High-resolution sea ice detection method based on rotating fan-shaped beam scatterometer

Abstract

The invention relates to the technical field of ocean engineering, in particular to a high-resolution sea ice detection method based on a rotating fan-shaped beam scatterometer, the sea ice detection method can realize ice and water distinguishing in 10km grids in south and north polar regions, and the sea ice can be detected by utilizing first-level data of the scatterometer and a polar stereographic projection conversion method. The spatial resolution of an existing scatterometer sea ice monitoring method is improved, a sliding time window is adopted for collecting training data, dynamic training and updating are conducted on the model, and the problem that radar backscattering coefficient ice-water characteristics change greatly along with seasons is solved to a certain extent.

Description

Background Art

[0002] Sea ice is usually formed in the oceans in the polar regions, and its area accounts for about 7% to 15% of the total ocean area. As an important component of the earth system, sea ice affects global climate change by exchanging heat with seawater and the atmosphere, and has long been considered one of the main indicators of global warming. In recent years, as global temperatures gradually rise, the melting rate of sea ice in the Arctic and Antarctic has continued to accelerate, making it possible to fully open the Arctic route, which has great economic value for China's ocean shipping and has an important impact on the world pattern. Therefore, sea ice monitoring is of great significance to human production and life, global climate change research, and military security.

[0003] Early sea ice monitoring was mainly carried out through ship surveys and aircraft aerial photography. Although detailed sea ice information can be obtained, the observation range is limited and the economic cost is high, which cannot meet the actual business needs. With the rapid development of satellite remote sensing technology, the use of satellite-borne sensors to obtain sea ice information has become the main means of sea ice monitoring, which mainly includes passive microwave radiometers and active microwave radars. Considering that scatterometer radar can cover a larger area of ​​the sea surface and can make multiple observations within the observation unit, ice-water classification can be achieved through the characteristic differences of ice-water radar backscatter coefficients. Therefore, scatterometer radar can be used for sea ice operational observation in the future to provide data support for route planning and climate change research. However, the existing scatterometer radar sea ice monitoring method mainly has the problem of low spatial resolution, and the model algorithm cannot be dynamically updated, resulting in the sea ice classification results often being affected by the temporal differences in radar sea ice characteristics.

[0004] Therefore, in view of this technical background, it is urgent to propose a high-resolution sea ice detection method based on a rotating fan-beam scatterometer to solve the problems in the background technology. Summary of the invention

[0005] The main purpose of the present invention is to provide a high-resolution sea ice detection method based on a rotating fan-beam scatterometer, which effectively solves the above-mentioned problems mentioned in the background technology.

[0006] The technical solution of the present invention is as follows:

[0007] A high-resolution sea ice detection method based on a rotating fan-beam scatterometer is proposed. The method includes the following steps:

[0008] S1. Obtain the L1B data of the rotating fan-beam scatterometer for the previous n days of sea ice monitoring. 0 The data is placed in the same grid;

[0009] S2. Extract the strips σ that meet the preset conditions in the L1B level data 0 The data is processed by arithmetic mean to obtain the observed grid point σ 0 data, and the strip σ 0 The incident angle, observation azimuth, longitude and latitude data corresponding to the data are also processed by arithmetic averaging to obtain the grid point σ 0 The corresponding incident angle, observation azimuth, polarization mode and longitude and latitude data;

[0010] S3, the grid point σ 0 The corresponding longitude and latitude data are converted into radians, and the coordinates are transformed according to the polar-stereoscopic projection coordinate transformation formula to obtain the coordinate values ​​under the 10km polar-stereoscopic projection grid;

[0011] S4. Obtain the background wind field data under the 10km polar stereographic projection grid and rotate the fan-beam scatterometer grid point σ 0 The data and the background wind field data are in one-to-one correspondence. By observing the azimuth and background wind direction information, each grid point σ 0 The relative azimuth of the data;

[0012] S5, according to the grid point σ 0 Corresponding latitude data, invalid observation data are eliminated, and grid points σ with the same number of columns and rows in the same hemisphere polar projection grid are 0 The data constitutes a single matching data pair;

[0013] S6. Determine the sea ice density of the polar stereographic projection grid where a single matching data pair is located, mark the observation grid according to the preset sea ice density threshold, and remove the matching data pairs whose observation times in the grid are lower than the threshold. Put the remaining matching data pairs into the XGBoost classification model for training, and then obtain the high-resolution sea ice monitoring models for the North and South Poles respectively.

[0014] S7. Extract the L1B data of the rotating fan-beam scatterometer on the day and repeat S1-S5 to obtain a set of matching data pairs of the rotating fan-beam scatterometer on the day. Put the matching data pairs into the high-resolution sea ice monitoring model of the North and South Poles obtained in S6 according to the position information of the northern and southern hemispheres, mark the ice and water category of each grid, and obtain the rotating fan-beam scatterometer's Arctic and Antarctic sea ice monitoring results.

[0015] A further improvement of the present invention is that the strip σ in S2 that meets the preset conditions 0 The data are as follows: all strips σ with the same polarization mode in the same grid in L1B data, within the range of 2° difference in observation incident angle and within the range of 20° difference in observation azimuth 0 data.

[0016] A further improvement of the present invention is that the specific formula for coordinate transformation in S3 is:

[0017]

[0018] Where a is the equatorial radius of the Earth, e is the eccentricity of the Earth, is the L1B level data of the rotating fan beam scatterometer σ 0 The latitude of , in radians; is the latitude of the projection reference point, which is a constant: when processing data in the northern hemisphere, When processing data from the Southern Hemisphere, Furthermore, the coordinate values ​​(x, y) under the 10km polar stereographic projection grid are obtained using the following formula:

[0019]

[0020] Where θ is the L1B level data of the rotating fan beam scatterometer σ 0 The longitude of the radian; p m and θ c (the longitude of the projection reference point) is a constant: When processing data in the northern hemisphere, p m =1,θ c =-0.7854rad, when processing data in the southern hemisphere, p m =-1,θ c =0.

[0021] A further improvement of the present invention is that the method for obtaining the background wind field data under the 10km polar stereographic projection grid in S4 is: obtaining the ECMWF wind field forecast data at the first two and last two forecast times closest to the observation time of the rotating fan-beam scatterometer L1B data, and obtaining the background wind field data under the 10km polar stereographic projection grid according to the time spline interpolation and space bilinear interpolation methods.

[0022] A further improvement of the present invention is that the specific content of S5 is: according to the grid point σ 0 The corresponding latitude data, the grid point σ 0 The data is divided into northern and southern hemisphere positions and the observation data at mid- and low-latitudes are eliminated, and then the grid points σ with the same number of columns and rows in the polar projection grid in the same hemisphere are 0 The data constitute a single matching data pair, and the observation data at mid- and low-latitudes are excluded with 60° north and south latitude as the boundary.

[0023] A further improvement of the present invention is that a single matching data pair in S5 contains at most 16 observation data and projection grid background wind speed data, and each observation contains σ 0 Natural value, polarization, angle of incidence, and relative azimuth data.

[0024] A further improvement of the present invention is that S6 comprises the following specific steps:

[0025] S61, obtain the Arctic and Antarctic sea ice density degree-day product of the 10 km polar stereographic projection grid that is consistent with the L1B data product of the rotating fan-beam scatterometer, and determine the sea ice density under the stereographic projection grid where each matching data pair is located;

[0026] S62, marking the observation grids according to a preset sea ice density threshold, with the categories being sea ice or sea water; and simultaneously counting the actual number of scatterometer observations in each polar stereographic projection grid, and removing matching data pairs in the grid that are below the observation number threshold;

[0027] S63. The matching data pairs retained in the previous n days are put into the XGBoost classification model for training according to the position information of the northern and southern hemispheres, and finally two rotating fan-beam scatterometer high spatial resolution sea ice monitoring models for the Antarctic and the Arctic are obtained respectively.

[0028] A further improvement of the present invention is that the sea ice density threshold in S62 is 15%, and the observation number threshold is 6.

[0029] The technical effects of the present invention are as follows:

[0030] (1) A high-resolution sea ice detection method based on rotating fan-beam scatterometer was constructed. The method can identify ice and water in the North and South Poles using rotating fan-beam scatterometer data and continuously update the rotating fan-beam scatterometer sea ice monitoring model under a sliding time window. This method solves the problem of temporal instability of the backscatter coefficient characteristics of sea ice radar to a certain extent and the problem of relatively large spatial resolution of the existing rotating fan-beam scatterometer sea ice monitoring model results.

[0031] (2) It can dynamically adapt to the changing characteristics of sea ice radar backscatter coefficients, while the existing technology focuses on the difference in the average state characteristics of sea ice and seawater radar backscatter coefficients, and needs to determine the radar observation characteristics that are sensitive to ice and water;

[0032] (3) The present invention can make full use of all actual observation data and the time-varying patterns of features. The sea ice monitoring method of the present invention directly uses the L1B data of the rotating fan-beam scatterometer to perform the required projection grid conversion, which determines that the present invention can reduce the error in data space matching and achieve sea ice monitoring with higher spatial resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0034] Figure 1 Detailed step diagram of Example 1 of the present invention;

[0035] Figure 2 This is a schematic diagram of the sea ice monitoring results of the target area according to the present invention;

[0036] Figure 3 Schematic diagram of sea ice monitoring results of a target area using a sea ice monitoring method under a non-sliding time window in Example 2 of the present invention;

[0037] Figure 4 This is a schematic diagram of the sea ice monitoring results of the target area using the Lambert equal-area projection 25km grid sea ice monitoring method in Example 3 of the present invention. DETAILED DESCRIPTION

[0038] The present invention aims to propose a high-resolution sea ice detection method based on a rotating fan beam scatterometer, which can use the rotating fan beam scatterometer data to identify ice and water in the North and South Poles, and continuously update the rotating fan beam scatterometer sea ice monitoring model under a sliding time window, which solves the problem of temporal instability of the backscatter coefficient characteristics of the sea ice radar to a certain extent, and solves the problem of relatively large spatial resolution of the results of the existing rotating fan beam scatterometer sea ice monitoring model; it can dynamically adapt to the changing backscatter coefficient characteristics of the sea ice radar, while the existing technology focuses on the difference in the average state characteristics of the backscatter coefficients of the sea ice and seawater radars, and needs to determine the radar observation characteristics that are sensitive to ice and water; it can make full use of all actual observation data and the law of changes of characteristics over time. The sea ice monitoring method of the present invention directly uses the L1B data of the rotating fan beam scatterometer to perform the required projection grid conversion, which determines that the present invention can reduce the error in data space matching and can achieve sea ice monitoring with higher spatial resolution.

[0039] Embodiment 1:

[0040] This embodiment proposes a high-resolution sea ice detection method based on a rotating fan-beam scatterometer, specifically, Figure 1 As shown, the following specific steps are included:

[0041] S1. Obtain all 139 L1B data from January 1, 2020 to January 10, 2020 and all 144 L1B data from July 5, 2020 to July 14, 2020. Set all strips σ in the L1B data within 5 km of the center point of the 10 km polar stereographic projection grid. 0 The data is placed in the same grid;

[0042] S2. Extract the strips σ that meet the preset conditions in the L1B level data 0 The data is processed by arithmetic mean to obtain the observed grid point σ 0 data, and the strip σ 0 The incident angle, observation azimuth, longitude and latitude data corresponding to the data are also processed by arithmetic averaging to obtain the grid point σ 0 The corresponding incident angle, observation azimuth, polarization mode and longitude and latitude data;

[0043] S3, the grid point σ 0 The corresponding longitude and latitude data are converted into radians, and the coordinates are transformed according to the polar-stereoscopic projection coordinate transformation formula to obtain the coordinate values ​​under the 10km polar-stereoscopic projection grid;

[0044] S4. Obtain the background wind field data under the 10km polar stereographic projection grid and rotate the fan-beam scatterometer grid point σ 0 The data and the background wind field data are in one-to-one correspondence. By observing the azimuth and background wind direction information, each grid point σ 0 The relative azimuth of the data;

[0045] S5, according to the grid point σ 0 Corresponding latitude data, invalid observation data are eliminated, and grid points σ with the same number of columns and rows in the same hemisphere polar projection grid are 0 The data constitutes a single matching data pair;

[0046] S6. Determine the sea ice density of the polar stereographic projection grid where a single matching data pair is located, mark the observation grid according to the preset sea ice density threshold, and remove the matching data pairs whose observation times in the grid are lower than the threshold. Put the remaining matching data pairs into the XGBoost classification model for training, and then obtain the high-resolution sea ice monitoring models for the North and South Poles respectively.

[0047] S7. Obtain all 15 tracks of L1B-level data from the rotating fan-beam scatterometer on July 15, 2020, and repeat S1-S5 to obtain a set of matching data pairs of the rotating fan-beam scatterometer on that day. Put the matching data pairs into the high-resolution sea ice monitoring model for the North and South Poles obtained in S6 according to the position information in the northern and southern hemispheres, and perform ice and water category inversion on each grid, and finally obtain the rotating fan-beam scatterometer Arctic and Antarctic sea ice monitoring results on July 15, 2020.

[0048] In this embodiment, the stripe σ in S2 that meets the preset condition 0 The data are as follows: all strips σ with the same polarization mode in the same grid in L1B data, within the range of 2° difference in observation incident angle and within the range of 20° difference in observation azimuth 0 data.

[0049] In this embodiment, the specific formula for coordinate transformation in S3 is:

[0050]

[0051] Where a is the equatorial radius of the Earth, e is the eccentricity of the Earth, is the L1B level data of the rotating fan beam scatterometer σ 0 The latitude of , in radians; is the latitude of the projection reference point, which is a constant: when processing data in the northern hemisphere, When processing data from the Southern Hemisphere, Furthermore, the coordinate values ​​(x, y) under the 10km polar stereographic projection grid are obtained using the following formula:

[0052]

[0053] Where θ is the L1B level data of the rotating fan beam scatterometer σ 0 The longitude of the radian; p m and θ c (the longitude of the projection reference point) is a constant: When processing data in the northern hemisphere, p m =1,θ c =-0.7854rad, when processing data in the southern hemisphere, p m =-1,θ c =0.

[0054] In this embodiment, the method for obtaining the background wind field data under the 10km polar stereographic projection grid in S4 is: obtaining the ECMWF wind field forecast data at the first two and last two forecast times closest to the observation time of the rotating fan-beam scatterometer L1B data, and obtaining the background wind field data under the 10km polar stereographic projection grid according to the time spline interpolation and spatial bilinear interpolation methods.

[0055] In this embodiment, the specific content of S5 is: according to the grid point σ 0 The corresponding latitude data, the grid point σ 0 The data is divided into northern and southern hemisphere positions and the observation data at mid- and low-latitudes are eliminated, and then the grid points σ with the same number of columns and rows in the polar projection grid in the same hemisphere are 0 The data constitute a single matching data pair, and the observation data at mid- and low-latitudes are excluded with 60° north and south latitude as the boundary.

[0056] In this embodiment, the single matching data pair in S5 contains at most 16 observation data and projection grid background wind speed data, and each observation contains σ 0 Natural value, polarization, angle of incidence, and relative azimuth data.

[0057] In this embodiment, S6 includes the following specific steps:

[0058] S61, obtain the Arctic and Antarctic sea ice density degree-day product of the 10 km polar stereographic projection grid that is consistent with the L1B data product of the rotating fan-beam scatterometer, and determine the sea ice density under the stereographic projection grid where each matching data pair is located;

[0059] S62, marking the observation grids according to a preset sea ice density threshold, with the categories being sea ice or sea water; and simultaneously counting the actual number of scatterometer observations in each polar stereographic projection grid, and removing matching data pairs in the grid that are below the observation number threshold;

[0060] S63. The matching data pairs retained in the previous n days are put into the XGBoost classification model for training according to the position information of the northern and southern hemispheres, and finally two rotating fan-beam scatterometer high spatial resolution sea ice monitoring models for the Antarctic and the Arctic are obtained respectively.

[0061] In this embodiment, the sea ice concentration threshold in S62 is 15%, and the observation number threshold is 6.

[0062] Embodiment 2:

[0063] Different from Example 1, in this embodiment, the L1B data source of the rotating fan-beam scatterometer in step S1 of Example 1 is changed to January 1 to January 31, 2020 (except the day of sea ice inversion, January 11, 2020) and July 1 to July 31, 2020 (except the day of sea ice inversion, July 15, 2020). This is because the scatterometer sea ice inversion method under the non-sliding window needs to input all data of the month in which the sea ice monitoring day is located for training, and obtain the sea ice monitoring model of a certain month to perform sea ice monitoring in that month in all subsequent years. The sea ice monitoring results are as follows: Figure 3 shown. Figure 3 (a) is a schematic diagram of sea ice monitoring results on January 11, 2020 (Antarctic summer). Figure 3 (b) is a schematic diagram of the sea ice monitoring results on July 15, 2020 (Arctic summer).

[0064] Combination Figure 2 , Figure 3It can be found that the results of the two methods in the Antarctic and the Arctic are relatively consistent in most spatial ranges. This is because the characteristics of sea ice or seawater are relatively concentrated and obvious in most areas. The advantage of the sea ice monitoring method of the present invention is that it can continuously adapt to the changing characteristics of the sea ice radar backscatter coefficient, especially in the summer when the sea ice characteristics change rapidly. Therefore, at the edge of the summer sea ice in the figure, it can be found that the spatial consistency of the sea ice monitoring results of the method of the present invention is higher, and the spatial noise area in the figure is smaller.

[0065] Example 3

[0066] Different from the above-mentioned embodiment, based on embodiment 1, this embodiment changes the rotating fan-beam scatterometer data in step S1 of embodiment 1 to L2A data with a spatial resolution of 25 km, and then changes the spatial resolution of the projection grids in steps 3 and 4 to 25 km, and obtains the sea ice density product with a spatial resolution of 25 km in step 7. Figure 4 The results of the rotating fan-beam scatterometer sea ice monitoring with a spatial resolution of 25 km were presented. Figure 4 (a) is a schematic diagram of sea ice monitoring results on January 11, 2020 (Antarctic summer). Figure 4 (b) is a schematic diagram of the sea ice monitoring results on July 15, 2020 (Arctic summer).

[0067] contrast Figure 2 and Figure 4 It can be found that the overall sea ice spatial distribution results have not changed much, but the larger spatial resolution sea ice edge monitoring results are relatively rough, with relatively few detailed features.

[0068] Embodiment 4:

[0069] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned high-resolution sea ice detection method based on a rotating fan-beam scatterometer by calling the computer program stored in the memory.

[0070] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a high-resolution sea ice detection method based on a rotating fan-beam scatterometer provided in the above method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment will not be described in detail here.

[0071] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0072] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0073] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A high-resolution sea ice monitoring method based on a rotating fan-beam scatterometer, characterized in that: The specific steps include: S1. Obtain the L1B data of the rotating fan-beam scatterometer for the previous 10 days of sea ice monitoring. 0 The data is placed in the same grid; S2. Extract the strips σ that meet the preset conditions in the L1B level data 0 The data is processed by arithmetic mean to obtain the observed grid point σ 0 data, and the strip σ 0 The incident angle, observation azimuth, longitude and latitude data corresponding to the data are also processed by arithmetic averaging to obtain the grid point σ 0 The corresponding incident angle, observation azimuth, polarization mode and longitude and latitude data; S3, the grid point σ 0 The corresponding longitude and latitude data are converted into radians, and the coordinates are transformed according to the polar-stereoscopic projection coordinate transformation formula to obtain the coordinate values ​​under the 10km polar-stereoscopic projection grid; S4. Obtain the background wind field data under the 10km polar stereographic projection grid and rotate the fan-beam scatterometer grid point σ 0 The data and the background wind field data are in one-to-one correspondence. By observing the azimuth and background wind direction information, each grid point σ 0 The relative azimuth of the data; S5, according to the grid point σ 0 Corresponding latitude data, invalid observation data are eliminated, and grid points σ with the same number of columns and rows in the same hemisphere polar projection grid are 0 The data constitutes a single matching data pair; S6. Determine the sea ice density of the polar stereographic projection grid where a single matching data pair is located, mark the observation grid according to the preset sea ice density threshold, and remove the matching data pairs whose observation times in the grid are lower than the threshold. Put the remaining matching data pairs into the XGBoost classification model for training, and then obtain the high-resolution sea ice monitoring models for the North and South Poles respectively. S7. Extract the L1B data of the rotating fan-beam scatterometer on the day and repeat S1-S5 to obtain a set of matching data pairs of the rotating fan-beam scatterometer on the day. Put the matching data pairs into the high-resolution sea ice monitoring model of the North and South Poles obtained in S6 according to the position information of the northern and southern hemispheres, mark the ice and water category of each grid, and obtain the rotating fan-beam scatterometer's Arctic and Antarctic sea ice monitoring results.

2. A high-resolution sea ice detection method based on a rotating fan-beam scatterometer according to claim 1, characterized in that: The strip σ in S2 that meets the preset conditions 0 The data are as follows: all strips σ with the same polarization mode in the same grid in L1B data, within the range of 2° difference in observation incident angle and within the range of 20° difference in observation azimuth 0 data.

3. A high-resolution sea ice detection method based on a rotating fan-beam scatterometer according to claim 2, characterized in that: The specific formula for coordinate transformation in S3 is: Where a is the equatorial radius of the Earth, e is the eccentricity of the Earth, is the L1B level data of the rotating fan beam scatterometer σ 0 The latitude of , in radians; is the latitude of the projection reference point, which is a constant: when processing data in the northern hemisphere, When processing data from the Southern Hemisphere, Furthermore, the coordinate values ​​(x, y) under the 10km polar stereographic projection grid are obtained using the following formula: Where θ is the L1B level data of the rotating fan beam scatterometer σ 0 The longitude of the radian; p m and the longitude θ of the projection reference point c is a constant: When processing data for the Northern Hemisphere, p m =1,θ c =-0.7854rad, when processing data in the southern hemisphere, p m =-1,θ c =0.

4. A high-resolution sea ice detection method based on a rotating fan-beam scatterometer according to claim 3, characterized in that: The method for obtaining the background wind field data under the 10km polar stereographic projection grid in S4 is: obtaining the ECMWF wind field forecast data at the first two and last two forecast times adjacent to the observation time of the rotating fan-beam scatterometer L1B data, and obtaining the background wind field data under the 10km polar stereographic projection grid according to the time spline interpolation and space bilinear interpolation methods.

5. A high-resolution sea ice detection method based on a rotating fan-beam scatterometer according to claim 4, characterized in that: The specific content of S5 is: according to the grid point σ 0 The corresponding latitude data, the grid point σ 0 The data is divided into northern and southern hemisphere positions and the observation data at mid- and low-latitudes are eliminated, and then the grid points σ with the same number of columns and rows in the polar projection grid in the same hemisphere are 0 The data constitute a single matching data pair, and the observation data at mid- and low-latitudes are excluded with 60° north and south latitude as the boundary.

6. A high-resolution sea ice detection method based on a rotating fan-beam scatterometer according to claim 5, characterized in that: The single matching data pair in S5 contains at most 16 observation data and projection grid background wind speed data, and each observation contains σ 0 Natural value, polarization, angle of incidence, and relative azimuth data.

7. A high-resolution sea ice detection method based on a rotating fan-beam scatterometer according to claim 6, characterized in that: The S6 comprises the following specific steps: S61, obtain the Arctic and Antarctic sea ice density degree-day product of the 10 km polar stereographic projection grid that is consistent with the L1B data product of the rotating fan-beam scatterometer, and determine the sea ice density under the stereographic projection grid where each matching data pair is located; S62, marking the observation grids according to a preset sea ice density threshold, with the categories being sea ice or sea water; and simultaneously counting the actual number of scatterometer observations in each polar stereographic projection grid, and removing matching data pairs in the grid that are below the observation number threshold; S63. The matching data pairs retained in the previous 10 days are put into the XGBoost classification model for training according to the position information of the northern and southern hemispheres, and finally two rotating fan-beam scatterometer high spatial resolution sea ice monitoring models for the Antarctic and the Arctic are obtained respectively.

8. A high-resolution sea ice detection method based on a rotating fan-beam scatterometer according to claim 7, characterized in that: The sea ice concentration threshold in S62 is 15%, and the observation number threshold is 6.

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