A method and system for remote sensing inversion of sea surface foam thickness
By constructing a foam radiative transfer model using the L-Ka dual-band collaborative inversion framework and the maximum likelihood estimation method, the problem of inverting sea surface foam thickness was solved, and the accurate acquisition of sea surface foam thickness was achieved, thus improving the accuracy and consistency of microwave remote sensing data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SECOND INST OF OCEANOGRAPHY MNR
- Filing Date
- 2025-08-05
- Publication Date
- 2026-06-02
AI Technical Summary
Existing technologies cannot effectively invert the thickness of sea surface foam, resulting in poor consistency and comparability of microwave remote sensing data. Furthermore, traditional models fail to fully consider the coupling effect between foam thickness and porosity, affecting model accuracy and applicability.
The L-Ka dual-band collaborative inversion framework is adopted. Based on the radiative transfer model and the dual-scale model, the thickness of sea surface foam is inverted by the maximum likelihood estimation method. The foam radiative transfer model is constructed and the radiative intensity is decomposed. The inversion is carried out by combining the foam coverage and thickness parameters.
It has enabled the accurate inversion of sea surface foam thickness, improved the accuracy and consistency of microwave remote sensing data, solved the technical problem of foam thickness acquisition, and promoted the refined quantitative study of air-sea interface processes.
Smart Images
Figure CN120993408B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of microwave remote sensing technology, specifically relating to a remote sensing inversion method and system for sea surface foam thickness. Background Technology
[0002] Sea surface foam is widely present on the ocean surface under the influence of wind and waves, and is an indispensable component of the air-sea interaction process. Foam formation is usually closely related to dynamic processes such as wave breaking, whitecap formation, and the upwelling of subsurface bubble clouds. As a direct product of wind and wave processes, foam not only affects sea surface roughness and gas exchange efficiency, but also significantly impacts passive remote sensing signals, especially the radiation characteristics of microwave and optical bands.
[0003] In microwave remote sensing, sea surface foam exhibits significantly enhanced radiative emission due to its unique electromagnetic properties, particularly displaying high brightness temperature characteristics in the low-frequency band. Conversely, in the visible light band, foam exhibits high reflectivity due to its white structure. This strong yet contradictory cross-band radiative behavior has long been a systemic problem in ocean remote sensing, severely impacting the consistency and comparability of multi-source satellite observation data.
[0004] Despite the widespread interest in the radiation effects of foam, current methods for obtaining foam physical structural parameters, particularly foam thickness, remain very limited. Foam thickness and porosity together determine its overall dielectric properties, which in turn affect microwave radiation transmission behavior. However, existing research largely focuses on remote sensing estimations of foam coverage or empirical models of foam generation based on macroscopic parameters such as wind speed. Research on foam thickness, a key structural parameter, is still in its early stages.
[0005] In existing research, some scholars have attempted to establish an empirical relationship between wind speed and foam thickness based on hydrodynamic theory to indirectly estimate sea surface foam thickness. However, such methods rely on a large amount of field experimental data for parameter fitting, limiting their applicability and failing to reflect the dynamic changes of foam under different sea states and observation angles. Furthermore, traditional air-sea flux models mostly use fixed empirical coefficients when dealing with foam parameters, failing to fully consider the coupling effect between foam thickness and porosity within the framework of radiative transfer theory, thus limiting the model's accuracy and applicability.
[0006] Current mainstream microwave inversion algorithms primarily focus on the spatial coverage of foam, lacking sensitivity to its vertical structural information. The contributions of foam thickness and porosity to radiation brightness temperature are difficult to decouple, leading to uncertainties and biases in the inversion model. The root of this problem lies in the highly heterogeneous, multi-scale structure of foam, whose radiation behavior is highly sensitive to multiple factors such as frequency, polarization, and incident angle. Furthermore, existing remote sensing methods lack the ability to effectively detect the vertical profile characteristics of foam.
[0007] In summary, current methods and techniques for obtaining and modeling sea surface foam thickness are significantly lacking, and there is an urgent need to develop research methods with a stronger physical basis and observational support to promote more refined and quantitative research on air-sea interface processes. Summary of the Invention
[0008] This invention provides a remote sensing inversion method and system for sea surface foam thickness to solve the problem that the thickness of sea surface foam cannot be inverted by remote sensing in the prior art.
[0009] To address the aforementioned technical problems, the present invention discloses the following technical solutions:
[0010] One aspect of the present invention provides a remote sensing inversion method for sea surface foam thickness, the method comprising:
[0011] Based on the preset radiative transfer model, the satellite brightness temperature data of the target sea area in the L-band and Ka-band are restored to the corresponding sea surface brightness temperature data respectively;
[0012] A pre-defined dual-scale model is used to extract the foam brightness temperature component from the sea surface brightness temperature data. The foam brightness temperature component includes a foam coverage parameter.
[0013] Construct a foam radiative transfer model;
[0014] A foam brightness temperature prediction model is obtained based on a foam radiative transfer model, and the foam brightness temperature prediction model includes a foam thickness parameter.
[0015] Using the maximum likelihood estimation method, based on the Ka-band sea surface brightness temperature data, the foam coverage rate is obtained by inverting the observed foam brightness temperature calculated from the foam brightness temperature component and the predicted foam brightness temperature calculated from the foam brightness temperature prediction model.
[0016] Based on the foam coverage, the observed foam brightness temperature in the L-band at 100% foam coverage was calculated, and the foam thickness on the sea surface was obtained by inversion using the maximum likelihood method.
[0017] Optionally, the step of restoring the satellite brightness temperature data of the target sea area in the L-band and Ka-band to the corresponding sea surface brightness temperature data based on a preset radiative transfer model includes:
[0018] Capture remote sensing images of the target sea area;
[0019] Acquire satellite brightness temperature data for each pixel in the remote sensing image in the L-band and Ka-band;
[0020] The following radiative transfer model was used to reconstruct sea surface brightness temperature data from satellite brightness temperature data:
[0021]
[0022] In the formula, T is the sea surface temperature of the target sea area; For satellite brightness temperature data; The restored sea surface brightness temperature data; τ is atmospheric transparency; T up and T dw These are the atmospheric spontaneous radiation brightness temperatures in the upward and downward directions, respectively; T sky It is the cosmic microwave background radiation, with values of 2.73K and 2.88K in the L-band and Ka-band, respectively.
[0023] Optionally, acquiring the satellite brightness temperature data corresponding to each pixel in the remote sensing image in the L-band and Ka-band includes:
[0024] For each pixel, the digital value is converted into radiance;
[0025] The radiance is converted into satellite brightness temperature data using a preset method.
[0026] Optionally, the extraction of foam brightness temperature components from sea surface brightness temperature data using a preset dual-scale model includes:
[0027] The sea surface brightness temperature data are expressed as water body brightness temperature component and foam brightness temperature component using the following formula:
[0028] In the formula, For sea surface brightness temperature data; The water body brightness temperature data is obtained through calculation using a preset model; For foam brightness temperature data; P(W) x W y ) represents the probability density function of sea surface slope, calculated using the Cox-Munk physical model; W x and W y θ0 represents the slope components of the sea surface in the x and y directions, respectively, where x represents the windward direction and y represents the crosswind direction; θ0 is the incident angle observed by the satellite. The rotation matrix is used to convert the local coordinate system of the tilted surface element into the global coordinate system; F is the foam coverage.
[0029] Optionally, constructing the foam radiative transfer model includes:
[0030] The foam radiative transfer model is constructed using the following formula:
[0031]
[0032] In the formula, κ represents the radiation intensity at the zenith angle θ along the vertical and horizontal polarization directions; e and κ aThese are the preset extinction coefficient and absorption coefficient, respectively; The scattering phase matrix is derived based on the viscous sphere scattering model; T is the sea surface temperature of the target sea area, and z is the depth inside the foam layer.
[0033] Based on the direction of radiative transfer propagation, the foam radiative transfer model can be decomposed into the following two parts:
[0034]
[0035] In the formula, and These represent the radiation intensity of the foam in the vertically upward and vertically downward directions, respectively. and These are respectively derived from the scattering phase matrix The forward and backward scattering phase matrices obtained from the decomposition; θ is the zenith angle of the incident direction; θ ' φ is the zenith angle of the scattering direction; φ is the azimuth angle of the incident direction; φ ' The azimuth angle represents the scattering direction.
[0036] Optionally, a foam brightness temperature prediction model can be obtained based on the foam radiative transfer model, including:
[0037] The matching boundary conditions are constructed using the following formula:
[0038]
[0039] In the formula, d is the thickness of the foam layer; and The reflectance matrices of the air-foam interface and the foam-seawater interface are respectively calculated using Fresnel's formula. The transmittance matrix from seawater to the foam layer is given by the following formula, where the actual transmission angle in the foam layer is determined by the following relationship:
[0040]
[0041] In the formula, θ f θ is the observation angle inside the foam layer; θ is the zenith angle; k0 is the vacuum wavenumber of electromagnetic waves, obtained by dividing the electromagnetic wave frequency by the speed of light in vacuum, where the speed of light in vacuum is taken as 3 × 10⁻⁶. 8 ;
[0042] The intermediate parameters p and q are represented as follows:
[0043] p(z)=2αβ
[0044] q(z)=β 2 -α 2 k0sin 2 θ
[0045] In the formula, α is the attenuation coefficient; β is the phase delay coefficient; ε f The dielectric constant of the foam is obtained by the following dipole approximation model:
[0046]
[0047] In the formula, the ξ function has the following form:
[0048]
[0049] In the formula, r is the bubble radius; p is the viscosity coefficient of the air bubble. fr For the bubble size distribution, a gamma distribution is used; the γ function has the following form:
[0050]
[0051] In the formula, ε w Let ξ be the dielectric constant of the water body, and let ξ be the bubble coverage density. It is obtained by calculating the bubble water film thickness δ: ξ=1-δ / r;
[0052] Determine the vertical upward radiation intensity of the foam based on the matching boundary conditions.
[0053] Based on the vertical upward radiation intensity of the foam Calculate the predicted value of the foam brightness temperature:
[0054]
[0055] in, Unit vector, Let be the Fresnel reflectance of the interface.
[0056] Optionally, the method of employing maximum likelihood estimation, based on Ka-band sea surface brightness temperature data, inverts the foam coverage rate by calculating the observed foam brightness temperature from the foam brightness temperature component and the predicted foam brightness temperature from the foam brightness temperature prediction model, including:
[0057] The preset cost function is as follows:
[0058]
[0059] In the formula, θ0 is the incident angle observed by the satellite, T is the sea surface temperature of the target sea area, U is the sea surface wind speed, and S is the sea surface salinity. This is the observed value of the foam brightness temperature. Here is the predicted brightness temperature of the foam; n is the inversion number.
[0060] The foam coverage rate is calculated when the cost function is minimized using the maximum likelihood estimation method, based on the parameters θ0,T,S,U matched with Ka-band satellite observations.
[0061] Optionally, the calculation of the observed foam brightness temperature in the L-band at 100% foam coverage based on foam coverage includes:
[0062] Based on the foam coverage value, the foam brightness temperature observation value in the L-band is calculated using the foam brightness temperature component;
[0063] The L-band sea surface foam brightness temperature observation values were normalized to the foam brightness temperature observation values under 100% foam coverage.
[0064] Optionally, the step of using the maximum likelihood method to invert and obtain the sea surface foam thickness includes:
[0065] The foam thickness value at which the cost function is minimized is calculated using the maximum likelihood estimation method based on the parameters θ0,T,S,U matched with L-band satellite observations.
[0066] Another aspect of the present invention provides a remote sensing inversion system for sea surface foam thickness, wherein the system applies the remote sensing inversion method for sea surface foam thickness provided in the foregoing aspect.
[0067] The remote sensing inversion method and system for sea surface foam thickness disclosed in this invention constructs an L-Ka dual-band collaborative inversion framework to establish a forward operator for multi-band radiative transfer based on the physical model constraints of foam emissivity-porosity-thickness. Simultaneously, based on the radiative transfer method, a direct inversion method for sea surface foam thickness based on satellite remote sensing is constructed.
[0068] The summary section is provided to present the chosen concepts in a simplified form, which will be further described in the detailed description below. The summary section is not intended to identify essential or necessary features of this disclosure, nor is it intended to limit the scope of this disclosure. Attached Figure Description
[0069] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.
[0070] Figure 1 This is a flowchart illustrating a remote sensing inversion method for sea surface foam thickness provided in an embodiment of the present invention. Detailed Implementation
[0071] Embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.
[0072] The term "comprising" and its variations as used herein signify open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.
[0073] Figure 1 This is a schematic flowchart of a remote sensing inversion method for sea surface foam thickness disclosed in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:
[0074] Step S100: Based on the preset radiative transfer model, the satellite brightness temperature data of the target sea area in the L-band and Ka-band are restored to the corresponding sea surface brightness temperature data.
[0075] Brightness temperature data describes the radiation intensity received by a remote sensing sensor in a specific electromagnetic frequency band. It represents the "blackbody temperature" corresponding to the radiation intensity detected by the sensor. That is, assuming the radiation is emitted by an ideal blackbody, what physical temperature the blackbody would need to have to emit the same radiation intensity. Therefore, brightness temperature is not equivalent to the actual physical temperature of an object, but rather an "equivalent temperature" obtained by inverting the radiation intensity.
[0076] Step S100 aims to use a preset radiative transfer model to process the brightness temperature data (denoted as L-band and Ka-band) of the target sea area obtained from remote sensing satellite observations. ) Restored to the true sea surface brightness temperature data (denoted as In one embodiment of the present invention, step S100 can be implemented in the following manner:
[0077] (1) Satellite brightness temperature data acquisition
[0078] First, the target sea area was observed using a satellite platform equipped with L-band and Ka-band microwave radiometers, and brightness temperature image data in the L-band and Ka-band were acquired. After radiometric calibration and geometric correction, the brightness temperature value of each pixel was determined. This indicates the equivalent radiation temperature at the top of the atmosphere at that location.
[0079] In the embodiments disclosed in this invention, remote sensing data with brightness temperature data can be downloaded directly, or satellite brightness temperature data for each pixel can be obtained in the following ways:
[0080] First, for each pixel, the digital value is converted into radiance.
[0081] Then, the radiance is converted into satellite brightness temperature data using inversion formulas or lookup tables.
[0082] (2) Inversion of sea surface brightness temperature using atmospheric radiative transfer model
[0083] To extract the true sea surface radiation characteristics of the target sea area from satellite-observed brightness temperature, the following radiative transfer model is used to model the atmospheric radiation process, and the sea surface brightness temperature value T is derived from it. B,sur :
[0084]
[0085] The meanings of each parameter in the formula are as follows:
[0086] Atmospheric top brightness temperature received by the satellite;
[0087] The sea surface brightness temperature that needs to be inverted is the equivalent brightness temperature of seawater transported upwards to the bottom of the atmosphere;
[0088] T: Sea surface temperature of the target sea area, which can be obtained through on-site buoys, models or thermal infrared remote sensing;
[0089] T up : The brightness temperature of spontaneous radiation transported upwards from the atmosphere;
[0090] T dw : Spontaneous emission brightness temperature transmitted downwards from the atmosphere;
[0091] T sky The brightness temperature of the cosmic microwave background radiation is 2.73 K in the L-band and 2.88 K in the Ka-band.
[0092] τ: Total atmospheric transparency, reflecting the degree of attenuation of electromagnetic waves as they pass through the atmosphere.
[0093] Among them, τ, T up T dw Data can be obtained from atmospheric profile models (such as MODTRAN), numerical weather prediction data (such as ECMWF and NCEP reanalysis data), or empirical parameterized models.
[0094] For each cell, the known Ts T up T dw T sky Substituting the value of τ into the above equation, the unknown can be solved through numerical iteration (such as Newton's method or quasi-Newton's method). The value is then used to obtain the sea surface brightness temperature.
[0095] In the formula disclosed in this invention, a parameter with one short line indicates that the parameter is vector data, a parameter with two short lines indicates that the parameter is matrix data, and a parameter without a short line indicates that the parameter is scalar data.
[0096] Step S200: Extract the foam brightness temperature component from the sea surface brightness temperature data using a preset dual-scale model.
[0097] In one embodiment of the present invention, the foam brightness temperature component model is extracted in the following manner:
[0098] The main influencing factors on sea surface brightness temperature (SBT) data include foam emission contribution and water body radiation contribution. After restoring satellite-observed SBT data to sea surface brightness temperature data, the foam emission contribution and water body radiation contribution are separated using the following two-scale model. This can be expressed as a weighted average of the brightness temperatures of all micro-sloping surfaces:
[0099]
[0100] In the formula, the geometric correction term 1-W x tanθ0 considers the effect of tilt on brightness temperature projection; P(W x W y ) represents the probability density function of sea surface slope, calculated using the Cox-Munk physical model (i.e., the wave spectrum model); W x and W y θ0 represents the slope components of the sea surface in the x and y directions, respectively, where x represents the windward direction and y represents the crosswind direction; θ0 is the incident angle observed by the satellite. The composite radiation of the inclined sea surface element is expressed as follows:
[0101]
[0102] In the formula, The water body brightness temperature data is obtained through calculation using a pre-set model; This refers to the brightness temperature data of the foam. The rotation matrix is used to convert the local coordinate system of the tilted surface element into the global coordinate system; F is the foam coverage.
[0103] Will Substituting the expression into the sea surface brightness temperature The expression for sea surface brightness temperature is as follows: water body brightness temperature component and foam brightness temperature component:
[0104]
[0105] The brightness temperature component of foam emission can be separated from the mixed sea surface radiation according to the above formula.
[0106] Calculated using the above formula This is data from foam brightness temperature observations.
[0107] Step S300: Construct a foam radiation transfer model.
[0108] In one embodiment of the present invention, the foam radiative transfer model is constructed using the following formula:
[0109]
[0110] In the formula, κ represents the radiation intensity at the zenith angle θ along the vertical and horizontal polarization directions; e κ is a preset extinction coefficient, characterizing the rate at which the radiation intensity in the foam decreases with distance. a κ is a preset absorption coefficient, characterizing the ability of a foam material to absorb radiant energy and convert it into heat energy. e and κ a The variation with depth z can be determined through experimental measurements or by deduction based on the physical properties of foam (bubble size, density, liquid properties); The scattering phase matrix is derived based on the viscous sphere scattering model, which describes the probability and polarization change of radiation scattering from the incident direction to the outgoing direction. T is the sea surface temperature of the target sea area, and z is the depth inside the foam layer.
[0111] This invention introduces a viscous sphere scattering phase matrix into the classical radiative transfer equation, which significantly improves the accuracy of foam brightness temperature simulation.
[0112] Based on the direction of radiative transfer propagation, the foam radiative transfer model can be decomposed into the following two parts:
[0113] One of them is:
[0114]
[0115] The second is:
[0116]
[0117] In the formula, and These represent the radiation intensity of the foam in the vertically upward and vertically downward directions, respectively. and These are respectively derived from the scattering phase matrix The forward and backward scattering phase matrices obtained from the decomposition. θ is the zenith angle at the incident direction; θ ' φ is the zenith angle of the scattering direction; φ is the azimuth angle of the incident direction.
[0118] φ ' The azimuth angle represents the scattering direction.
[0119] Step S400: Obtain the foam brightness temperature prediction model based on the foam radiative transfer model.
[0120] In one embodiment of the present invention, the foam brightness temperature prediction model can be obtained in the following manner:
[0121] The matching boundary conditions are constructed using the following formula:
[0122]
[0123] In the formula, at z = 0 (top of the foam layer), i.e., the air-foam interface, the upward radiation intensity is equal to the radiation obtained by reflection from the downward radiation intensity of the foam layer through the air-foam interface. The reflectivity matrix representing the air-foam interface is calculated using Fresnel's formula.
[0124] At z = -d (bottom of the foam layer), i.e., the foam-seawater interface, where d is the thickness of the foam layer, the downward radiation intensity consists of two parts: one is the portion of the upward radiation from the foam layer reflected back through the foam-seawater interface, and the other is the portion of the seawater thermal radiation transmitted into the foam layer. The reflectance matrices representing the air-foam interface and the foam-seawater interface are obtained through Fresnel's formula. θ is the transmittance matrix from seawater to the foam layer; T is the sea surface temperature of the target sea area; θ is the zenith angle.
[0125] Based on the above boundary conditions, the aforementioned foam radiative transfer model can be determined. The constant term of the homogeneous solution.
[0126] The actual transmission angle in the foam layer is determined by the following relationship, which reflects the propagation angle of the incident angle (zenith angle) θ after refraction through the foam layer with a complex dielectric constant:
[0127]
[0128] In the formula, θ f θ is the observation angle inside the foam layer; θ is the zenith angle; k0 is the vacuum wavenumber of electromagnetic waves, obtained by dividing the electromagnetic wave frequency by the speed of light in vacuum, where the speed of light in vacuum is taken as 3 × 10⁻⁶. 8 ;
[0129] The refraction angle here takes into account the complex refractive index, which includes attenuation caused by absorption and scattering. The intermediate parameters p and q are expressed as follows:
[0130] p(z)=2αβ
[0131] q(z)=β 2 -α 2 k0sin 2 θ
[0132] In the formula, α is the attenuation coefficient, describing the attenuation rate of the electromagnetic wave in the foam; β is the phase delay coefficient, representing the phase change; ε f The dielectric constant of the foam, in complex form, contains information about dielectric loss and is calculated using the following dipole approximation model:
[0133]
[0134] In the formula, the ξ function has the following form:
[0135]
[0136] This model is based on dipole theory, treating foam as a medium composed of bubbles of varying sizes. In the formula, p is the viscosity coefficient of the air bubble. fr For the bubble size distribution, the probability density of bubbles of different sizes is described using the Gamma distribution; the γ function describes the contribution of a single bubble to the dielectric constant, and has the following form:
[0137]
[0138] In the formula, r is the bubble radius, ε w Let ξ be the dielectric constant of the water body, and let ξ be the bubble coverage density. ξ can be calculated from the bubble water film thickness δ and the bubble radius r: ξ = 1 - δ / r.
[0139] Based on the aforementioned matching boundary conditions, the radiation intensity and its reflection and transmission at the upper and lower boundaries of the foam layer are linked together. When solving the internal radiation transfer equation, real physical interface constraints are introduced to ensure the physical accuracy of the model solution.
[0140] By solving the above formulas, we obtain the boundary conditions. and
[0141] Based on the following formula and Calculate the predicted value of foam brightness temperature
[0142]
[0143] in, Unit vector Let be the Fresnel reflectance of the interface.
[0144] Using the above method, the predicted value of foam brightness temperature for each pixel in the remote sensing image of the target sea area is calculated.
[0145] Step S500: Using the maximum likelihood estimation method, based on the Ka-band sea surface brightness temperature data, the foam coverage rate is obtained by inverting the observed foam brightness temperature calculated from the foam brightness temperature component and the predicted foam brightness temperature calculated from the foam brightness temperature prediction model.
[0146] In one embodiment of the present invention, step S500 can be implemented in the following manner:
[0147] The preset cost function is as follows:
[0148]
[0149] In the formula, θ0 is the incident angle observed by the satellite, T is the sea surface temperature of the target sea area, U is the sea surface wind speed, and S is the sea surface salinity. This is the observed value of the foam brightness temperature. is the predicted brightness temperature of the foam, and n is the inversion number.
[0150] The foam coverage rate is calculated when the cost function is minimized using the maximum likelihood estimation method, based on the parameters θ0,T,S,U matched with Ka-band satellite observations.
[0151] By continuously adjusting F, the optimal F can be obtained so that the predicted value of foam brightness temperature calculated by the model is closest to the observed value of foam brightness temperature.
[0152] Using the brightness temperature component of the foam, calculate the value corresponding to each F value under given parameters. For each F, a cost function is calculated, and the F corresponding to the minimum cost function is taken as the bubble coverage rate.
[0153] Step S600: Calculate the observed foam brightness temperature in the L-band at 100% foam coverage based on the foam coverage, and use the maximum likelihood method to invert the foam thickness on the sea surface.
[0154] In one embodiment of the present invention, the observed foam brightness temperature value of the L-band at 100% foam coverage is calculated based on the foam coverage:
[0155] (1) Combine the foam coverage value and use the foam brightness temperature observation model to calculate the foam brightness temperature observation value of L band.
[0156] Substitute the foam coverage value F into the aforementioned formula:
[0157]
[0158] The observed value T of the foam in the L-band was calculated. B,f .
[0159] (2) Normalize the L-band sea surface foam brightness temperature observation values to the foam brightness temperature observation values under 100% foam coverage.
[0160] In one embodiment of the present invention, the thickness of sea surface foam is obtained by inversion using the maximum likelihood method, which can be achieved in the following way:
[0161] The foam thickness value at which the cost function is minimized is calculated using the maximum likelihood estimation method based on the parameters θ0,T,S,U matched with L-band satellite observations.
[0162] The cost function is:
[0163]
[0164] The foam thickness d at which the cost function reaches its minimum value is calculated using the maximum likelihood estimation method based on the parameters θ0,T,S,U matched with L-band satellite observations.
[0165] By continuously adjusting d, the optimal d can be obtained so that the foam brightness temperature calculated by the model is closest to the observed brightness temperature.
[0166] Using the foam radiative transfer model, calculate the value of d for each given parameter.
[0167] For each d, a cost function is calculated, and the d corresponding to the minimum cost function is taken as the bubble thickness value.
[0168] The present invention also discloses a remote sensing inversion system for sea surface foam thickness, which applies the remote sensing inversion method for sea surface foam thickness disclosed in the foregoing embodiments.
[0169] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.
Claims
1. A method for remote sensing inversion of sea surface foam thickness, characterized in that, The method includes: Based on the preset radiative transfer model, the satellite brightness temperature data of the target sea area in the L-band and Ka-band are restored to the corresponding sea surface brightness temperature data respectively; A pre-defined dual-scale model is used to extract the foam brightness temperature component from the sea surface brightness temperature data. The foam brightness temperature component includes a foam coverage parameter. Construct a foam radiative transfer model; A foam brightness temperature prediction model is obtained based on a foam radiative transfer model, and the foam brightness temperature prediction model includes a foam thickness parameter. Using the maximum likelihood estimation method, based on the Ka-band sea surface brightness temperature data, the foam coverage rate is obtained by inverting the observed foam brightness temperature calculated from the foam brightness temperature component and the predicted foam brightness temperature calculated from the foam brightness temperature prediction model. Based on the foam coverage, the observed foam brightness temperature in the L-band at 100% foam coverage was calculated, and the foam thickness on the sea surface was obtained by inversion using the maximum likelihood method.
2. The method according to claim 1, characterized in that, The method, based on a preset radiative transfer model, reconstructs the satellite brightness temperature data of the target sea area in the L-band and Ka-band into corresponding sea surface brightness temperature data, including: Capture remote sensing images of the target sea area; Acquire satellite brightness temperature data for each pixel in the remote sensing image in the L-band and Ka-band; The following radiative transfer model was used to reconstruct sea surface brightness temperature data from satellite brightness temperature data: In the formula, T is the sea surface temperature of the target sea area; For satellite brightness temperature data; The restored sea surface brightness temperature data; τ is atmospheric transparency; T up and T dw These are the atmospheric spontaneous radiation brightness temperatures in the upward and downward directions, respectively; T sky It is the cosmic microwave background radiation, with values of 2.73K and 2.88K in the L-band and Ka-band, respectively.
3. The method according to claim 2, characterized in that, The acquisition of satellite brightness temperature data for each pixel in the remote sensing image in the L-band and Ka-band includes: For each pixel, the digital value is converted into radiance; The radiance is converted into satellite brightness temperature data using a preset method.
4. The method according to claim 1, characterized in that, The extraction of foam brightness temperature components from sea surface brightness temperature data using a pre-defined dual-scale model includes: The sea surface brightness temperature data are expressed as water body brightness temperature component and foam brightness temperature component using the following formula: In the formula, For sea surface brightness temperature data; The water body brightness temperature data is obtained through calculation using a preset model; For foam brightness temperature data; P(W) x W y ) represents the probability density function of sea surface slope, calculated using the Cox-Munk wave spectrum model; W x and W y θ0 represents the slope components of the sea surface in the x and y directions, respectively, where x represents the windward direction and y represents the crosswind direction; θ0 is the incident angle observed by the satellite. The rotation matrix is used to convert the local coordinate system of the tilted surface element into the global coordinate system; F is the foam coverage.
5. The method according to claim 1, characterized in that, The construction of the foam radiative transfer model includes: The foam radiative transfer model is constructed using the following formula: In the formula, κ represents the radiation intensity at the zenith angle θ along the vertical and horizontal polarization directions; e and κ a These are the preset extinction coefficient and absorption coefficient, respectively; The scattering phase matrix is derived based on the viscous sphere scattering model; T is the sea surface temperature of the target sea area; z is the depth inside the foam layer. Based on the direction of radiative transfer propagation, the foam radiative transfer model can be decomposed into the following two parts: In the formula, and These represent the radiation intensity of the foam in the vertically upward and vertically downward directions, respectively; and These are respectively derived from the scattering phase matrix The forward and backward scattering phase matrices obtained by decomposition; θ is the zenith angle of the incident direction; θ' is the zenith angle of the scattering direction; φ is the azimuth angle of the incident direction; φ' is the azimuth angle of the scattering direction.
6. The method according to claim 5, characterized in that, A foam brightness temperature prediction model is obtained based on the foam radiative transfer model, including: The matching boundary conditions are constructed using the following formula: In the formula, d is the thickness of the foam layer; and The reflectance matrices of the air-foam interface and the foam-seawater interface are respectively calculated using Fresnel's formula. The transmittance matrix from seawater to the foam layer is given by the following formula, where the actual transmission angle in the foam layer is determined by the following relationship: In the formula, θ f θ is the observation angle inside the foam layer; θ is the zenith angle; k0 is the vacuum wavenumber of electromagnetic waves, obtained by dividing the electromagnetic wave frequency by the speed of light in vacuum, where the speed of light in vacuum is taken as 3 × 10⁻⁶. 8 ; The intermediate parameters p and q are represented as follows: In the formula, α is the attenuation coefficient; β is the phase delay coefficient; ε f The dielectric constant of the foam is obtained by the following dipole approximation model: In the formula, the ξ function has the following form: In the formula, r is the bubble radius; p is the viscosity coefficient of the air bubble. fr For the bubble size distribution, a gamma distribution is used; the γ function has the following form: In the formula, ε w Let ξ be the dielectric constant of the water body, and let ξ be the bubble coverage density. It is obtained by calculating the bubble water film thickness δ: ξ=1-δ / r; Determine the vertical upward radiation intensity of the foam based on the matching boundary conditions. Based on the vertical upward radiation intensity of the foam Calculate the predicted value of the foam brightness temperature: in, Unit vector Let be the Fresnel reflectance of the interface.
7. The method according to claim 6, characterized in that, The method employs maximum likelihood estimation, based on Ka-band sea surface brightness temperature data, to invert foam coverage by combining observed foam brightness temperature values calculated from the foam brightness temperature component with predicted foam brightness temperature values calculated from the foam brightness temperature prediction model. This includes: The preset cost function is as follows: In the formula, θ0 is the incident angle of satellite observation; T is the sea surface temperature of the target sea area; U is the sea surface wind speed; and S is the sea surface salinity. These are observed values for the brightness temperature of the foam. Here is the predicted brightness temperature of the foam; n is the inversion number. The foam coverage rate is calculated when the cost function is minimized using the maximum likelihood estimation method, based on the parameters θ0,T,S,U matched with Ka-band satellite observations.
8. The method according to claim 7, characterized in that, The calculation of the observed foam brightness temperature in the L-band at 100% foam coverage based on foam coverage includes: Based on the foam coverage value, the foam brightness temperature observation value in the L-band is calculated using the foam brightness temperature component; The L-band sea surface foam brightness temperature observation values were normalized to the foam brightness temperature observation values under 100% foam coverage.
9. The method according to claim 8, characterized in that, The method of inverting the sea surface foam thickness using the maximum likelihood method includes: The foam thickness value at which the cost function is minimized is calculated using the maximum likelihood estimation method based on the parameters θ0,T,S,U matched with L-band satellite observations.
10. A remote sensing inversion system for sea surface foam thickness, characterized in that, The system uses the remote sensing inversion method for sea surface foam thickness as described in any one of claims 1-9.