A sea surface emissivity prediction method based on ship measurement experiment

Abstract

The application discloses a sea surface emissivity prediction method based on ship measurement experiment, and belongs to the field of marine data prediction. The method divides the sea surface emissivity into flat theoretical emissivity and roughness emissivity. Real sea surface radiation data and seawater and meteorological data are collected by ship measurement. The flat sea surface emissivity theoretical value is calculated according to the radiation frequency and seawater parameters. The angle, seawater and meteorological data are used as model inputs, and the sea surface roughness emissivity is used as target output to train a nonlinear regression model. The theoretical flat emissivity and the nonlinear regression model roughness emissivity prediction result are combined to obtain the sea surface emissivity under the corresponding environment. The method has certain generalization ability on parameters such as angle and wind speed, has higher angle resolution, and can supplement some angles and sea condition conditions that cannot be collected in the actual measurement process. The calculation speed of the model is obviously faster than that of the rough sea surface emissivity theoretical calculation based on the sea wave spectrum, and the calculation resources consumed are smaller.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of marine data prediction, and more particularly relates to a sea surface emissivity prediction method based on ship measurement experiment. BACKGROUND

[0002] The related research on sea surface radiation characteristics is the basis for the fields of remote sensing technology, sea surface target detection and identification, and has an important role in marine environment monitoring and climate analysis. The intensity and distribution of sea surface radiation are affected by multiple factors, such as sea surface temperature, salinity, sea surface roughness, etc. Therefore, by studying the sea surface radiation characteristics, the interaction mechanism between the ocean and the atmosphere can be understood in depth, and important parameters can be provided for climate prediction and oceanography research, among which the sea surface emissivity is a typical radiation parameter of the sea surface.

[0003] There are multiple methods to obtain sea surface emissivity data, such as the calculation method based on the dielectric constant model and the Fresnel reflectivity model, which directly obtains the emissivity of the sea surface through theoretical calculation. This method does not require actual measurement, but requires more input parameters and consumes a large amount of computing resources and time to obtain accurate results. It is also a feasible method to obtain sea surface emissivity through remote sensing data inversion, but the remote sensing data itself is affected by the atmospheric environment, and the accuracy of the data under complex sea conditions is challenging, and it is difficult to obtain emissivity in a wide range of angles. The most direct method of emissivity measurement is direct measurement of the actual sea surface environment to obtain sea surface emissivity data at different angles. This method has high accuracy, but is affected by the conditions of the actual measurement, making it difficult to achieve long-term monitoring and multi-angle multi-sea condition traversal. SUMMARY

[0004] In view of the above defects or improvement needs of the prior art, the present application provides a sea surface emissivity prediction method based on ship measurement experiment, thereby solving the problems of low prediction accuracy, complex calculation and difficulty in multi-angle multi-sea condition traversal of the existing sea surface emissivity acquisition methods.

[0005] To achieve the above-mentioned purpose, according to the first aspect of the present application, a sea surface emissivity prediction model construction method based on ship measurement experiment is provided, comprising:

[0006] S1, collecting ship measurement data; the ship measurement data includes the measured radiation voltage of the sea surface and the sky, meteorological data, and sea surface environmental parameters at different azimuth angles and elevation angles;

[0007] S2, calculating the actual emissivity of the sea surface at different azimuth angles and elevation angles according to the radiation voltage, and obtaining the roughness emissivity by subtracting the theoretical emissivity of the flat sea surface from the actual emissivity; wherein the theoretical emissivity of the flat sea surface is calculated by a flat sea surface theoretical emissivity calculation module according to the radiation frequency and the sea surface environmental parameters;

[0008] S3, training a plurality of different nonlinear regression models by taking the meteorological data and the sea surface environmental parameters at different azimuth angles and elevation angles as inputs and taking the roughness emissivity corresponding to the different azimuth angles and elevation angles as outputs, and taking the model with the best prediction effect as the roughness emissivity prediction model;

[0009] S4, constructing a sea surface emissivity prediction model; wherein the sea surface emissivity prediction model comprises the roughness emissivity prediction model, the flat sea surface theoretical emissivity calculation module and a summation module; the summation module is configured to sum the roughness emissivity predicted by the roughness emissivity prediction model and the flat sea surface theoretical emissivity calculated by the flat sea surface theoretical emissivity calculation module to obtain the sea surface emissivity.

[0010] According to a second aspect of the present application, a sea surface emissivity prediction method based on shipborne experiment is provided, comprising:

[0011] The radiation frequency, meteorological data and sea surface environmental parameters of the sea surface to be predicted are input into the sea surface emissivity prediction model constructed by the construction method of the first aspect to obtain the sea surface emissivity of the sea surface to be predicted.

[0012] According to a third aspect of the present application, an electronic device is provided, comprising: a computer readable storage medium and a processor;

[0013] The computer readable storage medium is configured to store executable instructions;

[0014] The processor is configured to read the executable instructions stored in the computer readable storage medium and execute the method of the first aspect or the second aspect.

[0015] According to a fourth aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for causing a processor to execute the method of the first aspect or the second aspect.

[0016] According to a fifth aspect of the present application, a computer program product is provided, comprising computer programs or instructions, which are executed by a processor to implement the method of the first aspect.

[0017] Overall, the above technical solutions conceived by the present application can achieve the following beneficial effects compared with the prior art:

[0018] The method provided by the application calculates the actual sea surface emissivity by using the radiation and angle data collected by the ship, calculates the theoretical emissivity of the flat sea surface according to the radiation frequency and seawater parameters, takes the real-time angle data and weather data as the model input, takes the difference (i.e., the roughness emissivity) between the actual emissivity and the theoretical emissivity of the sea surface as the target output, trains multiple different nonlinear regression models, and selects the model with the best prediction effect as the roughness emissivity prediction model; and combines the theoretical emissivity and the predicted roughness emissivity to obtain the sea surface emissivity under the corresponding conditions. According to the range distribution of the environmental parameters of the input data, the model has a certain generalization ability on the angle, wind speed and other parameters, has a higher angle resolution, can supplement some angles and sea condition conditions that cannot be collected in the actual measurement process, the calculation speed of the model is obviously faster than the rough sea surface emissivity theoretical calculation based on the sea wave spectrum, and the calculation resources consumed are smaller; so as to quickly obtain the rough sea surface emissivity under different sea condition wind speeds by consuming less calculation resources, and the missing data in the actual measurement data also has a good completion effect. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 FIG. 1 is a flowchart of a sea surface emissivity prediction model construction method based on a ship measurement experiment provided by an embodiment of the application;

[0020] Figure 2 FIG. 2 is another flowchart of a sea surface emissivity prediction model construction method based on a ship measurement experiment provided by an embodiment of the application;

[0021] Figure 3 FIG. 3 is a schematic diagram of a ship measurement data acquisition device provided by an embodiment of the application;

[0022] Figure 4 FIG. 4 is a schematic diagram of a 94GHz flat sea surface theoretical dual polarization emissivity calculation result provided by an embodiment of the application;

[0023] Figure 5 FIG. 5 is a schematic diagram of a dual polarization roughness emissivity result predicted by the method provided by an embodiment of the application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application. In addition, the technical features involved in each embodiment of the application described below can be combined with each other as long as they do not conflict with each other.

[0025] An embodiment of the application provides a sea surface emissivity prediction model construction method based on a ship measurement experiment, as shown in FIG. Figures 1-2 , comprising:

[0026] S1, collecting ship data; the ship data includes the measured radiation voltage of the sea surface and the sky at different azimuth angles and elevation angles, meteorological data, and sea surface environment parameters.

[0027] Specifically, the ship data is collected by a ship data collection device. The ship data collection device includes a radiometer, a weather station, a real-time tilt angle instrument, a black body, a thermometer, liquid nitrogen, and a foam box. The ship data to be collected includes radiation voltage data of the sea surface and the sky, meteorological data, and sea surface environment parameters.

[0028] The radiation voltage data is derived from the output of the radiometer during the ship measurement and includes the sea surface radiation measurement results and the sky radiation measurement results at multiple elevation angles and azimuth angles. Preferably, the radiation data includes dual-polarization radiation data of horizontal polarization and vertical polarization. Correspondingly, the radiometer can adopt a dual-polarization real-aperture radiometer for measuring the sea surface and the sky to obtain dual-polarization radiation data. That is, the radiation voltage includes horizontal polarization radiation voltage and vertical polarization radiation voltage; the actual sea surface emissivity includes horizontal polarization actual sea surface emissivity and vertical polarization actual sea surface emissivity; and the roughness emissivity includes roughness emissivity and roughness emissivity.

[0029] The meteorological data and the sea surface environment parameters can be selected according to actual conditions, and the embodiments of the present application do not make unique limitations thereon. As an example, the meteorological data includes air temperature, air pressure, humidity, wind speed, and wind direction corresponding to the measurement time; and the sea surface environment parameters include sea water temperature and sea water salinity corresponding to the measurement time.

[0030] The environmental meteorological data is derived from the output of the weather station during the ship measurement. Correspondingly, the weather station is a portable weather station installed on the ship in an unobstructed position and includes a meteorological module and a sea water measurement module for measuring parameters such as air temperature, air pressure, humidity, wind speed, wind direction, sea water temperature, and sea water salinity in real time.

[0031] The real-time tilt angle instrument is installed on the radiometer for recording the attitude of the radiometer in real time, obtaining real-time elevation angle data of the radiometer, and minimizing the time delay to avoid the influence of the ship body shaking on the measurement results. The azimuth angle data of the radiometer is with the ship body as the reference system. The corresponding incident angle can be obtained according to the elevation angle of the radiometer.

[0032] The black body is used for radiometer calibration as a calibration heat source.

[0033] The thermometer is used for radiometer calibration, specifically for measuring the temperature of the black body.

[0034] The liquid nitrogen is used for radiometer calibration as a calibration cold source, and the default temperature of the liquid nitrogen is 77K.

[0035] The foam box is used as a container of black body and liquid nitrogen, and the transmissivity of the foam box material is close to 1 in the measuring wave band.

[0036] The data recorded by the radiometer, the tiltmeter and the weather station should all contain time information, so as to facilitate the integration of multi-device data.

[0037] S2, according to the radiation voltage, the actual emissivity of the sea surface under different azimuth angles and elevation angles is calculated, and the roughness emissivity is obtained by subtracting the theoretical emissivity of the flat sea surface; wherein the theoretical emissivity of the flat sea surface is calculated by a flat sea surface theoretical emissivity calculation module according to the radiation frequency and the sea surface environmental parameters;

[0038] Specifically, the calculation method of the theoretical emissivity of the flat sea surface adopts the existing method, and the embodiments of the present application do not make unique limitation on this, for example: the double Debye model in the millimeter wave frequency band (3GHz-300GHz) has a relatively accurate description for the dielectric constant of seawater, according to the double Debye model, combined with the measured actual frequency (i.e. radiation frequency), the measured seawater temperature and salinity data, the theoretical value of the dielectric constant of seawater is calculated; according to the Fresnel law and the theoretical value of the dielectric constant of seawater, the theoretical emissivity of the flat sea surface is calculated As Figure 4 The p represents the polarization direction, and there are generally horizontal polarization and vertical polarization.

[0039] The two-point calibration data of the radiometer includes the calibration results of the cold source and the hot source, according to which the voltage data output by the radiometer can be converted into radiation brightness temperature data (including the measured value of the sea surface radiation brightness temperature and the corresponding measured value of the atmospheric radiation brightness temperature); according to the measured value of the sea surface radiation brightness temperature and the corresponding measured value of the atmospheric radiation brightness temperature, the intrinsic sea surface radiation measurement value can be calculated, and according to the measured seawater temperature of the weather station seawater module, the actual sea surface emissivity is obtained

[0040] The actual sea surface cannot be regarded as a mirror surface, so the calculated theoretical emissivity of the flat sea surface is not equal to the emissivity of the real sea surface, and the theoretical emissivity corresponding to the elevation angle is subtracted from the emissivity of the actual seawater to obtain the roughness emissivity

[0041] It can be understood that the radiometer integration time setting should be small and the sampling rate should be high to obtain more data for model training. The recommended value of the integration time can be between 5-100 ms, and the total integration time should not be too high, because when measuring the sea surface, the sea waves will fluctuate over time, and if the integration time is set too long, the radiation changes caused by the fluctuation of the sea waves may not be captured. Moreover, the collected pitch angle, wind speed, wind direction angle and other data should cover as many situations as possible, and the angle coverage range should be as large as possible. The wind speed covers a variety of sea conditions from low wind speed to high wind speed, and all data contain time information for subsequent time synchronization.

[0042] S3, using the weather data and the sea surface environment parameters under different azimuth angles and pitch angles as inputs, and using the roughness emissivity corresponding to the different azimuth angles and pitch angles as outputs, training a plurality of different nonlinear regression models, and selecting the model with the best prediction effect as the roughness emissivity prediction model.

[0043] Specifically, the weather data and the sea surface environment parameters are used as model inputs, and the roughness emissivity, i.e., the difference between the measured sea surface emissivity and the theoretical sea surface emissivity, is used as the target output. A plurality of different nonlinear regression models are trained. The indicators for measuring the prediction effect of the model include, but are not limited to, root mean square error, coefficient of determination, mean absolute error, etc.

[0044] The plurality of different nonlinear regression models can be selected as desired, and the embodiments of the present application do not make a unique limitation thereon. As an example, the plurality of different nonlinear regression models include regression trees, ensemble trees and neural networks.

[0045] Specifically, the existing physical model for calculating the emissivity of a flat surface is already accurate enough, and the present application will focus on the roughness emissivity Δe p The fitting of the regression model is performed, and a suitable regression model is selected, including but not limited to regression trees, ensemble trees, neural networks, etc. The independent variables of the model input are sea water temperature, sea water salinity, incident angle, wind speed, wind direction angle and pitch angle, and the dependent variable of the input is the roughness emissivity Δe p The absolute coefficient R 2 , root mean square error RMSE, mean absolute error, etc. are used to evaluate the prediction effect of the model on the roughness emissivity, and the optimal model is selected.

[0046] S4, constructing a sea surface emissivity prediction model; wherein the sea surface emissivity prediction model includes a roughness emissivity prediction model, a flat sea surface theoretical emissivity calculation module and a summation module; the summation module is used to sum the roughness emissivity predicted by the roughness emissivity prediction model and the flat sea surface theoretical emissivity calculated by the flat sea surface theoretical emissivity calculation module to obtain the sea surface emissivity.

[0047] This invention provides a method for predicting sea surface emissivity based on ship-based experiments, comprising:

[0048] The radiation frequency, meteorological data, and sea surface environmental parameters of the sea surface to be predicted are input into the sea surface emissivity prediction model constructed using the construction method described in any of the above embodiments to obtain the sea surface emissivity of the sea surface to be predicted.

[0049] The method provided by this invention will be further illustrated below with a specific example.

[0050] (1) Collect ship survey data using a ship survey data acquisition device.

[0051] The shipborne data acquisition equipment includes a dual-polarized solid aperture radiometer, a weather station, an inclinometer, and radiometer calibration equipment and related items such as liquid nitrogen, blackbody, foam box, and thermometer.

[0052] In the initial stage, connect the radiometer to the computer, turn it on and warm it up, and set the radiometer integration time. To ensure that the radiometer output has high real-time performance, the integration time should not be set too high.

[0053] The weather station is installed on an unobstructed location on the ship. The height of the weather station relative to the sea surface is recorded. The seawater measurement module of the weather station is placed in the seawater to obtain seawater temperature and salinity. The weather station is turned on and connected to a computer to collect and store environmental parameters such as time, air temperature, air pressure, humidity, wind speed, wind direction, seawater temperature, and seawater salinity in real time.

[0054] A real-time inclinometer is installed on the radiometer to record attitude parameters such as the pitch angle of the radiometer in real time. The sampling frequency of the inclinometer should be high enough and the delay should be low enough to facilitate the elimination of angle changes caused by the ship's rolling.

[0055] Two-point calibration of the radiometer is performed. The thermometer is connected to the blackbody, the radiometer is aligned with the blackbody at room temperature, and the bipolarized output voltage of the radiometer and the temperature of the blackbody are recorded. The blackbody is placed at the bottom of the foam box, liquid nitrogen is poured in, the radiometer is aligned with the blackbody in the liquid nitrogen, and the bipolarized output voltage of the radiometer is recorded. The calibration is now complete, and data acquisition begins.

[0056] like Figure 3 As shown, with the ship's hull as the reference coordinate system, select the azimuth angle, adjust the radiometer to measure the pitch angle, and scan and record the sky and sea surface with the largest possible pitch angle range (-90° to 90°). Alternatively, depending on the experimental conditions, the sky and sea surface can be continuously recorded sequentially in steps of a certain pitch angle. The recording time for a single azimuth angle should not be too long, and the saved radiation data should have a timestamp to facilitate real-time correspondence with the environmental parameters recorded by the meteorological station.

[0057] Take the bow pointing as the azimuth angle 0°, perform the same multi-elevation angle measurement at multiple azimuth angles in the range of 0°-360° with 30° steps, and perform multiple radiometer calibration during the measurement to avoid the influence of radiometer state change on measurement accuracy;

[0058] The azimuth angle and the elevation angle of the radiometer during the measurement can be adjusted according to the actual measurement conditions, and the whole follows the principle that the smaller the step is, the better it is;

[0059] An optional simplified measurement scheme considers the sky brightness temperature distribution at different azimuth angles to be the same, and in the case of relatively stable weather and climate, it is not necessary to collect sky data at each azimuth angle, but only to collect several times of sky data during the whole measurement process, and only to measure the sea surface data in the range of -90°-0° elevation angle during the measurement at different azimuth angles;

[0060] Multiple ship measurement data collection is performed at different sea state wind speeds, and sea surface radiation data at different wind speeds are recorded, and the collected wind speed data includes data covering 1-5 sea states;

[0061] (2) Data processing and model training: according to the calibration data, the radiometer voltage data is converted into brightness temperature data with the unit of K;

[0062] The meteorological station data is linearly interpolated to achieve the same sampling frequency as the radiometer data; according to the data time information, the meteorological station data, the inclination instrument data and the radiometer data at the same time are summarized; among them, the sky brightness temperature and the sea surface brightness temperature are summarized respectively according to the elevation angle;

[0063] According to the height of the meteorological station compared with the sea level, the measured wind speed is converted into the wind speed at the height of 10m, i.e. the basic wind speed;

[0064] The sea surface brightness temperature is subtracted by the sky brightness temperature reflected by the sea surface at the corresponding incident angle to obtain the intrinsic sea surface radiation brightness temperature, and combined with the real-time sea water temperature measured by the meteorological station, the measured sea surface emissivity under the corresponding conditions can be obtained;

[0065] According to the measured sea water temperature and salinity data, the sea water dielectric constant is calculated according to the double-debye equation, and according to the Fresnel law and the theoretical value of the sea water dielectric constant, the theoretical dual-polarization emissivity of the flat sea surface is calculated and h and v represent horizontal and vertical polarization respectively; the difference between the measured emissivity and the theoretical emissivity of the flat sea surface is obtained to obtain the sea surface roughness emissivity Δe h and Δe v ;

[0066] Taking MATLAB regression learner as an example, regression tree, integrated tree, neural network and other models are established, and the data input includes sea water temperature, sea water salinity, wind speed, wind direction angle, azimuth angle, elevation angle and roughness emissivity Δe.h and Δe v The independent variables are set as seawater temperature, seawater salinity, wind speed, wind direction angle, and pitch angle, and the response is the roughness emissivity Δe under the corresponding conditions. h and Δe v The validation scheme can be cross-validation, hold-out validation, or a combination of these methods. In this embodiment, 10% of the data is reserved as a validation set for subsequent model prediction performance verification. The absolute coefficient R0 is used. 2 The model's predictive performance on roughness emissivity is evaluated using parameters such as root mean square error (RMSE), and the optimal model is selected.

[0067] The roughness emissivity prediction model is used for prediction, and the roughness emissivity Δe is predicted using the validation set. h and Δe v The prediction is made and compared with the reserved measured roughness emissivity to verify the accuracy of the model's prediction. The final prediction result is as follows: Figure 5 As shown, the predictive coefficients of emissivity for dual-polarized roughness are all better than 97%.

[0068] By combining the output of the roughness emissivity prediction model with the theoretical emissivity of a flat sea surface, the emissivity of a rough sea surface with different polarization directions under corresponding meteorological conditions can be obtained. Based on the seawater temperature and sky brightness temperature, the brightness temperature of the seawater at the corresponding pitch angle can also be obtained.

[0069] (3) The trained regression model (i.e., the nonlinear roughness emissivity regression model) is encapsulated together with the theoretical flat sea surface emissivity calculation module and the summation module to obtain the final sea surface emissivity prediction model. In practical applications, the model output is the sea surface emissivity under the corresponding conditions.

[0070] This invention provides an electronic device, including: a computer-readable storage medium and a processor;

[0071] The computer-readable storage medium is used to store executable instructions;

[0072] The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in any of the above embodiments.

[0073] This invention provides a computer-readable storage medium storing computer instructions that cause a processor to perform the method described in any of the above embodiments.

[0074] This invention provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the method described in any of the above embodiments.

[0075] It is to be understood that the above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the application should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. A method for constructing a sea surface emissivity prediction model based on shipboard experiment, characterized in that, The method comprises: S1, collecting shipborne data; the shipborne data comprises radiation voltage of sea surface and sky measured at different azimuth angles and elevation angles, meteorological data, and sea surface environmental parameters; S2, calculating actual emissivity of the sea surface at different azimuth angles and elevation angles according to the radiation voltage and sea water temperature, and obtaining roughness emissivity by subtracting the theoretical emissivity of the flat sea surface from the actual emissivity; wherein the theoretical emissivity of the flat sea surface is calculated by a theoretical emissivity calculation module of the flat sea surface according to the radiation frequency and the sea surface environmental parameters; S3, training a plurality of different nonlinear regression models by taking the meteorological data and the sea surface environmental parameters at different azimuth angles and elevation angles as input and taking the roughness emissivity corresponding to the different azimuth angles and elevation angles as output, and selecting the model with the best prediction effect as the roughness emissivity prediction model; S4, constructing a sea surface emissivity prediction model; wherein the sea surface emissivity prediction model comprises the roughness emissivity prediction model, the theoretical emissivity calculation module of the flat sea surface, and a summation module; the summation module is configured to sum the roughness emissivity predicted by the roughness emissivity prediction model and the theoretical emissivity of the flat sea surface calculated by the theoretical emissivity calculation module of the flat sea surface to obtain the sea surface emissivity.

2. The method of claim 1, wherein, The meteorological data comprises wind speed and wind direction corresponding to the measurement time; The sea surface environmental parameters comprise sea water temperature and sea water salinity corresponding to the measurement time.

3. The method of claim 1, wherein, The plurality of different nonlinear regression models comprises regression tree, integrated tree and neural network.

4. The method of claim 1, wherein, At least one of absolute coefficient, root mean square error or mean absolute error is used as an evaluation index to evaluate the prediction effect of the plurality of different nonlinear regression models.

5. A method for predicting sea surface emissivity based on shipboard experiments, characterized in that, The method comprises: inputting the radiation frequency, meteorological data and sea surface environmental parameters of the sea surface to be predicted into the sea surface emissivity prediction model constructed by the construction method of any one of claims 1-4 to obtain the sea surface emissivity of the sea surface to be predicted.

6. An electronic device, comprising: The method comprises: a computer readable storage medium and a processor; the computer readable storage medium is configured to store executable instructions; the processor is configured to read the executable instructions stored in the computer readable storage medium and execute the method of any one of claims 1-5.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores computer instructions for causing the processor to execute the method of any one of claims 1-5.

8. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions are executed by the processor to implement the method of any one of claims 1-5.

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