A high-resolution sea ice detection method based on rotating fan-beam scatterometer

Abstract

The present invention relates to the field of marine engineering technology, and in particular to a high-resolution sea ice detection method based on a rotating fan-beam scatterometer. The sea ice monitoring method can distinguish between ice and water within a 10km grid in the Arctic and Antarctic regions. By utilizing scatterometer level 1 data and a polar stereographic projection conversion method, the spatial resolution of existing scatterometer sea ice monitoring methods is improved. In addition, a sliding time window is used to collect training data and dynamically train and update the model, which to a certain extent solves the problem that the ice-water characteristics of the radar backscatter coefficient vary greatly with the seasons.

Description

Technical Field

[0001] The present invention relates to the field of marine engineering technology, and in particular to a high-resolution sea ice detection method based on a rotating fan-beam scatterometer. Background Art

[0002] Sea ice typically forms in the polar oceans, covering approximately 7% to 15% of the total ocean surface. As a vital component of the Earth system, sea ice influences global climate change through heat exchange with ocean water and the atmosphere, and has long been considered a key indicator of global warming. In recent years, as global temperatures have gradually risen, the rate of sea ice melting in the Arctic and Antarctic has accelerated, making the full opening of Arctic shipping routes possible. This has significant economic value for China's ocean-going shipping and has a significant impact on the global landscape. 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 primarily relied on ship surveys and aircraft aerial photography. While these methods provided detailed sea ice information, their limited observation range and high cost made them inadequate for practical operational needs. With the rapid development of satellite remote sensing technology, the use of spaceborne sensors to obtain sea ice information has become the primary means of sea ice monitoring, primarily including passive microwave radiometers and active microwave radars. Scatterometer radars offer a wider coverage area and can perform multiple observations within an observation unit, enabling ice-water classification based on differences in radar backscatter coefficient characteristics. Therefore, scatterometer radars could be used for operational sea ice observations in the future, providing data support for route planning and climate change research. However, existing scatterometer radar sea ice monitoring methods suffer from low spatial resolution and an inability to dynamically update model algorithms. Consequently, sea ice classification results are often affected by temporal variations 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 solutions of the present invention are 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 n days before the day of sea ice monitoring. All strips σ in the L1B data within a spatial distance of 5 km from the center point of the 10 km polar stereographic projection grid are 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 averaging 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 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 coordinate transformation is performed according to the polar-stereoscopic projection coordinate transformation relationship to obtain the coordinate value 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 is obtained. 0 The relative azimuth of the data;

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

[0013] S6. Determine the sea ice density of the polar stereographic grid where a single matching data pair is located, mark the observation grid according to the preset sea ice density threshold, and eliminate matching data pairs with the number of observations within the grid below the threshold. Put the retained matching data pairs into the XGBoost classification model for training to obtain 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 conditions0 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 angle 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 10 km 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 Longitude, in radians; 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 background wind field data under the 10km polar stereographic projection grid in S4 is obtained by obtaining the ECMWF wind field forecast data at the two forecast moments closest to the observation moment 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.

[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 the northern and southern hemispheres 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 same hemisphere are divided into 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 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 mode, incidence angle and relative azimuth data.

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

[0025] S61. Obtain the Arctic and Antarctic sea ice density degree-day product for a 10 km polar stereographic grid that is consistent with the time of the rotating fan-beam scatterometer L1B data product, and determine the sea ice density for each matching data pair in the stereographic grid.

[0026] S62, marking the observation grids as sea ice or sea water according to a preset sea ice density threshold; simultaneously counting the actual number of scatterometer observations within each polar stereographic projection grid, and removing matching data pairs within the grid that fall 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 concentration 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 scatterometers was constructed. This method can identify ice and water in the Arctic and Antarctic regions using rotating fan-beam scatterometer data. The rotating fan-beam scatterometer sea ice monitoring model is continuously updated within a sliding time window. This method solves the temporal instability of the sea ice radar backscatter coefficient characteristics to a certain extent, and also solves 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 existing technologies focus on the differences in the average state characteristics of sea ice and seawater radar backscatter coefficients, and need to determine the radar observation characteristics that are sensitive to ice and water;

[0032] (3) The present invention can fully utilize all actual observation data and the temporal variation of features. The present sea ice monitoring method directly utilizes the L1B data of the rotating fan-beam scatterometer to perform the required projection grid conversion. This means that the present invention can reduce errors in data spatial matching and achieve higher spatial resolution sea ice monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0034] Figure 1 Detailed steps of Example 1 of the present invention are shown in FIG.

[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 for 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 Schematic diagram of sea ice monitoring results for a 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. This method can identify ice and water in the Antarctic and Arctic regions using rotating fan-beam scatterometer data, and continuously updates the rotating fan-beam scatterometer sea ice monitoring model within a sliding time window. This method addresses, to a certain extent, the temporal instability of sea ice radar backscatter coefficient characteristics and the relatively high spatial resolution of existing rotating fan-beam scatterometer sea ice monitoring models. It can dynamically adapt to the ever-changing characteristics of sea ice radar backscatter coefficients, whereas existing technologies focus on the differences in the average-state characteristics of sea ice and seawater radar backscatter coefficients and require the determination of radar observation features sensitive to ice and water. Furthermore, it can fully utilize all actual observation data and the temporal variations of these features. The present sea ice monitoring method directly utilizes the L1B data from the rotating fan-beam scatterometer for the required projection grid conversion, which reduces errors in data spatial matching and enables sea ice monitoring with higher spatial resolution.

[0039] Example 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 the rotating fan-beam scatterometer from January 1, 2020 to January 10, 2020, and all 144 L1B data from the rotating fan-beam scatterometer from July 5, 2020 to July 14, 2020. In the L1B data, all strips σ that are within 5 km of the center point of the 10 km polar stereographic projection grid are divided into 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 averaging 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 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 coordinate transformation is performed according to the polar-stereoscopic projection coordinate transformation relationship to obtain the coordinate value 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 is obtained. 0 The relative azimuth of the data;

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

[0046] S6. Determine the sea ice density of the polar stereographic grid where a single matching data pair is located, mark the observation grid according to the preset sea ice density threshold, and eliminate matching data pairs with the number of observations within the grid below the threshold. Put the retained matching data pairs into the XGBoost classification model for training to obtain 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 from the rotating fan-beam scatterometer on that day. Place the matching data pairs into the high-resolution sea ice monitoring models for the North and South Poles obtained in S6 according to their position information in the northern and southern hemispheres, perform ice and water category inversion on each grid, and finally obtain the rotating fan-beam scatterometer sea ice monitoring results for the North and South Poles on July 15, 2020.

[0048] In this embodiment, the stripe 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 angle 0 data.

[0049] In this embodiment, the specific formula for the 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 10 km 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 Longitude, in radians; 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 background wind field data under the 10 km polar stereographic projection grid in S4 is obtained by obtaining the ECMWF wind field forecast data at the two forecast moments closest to the observation moment of the rotating fan-beam scatterometer L1B data, and obtaining the background wind field data under the 10 km 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 the northern and southern hemispheres 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 same hemisphere are divided into 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 mode, incidence angle 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 for a 10 km polar stereographic grid that is consistent with the time of the rotating fan-beam scatterometer L1B data product, and determine the sea ice density for each matching data pair in the stereographic grid.

[0059] S62, marking the observation grids as sea ice or sea water according to a preset sea ice density threshold; simultaneously counting the actual number of scatterometer observations within each polar stereographic projection grid, and removing matching data pairs within the grid that fall 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] Example 2:

[0063] Different from Example 1, this embodiment changes the L1B data source of the rotating fan-beam scatterometer in step S1 of Example 1 to January 1 to January 31, 2020 (excluding the day of sea ice inversion, i.e. January 11, 2020) and July 1 to July 31, 2020 (excluding the day of sea ice inversion, i.e. 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 for 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] Combine Figure 2 、 Figure 3 It can be seen that the results from the two methods for the Antarctic and Arctic are relatively consistent across most of the spatial range. This is because the characteristics of sea ice or seawater are concentrated and prominent 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 sea ice radar backscatter coefficients, especially in the summer when sea ice characteristics change rapidly. Therefore, at the edge of the summer sea ice in the figure, the sea ice monitoring results of the present method are more spatially consistent and the spatial noise area is smaller.

[0065] Example 3

[0066] Different from the above 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 grid in steps 3 and 4 to 25 km, and obtains a sea ice concentration product with a spatial resolution of 25 km in step 7. Figure 4 The results of sea ice monitoring using a rotating fan-beam scatterometer 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, and relatively few detailed features are displayed.

[0068] Example 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 memories store at least one computer program, which 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-mentioned 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 will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0072] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, 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 thereof. 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 flowcharts and block diagrams, as well as combinations of processes and blocks in the flowcharts or block diagrams, 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 or block diagrams. Figure 1 A process or multiple processes and boxes Figure 1A 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 operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and boxes Figure 1 A step that specifies a function 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 guidance 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 are all protected by the present invention.

Claims

1. A high-resolution sea ice detection 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 first 10 days of sea ice monitoring. Compare all the 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; S2. Extract the strips σ that meet the preset conditions in the L1B level data 0 The data is processed by arithmetic averaging 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 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 coordinate transformation is performed according to the polar-stereoscopic projection coordinate transformation relationship to obtain the coordinate value 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 is obtained. 0 The relative azimuth of the data; S5, according to the grid σ 0 Corresponding latitude data, invalid observation data are eliminated, and the grid points with the same number of columns and rows in the same hemisphere polar projection grid σ 0 The data constitutes a single matching data pair; S6. Determine the sea ice density of the polar stereographic grid where the individual matching data pairs are located, mark the observation grid according to a preset sea ice density threshold, and remove matching data pairs with observation times below the threshold within the grid. The remaining matching data pairs are put into the XGBoost classification model for training to obtain 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. The high-resolution sea ice detection method based on rotating fan-beam scatterometer according to claim 1 is 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 angle 0 data.

3. The high-resolution sea ice detection method based on rotating fan-beam scatterometer according to claim 2 is 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 10 km 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 Longitude, in radians; p m and the longitude θ of the projection reference point c 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.

4. The high-resolution sea ice detection method based on rotating fan-beam scatterometer according to claim 3 is 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 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.

5. The high-resolution sea ice detection method based on rotating fan-beam scatterometer according to claim 4 is 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 the northern and southern hemispheres 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 same hemisphere are divided into 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. The high-resolution sea ice detection method based on rotating fan-beam scatterometer according to claim 5 is 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 mode, incidence angle and relative azimuth data.

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

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

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