Internal interference identification and elimination method for local data of satellite-borne microwave radiometer

Abstract

The invention discloses an internal interference identification and elimination method for local data of a satellite-borne microwave radiometer. The method comprises the following steps: 1, data preparation: obtaining earth observation voltage values, heat source observation voltage values and cold source observation voltage values of channels of a microwave radiometer, and inputting a coefficient matrix; 2, the input coefficient matrix serves as input, and scanning lines are marked; step 3, performing identification between observation points on the identification between the scanning lines; 4, performing cold and heat source identification on the identification between the observation points; and step 5, performing interference mitigation on the data after cold and heat source identification to realize interference elimination. The method is applied to the radiometric calibration product processing step of the satellite-borne microwave radiometer, and is the basis for ensuring that the measurement result is consistent with the real radiation characteristic.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing information technology, specifically relating to a method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer. Background Technology

[0002] A spaceborne microwave radiometer is a remote sensing instrument mounted on a satellite platform that retrieves physical parameters of the Earth system by receiving microwave thermal radiation signals emitted by the Earth's atmosphere, surface, and ocean. Taking the Fengyun series meteorological satellites as an example, microwave thermometers and microwave hygrometers are important payloads that play a crucial role in meteorological observation, weather forecasting, and climate research. Microwave thermometers can observe the vertical temperature distribution of the atmosphere around the clock and in all weather conditions. They can improve the initial field of numerical weather prediction models, provide important data support for meteorological and disaster monitoring, help improve the accuracy of weather forecasts, and are of great significance for the monitoring and forecasting of severe weather events such as typhoons and rainstorms. Microwave hygrometers are core payloads for detecting the vertical distribution of atmospheric humidity and related meteorological elements. By monitoring the radiation characteristics of water vapor in the atmosphere in specific microwave bands (such as the water vapor absorption line near 183 GHz), they achieve all-weather detection of clouds, fog, precipitation, and atmospheric humidity profiles.

[0003] Existing methods for detecting and eliminating internal interference in spaceborne microwave radiometers are based on two-dimensional features between scan lines and observation points, and require full-track data. These methods calculate large and small markers by identifying markers between scan lines, then mark serrations, and finally use markers between Earth observation points to mark the interfering diagonal lines before eliminating the interference. While these methods work well with full-track data, they suffer from false alarms and missed alarms when applied to localized data.

[0004] Localized data plays an irreplaceable role in areas such as regional governance, disaster response, and resource management. Because it is tailored to the specific needs of a particular region, this type of localized data can overcome the limitations of "averaging" in global-scale data, providing direct support for precise decision-making. Figure 1 The existing method is applied to local data. Blue indicates observation points that are not marked as interference, red indicates observation points marked as interference, and green circle indicates a false alarm.

[0005] In summary, the drawback of existing technologies is that they require the use of whole-track data and are applicable when there are false alarms and missed alarms in local data. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the purpose of this invention is to provide a method for identifying and eliminating internal interference in local data of a spaceborne microwave radiometer. Applying this method to the radiometric calibration product processing steps of a spaceborne microwave radiometer is the basis for ensuring that the measurement results are consistent with the true radiation characteristics.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer includes the following steps; Step 1: Data preparation: Obtain the ground observation voltage value, heat source observation voltage value, and cold source observation voltage value of each channel of the microwave radiometer, and construct the input coefficient matrix; Step 2: Use the input coefficient matrix as input to mark the areas between scan lines; Step 3: Mark the observation points after marking the scan lines; Step 4: After marking the observation points, mark the hot and cold sources; Step 5: Perform interference mitigation on the data after identifying the hot and cold sources to achieve interference removal.

[0008] The local data from the spaceborne microwave radiometer are presented in step 1 as “local raw observation results”, steps 2, 3, and 4 complete “identification”, and step 5 completes the removal.

[0009] In step 1, the ground observation voltage value, heat source observation voltage value, and cold source observation voltage value of each channel are the local raw observation results of the microwave radiometer; the local data of the spaceborne microwave radiometer are the embodiment of the local raw observation results.

[0010] The input coefficient matrix includes gain, gain coefficient indicating rapid changes in a single-point comparison threshold, slope of the interference line (observation point / scan line), threshold for determining all present, and threshold for determining all absent.

[0011] Step 2 specifically involves: Step 2-1: Calculate the large and small signs: Calculate the rate of change of the data: Change Change=abs(D(ii+3)+D(ii+2)-D(ii-3)-D(ii-2)) Where D(ii) represents the voltage value at the observation point on the ii-th scan line, and abs represents the absolute value; calculate the large and small flags; If Change < threshold S (S is obtained from the input coefficient matrix), according to the characteristics of the sawtooth pattern, if the value of a certain point is greater than the values ​​of its four adjacent points and the interval points between them, then its larger value is set to 1; if the value of a certain point is smaller than the values ​​of its four adjacent points and the interval points between them, then its smaller value is set to 1. If Change ≥ threshold S (S is obtained from the input coefficient matrix), then only neighboring points are compared; Step 2-2: Mark the serrations: If a scan line is marked as a large mark and the next scan line is marked as a small mark, then the scan line is marked as a triangle; each triangle corresponds to the high point of the serration. Steps 2-3: Combined Identification Serrated Edges: If three consecutive zigzag marks are marked, then the combined zigzag mark of these three marks is marked, as are the combined zigzag marks of the two preceding and two following marks.

[0012] Step 3 specifically involves: Step 3-1: Accumulate diagonal lines Based on the positional pattern of the interference cycle, the proportion Ts of the joint identifier sawtooth on the cumulative diagonal line is obtained by the slope of the diagonal line from the input coefficient matrix; Positional patterns: Interference is generated by radar, and its period is twice the observation period of microwave radiometer; diagonal lines: Step 1 is performed between scan lines (the concept of scan lines is a basic consensus in this field), and Step 2 is performed on diagonal lines. There is only one parameter that determines the diagonal lines, namely the slope of the diagonal lines.

[0013] Step 3-2: Determining the Diagonal Line If Ts > threshold (the threshold is obtained from the input coefficient matrix) and the total number of points on the diagonal line is > 3, then the line is considered to have interference, and an additional line is added before and after the line to mark the interference. If Ts > 0.6, and the total number of points on the diagonal line is 2 or 3 (i.e., at the corner), then the line is considered to have interference. If there are two scan lines before and after, then the first line is marked as interference and the second line is marked as interference.

[0014] Step 3-3: Diagonal marking: If the diagonal line is marked as interference, then all positions of the diagonal line are considered to contain interference.

[0015] Step 4 specifically involves: Step 4-1: Accumulate interference within the scan line: The input cold source observation voltage value is used to accumulate the number of interference observation points marked in step 2 within each scan line for the four observation points of the cold air. Step 4-2: Find the interfering odd or even rows: The total number of marked points in odd and even rows is counted. If the total number of marked points in odd rows is large, then the odd rows are disturbed; otherwise, the even rows are disturbed. Step 4-3: Interference Identification If step 4-2 determines that odd-numbered rows are causing interference, then the accumulation starts from row 1; if step 4-2 determines that even-numbered rows are causing interference, then the accumulation starts from row 2. The accumulation rule is as follows: Sum the number of interference markers in the 5-row interval. If the sum of the number of interference markers is greater than 5, then all 5 scan lines are marked as interference, and the line is expanded by 1 row before and 1 row after the line. All 4 points in the marked line are marked as interference. Step 4-4: Heat source Repeat steps 4-1 to 4-3 for the heat source; Right now: The input heat source observation voltage value is used to accumulate the number of interference observation points marked in step 2 within each scan line for the four observation points of the heat source. The total number of marked points in odd and even rows is counted. If the total number of marked points in odd rows is large, then the odd rows are disturbed; otherwise, the even rows are disturbed. If the odd-numbered rows are determined to be the source of interference, the accumulation begins from row 1. If step 4-2 determines that the even-numbered rows are the source of interference, the accumulation begins from row 2. The accumulation rule is as follows: Sum the number of interference markers in the 5-row interval. If the sum of the number of interference markers is greater than 5, then all 5 scan lines are marked as interference, and the line is expanded by 1 row before and 1 row after, marking all 4 points in the marked line as interference.

[0016] Step 5 specifically involves: For the first scan line: if it is marked as interfering, use the data from the second scan line instead; for the last scan line: if it is marked as interfering, use the data from the second-to-last scan line instead; for scan lines 2 to the second-to-last: if it is marked as interfering, use the average value of the preceding and following scan lines instead.

[0017] The voltage values ​​of the ground observation points and the voltage values ​​of the cold and heat sources after removing interference are input into the calibration program to obtain the observed brightness temperature value. The difference between the observed and simulated brightness temperature values ​​is obtained by subtracting the observed and simulated brightness temperature values ​​after removing interference.

[0018] The method for identifying and eliminating internal interference in local data from spaceborne microwave radiometers is applied to meteorological, marine, terrestrial, and disaster monitoring.

[0019] The beneficial effects of this invention are: Local data from spaceborne microwave radiometers play a crucial role in resolving detailed requirements and regional issues. However, internal interference degrades data quality, hindering accurate retrieval of Earth's surface and atmospheric physical parameters. Specifically, internal interference causes observed data to be inflated, deviating from true values ​​and making it impossible to obtain accurate brightness temperatures, thus complicating the accurate retrieval of Earth's surface and atmospheric physical parameters. This invention proposes an interference identification and removal method, reconstructing the Earth observation and cold / hot source voltages after interference removal. This method is of great significance for data preprocessing and local data applications from spaceborne microwave radiometers. Attached Figure Description

[0020] Figure 1The existing method is applied to local data. Blue indicates observation points that are not marked as interference, red indicates observation points marked as interference, and green circle indicates a false alarm.

[0021] Figure 2 The simulated brightness temperature difference is shown in the image, with the location circled in red indicating a typical disturbance.

[0022] Figure 3 This is a flowchart of the interference identification and removal method.

[0023] Figure 4 A diagram for calculating large and small signs.

[0024] Figure 5 This is a diagram illustrating the sawtooth pattern.

[0025] Figure 6 The combined identification serrations are obtained after traversing all scan lines. Red indicates marked interfering observation points, and blue indicates unmarked observation points.

[0026] Figure 7 These are interference markers after the observation points have been identified. Red indicates marked interfering observation points, and blue indicates unmarked observation points.

[0027] Figure 8 shows a comparison before and after interference removal. (a) Observed simulated brightness temperature difference before interference removal; (b) Observed simulated brightness temperature difference after interference removal; (c) The interference removed. Detailed Implementation

[0028] The present invention will now be described in further detail with reference to the accompanying drawings.

[0029] Spaceborne microwave radiometers have irreplaceable application value in meteorology, oceanography, land monitoring, and disaster monitoring. Whole-orbit data and local data have different roles and values ​​in application: whole-orbit data focuses on macroscopic patterns and large-scale dynamics, while local data focuses on detailed needs and regional issues. They each have their own focus yet complement each other, together forming a complete application chain from "global understanding" to "regional decision-making." Addressing the internal interference to the signals received by the spaceborne microwave radiometer caused by electromagnetic coupling between various payloads and equipment on the satellite, the interference identification and elimination method of this invention is applicable to local data (such as 70 scan lines), forming the basis for solving detailed needs and regional problems.

[0030] This invention relates to a method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer. The method is illustrated using the FY-3E microwave thermometer as an example. Figure 2The simulated brightness-temperature difference caused by interference is shown. The horizontal axis represents observation points 1 to 98, and the vertical axis represents scan lines 1130 to 1169. The location circled in red indicates a typical interference. The interference manifests as a relatively large value on one scan line and a relatively small value on the next scan line, alternating between them.

[0031] like Figure 3 As shown, the method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer includes the following steps; Step 1: Data Preparation. Obtain the ground observation voltage values, heat source observation voltage values, and cold source observation voltage values ​​for each channel of the microwave radiometer, and input the coefficient matrix. The purpose of this step is to provide basic data and key parameter support for subsequent interference identification and processing. The effect is to lay the data foundation for completing interference processing, ensuring that Step 2 has a clear analysis object and judgment criteria, and providing a fundamental guarantee for the accuracy of interference identification.

[0032] Step 2, Sub-module 1: Scan Line Identification. The purpose of this step is to identify interference points with "sawtooth" characteristics by analyzing the voltage value change characteristics between scan lines. The effect is to initially locate sawtooth interference from the scan line dimension, providing a foundation for Step 3.

[0033] Step 2-1: Calculate the large and small signs Calculate the rate of change of the data: Change Change=abs(D(ii+3)+D(ii+2)-D(ii-3)-D(ii-2)) Where D(ii) represents the voltage value at the observation point on the ii-th scan line, and abs represents the absolute value.

[0034] If Change < threshold S (S is obtained from the input coefficient matrix), according to the characteristics of the sawtooth pattern, if the value of a certain point is greater than the values ​​of its four neighboring points and the points between them, then its larger value is set to 1. Figure 4 The high point of the sawtooth pattern (*). If the value of a point is smaller than the values ​​of its four adjacent points and the points between those adjacent points (a total of four points), then its smaller value is set to 1. Figure 4 The lowest point (o) of the middle sawtooth.

[0035] If Change ≥ threshold S (S is obtained from the input coefficient matrix), then only neighboring points are compared.

[0036] Step 2-2: Mark the serrations If a scan line is marked with a large mark and the next scan line is marked with a small mark, then that scan line is marked as large, i.e. Figure 5 A triangle.

[0037] Steps 2-3: Combined Identification Serrated Edges If three consecutive zigzag marks are marked, then the combined zigzag mark of these three marks is marked, as are the combined zigzag marks of the two preceding and two following marks.

[0038] Figure 6 The combined identification serrations are obtained after traversing all scan lines. Red indicates marked interfering observation points, and blue indicates unmarked observation points.

[0039] Step 3, Sub-module 2: Inter-observation point identification. Based on the periodic location pattern of interference, systematic interference with diagonal distribution characteristics is identified from a global perspective of the observation points. Effect: This supplements the shortcomings of local identification between scan lines, identifying periodically distributed diagonal interference from a global perspective, making interference labeling more comprehensive.

[0040] Step 3-1: Accumulate diagonal lines Based on the positional pattern of the interference cycle, the proportion Ts of the serrated edges on the cumulative diagonal line is obtained from the input coefficient matrix.

[0041] Step 3-2: Determining the Diagonal Line If Ts > threshold (the threshold is obtained from the input coefficient matrix) and the total number of points on the diagonal line is > 3, then the line is considered to have interference, and an additional line is added before and after the line to mark the interference.

[0042] If Ts > 0.6, and the total number of points on the diagonal line is 2 or 3 (i.e., at the corner), then the line is considered to have interference. If there are two scan lines before and after, then the first line is marked as interference and the second line is marked as interference.

[0043] Step 3-3: Diagonal marking If the diagonal line is marked as interference, then all positions of the diagonal line are considered to contain interference.

[0044] Step 4, Sub-module 3: Cold and hot source identification. The purpose is to obtain accurate cold and hot source observations, as these sources are crucial references for microwave radiometer calibration. Interference in their data directly contaminates the calibration coefficients. The effect is to achieve accurate identification and full-coverage marking of periodic interference in the cold and hot source data, ensuring the reliability of the calibration data.

[0045] Step 4-1: Accumulate interference within the scan line; For the four observation points in the cold air, the number of interference observation points identified in submodule 1 is accumulated within each scan line.

[0046] Step 4-2: Find the odd or even rows that are causing interference; The total number of marked points in odd and even rows is counted. If the total number of marked points in odd rows is large, then the odd rows are considered to be affected by interference; otherwise, the even rows are considered to be affected by interference.

[0047] Step 4-3: Interference Identification If step 4-2 determines that odd-numbered rows are causing interference, then the accumulation starts from row 1; if step 4-2 determines that even-numbered rows are causing interference, then the accumulation starts from row 2. The accumulation rule is as follows: Sum the number of interference markers in the 5-row interval. If the sum of the number of interference markers is greater than 5, then all 5 scan lines are marked as interference, and the line is expanded by 1 row before and 1 row after, marking all 4 points in the marked line as interference.

[0048] This rule uses the logic of "periodic unit division (5 rows) → statistical threshold filtering (sum > 5) → overall cluster marking → edge expansion coverage" to accurately locate systematic interference related to the instrument hardware cycle in cold source data. This avoids misjudgment of random noise and ensures that the interference area (including the core and the edge) is fully marked.

[0049] Step 4-4: Heat source Repeat steps 4-1 to 4-3 for the heat source.

[0050] Right now: The input heat source observation voltage value is used to accumulate the number of interference observation points marked in step 2 within each scan line for the four observation points of the heat source. The total number of marked points in odd and even rows is counted. If the total number of marked points in odd rows is large, then the odd rows are disturbed; otherwise, the even rows are disturbed. If the odd-numbered rows are determined to be the source of interference, the accumulation begins from row 1. If step 4-2 determines that the even-numbered rows are the source of interference, the accumulation begins from row 2. The accumulation rule is as follows: Sum the number of interference markers in the 5-row interval. If the sum of the number of interference markers is greater than 5, then all 5 scan lines are marked as interference, and the line is expanded by 1 row before and 1 row after, marking all 4 points in the marked line as interference.

[0051] Step 4, Submodule 4: Interference Mitigation. The aim is to specifically eliminate identified interference and restore the continuity and accuracy of the data. The effect is that scan line data marked as interference are effectively replaced, and the processed data more closely approximates the true observations, providing reliable input for subsequent brightness temperature inversion.

[0052] For scan line 1: If it is marked as interfering, use the data from scan line 2 instead. For the last scan line: If it is marked as interfering, use the data from scan line 2-3 to 2-4 to 2-5 to 2-6 to 2-7 to 2-8 to 2-9 to 2-10 to 2-11 to 2-12 to 2-13 to 2-14 to 2-15 to 2-13 to 2-14 to 2-15 to 2-15 to 2-16 to 2-15 to 2-16 to 2-17 to 2-18 to 2-19 to 2-10 ...

[0053] The physical properties of the Earth's surface or atmosphere (such as temperature and humidity) do not change abruptly within a short distance (adjacent scan lines), and the valid data from adjacent scan lines can serve as a reasonable substitute for interfering data.

[0054] Figure 8(a) shows the simulated brightness temperature difference before interference removal. The voltage values ​​of the ground observation points and the voltage values ​​of the cold and heat sources after interference removal are input into the calibration program to obtain the observed brightness temperature value. The difference between the observed and simulated brightness temperature values ​​is then calculated to obtain the simulated brightness temperature difference after interference removal, as shown in Figure 8(b). The removed interference is shown in Figure 8(c).

Claims

1. A method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer, characterized in that, Includes the following steps; Step 1: Obtain the ground observation voltage value, heat source observation voltage value, and cold source observation voltage value of each channel of the microwave radiometer, and construct the input coefficient matrix; Step 2: Use the input coefficient matrix as input to mark the areas between scan lines; Step 3: Mark the observation points after marking the scan lines; Step 4: After marking the observation points, mark the hot and cold sources; Step 5: Perform interference mitigation on the data after identifying the hot and cold sources to achieve interference removal.

2. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 1, characterized in that, In step 1, the ground observation voltage value, heat source observation voltage value, and cold source observation voltage value of each channel are the local raw observation results of the microwave radiometer; the local data of the spaceborne microwave radiometer are a reflection of the local raw observation results. The input coefficient matrix includes gain, gain coefficient identifier for single-point comparison threshold, gain coefficient for rapidly changing gain, slope of interference lines, threshold for all present, and threshold for all absent.

3. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 2, characterized in that, Step 2 specifically involves: Step 2-1: Calculate the rate of change of the data: Change Change=abs(D(ii+3)+D(ii+2)-D(ii-3)-D(ii-2)) Where D(ii) represents the voltage value at the observation point on the ii-th scan line, and abs represents the absolute value; calculate the large and small flags; Step 2-2: If a scan line is marked as a large mark and the next scan line is marked as a small mark, then the scan line is marked as a triangle; each triangle corresponds to the high point of the serration. Steps 2-3: If three consecutive zigzag marks are marked, then the combined zigzag mark of these three marks is marked, as are the combined zigzag marks of the two preceding and two following marks.

4. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 3, characterized in that, In step 2-1, if Change < threshold S, S is obtained from the input coefficient matrix. According to the characteristics of the sawtooth pattern, if the value of a certain point is greater than the values ​​of the four points (adjacent points and the interval points between adjacent points), then its larger value is 1. If the value of a certain point is smaller than the values ​​of the four points (adjacent points and the interval points between adjacent points), then its smaller value is 1. If Change ≥ threshold S, then only the neighboring points are compared.

5. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 4, characterized in that, Step 3 specifically involves: Step 3-1: Based on the positional pattern of the interference cycle, the proportion Ts of the joint identifier sawtooth on the cumulative diagonal line is obtained by the slope of the diagonal line from the input coefficient matrix; Step 3-2: If Ts > threshold (the threshold is obtained from the input coefficient matrix) and the total number of points on the diagonal line is > 3, then the line is considered to have interference, and an additional line is added before and after the line to mark the interference. If Ts > 0.6, and the total number of points on the diagonal line is 2 or 3 (i.e., at the corner), then the line is considered to have interference. If there are two scan lines before and after, then the first line is marked as interference and the second line is marked as interference. Step 3-3: If the diagonal line is marked as interference, then all positions of the diagonal line are considered to contain interference.

6. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 5, characterized in that, In step 3-1, the location pattern is as follows: the interference is generated by the radar, and its period is twice the observation period of the microwave radiometer. Diagonal lines: Step 1 is performed between scan lines, and Step 2 is performed on diagonal lines. The parameter that determines the diagonal lines is the slope of the diagonal lines.

7. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 6, characterized in that, Step 4 specifically involves: Step 4-1: Input the cold source observation voltage value, and for the four observation points of the cold air, accumulate the number of interference observation points marked in Step 2 within each scan line; Step 4-2: Count the total number of marked points in odd and even rows. If the total number of marked points in odd rows is large, then the odd rows are disturbed; otherwise, the even rows are disturbed. Step 4-3: If step 4-2 determines that odd-numbered rows are causing interference, then the accumulation starts from row 1; if step 4-2 determines that even-numbered rows are causing interference, then the accumulation starts from row 2. The accumulation rule is as follows: Sum the number of interference markers in the 5-row interval. If the sum of the number of interference markers is greater than 5, then all 5 scan lines are marked as interference, and the line is expanded by 1 row before and 1 row after the line. All 4 points in the marked line are marked as interference. Step 4-4: The input heat source observation voltage value is used to accumulate the number of interference observation points marked in step 2 within each scan line for the four observation points of the heat source. The total number of marked points in odd and even rows is counted. If the total number of marked points in odd rows is large, then the odd rows are disturbed; otherwise, the even rows are disturbed. If the interference is determined to be in odd-numbered rows, the accumulation starts from row 1; if the interference is determined to be in even-numbered rows, the accumulation starts from row 2. The accumulation rule is as follows: Sum the number of interference markers in the 5-row interval. If the sum of the number of interference markers is greater than 5, then all 5 scan lines are marked as interference, and the line is expanded by 1 row before and 1 row after, marking all 4 points in the marked line as interference.

8. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 7, characterized in that, Step 5 specifically involves: For the first scan line: if it is marked as interfering, then use the data from the second scan line to replace it; for the last scan line: if it is marked as interfering, then use the data from the second to last scan line to replace it. For scan lines 2 to the second to last: if they are marked as interference, use the average value of the preceding and following scan lines instead.

9. The method for identifying and eliminating internal interference in local data from a spaceborne microwave radiometer according to claim 8, characterized in that, The voltage values ​​of the ground observation points and the voltage values ​​of the cold and heat sources after removing interference are input into the calibration program to obtain the observed brightness temperature value. The difference between the observed and simulated brightness temperature values ​​is obtained by subtracting the observed and simulated brightness temperature values ​​after removing interference.

10. The application of the method for identifying and eliminating internal interference in local data of a spaceborne microwave radiometer as described in any one of claims 1-9, characterized in that, It is applied to meteorological, marine, terrestrial, and disaster monitoring.

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