A method and device for detecting sea fog based on spaceborne lidar

Abstract

The present application provides a sea fog detection method and device based on satellite-borne laser radar, which belongs to the field of atmospheric remote sensing monitoring. The method includes: setting a multi-parameter joint threshold for cloud-aerosol differentiation; based on the set multi-parameter joint threshold, completing the preliminary differentiation of cloud-aerosol, and screening out cloud layers within a specified height from the sea surface from the cloud-aerosol range; combining the cloud base height and scattering characteristic parameters of the cloud layer, calibrating the area that meets the scattering characteristic criteria as the sea fog layer; calculating the continuity characteristics of the sea fog layer in the vertical and horizontal distribution, identifying the signal interruption point and the abnormal distribution area; interpolating and completing the scattering characteristic data of the signal interruption point area, eliminating the abnormal distribution area, and re-calibrating the vertical and horizontal distribution layer characteristics, and outputting the optimized sea fog layer distribution result. The sea fog detection method and device based on satellite-borne laser radar provided in the present application can accurately and effectively realize the detection of the vertical distribution of sea fog.

Description

Technical Field

[0001] The present application relates to the field of atmospheric remote sensing monitoring technology, and in particular to a sea fog detection method and device based on spaceborne lidar. Background Art

[0002] Sea fog is a low-level atmospheric phenomenon formed by condensation or bloom. It typically consists of large accumulations of water droplets or ice crystals (or both), and can reduce horizontal visibility to below 1 km. Sea fog is common in oceans and coastal areas, severely impacting air, sea, and land transportation. It also significantly impacts communications, fishing, and maritime military activities. Therefore, the formation mechanism, evolution, and spatiotemporal distribution characteristics of sea fog have long been important topics in meteorological research.

[0003] Due to the sparseness of meteorological stations in ocean areas, traditional observation methods are unable to meet the needs of obtaining large-scale, high-precision sea fog data. Existing sea fog distribution detection algorithms mainly detect the sea fog coverage area and rely on multi-channel spectral data from satellite-borne passive remote sensing, without fully utilizing 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 vertical profile data, 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 spaceborne lidar, which can accurately and effectively detect the vertical distribution of sea fog.

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

[0006] In a first aspect, the present application provides a sea fog detection method based on a spaceborne laser radar, the method comprising:

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

[0008] Based on a 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 cloud-aerosol distinction, and cloud layers within a specified altitude above the sea surface are screened from the cloud-aerosol range.

[0009] The vertical structure information of the atmospheric layer is obtained based on the spaceborne lidar, and the cloud base height and scattering characteristic parameters of the cloud layer are combined to calibrate the area that meets the scattering characteristic criteria as the sea fog 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 signal interruption points and abnormal distribution areas based on the spatial topological characteristics of the sea fog layer in the horizontal distribution;

[0011] The scattering characteristic data of 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 values ​​according to the scattering characteristics, and 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 filter out the cloud layers within a specified height above the sea surface from the cloud-aerosol range;

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

[0016] The recognition module is used to calculate the continuity characteristics of the sea fog layer in the vertical and horizontal distribution, determine the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixels in the vertical direction, and identify signal interruption points and abnormal distribution areas based on 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 to output the optimized sea fog hierarchical distribution result.

[0018] The sea fog detection method and device based on satellite-borne laser radar provided in this application calculates the statistical distribution characteristics of the scattering characteristic parameter values ​​and sets a multi-parameter joint threshold. Compared with the traditional method of relying on 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 the judgment is made in combination with the cloud base height and scattering characteristic parameters. The area that meets the scattering characteristic criteria can be accurately calibrated as the sea fog layer. This utilizes the advantages of laser radar in vertical detection, overcomes the defect that traditional observation methods are difficult to obtain atmospheric vertical structure information, and makes the identification of sea fog layers 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. This 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 This is a flow chart of Example 1 of the sea fog detection method based on space-borne lidar 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 this application;

[0021] Figure 3 This is a diagram showing the verification results of the spaceborne lidar sea fog recognition results shown in this application;

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

[0023] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0024] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as 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 solutions of the present application in detail.

[0027] Example 1:

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

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

[0030] It should be noted that scattering characteristics refer to a series of properties exhibited by atmospheric particles (such as aerosols, cloud droplets, and ice crystals) when scattering laser or other electromagnetic waves. In the context of sea fog detection using spaceborne lidar, the main scattering characteristic parameters involved include the scattering ratio, attenuated backscattering coefficient, and backscattering coefficient. The scattering ratio is the ratio of the intensity of light scattered in the atmosphere at a certain wavelength to the total intensity of the reflected light at that wavelength. It reflects the differences in the scattering characteristics of different particles in the atmosphere and helps distinguish different atmospheric components. For example, in sea fog detection, it can be used to distinguish sea fog from other atmospheric layers (such as low clouds or aerosols). The attenuated backscattering coefficient describes the attenuation of the energy of a laser or other electromagnetic beam as it propagates through the atmosphere 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, as well as light energy loss, and accurately reflects the actual detected signal intensity. It 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 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 attenuated backscattering coefficient of the first-level 532nm wavelength channel, and the backscattering coefficient, scattering ratio, radar ratio, depolarization ratio and extinction coefficient of the second-level 532nm wavelength. 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 spaceborne lidar observation data as scattering characteristic parameters.

[0034] During operation, spaceborne lidars continuously observe the atmosphere and collect a large amount of echo data. Using specialized signal processing algorithms and data parsing modules, key scattering characteristics, such as the scattering ratio, attenuated backscatter coefficient, and backscatter coefficient, are extracted from this raw observation data. These algorithms and modules, typically based on optical principles and radar signal processing techniques, accurately identify and separate scattered signals of varying wavelengths, intensities, and angles, and then calculate the corresponding parameter values. For example, calculating the scattering ratio requires filtering and calculating a suitable value from complex signal data based on the ratio of the atmospheric scattering intensity of light of a specific wavelength to the total reflected light intensity. The attenuated backscatter coefficient is calculated based on the initial laser energy, propagation distance, and received backscattered energy, combining relevant theories and the radar equation. (The attenuated backscatter coefficient is related to the energy attenuation and backscattering of the laser beam as it propagates through the atmosphere. As the laser beam propagates through the atmosphere, its energy is attenuated by scattering and absorption by 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] Statistical analysis software or other tools can be used to summarize and organize the extracted scattering characteristic parameters. Specifically, when calculating the mean, all parameter values ​​are added together 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 using a series of mathematical formulas to measure the dispersion of parameter values ​​relative to the mean, reflecting data fluctuations. For the data distribution range, the maximum and minimum values ​​of the parameter are identified to determine the range covered on the numerical axis.

[0037] (3) 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. The existing scattering characteristic database is combined to form a distinction rule.

[0038] It should be noted that the researchers have previously collected and compiled a large amount of data on the scattering characteristics of clouds and aerosols. These data 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, while 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 can be made on 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, further in-depth analysis and determination of the atmospheric component characteristics corresponding to the current parameters, such as the differences in the 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 multi-parameter joint threshold is derived from a large number of historical observation results. Based on 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 a set multi-parameter joint threshold, the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data are analyzed to perform a preliminary cloud-aerosol distinction, and cloud layers within a specified height above the sea level 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 cloud-aerosol distinction, 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 from the spaceborne lidar is presented in profile format. Within the software system, specific data extraction programs are applied to each vertical resolution unit. These programs, based on pre-defined algorithms, accurately calculate scattering parameters such as the scattering ratio, attenuated backscatter coefficient, and backscatter coefficient from the large amount of raw echo data for each unit. Next, the extracted scattering parameters for each unit are individually compared against a pre-set multi-parameter joint threshold. This comparison process involves evaluating each parameter within a set threshold range. Specifically, if the scattering ratio threshold is greater than a certain value, the attenuated backscatter coefficient is within another specific range, and the backscatter coefficient also has a corresponding value range, then only if all three parameters for a vertical resolution unit meet their respective thresholds will it be considered cloud-like and marked as a cloud region. Conversely, if any parameter fails to meet the threshold, the region is marked as an aerosol region. For example, taking the scattering ratio threshold as an example, if the set threshold is greater than 10, this condition is met when the calculated scattering ratio 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 a 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 spaceborne lidar data collection is inevitably subject to interference from various factors, resulting in noise points and outliers. These anomalies can affect the accuracy of cloud-aerosol differentiation. To mitigate this effect, a neighborhood smoothing algorithm is used for filtering. This algorithm is based on the spatial correlation of the data. For each data point, the values ​​of neighboring data points within a certain range are considered. Using a weighted average or other smoothing method, noise points and outliers are corrected or eliminated.

[0054] Specifically, for the identification of noise points and outliers, the statistical characteristics of the scattering characteristic parameters within a certain neighborhood range around each vertical resolution unit (for example, a 3x3 or 5x5 unit matrix), such as the mean and standard deviation, can be calculated. If the deviation of the parameter value of a certain 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 include the simple averaging method. For a unit determined to be abnormal, the scattering characteristic parameters of the normal units in its neighborhood are averaged and the average value is used to replace the parameter value of the abnormal unit; or the weighted averaging method is used. Different weights are assigned according to the distance between the neighboring unit and the abnormal unit for weighted averaging calculation. The closer the distance, the greater the weight. In this way, the abnormal data is corrected 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 lidar profile data are analyzed, including: obtaining the spaceborne lidar profile data; determining the minimum vertical resolution unit according to the data distribution characteristics of the profile data, where the minimum vertical resolution unit is 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, to obtain a preliminary cloud-aerosol distinction result.

[0056] Specifically, in the process of sea fog detection using spaceborne lidar, the processing of profile data is crucial. First, the acquired spaceborne lidar profile data exhibits certain spatial distribution characteristics, with an overall rectangular shape. To more precisely analyze the data, it is necessary to determine the minimum vertical resolution unit (MVR) based on the data distribution characteristics of the profile data. This process involves dividing a large rectangular canvas into many smaller rectangular grids, with each small rectangle representing a MVR unit. After the unit division is completed, the profile data is finely segmented based on these MVR units. Next, the region to which each unit belongs is determined based on a pre-set multi-parameter joint threshold, resulting in a preliminary partitioning conclusion for the profile data. However, this preliminary partitioning conclusion may contain some error. At this point, the center point of each unit is further determined, focusing on adjacent units with horizontal distribution and a center point height difference below the threshold. Due to the complexity of the atmospheric environment and data volatility, the partitioning results for units on the same horizontal line may fluctuate. By analyzing the partitioning results of these adjacent units, suspected areas with significant variation in the partitioning results can be identified.

[0057] Finally, these suspected areas are corrected using a method that fits the partitioning trend. Specifically, mathematical models and algorithms are used to predict a reasonable partitioning result for the suspected area based on reliable partitioning results of adjacent areas and existing trends. This is used as the corrected partitioning result. For example, if adjacent areas are mostly identified as aerosol areas and there is a trend of gradual transition, if the initial judgment of the suspected area does not match this, the suspected area is re-evaluated and corrected in conjunction with trend analysis, resulting in a more accurate initial cloud-aerosol distinction.

[0058] Furthermore, after completing the initial cloud-aerosol distinction, cloud layers within a specified altitude above sea level will be screened from the cloud-aerosol range. In specific implementation, based on long-standing research and understanding of the distribution characteristics of sea fog near sea level, sea fog typically exists within a certain altitude range above sea level. Based on a large number of past field observations, statistical analyses, and relevant theoretical research results, researchers will set an appropriate vertical altitude range (for example, 100 meters to 1 kilometer from sea level, with the specific value determined based on actual research and application scenarios).

[0059] Then, within the previously identified cloud-aerosol range, data is filtered according to this set altitude range, retaining only cloud layer-related data within this altitude range and excluding cloud data outside this range. This preliminarily narrows the target range and focuses 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 altitude range above the sea surface, spaceborne lidar can be used to obtain accurate cloud base heights and calculate their distance from the sea surface, which serves as the basis for distinguishing sea fog from stratus clouds. Specifically, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) method indicates that CALIOP's Vertical Feature Mask (VFM) data may misidentify sea fog as sea surface. However, since ACDL also carries an altimetry payload and has real-time surface elevation measurements, this problem does not exist when using ACDL to obtain sea fog layers.

[0061] S103. Obtain vertical structure information of the atmospheric layer according to the spaceborne laser radar, combine the cloud base height and scattering characteristic parameters of the cloud layer, and calibrate the area that meets the scattering characteristic criteria as the sea fog layer.

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

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

[0064] It's important to note that spaceborne lidar emits laser pulses into the atmosphere and receives signals scattered back from atmospheric particles at different altitudes. The lidar's echo signal processing mechanism can analyze how signal strength varies with altitude—that is, how the intensity of the scattering characteristic parameters changes. As altitude increases, the concentration and size of particles in different atmospheric layers vary, causing the intensity of the scattered laser signal to change accordingly. By analyzing these echo intensities, a curve of signal strength versus altitude can be generated.

[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 altitude range, it can be determined that this is the height of the cloud base. Specifically, calculating the cloud base height includes: the satellite-borne lidar continuously emits laser pulses into the atmosphere, and receives signals scattered back by atmospheric particles at different altitudes, recording the time information and signal strength information corresponding to each echo signal; real-time monitoring and analysis of the received echo signal intensity, marking the signal intensity value and the corresponding time point when the echo signal intensity begins to show an increasing trend; monitoring the subsequent echo signal intensity, and recording the altitude information when the echo signal intensity reaches a pre-set threshold and maintains a high intensity state within a specified altitude range; the starting altitude 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 vertical distribution characteristics of cloud layers, such as cloud thickness, the distance 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 the cloud layer 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 selected cloud layer data within a specified altitude, appropriate data extraction methods can be used to accurately obtain key scattering characteristic parameters such as the scattering ratio, attenuated backscatter coefficient, and backscatter coefficient. (The vertical values ​​of these parameters vary with altitude and have their own distribution patterns.) Statistical analysis methods can then be used to study the vertical distribution characteristics of these parameters. For example, the mean and standard deviation of the scattering ratio at different altitudes can be calculated, and its trend with altitude can be analyzed to determine whether it increases, decreases, or fluctuates. This statistical analysis can further understand the specific scattering characteristics of these cloud layers.

[0068] (3) Based on the cloud base height information detected by the satellite-borne lidar, 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 cloud base height information detected by spaceborne lidar, the system checks whether low clouds exist within a set vertical height range. For existing low clouds, its scattering characteristics (scattering ratio, attenuated backscatter coefficient, and backscatter coefficient) are analyzed. If these scattering characteristics of the low clouds overlap or are highly similar to those of previously identified aerosol regions, the low cloud layer can be identified as sea fog in this specific case, based on our understanding of sea fog formation mechanisms and its relationship with aerosols and low clouds. Compared to aerosols, sea fog exhibits stronger backscattering, a smaller lidar ratio, greater attenuation of lidar signals, a higher scattering ratio, and sharper layer edges. The key difference between sea fog and low-level clouds is that low-level clouds are not directly connected to the surface, while the base of sea fog is directly in contact with the ocean surface. This overlap in characteristics allows the identification of possible sea fog locations in complex atmospheric environments.

[0070] (4) Compare the scattering characteristic parameters of the screened areas with the set scattering characteristic criteria one by one to confirm whether they meet the multi-parameter joint threshold conditions of sea fog characteristics.

[0071] The scattering characteristic parameters of the selected areas (including low clouds previously identified as likely sea fog and other cloud layers within a specified altitude) are compared parameter by parameter with pre-set multi-parameter joint thresholds for sea fog characteristics. For example, the multi-parameter joint thresholds for sea fog characteristics specify that the scattering ratio must be within a specific range and the attenuated backscatter coefficient must also fall within a corresponding range.

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

[0073] (5) Calibrate the aerosol area that meets the scattering characteristic criteria, record the vertical distribution position and corresponding scattering characteristic parameters of the aerosol area that meets the scattering characteristic criteria, and complete the preliminary identification of the sea fog layer.

[0074] In this way, the preliminary identification of sea fog layers is completed. 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. 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 signal interruption points and abnormal distribution areas based on the spatial topological characteristics of the sea fog layer in the horizontal distribution.

[0076] Specifically, identifying signal interruption points and abnormal distribution areas includes:

[0077] (1) Extract the scattering characteristic parameters of the calibrated sea fog layer from the spaceborne lidar data, and record the vertical and horizontal distribution positions of the scattering characteristic parameters.

[0078] It should be noted that from the massive amount of data acquired by the spaceborne lidar, data extraction operations are specifically performed on the portion previously calibrated as the sea fog layer. Using the corresponding data extraction algorithms and programs, scattering characteristic parameters (key parameters such as scattering ratio, attenuated backscatter coefficient, and backscatter coefficient) are accurately separated from it. Furthermore, in order to subsequently analyze the spatial variations of these parameters, their corresponding vertical and horizontal distribution positions need to be recorded. For example, the height value of each pixel from the sea level is recorded in the vertical direction, and its latitude and longitude coordinates or relative coordinate position within the radar scanning plane are recorded in the horizontal direction. This position information corresponds one-to-one with the scattering characteristic parameters.

[0079] (2) Perform differential calculation on the scattering characteristic parameters of adjacent pixel points in the vertical direction, analyze the changing 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 to be a discontinuity point in the vertical direction.

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

[0081] Next, the changes in these parameters are compared with the preset continuity threshold for judgment. 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 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 scattering ratio between 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 a figure (here, this can be compared to the distribution of the sea fog layer on the horizontal plane) that remain unchanged under continuous deformation, such as connectivity and closure. Using relevant topological analysis algorithms, the boundary shape of the sea fog layer is detected to determine its integrity and continuity, as well as the distribution and connectivity between different areas of the sea fog layer on the horizontal plane. For example, if a small, isolated area of ​​the sea fog layer is found in the horizontal direction, disconnected from the surrounding sea fog layers, or if there are areas that should be continuous but appear discontinuous, these isolated and discontinuous areas are marked as horizontal discontinuities.

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

[0085] Figure 2 This is a schematic diagram of identifying signal discontinuity points and abnormal distribution areas based on continuity features shown in this application. Please refer to Figure 2 , Figure 2 Figure a in the figure is the attenuated backscatter coefficient identification result diagram, Figure 2 Figure b in the figure 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 shows the sea fog identification results. The distribution patterns of sea fog layers are typically derived from meteorological principles and past observational experience, such as the approximate horizontal and vertical range, continuity, and relationship with the surrounding atmospheric environment under specific meteorological conditions. By comparing the actual detected sea fog distribution with discontinuities with these patterns, it is possible to identify areas where the distribution does not conform to normal conditions. These abnormal distribution areas are likely caused by noise interference or measurement errors (data deviations caused by instrument accuracy and calibration issues).

[0086] S105. 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 to output 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 discontinuity points, the scattering characteristic parameters of adjacent units are extracted and the missing data are supplemented using linear interpolation.

[0089] It's important to note that once a vertical breakpoint is determined, it's necessary to examine its neighboring cells. These neighboring cells include pixels located vertically above and below the breakpoint. After extracting the scattering characteristic parameters of the neighboring cells, linear interpolation can be used to fill in the missing data. Linear interpolation is based on a simple linear relationship assumption: the change in the scattering characteristic parameters between two adjacent cells is linear. For example, if the scattering ratio of the neighboring cell above the breakpoint is A, and the scattering ratio of the neighboring cell below the breakpoint is B, and the vertical distance from the breakpoint to the cell above is d1, and the vertical distance from the breakpoint to the cell below is d2 (the total distance is d1 + d2), then the scattering ratio value to be supplemented at the breakpoint can be calculated using the linear interpolation formula (interpolation result = A + (B-A) * (d1 / (d1 + d2))).

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

[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). Using the known, continuous scattering ratio values ​​of pixels on either side of the horizontal break as sample data, the coefficients a, b, and c in the polynomial are determined using a fitting algorithm such as the least squares method, resulting in a complete fitting function. This function can be used to calculate the supplementary scattering ratio values ​​for each location within the horizontal break area. Similar polynomial fitting operations are performed for parameters such as the attenuated backscatter coefficient and the backscatter coefficient, completing the data in the horizontal break area and ensuring a more consistent horizontal distribution of sea fog.

[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). This difference is then compared with a pre-specified threshold. This specified threshold 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 sea fog distribution continuity requirements, then the interpolation result needs to be readjusted. 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) 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.

[0097] Specifically, vertically, we can check whether the scattering characteristic parameters of each pixel change continuously along the height direction according to a reasonable change pattern, such as whether there are sudden jumps or breakpoints that do not conform to the trend. Horizontally, we can observe whether the distribution of the sea fog layer within the plane is continuous, and whether there are isolated areas that are inconsistent with the overall distribution. 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 the scattering characteristic parameters of the abnormal area. If the scattering characteristic parameter values ​​of the abnormal area deviate from the statistical distribution characteristics of the calibrated sea fog layer, the abnormal area is determined to be an area to be deleted.

[0099] It should be noted that the scattering characteristic parameter values ​​of the abnormal area are compared with the statistical distribution characteristics of the previously calibrated normal sea fog layer. If the scattering characteristic parameter values ​​of the abnormal area deviate significantly from the statistical distribution characteristics of the normal sea fog layer, for example, its scattering ratio value far exceeds 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). 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 information such as the mean, standard deviation, and common value range of each scattering characteristic parameter.

[0100] In conjunction with the above description, specifically, scattering characteristic parameters are extracted from the calibrated abnormal area data. The difference between each scattering characteristic parameter and the mean of the scattering characteristic parameter corresponding to the normal sea fog layer is calculated, and the difference is divided by the standard deviation of the scattering characteristic parameter of the normal sea fog layer to obtain a standardized deviation value. A standardized deviation threshold is set. When the scattering characteristic parameters in a region all meet the standardized deviation threshold, the region is determined to be a candidate abnormal region. The changes in the scattering characteristic parameters in the candidate abnormal region and the preset region are analyzed, and the cause of the abnormality is determined based on the change trend and relevant records during the data collection process. When the cause of the abnormality is noise, detection error, or misjudgment, the region is determined to be an abnormal region and marked as a region to be deleted. It should also be noted that if the cause of the abnormality cannot be clearly determined, the region is further manually analyzed or verified using a more complex algorithm until it is determined whether it is an abnormal region.

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

[0102] Once certain areas are identified as outliers, the data from these areas is directly removed from the calibrated sea fog layer data during data processing, eliminating their inclusion in subsequent analysis and result presentation. This prevents these inaccurate data from misleading the final sea fog layer distribution results. Furthermore, due to the removal of outliers, the originally calibrated sea fog layer range needs to be readjusted. Based on the remaining data after removal and the surrounding sea fog areas that meet the continuity characteristics, the appropriate vertical and horizontal distribution ranges of the sea fog layer are re-determined to ensure that the final output sea fog layer distribution results truly and accurately reflect the actual sea fog distribution state in the atmosphere.

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

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

[0105] Professional mapping software or the built-in mapping function of a data analysis platform can be used to plot the distribution of sea fog layers at various heights into an intuitive vertical profile, with altitude as the vertical coordinate (usually the height above sea level) and horizontal position or related identification parameters as the horizontal coordinate (for example, the horizontal coordinate position along a radar scan line). Such a profile can clearly demonstrate the vertical thickness, layer structure, and boundary between the sea fog and the surrounding atmosphere.

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

[0107] Using geographic information system (GIS) software or tools with geographic coordinate mapping capabilities, the distribution of sea fog on a horizontal plane is plotted using longitude and latitude as the coordinate system. This map visually shows the extent and shape of sea fog coverage, as well as the density of 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 previously generated vertical profile and sea fog horizontal distribution map 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 and the height from sea level as the vertical coordinates, a three-dimensional model is constructed using the relevant characteristics of sea fog (such as scattering characteristic parameters, which can be represented by different colors, transparency, etc.) 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 status 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 for in-depth research on the formation mechanism, evolution law and impact of sea fog on the ocean and atmospheric environment.

[0110] Furthermore, after outputting the optimized sea fog layer distribution results, they can be compared with actual observational data from the International Integrated Ocean-Atmosphere Dataset (ICOADS) to evaluate the accuracy of the recognition results. By comparing with ICOADS data, the accuracy of the sea fog recognition method can be verified, its recognition capabilities under different meteorological conditions can be evaluated, and the model can be optimized. During this comparative analysis, ACDL trajectories with a time interval of less than 2 hours and a spatial distance of less than 100 km can be selected for comparison with ICOADS data points.

[0111] Figure 3 Please refer to the verification result diagram of the space-borne lidar sea fog recognition result shown in this application. Figure 3 Within the selected time range and spatial range of the four major sea areas (Regions 1, 2, 3, and 4), there were 1,873 cases of spatiotemporal matches between ACDL trajectories and ICOADS records. Of these, 26 were found in Region 1, 173 in Region 2, 420 in Region 3, and 1,254 in Region 4. Comparing the ACDL sea fog identification results with the ICOADS records, there were 1,688 cases where both ACDL and ICOADS records showed fog or no fog. ICOADS recorded fog as foggy but ACDL identified no fog in 16 cases, and ICOADS recorded no fog as foggy but ACDL identified foggy in 169 cases. This resulted in a correct identification rate of 90.12%, a missed alarm rate of 0.85%, and a false alarm rate of 9.02%.

[0112] The method provided in this embodiment calculates the statistical distribution characteristics of scattering parameter values ​​and sets a multi-parameter joint threshold. It comprehensively considers multiple scattering characteristics, such as the scattering ratio, attenuated backscattering coefficient, and backscattering coefficient. This overcomes the limitations of traditional judgments that rely on a single or limited number of parameters, greatly reduces the possibility of misjudgment, and makes cloud-aerosol differentiation more accurate. The atmospheric environment is complex and changeable, and a single parameter cannot fully reflect the differences in cloud and aerosol characteristics. The scattering ratio reflects the scattering characteristics of different particles, the attenuated backscattering coefficient reflects the energy attenuation and the combined characteristics of particles, and the backscattering coefficient represents the scattering ability of particles. The combination of these three can accurately define clouds and aerosols from multiple dimensions. By statistically analyzing a large amount of observation data and fully considering the distribution patterns and interrelationships of various parameters under different atmospheric conditions, the accuracy of cloud-aerosol differentiation is significantly improved, effectively reducing the risk of misjudgment. By utilizing the vertical structure information of spaceborne lidar, cloud layers within a specified height above the sea surface are screened based on cloud-aerosol differentiation. Combining cloud base height and scattering characteristic parameters, this method fully utilizes the advantages of lidar vertical detection, overcomes the difficulties of traditional methods in obtaining atmospheric vertical structure information, and makes sea fog layer identification more accurate and reliable. Furthermore, by calculating the vertical and horizontal continuity characteristics of sea fog layers, identifying signal discontinuities and areas of abnormal distribution, interpolating and completing discontinuity areas and removing abnormal areas, the method ultimately recalibrates the layer characteristics and outputs an optimized sea fog layer distribution. This effectively addresses issues such as discontinuity and misidentification of sea fog layers caused by factors such as signal attenuation and data noise, improving the integrity and accuracy of the sea fog distribution results. Furthermore, this operation helps remove misidentified sea fog layers and ensures the physical and geographical plausibility of all identified layers. Finally, comparisons with actual observational data from the International Comprehensive Ocean-Atmosphere Dataset strongly validate the accuracy and effectiveness of this method, 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 status of the sea fog layer by focusing only 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 a precise 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 space-borne laser radar, the present application also provides an embodiment of a sea fog detection device based on a space-borne laser radar.

[0115] Example 2:

[0116] Figure 4 This is a schematic diagram of the structure of the second embodiment of the sea fog detection device based on space-borne laser radar provided by 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 configured to calculate statistical distribution characteristics of scattering characteristic parameter values ​​based on the scattering characteristics, and to set a multi-parameter joint threshold for cloud-aerosol differentiation based on the statistical characteristic distribution and an existing scattering characteristic database;

[0118] The differentiation module 420 is configured 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, perform preliminary cloud-aerosol differentiation, and filter out cloud layers within a specified altitude from the sea surface from the cloud-aerosol range;

[0119] The calibration module 430 is configured to obtain vertical structure information of the atmospheric layer based on the spaceborne laser radar, and to calibrate the area that meets the scattering characteristic criteria as the sea fog layer based on 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 the vertical and horizontal distribution, determine the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixels in the vertical direction, and identify signal interruption points and abnormal distribution areas based on 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 to 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 embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely 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 replacements, 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 spaceborne laser radar, characterized in that: The method comprises: Calculating statistical distribution characteristics of scattering characteristic parameter values ​​based on the scattering characteristics, and setting a multi-parameter joint threshold for cloud-aerosol differentiation based on the statistical distribution characteristics and an existing scattering characteristic database; Based on a 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 cloud-aerosol distinction and filter out cloud layers within a specified altitude above the sea surface from the cloud-aerosol range. The vertical structure information of the atmospheric layer is obtained based on the spaceborne lidar, and the cloud base height and scattering characteristic parameters of the cloud layer are combined to calibrate the area that meets the scattering characteristic criteria as the sea fog 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 signal interruption points and abnormal distribution areas based on the spatial topological characteristics of the sea fog layer in the horizontal distribution; Interpolate and complete the scattering characteristic data of the signal interruption point area, eliminate the abnormal distribution area, and recalibrate the vertical and horizontal distribution layer characteristics to output the optimized sea fog layer distribution results; The multi-parameter joint threshold for cloud-aerosol differentiation is set as follows: 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. The existing scattering characteristic database is combined to form a distinction rule. A multi-parameter joint threshold is set according to the statistical distribution characteristics of the scattering ratio, the attenuated backscattering coefficient and the backscattering coefficient and the distinction rule.

2. The method according to claim 1, characterized in that The method analyzes the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data based on the set multi-parameter joint threshold to complete the preliminary cloud-aerosol distinction, 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 a 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.

3. The method according to claim 1, characterized in that Defining the area satisfying the scattering characteristic criterion as the sea fog layer includes: Obtain vertical distribution information of the atmospheric layers from 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, including scattering ratio, attenuated backscattering coefficient, and backscattering coefficient, from the cloud-aerosol range, and performing statistical analysis on vertical distribution characteristics of the scattering characteristic parameters; Based on the cloud base height information detected by the spaceborne lidar, 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; The scattering characteristic parameters of the screened areas are compared one by one with the set scattering characteristic criteria to confirm whether they meet the multi-parameter joint threshold conditions of sea fog characteristics; 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.

4. The method according to claim 1, wherein Identify signal discontinuities and abnormal distribution areas, including: Extracting scattering characteristic parameters of the calibrated sea fog layer from the spaceborne lidar 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 changing 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 to be a discontinuity point in the vertical direction; Based on the spatial connectivity of the calibrated sea fog layer in the horizontal distribution, a 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. Based on the vertical and horizontal breakpoints and the distribution patterns of sea fog layers, abnormal distribution areas caused by noise and errors are identified.

5. The method according to claim 1, wherein 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, 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. The completed data is checked for continuity. If the difference between the completed data and the neighborhood scattering characteristic parameters is greater than the specified threshold, the interpolation result is readjusted.

6. 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 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, and if the scattering characteristic parameter values ​​of the abnormal area deviate from the statistical distribution characteristics of the calibrated sea fog layer, determining that the abnormal area is an area to be deleted; The areas judged to be abnormal are eliminated and the calibrated sea fog level range is readjusted.

7. 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 graph are integrated to form a three-dimensional distribution result.

8. The method according to claim 1, characterized in that The analyzing of the scattering characteristic parameters of each vertical resolution unit in the spaceborne lidar profile data includes: Obtain spaceborne 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 using the minimum vertical resolution unit as a unit; Based on the multi-parameter joint threshold, the area 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 differentiation result.

9. A sea fog detection device based on spaceborne laser radar, characterized in that: The device includes a setting module, a differentiation 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 values ​​according to the scattering characteristics, and set the multi-parameter joint threshold for cloud-aerosol differentiation according to the statistical distribution characteristics 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 filter out the cloud layers within a specified height above the sea surface from the cloud-aerosol range; The calibration module is used to obtain vertical structure information of the atmospheric layer based on the space-borne laser radar, and to calibrate the area that meets the scattering characteristic criteria as the sea fog layer in combination with the cloud base height and scattering characteristic parameters of the cloud layer; The recognition module is used to calculate the continuity characteristics of the sea fog layer in the vertical and horizontal distribution, determine the vertical continuity of the layer based on the scattering characteristic parameters of adjacent pixels in the vertical direction, and identify signal interruption points and abnormal distribution areas based on 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 vertical and horizontal distribution layer characteristics to output the optimized sea fog layer distribution result; The multi-parameter joint threshold for cloud-aerosol differentiation is set as follows: 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. The existing scattering characteristic database is combined to form a distinction rule. A multi-parameter joint threshold is set according to the statistical distribution characteristics of the scattering ratio, the attenuated backscattering coefficient and the backscattering coefficient and the distinction rule.

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