a three-dimensional distribution result of the sea fog layer. Such a three-dimensional distribution result can fully and three-dimensionally display the actual distribution state of sea fog in the atmosphere, allowing researchers to observe the shape, structure and relationship of sea fog with the surrounding environment from multiple angles, providing a very intuitive and detailed data presentation form for in-depth research on the formation mechanism, evolution law and impact of sea fog on the marine and atmospheric environment.

[0110] In addition, after outputting the optimized sea fog layer distribution results, the output optimized sea fog layer distribution results can also be compared and analyzed with the actual observation data in the International Integrated Ocean-Atmosphere Dataset (ICOADS) to evaluate the accuracy of the recognition results. By comparing with the ICOADS data, the accuracy of the sea fog recognition method can be verified, the recognition ability under different meteorological conditions can be evaluated, and the model can be optimized and adjusted. Among them, in this comparative analysis process, ACDL trajectories with a time interval of less than 2 hours and a spatial distance of less than 100km can be screened for comparison with ICOADS data points.

[0111] Figure 3 Please refer to the verification result diagram of the space-borne laser radar sea fog recognition result shown in this application. Figure 3 , within the selected time range and spatial range in the four major sea areas of Region 1, Region 2, Region 3, and Region 4, there are 1873 cases of spatiotemporal matching between ACDL trajectories and ICOADS records. There are 26 cases in Region 1, 173 cases in Region 2, 420 cases in Region 3, and 1254 cases in Region 4. Comparing the ACDL sea fog recognition results with the ICOADS records: there are 1688 cases where both results are foggy or fog-free, 16 cases where ICOADS records foggy and ACDL identifies fog-free, and 169 cases where ICOADS records fog-free and ACDL identifies foggy, that is, the correct recognition rate is 90.12%, the missed alarm rate is 0.85%, and the false alarm rate is 9.02%.

[0112] The method provided in this embodiment calculates the statistical distribution characteristics of the scattering characteristic parameter values ​​and sets a multi-parameter joint threshold, comprehensively considers multiple scattering characteristics such as scattering ratio, attenuated backscattering coefficient and backscattering coefficient, changes the limitations of traditional judgments that rely on a single or fewer parameters, greatly reduces the possibility of misjudgment, and makes cloud-aerosol distinction more accurate. The atmospheric environment is complex and changeable, and a single parameter is difficult to fully reflect the characteristic differences between clouds and aerosols. The scattering ratio can reflect the scattering characteristics of different particles, the attenuated backscattering coefficient reflects the energy attenuation and the comprehensive situation of particles, and the backscattering coefficient represents the scattering ability of particles. The combination of the three can accurately define clouds and aerosols from multiple dimensions. Through statistical analysis of a large amount of observation data, the distribution law and mutual relationship of each parameter under different atmospheric conditions are fully considered, thereby greatly improving the accuracy of cloud-aerosol distinction and effectively reducing the risk of misjudgment. Using the vertical structure information of the satellite-borne laser radar, on the basis of cloud-aerosol distinction, the cloud layer within a specified height from the sea surface is screened, combined with the cloud bottom height and scattering characteristic parameter judgment, giving full play to the advantages of laser radar vertical detection, overcoming the difficulties of traditional means in obtaining atmospheric vertical structure information, and making sea fog layer identification more accurate and reliable. In addition, by calculating the continuity characteristics of the sea fog layer in the vertical and horizontal directions, identifying the signal interruption points and abnormal distribution areas, and interpolating and completing the interruption point areas and eliminating the abnormal areas, the layer characteristics are finally recalibrated and the optimized sea fog layer distribution results are output, which can effectively solve the discontinuity and misidentification problems of the sea fog layer caused by factors such as weakened detection signals and data noise, and improve the integrity and accuracy of the sea fog distribution results. At the same time, this operation helps to remove the misidentified sea fog layers and ensure the physical and geographical rationality of all identified layers. Finally, by comparing with the actual observation data in the international comprehensive ocean-atmosphere dataset, the accuracy and effectiveness of this method are effectively verified, providing reliable data support for sea fog research.

[0113] It should also be noted that in the continuity judgment link, it is difficult to fully capture the true distribution state of the sea fog layer by only focusing on a single direction. In the vertical direction, the differential calculation of the scattering characteristic parameters of adjacent pixel points can accurately monitor the subtle changes in the height of the sea fog. Once the preset continuity threshold is exceeded, the vertical discontinuity point can be quickly located to ensure the continuity of the vertical structure. In the horizontal direction, with the help of topological analysis methods, the boundaries and connectivity are detected based on the spatial connectivity of the sea fog layer, and isolated or discontinuous areas are identified as horizontal discontinuity points. This two-way collaborative judgment mechanism can comprehensively and accurately discover abnormal distribution areas, provide accurate basis for subsequent interpolation, completion and elimination operations, and effectively ensure the integrity and accuracy of the sea fog layer distribution results in the vertical and horizontal dimensions, greatly improving the reliability and accuracy of sea fog detection.

[0114] Corresponding to the aforementioned embodiment of a sea fog detection method based on a satellite-borne laser radar, the present application also provides an embodiment of a sea fog detection device based on a satellite-borne laser radar.

[0115] Embodiment 2:

[0116] Figure 4 This is a schematic diagram of the structure of the second embodiment of the sea fog detection device based on the satellite-borne laser radar provided in this application. Figure 4 , the device provided in this embodiment includes a setting module 410, a distinguishing module 420, a calibration module 430, an identification module 440 and an output module 450;

[0117] The setting module 410 is used to calculate the statistical distribution characteristics of the scattering characteristic parameter value according to the scattering characteristics, and set the multi-parameter joint threshold for cloud-aerosol differentiation according to the statistical characteristic distribution and the existing scattering characteristic database;

[0118] The differentiation module 420 is used to analyze the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data based on the set multi-parameter joint threshold, complete the preliminary cloud-aerosol differentiation, and filter out the cloud layers within the specified height from the sea surface from the cloud-aerosol range;

[0119] The calibration module 430 is used to obtain vertical structure information of the atmospheric layer according to the satellite-borne laser radar, and calibrate the area that meets the scattering characteristic criterion as the sea fog layer in combination with the cloud base height and scattering characteristic parameters of the cloud layer;

[0120] The identification module 440 is used to calculate the continuity characteristics of the sea fog layer in vertical and horizontal distribution, determine the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixel points in the vertical direction, and identify the signal interruption point and abnormal distribution area in combination with the spatial topological characteristics of the sea fog layer in the horizontal distribution;

[0121] The output module 450 is used to interpolate and complete the scattering characteristic data of the signal interruption point area, eliminate the abnormal distribution area, and recalibrate the hierarchical characteristics of the vertical and horizontal distribution, and output the optimized sea fog hierarchical distribution result.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 sea fog detection method based on space-borne laser radar, characterized in that: The method comprises: Calculating statistical distribution characteristics of scattering characteristic parameter values ​​according to the scattering characteristics, and setting a multi-parameter joint threshold for cloud-aerosol differentiation according to the statistical characteristic distribution and an existing scattering characteristic database; Based on the set multi-parameter joint threshold, the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data are analyzed to complete the preliminary distinction between clouds and aerosols, and the cloud layers within the specified height from the sea surface are screened out from the cloud-aerosol range; The vertical structure information of the atmospheric layer is obtained according to the spaceborne laser radar, and the area meeting the scattering characteristic criterion is calibrated as the sea fog layer in combination with the cloud base height and scattering characteristic parameters of the cloud layer; Calculating the continuity characteristics of the sea fog layer in vertical and horizontal distribution, judging the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixel points in the vertical direction, and identifying the signal interruption points and abnormal distribution areas in combination with the spatial topological characteristics of the sea fog layer in the horizontal distribution; The scattering characteristic data in the signal interruption point area are interpolated and supplemented, the abnormal distribution area is eliminated, and the hierarchical characteristics of the vertical and horizontal distribution are recalibrated to output the optimized sea fog hierarchical distribution results.

2. The method according to claim 1, characterized in that: The multi-parameter joint threshold for cloud-aerosol differentiation is set, including: The scattering ratio, attenuated backscattering coefficient and backscattering coefficient are extracted from the spaceborne lidar observation data as scattering characteristic parameters; Perform statistical analysis on the extracted scattering characteristic parameters and calculate the mean, standard deviation and data distribution range of each scattering characteristic parameter; Based on the existing cloud and aerosol scattering characteristic criteria, the extracted scattering characteristic parameters are compared and analyzed with the cloud and aerosol distribution range to determine the scattering ratio, attenuated backscattering coefficient, and the characteristics of the backscattering coefficient in different atmospheric components, and the existing scattering characteristic database is combined to form a distinction rule; A multi-parameter joint threshold is set according to the scattering ratio, the attenuated backscattering coefficient and the statistical distribution characteristics of the backscattering coefficient and the distinction rule.

3. The method according to claim 1, characterized in that Based on the set multi-parameter joint threshold, the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data are analyzed to complete the preliminary distinction between clouds and aerosols, including: Extract the scattering characteristic parameters of each vertical resolution unit, and match the scattering characteristic parameters of each unit with the set multi-parameter joint threshold one by one; If the scattering characteristic parameters of the cell meet the joint threshold, it is marked as a cloud area; if not, it is marked as an aerosol area; The noise points and outlier data in the spaceborne lidar profile data are filtered using a neighborhood smoothing algorithm to remove abnormal data and output the cloud-aerosol distribution results that have been preliminarily distinguished.

4. The method according to claim 1, characterized in that Defining the area satisfying the scattering characteristic criterion as the sea fog level comprises: Obtain vertical distribution information of the atmospheric layers from the spaceborne lidar profile data, including signal intensity changes of scattering characteristic parameters, cloud base height, and distribution characteristics of cloud layers; Extracting scattering characteristic parameters of cloud layers within a specified height from the sea surface screened out from the cloud-aerosol range, including scattering ratio, attenuated backscattering coefficient and backscattering coefficient, and performing statistical analysis on vertical distribution characteristics of the scattering characteristic parameters; Based on the cloud base height information detected by the satellite-borne laser radar, it is determined whether there is a low cloud layer within the vertical height range. If the scattering characteristics of the low cloud layer coincide with the aerosol area, the low cloud layer is determined to be a sea fog layer; The scattering characteristic parameters of the screened area are compared with the set scattering characteristic criteria one by one to confirm whether the multi-parameter joint threshold conditions of the sea fog characteristics are met; The aerosol areas that meet the scattering characteristic criteria are calibrated, and the vertical distribution positions and corresponding scattering characteristic parameters of the aerosol areas that meet the scattering characteristic criteria are recorded to complete the preliminary identification of the sea fog layers.

5. The method according to claim 1, characterized in that Identify signal interruption points and abnormal distribution areas, including: Extracting scattering characteristic parameters of the calibrated sea fog layer from the satellite-borne laser radar data, and recording the vertical and horizontal distribution positions of the scattering characteristic parameters; Perform differential calculation on the scattering characteristic parameters of adjacent pixel points in the vertical direction, analyze the change trend of the scattering characteristic parameters, and determine whether the change of the scattering characteristic parameters is continuous; if the change of the scattering characteristic parameters exceeds the preset continuity threshold, it is determined as a discontinuity point in the vertical direction; According to the spatial connectivity of the calibrated sea fog layer in the horizontal distribution, the topological analysis method is used to detect the boundary shape and distribution connectivity of the sea fog layer, identify isolated distribution areas and discontinuous areas, and mark them as horizontal discontinuity points. According to the vertical and horizontal break points, as well as the distribution patterns of sea fog layers, the abnormal distribution areas caused by noise and errors are identified.

6. The method according to claim 1, characterized in that The interpolation and completion of the scattering characteristic data of the signal interruption point area includes: For vertical breakpoints, the scattering characteristic parameters of adjacent units are extracted, and the missing data are supplemented using linear interpolation method; For horizontal discontinuity points, based on the trend characteristics of sea fog layers in horizontal distribution, a polynomial fitting algorithm is used to complete the scattering characteristic parameters of the discontinuity area. The completed data is checked for continuity. If the difference with the neighborhood scattering characteristic parameters is greater than the specified threshold, the interpolation result is readjusted.

7. The method according to claim 1, characterized in that The step of removing abnormal distribution areas includes: Conduct continuity analysis on the calibrated sea fog layer data, extract the areas that do not meet the continuity characteristics in the vertical and horizontal distribution, and mark them as abnormal areas; Extracting scattering characteristic parameters of the abnormal area, if the scattering characteristic parameter value of the abnormal area deviates from the statistical distribution characteristics of the calibrated sea fog layer, then determining the abnormal area as an area to be deleted; The areas judged to be abnormal are eliminated and the calibrated sea fog level range is readjusted.

8. The method according to claim 1, characterized in that The output optimized sea fog layer distribution result includes: Generate a vertical profile based on the completed and optimized sea fog layer distribution data; Obtain geographic coordinate information and extract horizontal distribution information of sea fog layers to generate a sea fog horizontal distribution map; The vertical profile and horizontal distribution diagram are integrated to form a three-dimensional distribution result.

9. The method according to claim 1, characterized in that: The analyzing of the scattering characteristic parameters of each vertical resolution unit in the spaceborne laser radar profile data includes: Obtain space-borne lidar profile data; Determine a minimum vertical resolution unit according to data distribution characteristics of the profile data, wherein the minimum vertical resolution unit is a rectangular area; Dividing the profile data by taking the minimum vertical resolution unit as a unit; Based on the multi-parameter joint threshold, the region to which each unit belongs is determined, and the preliminary partition conclusion of the profile data is obtained; The center point of each unit is determined, and the preliminary partitioning conclusion is corrected with the partitioning results between adjacent units that are horizontally distributed and whose center point height difference is lower than the threshold, so as to obtain the preliminary cloud-aerosol distinction result.

10. A sea fog detection device based on satellite-borne laser radar, characterized in that: The device includes a setting module, a distinguishing module, a calibration module, an identification module and an output module; The setting module is used to calculate the statistical distribution characteristics of the scattering characteristic parameter value according to the scattering characteristics, and to set the multi-parameter joint threshold for cloud-aerosol differentiation according to the statistical characteristic distribution and an existing scattering characteristic database; The differentiation module is used to analyze the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data based on a set multi-parameter joint threshold, complete the preliminary cloud-aerosol differentiation, and screen out the cloud layers within a specified height from the sea surface from the cloud-aerosol range; The calibration module is used to obtain vertical structure information of the atmospheric layer according to the satellite-borne laser radar, and to calibrate the area that meets the scattering characteristic criterion as the sea fog layer in combination with the cloud base height and scattering characteristic parameters of the cloud layer; The identification module is used to calculate the continuity characteristics of the sea fog layer in vertical and horizontal distribution, judge the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixel points in the vertical direction, and identify the signal interruption point and abnormal distribution area in combination with the spatial topological characteristics of the sea fog layer in the horizontal distribution; The output module is used to interpolate and complete the scattering characteristic data of the signal interruption point area, eliminate the abnormal distribution area, and recalibrate the hierarchical characteristics of the vertical and horizontal distribution, and output the optimized sea fog hierarchical distribution result.

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