A method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing

Abstract

The present invention discloses a method for detecting the three-dimensional distribution of sea fog based on the fusion of satellite-borne active and passive remote sensing. This method fully utilizes the high-precision vertical profile detection capability of satellite-borne active remote sensing lidar and the large-scale, long-term continuous observation capability of geostationary satellite passive remote sensing. The method selects the best band combination through reconstruction and verification, evaluates the multi-band spectral similarity, selects the most appropriate active remote sensing profile, and extends it to a certain distance outside the active remote sensing detection range to construct a three-dimensional distribution of sea fog layers. Compared with the sea fog layer detection results of existing methods, this method effectively expands the satellite's three-dimensional detection range, has the advantages of high spatial resolution and detectable vertical layers, is applicable to multiple scenarios, helps understand the formation and dissipation mechanism of sea fog, and can also serve as a high-precision three-dimensional initial field for sea fog forecasting.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric remote sensing monitoring, and in particular to a method for detecting three-dimensional distribution of sea fog based on the fusion of satellite-borne active and passive remote sensing. Background Art

[0002] Sea fog is a condensation phenomenon in the lower atmosphere above the ocean. Formed by the accumulation of large amounts of water droplets or ice crystals (or both), it can reduce horizontal visibility to below 1 km. As a common meteorological disaster in marine or coastal areas, sea fog directly affects air, sea, and land transportation, while also causing significant inconvenience to communications, fishing, and maritime military activities. Therefore, the formation, diffusion mechanisms, and spatiotemporal distribution of sea fog have long been a research focus for scholars at home and abroad. To better understand the formation, evolution, and dissipation processes of sea fog, it is necessary to accurately understand its three-dimensional distribution information. High-precision three-dimensional sea fog distribution grid data can serve as the initial field input for predicting sea fog formation, diffusion, and dissipation, thereby improving the accuracy of sea fog predictions.

[0003] Due to the sparse distribution of meteorological stations over the ocean, spaceborne passive remote sensing has become an important tool for sea fog monitoring, satisfying the need for obtaining high-quality, large-scale sea fog observation data. It offers advantages such as a wide observation range, high real-time availability, and rich information. However, due to the extreme similarity in radiation and brightness temperature across channels between sea fog and stratus clouds, distinguishing between the two has always been a challenge in sea fog monitoring. The only difference between the two is that the base of sea fog lies close to the ocean surface, while the base of stratus clouds rises a certain distance above the surface.

[0004] Relying solely on passive remote sensing, it's difficult to obtain information on the vertical structure of clouds and fog. Whether it's obstruction by high clouds or misclassification between low-level clouds and sea fog, the accuracy of passive remote sensing sea fog detection is limited. Unlike passive remote sensing, active remote sensing, such as spaceborne lidar, can capture the vertical structure of the atmosphere and has the potential to distinguish sea fog from low-level clouds.

[0005] However, spaceborne active remote sensing (lidar, cloud radar, etc.) also has significant shortcomings. Their observation targets are distributed in a point-like pattern along the satellite's subsatellite track, along the direction of flight. This narrow coverage allows for three-dimensional observation only within the orbital coverage area. Sea fog typically covers a wide area, and using spaceborne lidar to obtain only a single profile is insufficient to support comprehensive three-dimensional monitoring of sea fog over the entire ocean.

[0006] The invention patent with authorization announcement number CN 114022782A discloses a sea fog detection method based on MODIS satellite data, including preprocessing MODIS satellite data and performing feature extraction; selecting ground object samples and dividing them into training and test sets in combination with CALIOP VFM satellite data; using the information gain rate of node entropy as the impurity criterion for node splitting, and assigning a weight to each typical correlation tree in the typical correlation forest, and training with the training set to obtain an improved typical correlation forest model; adjusting and updating the weight of each typical correlation tree during post-testing to obtain a continuously optimized improved typical correlation forest model; and using the model to identify satellite images to obtain sea fog recognition results.

[0007] Current sea fog distribution detection algorithms primarily focus on detecting fog coverage, relying on multi-channel spectral data from spaceborne passive remote sensing. These algorithms fail to fully utilize the vertical structure detection capabilities of spaceborne active remote sensing, resulting in limited two-dimensional sea fog distribution results. Therefore, a three-dimensional sea fog distribution detection method based on the fusion of spaceborne active and passive remote sensing is needed. This method would facilitate the construction of a high-precision three-dimensional sea fog model, enabling better research on sea fog mechanisms and forecasting. Summary of the Invention

[0008] To address the problem that most existing spaceborne sea fog distribution detection algorithms only obtain two-dimensional distributions and are unable to perform large-scale sea fog layer height distribution, the present invention proposes a three-dimensional sea fog distribution detection method based on the fusion of spaceborne active and passive remote sensing. The algorithm uses the 532nm attenuated backscatter coefficient, scattering ratio and backscatter coefficient obtained by spaceborne lidar and the multi-band albedo or brightness temperature data of geostationary satellites as input to identify sea fog layers and construct three-dimensional sea fog structures, thereby obtaining continuous, high-spatial-resolution three-dimensional distribution information of sea fog layers.

[0009] The specific technical solutions adopted are as follows:

[0010] A method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft, comprising the following steps:

[0011] (1) Obtaining spaceborne lidar remote sensing observation data as active remote sensing data, including obtaining the calibrated attenuated backscatter coefficient of level 1 (i.e., level 1), and simultaneously obtaining the corresponding backscatter coefficient and scattering ratio of level 2 (i.e., level 2) as the basis for subsequent identification of sea fog layers; obtaining geostationary satellite remote sensing observation data as passive remote sensing data, including multi-band spectral information, specifically including the albedo of each channel in the visible and near-infrared bands and the brightness temperature of each channel in the infrared band; performing spatiotemporal matching between the spaceborne lidar remote sensing observation data and the geostationary satellite remote sensing observation data;

[0012] (2) By using the optical property criterion, the thresholds of the scattering ratio, attenuated backscattering coefficient, and backscattering coefficient are set, and the cloud and aerosol layers in the detection profile are distinguished based on the thresholds. By using the physical position criterion, the vertical detection advantage of the spaceborne lidar is brought into play, and the cloud layers within a certain threshold range above the sea surface are screened, and the pixels in the screened layers are identified as sea fog. On this basis, the layer continuity criterion is used to obtain a complete and continuous sea fog layer.

[0013] (3) Compare the sea fog identification results in step (2) with the actual observation data in the International Integrated Ocean-Atmosphere Dataset (ICOADS) to analyze the accuracy of sea fog identification in step (2);

[0014] (4) Based on the time-space matching of the vertical detection profile of the spaceborne lidar and the multi-band spectral information of the geostationary satellite, the information of the measured vertical profile is expanded to a certain range outside the orbit by analyzing the multi-channel spectral similarity between the points outside the orbit of the spaceborne lidar and the points on the orbit;

[0015] (5) Reconstruct the vertical detection profile of the spaceborne lidar using the method in step (4), and compare the sea fog layer height before and after reconstruction under different band conditions to select the band combination most suitable for detecting the sea fog layer height distribution;

[0016] (6) Apply the method of step (4) in combination with the band combination selected in step (5) to construct the three-dimensional distribution of sea fog within a certain range from the orbit of the satellite-borne lidar.

[0017] The present invention combines the high-precision atmospheric vertical profile detection capability of spaceborne lidar with the large-scale continuous observation capability of geostationary satellites, and realizes the three-dimensional distribution detection of sea fog based on the active and passive fusion method. It can effectively expand the satellite's three-dimensional detection range and improve the ability to distinguish sea fog from low clouds.

[0018] Preferably, in step (1), the channel attenuation backscatter coefficient, scattering ratio, and backscattering coefficient are obtained to better identify sea fog. Of course, optical characteristic parameters such as extinction coefficient, depolarization ratio, and radar ratio can also be obtained. These optical characteristic parameters can assist in sea fog identification. Generally speaking, sea fog has stronger backscattering and a smaller lidar ratio than aerosols, has a stronger attenuation of lidar signals, a larger scattering ratio, and clearer layer edges.

[0019] The difficulty in identifying sea fog lies mainly in distinguishing it from stratus clouds. Stratus clouds belong to the low cloud family, and the cloud base is usually less than 2 km above the ground or ocean surface, which often seriously affects the safety and stability of aviation. The large amount of water vapor in the cloud can also interfere with communication electromagnetic wave signals. The main difference between sea fog and stratus clouds is that stratus clouds are not directly connected to the surface, while the bottom of sea fog is in direct contact with the ocean surface. There is no obvious difference in physical properties between the two. Due to the inability to obtain high-precision layer information, passive remote sensing often finds it difficult to accurately distinguish sea fog from stratus clouds. Therefore, in step (2), it is necessary to give full play to the high-resolution atmospheric vertical profile detection capability of the satellite-borne lidar to accurately obtain the layer height and improve the ability to distinguish sea fog from stratus clouds.

[0020] Preferably, in step (2), the optical property judgment threshold is derived from a large number of historical observation results. Based on the long-term observation data of ground-based lidar, the optical property judgment formula for distinguishing cloud aerosols is:

[0021] SR>10

[0022] ATB≥0.02km -1 sr -1

[0023] β≥0.03km -1 sr -1

[0024] Where SR is the scattering ratio, ATB is the attenuated backscatter coefficient, and β is the backscatter coefficient. When the thresholds for SR, ATB, and β are met simultaneously, the cloud is considered to be a cloud; otherwise, it is considered to be an aerosol.

[0025] When using the extinction coefficient, depolarization ratio, and radar ratio to aid sea fog identification, clouds are considered likely to be clouds if the scattering ratio is less than 10, the depolarization ratio is greater than 0.15, and the radar ratio is less than 40. Since the extinction coefficient of clouds tends to increase with cloud thickness due to multiple scattering, this can also be used to aid sea fog identification.

[0026] Preferably, in step (2), the cloud layers within a certain threshold range above the sea surface are screened, and the accurate cloud base height is obtained by satellite-borne laser radar, and the distance between the cloud base and the sea surface is calculated as the basis for distinguishing sea fog from stratus clouds, and the pixels within the distance are identified as sea fog. The CALIOP-based method points out that CALIOP's VFM data may mistakenly identify sea fog as sea surface. Since satellite-borne laser radar is also equipped with an altimeter payload and has real-time surface elevation measurement data, this problem will not exist when using satellite-borne laser radar to obtain sea fog layers.

[0027] Preferably, in step (2), the method for obtaining a complete and continuous sea fog layer through the layer continuity criterion is as follows: for each pixel point identified as sea fog, if the pixel point directly connected to it meets the optical characteristic criterion, then the directly connected pixel point is identified as sea fog.

[0028] Preferably, in step (3), when making the comparison, the trajectory points in the sea fog identification results with a time interval of less than 2 hours and a spatial distance of less than 100 km are screened for comparison with the ICOADS data points. If both are identified as sea fog or both are identified as no sea fog, it is determined that the identification is accurate; if it is identified as sea fog but ICOADS does not record sea fog, it is determined to be a false identification; if it is not identified as sea fog but ICOADS records it as sea fog, it is determined to be missing. All matching cases are counted, and the accuracy rate, missing rate and false identification rate are analyzed to determine the accuracy of the method.

[0029] Preferably, in step (4), the basic assumption of the method of fusing active and passive remote sensing data to expand the measured vertical profile information to a certain range outside the orbit is that, in the case of close geographical locations and similar background conditions such as surface type, if the radiation amounts of two pixels in multiple spectral channels related to atmospheric characteristics such as clouds and aerosols in the passive remote sensing data are similar, then it can be considered that the vertical structures of clouds and aerosols at these two pixels are also similar. In the area where active and passive remote sensing are simultaneously detected, the vertical structure of clouds and aerosols and the spectral information of each band are known, and this information can be used as the "donor" in the expansion process. The pixels outside the orbit only have the spectral information of each band of passive remote sensing. Therefore, these pixels can be used as "receptors" and matched by selecting "donor" points with sufficiently similar spectral information to themselves.

[0030] Preferably, in step (4), the information of the measured vertical profile is expanded to a certain range outside the orbit by analyzing the multi-channel spectral similarity between the points outside the orbit of the satellite-borne laser radar and the points on the orbit:

[0031] First, record the position information of the donor pixel points on the ACHSRL (Aerosol-Cloud High-Spectral-Resolution Lidar) track and the receptor pixel points in the area to be expanded outside the ACHSRL track; the coordinates of the "donor" pixel point on the ACHSRL track are (a, 0), a∈[0, I], and the width range to be constructed is the range of J pixels on both sides of the active track. Then the coordinates of the receptor pixel point to be constructed are (i, j), j∈[-J, 0) ∪(0, J];

[0032] Then, the loss function C(i, j; a) is calculated using the weighted minimum square error of the albedo or brightness temperature data of the multiple channels of the donor pixel and the acceptor pixel:

[0033]

[0034] Among them, Al m The albedo of the mth channel selected by the geostationary satellite for spectral similarity comparison, BT n is the brightness temperature of the nth channel;

[0035] Next, the most suitable donor pixel is selected based on background constraints and distance index. The background constraints are: 1) the acceptor pixel and the donor pixel have the same surface type; 2) the acceptor pixel and the donor pixel have similar solar zenith angle and azimuth, that is, the difference in solar zenith angle and azimuth is less than 10 degrees. The optimal donor is the set of pixels with the smallest cost function to the acceptor pixel among all the candidate donor pixels that meet these constraints, and then the point with the closest distance is selected from them.

[0036] Finally, in the target area, the vertical profile information of the donor pixel is used as the vertical profile information of each matching receptor pixel to construct the three-dimensional structure of the sea fog.

[0037] Preferably, in step (5), a band combination is selected using a reconstruction algorithm. The following band combinations are tested by setting the blind zone range to 30km, 60km, and 100km: Combination 1: the closest band to the band selected by MODIS, namely 0.64μm, 2.3μm, 8.6μm, and 12.4μm; Combination 2: the bands commonly used for daytime sea fog monitoring, namely 0.64μm, 0.86μm, and 1.6μm; Combination 3: the band combination commonly used for daytime and nighttime sea fog monitoring, namely 0.64μm, 0.86μm, 1.6μm, 3.9μm, and 11.2μm; Combination 4: all geostationary satellite bands. By comparing and analyzing the profiles before and after reconstruction, evaluating the overall matching rate TMR and the root mean square error of the sea fog height before and after reconstruction, the band combination that is most conducive to the expansion of the three-dimensional structure of sea fog is obtained. The definition of TMR is:

[0038] TMR=(N cc +N aa +N ff +N 00 ) / N

[0039] where N cc is the number of pixels identified as clouds before and after reconstruction, N aa is the number of pixels identified as aerosols before and after reconstruction, N ff N is the number of pixels identified as sea fog before and after reconstruction.00 The number of pixels identified as clear (i.e., without cloud or aerosol) before and after reconstruction is 0.64μm, 2.3μm, 8.6μm, and 12.4μm, respectively.

[0040] Preferably, in step (6), the method for constructing the three-dimensional distribution of sea fog within a certain range from the ACDL track is: select the profile of the known point with the highest spectral similarity to the point to be expanded, and move it outward to the expansion point, so as to obtain the complete three-dimensional structure of the sea fog and obtain the distribution of the sea fog layer height.

[0041] Preferably, in step (6), the three-dimensional distribution of sea fog within a certain range from the ACDL track is constructed, and this distance is also evaluated in the reconstruction verification of step (5).

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) The present invention's three-dimensional distribution detection method for sea fog based on the fusion of satellite-borne active and passive remote sensing combines the high-precision atmospheric vertical profile detection capability of ACDL with the large-scale continuous observation capability of Himawari-8 to achieve three-dimensional distribution detection of sea fog, which can effectively expand the satellite's three-dimensional detection range.

[0044] (2) The sea fog recognition results obtained by the three-dimensional distribution detection method of sea fog based on the fusion of active and passive remote sensing onboard the present invention are compared with the ICOADS records matched with time and space. The results show that the accuracy rate reaches more than 90%, the missing rate is about 1%, and the misidentification rate is about 9%.

[0045] (3) Compared with the existing satellite-based remote sensing sea fog layer distribution detection algorithm, the proposed method has the advantages of high spatial resolution and the ability to detect vertical layers. It is applicable to multiple scenarios, helps to understand the formation and dissipation mechanism of sea fog, and can also be used as a high-precision three-dimensional initial field for sea fog forecasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the flow of the method for detecting three-dimensional distribution of sea fog based on the fusion of spaceborne active and passive remote sensing in this embodiment.

[0047] Figure 2 Schematic diagram of a case of sea fog recognition results in this embodiment.

[0048] Figure 3 This is a comparison chart of the sea fog recognition results in this embodiment and the ICOADS data.

[0049] Figure 4 3 is a comparison diagram of the top height of the sea fog layer before and after reconstruction of different blind area ranges in this embodiment.

[0050] Figure 5 (a) is a three-dimensional map of the sea fog layers constructed in this embodiment, and (b) is a distribution map of the top height of the sea fog layer. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0052] This embodiment provides a method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft. Figure 1 As shown, the three-dimensional distribution detection method of sea fog includes five steps, each of which is described in detail below:

[0053] (1) Active and passive remote sensing data acquisition and spatiotemporal matching

[0054] Active remote sensing data are acquired from the spaceborne ACDL lidar. The time range is June 1, 2022, to December 31, 2023. The spatial range includes the Bohai Sea (116°E-124°E, 36°N-42°N), the Yellow Sea (118°E-128°E, 32°N-40°N), the East China Sea (120°E-130°E, 25°N-32°N), and the South China Sea (108°E-124°E, 0°N-25°N). The main data products include the attenuated backscatter coefficient of the 532nm channel at level 1, the backscatter coefficient, scattering ratio, radar ratio, depolarization ratio, and extinction coefficient of the 532nm channel at level 2.

[0055] The passive remote sensing data are obtained from the Himawari-8 geostationary satellite. The main data products include albedo data of visible and near-infrared channels with a temporal resolution of 10 minutes and a spatial resolution of 5 km, as well as brightness temperature data of the infrared channel, forming multi-band spectral information.

[0056] For ACDL and Himawari-8, since Himawari-8 is a geostationary satellite, the R space-time matching is the part of the ACDL trajectory that passes through the Himawari-8 detection area.

[0057] (2) Spaceborne LiDAR ACDL sea fog recognition

[0058] There are three main criteria for identifying sea fog based on the remote sensing observation data of the space-borne lidar ACDL:

[0059] Optical characteristic criteria: ACDL detected layer SR>10, ATB≥0.02km-1 sr -1 , β≥0.03km -1 sr -1 ;

[0060] Physical location criteria: The bottom of the layer is directly connected to the ocean surface, or the distance d from the bottom of the layer to the ocean surface is ≤ 2Δh, where Δh is the vertical resolution of the ACDL data product; and the surface type corresponding to the pixel point is ocean;

[0061] Hierarchical continuity criterion: For each pixel identified as sea fog, if the pixels directly connected to it meet the optical property criterion, it can be determined to be sea fog.

[0062] Figure 2 This example illustrates sea fog identification based on the aforementioned criteria. This sea fog event was detected by ACDL around 17:40 UTC on June 2, 2022. The ACDL optical property data product and sea fog identification results are presented.

[0063] (3) Verification of ACDL sea fog recognition results by spaceborne lidar

[0064] Within the selected timeframe of June 1, 2022, to December 31, 2023, and across the four major sea areas of the Bohai, Yellow, East China, and South China Seas, there were 1,873 cases of spatiotemporal matches between ACDL trajectories and ICOADS records. These included 26 cases in the Bohai, 173 in the Yellow, 420 in the East China, and 1,254 in the South China Sea. Comparing ACDL sea fog identification results with ICOADS records revealed 1,688 cases where both indicated fog or no fog. ICOADS recorded fog as no fog in 16 cases, and ICOADS recorded no fog as foggy in 169 cases where ACDL identified fog as present. This yields an accuracy rate of 90.12%, a missing error rate of 0.85%, and a false positive rate of 9.02%. Figure 3 The accuracy of the ACDL sea fog recognition algorithm of spaceborne lidar is demonstrated.

[0065] (4) Reconstruction verification for band combination selection and extended range evaluation

[0066] A reconstruction algorithm was used to select band combinations. The following band combinations were tested with blind zones set at 30 km, 60 km, and 100 km: Band 1: Bands closest to the MODIS bands, namely 0.64 μm, 2.3 μm, 8.6 μm, and 12.4 μm; Band 2: Bands commonly used for daytime sea fog monitoring, namely 0.64 μm, 0.86 μm, and 1.6 μm; Band 3: Bands commonly used for daytime and nighttime sea fog monitoring, namely 0.64 μm, 0.86 μm, 1.6 μm, 3.9 μm, and 11.2 μm; Band 4: All Himawari-8 bands. The overall matching rate (TMR) and the root mean square error (RMS) of the sea fog height before and after reconstruction were evaluated by comparing the pre- and post-reconstruction profiles. When the blind zone range is 30 km, the TMRs of the four band combinations are 89.08%, 87.27%, 89.11%, and 89.07%, respectively. When the blind zone range is 60 km, the TMRs of the four band combinations are 86.82%, 86.17%, 86.84%, and 86.80%, respectively. When the blind zone range is 100 km, the TMRs of the four band combinations are 84.41%, 82.45%, 84.42%, and 84.39%, respectively. Therefore, Band 3 is selected as the optimal band combination. Figure 4 The comparison of the top height of the sea fog layer before and after reconstruction under different blind area ranges is shown.

[0067] (4) Construction of active and passive integrated sea fog three-dimensional structure

[0068] This step relies on the band combination selection results obtained by reconstruction and verification in step (5) and the evaluation of the extended range. Band 3 is selected as the band combination for evaluating spectral similarity, and the three-dimensional structure of sea fog within a certain distance of the active remote sensing detection range is constructed through active and passive fusion. Figure 5 It shows a stereoscopic image of the three-dimensional structure of sea fog and a distribution map of the height of the top of the sea fog layer.

Claims

1. A method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft, characterized in that: The following steps are involved: (1) Obtaining remote sensing observation data from spaceborne lidar as active remote sensing data, including obtaining the calibrated attenuated backscatter coefficient of level 1, and simultaneously obtaining the corresponding backscatter coefficient and scattering ratio of level 2 as the basis for subsequent identification of sea fog layers; obtaining remote sensing observation data from geostationary satellites as passive remote sensing data, including multi-band spectral information, specifically including the albedo of each channel in the visible and near-infrared bands and the brightness temperature of each channel in the infrared band; performing spatiotemporal matching between the remote sensing observation data from spaceborne lidar and the remote sensing observation data from geostationary satellites; (2) By using the optical property criterion, the scattering ratio, attenuated backscatter coefficient, and backscatter coefficient thresholds are set, and based on the thresholds, the cloud and aerosol layers in the detection profile of the spaceborne lidar are distinguished; by using the physical position criterion, the cloud layers within a certain threshold range above the sea surface are screened, the distance between the cloud base height and the sea surface is calculated, and the pixels within this distance are identified as sea fog; on this basis, the layer continuity criterion is used to obtain a complete and continuous sea fog layer; (3) Compare the sea fog identification results in step (2) with the actual observation data in the International Integrated Ocean-Atmosphere Dataset (ICOADS) to analyze the accuracy of sea fog identification in step (2); (4) Based on the time-space matching of the vertical detection profile of the spaceborne lidar and the multi-band spectral information of the geostationary satellite, the information of the measured vertical profile is expanded to a certain range outside the orbit by analyzing the multi-channel spectral similarity between the points outside the orbit of the spaceborne lidar and the points on the orbit; In step (4), the multi-channel spectral similarity analyzed is a loss function that calculates the albedo or brightness temperature of the selected channel between the pixels outside the track and the points on the ACDL track; By analyzing the multi-channel spectral similarity between points outside the orbit and points on the orbit of the spaceborne lidar, the information of the measured vertical profile is extended to a certain range outside the orbit, including: First, record the position information of the donor pixel point on the space-borne laser radar track and the receptor pixel point in the area to be expanded outside the track; the coordinates of the donor pixel point on the space-borne laser radar track are (a, 0), a∈[0, I], and the width range to be constructed is the range of J pixels on both sides of the active track, then the coordinates of the receptor pixel point to be constructed are (i, j), j∈[-J, 0) ∪(0, J]; Then, the loss function C(i, j; a) is calculated using the weighted minimum square error of the albedo or brightness temperature data of the multiple channels of the donor pixel and the acceptor pixel: Among them, Al m The albedo of the mth channel selected by the geostationary satellite for spectral similarity comparison, BT n is the brightness temperature of the nth channel; Next, the most suitable donor pixel is selected based on background constraints and a distance index. Background constraints include: 1) the acceptor pixel and the donor pixel have the same surface type; 2) the acceptor pixel and the donor pixel have similar solar zenith angles and azimuths, that is, the difference in solar zenith angle and azimuth is less than 10 degrees. The optimal donor pixel is the set of pixels with the smallest cost function to the acceptor pixel among all the candidate donor pixels that meet these constraints, and then the closest point is selected from these pixels. Finally, in the target area, the vertical profile information of the donor pixel is used as the vertical profile information of each receptor pixel to match, so as to construct the three-dimensional structure of the sea fog; (5) Reconstruct the vertical detection profile of the spaceborne lidar using the method in step (4), and compare the sea fog layer height before and after reconstruction under different band conditions to select the band combination most suitable for detecting the sea fog layer height distribution; (6) Apply the method of step (4) in combination with the band combination selected in step (5) to construct the three-dimensional distribution of sea fog within a certain range from the orbit of the satellite-borne lidar.

2. The method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft according to claim 1 is characterized in that: In step (2), the thresholds for cloud and aerosol classification using optical characteristics are: scattering ratio greater than 10, attenuated backscatter coefficient greater than or equal to 0.02 km -1 sr -1 , backscatter coefficient is greater than or equal to 0.03km -1 sr -1 When the above scattering ratio, attenuated backscattering coefficient, and backscattering coefficient threshold are met at the same time, it is considered to be cloud, otherwise it is aerosol.

3. The method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft according to claim 1 is characterized in that: In step (2), the method for obtaining a complete and continuous sea fog layer through the layer continuity criterion is as follows: for each pixel point identified as sea fog, if the pixel point directly connected to it meets the optical characteristic criterion, then the directly connected pixel point is identified as sea fog.

4. The method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft according to claim 1 is characterized in that: In step (3), when making comparisons, the trajectory points in the sea fog identification results with a time interval of less than 2 hours and a spatial distance of less than 100 km are screened for comparison with the actual observation data points in ICOADS. If both are identified as sea fog or both are identified as no sea fog, the identification is determined to be accurate; if it is identified as sea fog but ICOADS does not record sea fog, it is determined to be a false identification; if it is not identified as sea fog but ICOADS records it as sea fog, it is determined to be missing. All matching cases are counted and the accuracy rate, missing rate and false identification rate are analyzed.

5. The method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft according to claim 1 is characterized in that: In step (5), the method for reconstructing the vertical detection profile of the satellite-borne laser radar is as follows: a certain blind area is set around each position, and the vertical profile inside the blind area is assumed to be unknown, and the corresponding position is reconstructed using the part outside the blind area.

6. The method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft according to claim 1 is characterized in that: In step (5), the goodness of fit and root mean square error under different band combinations are calculated based on the results of comparative analysis of the sea fog layer height before and after reconstruction, so as to select the band combination that is most suitable for reconstructing the sea fog layer height.

7. The method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft according to claim 1 is characterized in that: In step (5), the most suitable band combination for detecting the three-dimensional distribution of sea fog is 0.64μm, 2.3μm, 8.6μm and 12.4μm.

8. The method for detecting three-dimensional distribution of sea fog based on the fusion of active and passive remote sensing on board spacecraft according to claim 1 is characterized in that: In step (6), the method for constructing the three-dimensional distribution of sea fog within a certain range from the satellite-borne lidar track is: select the profile of the known point with the highest spectral similarity to the point to be expanded, move it outward to the point with expansion, thereby obtaining the complete three-dimensional structure of the sea fog and the distribution of the sea fog layer height.

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