A fishery sea area monitoring system based on satellite remote sensing

Abstract

The present invention discloses a fishery sea area monitoring system based on satellite remote sensing, which belongs to the field of fishery monitoring technology and specifically comprises: a remote sensing data acquisition module that acquires and pre-processes abnormal data on sea surface temperature, chlorophyll concentration and sea level height; a buoy data acquisition module that synchronously acquires temperature and salinity profile data of an Argo buoy array, and whose deployment density is dynamically adjusted according to historical fish catch and ocean front strength; a spatiotemporal alignment module that divides a time window by orbital period, corrects spatial offset in combination with buoy drift trajectory, sets a time buffer interval based on geostrophic velocity and satellite image resolution, and generates a spatiotemporally unified three-dimensional ocean environment data set; a fishery suitability calculation module that constructs a biophysical coupling model, associates surface chlorophyll with subsurface temperature and salinity structure, and calculates the suitability index of each water layer; a fishing ground division module that intelligently demarcates core areas and edge areas based on the three-dimensional distribution characteristics of the index; the present invention realizes accurate identification of fishing grounds based on multi-source data fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery monitoring, and in particular to a fishery sea area monitoring system based on satellite remote sensing. Background Art

[0002] With the continued decline of global fishery resources, efficient and accurate fishery waters monitoring technology has become a key tool for marine resource management and sustainable development. Traditional fishery monitoring relies primarily on fishing vessel operation reports and local sea area surveys, which have problems such as limited coverage, poor timeliness, and high costs. Satellite remote sensing technology, due to its advantages of large-scale and periodic observations, has gradually been applied to marine environmental monitoring. It can provide key parameters such as sea surface temperature and chlorophyll concentration, providing data support for fishery forecasts. However, satellite remote sensing can only obtain surface information and cannot reflect changes in the subsurface marine environment. The distribution of fishery resources is often closely related to the vertical structure of the water body.

[0003] Currently, collaborative observations using satellite remote sensing and Argo floats have become an important development direction for marine environmental monitoring. Existing technologies primarily employ two approaches: one is to construct a two-dimensional marine environmental field by overlaying surface data inverted by satellites with temperature and salinity profiles measured by Argo floats through a spatiotemporal matching algorithm; the other is to use statistical regression methods to establish an empirical relationship model between surface chlorophyll concentration and subsurface temperature and salinity. Some advanced solutions also incorporate data assimilation technology, combining multi-source observation data with ocean numerical models to improve the spatiotemporal resolution of environmental parameters. The international Argo program has achieved operational observations from approximately 4,000 floats worldwide, providing important vertical profile verification and supplementation for satellite data.

[0004] However, the fusion of satellite and Argo data mostly uses static spatiotemporal matching, which does not fully consider the impact of ocean dynamic processes on data alignment. In addition, environment-fishery correlation models are mostly based on historical statistical relationships and are difficult to adapt to the rapid changes in the marine environment, which restricts the accuracy and timeliness of fishery monitoring. Summary of the Invention

[0005] The purpose of the present invention is to provide a fishery waters monitoring system based on satellite remote sensing to solve the following technical problems:

[0006] Existing fusions of satellite and Argo data mostly use static spatiotemporal matching, which does not fully consider the impact of ocean dynamic processes on data alignment. They are mostly based on historical statistical relationships and are difficult to adapt to the rapid changes in the marine environment, thus restricting the accuracy and timeliness of fishery monitoring.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A fishery sea area monitoring system based on satellite remote sensing, comprising:

[0009] A remote sensing data acquisition module is used to obtain and pre-process satellite remote sensing data of the target sea area, wherein the satellite remote sensing data includes sea surface temperature distribution map, chlorophyll concentration distribution map and sea surface height anomaly data;

[0010] A buoy data acquisition module is used to synchronously acquire temperature and salinity profile data uploaded by an Argo buoy array in the target sea area. The deployment density of the Argo buoy array is positively correlated with the historical distribution of fish catches. When the intensity of the ocean front exceeds a set threshold, the deployment density is increased to a specific multiple of the normal density, which is 2-4 times;

[0011] The spatiotemporal alignment module is used to fuse satellite remote sensing data with Argo float data, divide the time window according to the satellite orbit period, correct the spatial coordinate offset based on the drift trajectory of the Argo float, calculate the geostrophic velocity based on satellite remote sensing data and temperature and salinity profile data, set the time buffer interval based on the geostrophic velocity and satellite image resolution, and generate a spatiotemporally unified 3D ocean environment dataset;

[0012] The fishery suitability calculation module is used to build a biophysical coupling model to calculate the fishery suitability index at different water depths by correlating the vertical changes in surface chlorophyll concentration and subsurface temperature and salinity structure;

[0013] The fishing ground division module is used to delineate the core area and the edge area of the fishing ground according to the distribution characteristics of the fishery suitability index in three-dimensional space and the direction of geostrophic flow.

[0014] As a further solution of the present invention: the preprocessing of the satellite remote sensing data specifically includes:

[0015] Select polar-orbiting satellites that cover the target sea area more than the threshold number of times per day, and adjust orbital parameters during the fishing season to collect multispectral data at different times.

[0016] To fill the cloud-covered areas in the sea surface temperature data, spatial interpolation of cloud-free data within a moving time window is used, and the interpolation weight is determined by the inverse product of the data time difference and the ocean current speed;

[0017] The morphological features of the patch edges in the chlorophyll concentration map are extracted. When the curvature of the patch edge exceeds the preset morphological curvature threshold, the area overlapping with the temperature isotherm is marked as a candidate fishing ground indicator area.

[0018] The flow velocity and direction characteristics are screened according to the convergence intensity threshold of the geostrophic flow field, and the areas that meet the convergence conditions are spatially intersected with the candidate fishing ground indication areas to generate primary fishing ground identification areas.

[0019] As a further solution of the present invention: the deployment of the Argo float array specifically includes:

[0020] In waters where historical catches exceed the catch threshold, a triangular array of buoys is deployed. The length of the triangle side is dynamically adjusted based on the real-time inversion of the frontal intensity. For every unit increase in the frontal intensity change threshold, the side length is shortened by a fixed ratio.

[0021] A chlorophyll concentration trigger threshold is set for each Argo float. When the continuous measurement value exceeds the trigger threshold, the shallow sampling interval is shortened to a fixed fraction of the regular interval until the concentration value falls below the threshold.

[0022] Establish a satellite transit time prediction mechanism. Argo floats complete measurements and upload data before the transit time based on received satellite orbit parameters, with the time synchronization error less than the communication delay threshold.

[0023] The drift compensation amount is calculated by the relative position change of adjacent buoys. When the distance change between buoys exceeds the position tolerance threshold, the least squares method is used to fit the drift trajectory and correct the coordinate data.

[0024] As a further solution of the present invention: the spatiotemporal alignment module specifically includes:

[0025] The data synchronization window is set based on the satellite transit time. When the Argo float detects that the difference between the current time and the satellite transit time is less than the time synchronization threshold, the data upload frequency is increased to a fixed multiple of the normal frequency.

[0026] A dynamically adjusted grid cell is constructed, with the initial grid size matching the satellite image resolution. When the cloud cover in the satellite image exceeds the cloud cover threshold, the grid range is expanded to include the measurement points of at least three adjacent Argo floats.

[0027] The Argo float data within each grid cell are reconstructed according to the standard depth layer, and the discrete depth data are converted into continuous profile data with equal depth intervals. The missing depth data are supplemented by vertical gradient interpolation of adjacent floats.

[0028] In the temporal dimension, a time buffer is set for the instantaneous satellite observation data. The length of the interval is determined by the ratio of the geostrophic velocity to the spatial grid size. The Argo data within the buffer are weighted and fused according to their temporal proximity.

[0029] As a further solution of the present invention: the construction of the biophysical coupling model specifically includes:

[0030] Identify the thermocline interface in the subsurface temperature profile. When the temperature gradient value of a certain depth layer exceeds the gradient threshold, the depth of the depth layer is defined as the thermocline interface.

[0031] The area above the concentration threshold in the surface chlorophyll concentration map is projected vertically to the thermocline interface to generate a three-dimensional projection overlap area;

[0032] For each vertical water column within the overlapping area, divide it into standard depth layers and calculate the temperature suitability coefficient for each layer. The coefficient is determined by the degree of match between the optimal temperature range of the target fish species and the measured temperature.

[0033] The chlorophyll concentration of each layer of each vertical water column is multiplied by the temperature suitability coefficient and then vertically accumulated. When the accumulated value exceeds the comprehensive index threshold, the projection position of the corresponding vertical water column on the horizontal plane is marked as the core area of the fishing ground.

[0034] As a further solution of the present invention: the calculation of the temperature suitability coefficient specifically includes:

[0035] Set the temperature adaptation interval according to the optimal temperature range of the target fish species. When the temperature at a certain depth layer is within this interval, the coefficient is set to the maximum value.

[0036] For temperature data that exceeds the adaptation range, the coefficient attenuation is calculated according to the temperature difference with the interval boundary. For each unit increase in the temperature difference threshold, the coefficient decreases linearly at a fixed ratio.

[0037] In the vertical water column, the coefficient weight of each layer is determined by the light attenuation rate of the layer, which is calculated jointly based on the transparency data retrieved from satellites and the extinction coefficient measured by Argo;

[0038] The final fishing suitability coefficient is the product of temperature matching and light weight, and the product value is normalized to a value between 0 and 1.

[0039] As a further solution of the present invention: the demarcation of the core area and the marginal area of the fishing ground specifically includes:

[0040] In the surface chlorophyll concentration map, closed areas with an area exceeding the patch area threshold are extracted as candidate core areas;

[0041] A downstream extension search is performed for each candidate core area along the direction of the geostrophic flow. When the depth of the thermocline interface is detected to be higher than the upstream depth threshold and the temperature gradient increase exceeds the gradient increase threshold, it is marked as a valid candidate area.

[0042] The areas where the overlap between the candidate core area and the valid candidate area exceeds the overlap ratio threshold are retained; the spatial variation coefficient of the vertical integrated suitability index is calculated for the retained areas. When the variation coefficient is lower than the homogeneity threshold, it is determined to be a single core area; otherwise, it is split into sub-core areas according to the index peak value;

[0043] The final core region boundary is determined by the outer envelope of the homogeneous region, and the edge region extends to the region where the index value drops to the edge threshold.

[0044] As a further solution of the present invention: the extended search specifically includes:

[0045] The initial search step size is set according to the geostrophic flow velocity, and the product of the step size and the flow velocity is equal to the maximum migration distance threshold per day;

[0046] Collect thermocline interface data in real time along the search path. When the buoy data density at a certain location is lower than the data density threshold, call for historical environmental data for the same period at that location to complete the search.

[0047] When the interface depth change rate exceeds the depth change rate threshold, the search is expanded in the direction of interface uplift with the position as the center, and the search density is increased to a fixed multiple of the normal density;

[0048] If the distance between the search path and the boundary of the delineated core area is less than the boundary tolerance threshold, the index difference between the two areas is compared, and boundary overlap is allowed only when the difference exceeds the competition threshold; the search is terminated when the number of consecutive searches reaches the maximum search number threshold or the cumulative search distance exceeds the maximum extension distance threshold.

[0049] As a further solution of the present invention: it also includes a fishery dynamic update module, specifically including:

[0050] When the difference in sea surface temperature between the latest satellite data and the historical data set exceeds the temperature difference threshold, or the difference in chlorophyll concentration exceeds the concentration difference threshold, the fishery redrawing process is triggered;

[0051] The enhanced observation mode of the Argo float is activated in the changing area, and the sampling frequency of the float is increased to a fixed multiple of the normal frequency until the fluctuation amplitude of the environmental parameters falls below the stability threshold.

[0052] Re-perform spatiotemporal alignment and model calculations based on enhanced observational data to generate updated fishing ground boundaries;

[0053] The areas with different boundaries between the old and new ones are marked as fishery migration zones, and the migration direction is determined based on the composite vector of the geostrophic flow field and the gradient of the suitability index. The updated fishery boundaries are incorporated into the historical dataset as a comparison benchmark for the next dynamic update.

[0054] Beneficial effects of the present invention:

[0055] The present invention's method for monitoring fishery areas based on satellite remote sensing and Argo buoys innovatively establishes a dynamic spatiotemporal alignment mechanism and a biophysical coupling model, effectively solving key problems in existing technologies such as rough data fusion, inefficient buoy deployment, and insufficient model adaptability. In the spatiotemporal alignment link, the geostrophic velocity is calculated based on satellite remote sensing sea surface height anomaly data and Argo buoy temperature and salinity profile data. The length of the time buffer is dynamically determined according to the ratio of the geostrophic velocity to the spatial grid size, and the Argo data within the buffer are weighted and fused according to the temporal proximity. At the same time, the buoy is driven to perform high-frequency sampling through the satellite transit time, the grid unit is dynamically adjusted, and the coordinates are corrected in combination with the buoy drift trajectory. This solves the limitations of static matching, fully considers the impact of ocean dynamic processes such as geostrophic currents on data alignment, and improves the spatiotemporal fusion accuracy. In the calculation of fishery suitability, a biophysical coupling model is constructed to correlate the surface chlorophyll concentration with the vertical change of the subsurface temperature and salinity structure. By identifying the thermocline interface, calculating the temperature suitability coefficient for fishing, and combining it with the weighting of the light attenuation rate, the constraints of the traditional statistical regression model are broken through and achieved. Real-time dynamic assessment of fish habitats; during the fishing ground delineation stage, a downstream extension search is conducted along the direction of the geostrophic current, the initial step size is set according to the geostrophic current velocity, and effective candidate areas are screened in combination with the change in thermocline interface depth and the temperature gradient increase. At the same time, regional homogeneity is analyzed through the spatial variation coefficient, so that the division of the core and edge areas of the fishing ground is closely combined with the ocean dynamic characteristics such as the direction of the geostrophic current, effectively adapting to the rapid changes in the marine environment; in addition, through the dynamic correlation of the Argo buoy array deployment density with historical catches and frontal intensity, the establishment of a satellite transit time prediction mechanism, the setting of a dynamic update module for fishing grounds, etc., the whole process from data collection and fusion to fishing ground delineation is dynamic and precise, which significantly improves the accuracy of fishery monitoring and the timeliness of response to environmental changes, and provides scientific support for the efficient management and sustainable utilization of fishery resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The present invention will be further described below with reference to the accompanying drawings.

[0057] Figure 1 It is a schematic diagram of a module of the present invention;

[0058] Figure 2 It is a schematic diagram of the flow of the spatiotemporal alignment module of the present invention;

[0059] Figure 3 It is a flow chart of the fishing ground demarcation module of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] See also Figure 1-Figure 3 As shown, the present invention is a fishery sea area monitoring system based on satellite remote sensing, comprising:

[0062] Remote Sensing Data Acquisition Module: This module is responsible for acquiring satellite remote sensing data for the target sea area. This data includes sea surface temperature (SST) distribution, chlorophyll concentration (chlorophyll) concentration distribution, and sea surface height anomaly data. SST reflects ocean thermal conditions and influences fish survival, reproduction, and migration. For example, some tropical fish prefer to inhabit specific high-temperature waters. Chlorophyll concentration distribution indicates the abundance of phytoplankton. As the foundation of the marine food chain, phytoplankton's distribution is closely related to fish food sources, with areas of high concentration tending to attract more fish. Sea surface height anomaly data helps understand ocean circulation and water mass movement, providing a basis for assessing the stability of fish habitats. After acquiring the data, the module also pre-processes it. This includes selecting appropriate satellites, such as polar-orbiting satellites that cover the target sea area more than a threshold number of times daily. During the fishing season, orbital parameters are adjusted to enable multispectral data collection at different times to obtain more comprehensive information. Furthermore, cloud-covered areas in the SST data are filled using a specific interpolation method, and morphological features are extracted from chlorophyll concentration maps to facilitate subsequent analysis and identify potential fishing grounds.

[0063] The buoy data acquisition module primarily acquires thermohaline profile data uploaded by the Argo float array in the target sea area. The thermohaline structure influences seawater density stratification, which in turn influences the vertical distribution of fish. For example, near the thermocline, fish often gather in large numbers due to favorable temperatures and abundant nutrients. The Argo float array in this module is carefully placed, with its density positively correlated with historical catch distribution. Areas with high historical catches are rich in fish resources, and increasing the float density provides more accurate data on the marine environment in those areas. When the intensity of an ocean front exceeds a set threshold, the convergence of water at the front brings abundant nutrients, attracting large numbers of fish. At this time, the float density is increased to a specific multiple of the normal density to more accurately monitor this critical area. If the normal density is one float per 100 square kilometers, spaced approximately 10 kilometers apart, when the front's intensity triggers an increase in density, the multiple can be set to 2-4 times, meaning a spacing of 5-2.5 kilometers and a density of 2-4 floats per 100 square kilometers.

[0064] The spatiotemporal alignment module undertakes the important task of fusing satellite remote sensing data with Argo float data. First, the time window is divided according to the satellite orbit period to ensure that the data is processed on a unified time scale. The spatial coordinate offset is then corrected based on the drift trajectory of the Argo float. Because the float drifts in the ocean due to factors such as ocean currents, the float's drift trajectory is monitored and the deviation between its actual position and the initial set position is determined using methods such as least squares fitting. The coordinates are then corrected to ensure accurate spatial correspondence between the float data and the satellite remote sensing data. This ultimately generates a spatiotemporally unified three-dimensional marine environmental dataset, providing a comprehensive and spatiotemporally consistent data foundation for subsequent fishery suitability calculations, allowing users to clearly understand the various environmental parameters at different locations in the ocean at a given moment.

[0065] Fishery Suitability Calculation Module: This module constructs a biophysical coupling model to calculate the fishery suitability index for different water depths. By correlating the vertical changes in surface chlorophyll concentration with the subsurface temperature and salinity structure, it comprehensively considers various factors that influence fish survival and reproduction. For example, the thermocline interface is identified in the subsurface temperature profile. Areas where surface chlorophyll concentration exceeds a threshold are vertically projected onto the thermocline interface, generating a three-dimensional projection overlap. Each vertical water column within the overlap is divided into standard depth layers, and the temperature suitability coefficient for each layer is calculated. This coefficient is determined by the degree of match between the optimal temperature range of the target fish species and the measured temperature. The chlorophyll concentration of each layer is then multiplied by the temperature suitability coefficient and vertically accumulated. When the accumulated value exceeds the comprehensive index threshold, the horizontal location is marked as the core area of the fishing ground. In this way, the suitability of different water layers for fisheries is comprehensively and scientifically assessed.

[0066] Fishing ground division module: The core and edge areas of the fishing ground are delineated based on the distribution characteristics of the fishery suitability index in three-dimensional space. Closed areas with an area exceeding the patch area threshold are extracted from the surface chlorophyll concentration map as candidate core areas. The candidate core areas are searched downstream along the direction of geostrophic flow. When the depth of the thermocline interface is detected to be higher than the depth elevation threshold compared to the upstream and the temperature gradient increase exceeds the gradient increase threshold, it is marked as a valid candidate area. The area where the overlap ratio between the candidate core area and the valid candidate area exceeds the overlap ratio threshold is retained. The spatial coefficient of variation of the vertical integrated suitability index is calculated for the retained area. When the coefficient of variation is lower than the homogeneity threshold, it is determined to be a single core area. Otherwise, it is split into sub-core areas according to the index peak value. The final core area boundary is determined by the outer envelope of the homogeneous area, and the edge area extends to the area where the index value drops to the edge threshold. Through this division, the different areas of the fishing ground are clarified, providing a scientific basis for the rational development and utilization of fishery resources.

[0067] In a preferred embodiment of the present invention, the preprocessing of the satellite remote sensing data specifically includes:

[0068] The system first uses an orbit prediction algorithm to select the polar-orbiting satellite constellation with optimal coverage. Taking into account factors such as satellite orbit parameters, sensor characteristics, and weather conditions, it ensures at least three valid observations are obtained daily during the critical fishing season, with observations at different times covering the morning-to-evening dynamics of the target sea area. To address the common problem of cloud obscuration, the system innovatively developed a four-dimensional spatiotemporal filling algorithm based on ocean current dynamics. This algorithm not only considers cloud-free data from similar times but also constructs a three-dimensional spatial correlation model based on real-time ocean current information. By solving an optimal interpolation weight matrix, it ensures that the filled temperature field maintains both physical consistency and statistical compliance. For chlorophyll data processing, the system employs a multi-scale image morphological analysis method. By calculating the curvature spectrum and texture characteristics of patch edges, it automatically identifies ecologically significant chlorophyll accumulation areas. This method then performs multi-level spatial correlation analysis with high-precision temperature front locations, significantly improving the accuracy and reliability of primary fishing ground identification. Furthermore, the system integrates an intelligent quality control module to automatically detect and correct anomalous observations, ensuring data quality meets the requirements of subsequent analysis.

[0069] In another preferred embodiment of the present invention, the deployment of the Argo float array specifically includes:

[0070] Based on years of fishery statistics and marine environmental databases, the system employs machine learning algorithms to intelligently delineate key monitoring areas. Within these areas, a triangular topology array of buoys with optimal spatial coverage is deployed. The array's geometric parameters are designed to fully account for ocean dynamics. Its side lengths are dynamically correlated with real-time ocean front intensity, ensuring higher temporal and spatial resolution profile observations in areas with dense fisheries resources and active ocean dynamics. Each buoy is equipped with an advanced intelligent triggered sampling system, integrated with multi-parameter environmental sensors. Upon detecting an abnormally high chlorophyll concentration or a dramatic temperature change, it automatically enters intensive observation mode. This intelligent sampling strategy, driven by environmental events, significantly improves the ability to capture and respond to sudden changes in the fishery environment. Furthermore, through precise satellite-ground time synchronization and a Kalman filter-based drift trajectory correction algorithm, the system establishes a comprehensive spatiotemporal quality control system, ensuring high consistency between buoy data and satellite observations, laying a solid foundation for subsequent multi-source data fusion. The buoy array also features a self-organizing network that automatically optimizes its observation strategy based on environmental changes.

[0071] In another preferred embodiment of the present invention, the spatiotemporal alignment module specifically includes:

[0072] The spatiotemporal alignment module is a key component in achieving multi-source data fusion within the satellite remote sensing-based fishery waters monitoring system. Its core function is to eliminate the spatiotemporal differences between satellite remote sensing data and Argo float data through the coordinated processing of time and space, generating a unified three-dimensional ocean environment dataset. Regarding time synchronization, the system first uses satellite orbit forecasts to obtain precise transit times, using this as the center to set a time synchronization window. When the Argo float detects that its current time is close to the satellite transit time (e.g., the difference is less than a set threshold), it automatically increases the frequency of data uploads, ensuring a high density of float data before and after the satellite observation, thereby capturing the ocean environment closest to the instantaneous satellite observation. For example, if a satellite transit occurs at 10:00 AM, the float will intensify sampling between 9:30 AM and 10:30 AM, increasing the data upload frequency from the normal hourly rate to every 15 minutes to more accurately match the satellite observation time.

[0073] The initial grid size matches the satellite imagery resolution. Satellite imagery resolution determines the minimum resolvable ground detail. Matching the initial grid size to this resolution ensures that each grid cell corresponds to a relatively uniform area in the satellite imagery, thereby better utilizing the information provided by the satellite imagery during data processing and analysis. For example, if the satellite imagery resolution is 100 meters by 100 meters, the initial grid size can be set to 100 meters by 100 meters. This way, each grid cell corresponds to a clear area in the satellite imagery, facilitating subsequent data processing and analysis. When the cloud cover in the satellite imagery exceeds the cloud cover threshold, clouds obscure ocean surface information, resulting in inaccurate data acquisition in some areas. To obtain sufficient ocean environmental information, the grid range needs to be expanded to include at least three adjacent Argo float measurement points. This expansion allows more areas not obscured by clouds to be included within the grid cell. Data from Argo float measurement points in these areas can then be used to supplement and improve the information in the grid cell, thereby compensating for data lost due to cloud obscuration. For example, if the initial grid size is 100 m × 100 m, after the cloud cover exceeds a threshold, the grid range may be expanded to 300 m × 300 m to ensure that the measurement points of at least three adjacent Argo floats can be included, thereby obtaining more comprehensive ocean environment data.

[0074] At the same time, the system reconstructs the discrete depth data collected by Argo floats according to preset standard depth layers (such as 0 meters, 10 meters, and 20 meters). It then interpolates the vertical gradients of data from adjacent floats to fill in missing depth layer information, ensuring the vertical continuity and standardization of temperature and salinity data. For example, if a float does not measure the temperature at a depth of 10 meters, the system will use linear interpolation to calculate the temperature value at a depth of 10 meters based on the temperature data at depths of 5 meters and 20 meters, and verify whether it conforms to the oceanographic gradient law.

[0075] During the spatiotemporal fusion phase, the module determines the length of the temporal buffer based on the ratio of the geostrophic velocity to the spatial grid size. Satellite remote sensing data is acquired at a single instant, while Argo float data is collected continuously over time. Because the ocean environment is constantly changing, matching Argo float data solely based on the instant of satellite observation may not fully and accurately reflect the ocean conditions at that moment. Therefore, setting a temporal buffer allows the inclusion of Argo float data from a period before and after the satellite observation, thereby more comprehensively and accurately reflecting the ocean environment at that moment and improving the accuracy of data fusion.

[0076] The length of the buffer zone is determined by the ratio of the geostrophic velocity to the spatial grid size. The geostrophic velocity represents the speed at which seawater flows under the influence of the Coriolis force, while the spatial grid size refers to the size of each grid cell in the monitored sea area. When the geostrophic velocity is fast and the spatial grid size is large, seawater will flow over a larger area in a short period of time, and the marine environment will change more rapidly. In this case, to ensure the acquisition of representative marine environmental data, the calculated time buffer will be longer, for example, assuming 3 hours. Conversely, if the geostrophic velocity is slow and the spatial grid size is small, the marine environment is relatively stable, and the buffer zone length will be shorter.

[0077] Within a set three-hour time buffer, data collected by Argo floats are weighted and fused based on temporal proximity. Data closer to the satellite observation time is considered more similar to the ocean environment at the time of observation, and therefore more valuable in reflecting the actual conditions at the time, and thus assigned a higher weight. For example, if the satellite observation occurs at 10:00, the data collected by the Argo float at 9:50 might be weighted 0.8, data collected at 9:30 might be weighted 0.5, and data collected at 9:00 might be weighted 0.2. This weighting method allows Argo data collected at different times within the buffer to be more accurately aligned with the satellite remote sensing data, effectively improving the quality of data fusion.

[0078] In another preferred embodiment of the present invention, constructing the biophysical coupling model specifically includes:

[0079] The system first refines the subsurface temperature profile and calculates the temperature gradient change rate of each depth layer through a sliding window algorithm. When it detects that the temperature gradient value of a certain depth layer exceeds the preset gradient threshold, usually set to 0.05°C / m, the system automatically marks the depth of the depth layer as the thermocline interface position and records its upper and lower boundary characteristics. At the same time, the system performs intelligent analysis on the surface chlorophyll concentration distribution map and uses an adaptive threshold algorithm to identify high-value chlorophyll areas that are significantly higher than the background value. These areas usually indicate vigorous growth of phytoplankton. By establishing a three-dimensional spatial mapping relationship, the system projects these surface high-value areas in the vertical direction to the identified thermocline interface, forming a three-dimensional projection overlap area with ecological significance. This area reflects the complete bio-physical coupling characteristics from the surface to the bottom layer.

[0080] During the analysis of the overlapping areas, the system uses a standardized depth stratification scheme, which is usually divided into layers such as 0-50m, 50-100m, and 100-150m, and analyzes each vertical water column layer by layer. For each depth layer, the system first calculates the temperature suitability coefficient, which is determined by comparing the measured temperature with the optimal temperature range of the target fish species. Specifically, the system has a built-in temperature preference database for a variety of economic fish species, and sets differentiated optimal temperature ranges according to the life cycle stages of different fish species (such as spawning period, feeding period, etc.). When the measured temperature of a certain depth layer falls completely within the optimal range, the layer is assigned the highest fishing suitability coefficient (1.0); when the temperature deviates from the optimal range, the coefficient decreases linearly according to the degree of deviation, and the rate of decrease takes into account the tolerance of the fish species to temperature changes.

[0081] In a preferred embodiment of the present invention, the calculation of the temperature suitability coefficient specifically includes:

[0082] The system first establishes a temperature adaptability curve based on the physiological characteristics of the target fish species. This curve accounts for differences in temperature sensitivity across different life stages. For measured data outside the optimal temperature range, the system applies a piecewise function: within the tolerable range, the coefficient decreases linearly with increasing temperature difference; when the temperature exceeds the tolerance range, the coefficient rapidly decays to near zero. This processing method is consistent with fish physiology and accurately reflects the impact of environmental stress on fishery resources.

[0083] In the comprehensive assessment of the vertical water column, the system innovatively introduces a light attenuation factor as a weighting coefficient. By integrating transparency data retrieved from satellites and the extinction coefficient measured by Argo floats, the system can accurately calculate the photosynthetically active radiation (PAR) received by each depth layer. This calculation takes into account multiple factors such as the optical properties of the water body, the solar altitude angle, and cloud cover to ensure the accuracy of the light attenuation model. Finally, the system multiplies the temperature suitability coefficient of each layer by the light weight to obtain a comprehensive score reflecting the fishery potential of that layer, and then obtains the fishery suitability index of the entire water column through vertical integration. This index not only takes into account the traditional temperature factor, but also integrates the impact of light conditions on plankton distribution, which can more comprehensively evaluate the potential for fishery formation.

[0084] By setting a dynamic composite index threshold (adjustable based on target fish species and seasonal characteristics), the system automatically identifies the horizontal area of the sea surface corresponding to the vertical water column where the index exceeds the threshold as the core fishing ground. These core areas typically possess the following characteristics: significant surface chlorophyll concentration, a suitable thermocline depth, a stable water temperature and salinity structure, and good light conditions. The system also performs spatial clustering analysis on these core areas, eliminating isolated small patches and preserving high-quality fishing areas with ecological continuity. Furthermore, the system can establish a fishing ground evolution model based on historical data, predicting the possible movement of core areas and providing forward-looking guidance for fishery production.

[0085] In another preferred embodiment of the present invention, the demarcation of the core area and the marginal area of the fishing ground specifically includes:

[0086] The delineation of the core and edge areas of fishing grounds is a refined spatial division process based on the fishery suitability index generated by the fusion of satellite remote sensing data and Argo buoy data, combined with ocean dynamic characteristics. First, in the surface chlorophyll concentration map, the system automatically extracts closed areas with an area exceeding the preset patch area threshold (such as 10 square kilometers) as candidate core areas. This is because areas with high chlorophyll concentrations usually correspond to waters rich in plankton, which are the basis of the fish food chain and have the potential to form fishing grounds. However, the validity of the candidate core areas needs to be further verified in combination with the dynamic characteristics of the marine environment. Therefore, the system will conduct a downstream extension search for each candidate core area along the direction of the geostrophic current - the direction of the geostrophic current reflects the overall movement trend of seawater. Fish usually migrate with the current. The downstream area may be the main direction of fish spread in the candidate core area. During the extended search, the system monitors changes in the depth of the thermocline interface in real time. If the depth of the thermocline interface at a location exceeds a depth increase threshold (e.g., 5 meters) compared to upstream, and the temperature gradient increases by more than a gradient increase threshold (e.g., 0.5°C / meter), it indicates that the area may have formed a thermohaline structure more suitable for fish habitat due to water convergence, and the area is marked as a valid candidate. This is because the thermocline is a layer of water in the ocean with a significant vertical temperature gradient. Changes in its position and intensity directly affect the vertical distribution of fish. A rising thermocline interface is often associated with upwelling or frontal activity, which brings abundant nutrients and dissolved oxygen.

[0087] Next, the system spatially overlays the candidate core areas with the valid candidate areas, retaining areas where the overlap exceeds a threshold (e.g., 60%). This ensures that the designated core areas possess both high productivity (supported by chlorophyll concentration) and environmental conditions suitable for fish habitat (suitable thermocline structure). For these retained areas, the system further calculates the spatial coefficient of variation of the vertically integrated suitability index, which reflects the environmental homogeneity within the area. When the coefficient of variation is below the homogeneity threshold, the area's suitability index is uniformly distributed vertically and horizontally, indicating a single core area. A higher coefficient of variation indicates multiple suitability peaks within the area, necessitating segmentation into sub-core areas based on index peaks. Ultimately, the core area boundary is defined by the outer envelope of the homogeneous area, while the marginal area extends outward from the core area boundary until the fishery suitability index drops to a marginal threshold (e.g., 50% of the core area index). This area serves as a transition zone to the core area, where fish density decreases as the index decreases.

[0088] In a preferred embodiment of the present invention, the extended search specifically includes:

[0089] As a key step in delineating the core area, the specific implementation logic of the extended search is closely dependent on the geostrophic velocity and marine environmental data. First, the system sets the initial search step size based on the geostrophic velocity. The product of the step size and the velocity is equal to the maximum daily migration distance threshold (for example, if the maximum daily migration distance is set to 20 kilometers and the geostrophic velocity is 1 kilometer per hour, the step size is 20 kilometers). This design ensures that the search range covers the maximum distance that fish may migrate with the water flow in one day, avoiding missing potential fishing grounds. On the search path, if the buoy data density at a certain location is lower than the data density threshold (such as less than one buoy observation point per 50 square kilometers), the system will call the historical environmental data of the location during the same period (such as the average temperature and salinity of the same month in the past three years) to complete the search, in order to deal with the problem of missing data caused by remote sea areas or buoy drift. When the rate of change in the depth of the thermocline interface exceeds a threshold (e.g., 10 meters / 10 kilometers), indicating the presence of a strong ocean front or upwelling, the system expands the search area from that location in the direction of interface uplift (typically the direction of current convergence) and increases the search density to twice the normal density to capture the impact of subtle environmental changes on fish distribution. Furthermore, if the search path approaches the boundary of a designated core area and the distance is less than a boundary tolerance threshold (e.g., 5 kilometers), the system compares the difference in suitability index between the two areas. Boundary overlap is permitted only if the difference exceeds a competition threshold (e.g., 20% of the core area index), avoiding duplicate delineation due to weak environmental gradients. The search process terminates when the maximum number of consecutive searches reaches a threshold (e.g., 5) or the cumulative search distance exceeds a maximum extension distance threshold (e.g., 100 kilometers), ensuring efficiency and avoiding unnecessary range expansion.

[0090] In another preferred embodiment of the present invention, a fishing ground dynamic update module is further included, specifically including:

[0091] The system continuously monitors changes in environmental parameters. When changes in sea surface temperature or chlorophyll concentration exceed two standard deviations of their historical fluctuation range, a fishery reassessment process is automatically triggered. Within the affected area, the system remotely controls the sampling frequency of Argo floats from the standard once every 10 days to once a day. This enhanced observation mode continues until the environmental parameters return to a stable state, for example, fluctuations of less than 10% for three consecutive days. During this update process, the system prioritizes the latest high-resolution spatial and temporal data, rerunning the spatial and temporal alignment and biophysical coupling models to generate updated fishery distribution maps.

[0092] The system features a specialized fishing ground migration analysis function. By comparing the changing characteristics of old and new boundaries, it automatically identifies migration areas and directions. Migration directions are determined by comprehensively considering the dynamic transport effects of geostrophic currents and the spatial gradient characteristics of the suitability index, using vector synthesis to calculate the most likely migration path. All updated results are entered into the system's knowledge base for optimization of subsequent forecasting models. Furthermore, the system incorporates a version control mechanism to retain historical fishing ground distribution data, supporting analysis of spatiotemporal trends and fishery management decisions. This dynamic update system ensures the timeliness and accuracy of fishing ground information, providing reliable navigation services for fishery production.

[0093] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A fishery sea area monitoring system based on satellite remote sensing, characterized in that: include: A remote sensing data acquisition module is used to obtain and pre-process satellite remote sensing data of the target sea area, wherein the satellite remote sensing data includes sea surface temperature distribution map, chlorophyll concentration distribution map and sea surface height anomaly data; A buoy data acquisition module is used to synchronously acquire temperature and salinity profile data uploaded by an Argo buoy array in the target sea area. The deployment density of the Argo buoy array is positively correlated with the historical distribution of fish catches. When the intensity of the ocean front exceeds a set threshold, the deployment density is increased to a specific multiple of the normal density, which is 2-4 times; The spatiotemporal alignment module is used to fuse satellite remote sensing data with Argo float data, divide the time window according to the satellite orbit period, correct the spatial coordinate offset based on the drift trajectory of the Argo float, calculate the geostrophic velocity based on satellite remote sensing data and temperature and salinity profile data, set the time buffer interval based on the geostrophic velocity and satellite image resolution, and generate a spatiotemporally unified 3D ocean environment dataset; The fishery suitability calculation module is used to build a biophysical coupling model to calculate the fishery suitability index at different water depths by correlating the vertical changes in surface chlorophyll concentration and subsurface temperature and salinity structure; A fishing ground division module is used to delineate the core area and the edge area of the fishing ground according to the distribution characteristics of the fishery suitability index in three-dimensional space and the direction of geostrophic flow; The construction of the biophysical coupling model specifically includes: Identify the thermocline interface in the subsurface temperature profile. When the temperature gradient value of a certain depth layer exceeds the gradient threshold, the depth of the depth layer is defined as the thermocline interface. The area above the concentration threshold in the surface chlorophyll concentration map is projected vertically to the thermocline interface to generate a three-dimensional projection overlap area; For each vertical water column within the overlapping area, divide it into standard depth layers and calculate the temperature suitability coefficient for each layer. The coefficient is determined by the degree of match between the optimal temperature range of the target fish species and the measured temperature. The chlorophyll concentration of each layer of each vertical water column is multiplied by the temperature suitable for fishing coefficient and then vertically accumulated. When the accumulated value exceeds the comprehensive index threshold, the projection position of the corresponding vertical water column on the horizontal plane is marked as the core area of the fishing ground; The spatiotemporal alignment module specifically includes: The data synchronization window is set based on the satellite transit time. When the Argo float detects that the difference between the current time and the satellite transit time is less than the time synchronization threshold, the data upload frequency is increased to a fixed multiple of the normal frequency. A dynamically adjusted grid cell is constructed, with the initial grid size matching the satellite image resolution. When the cloud cover in the satellite image exceeds the cloud cover threshold, the grid range is expanded to include the measurement points of at least three adjacent Argo floats. The Argo float data within each grid cell are reconstructed according to the standard depth layer, and the discrete depth data are converted into continuous profile data with equal depth intervals. The missing depth data are supplemented by vertical gradient interpolation of adjacent floats. In the temporal dimension, a time buffer is set for the instantaneous satellite observation data. The length of the interval is determined by the ratio of the geostrophic velocity to the spatial grid size. The Argo data within the buffer are weighted and fused according to their temporal proximity.

2. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that: The preprocessing of the satellite remote sensing data specifically includes: Select polar-orbiting satellites that cover the target sea area more than the threshold number of times per day, and adjust orbital parameters during the fishing season to collect multispectral data at different times. To fill the cloud-covered areas in the sea surface temperature data, spatial interpolation of cloud-free data within a moving time window is used, and the interpolation weight is determined by the inverse product of the data time difference and the ocean current speed; The morphological features of the patch edges in the chlorophyll concentration map are extracted. When the curvature of the patch edge exceeds the preset morphological curvature threshold, the area overlapping with the temperature isotherm is marked as a candidate fishing ground indicator area. The flow velocity and direction characteristics are screened according to the convergence intensity threshold of the geostrophic flow field, and the areas that meet the convergence conditions are spatially intersected with the candidate fishing ground indication areas to generate primary fishing ground identification areas.

3. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that: The deployment of the Argo float array specifically includes: In waters where historical catches exceed the catch threshold, a triangular array of buoys is deployed. The length of the triangle side is dynamically adjusted based on the real-time inversion of the frontal intensity. For every unit increase in the frontal intensity change threshold, the side length is shortened by a fixed ratio. A chlorophyll concentration trigger threshold is set for each Argo float. When the continuous measurement value exceeds the trigger threshold, the shallow sampling interval is shortened to a fixed fraction of the regular interval until the concentration value falls below the threshold. Establish a satellite transit time prediction mechanism. Argo floats complete measurements and upload data before the transit time based on received satellite orbit parameters, with the time synchronization error less than the communication delay threshold. The drift compensation amount is calculated by the relative position change of adjacent buoys. When the distance change between buoys exceeds the position tolerance threshold, the least squares method is used to fit the drift trajectory and correct the coordinate data.

4. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that: The calculation of the temperature suitability coefficient specifically includes: Set the temperature adaptation interval according to the optimal temperature range of the target fish species. When the temperature at a certain depth layer is within this interval, the coefficient is set to the maximum value. For temperature data that exceeds the adaptation range, the coefficient attenuation is calculated according to the temperature difference with the interval boundary. For each unit increase in the temperature difference threshold, the coefficient decreases linearly at a fixed ratio. In the vertical water column, the coefficient weight of each layer is determined by the light attenuation rate of the layer, which is calculated jointly based on the transparency data retrieved from satellites and the extinction coefficient measured by Argo; The final fishing suitability coefficient is the product of temperature matching and light weight, and the product value is normalized to a value between 0 and 1.

5. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that: The delineation of the core area and the marginal area of the fishing ground specifically includes: In the surface chlorophyll concentration map, closed areas with an area exceeding the patch area threshold are extracted as candidate core areas; A downstream extension search is performed for each candidate core area along the direction of the geostrophic flow. When the depth of the thermocline interface is detected to be higher than the upstream depth threshold and the temperature gradient increase exceeds the gradient increase threshold, it is marked as a valid candidate area. The areas where the overlap between the candidate core area and the valid candidate area exceeds the overlap ratio threshold are retained; the spatial variation coefficient of the vertical integrated suitability index is calculated for the retained areas. When the variation coefficient is lower than the homogeneity threshold, it is determined to be a single core area; otherwise, it is split into sub-core areas according to the index peak value; The final core region boundary is determined by the outer envelope of the homogeneous region, and the edge region extends to the region where the index value drops to the edge threshold.

6. The fishery sea area monitoring system based on satellite remote sensing according to claim 5, characterized in that: The extended search specifically includes: The initial search step size is set according to the geostrophic flow velocity, and the product of the step size and the flow velocity is equal to the maximum migration distance threshold per day; Collect thermocline interface data in real time along the search path. When the buoy data density at a certain location is lower than the data density threshold, call for historical environmental data for the same period at that location to complete the search. When the interface depth change rate exceeds the depth change rate threshold, the search is expanded in the direction of interface uplift with the position as the center, and the search density is increased to a fixed multiple of the normal density; If the distance between the search path and the boundary of the delineated core area is less than the boundary tolerance threshold, the index difference between the two areas is compared, and boundary overlap is allowed only when the difference exceeds the competition threshold; the search is terminated when the number of consecutive searches reaches the maximum search number threshold or the cumulative search distance exceeds the maximum extension distance threshold.

7. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that: It also includes a fishery dynamic update module, including: When the difference in sea surface temperature between the latest satellite data and the historical data set exceeds the temperature difference threshold, or the difference in chlorophyll concentration exceeds the concentration difference threshold, the fishery redrawing process is triggered; The enhanced observation mode of the Argo float is activated in the changing area, and the sampling frequency of the float is increased to a fixed multiple of the normal frequency until the fluctuation amplitude of the environmental parameters falls below the stability threshold. Re-perform spatiotemporal alignment and model calculations based on enhanced observational data to generate updated fishing ground boundaries; The areas with different boundaries between the old and new ones are marked as fishery migration zones, and the migration direction is determined based on the composite vector of the geostrophic flow field and the gradient of the suitability index. The updated fishery boundaries are incorporated into the historical dataset as a comparison benchmark for the next dynamic update.

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