Multi-angle brightness temperature noise suppression method for satellite-borne synthetic aperture microwave radiometer

Abstract

The invention discloses a satellite-borne synthetic aperture microwave radiometer multi-angle brightness temperature noise suppression method, which belongs to the technical field of microwave remote sensing, and comprises the following steps: acquiring historical marine physical parameters, and calculating to obtain multi-angle brightness temperature according to the historical marine physical parameters; fitting the multi-angle brightness temperature, and establishing a relational expression of the brightness temperature and the observation angle under different physical parameters; and calculating the slope of the fitting curve, calculating the deviation between the brightness temperature of the multiple angles and the brightness temperature of any single angle, and establishing a deviation lookup table of the brightness temperature of the multiple angles and the brightness temperature of the specific angle under different physical parameters. The brightness temperature correction value of the corresponding angle is searched in the lookup table, and the brightness temperature of SMOS multi-angle observation is mapped to a single angle; and averaging the mapped single-angle brightness temperature to obtain the noise-suppressed single-angle brightness temperature.

Description

Technical Field

[0001] The present invention belongs to the technical field of microwave remote sensing, and more specifically, relates to a method for suppressing multi-angle brightness temperature noise of a spaceborne synthetic aperture microwave radiometer. Background Art

[0002] The synthetic aperture microwave radiometer is an effective payload for realizing all-weather satellite remote sensing of sea surface salinity at present, and has been used in the European Space Agency's SMOS (Soil Moisture and Ocean Salinity) ocean salinity remote sensing satellite mission. The synthetic aperture radiometer reconstructs the two-dimensional brightness temperature of the entire field of view from the directly measured visibility function based on the interferometric principle, which is called a snapshot in the SMOS measurement and represents a single measurement of SMOS. Most of the adjacent snapshots of SMOS overlap, so that each grid point on the earth's surface can be observed by multiple consecutive snapshots. Since the observation incident angles of each snapshot on the same grid point on the earth's surface are different, SMOS is endowed with the unique ability to measure multi-angle brightness temperature at each grid point. The L1C (Level 1C, the first-level data product after radiometric calibration and geometric correction) brightness temperature product of SMOS stores the multi-angle brightness temperature data of each ISEA (Icosahedral Snyder Equal Area) grid point.

[0003] Compared with traditional real-aperture radiometers, synthetic aperture radiometers have the advantages of fast measurement speed, large measurement range, and high spatial resolution. However, this operating system also causes the sensitivity of synthetic aperture radiometers to be significantly lower than that of real-aperture radiometers under the same measurement time. The nominal sensitivity of SMAP (Soil Moisture Active and Passive satellite) using an L-band real-aperture microwave radiometer is about 1K, while the nominal sensitivity of SMOS is 2.6K to 5K. The sensitivity of SMOS has a range because the sensitivity of synthetic aperture radiometers depends on the spatial cosine coordinates of the grid points within the field of view, and the sensitivity deteriorates as it gets closer to the edge of the field of view. The measured brightness temperatures at a grid point of SMOS have different angles, and the L-band sea surface brightness temperature is very sensitive to angles. Therefore, it is impossible to reduce the noise by averaging the measured brightness temperatures at the same grid point multiple times. This results in the fact that from the perspective of brightness temperature, although the measurement information of SMOS is very rich (dozens of angle measurement values can be provided for each grid point), it is impossible to give a sufficiently accurate sea surface brightness temperature value at any angle (the measured brightness temperature noise at each angle is very large). Although multi-angle brightness temperature joint inversion can be used to ensure the accuracy of salinity products, it is still an uncertain issue as to whether multiple inaccurate brightness temperatures or one accurate brightness temperature can achieve a more accurate salinity product. On the other hand, SMOS cannot provide a sufficiently accurate brightness temperature at a single angle, which is disadvantageous for cross-calibration or data assimilation with SMAP, etc. Summary of the Invention

[0004] Aiming at the defects of the prior art, the purpose of the present invention is to provide a method for suppressing multi-angle brightness temperature noise of spaceborne synthetic aperture microwave radiometers, aiming to solve the problem that there is relatively large noise in multi-angle brightness temperature products of L-band synthetic aperture radiometers, so as to obtain a sufficiently accurate brightness temperature at a single angle.

[0005] To achieve the above purpose, according to the first aspect of the present invention, a method for suppressing multi-angle brightness temperature noise of spaceborne synthetic aperture microwave radiometers is provided, including:

[0006] Obtain historical ocean physical parameters, and calculate the corresponding brightness temperatures at different observation angles according to the historical ocean physical parameters;

[0007] Fit the brightness temperatures into multiple first curves, and establish a relationship between brightness temperature and observation angle under different historical ocean physical parameters;

[0008] Select a specific observation angle from the different observation angles as the reference angle, calculate the deviation between the corresponding brightness temperatures at each observation angle and the corresponding brightness temperature at the reference angle, establish a lookup table of observation angle and the deviation under different physical parameters, and the slope of each first curve is the keyword of the lookup table;

[0009] When correcting the brightness temperature of SMOS multi-angle observations, obtain the current ocean physical parameters, calculate the corresponding brightness temperatures at different observation angles according to the current ocean physical parameters, fit the brightness temperatures into multiple second curves, calculate the slopes of each second curve, look up the brightness temperature correction values of each SMOS observation angle in the look-up table according to the slopes of each second curve, and subtract the brightness temperature correction value from the brightness temperature corresponding to the SMOS observation angle to obtain multiple reference angle brightness temperatures;

[0010] Average the multiple reference angle brightness temperatures to obtain the reference angle brightness temperature with noise suppression.

[0011] Further, the steps of obtaining the brightness temperature include:

[0012] S11. Obtain the sea surface physical parameters from ECMWF auxiliary data;

[0013] S12. Obtain the sea surface salinity data from SMOS L2;

[0014] S13. Input the data into the FASTEM-6 model to obtain the brightness temperatures at multiple observation angles.

[0015] Further, the fitting of the brightness temperature with respect to the observation angle is a binomial fitting.

[0016] Further, after establishing the look-up table, it is necessary to delete the redundant curves with high similarity in the look-up table to improve the look-up efficiency.

[0017] Further, before averaging the multiple reference angle brightness temperatures, perform outlier detection on the brightness temperature and remove the abnormal brightness temperature values.

[0018] Further, the outlier detection is performed using the following formula, including:

[0019] |TB L1c -Tb model -median(TB L1c -Tb model )|>nσTB#

[0020]

[0021] where TB L1c is the L1C product brightness temperature, Tb model is the model calculated brightness temperature, σTB radiometric noise is the radiometer error, and σTB model is the model error.

[0022] According to another aspect of the present invention, an electronic device is provided, including: a computer-readable storage medium and a processor;

[0023] The computer-readable storage medium is used to store executable instructions;

[0024] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the above-mentioned method.

[0025] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to execute the above-mentioned method.

[0026] According to another aspect of the present invention, there is provided a computer program product including a computer program or instructions, and when the computer program or instructions are executed by a processor, the above-mentioned method is implemented.

[0027] Generally speaking, through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0028] (1) The method for suppressing multi-angle brightness temperature noise of the spaceborne synthetic aperture microwave radiometer provided by the present invention extracts the relationship between the observation angle and the brightness temperature, and establishes a deviation table between the multi-angle brightness temperature and the brightness temperature at a specific angle under different ocean physical parameters. In this way, after establishing the look-up table, the brightness temperature at different angles can be quickly mapped to a specific angle to obtain multiple brightness temperature values at the same angle. It establishes the premise of reducing noise by averaging, and can more effectively realize the work of converting the high-noise brightness temperatures at multiple different angles into a single-angle low-noise brightness temperature. By averaging this, the single-angle brightness temperature after effectively suppressing random noise can be obtained.

[0029] (2) In order to distinguish the mapping relationships in different situations, the present invention uses the slope of the multi-angle brightness temperature curve as the key word. When the slopes of the curves are different, the deviation between the brightness temperatures at two angles is different. For the slope of the second curve, select the slope value in the table that is closest to this, and the corresponding deviation is the required deviation.

[0030] (3) In order to obtain the multi-angle brightness temperature curve, the present invention needs to calculate the multi-angle brightness temperature. The historical ocean physical parameters come from ECMWF auxiliary data, and the sea surface salinity data comes from SMOS L2 level. The historical ocean physical parameters are input into the FASTEM6 model to calculate the multi-angle brightness temperature matching the SMOS L1C brightness temperature, including X and Y polarizations.

[0031] (4) The fitting of the brightness temperature with respect to the observation angle in the present invention is a binomial fitting, which reduces the complexity while maintaining the fitting accuracy.

[0032] (5) Before averaging the single-angle brightness temperature after mapping, the present invention performs outlier detection on the brightness temperature and removes the abnormal brightness temperature values.

[0033] (6) The method proposed by the present invention is to reduce the noise of the observed brightness temperature of the SMOS satellite. Therefore, the SMOS brightness temperature and SMAP brightness temperature data with similar time and the same space are matched to verify the noise reduction effect on the observed brightness temperature of the SMOS satellite. The SMOS L1C multi-angle brightness temperature, the processed SMOS single-angle brightness temperature, and the SMAP single-angle brightness temperature are respectively used to invert the sea surface salinity. The algorithm is the LM algorithm used in the SMOS L2 salinity data product. The salinity data in the ISAS20_ARGO (ISAS: In-situ Analysis System) product downloaded from the ARGO official website is used as the reference true value, and the inversion accuracy is compared to verify the noise reduction result. Description of the Drawings

[0034] Figure 1 Schematic diagram of the noise suppression method for the multi-angle brightness temperature product of the L-band synthetic aperture radiometer of the present invention.

[0035] Figure 2 Curve of the brightness temperature varying with the angle in the embodiment of the present invention.

[0036] Figure 3 Standard deviation of the half-orbit observed brightness temperature at 20200715T0133 in the embodiment of the method of the present invention.

[0037] Figure 4 Standard deviation of the half-orbit observed brightness temperature after noise reduction at 20200715T0133 in the embodiment of the method of the present invention.

[0038] Figure 5 Standard deviation of the SMAP data closest to 20200715T0133 in the embodiment of the method of the present invention.

[0039] Figure 6 Three kinds of brightness temperature inversion salinities and Argo float salinities of the half-orbit brightness temperature data at 20200430T0231 in the embodiment of the method of the present invention. Detailed Embodiment

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] In the present invention, terms such as "first" and "second" in the present invention and the drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0042] Such as Figure 1As shown in the figure, the method for suppressing multi-angle brightness temperature noise of the spaceborne synthetic aperture microwave radiometer of the present invention includes:

[0043] Obtain historical ocean physical parameters, and calculate the corresponding brightness temperature at different observation angles according to the historical ocean physical parameters;

[0044] Fit the brightness temperature into multiple first curves, and establish a relationship between the brightness temperature and the observation angle under different historical ocean physical parameters;

[0045] Select a specific observation angle from the different observation angles as the reference angle, calculate the deviation between the corresponding brightness temperature at each observation angle and the corresponding brightness temperature at the reference angle, establish a look-up table of the observation angle and the deviation under different physical parameters, and the slope of each first curve is the keyword of the look-up table;

[0046] When correcting the brightness temperature of SMOS multi-angle observation, obtain the current ocean physical parameters, calculate the corresponding brightness temperature at different observation angles according to the current ocean physical parameters, fit the brightness temperature into multiple second curves, calculate the slope of each second curve, and look up the brightness temperature correction value of each SMOS observation angle in the look-up table according to the slope of each second curve, and subtract the brightness temperature correction value from the brightness temperature of the corresponding SMOS observation angle to obtain multiple reference angle brightness temperatures;

[0047] Average the multiple reference angle brightness temperatures to obtain the reference angle brightness temperature for suppressing noise.

[0048] Specifically, the steps for obtaining the multi-angle brightness temperature include:

[0049] S11. Obtain the sea surface physical parameters from the ECMWF auxiliary data;

[0050] S12. Obtain the sea surface salinity data from SMOS L2;

[0051] S13. Input the data into the FASTEM-6 model to obtain the multi-angle brightness temperature.

[0052] Specifically, the fitting of the brightness temperature with respect to the observation angle is a binomial fitting.

[0053] Specifically, after establishing the look-up table, it is necessary to delete the redundant curves with high similarity in the look-up table to improve the look-up efficiency.

[0054] Specifically, before averaging the single-angle brightness temperature, perform outlier detection on the brightness temperature and remove the abnormal brightness temperature values.

[0055] Specifically, the following formula is used for outlier detection, including:

[0056] |TB L1c -Tb model-median(TB L1c -Tb model )|>nσTB#

[0057]

[0058] The method of the present invention further includes: using the historical true observation brightness temperature of the SMOS satellite and the corresponding historical ocean physical parameters to obtain a brightness temperature image after noise suppression;

[0059] Calculate the standard deviation of the brightness temperature image after noise suppression and the original observed brightness temperature TB, and compare it with the standard deviation of the matched SMAP brightness temperature. The greater the decrease in the standard deviation after noise suppression, the better the result of noise suppression.

[0060] Use the SMOS L1C multi-angle brightness temperature, the processed SMOS single-angle brightness temperature, and the SMAP single-angle brightness temperature to retrieve the sea surface salinity respectively. The algorithm is the LM algorithm used in the SMOS L2 salinity data product, and the salinity data in the ISAS20_ARGO (ISAS: In-situ Analysis System) product downloaded from the ARGO official website is used as the reference true value. Compare the retrieval accuracy to verify the noise reduction result.

[0061] In the embodiment of the present invention, the method of the present invention is further described by taking the L-band remote sensing brightness temperature image of the SMOS satellite in 2020 as an example.

[0062] 300,000 historical sea surface physical parameters corresponding to SMOS observation samples were randomly selected from 2020 for simulation. Download the SMOS satellite L2 product data to obtain the sea surface salinity data, and obtain the physical information such as the matched sea surface temperature, wind speed, and wind direction from ECMWF. Based on the FASTEM-5 model, multi-angle brightness temperatures including X and Y polarizations are forwardly generated according to the above physical parameters.

[0063] Perform a quadratic term fitting of the multi-angle brightness temperatures under different physical parameters with respect to the angle. Use the fitting curve to calculate the brightness temperature at intervals of 0.1° from 0° to 70°.

[0064] Calculate the slope of the fitting curve, and perform a statistical analysis on the deviation between the multi-angle brightness temperature (0° to 70°) and the brightness temperature of any single angle. Since it is necessary to evaluate the SMOS brightness temperature noise level through the comparison of the SMAP brightness temperature product, and the incident angle of the SMAP brightness temperature of the real aperture radiometer using conical scanning is uniformly 40°, 40° is used as the specified single angle. The slope of the fitting curve is used as the lookup table matching keyword, and the corresponding deviation is used as the brightness temperature correction value.

[0065] The redundant curves in the lookup table were deleted, and a lookup table for the multi-angle sea surface brightness temperature relationship was generated, which contains approximately 50,000 entries. Figure 2Shows the look-up table corresponding to 40°, where (a) represents X polarization and (b) represents Y polarization. Each curve in the figure corresponds to the brightness temperature correction values at different incident angles under given historical ocean physical parameters.

[0066] Store the above look-up table for future use.

[0067] For the SMOS observed brightness temperature data, select the matching ECMWF auxiliary data and SMOS L2 sea surface salinity data to calculate the matching multi-angle brightness temperature.

[0068] Fit the multi-angle brightness temperature and calculate the slope of the fitted curve. Then use the slope as the retrieval value to look up the brightness temperature correction value corresponding to the angle in the look-up table, and map the brightness temperature of SMOS multi-angle observation to 40°.

[0069] Formulas (1) and (2) are used to detect outliers in the brightness temperature and remove the abnormal brightness temperature values. Then, average the mapped single-angle brightness temperature to obtain the single-angle brightness temperature with noise suppressed.

[0070] |TB L1c -Tb model -median(TB L1c -Tb model )|>nσTB#(1)

[0071]

[0072] As Figure 3 shown is the standard deviation of the brightness temperature of the half-orbit observation at 01:33 on July 15, 2020 in the embodiment of the method of the present invention, where (a) represents X polarization and (b) represents Y polarization; Figure 4 is the standard deviation result after noise reduction for the observed brightness temperature map in Figure 3 , where (a) represents X polarization and (b) represents Y polarization; Figure 5 is the standard of SMAP data interpolated to the SMOS grid, where (a) represents X polarization and (b) represents Y polarization. Since the SMAP brightness temperature data is in H and V polarizations in the Earth coordinate system, it also goes through the step of rotating the SMOS brightness temperature polarization from XY polarization to HV polarization.

[0073] Obviously, for all samples, the standard deviation of the processed SMOS brightness temperature in H and V polarizations is reduced by about 3K compared to the original value, and the standard deviation is almost the same as that of the SMAP TOA. This indicates that the noise level of the corrected SMOS H polarization brightness temperature is comparable to that of the SMAP

[0074] Tables 1 and 2 summarize the biases and root mean square errors (RMSE) of SMOS and SMAP TOA before and after H and V polarization corrections, respectively. Compared with the uncorrected results, the overall bias between the corrected SMOS and SMAP TOA slightly decreases, and the RMSE decreases by approximately 2 to 3 K. This indicates that the matching accuracy between the corrected SMOS L1C TB and SMAP TOA is higher, suggesting that the accuracy of the corrected SMOS L1C TB has been improved. Therefore, the corrected SMOS L1C TB is more suitable for cross-calibration with the brightness temperature data of spaceborne real aperture radiometers at the same angle.

[0075] Table 1 Standard Deviation Statistics

[0076]

[0077]

[0078] Table 2 Bias Statistics

[0079]

[0080] For the salinity inversion experiment, SMOS measured brightness temperatures and SMAP brightness temperatures that were temporally and spatially matched with Argo float data were selected. The LM algorithm used in the SMOS L2 salinity data product was adopted as the inversion algorithm. The experiment used SMOS L1C multi-angle brightness temperatures, processed SMOS single-angle brightness temperatures, and SMAP single-angle brightness temperatures to invert sea surface salinity, and the inversion results were compared with ARGO (ISAS) data. Figure 6 Three salinity inversions of the half-orbit brightness temperature data at 20200430T0231 are shown. (a) is the Argo float salinity, (b) is the inversion result of the corrected SMOS L1C TB, (c) is the inversion result of the initial SMOS L1C TB, (d) is the SMAP brightness temperature inversion result. Tables 3 and 4 summarize the biases and root mean square errors (RMSE) between the salinity inversions of the original SMOS brightness temperature, processed SMOS brightness temperature, and SMAP brightness temperature of multiple half-orbits and the ARGO salinity. The statistical results show that the accuracy of inverting salinity using the single-angle SMOS brightness temperature after noise suppression is significantly better than that using the original multi-angle SMOS brightness temperature inversion result, and is comparable to the SMAP brightness temperature inversion result.

[0081] Table 3 Absolute Value Bias Statistics (V polarization)

[0082]

[0083]

[0084] Table 4 Root Mean Square Error Statistics (V polarization)

[0085] Orbit Time RAW SMOS CALIBRATED SMOS SMAP 20200430T0231 1.82 1.28 1.34 20200131T0234 1.88 1.25 1.27 20200715T0133 1.75 1.35 1.32 20200815T1356 1.52 1.46 1.47 20200915T1759 1.95 1.54 1.40 20201015T1742 1.63 1.33 1.32

[0086] In summary, it can be clearly seen that the single-angle brightness temperature obtained by the present invention can effectively suppress noise and achieve better results in inversion. By comparing with the brightness temperature observed by SMAP, it is found that the processed brightness temperature has a higher matching accuracy with SMAP TOA and is more suitable for cross-calibration with the brightness temperature data of a spaceborne real aperture radiometer at the same angle.

[0087] In the embodiment of the present invention, by utilizing the characteristic that the relative change relationship of the forward-modeled brightness temperature with the angle has good consistency with the actual observed value, a lookup table of the multi-angle brightness temperature relationship of the sea surface is established based on the multi-angle forward-modeled brightness temperature. Thus, the multi-angle brightness temperature of SMOS L1C can be mapped to any single angle, and a more accurate single-angle brightness temperature product can be obtained by averaging to suppress noise, which is expected to improve the effects of applications such as salinity inversion, cross-calibration, and data assimilation.

[0088] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps corresponding to the method for suppressing noise of the multi-angle brightness temperature of a synthetic aperture radiometer as described above are implemented.

[0089] The method for suppressing noise of the multi-angle brightness temperature of a spaceborne synthetic aperture microwave radiometer provided by the present invention utilizes the characteristic that the trends of the forward-modeled brightness temperature and the observed brightness temperature with the angle are basically the same. By extracting the characteristics of the relationship between the observed angle and the brightness temperature, a deviation table of the multi-angle brightness temperature and the brightness temperature at a specific angle in different situations is established. When calculating the multi-angle brightness temperature, the historical ocean physical parameters are from ECMWF auxiliary data, and the sea surface salinity data is from SMOS L2 level. The historical ocean physical parameters are input into the FASTEM6 model to calculate the multi-angle brightness temperature that matches the SMOS L1C brightness temperature, including X and Y polarizations. In order to distinguish the mapping relationships in different situations, the curve slope of the multi-angle brightness temperature is used as a keyword. When the curve slopes are different, the deviation between the brightness temperatures at two angles is different. For the slope of the second curve, the slope value in the table that is closest to this is selected, and the corresponding deviation is the required deviation. In this way, after establishing the lookup table, the brightness temperatures at different angles can be quickly mapped to a specific angle to obtain multiple brightness temperature values at the same angle. Before averaging the mapped single-angle brightness temperatures, outlier detection is performed on the brightness temperatures, and the abnormal brightness temperature values are removed. By averaging this, the single-angle brightness temperature with effectively suppressed random noise can be obtained.

[0090] Based on the multi - angle brightness temperatures in the L - band of the spaceborne synthetic aperture radiometer on the SMOS satellite, this invention obtains a single - angle brightness temperature with higher accuracy from the observed multi - angle brightness temperatures of the synthetic aperture radiometer with relatively large input errors. This is beneficial for helping the application of the synthetic aperture radiometer brightness temperature products in aspects such as salinity inversion, cross - calibration, and data assimilation. The look - up table in this invention is calculated from the forward - modeled brightness temperatures in a large number of different physical environments. According to the slopes of different curves, it matches the brightness temperature deviations caused by angles in different situations. This method can efficiently map the multi - angle brightness temperatures to a single angle, effectively improve the measurement brightness temperature accuracy of the synthetic aperture radiometer in the offshore area at this angle, and reduce the computational complexity and required time. It is a new method for noise suppression of the multi - angle brightness temperatures in the L - band of the synthetic aperture radiometer.

[0091] It is easy for those skilled in the art to understand that the above - mentioned is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for suppressing multi-angle brightness temperature noise of a spaceborne synthetic aperture microwave radiometer, characterized in that: include: Acquire historical ocean physical parameters, and calculate brightness temperatures corresponding to different observation angles according to the historical ocean physical parameters; Fitting the brightness temperature into a plurality of first curves, and establishing a relationship between the brightness temperature and the observation angle under different historical ocean physical parameters; Selecting a specific observation angle from the different observation angles as a reference angle, calculating the deviation between the brightness temperature corresponding to each observation angle and the brightness temperature corresponding to the reference angle, and establishing a lookup table of observation angles and the deviation under different physical parameters, wherein the slope of each first curve is a keyword of the lookup table; When correcting the brightness temperature observed by SMOS at multiple angles, obtain the current ocean physical parameters, calculate the brightness temperatures corresponding to different observation angles according to the current ocean physical parameters, fit the brightness temperatures to multiple second curves, calculate the slopes of the second curves, find the brightness temperature correction value of each SMOS observation angle in the lookup table according to the slopes of the second curves, subtract the brightness temperature correction value from the brightness temperature corresponding to the SMOS observation angle, and obtain multiple reference angle brightness temperatures; The plurality of reference angle brightness temperatures are averaged to obtain a reference angle brightness temperature that suppresses noise.

2. The method according to claim 1, characterized in that The brightness temperature was fitted as a binomial fit.

3. The method according to claim 1, characterized in that Before averaging the brightness temperatures at the multiple reference angles, the method further includes performing abnormal value detection on the brightness temperatures and removing abnormal brightness temperature values.

4. The method according to claim 3, characterized in that The following formula is used for outlier detection, including: |TB L1c -Tb model -median(TB L1c -Tb model )|>nσTB Among them, TB L1c is the brightness temperature of L1C product, Tb model Calculate brightness temperature for the model, σTB radiometricnoise is the radiometer error, σTB model is the model error.

5. An electronic device, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 4.

7. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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