Node sampling-based one-dimensional synthetic aperture microwave radiometer near-shore brightness temperature increasing method

Abstract

The invention belongs to the technical field of one-dimensional synthetic aperture near-shore data processing, and relates to a one-dimensional synthetic aperture microwave radiometer near-shore brightness temperature increasing method based on node sampling, which comprises the following steps: initializing parameters, and carrying out oversampling processing on frequency domain signals containing Gibbs pollution; nodes in the oversampling brightness temperature image are searched through positive and negative values of a second derivative, and common nodes and abrupt change nodes are screened; performing iterative correction on the common nodes, including calculating a high-resolution Laplacian operator, updating brightness temperature values of the common nodes, and performing loop iteration; brightness temperature correction is performed on the abrupt change node, a trend factor is calculated according to the brightness temperature difference value of the nearest adjacent node, and the brightness temperature value of the abrupt change node is updated; and outputting the corrected near-shore data brightness temperature value. According to the method, Gibbs oscillation elimination and sea-land buffer area reduction are carried out by using a node sampling method, and on the basis that iteration of all common nodes is completed, mutation nodes are corrected by calculating trend factors, so that the purpose of reducing the sea-land buffer area is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of one-dimensional synthetic aperture nearshore data processing, and in particular to the field of one-dimensional synthetic aperture microwave radiometer nearshore influence elimination. Background Art

[0002] The application of synthetic aperture technology in microwave radiometers has successfully solved the contradiction between spatial resolution and antenna size, greatly improved the spatial resolution of observations, and significantly reduced the size and weight of the radiometer. However, these advantages come at the expense of increased complexity in system design and signal processing. Compared with traditional real-aperture microwave radiometers, synthetic aperture microwave radiometers use complex antenna arrays and interferometric imaging principles, which introduces a variety of errors. These errors not only come from the instrument itself, but are also affected by external factors, bringing certain challenges to high-precision brightness temperature measurements.

[0003] The advantage of the synthetic aperture radiometer technology is that it improves the spatial resolution of low-frequency radiometers without increasing the physical size of the antenna through aperture synthesis technology. However, due to its high system complexity, the difficulty of data processing also increases, especially in satellite payload interferometry, which shows discreteness and frequency domain truncation problems. When brightness temperature transitions occur in the field of view, a large-scale Gibbs phenomenon is likely to occur, which is unacceptable for salinity measurements that require extremely high brightness temperature accuracy.

[0004] The synthetic aperture microwave radiometer will face the problem of land and ocean areas aliasing into the synthetic aperture measurement field of view in the nearshore area. Due to the characteristics of aperture synthesis, it will lead to the loss of frequency domain information during interferometric measurement. On the one hand, it causes the aliasing phenomenon of the instrument's field of view, narrowing the available mapping width; on the other hand, if there is a brightness temperature transition phenomenon in the field of view, it will cause Gibbs contamination in the inverted brightness temperature image, so the nearshore area will face a more difficult problem of sea-land mutual contamination than the real aperture radiometer. Salinity inversion requires the input of high-precision brightness temperature, and the land contamination problem in the nearshore brightness temperature measurement must be corrected.

[0005] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0006] In order to overcome the Gibbs contamination problem and the blurred sea-land boundary problem in the field of view of the one-dimensional synthetic aperture microwave radiometer during interferometric measurement in the prior art, the present invention proposes a nearshore brightness temperature improvement method for a one-dimensional synthetic aperture microwave radiometer based on node sampling. After the inversion parameters are initialized, the method performs oversampling processing on the frequency domain signal, and then performs node screening on the brightness temperature image obtained by oversampling to identify the mutation nodes and non-mutation nodes. For the non-mutation nodes, an iterative correction operation is performed. On this basis, for the mutation nodes, the trend factor calculated according to the brightness temperature difference between the mutation nodes and the adjacent nodes is adjusted, and finally the brightness temperature at the mutation nodes is corrected. After the whole process is completed, the corrected nearshore data brightness temperature value is output.

[0007] Therefore, the present invention utilizes node sampling and iterative calculation of the high-resolution Laplace operator of the nodes to suppress Gibbs contamination, and on this basis, achieves the purpose of reducing the sea-land fuzzy area by calculating the trend factor.

[0008] The technical solution of the present invention is: A method for improving nearshore brightness temperature using a one-dimensional synthetic aperture microwave radiometer based on node sampling includes the following steps: Step 1: Initialize the nearshore data parameters of the one-dimensional synthetic aperture microwave radiometer, oversample the frequency domain signal containing Gibbs contamination, set the oversampling factor, and obtain a one-dimensional oversampled brightness temperature image; Step 2: Find the nodes in the oversampled brightness temperature image through the positive and negative values ​​of the second-order derivative, set the threshold S, and filter the common nodes and mutation nodes; when the difference between the two adjacent sides of the node is greater than the threshold S at the same time, the node is regarded as a mutation node, and the other nodes are regarded as common nodes; Step 3: perform iterative correction on the common nodes, including calculating the high-resolution Laplace operator, updating the brightness temperature value of the common nodes, and iterating in a loop; the calculation expression of the high-resolution Laplace operator is: (1) in, represents the high-resolution Laplace operator, T represents the brightness temperature, represents the node position, λ represents the high-resolution Laplace operator calculation range, T(u,v) represents the brightness temperature value at the node position, and Represent the brightness temperature values ​​at the corresponding positions respectively; Step 4: perform brightness temperature correction on the mutation node, calculate the trend factor ρ according to the brightness temperature difference of the nearest adjacent node, and then adjust and update the brightness temperature value of the mutation node; the expression of the trend factor ρ is as follows: (2) or (3); in, represents the relative distance between the node and the mutation node, and k represents the calculation range of the trend factor; and Represent the brightness temperature values ​​at the corresponding positions respectively; Step 5: Output the corrected brightness temperature value of the nearshore data.

[0009] Furthermore, the parameter initialization of step one includes radiometer system parameter definition, scene definition, channel and error parameter definition, and inversion parameter definition.

[0010] Furthermore, in the step 1, before the oversampling process is performed, the number of zero padding is determined in the parameter definition. The visibility function of the nearshore data of the one-dimensional synthetic aperture microwave radiometer is a one-dimensional matrix with a side length of N. After the oversampling process, it becomes a one-dimensional matrix with a side length of N. L The new one-dimensional matrix, N and N L There is an integer relationship between them: N L =N×β, β is the oversampling factor.

[0011] Furthermore, in the step 2, all nodes are found based on the existence of zero values ​​between positive and negative values ​​of the second-order derivative, and a threshold value S is set according to the brightness temperature variation characteristics of the nearshore area, and the range of S is 40-60. The nodes are screened according to the threshold S. If the difference between the two adjacent sides of the node exceeds S at the same time, the node is regarded as a mutation node, and a 1×n one-dimensional matrix of mutation node positions is obtained, where n is the number of mutation nodes.

[0012] Furthermore, the number of loop iterations in step three is determined according to the correction effect; an interrupt condition is introduced during the iterative loop process to limit the number of iterations to m, where m is any integer between 20 and 30, and the specific value of m is adjusted according to the correction result.

[0013] Furthermore, in step 4, if the number of mutation nodes is an even number, the two mutation nodes are respectively corrected to the positions calculated based on the trend factor obtained from the nearest non-mutation node. At this time, the trend factor ρ corresponding to the mutation node on the left is calculated using formula (2), and the trend factor ρ corresponding to the mutation node on the right is calculated using formula (3).

[0014] Furthermore, in step 4, if the number of mutation nodes is an odd number, firstly, the difference between the brightness temperature values ​​of the mutation nodes and the ordinary nodes on both sides is calculated respectively. If the difference between the mutation node and the ordinary node on the left is small, then the trend factor is calculated using formula (2); if the difference between the mutation node and the ordinary node on the right is small, then the trend factor is calculated using formula (3).

[0015] Furthermore, in step 4, if the number of ordinary nodes on the corresponding side is less than k when the trend factor is calculated, and k is 1-3, the trend factor calculation here is skipped and the original node brightness temperature value is retained.

[0016] Beneficial effects of the present invention: (1) The present invention provides a method for improving the nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling. The node sampling method is used to eliminate the Gibbs oscillation and reduce the land-sea buffer zone. In each iteration, the high-resolution Laplace operator is calculated to eliminate the Gibbs oscillation, thereby suppressing Gibbs pollution. After all non-mutation nodes are iterated, the mutation nodes are corrected by calculating the trend factor to reduce the land-sea buffer zone.

[0017] (2) The present invention adopts the node sampling method to suppress Gibbs contamination, and obtains an oversampled brightness temperature image by zero padding; the suppression process is an iterative process, and the number of iterations plays a role in the suppression effect and the speed of the program; λ represents the high-resolution Laplace operator calculation range; when correcting the sudden node brightness temperature, the trend factor calculation range k is used as the sudden node brightness temperature calculation parameter; The present invention abandons the windowing operation in the traditional method when suppressing the Gibbs effect, and adopts the one-dimensional matrix operation of the entire oversampled brightness temperature image, making full use of the difference information of the brightness temperature values ​​of adjacent nodes, effectively avoiding the phenomenon that the brightness temperature value of a single node is too high or too low, and the boundary information can be retained to a certain extent when correcting the brightness temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention provides a flow chart of the method.

[0019] Figure 2 : is a comparison image of the correction result of Example 1 of the present invention, wherein the solid line represents the set scene brightness temperature, the dashed line represents the original inverted brightness temperature, and the dotted line represents the corrected brightness temperature; the horizontal coordinate ξ represents the direction cosine.

[0020] Figure 3 : is a comparison diagram of the difference before and after correction of Example 1 of the present invention, wherein the solid line represents the brightness temperature difference between the set scene and the set scene before correction, and the dotted line represents the brightness temperature difference between the set scene and the set scene after correction; the horizontal axis ξ represents the direction cosine. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In order to further understand the present invention, the present invention will be further described in conjunction with the accompanying drawings and embodiments.

[0023] like Figure 1 As shown, the present invention provides a method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling, comprising the following steps: Step 1: Initialize the nearshore data parameters of the one-dimensional synthetic aperture microwave radiometer, that is, initialize the system inversion parameters, including: radiometer system parameter definition, scene definition, channel and error parameter definition, inversion parameter definition. And perform oversampling on the frequency domain signal containing Gibbs contamination.

[0024] Accurate definition of parameters can achieve good inversion results, so that the brightness temperature image can be reconstructed more accurately in subsequent iterations and corrections.

[0025] The spatial oversampling of the brightness temperature image is achieved by embedding the Fourier coefficients of the basic one-dimensional matrix into a larger one-dimensional matrix and filling in new spatial frequency domain zeros. In this process, the original Fourier coefficients are retained, ensuring that the original information is completely retained during the oversampling process. During the oversampling process, no assignment operation is performed on the Fourier coefficients. The side length of the one-dimensional matrix of the visibility function of the original one-dimensional synthetic aperture microwave radiometer nearshore data is defined as N, and the original Fourier coefficient matrix is ​​embedded in the matrix with a side length of N. L In a larger one-dimensional matrix, N and N L There is an integer relationship β between them: N L =N×β, β is named as the oversampling factor, all new Fourier coefficients are set to zero, the inverse Fourier transform is applied to a larger Fourier domain, and finally an oversampled brightness temperature image is obtained, which has the same spatial coverage as the original image, but the density is β times.

[0026] Step 2: The node is the position on the data curve where the second-order derivative is zero and the direction changes. Since there is a zero value between the positive and negative values ​​of the second-order derivative, all nodes can be found, and the threshold is set to S according to the brightness temperature change characteristics of the nearshore area. The nodes in the oversampled image are screened to distinguish between common nodes and mutation nodes.

[0027] The range of threshold S is 40-60, which is determined by the difference between the sea surface brightness temperature and the land brightness temperature. In the simulation system, since the scene brightness temperature can be set freely, the threshold value should be changed according to the setting of the scene brightness temperature, and the setting of the threshold is relatively flexible. The nodes are screened, and if the difference between the adjacent two sides of the node exceeds S at the same time, the node is regarded as a mutation node.

[0028] Since there may be continuous mutation nodes in complex high and low brightness temperature data (nearshore data), after the continuous mutation nodes are screened out, if the number of mutation nodes is an odd number, the middle mutation node is regarded as the final mutation node; if the number of continuous mutation nodes is an even number, the two middle mutation nodes are retained.

[0029] Step 3: Iteratively correct the common nodes. Except for the mutation node and the nodes before and after it, the other nodes calculate the high-resolution Laplacian operator on the oversampled image. The calculation method is the difference between the average value of the brightness temperature of the first adjacent point of each node on the image and the point. If there is no adjacent non-mutation node at the node position, the original value is retained. The expression is as follows:

[0030] in, represents the high-resolution Laplace operator, T represents the brightness temperature, and λ represents the calculation range of the high-resolution Laplace operator. Represents the node position. Where u represents the position number of the node in the node sequence. Since the one-dimensional synthetic aperture imaging is a one-dimensional line, Set to 0. The high-resolution Laplace operator calculated in each iteration needs to be added to the brightness temperature of the ordinary node to obtain the updated brightness temperature. The brightness temperature needs to be updated once every iteration. Finally, the iteratively corrected ordinary node and the uncorrected mutation node are represented as T1.

[0031] The number of loop iterations should be determined based on the correction effect. When the number of iterations is early, you can visually confirm that large oscillations are being eliminated, and then gradually stagnate. At this time, from the i-th to the i+1-th iteration, oscillations may occur: the value of the same point is updated to a new value, and then returns to the previous value after another iteration. The value of this point changes between these two values, and the cycle repeats. Therefore, an interrupt condition should be introduced here to avoid invalid iterations, and the number of iterations is limited to m, where m is any integer between 20-30 and can be adjusted according to the correction effect.

[0032] For simplicity, a very simple interruption condition is introduced: the algorithm stops iterating when it reaches iteration 25. Because from the test results, no obvious image improvement is observed after iteration 25.

[0033] Step 4: When correcting the brightness temperature of the mutation node, the brightness temperature of the ordinary nodes and the brightness temperature of the mutation node involved in the calculation are both obtained in T1. If the number of mutation nodes is even, the two mutation nodes are corrected to the position calculated based on the trend factor obtained from the nearest ordinary node; at this time, the trend factor ρ corresponding to the mutation node on the left is calculated as follows: ; The trend factor ρ corresponding to the mutation node on the right side is calculated using the formula ; If the number of mutation nodes is odd, first calculate the difference between the brightness temperature of the mutation nodes and the ordinary nodes on both sides, and then calculate the trend factor based on the side with the smaller difference.

[0034] If the difference between the mutation node and the ordinary node on the left is small, the trend factor is calculated based on the left node of its relative position. At this time, the trend factor ρ calculation formula is: ; If the difference between the mutation node and the ordinary node on the right is small, the trend factor is calculated based on the right node of its relative position. At this time, the trend factor ρ calculation formula is: ; where ρ is the brightness temperature value of the trend factor, represents the relative distance between the node and the mutation node, and k represents the calculation range of the trend factor; and They represent the brightness temperature values ​​at the corresponding positions respectively.

[0035] If the number of ordinary nodes on the corresponding side is less than k when the trend factor is calculated, the trend factor calculation here is skipped and the original node brightness temperature value is retained. The value range of k is between 1-3.

[0036] Usually, k is set to 2 because the values ​​of ordinary nodes have been iteratively corrected by calculating the high-resolution Laplace operator in the above steps, and the difference between them and adjacent ordinary nodes is not very large, so the value of k of 2 is sufficient to meet the requirements of trend factor ρ calculation.

[0037] Step 5: Output the corrected nearshore data brightness temperature value, and then end. This embodiment uses a one-dimensional synthetic aperture microwave radiometer simulation system to illustrate a one-dimensional synthetic aperture microwave radiometer nearshore brightness temperature enhancement method based on node sampling.

[0038] The observation scene of a one-dimensional synthetic aperture microwave radiometer is a one-dimensional line, and in the simulation process, various parameter inputs will affect the node correction effect and the final brightness temperature image result. Too few parameter definitions will have a negative impact on the node screening and correction; too many parameter definitions may cause too much redundancy in the correction process, reduce operating efficiency, and increase the algorithm correction time.

[0039] Radiometer system parameter definition: The one-dimensional synthetic aperture microwave radiometer antenna is set as an antenna array with 10 feeding units [0 2 4 5 6 7 16 19 21 24], the minimum antenna spacing between each feeding unit is 0.618du of the L-band wavelength, the RF signal center frequency is 1.413GHz, the bandwidth is set to 20MHz, and the receiver noise is 140K during the simulation.

[0040] Scene definition: In order to simulate the boundary between land and sea during the simulation process, the simulation scene is set to a shape with high in the middle and low on both sides, the source is a number of points with evenly distributed pixels, the brightness temperature of the scenes on both sides is set to 0k, and the brightness temperature of the middle scene is set to 200k.

[0041] Channel and error parameter definition: This part is an ideal simulation of various hardware, and all errors are set to 0.

[0042] Definition of response parameters: The inversion method adopts ideal Fourier inversion, the number of zero padding is determined to be 370, and no windowing is selected.

[0043] After the various parameters are defined, the simulated one-dimensional synthetic aperture L1A level data is obtained through the simulation system and oversampled. The oversampling factor is β, which is set to 37 here. Then, an inverse Fourier transform is performed to obtain a one-dimensional oversampled brightness temperature image T.

[0044] according to Figure 1 , find all nodes in the brightness temperature image T by the existence of zero values ​​between the second-order positive and negative derivatives. When the difference between the two adjacent sides of the node exceeds the threshold S at the same time, where S is set to 50, the node is marked as a mutation node. Note that the threshold here cannot be set too large, otherwise the mutation nodes will not be screened out, and it cannot be too small, otherwise the screened mutation nodes will not play the role of marking the boundary between land and sea. After marking all mutation nodes, calculate the high-resolution Laplacian operator of the ordinary node After calculation, the Laplace operator is added to the node brightness temperature T, and the mutation node brightness temperature value remains unchanged, and finally the correction brightness temperature T1 is formed. The calculation formula is: Where T is the brightness temperature after oversampling, (u,v) represents the node position, and λ represents the high-resolution Laplace operator calculation range. Here, λ is set to 1. Since the one-dimensional synthetic aperture imaging is a one-dimensional line, v Set to 0, the number of iterations m here is set to 25 times, and T1 represents the brightness temperature value obtained by adding the node to the Laplace operator.

[0045] On the basis of T1, the mutation nodes are corrected. If the mutation nodes are even, the two mutation nodes are corrected to the positions calculated based on the trend factor obtained from the nearest normal node. If the mutation nodes are odd, the difference between the brightness temperature values ​​of the nodes on both sides is calculated first, and then the value is corrected to the side with the smaller difference. The value will be calculated based on the trend factor. The trend factor The expression is as follows: is the brightness temperature value of the trend factor, (u,v) represents the node position, and since the one-dimensional synthetic aperture imaging is a one-dimensional line, Set to 0, j represents the relative distance between the node and the mutation node, k represents the trend factor calculation range, and the trend factor calculation range k is set to 2. Update the brightness temperature value of the mutation node and output the brightness temperature.

[0046] like Figure 2 As shown in Figure 1, Gibbs contamination has been significantly suppressed; at the point where the brightness temperature suddenly changes, the sea-land boundary is more obvious and the blurred area is reduced. Figure 3 As shown in the figure, the Gibbs error is obviously corrected after the correction when the brightness temperature of the set scene is flat.

[0047] The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, modification, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for improving nearshore brightness temperature using a one-dimensional synthetic aperture microwave radiometer based on node sampling, characterized in that: The steps include: Step 1: Initialize the nearshore data parameters of the one-dimensional synthetic aperture microwave radiometer, oversample the frequency domain signal containing Gibbs contamination, set the oversampling factor, and obtain a one-dimensional oversampled brightness temperature image; Step 2: Find the nodes in the oversampled brightness temperature image through the positive and negative values ​​of the second-order derivative, set the threshold S, and filter the common nodes and mutation nodes; when the difference between the two adjacent sides of the node is greater than the threshold S at the same time, the node is regarded as a mutation node, and the other nodes are regarded as common nodes; Step 3: perform iterative correction on the common nodes, including calculating the high-resolution Laplace operator, updating the brightness temperature value of the common nodes, and iterating in a loop; the calculation expression of the high-resolution Laplace operator is: （1） in, represents the high-resolution Laplace operator, T represents the brightness temperature, represents the node position, λ represents the high-resolution Laplace operator calculation range, T(u,v) represents the brightness temperature value at the node position, and Represent the brightness temperature values ​​at the corresponding positions respectively; Step 4: perform brightness temperature correction on the mutation node, calculate the trend factor ρ according to the brightness temperature difference of the nearest adjacent node, and then adjust and update the brightness temperature value of the mutation node; the expression of the trend factor ρ is as follows: (2) or （3）； in, represents the relative distance between the node and the mutation node, and k represents the calculation range of the trend factor; and Represent the brightness temperature values ​​at the corresponding positions respectively; Step 5: Output the corrected brightness temperature value of the nearshore data.

2. The method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling according to claim 1 is characterized in that: The parameter initialization of step one includes radiometer system parameter definition, scene definition, channel and error parameter definition, and inversion parameter definition.

3. The method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling according to claim 1 is characterized in that: In the step 1, before the oversampling process is performed, the number of zero padding is determined in the parameter definition. The visibility function of the nearshore data of the one-dimensional synthetic aperture microwave radiometer is a one-dimensional matrix with a side length of N. After the sampling process, it becomes a one-dimensional matrix with a side length of N. L The new one-dimensional matrix, N and N L There is an integer relationship between them: N L =N×β, β is the oversampling factor.

4. The method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling according to claim 1 is characterized in that: In the step 2, all nodes are found according to the existence of zero value between positive and negative values ​​of the second-order derivative, and a threshold value S is set according to the brightness temperature variation characteristics of the nearshore area, and the range of S is 40-60. The nodes are screened according to the threshold value S. If the difference between the two adjacent sides of the node exceeds S at the same time, the node is regarded as a mutation node, and a 1×n one-dimensional matrix of mutation node positions is obtained, where n is the number of mutation nodes.

5. The method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling according to claim 1 is characterized in that: The number of loop iterations in step three is determined according to the correction effect; an interrupt condition is introduced during the iterative loop process to limit the number of iterations to m, where m is any integer between 20 and 30, and the specific value of m is adjusted according to the correction result.

6. The method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling according to claim 1 is characterized in that: In step 4, if the number of mutation nodes is an even number, the two mutation nodes are corrected to the positions calculated based on the trend factor obtained from the nearest non-mutation node. At this time, the trend factor ρ corresponding to the mutation node on the left is calculated using formula (2), and the trend factor ρ corresponding to the mutation node on the right is calculated using formula (3).

7. The method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling according to claim 1 is characterized in that: In step 4, if the number of mutation nodes is an odd number, firstly, the difference between the brightness temperature values ​​of the mutation nodes and the ordinary nodes on both sides is calculated respectively. If the difference between the mutation node and the ordinary node on the left is smaller than the difference between the mutation node and the ordinary node on the right, then the trend factor is calculated using formula (2). If the difference between the mutation node and the ordinary node on the right is smaller than the difference between the mutation node and the ordinary node on the left, then the trend factor is calculated using formula (3).

8. The method for improving nearshore brightness temperature of a one-dimensional synthetic aperture microwave radiometer based on node sampling according to claim 1 is characterized in that: In the step 4, if the number of ordinary nodes on the corresponding side is less than k when the trend factor is calculated, and k is 1-3, the trend factor calculation here is skipped and the original node brightness temperature value is retained.

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