High-resolution ocean surface physical state detection method and device based on satellite-borne GNSS-R complex waveform data

Abstract

The invention discloses a high-resolution ocean surface physical state detection method and device based on satellite-borne GNSS-R complex waveform data. The method comprises the following steps: acquiring complex waveform data; preprocessing the complex waveform data to obtain a complex waveform data sequence for detecting the physical state of the ocean surface, dividing the detected complex waveform data sequence into a plurality of complex waveform groups according to a time sequence, and determining a coherence coefficient between two continuous complex waveforms in each complex waveform group; taking the complex waveform data sequence as a test set, selecting 20% of data of the test set as a training set, determining a threshold value of an average coherence coefficient characteristic quantity according to verification data and the training set by adopting a histogram method, and taking the threshold value of the average coherence coefficient characteristic quantity as a boundary for judging sea ice and seawater; and obtaining the surface physical state of the detection area determined by the plurality of sampling points corresponding to each complex waveform group in the test set according to the threshold value and the average coherence coefficient of each complex waveform group in the test set.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing, and in particular to a method and a device for high-resolution detection of the physical state of the ocean surface based on satellite-borne GNSS-R complex waveform data. Background Art

[0002] Sea ice is an important variable in global climate patterns and an important component of high-latitude oceans. Sea ice covers most of the ocean surface in polar regions. About 71% of the Earth's surface is occupied by oceans, of which about 7% to 15% of the sea area is covered by sea ice. Against the backdrop of global warming, the sea ice coverage in the Antarctic and Arctic sea ice regions is decreasing, which is profoundly affecting the natural ecological environment and human economic society in the polar regions and the world. Therefore, accurate and efficient acquisition of sea ice range and change information is of great significance to maritime transportation, marine resource development, global climate change, and ecosystem stability.

[0003] Initial sea ice monitoring mainly relied on in-situ measurement methods such as icebreakers and buoys. These methods provided accurate sea ice information, but the coverage was limited, the measurement process was time-consuming and labor-intensive, and it was difficult to achieve long-term, large-scale, and efficient dynamic monitoring. Satellite remote sensing originated in the early 1970s, mainly using visible light, near-infrared, and microwave (active / passive) sensors for earth observation. However, optical remote sensing (visible light, near-infrared) is easily affected by cloud cover and lighting conditions, and cannot achieve all-day and all-weather sea ice monitoring. Microwave remote sensing is difficult to balance the temporal and spatial resolution. With the development of satellite technology, spaceborne GNSS-R, as an emerging remote sensing detection method, has the advantages of low cost, low power consumption, all-day, all-weather, large scale, and high temporal and spatial resolution. As a new remote sensing technology, it has shown great potential in the field of sea ice remote sensing. Therefore, sea ice detection can be achieved using the reflected signals collected by the spaceborne GNSS-R mission.

[0004] Prior to this, multiple characteristic observations were usually extracted from the Delay Doppler map (DDM map) collected by the spaceborne GNSS-R mission for sea ice detection. However, the along-track resolution of the spaceborne GNSS-R mission, that is, the spatial distance between the centers of two consecutive GNSS-R data signal peaks (SP), is affected by the 1-second incoherent averaging process in the generation of DDM products, resulting in the obtained along-track resolution usually being less than 6 kilometers. This limitation means that traditional DDM products are difficult to achieve high-resolution sea ice detection and cannot meet the needs of accurate monitoring of the subtle structure and dynamic changes of sea ice. Summary of the invention

[0005] In view of this, a first aspect of the present invention provides a method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data, the method comprising:

[0006] Acquire complex waveform data, wherein the complex waveform data is obtained by cross-correlating the reflected signal received by the onboard GNSS-R with the local replica code, and the complex waveform data includes the amplitude and phase information of the reflected signal acquired at a high sampling rate of 1kHz, and by selecting the coherent and incoherent times by itself, detection with controllable resolution along the track can be performed; the reflected signal is a satellite signal emitted by the target satellite reflected from the surface of the earth;

[0007] Preprocessing the complex waveform data to obtain a complex waveform data sequence for detecting the physical state of the ocean surface; wherein the complex waveform data sequence includes a plurality of complex waveforms, each of which corresponds to a sampling point on the ocean surface;

[0008] Divide the detected complex waveform data sequence into a plurality of complex waveform groups in time sequence, and determine the coherence coefficient between any two consecutive complex waveforms in each complex waveform group; wherein the number of complex waveforms in each complex waveform group ranges from 1 to 20, and each complex waveform corresponds to a time resolution of 1 ms;

[0009] Extracting, based on all the coherence coefficients of each complex waveform group, an average coherence coefficient feature of the complex waveform group with a time resolution of 20 ms for quantifying the degree of phase change;

[0010] The complex waveform data sequence is used as a test set, 20% of the data in the test set is selected as a training set, and a threshold value of an average coherence coefficient feature quantity is determined based on the verification data and the training set using a histogram method;

[0011] The threshold of the average coherence coefficient characteristic quantity is used as the boundary line for judging sea ice and sea water, and based on the threshold and the average coherence coefficient of each complex waveform group in the test set, the surface physical state of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set is obtained.

[0012] According to an embodiment of the present invention, a method for preprocessing complex waveform data includes:

[0013] Get initial complex waveform data;

[0014] The initial complex waveform data is filtered using a land mask method to filter out complex waveforms corresponding to sampling points located on land and within a preset distance from the land in the complex waveform data, thereby obtaining filtered complex waveform data;

[0015] The signal-to-noise ratio is used to evaluate the quality of the filtered complex waveform data, and the filtered complex waveform data with a signal-to-noise ratio greater than a preset value is retained to obtain a complex waveform data sequence for detecting the physical state of the ocean surface.

[0016] According to an embodiment of the present invention, the coherence coefficient between two consecutive complex waveforms is expressed as follows:

[0017]

[0018] in, represents the coherence coefficient of the two consecutive complex waveforms, t c Represents the time interval between two consecutive complex waveforms, Indicates t m The complex waveform value corresponding to the peak power of the complex waveform at time, Indicates t m +t c The complex waveform value corresponding to the peak power of the complex waveform at time, for The conjugation of.

[0019] According to an embodiment of the present invention, each complex waveform group includes M complex waveforms arranged in time sequence;

[0020] According to all the coherence coefficients of each complex waveform group, the average coherence coefficient feature of the complex waveform group with a time resolution of 20ms for quantifying the phase change degree is extracted, including:

[0021] All the coherence coefficients in the complex waveform group are averaged to obtain the average coherence coefficient feature of the complex waveform group, and the average coherence coefficient feature is expressed as follows:

[0022]

[0023] in, represents the average coherence coefficient feature.

[0024] According to an embodiment of the present invention, the surface physical state includes a sea ice state and a sea water state;

[0025] According to the threshold and the average coherence coefficient of each complex waveform group in the test set, the physical state of the surface of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set is obtained, including:

[0026] In each complex waveform group in the test set, the average coherence coefficient characteristic quantity is compared with the average coherence coefficient characteristic quantity threshold. When the average coherence coefficient characteristic quantity is greater than the average coherence coefficient characteristic quantity threshold, the physical state avoided by the detection area determined by the M sampling points is the sea ice state; when the average coherence coefficient characteristic quantity is less than the average coherence coefficient characteristic quantity threshold, the physical state of the surface of the detection area determined by the M positions is the sea water state.

[0027] According to an embodiment of the present invention, 20% of the data in the test set is selected as the training set, and the threshold of the average coherence coefficient feature quantity is determined according to the verification data and the training set using the histogram method, including:

[0028] The training data set is grouped to obtain a plurality of complex waveform group samples, and the average coherence coefficient characteristic quantity of each group is calculated;

[0029] Acquire verification data that is time-space matched with each complex waveform group sample in the training set, the verification data representing the actual physical state of the surface of the target area corresponding to the complex waveform group sample;

[0030] According to the actual physical state given by the verification data, a histogram of the average coherence coefficient characteristic quantity of the sea ice and sea water surface is drawn, and the intersection of the sea ice and sea water histograms is determined by a histogram method, and the intersection is the average coherence coefficient characteristic quantity threshold.

[0031] According to an embodiment of the present invention, the preset threshold is 3db, and the preset distance is 10 kilometers.

[0032] As a second aspect of the present invention, there is also provided a device for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data, for implementing the above method, the device comprising:

[0033] An acquisition module is used to acquire complex waveform data. The complex waveform data is obtained by cross-correlating the reflected signal received by the onboard GNSS-R with the local replica code. The complex waveform data includes the amplitude and phase information of the reflected signal acquired at a high sampling rate of 1kHz. By selecting the coherent and incoherent times, detection with controllable resolution along the track can be performed. The reflected signal is a satellite signal emitted by the target satellite reflected from the surface of the earth.

[0034] A preprocessing module, adapted to preprocess the complex waveform data to obtain a complex waveform data sequence for detecting the physical state of the ocean surface; wherein the complex waveform data sequence includes a plurality of complex waveforms, each of which corresponds to a sampling point on the ocean surface;

[0035] The grouping module is suitable for dividing the detected complex waveform data sequence into a plurality of complex waveform groups according to time sequence, and determining the coherence coefficient between any two consecutive complex waveforms in each complex waveform group, wherein the number of complex waveforms in each complex waveform group ranges from 1 to 20, and each complex waveform corresponds to a time resolution of 1 ms.

[0036] A first determination module is adapted to extract, based on all coherence coefficients of each complex waveform group, an average coherence coefficient feature quantity of the complex waveform group with a time resolution of 20 ms for quantifying the phase change degree;

[0037] The second determination module is adapted to use the complex waveform data sequence as a test set, select 20% of the data in the test set as a training set, and use a histogram method to determine a threshold value of an average coherence coefficient feature quantity based on the verification data and the training set;

[0038] The detection module is suitable for using the threshold of the average coherence coefficient characteristic quantity as the boundary line for judging sea ice and sea water, and is suitable for obtaining the surface physical state of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set based on the threshold and the average coherence coefficient of each complex waveform group in the test set.

[0039] The complex waveform data (CWF data) sequence according to the embodiment of the present invention is obtained based on the TDS-1CWF product, which is obtained by cross-correlating the reflected signal received by TDS-1 with the local replica code, and the sampling rate is 1kHz. Since the number of complex waveforms included in each complex waveform group ranges from 1 to 20, the method in the embodiment of the present invention is equivalent to being able to detect the ocean surface at a sampling rate of 1 to 20ms. Therefore, the method in the embodiment of the present invention has a higher resolution, which makes it possible to detect the ice and water state in small-scale areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0041] Figure 1 A flow chart showing a method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data provided according to an embodiment of the present invention;

[0042] Figure 2 A flow chart of a method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data provided according to another embodiment of the present invention is shown;

[0043] Figure 3 A flowchart of a method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data provided according to another embodiment of the present invention is shown;

[0044] Figure 4A shows a CWF trajectory of a polynya provided according to an embodiment of the present invention;

[0045] Figure 4B Shows Figure 4A The MODIS surface reflectance and Figure 4A The MCC feature quantities obtained from the trajectory points at the CWF trajectory shown;

[0046] Figure 4C Shows Figure 4B The correspondence between MODIS surface reflectance and MCC characteristic quantity scale after unification;

[0047] Figure 4D Shows Figure 4C Scatter comparison diagram of surface reflectance and MCC feature quantity in;

[0048] Figure 5A shows a CWF track of a sea ice region provided according to an embodiment of the present invention;

[0049] Figure 5B Shows Figure 5A The MODIS surface reflectance and Figure 5A The MCC feature quantity obtained from the trajectory points at the local CWF trajectory in;

[0050] Figure 5C Shows Figure 5B The correspondence between MODIS surface reflectance and MCC characteristic quantity scale after unification;

[0051] Figure 5D Shows Figure 5C Scatter comparison diagram of surface reflectance and MCC feature quantity in;

[0052] Figure 6 A schematic diagram of a device for detecting the physical state of the ocean surface provided according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0054] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0055] All terms used herein, including technical and scientific terms, have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0056] When using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted as the meaning of the expression generally understood by those skilled in the art. For example, "a system having at least one of A, B, and C" should include but not be limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc. When using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted as the meaning of the expression generally understood by those skilled in the art. For example, "a system having at least one of A, B, or C" should include but not be limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.

[0057] It should also be noted that the directional terms mentioned in the embodiments, such as "upper", "lower", "front", "back", "left", "right", etc., are only reference directions of the drawings and are not intended to limit the scope of protection of the present invention. Throughout the drawings, the same elements are represented by the same or similar reference numerals. Conventional structures or configurations will be omitted when they may cause confusion in the understanding of the present invention.

[0058] In the embodiments of the present invention, the collection, updating, analysis, processing, use, transmission, provision, invention, storage and other aspects of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information and network security.

[0059] Figure 1 A flow chart of a method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data provided according to an embodiment of the present invention is shown.

[0060] like Figure 1 As shown, the method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data includes operations S1 to S6.

[0061] In operation S1, complex waveform data is obtained. The complex waveform data is obtained by cross-correlating the reflected signal received by the onboard GNSS-R with the local replica code. The complex waveform data includes the amplitude and phase information of the reflected signal acquired at a high sampling rate of 1kHz. By selecting the coherent and incoherent times by oneself, detection with controllable resolution along the track can be performed. The reflected signal is a satellite signal transmitted by the target satellite reflected from the surface of the earth.

[0062] According to an embodiment of the present invention, the cross-correlation process can be expressed as the following formula:

[0063]

[0064] Among them, in formula (1), is the complex waveform of the reflected signal, t0 represents the start time of the cross-correlation, represents the time of each complex waveform; τ is the waveform delay; T I is the coherent integration time, which is 1 ms; C(·) is the PRN code generated locally by the receiver, s r is the reflected signal received by the onboard GNSS-R, ψ C The code phase of the replica code generated locally by the receiver, f r and φ r It is the frequency and phase of the carrier generated locally by the receiver. The detection complex waveform data sequence includes the in-phase component and the orthogonal component of the reflected signal, and provides the amplitude and phase information of the reflected signal at a high sampling rate of 1kHz. By selecting the coherent and incoherent times, the detection with controllable resolution along the track can be performed.

[0065] In operation S2, the complex waveform data is preprocessed to obtain a complex waveform data sequence for detecting the physical state of the ocean surface; wherein the complex waveform data sequence includes a plurality of complex waveforms, each of which corresponds to a sampling point on the ocean surface.

[0066] In operation S3, the detected complex waveform data sequence is divided into multiple complex waveform groups in time sequence, and the coherence coefficient (MCC) between any two consecutive complex waveforms in each complex waveform group is determined; wherein the number of complex waveforms in each complex waveform group ranges from 1 to 20, and each complex waveform corresponds to a time resolution of 1 ms.

[0067] In operation S4, a mean coherence coefficient (MCC) feature of the complex waveform group with a time resolution of 20 ms for quantifying the degree of phase change is extracted based on all coherence coefficients of each complex waveform group.

[0068] In operation S5, the complex waveform data sequence is used as a test set, 20% of the data in the test set is selected as a training set, and a threshold of the average coherence coefficient feature value is determined based on the verification data and the training set using a histogram method.

[0069] In operation S6, the threshold of the average coherence coefficient characteristic quantity is used as the boundary line for judging sea ice and sea water, and based on the threshold and the average coherence coefficient of each complex waveform group in the test set, the surface physical state of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set is obtained.

[0070] The complex waveform data (CWF data) sequence according to the embodiment of the present invention is obtained based on the TDS-1CWF product, which is obtained by cross-correlating the reflected signal received by TDS-1 with the local replica code, and the sampling rate is 1kHz. Since the number of complex waveforms included in each complex waveform group ranges from 1 to 20, the method in the embodiment of the present invention is equivalent to being able to detect the ocean surface at a sampling rate of 1 to 20ms. Therefore, the method in the embodiment of the present invention has a higher resolution, which makes it possible to detect the ice and water state in small-scale areas.

[0071] Figure 2 A flow chart of a method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data provided according to an embodiment of the present invention is shown.

[0072] Combination Figure 1-2 According to an embodiment of the present invention, the process of determining the complex waveform data may include a TDS-1CWF product data preparation process and a data selection process. The first is the TDS-1CWF product data preparation process: the embodiment of the present invention processes and explains all CWF trajectories in a certain time period in the TDS-1CWF product (for example, a total of 659 CWF trajectories from December 2014 to March 2, 2019). Each trajectory contains complex waveforms and related metadata of direct and reflected signals from the target satellite (i.e., a given GPS satellite or a given GNSS satellite). Each trajectory has a data sequence of approximately 130s, and the sampling rate is 1ms, so each trajectory has approximately 130,000 complex waveforms. The complex waveform consists of 500 time delays, with a resolution of 1 / 16 chips (approximately 18.3169m). The second is the data selection process. Before detection, we must first determine the temporal and spatial scope of sea ice detection, extract the CWF data sequence and corresponding metadata within the detection range, and select complex waveform data from all trajectories obtained within the above time period based on the determined temporal and spatial scope.

[0073] In operation S2, the method for determining the complex waveform data sequence includes the following steps.

[0074] First, data filtering. Specifically, the complex waveform data is filtered using the land mask method to filter out the complex waveforms corresponding to the sampling points located on land and within a preset distance from the land (for example, 10 kilometers) in the complex waveform data, and to obtain filtered complex waveform data. Sea ice detection only focuses on the CWF data of the sea surface, and the land mask method is used to filter the initial complex waveform data sequence to filter out the complex waveforms corresponding to the sampling points located on land and within a preset distance from the land in the complex waveform data sequence. Secondly, the signal-to-noise ratio is used to evaluate the quality of the filtered complex waveform data, and the filtered complex waveform data with a signal-to-noise ratio greater than a preset value is retained to obtain a complex waveform data sequence for detecting the physical state of the ocean surface. According to an embodiment of the present invention, since the effective information of part of the CWF data is completely submerged by noise, the signal-to-noise ratio (SNR) is selected as a quality control condition to evaluate the CWF quality, and low-quality complex waveform data is eliminated. Only the complex waveform data with an SNR value greater than a preset threshold (for example, 3 dB) is retained. Finally, a detection complex waveform data sequence is obtained. The selected detection complex waveform data sequence is also called a test data set.

[0075] According to an embodiment of the present invention, through TDS-1CWF product data preparation, data selection and data filtering, the processing efficiency is improved, the influence of various noises is reduced, and the data quality and the reliability of the sea surface state detection results are guaranteed.

[0076] According to the embodiment of the present invention, the physical state of the ocean surface includes the state of sea ice and the state of sea water. The satellite signals reflected by sea ice and sea water have obvious phase change differences. The embodiment of the present invention extracts the average coherence coefficient (i.e., MCC feature quantity) and selects a suitable MCC feature quantity threshold as the boundary line for judging sea ice and sea water to determine the state of sea ice and sea water, thereby determining the physical state of the ocean surface. The following will introduce them in detail.

[0077] According to another embodiment of the present invention, the detection of the physical state of the ocean surface is achieved by extracting the average coherence coefficient (i.e., MCC feature quantity). Specifically, in operation S3, the detected complex waveform data sequence is divided into a plurality of complex waveform groups according to the time sequence, and the coherence coefficient between two consecutive complex waveforms in each complex waveform group is determined. For any two adjacent complex waveforms, for example, the sampling times corresponding to the mth complex waveform and the m+1th complex waveform are t m and t m+1 , t m and t m+1 The time interval between c The coherence coefficient between the mth complex waveform and the m+1th complex waveform (i.e. the coherence coefficient between two consecutive complex waveforms) It is expressed as follows:

[0078]

[0079] Among them, among them, represents the coherence coefficient of the two consecutive complex waveforms, t c Represents the time interval between two consecutive complex waveforms, Indicates t m The complex waveform value corresponding to the peak power of the complex waveform at time, Indicates t m +t c The complex waveform value corresponding to the peak power of the complex waveform at time, for The conjugation of.

[0080] According to an embodiment of the present invention, each complex waveform group includes M complex waveforms arranged in time sequence. Operation S4 specifically includes averaging all the coherence coefficients in the complex waveform group to obtain an average coherence coefficient feature of the complex waveform group, and the average coherence coefficient feature is expressed as follows:

[0081]

[0082] in, represents the average coherence coefficient feature.

[0083] According to an embodiment of the present invention, operation S5 specifically includes the following steps. First, the training data set is grouped, and the average coherence coefficient feature quantity of each group is calculated. Secondly, verification data that matches each complex waveform group sample in the training set in time and space is obtained, and the verification data characterizes the actual physical state of the surface of the target area corresponding to the complex waveform group sample. Thirdly, according to the actual physical state given by the verification data, a histogram of the average coherence coefficient feature quantity of the sea ice and sea water surface is drawn, and the intersection of the sea ice and sea water histograms is determined by the histogram method, and the intersection is the average coherence coefficient feature quantity threshold.

[0084] According to an embodiment of the present invention, there is an obvious difference in phase change in the CWF data sequence obtained from the satellite signal reflected by sea ice and sea water, and the MCC feature quantity quantifies the phase difference change between adjacent complex waveforms. The surface physical state of the detection area can be obtained by comparing the MCC feature quantity with the MCC feature quantity threshold. The MCC feature quantity threshold is determined by the histogram method, and the feature quantity threshold is the intersection of the sea ice and sea water histograms. In essence, the MCC feature quantity measures the degree of phase change of the complex waveform. The MCC feature quantity obtained based on sea ice is greater than the feature quantity preset value, and the MCC feature quantity obtained based on sea water is less than the feature quantity threshold, which is expected to be higher than the MCC feature quantity obtained from sea water, that is:

[0085]

[0086] In the above formula, CWF para represents the CWF feature quantity, para thres Indicates the CWF feature value threshold. For example, the MCC feature value corresponding to the sea ice state is greater than the MCC feature value, and the MCC feature value corresponding to the sea water state is less than the MCC feature value.

[0087] According to an embodiment of the present invention, the embodiment of the present invention also includes selecting data that can characterize the state of the ocean surface, such as OSISAF SIC-SH and NSIDC MASIE-NH sea ice product data sets, to evaluate the reliability and accuracy of the method provided by the embodiment of the present invention. The present invention is an embodiment that establishes a corresponding relationship between the ice-water state of the verification data and the ice-water state determined by the MCC feature quantity, compares the sea ice detection results of the MCC feature quantity with the verification data, and evaluates the detection accuracy of the method provided by the embodiment of the present invention for the ocean surface.

[0088] Figure 3 A flow chart of a method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data provided according to yet another embodiment of the present invention is shown.

[0089] like Figure 3 As shown, according to an embodiment of the present invention, the above method also includes the following operations.

[0090] First, when the surface physical state of the detection area determined by the multiple positions corresponding to the complex waveform group of the test set is a sea ice state, the above method also includes obtaining an image of the detection area, that is, obtaining a MODIS synthetic image. Secondly, it is necessary to preprocess the TDS-1CWF data to obtain the CWF data trajectory. According to the MODIS synthetic image, it is determined whether the CWF data trajectory passes through the ice channel. Finally, the MCC feature value of the CWF data trajectory point passing through the ice channel is extracted (i.e., CWFMCC feature value extraction), and its consistency with the MODIS surface reflectance data extracted by the MODIS MOD09GQ product is judged (i.e., correlation determination).

[0091] According to an embodiment of the present invention, the surface of the polynya is smoother than sea ice, and the Fresnel reflection coefficient of water is much larger than that of ice. Therefore, the CWF feature quantity obtained at the polynya is greater than the CWF feature quantity at the sea ice. According to the MODIS synthetic image, a CWF track passing through the polynya in a time-space matching and cloud-free area is selected, and the MCC feature quantity of the CWF track is extracted. The surface reflectivity of the CWF track points extracted from the MODIS MOD09GQ product can be used to evaluate the polynya recognition capability of the CWF data.

[0092] In order to facilitate ordinary technicians in the field to understand and implement the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the implementation examples described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0093] All TDS-1CWF tracks in a certain time period (659 tracks in total from December 2014 to March 2019) are selected for processing and interpretation. Each track contains the complex waveforms and related metadata of the direct and reflected signals from a given GPS satellite. The OSISAF SIC-SH and NSIDC MASIE-NH sea ice product datasets are selected as validation products to evaluate the reliability and accuracy of sea ice detection using spaceborne GNSS-R technology.

[0094] The present invention is based on the high-resolution sea ice detection method of satellite-borne GNSS reflectometry technology (GNSS-Reflectometry, GNSS-R) complex waveform (Complex Waveform, CWF) products, which consists of four main parts: CWF product acquisition and preprocessing, CWF data high-resolution sea ice detection scheme construction, CWF data sea ice detection performance evaluation, and CWF data interglacial waterway identification application. The following are specific embodiments, and combined with Figure 3 The process of the method for high-resolution detection of ocean surface physical state based on satellite-borne GNSS-R complex waveform data provided by an embodiment of the present invention is described in detail. The present invention includes the following steps:

[0095] Step 1: Data acquisition and preparation. Download the TDS-1CWF product (659 tracks from December 2014 to March 2019) and extract the required CWF data and related metadata, including time, GNSS satellite, GNSS-R satellite (or spaceborne GNSS-R), position and velocity of the mirror reflection point; download the OSISAF SIC-SH and NSIDC MASIE-NH sea ice product datasets.

[0096] Step 2: TDS-1CWF data selection and filtering. Select DS-1CWF data that matches the time and spatial location of the verification product; select SNR to evaluate CWF quality, and remove DS-1CWF data with SNR values ​​less than the preset threshold; use land mask to filter CWF data collected on land and within 10 kilometers of land. The filtered data is a complex waveform data sequence determined for detecting the physical state of the ocean surface.

[0097] Step 3: Data training and test data set construction. The analysis data is a determined detection complex waveform data sequence, of which there are 29025 complex waveform data in the southern hemisphere and 343438 complex waveform data in the northern hemisphere. Training and test data sets are established for the northern and southern hemispheres respectively, with the training data set accounting for 20% of the overall data of each, and the test data set is all the analysis data.

[0098] Step 4: Feature extraction. The complex waveform data sequence is divided into multiple complex waveform groups, the number of complex waveforms in each complex waveform group is 20, and the coherence coefficient between two consecutive complex waveforms in each complex waveform group is calculated. Based on all the coherence coefficients of each complex waveform group, the mean coherence coefficient (MCC) feature with a time resolution of 20ms that quantifies the phase change degree is extracted to quantify the phase change degree of the TDS-1CWF data.

[0099] Step 5: Determine the state of sea ice and sea water. Take the NSIDC MASIE-NH and OSISAF SIC-SH data as validation data, determine the MCC feature threshold through the training set and validation data, use the MCC feature threshold as the dividing line for judging sea ice and sea water, and determine the state of sea ice and sea water on the test set.

[0100] Step 6: Evaluation of sea ice detection performance. Establish a spatiotemporal matching relationship between the ice-water state of the verification data and the characteristic quantities of each complex waveform group in the test set. The temporal matching standard is that the TDS-1CWF and the verification data are collected on the same day, and the spatial position matching standard is that the distance between the CWF SP point and the verification data grid point is the shortest. Compare the sea ice detection results of the test set with the ice-water state of the corresponding verification data to evaluate the sea ice detection performance of the CWF.

[0101] Step 7: Application of CWF data in identifying polynyas. The CWF tracks passing through polynyas are determined through MODIS synthetic images. Taking the CWF tracks obtained in a certain area during a certain period of time as an example, the sensitivity of the CWF feature to polynyas is observed through qualitative analysis. The 20ms MCC feature of the CWF track and the surface reflectance data of MODIS MOD09GQ band 1 at the corresponding position are extracted, and the sliding average is performed 20 times to compare the consistency of the two. Through further quantitative analysis, the recognition ability of the CWF feature to polynyas is evaluated.

[0102] Figure 4A A CWF trajectory of a polynya provided according to an embodiment of the present invention is shown.

[0103] Figure 4B Shows Figure 4A The MODIS surface reflectance and Figure 4A The MCC feature values ​​obtained from the trajectory points on the CWF trajectory shown.

[0104] Figure 4C Shows Figure 4B The correspondence between the MODIS surface reflectance and the MCC characteristic quantity after the scales are unified.

[0105] Figure 4D Shows Figure 4C Scatter comparison of surface reflectance and MCC feature quantities in .

[0106] Figure 4A The CWF track is based on the MOD09GQ band 1 image. The MODIS surface reflectance at the polynya and the MCC feature values ​​obtained using the CWF track points at the polynya are shown in Figure 2. Figure 4B As shown. Figure 4A-4D As shown in the figure, the MODIS surface reflectance at the polynya waterway has a strong correlation with the MCC characteristic quantity. The MODIS surface reflectance at the polynya waterway and the MCC characteristic quantity at the polynya waterway are respectively deducted from the average value and normalized to the 0-1 scale, and the corresponding relationship between the surface reflectance data and the CWF characteristic quantity is established. It can be seen that the change trend of the MCC characteristic quantity is basically consistent with that of the surface reflectance. By calculating the scatter plot, the regression slope k of the MODIS surface reflectance and the MCC characteristic quantity is 0.9456, the correlation coefficient is 95.33%, and the deviation is 0.0045.

[0107] Figure 5A A local CWF trajectory in a sea ice area provided according to an embodiment of the present invention is shown. Figure 5B Shows Figure 5A The MODIS surface reflectance and Figure 5A The MCC feature quantity obtained from the trajectory points at the local CWF trajectory in . Figure 5A It is the local track point of CWF in the sea ice area with MOD09GQ band 1 image as the base map.

[0108] Figure 5C Shows Figure 5B The correspondence between the MODIS surface reflectance and the MCC characteristic quantity after the scales are unified. Figure 5D Shows Figure 5C Scatter comparison of surface reflectance and MCC feature quantities in .

[0109] like Figure 5C-5D As shown in the figure, if only the sea ice area is considered, the regression slope k of the MODIS surface reflectance and the MCC characteristic quantity is 0.7765, the correlation coefficient is 78.42%, and the deviation is 0.0045.

[0110] As a second aspect of the present invention, a device for detecting the physical state of the ocean surface is also provided to implement the above method.

[0111] Figure 6 A schematic diagram of a device for detecting the physical state of the ocean surface provided according to an embodiment of the present invention is shown.

[0112] like Figure 6 As shown, the device includes: an acquisition module 601, a preprocessing module 602, a grouping module 603, a first determination module 604, a second determination module 605, and a detection module 606.

[0113] The acquisition module 601 is used to acquire complex waveform data. The complex waveform data is obtained by cross-correlating the reflected signal received by the onboard GNSS-R with the local replica code. The complex waveform data includes the amplitude and phase information of the reflected signal obtained at a high sampling rate of 1kHz. By selecting the coherent and incoherent times, the detection with controllable resolution along the track can be performed; the reflected signal is a satellite signal emitted by the target satellite reflected by the earth's surface. The pre-processing module 602 is suitable for pre-processing the complex waveform data to obtain a complex waveform data sequence for detecting the physical state of the ocean surface; wherein the complex waveform data sequence includes multiple complex waveforms, each complex waveform corresponding to a sampling point on the ocean surface. The grouping module 603 is suitable for dividing the detected complex waveform data sequence into multiple complex waveform groups in time sequence, and determining the coherence coefficient between any two consecutive complex waveforms in each complex waveform group, wherein the number of complex waveforms in each complex waveform group ranges from 1 to 20, and each complex waveform corresponds to a time resolution of 1ms. The first determination module 604 is suitable for extracting the average coherence coefficient feature of the complex waveform group with a time resolution of 20ms that quantifies the phase change degree based on all the coherence coefficients of each complex waveform group. The second determination module 605 is suitable for using the complex waveform data sequence as a test set, selecting 20% ​​of the data in the test set as a training set, and using the histogram method to determine the threshold of the average coherence coefficient feature based on the verification data and the training set. The detection module 606 is suitable for using the threshold of the average coherence coefficient feature as the boundary for judging sea ice and seawater, and is suitable for obtaining the surface physical state of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set based on the threshold and the average coherence coefficient of each complex waveform group in the test set.

[0114] According to the high-resolution sea ice detection method based on satellite-borne GNSS reflectometry (GNSS-Reflectometry, GNSS-R) complex waveform (Complex Waveform, CWF) products proposed in the present invention, the CWF data of the TDS-1 satellite provides a new data source for high-resolution sea ice detection without adding additional costs, which supplements the deficiencies of existing sea ice detection data; the correlation coefficient feature extracted from the 1ms CWF data is averaged 20 times to obtain the MCC feature, which can detect sea ice at a sampling rate of 20ms. Compared with the DDM data with a sampling rate of 1s, the along-track resolution is improved by 50 times; in addition, the high-resolution sea ice detection capability of CWF data makes it possible to detect the ice and water status in small-scale areas, and can be used to identify narrow interglacial waterways.

[0115] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination. The scope of the present invention is defined by the attached claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data, the method comprising: Acquire complex waveform data, wherein the complex waveform data is obtained by cross-correlating the reflected signal received by the onboard GNSS-R with the local replica code, and the complex waveform data includes the amplitude and phase information of the reflected signal acquired at a high sampling rate of 1kHz, and by selecting the coherent and incoherent times by itself, detection with controllable resolution along the track can be performed; the reflected signal is a satellite signal emitted by the target satellite reflected from the surface of the earth; Preprocessing the complex waveform data to obtain a complex waveform data sequence for detecting the physical state of the ocean surface; wherein the complex waveform data sequence includes a plurality of complex waveforms, each of which corresponds to a sampling point on the ocean surface; Divide the detected complex waveform data sequence into a plurality of complex waveform groups in time sequence, and determine the coherence coefficient between any two consecutive complex waveforms in each complex waveform group; wherein the number of complex waveforms in each complex waveform group ranges from 1 to 20, and each complex waveform corresponds to a time resolution of 1 ms; Extracting, based on all the coherence coefficients of each complex waveform group, an average coherence coefficient feature of the complex waveform group with a time resolution of 20 ms for quantifying the degree of phase change; The complex waveform data sequence is used as a test set, 20% of the data in the test set is selected as a training set, and a threshold value of an average coherence coefficient feature quantity is determined based on the verification data and the training set using a histogram method; The threshold of the average coherence coefficient characteristic quantity is used as the boundary line for judging sea ice and sea water, and based on the threshold and the average coherence coefficient of each complex waveform group in the test set, the surface physical state of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set is obtained.

2. The method according to claim 1, wherein: The preprocessing methods of complex waveform data include: The complex waveform data is filtered using a land mask method to filter out complex waveforms corresponding to sampling points located on land and within a preset distance from the land in the complex waveform data, thereby obtaining filtered complex waveform data; The signal-to-noise ratio is used to evaluate the quality of the filtered complex waveform data, and the filtered complex waveform data with a signal-to-noise ratio greater than a preset value is retained to obtain a complex waveform data sequence for detecting the physical state of the ocean surface.

3. The method according to claim 1, wherein: The coherence coefficient between two consecutive complex waveforms is expressed as follows: in, represents the coherence coefficient of the two consecutive complex waveforms, t c Represents the time interval between two consecutive complex waveforms, Indicates t m The complex waveform value corresponding to the peak power of the complex waveform at time, Indicates t m +t c The complex waveform value corresponding to the peak power of the complex waveform at time, for The conjugation of.

4. The method according to claim 1, wherein: Each complex waveform group includes M complex waveforms arranged in time sequence; According to all the coherence coefficients of each complex waveform group, the average coherence coefficient feature of the complex waveform group with a time resolution of 20ms for quantifying the phase change degree is extracted, including: All the coherence coefficients in the complex waveform group are averaged to obtain the average coherence coefficient feature of the complex waveform group, and the average coherence coefficient feature is expressed as follows: in, represents the average coherence coefficient feature.

5. The method according to claim 4, wherein: The surface physical state includes sea ice state and sea water state; According to the threshold and the average coherence coefficient of each complex waveform group in the test set, the physical state of the surface of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set is obtained, including: In each complex waveform group in the test set, the average coherence coefficient characteristic quantity is compared with the average coherence coefficient characteristic quantity threshold. When the average coherence coefficient characteristic quantity is greater than the average coherence coefficient characteristic quantity threshold, the physical state avoided by the detection area determined by the M sampling points is the sea ice state; when the average coherence coefficient characteristic quantity is less than the average coherence coefficient characteristic quantity threshold, the physical state of the surface of the detection area determined by the M positions is the sea water state.

6. The method according to claim 3, wherein: 20% of the test set data is selected as the training set, and the threshold of the average coherence coefficient feature quantity is determined based on the verification data and the training set using the histogram method, including: The training data set is grouped to obtain a plurality of complex waveform group samples, and the average coherence coefficient characteristic quantity of each group is calculated; Acquire verification data that is time-space matched with each complex waveform group sample in the training set, the verification data representing the actual physical state of the surface of the target area corresponding to the complex waveform group sample; According to the actual physical state given by the verification data, a histogram of the average coherence coefficient characteristic quantity of the sea ice and sea water surface is drawn, and the intersection of the sea ice and sea water histograms is determined by a histogram method, and the intersection is the average coherence coefficient characteristic quantity threshold.

7. The method according to claim 2, wherein: The preset threshold is 3db, and the preset distance is 10 kilometers.

8. A device for high-resolution detection of ocean surface physical conditions based on satellite-borne GNSS-R complex waveform data, used to implement the method according to claims 1 to 7, the device comprising: An acquisition module is used to acquire complex waveform data. The complex waveform data is obtained by cross-correlating the reflected signal received by the onboard GNSS-R with the local replica code. The complex waveform data includes the amplitude and phase information of the reflected signal acquired at a high sampling rate of 1kHz. By selecting the coherent and incoherent times, detection with controllable resolution along the track can be performed. The reflected signal is a satellite signal emitted by the target satellite reflected from the surface of the earth. A preprocessing module, adapted to preprocess the complex waveform data to obtain a complex waveform data sequence for detecting the physical state of the ocean surface; wherein the complex waveform data sequence includes a plurality of complex waveforms, each of which corresponds to a sampling point on the ocean surface; The grouping module is suitable for dividing the detected complex waveform data sequence into a plurality of complex waveform groups according to time sequence, and determining the coherence coefficient between any two consecutive complex waveforms in each complex waveform group, wherein the number of complex waveforms in each complex waveform group ranges from 1 to 20, and each complex waveform corresponds to a time resolution of 1 ms. A first determination module is adapted to extract, based on all coherence coefficients of each complex waveform group, an average coherence coefficient feature quantity of the complex waveform group with a time resolution of 20 ms for quantifying the phase change degree; The second determination module is adapted to use the complex waveform data sequence as a test set, select 20% of the data in the test set as a training set, and use a histogram method to determine a threshold value of an average coherence coefficient feature quantity based on the verification data and the training set; The detection module is suitable for using the threshold of the average coherence coefficient characteristic quantity as the boundary line for judging sea ice and sea water, and is suitable for obtaining the surface physical state of the detection area determined by multiple sampling points corresponding to each complex waveform group in the test set based on the threshold and the average coherence coefficient of each complex waveform group in the test set.

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