Active and passive microwave combined sea surface multi-element inversion method and system without background field
By employing a multi-band active and passive microwave joint inversion method, and utilizing multi-polarized radiation brightness temperature and scatterometer data in the L, C, and K bands, the problem of dependence on external data in existing technologies has been solved, and high-precision independent inversion of sea surface salinity, temperature, wind speed, total water vapor, and total liquid water has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NAT SPACE SCI CENT CAS
- Filing Date
- 2025-12-29
- Publication Date
- 2026-05-19
AI Technical Summary
Existing sea surface salinity monitoring methods rely on external auxiliary data as background fields, making it impossible to completely eliminate the dependence on external data. Furthermore, the contributions of total water vapor and liquid water in the inversion process are not considered, resulting in limited monitoring accuracy.
Using observational data from a multi-band active and passive microwave imager, a set of polynomial coefficients was generated through a microwave radiative transfer model and historical atmospheric and oceanic data. Combined with linear regression and the least squares method, multiple sea surface elements, including sea surface salinity, temperature, wind speed, total water vapor, and total liquid water, were inverted using multi-polarized radiative brightness temperature and scatterometer data in the L, C, and K bands.
It achieves high-precision inversion of multiple sea surface elements without relying on external auxiliary data, avoids crosstalk between multiple element inversions, and improves monitoring accuracy and independence.
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Figure CN122065644A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of marine exploration technology, and in particular to a method and system for inverting multiple elements of the sea surface using a combination of active and passive microwaves without requiring a background field. Background Technology
[0002] Sea surface salinity (SSS) is one of the most important physical parameters of the ocean. Under constant atmospheric pressure, ocean salinity and temperature together determine the density of seawater. Seawater density affects the formation and distribution of ocean water masses, and differences in density lead to seawater flow, determining the intensity and direction of ocean circulation. Therefore, monitoring sea surface salinity will help deepen our understanding of ocean water masses and ocean circulation. Internationally, the European SMOS satellite uses the L-band integrated aperture microwave radiometer to detect salinity, while NASA's Aquarius and SMAP missions in the United States employ a combined active and passive technique using the L-band real aperture microwave radiometer and microwave scatterometer for salinity detection. Since only the L-band is used, sea surface temperature (SST), wind speed (WS), total column water vapor (TCWV), and total cloud liquid water (TCLW) synchronized with the observation time are required as background field inputs when retrieving salinity.
[0003] my country's ocean salinity probe, launched in 2025, carries the world's first integrated active and passive microwave imager (MICAP) combining a three-band (L-band, 1.4 GHz, C-band, and K-band, 18.7 GHz) one-dimensional integrated aperture microwave radiometer with an L-band digital beamforming microwave scatterometer. This enables integrated and coordinated detection of multiple sea surface elements. The L-band is sensitive to salinity, the C-band to sea temperature and high wind speeds, and the K-band to medium and low wind speeds, total water vapor, and total liquid water, thus providing multi-element detection capabilities. However, existing methods, even those considering multi-band inversion, do not account for the contributions of water vapor and total liquid water in the inversion process. They still require simultaneous acquisition of external auxiliary data (such as NWP (Numerical Weather Prediction) models) as background field input, making it impossible to completely eliminate dependence on external data. Summary of the Invention
[0004] The purpose of this application is to overcome the above-mentioned defects of the prior art and provide a method and system for multi-element inversion of the sea surface by active and passive microwave imaging without background field. This method and system can perform multi-element inversion of the sea surface by relying solely on the observation data of the active and passive microwave imager itself without relying on external auxiliary data.
[0005] To solve the above-mentioned technical problems, the technical solution of this application provides a method for multi-element inversion of the sea surface without background field using a combined active and passive microwave method, including: Step 1: Using a microwave radiative transfer model and historical atmospheric and oceanic data, simulated brightness temperatures in the L, C, and K bands at a specified incident angle are generated. Based on a linear regression model, a set of polynomial coefficients corresponding one-to-one with multiple sea surface elements is generated. These multiple sea surface elements include sea surface salinity, sea surface temperature, sea surface wind speed, total water vapor, and total liquid water. Step 2: Obtain the multi-polarized sea surface radiation brightness temperature in the L-band, C-band, and K-band observed by the spaceborne active and passive microwave imager. Substitute the observed multi-polarized sea surface radiation brightness temperature into the regression equation corresponding to the polynomial coefficient set to obtain the initial values of multiple sea surface elements. Step 3: Based on the C-band multi-polarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager, after quality control of the initial value, the preliminary inversion result of sea surface temperature is obtained by least squares inversion, and the initial value of sea surface temperature is updated. Step 4: Based on the multi-polarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager in the C and K bands, the optimized inversion results of sea surface temperature, sea surface wind speed, total water vapor and total liquid water are obtained by least squares inversion, and the corresponding initial values are updated. Step 5: Based on the L-band multipolarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager and the backscattering coefficient observed by the L-band microwave scatterer, combined with the active and passive joint observation data, the final inversion results of sea surface salinity and sea surface wind speed are obtained by least squares inversion. Step 6: Output the inversion results of multiple sea surface elements, including: the optimized inversion results of sea surface temperature, total water vapor and total liquid water obtained in Step 4, and the final inversion results of sea surface salinity and sea surface wind speed obtained in Step 5.
[0006] As an improvement to the above method, the range of the specified incident angle in step 1 is 30°~60°.
[0007] As an improvement to the above method, the polynomial coefficient set in step 1 It can be obtained through the following formula: ; in, The symbol for solving indicates solving for the polynomial coefficients a~g within the parentheses. Includes: a~g, where, As the first coefficient, As the second coefficient, The third coefficient, It is the fourth coefficient. It is the fifth coefficient. It is the sixth coefficient. It is the seventh coefficient. The brightness temperature of polarized sea surface radiation. This refers to the brightness temperature of horizontally polarized sea surface radiation in the L-band. The brightness temperature of vertically polarized sea surface radiation in the L-band. The brightness temperature of horizontally polarized sea surface radiation in the C-band. This refers to the C-band vertically polarized sea surface radiation brightness temperature. The K-band horizontally polarized sea surface radiation brightness temperature. This refers to the brightness temperature of vertically polarized sea surface radiation in the K-band.
[0008] As an improvement to the above method, the specific method of quality control in step 3 is as follows: determine whether there is an anomaly in the initial value obtained in step 2, and if so, replace the anomaly value with the corresponding atmospheric and oceanic historical data.
[0009] As an improvement to the above method, the least squares method in step 3 is solved using the Levenberg-Marquardt iterative method, and the inversion process employs the first cost function. The first cost function for: ; in, Sea surface temperature, Represents polarization mode, Represents horizontal polarization. Represents vertical polarization. For C-band Sea surface radiation brightness temperature by polarization for The corresponding simulated brightness temperature for radiative transfer. For microwave radiometer observations The sum of the system sensitivity of the polarization mode and the error of the radiative transfer model.
[0010] As an improvement to the above method, the least squares method in step 4 is solved using the Levenberg-Marquardt iterative method, and the inversion process employs a second cost function. The second cost function for: ; in, Sea surface temperature, For sea surface wind speed, Total water vapor The total amount of liquid water, For frequency band identification, among which, Represents the C band. Represents the K-band. for frequency band Sea surface radiation brightness temperature by polarization for frequency band Simulated brightness temperature of radiative transfer by polarization mode For microwave radiometer observations frequency band The sum of the system sensitivity by polarization mode and the error of the radiative transfer model. This represents the preliminary inversion result of the sea surface temperature obtained in step 3. This represents the error estimate of the preliminary sea surface temperature inversion result obtained in step 3.
[0011] As an improvement to the above method, the least squares method in step 5 is solved using the Levenberg-Marquardt iterative method, and the inversion process employs a third cost function. The third cost function for: ; in, The salinity of the sea surface. For sea surface wind speed, The Faraday rotation angle of the ionosphere. L-band Sea surface radiation brightness temperature by polarization L-band Simulated brightness temperature of radiative transfer by polarization mode For microwave radiometer observations The sum of the system sensitivity by polarization mode and the error of the radiative transfer model. Represents polarization mode, Vertical transmission and vertical reception polarization. For horizontal transmission and horizontal reception polarization, Acquired by L-band microwave scattering meter Backscattering coefficient of polarization mode To acquire observations for L-band microwave scattering meters Polarization system sensitivity Represents the simulation of geophysical model functions Backscattering coefficient of polarization mode; The optimized inversion result of the sea surface wind speed obtained in step 4 represents the result of the inversion. The initial value of the Faraday rotation angle. This is an error estimate of the initial value of the Faraday rotation angle of the ionosphere. This is the error estimate of the optimized inversion result of the sea surface wind speed obtained in step 4.
[0012] As an improvement to the above method, the initial value of the Faraday rotation angle is obtained from the data of the previous satellite.
[0013] To achieve another objective of this application, this application also provides a combined active and passive microwave system for multi-element sea surface inversion without background field requirements, comprising: The regression module uses a microwave radiative transfer model and historical atmospheric and oceanic data to generate simulated brightness temperatures in the L, C, and K bands at a specified incident angle. Based on a linear regression model, it generates a set of polynomial coefficients that correspond one-to-one with multiple sea surface elements, including sea surface salinity, sea surface temperature, sea surface wind speed, total water vapor, and total liquid water. The initial value inversion module is used to obtain the multi-polarized sea surface radiation brightness temperature in the L-band, C-band and K-band observed by the spaceborne active and passive microwave imager. The observed multi-polarized sea surface radiation brightness temperature is substituted into the regression equation corresponding to the polynomial coefficient set to invert and obtain the initial values of multiple sea surface elements. The preliminary sea surface temperature inversion module, based on the C-band multi-polarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager, performs quality control on the initial value, and then uses the least squares method to invert the preliminary sea surface temperature to obtain the preliminary inversion result, and updates the initial value of the sea surface temperature. The multi-element joint optimization inversion module, based on the multi-polarized sea surface radiation brightness temperature in the C and K bands observed by the spaceborne active and passive microwave imager, uses the least squares method to invert the optimized inversion results of sea surface temperature, sea surface wind speed, total water vapor and total liquid water, and updates the corresponding initial values. The active-passive joint inversion module, based on the L-band multi-polarized sea surface radiative brightness temperature observed by the spaceborne active-passive microwave imager and the backscattering coefficient observed by the L-band microwave scatterometer, combines the active-passive joint observation data and uses the least squares method to invert the final inversion results of sea surface salinity and sea surface wind speed; and The output module outputs the inversion results of multiple sea surface elements, including: the optimized inversion results of sea surface temperature, total water vapor and total liquid water obtained in step 4, and the final inversion results of sea surface salinity and sea surface wind speed obtained in step 5.
[0014] The advantage of this application is that it proposes a method and system for multi-element sea surface inversion that does not require the use of a background field. Existing inversion methods require the use of forecast or reanalysis data to provide the background field of the sea surface or atmosphere. This method utilizes the characteristics of multi-channel brightness temperature itself and avoids crosstalk between multi-element inversions by separating frequency band combinations with different sensitive parameters. First, initial values of these parameters are obtained from multi-band brightness temperatures using linear regression. Then, brightness temperatures in the C-band, which is most sensitive to sea surface temperature and relatively less sensitive to other parameters, are used to invert and obtain high-precision sea surface temperatures, avoiding crosstalk issues when inverting multiple parameters simultaneously. Next, joint inversion of the C and K bands is performed, constraining the sea surface temperature background field to the inversion values from the previous step, and simultaneously iterating sea surface wind speed, total water vapor, and total liquid water to obtain even higher-precision sea surface temperature, sea surface wind speed, total water vapor, and total liquid water. Because sea surface temperature is constrained, mutual interference of other parameters is avoided. Finally, combining radiometers and scatterometers in the L-band, which are sensitive to salinity and wind speed respectively, high-precision salinity and wind speed are obtained. Attached Figure Description
[0015] Figure 1 A flowchart of a method for inverting multiple sea surface elements without background field using a combined active and passive microwave method provided in an embodiment of the present invention. Detailed Implementation
[0016] The technical solutions provided in this application are further illustrated below with reference to the embodiments.
[0017] Example 1 In current multi-element sea surface inversion methods, atmospheric parameters such as total water vapor and total liquid water are generally obtained through external auxiliary data (such as the NWP model). External data has certain lag and incompleteness, and has a smoothing effect in time and space, so it cannot fully represent the state at the time of observation. In particular, water in the atmosphere has a great influence on K-band brightness temperature. If it is inaccurate, it will affect the inversion of wind speed.
[0018] The active-passive microwave combined multi-element sea surface inversion method provided in this embodiment, which does not require a background field, can perform multi-element sea surface inversion solely based on observation data from the active-passive microwave imager itself, without relying on external auxiliary data. The elements include five parameters: sea surface salinity (SSS), sea surface temperature (SST), sea surface wind speed (WS), total water vapor volume (TCWV), and total liquid water volume (TCLW).
[0019] First, initial value inversion coefficients are prepared, namely, simulated brightness temperatures in three frequency bands (L, C, K) with incident angles of 30°~60° are generated using the Radiative Transfer Model (RTM) and approximately one year of atmospheric and oceanic historical data. Then, five polynomial coefficient sets are generated using a linear regression model, each corresponding to sea surface salinity (SSS), sea surface temperature (SST), sea surface wind speed (WS), total water vapor (TCWV), and total liquid water (TCLW). Then, using the L, C, and K band multipolarized sea surface radiation brightness temperature obtained by the spaceborne active and passive microwave imager, the initial values of sea surface salinity (SSS), sea surface temperature (SST), sea surface wind speed (WS), total water vapor (TCWV), and total liquid water (TCLW) were obtained by multiple linear regression inversion. Secondly, using the initial values mentioned above, the C-band dual-polarized sea surface radiation brightness temperature obtained by the spaceborne active and passive microwave imager is used to obtain the preliminary result of sea surface temperature (SST) by least squares inversion, and the initial value of sea surface temperature (SST) is updated. Furthermore, by using the dual-polarized sea surface radiation brightness temperatures in the C and K bands obtained by the spaceborne active and passive microwave imager, more accurate sea surface temperature (SST), sea surface wind speed (WS), total water vapor volume (TCWV), and total liquid water volume (TCLW) are retrieved, and the above initial values are updated. Finally, using the L-band sea surface radiation brightness temperature and L-band backscattering coefficient obtained by the spaceborne active and passive microwave imager, along with the joint active and passive observation data, high-precision inversion values of sea surface salinity (SSS) and sea surface wind speed (WS) were simultaneously obtained by using the least squares method.
[0020] To facilitate understanding of this embodiment, the following is in conjunction with... Figure 1 The method disclosed in the embodiments of the present invention is further described, and the method includes: Step 1: Before processing satellite data, simulated brightness temperatures in the L, C, and K bands at incident angles of 30°–60° are generated using the microwave radiative transfer model (RTM) and approximately one year of historical atmospheric and oceanic data. Then, a linear regression model is used to generate five sets of polynomial coefficients that correspond one-to-one with sea surface salinity (SSS), sea surface temperature (SST), sea surface wind speed (WS), total water vapor volume (TCWV), and total liquid water volume (TCLW). Polynomial coefficient set Obtained through the following formula:
[0021] in, The symbol for solving indicates solving for the polynomial coefficients a~g within the parentheses. Includes: a~g, where, As the first coefficient, As the second coefficient, The third coefficient, It is the fourth coefficient. It is the fifth coefficient. It is the sixth coefficient. It is the seventh coefficient. The brightness temperature of polarized sea surface radiation. This refers to the brightness temperature of horizontally polarized sea surface radiation in the L-band. The brightness temperature of vertically polarized sea surface radiation in the L-band. The brightness temperature of horizontally polarized sea surface radiation in the C-band. This refers to the C-band vertically polarized sea surface radiation brightness temperature. The K-band horizontally polarized sea surface radiation brightness temperature. This refers to the brightness temperature of vertically polarized sea surface radiation in the K-band.
[0022] Step 2: Start processing satellite data. Use the vertically polarized sea surface radiation brightness temperature and the horizontally polarized sea surface radiation brightness temperature in the L, C, and K bands obtained by the microwave radiometer, and substitute them into the above regression equation to obtain the initial values of sea surface salinity (SSS), sea surface temperature (SST), sea surface wind speed (WS), total water vapor (TCWV), and total liquid water (TCLW). Step 3: Using the vertically polarized and horizontally polarized sea surface radiative brightness temperatures in the C-band obtained by the microwave radiometer as input, and performing quality control on the initial values obtained in Step 2, if the values are abnormal, historical climate data is used. Based on the microwave radiative transfer model, the preliminary inversion results of sea surface temperature (SST) are obtained using the least squares method and the Levenberg-Marquardt iterative method, where the first cost function... as follows:
[0023] in, Represents polarization mode, Represents horizontal polarization. Represents vertical polarization. For C-band Sea surface radiation brightness temperature by polarization for The corresponding simulated brightness temperature for radiative transfer. For microwave radiometer observations The sum of the system sensitivity of the polarization mode and the error of the radiative transfer model.
[0024] Step 4: Update the initial value of sea surface temperature (SST) using the preliminary inversion results obtained in Step 3. Using the vertically polarized and horizontally polarized sea surface radiation brightness temperatures in the C and K bands acquired by the microwave radiometer as input, repeat the above steps. Invert using the least squares method, and solve the least squares problem using the Levenberg-Marquardt iterative method to obtain optimized inversion results for sea surface temperature (SST), sea surface wind speed (WS), total water vapor volume (TCWV), and total liquid water volume (TCLW). Update the initial values using the optimized inversion results, where the second cost function... as follows:
[0025] in, For frequency band identification, among which, Represents the C band. Represents the K-band. for frequency band Sea surface radiation brightness temperature by polarization for frequency band Simulated brightness temperature of radiative transfer by polarization mode For microwave radiometer observations frequency band The sum of the system sensitivity by polarization mode and the error of the radiative transfer model. This represents the preliminary inversion result of the sea surface temperature (SST) obtained in step 3. This represents the error estimate of the preliminary inversion result of sea surface temperature (SST) obtained in step 3.
[0026] Step 5: Update the initial values of sea surface temperature (SST), sea surface wind speed (WS), total water vapor volume (TCWV), and total liquid water volume (TCLW) using the optimized inversion results obtained in Step 4. Use the vertically polarized sea surface radiation brightness temperature and horizontally polarized sea surface radiation brightness temperature obtained from the L-band microwave radiometer, and the backscattering coefficient obtained from the L-band microwave scatterometer as inputs. Repeat the above steps, using the least squares inversion method, and solve the least squares problem using the Levenberg-Marquardt iterative method to solve for sea surface salinity (SSS), where the third cost function... as follows:
[0027] in, This represents the Faraday rotation angle of the ionosphere. Since the L-band is greatly affected by the ionosphere, the Faraday rotation angle can be synchronously inverted here. L-band Sea surface radiation brightness temperature by polarization L-band Simulated brightness temperature of radiative transfer by polarization mode For microwave radiometer observations The sum of the system sensitivity by polarization mode and the error of the radiative transfer model. Represents polarization mode, Vertical transmission and vertical reception polarization. For horizontal transmission and horizontal reception polarization, Acquired by L-band microwave scattering meter Backscattering coefficient of polarization mode To acquire observations for L-band microwave scattering meters Polarization system sensitivity Represents the simulation of geophysical model function (GMF) Backscattering coefficient of polarization mode; The optimized inversion result of the sea surface wind speed (WS) obtained in step 4 represents the result of the inversion. The initial value of the Faraday rotation angle can be obtained from the data of the previous satellite. This is an error estimate of the initial value of the Faraday rotation angle of the ionosphere. The error estimate is used to optimize the inversion result of the sea surface wind speed (WS) obtained in step 4.
[0028] The final inversion products are the final inversion results of sea surface salinity (SSS) and sea surface wind speed (WS) obtained in step 5, and the optimized inversion results of sea surface temperature (SST), total water vapor (TCWV) and total liquid water (TCLW) obtained in step 4.
[0029] Example 2 This embodiment provides a combined active and passive microwave system for sea surface multi-element inversion without background field, including: The regression module uses a microwave radiative transfer model and historical atmospheric and oceanic data to generate simulated brightness temperatures in the L, C, and K bands at a specified incident angle. Based on a linear regression model, it generates a set of polynomial coefficients that correspond one-to-one with multiple sea surface elements, including sea surface salinity, sea surface temperature, sea surface wind speed, total water vapor, and total liquid water. The initial value inversion module is used to obtain the multi-polarized sea surface radiation brightness temperature in the L-band, C-band and K-band observed by the spaceborne active and passive microwave imager. The observed multi-polarized sea surface radiation brightness temperature is substituted into the regression equation corresponding to the polynomial coefficient set to invert and obtain the initial values of multiple sea surface elements. The preliminary sea surface temperature inversion module, based on the C-band multi-polarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager, performs quality control on the initial value, and then uses the least squares method to invert the preliminary sea surface temperature to obtain the preliminary inversion result, and updates the initial value of the sea surface temperature. The multi-element joint optimization inversion module, based on the multi-polarized sea surface radiation brightness temperature in the C and K bands observed by the spaceborne active and passive microwave imager, uses the least squares method to invert the optimized inversion results of sea surface temperature, sea surface wind speed, total water vapor and total liquid water, and updates the corresponding initial values. The active-passive joint inversion module, based on the L-band multi-polarized sea surface radiative brightness temperature observed by the spaceborne active-passive microwave imager and the backscattering coefficient observed by the L-band microwave scatterometer, combines the active-passive joint observation data and uses the least squares method to invert the final inversion results of sea surface salinity and sea surface wind speed; and The output module outputs the inversion results of multiple sea surface elements, including: the optimized inversion results of sea surface temperature, total water vapor and total liquid water obtained in step 4, and the final inversion results of sea surface salinity and sea surface wind speed obtained in step 5.
[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for inverting multiple sea surface elements using a combination of active and passive microwaves without requiring a background field, characterized in that... include: Step 1: Using a microwave radiative transfer model and historical atmospheric and oceanic data, simulated brightness temperatures in the L, C, and K bands at a specified incident angle are generated. Based on a linear regression model, a set of polynomial coefficients corresponding one-to-one with multiple sea surface elements is generated. These multiple sea surface elements include sea surface salinity, sea surface temperature, sea surface wind speed, total water vapor, and total liquid water. Step 2: Obtain the multi-polarized sea surface radiation brightness temperature in the L-band, C-band, and K-band observed by the spaceborne active and passive microwave imager. Substitute the observed multi-polarized sea surface radiation brightness temperature into the regression equation corresponding to the polynomial coefficient set to obtain the initial values of multiple sea surface elements. Step 3: Based on the C-band multi-polarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager, after quality control of the initial value, the preliminary inversion result of sea surface temperature is obtained by least squares inversion, and the initial value of sea surface temperature is updated. Step 4: Based on the multi-polarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager in the C and K bands, the optimized inversion results of sea surface temperature, sea surface wind speed, total water vapor and total liquid water are obtained by least squares inversion, and the corresponding initial values are updated. Step 5: Based on the L-band multipolarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager and the backscattering coefficient observed by the L-band microwave scatterer, combined with the active and passive joint observation data, the final inversion results of sea surface salinity and sea surface wind speed are obtained by least squares inversion. Step 6: Output the inversion results of multiple sea surface elements, including: the optimized inversion results of sea surface temperature, total water vapor and total liquid water obtained in Step 4, and the final inversion results of sea surface salinity and sea surface wind speed obtained in Step 5.
2. The active and passive microwave joint sea surface multi-element inversion method without background field as described in claim 1, characterized in that, The range of the specified incident angle in step 1 is 30° to 60°.
3. The active and passive microwave joint sea surface multi-element inversion method without background field as described in claim 1, characterized in that, The polynomial coefficient set in step 1 It can be obtained through the following formula: ； in, The symbol for solving indicates solving for the polynomial coefficients a~g within the parentheses. Includes: a~g, where, As the first coefficient, As the second coefficient, The third coefficient, It is the fourth coefficient. It is the fifth coefficient. It is the sixth coefficient. It is the seventh coefficient. The brightness temperature of polarized sea surface radiation. This refers to the brightness temperature of horizontally polarized sea surface radiation in the L-band. This refers to the brightness temperature of vertically polarized sea surface radiation in the L-band. The brightness temperature of horizontally polarized sea surface radiation in the C-band. This refers to the C-band vertically polarized sea surface radiation brightness temperature. The K-band horizontally polarized sea surface radiation brightness temperature. This refers to the K-band vertically polarized sea surface radiation brightness temperature.
4. The active and passive microwave combined sea surface multi-element inversion method without background field as described in claim 1, characterized in that, The specific method of quality control in step 3 is as follows: determine whether there is any abnormality in the initial value obtained in step 2. If so, replace the abnormal value with the corresponding historical atmospheric and oceanic data.
5. The active and passive microwave joint sea surface multi-element inversion method without background field as described in claim 1, characterized in that, The least squares method in step 3 is solved using the Levenberg-Marquardt iterative method, and the inversion process employs the first cost function. The first cost function for: ； in, Sea surface temperature, Represents polarization mode, Represents horizontal polarization, Represents vertical polarization. For C-band Sea surface radiation brightness temperature by polarization for The corresponding simulated brightness temperature for radiative transfer. For microwave radiometer observations The sum of the system sensitivity of the polarization mode and the error of the radiative transfer model.
6. The active and passive microwave joint sea surface multi-element inversion method without background field as described in claim 1, characterized in that, The least squares method in step 4 is solved using the Levenberg-Marquardt iterative method, and the inversion process employs a second cost function. The second cost function for: ； in, Sea surface temperature, For sea surface wind speed, Total water vapor The total amount of liquid water, For frequency band identification, among which, Represents the C band. Represents the K-band. for frequency band Sea surface radiation brightness temperature by polarization for frequency band Simulated brightness temperature of radiative transfer by polarization mode For microwave radiometer observations frequency band The sum of the system sensitivity by polarization mode and the error of the radiative transfer model. This represents the preliminary inversion result of the sea surface temperature obtained in step 3. This represents the error estimate of the preliminary sea surface temperature inversion result obtained in step 3.
7. The active and passive microwave joint sea surface multi-element inversion method without background field as described in claim 1, characterized in that, The least squares method in step 5 is solved using the Levenberg-Marquardt iterative method, and the inversion process employs a third cost function. The third cost function for: ； in, The salinity of the sea surface. For sea surface wind speed, The Faraday rotation angle of the ionosphere. L-band Sea surface radiation brightness temperature by polarization L-band Simulated brightness temperature of radiative transfer by polarization mode For microwave radiometer observations The sum of the system sensitivity by polarization mode and the error of the radiative transfer model. Represents polarization mode, Vertical transmission and vertical reception polarization. For horizontal transmission and horizontal reception polarization, Acquired by L-band microwave scattering meter Backscattering coefficient of polarization mode To acquire observations for L-band microwave scattering meters Polarization system sensitivity Represents the simulation of geophysical model functions Backscattering coefficient of polarization mode; The optimized inversion result of the sea surface wind speed obtained in step 4 represents the result of the inversion. The initial value of the Faraday rotation angle. This is an error estimate of the initial value of the Faraday rotation angle of the ionosphere. This is the error estimate of the optimized inversion result of the sea surface wind speed obtained in step 4.
8. The active and passive microwave joint sea surface multi-element inversion method without background field as described in claim 7, characterized in that, The initial value of the Faraday rotation angle is obtained from the data of the previous satellite.
9. A combined active and passive microwave system for sea surface multi-element inversion without background field, characterized in that, include: The regression module uses a microwave radiative transfer model and historical atmospheric and oceanic data to generate simulated brightness temperatures in the L, C, and K bands at a specified incident angle. Based on a linear regression model, it generates a set of polynomial coefficients that correspond one-to-one with multiple sea surface elements, including sea surface salinity, sea surface temperature, sea surface wind speed, total water vapor, and total liquid water. The initial value inversion module is used to obtain the multi-polarized sea surface radiation brightness temperature in the L-band, C-band and K-band observed by the spaceborne active and passive microwave imager. The observed multi-polarized sea surface radiation brightness temperature is substituted into the regression equation corresponding to the polynomial coefficient set to invert and obtain the initial values of multiple sea surface elements. The preliminary sea surface temperature inversion module, based on the C-band multi-polarized sea surface radiation brightness temperature observed by the spaceborne active and passive microwave imager, performs quality control on the initial value, and then uses the least squares method to invert the preliminary sea surface temperature to obtain the preliminary inversion result, and updates the initial value of the sea surface temperature. The multi-element joint optimization inversion module, based on the multi-polarized sea surface radiation brightness temperature in the C and K bands observed by the spaceborne active and passive microwave imager, uses the least squares method to invert the optimized inversion results of sea surface temperature, sea surface wind speed, total water vapor and total liquid water, and updates the corresponding initial values. The active-passive joint inversion module, based on the multi-polarized sea surface radiation brightness temperature in the L-band observed by the spaceborne active-passive microwave imager and the backscattering coefficient observed by the L-band microwave scatterometer, combines the active-passive joint observation data and uses the least squares method to invert the final inversion results of sea surface salinity and sea surface wind speed. and The output module outputs the inversion results of multiple sea surface elements, including: the optimized inversion results of sea surface temperature, total water vapor and total liquid water obtained in step 4, and the final inversion results of sea surface salinity and sea surface wind speed obtained in step 5.