CN116935244A - Small and medium-sized vortex characteristic identification method integrating active and passive remote sensing data - Google Patents
Small and medium-sized vortex characteristic identification method integrating active and passive remote sensing data Download PDFInfo
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Abstract
The application discloses a medium-small scale vortex characteristic identification method for fusing active and passive remote sensing data, which comprises the steps of acquiring a vortex tracking data set by utilizing sea surface height abnormal data to obtain vortex date, vortex center point longitude and latitude, vortex radius and vortex duration; acquiring chlorophyll concentration data in a vortex range by using a static water color remote sensing image; acquiring a surface vortex vector by using chlorophyll concentration as a tracer by using a maximum correlation coefficient flow field inversion algorithm; acquiring vortex surface temperature by utilizing a sea surface temperature product; establishing a time data set of longitude and latitude coordinates, vortex radius, vortex duration, chlorophyll concentration, sea surface flow field vector and sea surface temperature of a vortex center point; and acquiring key characteristic parameters of vortex surface vortex, morphological structure, inner and outer chlorophyll concentration and surface temperature change in the life cycle. The application combines active and passive remote sensing data, and can accurately capture the changes of marine biochemistry and physical parameters and daily changes in the vortex life cycle.
Description
Technical Field
The application relates to the field of marine remote sensing, in particular to a medium and small scale vortex characteristic identification method for fusing active and passive remote sensing data.
Background
Ocean mesoscale vortices refer to rotating fluid structures that are spread out over time for days to months, spatially over tens to hundreds of kilometers. These vortices store energy an order of magnitude or more higher than the surrounding average flow velocity, which are an essential component of the dynamic oceanographic study. Mesoscale vortices have a rich kinetic energy during motion and play an important role in momentum transport, chemical transport, heat and mass transfer for the upper ocean. They have important effects on the circulation structure within the sea, large scale water mass distribution, marine life and global climate change induced by interactions with the sea. So far, in the land-based shallow sea and the ocean of the world, scientists are always researching the interaction between the mesoscale phenomenon of the ocean and the biochemical process of the ocean and have made remarkable breakthrough progress, and the research has important scientific significance and practical application value.
In the whole, the ocean vortex has the advantages of numerous quantity, wide distribution, abundant energy and strong mobility, so that the ocean vortex becomes an ideal carrier for researching the material circulation, energy transmission and interaction among different circle layers in the ocean. With the realization of vortex full life cycle tracking observation, the remote sensing technology in the field has made a major breakthrough in the 21 st century, and a new hot trend in the vortex research field is initiated. Starting from the basic characteristics of ocean vortexes, the former proposes 4 remote sensing principles for effectively identifying and tracking the ocean vortexes: (1) abnormal temperature; (2) substance tracing; (3) a closed topology; (4) rotating the flow field. First, warm and cold vortices exist in the ocean, and they cause positive and negative anomalies in sea water temperature at the vortex core, respectively. Ideally, these anomalies can directly affect the ocean surface, creating synchronous anomalies in the sea surface temperature SST (Sea Surface Temperature). Such anomalies may be detected by satellite-mounted infrared sensors. Second, the vortex has strong material wrapping and transporting capability, and can rotate and migrate physical, biological and chemical substances such as chlorophyll, organic carbon, plankton and even offshore ice flocs. The motion of these materials can be perceived by a spectrometer with some underwater penetration capability and synthetic aperture radar SAR (Synthetic Aperture Radar) with sea surface imaging capabilities. Third, the ground-turning effect of vortex circulation causes the sea surface topology to assume a relatively stable closed configuration. In this configuration, the center of the warm vortex corresponds to the maximum of sea level height SSH (Sea Surface Height), while the center of the cold vortex corresponds to the minimum. Thus, radar altimeters can be used to identify and track these closed structures. Fourth, the flow of seawater inside the vortex can form a clockwise or anticlockwise rotating flow field, and meanwhile, the scattering and the radial aggregation of the surface seawater can be caused to form an ascending flow and a descending flow. This flow changes the roughness of the sea surface and the Sea Surface Height (SSH) so that it can be captured by sensors such as SAR and radar altimeters. Based on the above principle, a series of ocean vortex remote sensing technologies and methods have been developed by people in the past half century using various visible light, infrared and microwave sensors, greatly promoting the development of ocean science and even earth system science.
Vortex water color remote sensing based on the principle of substance tracing starts in the 80 s of the 20 th century. To date, the early exploration phase (before the 2l century) based on CZCS satellite data and mainly based on vortex identification application is approximately experienced, and the extensive application phase of utilizing fusion data mainly based on novel remote sensors such as SeaWiFS, MODIS, MERIS and the like and developing vortex areas and global feature statistics and ecological researches is later carried out. GOCI (Geostationary Ocean Color Imager), due to its relatively high temporal resolution (one hour) and spatial resolution (500 m), it is possible to achieve continuous observations per hour of Bohai sea, yellow sea, east sea, japanese sea and North Pacific ocean regions of China (Ryu J H, han H J, cho S, et al, overlay of Geostationary Ocean Color Imager (GOCI) and GOCI Data Processing System (GDPS) [ J ]. Ocean Science Journal,2012,47 (3): 223-233.). Compared with a polar orbit satellite, the unprecedented high space-time observation result of GOCI greatly improves the capability of monitoring the ocean high dynamic environment change, and also provides a new data support for the characteristic observation of the vortex intra-day change. The northeast asia sea area of the GOCI coverage area is one of the sea areas where medium-scale vortices are more active and is also a sensitive area for global atmospheric and ocean changes. The China is located on the North Pacific ocean west, the climate and offshore ocean environment are directly influenced by the variation of tropical cyclone and ocean current of the North Pacific ocean, and the exploration of the vortex characteristics of the region has important scientific significance and application value on the influence on the ecological environment. However, the technology still has the problem that the same vortex is difficult to mark due to the influence of cloud shielding. Therefore, feature identification within the full life cycle of small and medium scale vortices incorporating a variety of technologies is highly desirable in the northeast asia sea area.
Disclosure of Invention
Aiming at the problems that multisource remote sensing data are relatively independent and are not fully and comprehensively utilized in the prior art, the application provides a medium-small scale vortex characteristic identification method, which overcomes the defects that the same vortex cannot be marked due to the influence of cloud cover shielding in water color remote sensing and vortex characteristic parameters cannot be obtained by microwave remote sensing.
The aim of the application is realized by the following technical scheme:
the medium-small scale vortex characteristic identification method for fusing active and passive remote sensing data is characterized by comprising the following steps of:
step S1: acquiring a tracking data set of the medium-small scale vortex by utilizing sea surface height abnormal data, and calculating to obtain the life cycle (duration time), longitude and latitude coordinates of a central point and the radius of the vortex;
step S2: acquiring chlorophyll concentration data in a vortex range by using a static water color remote sensing satellite image through a matching algorithm;
step S3: acquiring a surface vortex vector by using chlorophyll concentration as a tracer by using a maximum correlation coefficient flow field inversion algorithm;
step S4: acquiring the surface temperature of the medium-small scale vortex by utilizing sea surface temperature product data;
step S5: combining the data obtained in the steps S1 to S4 to establish a time data set of longitude and latitude coordinates, vortex radius, vortex duration, chlorophyll concentration, surface vortex vector and vortex surface temperature of a vortex center point in a vortex life cycle;
step S6: and (5) selecting key time point data of part of time intervals in the step (S5), constructing key characteristic parameters of surface vortex change, morphological structure change, chlorophyll concentration change and surface temperature change of the medium-small scale vortex in the whole life cycle, and facilitating the utilization, understanding and illustration of people.
By the medium-small scale vortex characteristic identification method, the service life cycle, the longitude and latitude coordinates of the center point and the radius of the vortex are creatively acquired by utilizing SLA data; acquiring sea surface flow field and sea surface temperature data corresponding to the vortex by utilizing GOCI chlorophyll data; and then, the SLA data and the GOCI chlorophyll data are integrated to obtain the evolution process (comprising the change process of sea surface vortex, morphological structure, chlorophyll distribution and concentration and sea surface temperature) in the whole life cycle of the vortex, so that relevant personnel and managers can know, utilize and analyze the influence of the vortex on the sea engineering, the sea fishery and the sea ecological environment.
The step S1 of the application comprises the following steps:
s11: will T 0 The moment vortex centroid is defined as a vortex center point; number T of consecutive undetected vortex 1 Set to 0; t=t 0；
S12: taking a vortex center point as an origin, taking larger values of a length which is 1.75 times of the ocean propagation distance and 150km from 7 days at a diagonal pressure long Rossby wave phase speed to the west, taking 150km from 3 directions of east, south and north, selecting a vortex with the minimum similarity value and meeting the T=T+1 moment with the similarity value less than 0.5 in the range, wherein a similarity calculation formula is as follows:
wherein DeltaS, deltaL s ，Δξ，ΔE k Respectively representing the difference between the vortex space distance and the radius, the difference between the relative vorticity and the EKE of two adjacent moments; s is S 0 ，L s0 ，ξ 0 ，E k0 Respectively, 100km,500km,10 -6 s -1 And 100cm 2 s -2 ；
S13: if homologous vortex can be detected at the moment T, the vortex is regarded as existence continuously and the number T of times of continuously not detecting the vortex is cleared 1 Step S12, backward turning; if the vortex is not detected, the vortex is recorded as missing once, and the number T of times of continuously detecting the vortex is not detected 1 1 is added in the process, so that the temperature of the product is increased,
s14: number T of consecutive undetected vortex 1 If the life cycle of the vortex is greater than the vortex disappearance confirmation set value, the step S12 is not performed, and if the life cycle of the vortex is terminated at the T-vortex disappearance confirmation set value time (i.e., the vortex disappearance confirmation set value time is reversed from the current time).
The radius of chlorophyll extracted in the step S2 is equal to the radius of vortex.
The chlorophyll concentration and the time resolution of the surface vortex vector in the time data set obtained in the step S5 are hours, and the coverage range is Beijing time 8:30-15:30; the longitude and latitude coordinates, the vortex radius, the vortex life cycle and the time resolution of the vortex surface temperature of the vortex center point are days.
The chlorophyll coverage area in the vortex is more than 30% of the total area of the vortex.
The beneficial effects of the application are as follows:
(1) The application merges active and passive remote sensing data, and establishes a characteristic time data set in the vortex full life cycle in the GOCI coverage range of 10 years through a series of post-processing operations such as identification, matching and the like.
(2) The method overcomes the defect that the passive water color remote sensing cannot mark the same vortex and the microwave remote sensing cannot acquire the characteristic parameters of the vortex due to the influence of cloud layer shielding.
(3) The method can accurately capture the changes of ocean biochemistry and physical parameters in the full life cycle of the medium-small scale vortex, and provides a more reliable basis for the subsequent research on ocean engineering, ocean fishery, ocean ecological environment and the like.
Drawings
FIG. 1 is a flow chart of an embodiment of the present application;
FIG. 2 is a flow chart of identifying vortices using SLA data according to an embodiment of the present application;
FIG. 3 is a feature visualization of various moments in the life cycle of a scroll in accordance with an embodiment of the present application; the life cycle of the vortex is normalized, and vortex characteristic distribution at the moments of 0, 0.25, 0.5, 0.75 and 1 is selected; time=0 represents the time of vortex generation, and similarly time=1 represents the time of vortex extinction. Figures (a) - (e) show chlorophyll and surface vortex distribution at various moments of vortex, the background field is chlorophyll, and the black vector is surface vortex; figures (f) - (j) are SLA and morphological size distribution at each moment of vortex, background field is SLA, and black dotted line is fitting circle of vortex radius size; graphs (k) - (o) are surface temperature distributions at various moments of the vortex.
FIG. 4 is a graph showing the change in concentration of vortex chlorophyll and surface vortex over time in an embodiment of the present application; wherein the daily variation time range is 8:30-15:30 (Beijing time), the background field is chlorophyll, and the black vector is surface vortex.
Detailed Description
The northeast asia sea area of the GOCI coverage area is one of the sea areas where medium-scale vortices are more active and is also a sensitive area for global atmospheric and ocean changes. The China is located on the North Pacific ocean west, the climate and offshore ocean environment are directly influenced by the variation of tropical cyclone and ocean current of the North Pacific ocean, and the exploration of the vortex characteristics of the region has important scientific significance and application value on the influence on the ecological environment. Therefore, feature recognition in the middle-small scale vortex full life cycle combined with multi-source remote sensing data is developed in the northeast Asia sea area, and mutual fusion, verification and correction of various technologies are facilitated.
The application discloses a medium-small scale vortex characteristic identification method for fusing active and passive remote sensing data, which comprises the following steps of:
step S1: acquiring a tracking data set of the medium-small scale vortex by utilizing sea surface height abnormal data, and calculating to obtain the life cycle (duration time), longitude and latitude coordinates of a central point and the radius of the vortex;
sea surface height anomaly data (SLA) is TOPEX/Poseidon (T/P) provided by AVISO in 2011-2021 for 10 years, and fusion data (http:// www.aviso.oceanobs.com/en/data.html) of altimeters such as Jason and ERS1/2, and the fused data can better reflect energy and structural characteristics of ocean mesoscale vortex. The identification method of vortex refers to the criterion of Chelton et al (2011) (Chelton DB, schlax M G and SamelsonR M.2011.Global observationsof nonlinear mesoscale eddies. Progress in Oceanography,91 (2): 167-216), and the specific criteria are as follows:
(1) Smoothing the data by adopting high-pass filtering to remove Rossby wave coherent signals;
(2) For vortex (with both anti-gas vortex and gas vortex, the same applies below), the Sea Surface Height (SSH) values of all pels are greater than a given threshold, where the threshold is the minimum of all pels; for gas vorticity, the Sea Surface Height (SSH) values of all pels are less than a given threshold, where the threshold is the maximum of all pels;
(3) The number of pixels in the communication area is more than 8 and less than 1000;
(4) For vortex, there is at least one local SLA extremum (maximum or minimum);
(5) The amplitude of the vortex is more than or equal to 7.5cm;
(6) The distance between any two points in the communication area is smaller than a given maximum value. It is specified that 1200km at the equator and 400km at 25 ° N north, this value being linear with latitude between the equator and 25 ° N.
The tracking algorithm combining the nearest distance method and the similarity method is adopted to track all the mesoscale vortexes at each moment (Qin Lijuan, dong Qing, fan Xing, xue Cunjin, hou Xueyan, suburb in Song, north Pacific mesoscale vortex space-time analysis of satellite altimeter [ J ]. Remote sensing school report, 2015,19 (05): 806-817.) specifically comprises the following steps:
s11: will T 0 The centroid of the geometric figure (the outermost edge of the detected vortex range) within the vortex boundary range at the moment (the initial detection moment, the initial value of the detection moment T) is defined as the center point of the vortex; number T of consecutive undetected vortex 1 Set to 0;
s12: taking the central point as the origin, taking the length (150 km in the case of less than 150 km) which is 1.75 times of the ocean propagation distance in 7 days (d) of the oblique pressure long Rossby wave phase speed to the west, taking 150km in all the other 3 directions, and selecting the vortex with the smallest similarity value and meeting the next moment (T=T+1) with the similarity value less than 0.5 in the range. Similarity calculation formula:
wherein DeltaS, deltaL s ，Δξ，ΔE k Respectively representing the difference between the vortex space distance and the radius, the difference between the relative vorticity and the EKE of two adjacent moments; s is S 0 ，L s0 ，ξ 0 ，E k0 Respectively, 100km,500km,10 -6 s -1 And 100cm 2 s -2 ；
S13: if homologous vortex can be detected at the moment T, the vortex is regarded as existence continuously and the number T of times of continuously not detecting the vortex is cleared 1 Step S12, backward turning; if the vortex is not detected, the vortex is recorded as missing once, and the number T of times of continuously detecting the vortex is not detected 1 1 is added in the process, so that the temperature of the product is increased,
s14: number T of consecutive undetected vortex 1 If the life cycle of the vortex is greater than the vortex disappearance confirmation set value, the step S12 is started, if the life cycle of the vortex is not greater than the vortex disappearance confirmation set value, the vortex is stopped at the moment of the T-vortex disappearance confirmation set value, and the current vortex tracking is completed.
The moment of the application is a set detection period, the detection period is usually 1 day or more, in particular 1 day, and the data can be comprehensively determined according to requirements, possibility and data size. The vortex disappearance confirmation setting value is usually set to 4, that is, the vortex is continuously lost for more than 5 times, and then the disappearance is calculated.
Step S2: chlorophyll concentration data in the vortex range is obtained by utilizing a static water color remote sensing satellite image through a matching algorithm, wherein the chlorophyll coverage ratio is up to 30% or more of the vortex area.
And (3) acquiring chlorophyll concentration data in a vortex range by utilizing the static water color remote sensing satellite image through a matching algorithm for the vortex tracking data set acquired in the step (S1). Wherein, for the vortex tracking data set, classifying according to vortex type and year; for GOCI chlorophyll data, the data with missing part of days and time periods are filled with 0-value data, and the data are classified according to years, so that the marine flow field operation of a matching program is facilitated. The matched information mainly comprises vortex number and vortex chlorophyll concentration mean value which meet the matching conditions.
Step S3: acquiring a surface vortex vector by using chlorophyll concentration as a tracer by using a maximum correlation coefficient flow field inversion algorithm;
the maximum correlation coefficient method is based on a template matching technology, and the sea surface flow field vector of the period can be obtained from two continuous GOCI images on the same day. The former image used to estimate the current position is referred to as a "template window", and the latter image is referred to as a "search window". The MCC algorithm uses correlations to track changes in tracer structure based on template matching techniques. And determining a proper matching window by calculating the maximum correlation coefficient between the template window and the search window, and if the correlation coefficient of the template window and the search window is larger than the similarity threshold value of the template window and the search window, considering the matching window as the correct position reached after the template window moves for 1 hour. The above steps are then repeated to obtain a relatively complete sea surface flow field.
Step S4: acquiring the surface temperature of the medium-small scale vortex by utilizing sea surface temperature product data;
the Sea level temperature data were from the OSTIA (Operational SST & Sea Ice Analysis) product (http:// ghrstppmetofface. Com/pages/last_analysis/OSTIA. Html) day data with a resolution of 0.05 DEG x 0.05 deg. The product uses data of a sensor such as AVHRR, AMSR, AATSR and measured data. In order to eliminate errors caused by daytime sea surface temperature rise, the base temperature of the OSTIA product filters out observations of daytime wind speeds less than 6m/s, and the errors are adjusted by referring to AATSR data and satellite tracking buoy data. The sea surface temperature product data can be used for acquiring the surface temperature of the medium-small scale vortex.
Step S5: combining the data obtained in the steps S1 to S4, and establishing a time data set of longitude and latitude coordinates of a vortex center point, a vortex radius, the vortex duration, chlorophyll concentration, a surface vortex vector and vortex surface temperature in the vortex duration;
the established time data set comprises basic parameters for identifying vortex, such as longitude and latitude coordinates of a central point, the date of the central point, a track identification number and the like; specific biochemical and physical parameters such as chlorophyll concentration, radius, etc. are also included. Chlorophyll concentration, time resolution of surface vortex vector is hours, coverage is 8:30-15:30 (Beijing time), time resolution of the remaining parameters is days.
Step S6: according to step S5, key characteristic parameters such as surface vortex change, morphological structure change, chlorophyll concentration change, surface temperature change and the like of the medium-small scale vortex in the whole life cycle are obtained, and part of representative time point data in the step S5, such as vortex data of five key time points of the vortex life cycle, namely 0, 0.25, 0.5, 0.75 and 1, or vortex data of a plurality of time points (more than three and at least including the three key time points of the vortex life cycle) of the uniform vortex life cycle are selected, so that people can intuitively feel the vortex change process and the vortex illustration.
According to the established vortex time data set, the vortex can be selected according to the vortex identification number and the observation sequence number, so that key characteristic parameters such as surface vortex change, morphological structure change, chlorophyll concentration change, surface temperature change and the like of the medium-small scale vortex in the whole life cycle are obtained.
The following describes the steps of the present application and the recognition results of specific examples with reference to the drawings.
FIG. 1 is a flow chart of a method for identifying characteristics of medium-and-small-scale vortices, the flow chart comprises 3 most of processing, and the first part is a tracking data set for acquiring the medium-and-small-scale vortices by utilizing sea surface height abnormal data; the second part is to obtain chlorophyll concentration data in a vortex range by utilizing a static water color remote sensing satellite image through a matching algorithm, and further obtain a surface vortex vector by utilizing a maximum correlation coefficient algorithm; and the third part is to combine the results of the first two parts and sea surface temperature data to establish a characteristic parameter data set in the duration of vortex, so as to obtain the characteristic parameter change of the medium-small scale vortex in the whole life cycle.
Fig. 2 is a flow chart for identifying vortices by using sea level height anomaly data, which can be identified by removing Rossby wave coherent signals by high pass filtering preprocessing of the sea level height anomaly data, and then according to the vortex discrimination criteria of Chelton et al (2011).
FIG. 3 is a feature visualization of various moments in the life cycle of a scroll in accordance with an embodiment of the present application; the life cycle of the vortex is normalized, and vortex characteristic distribution at the moments of 0, 0.25, 0.5, 0.75 and 1 is selected; time=0 represents the time of vortex generation, and similarly time=1 represents the time of vortex extinction. The distribution of chlorophyll and surface vortex at each moment of vortex is shown in the figures (a) - (e), the background field is chlorophyll, the black vector is surface vortex, the trend of increasing the concentration of chlorophyll in the vortex from generation to extinction of the vortex can be seen, and the flow velocity of the surface vortex is a trend of decreasing; figures (f) - (j) are SLA and morphological size distribution at each moment of vortex, a background field is SLA, a black dotted line is a fitting circle of the radius of the vortex, the vortex can be seen to be changed from generation to extinction, the position is changed, the whole body moves like a Western style, and the morphological size is not changed obviously; graphs (k) - (o) show the surface temperature distribution at various moments of the vortex, and it can be seen that the vortex tends to increase in surface temperature from generation to extinction.
FIG. 4 is a graph showing the change in concentration of vortex chlorophyll and surface vortex over time in an embodiment of the present application; the daily change time range is 8:30-15:30 (Beijing time), the background field is chlorophyll, the black vector is surface vortex, the daily change trend of the concentration of chlorophyll in the vortex is shown, the concentration of chlorophyll in the vortex is increased firstly, then the concentration of chlorophyll in the vortex is reduced, and the number of vortex vectors in the vortex center is also shown to be increased firstly and then reduced. As can be seen by combining fig. 3 and fig. 4, the application can accurately capture the changes of ocean biochemistry and physical parameters in the full life cycle of the medium-small scale vortex, and can reflect the intra-day change characteristics of the medium-small scale vortex.
Claims (5)
1. The medium-small scale vortex characteristic identification method for fusing active and passive remote sensing data is characterized by comprising the following steps of:
step S1: acquiring a tracking data set of the medium-small scale vortex by utilizing sea surface height abnormal data, and calculating to obtain the life cycle, longitude and latitude coordinates of a central point and the radius of the vortex;
s11: will T 0 The moment vortex centroid is defined as a vortex center point; number of times of no vortex being detected T 1 Set to 0; t=t 0 ；
S12: taking a vortex center point as an origin, taking larger values of a length which is 1.75 times of the ocean propagation distance and 150km from 7 days at a diagonal pressure long Rossby wave phase speed to the west, taking 150km from 3 directions of east, south and north, selecting a vortex with the minimum similarity value and meeting the T=T+1 moment with the similarity value less than 0.5 in the range, wherein a similarity calculation formula is as follows:
wherein DeltaS, deltaL s ，Δξ，ΔE k Respectively representing the difference between the vortex space distance and the radius, the difference between the relative vorticity and the EKE of two adjacent moments; s is S 0 ，L s0 ，ξ 0 ，E k0 Respectively, 100km,500km,10 -6 s -1 And 100cm 2 s -2 ；
S13: if homologous vortex can be detected at the moment T, the vortex is regarded as existence continuously and the number T of times of continuously not detecting the vortex is cleared 1 Step S12, backward turning; if it is not detected, the vortex is recorded as missing once and is continuedNumber of times of no vortex being detected T 1 1 is added in the process, so that the temperature of the product is increased,
s14: number T of consecutive undetected vortex 1 If the life cycle of the vortex is larger than the vortex disappearance confirmation set value, if the life cycle of the vortex is not larger than the vortex disappearance confirmation set value, turning to the step S12, and if the life cycle of the vortex is ended at the moment of the T-vortex disappearance confirmation set value;
step S2: acquiring chlorophyll concentration data in a vortex range by using a static water color remote sensing satellite image through a matching algorithm;
step S3: acquiring a surface vortex vector by using chlorophyll concentration as a tracer by using a maximum correlation coefficient flow field inversion algorithm;
step S4: acquiring the surface temperature of the medium-small scale vortex by utilizing sea surface temperature product data;
step S5: combining the data obtained in the steps S1 to S4 to establish a time data set of longitude and latitude coordinates, vortex radius, vortex duration, chlorophyll concentration, surface vortex vector and vortex surface temperature of a vortex center point in a vortex life cycle;
step S6: and (5) selecting key time point data of part of time intervals in the step (S5), and constructing key characteristic parameters of surface vortex change, morphological structure change, chlorophyll concentration change and surface temperature change of the medium-small scale vortex in the whole life cycle.
2. The method for identifying the medium-small scale vortex characteristics by fusing active and passive remote sensing data according to claim 1, wherein the vortex identification meets the following requirements:
(1) Smoothing the data by adopting high-pass filtering to remove Rossby wave coherent signals;
(2) The SSH values of all pixels are within the SSH threshold range;
(3) The number of pixels in the communication area is more than 8 and less than 1000;
(4) At least one local SLA extremum;
(5) The vortex amplitude is more than or equal to 7.5cm;
(6) The distance between any two points in the communication area is smaller than a given maximum value.
3. The method for identifying the medium-small scale vortex characteristics by fusing active and passive remote sensing data according to claim 1, wherein the radius of chlorophyll extracted in the step S2 is equal to the radius of vortex.
4. The method for identifying the medium-small scale vortex characteristics by fusing active and passive remote sensing data according to claim 1, wherein the chlorophyll concentration and the time resolution of the surface vortex vector in the time data set obtained in the step S5 are hours, and the coverage range is Beijing time 8:30-15:30; the longitude and latitude coordinates, the vortex radius, the vortex life cycle and the time resolution of the vortex surface temperature of the vortex center point are days.
5. The method for identifying the medium-small scale vortex characteristics by fusing active and passive remote sensing data according to claim 1, wherein the chlorophyll coverage area in the vortex accounts for more than 30% of the total area of the vortex.
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| CN117932362A (en) * | 2024-03-25 | 2024-04-26 | 自然资源部第一海洋研究所 | Mesoscale vortex recognition and track tracking method and device |
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| CN117213448A (en) * | 2023-11-07 | 2023-12-12 | 中国人民解放军国防科技大学 | Ocean secondary mesoscale frontal surface investigation method |
| CN117213448B (en) * | 2023-11-07 | 2024-01-30 | 中国人民解放军国防科技大学 | Ocean secondary mesoscale frontal surface investigation method |
| CN117932362A (en) * | 2024-03-25 | 2024-04-26 | 自然资源部第一海洋研究所 | Mesoscale vortex recognition and track tracking method and device |
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