Inorganic nitrogen concentration inversion method and system

Abstract

The invention discloses an inorganic nitrogen concentration inversion method, and belongs to the technical field of informatization management systems, and the method comprises the following steps: water quality data preprocessing, Sentinel-2 image preprocessing, data set generation, correlation analysis, inversion model establishment, inversion image generation, inversion image splicing and thematic map generation. The invention also discloses an inorganic nitrogen concentration inversion system. The system comprises a water quality data acquisition module, a water quality data processing module, a remote sensing image acquisition module, a remote sensing image processing module, a data set generation module, a correlation analysis module and an inversion module. The concentration of inorganic nitrogen in seawater is predicted through a remote sensing inversion technology, intelligent monitoring is achieved, consumed manpower and material resources are small, the monitoring range is wide, the monitoring frequency is high, and the problems that a traditional inorganic nitrogen monitoring mode needs to consume a large amount of manpower and material resources, and the monitoring range and the monitoring frequency are limited can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of information management systems, and particularly relates to a method and system for inverting inorganic nitrogen concentration. Background Art

[0002] Inorganic nitrogen is one of the pollutants that marine management pays more attention to. The traditional monitoring method is to measure the positioning samples along the route by a marine survey ship. This process not only requires a large amount of manpower and material resources, but also is very limited in terms of monitoring scope and frequency. In addition, there are problems of wide management scope and weak management personnel in the current management of coastal waters, resulting in difficulty in achieving high-frequency and comprehensive management. However, the remote sensing inversion technology can effectively solve the above pain points, and at the same time, it can display the change trend of the pollution status at different time periods on the map after the inversion, which is beneficial to the management of coastal waters. Therefore, based on the remote sensing inversion technology, the present invention proposes a method and system for inverting inorganic nitrogen concentration. Summary of the Invention

[0003] In order to overcome the above technical problems, the present invention provides a method and system for inverting inorganic nitrogen concentration, which predicts the inorganic nitrogen concentration in seawater through remote sensing inversion technology, realizes intelligent monitoring, consumes less manpower and material resources, and has a wide monitoring scope and high monitoring frequency, so as to solve the problems that the traditional inorganic nitrogen monitoring method requires a large amount of manpower and material resources and is relatively limited in terms of monitoring scope and frequency.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] The present invention provides a method for inverting inorganic nitrogen concentration, including the following steps:

[0006] S1: Collect water quality data of the monitoring area through multiple water quality monitoring stations, and preprocess the water quality data to obtain the measured data of inorganic nitrogen concentration in the monitoring area;

[0007] S2: Download Sentinel-2 images of the monitoring area, and preprocess the Sentinel-2 images to obtain remote sensing data of the monitoring area; wherein, the remote sensing data is the reflectance values of each band;

[0008] S3: Take the monitoring date and the longitude and latitude of the water quality monitoring station as conditions, and correspond the measured data of inorganic nitrogen concentration obtained in step S1 with the remote sensing data obtained in step S2 to generate a data set;

[0009] S4: Conduct a correlation analysis on the measured data of inorganic nitrogen concentration and the corresponding remote sensing data in the data set generated in step S3, and establish an inversion model based on the correlation analysis;

[0010] S5: Input the preprocessed Sentinel-2 image in step S2 into the inversion model established in step S4 for inversion to generate an inversion image. If there is more than one inversion image, execute step S6; if there is only one inversion image, execute step S7.

[0011] S6: Perform coordinate system conversion on all the inversion images obtained in step S5 to convert them into a unified WGS84 geographic coordinate system, and splice all the inversion images after coordinate system conversion to obtain a spliced image.

[0012] S7: Render the inversion image obtained in step S5 or the spliced image obtained in step S6 according to the inorganic nitrogen concentration from low to high to generate a thematic map.

[0013] Further, in step S1, the preprocessing of the water quality data includes:

[0014] S11: Remove outliers, where the outliers include null values, values less than 0 mg / L, and values greater than 70 mg / L.

[0015] S12: Eliminate irrelevant water quality monitoring indicators and only retain the inorganic nitrogen concentration data.

[0016] S13: Calculate the daily average value of the retained inorganic nitrogen concentration data to obtain the measured inorganic nitrogen concentration data.

[0017] Further, in step S2, the preprocessing of the Sentinel-2 image includes completing atmospheric correction and resampling processing through the Sen2Cor plugin.

[0018] Further, in step S3, during the generation of the dataset, it is necessary to screen the reflectance values of each band and remove outliers.

[0019] Further, in step S3, the screening of the reflectance values of each band includes introducing the normalized difference water index to extract water bodies.

[0020] Further, in step S4, the analysis methods for correlation analysis include the double-band ratio method, the BP neural network model, the three-band factor method, and the four-band index method; by combining the reflectance values of each band and performing correlation analysis with the measured inorganic nitrogen concentration data, evaluate the effects of different combinations to determine the band combination with the highest correlation.

[0021] Further, in step S4, the finally established inversion model is: y = 3.9585e 3.0022*x , x = (B03 - B04) / (B03 - B06), where y is the predicted value and x is the combined value of the three bands B03, B04, and B06.

[0022] Further, in step S6, after all the inverted images obtained through coordinate system conversion are cropped by the coastline vector and then stitched together.

[0023] The present invention also provides an inorganic nitrogen concentration inversion system, including:

[0024] A water quality data acquisition module, connected to multiple water quality monitoring stations in the monitoring area, for acquiring water quality data in the monitoring area and sending the water quality data to the water quality data processing module;

[0025] A water quality data processing module, connected to the water quality data acquisition module, for preprocessing the water quality data to obtain measured inorganic nitrogen concentration data in the monitoring area and sending the measured inorganic nitrogen concentration data to the data set generation module;

[0026] A remote sensing image acquisition module, connected to a satellite, for acquiring Sentinel-2 images of the monitoring area and sending the Sentinel-2 images to the remote sensing image processing module;

[0027] A remote sensing image processing module, connected to the remote sensing image acquisition module, for preprocessing the Sentinel-2 images to obtain remote sensing data of the monitoring area and sending the remote sensing data to the data set generation module; wherein, the remote sensing data are reflectance values of each band;

[0028] A data set generation module, connected to the water quality data processing module and the remote sensing image processing module, for corresponding the measured inorganic nitrogen concentration data with the remote sensing data on the condition of the monitoring date and the longitude and latitude of the water quality monitoring station, generating a data set and sending the data set to the correlation analysis module;

[0029] A correlation analysis module, connected to the data set generation module, for performing a correlation analysis on the measured inorganic nitrogen concentration data and the corresponding remote sensing data in the data set and establishing an inversion model according to the correlation analysis;

[0030] An inversion module, connected to the correlation analysis module and the remote sensing image processing module, for inputting the Sentinel-2 images processed by the remote sensing image processing module into the inversion model established by the correlation analysis module for inversion to generate inverted images, and generating a thematic map after color rendering of the inverted images.

[0031] Further, the inversion module includes an inorganic nitrogen inversion unit, a coordinate system conversion unit, a coastal boundary clipping unit, an image mosaicking unit, and a thematic map generation unit; the inorganic nitrogen inversion unit is connected to the correlation analysis module and the remote sensing image processing module for generating an inversion image; if the inversion image is a single image, the inorganic nitrogen inversion unit directly sends the single inversion image to the thematic map generation unit for color rendering; if the inversion image is multiple images, the inorganic nitrogen inversion unit first sends the multiple inversion images to the coordinate system conversion unit for coordinate system conversion; after the conversion is completed, the coordinate system conversion unit then sends the multiple inversion images to the coastal boundary clipping unit for coastal boundary clipping; after the clipping is completed, the coastal boundary clipping unit then sends the multiple inversion images to the image mosaicking unit for mosaicking to obtain a mosaicked image; after the mosaicking is completed, the image mosaicking unit then sends the mosaicked image to the thematic map generation unit for color rendering.

[0032] Due to the above technical solutions, the present invention has the following beneficial effects:

[0033] 1. The present invention predicts the concentration of inorganic nitrogen in seawater through remote sensing inversion technology, with intelligent monitoring, consuming less manpower and material resources, and having a wide monitoring range and high monitoring frequency, which can effectively solve the problems that traditional inorganic nitrogen monitoring methods require a large amount of manpower and material resources and are relatively limited in monitoring range and frequency.

[0034] 2. The present invention introduces the normalized difference water index in the process of generating the data set, which can avoid the interference of non-water bodies such as clouds, bare land, and buildings, making the generated inversion image better fit the actual marine water body boundary. Before mosaicking the inversion images, the remote sensing images are clipped using the coastline vector, making the boundary of the generated inversion image more accurate and avoiding the influence of other water bodies in non-marine areas such as lakes, rivers, and irrigated farmland. By mosaicking the inversion images, a wide monitoring area can be covered to meet the usage requirements of different regions.

[0035] 3. The present invention can automatically generate a thematic map based on the data of remote sensing inversion, and reflect the level of inorganic nitrogen concentration through different colors, which is convenient for managers to intuitively understand the degree of inorganic nitrogen pollution. In addition, after the inversion is completed, the change trend of the pollution status at different time periods can be displayed on the map, which is conducive to the management of the coastal waters.

[0036] 4. The present invention is applied to the monitoring and management of inorganic nitrogen in coastal waters and has a good early warning effect. The coastal waters around are divided into grids, and then each grid is compared with the historical data of its surrounding and its own grid. If it is higher than a certain proportion of the surrounding grids and also higher than the historical data by a certain proportion, then the high-value grids are identified through a set algorithm and early warning is given to the high-value grids. If adjacent grids are all high-value grids, they can form a surface for early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flowchart of the present invention;

[0038] Figure 2 is a technical roadmap of the present invention;

[0039] Figure 3 is part of the data in the process of generating the dataset of the present invention;

[0040] Figure 4 is the correlation between some dual-band and triple-band combinations and the inorganic nitrogen concentration in the process of correlation analysis of the present invention;

[0041] Figure 5 is the inversion model of inorganic nitrogen concentration without removing discrete values in the process of establishing the inversion model of the present invention;

[0042] Figure 6 is the inversion model of inorganic nitrogen concentration after removing discrete values in the process of establishing the inversion model of the present invention;

[0043] Figure 7 is a comparison schematic diagram of the predicted value of inorganic nitrogen concentration obtained by inversion of the present invention and the measured value of inorganic nitrogen concentration;

[0044] Figure 8 is the inversion image of inorganic nitrogen concentration in the monitoring area obtained by the present invention;

[0045] Figure 9 is the thematic map of inversion of inorganic nitrogen concentration in the monitoring area obtained by the present invention;

[0046] Figure 10 is the overview of inorganic nitrogen concentration in the monitoring area obtained by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The accompanying drawings are only for illustrative purposes and show only schematic diagrams, not physical diagrams, and should not be construed as a limitation to this patent. To better illustrate the specific embodiments of the present invention, some components in the accompanying drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product. For those skilled in the art, it is understandable that some well-known structures, components and their descriptions in the accompanying drawings may be omitted. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0048] In the description of the present invention, it should also be noted that unless otherwise clearly specified and defined, the terms "arrangement" and "connection" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] Embodiment

[0050] As Figure 1 shown, the present invention provides a method for inverting the inorganic nitrogen concentration, including the following steps:

[0051] S1: Collect the water quality data of the monitoring area through multiple water quality monitoring stations, and preprocess the water quality data to obtain the measured data of the inorganic nitrogen concentration in the monitoring area.

[0052] S2: Download the Sentinel-2 images of the monitoring area, and preprocess the Sentinel-2 images to obtain the remote sensing data of the monitoring area; wherein, the remote sensing data are the reflectance values of each band. Sentinel-2 has a total of 13 bands, and the remote sensing data include the reflectance values of 13 bands.

[0053] S3: Take the monitoring date and the longitude and latitude of the water quality monitoring station as conditions, and correspond the measured data of the inorganic nitrogen concentration obtained in step S1 with the remote sensing data obtained in step S2 to generate a data set.

[0054] S4: Conduct a correlation analysis on the measured data of the inorganic nitrogen concentration and the corresponding remote sensing data in the data set generated in step S3, and establish an inversion model based on the correlation analysis.

[0055] S5: Input the preprocessed Sentinel-2 images in step S2 into the inversion model established in step S4 for inversion to generate an inversion image; if there is more than one inversion image, execute step S6, if there is only one inversion image, then execute step S7.

[0056] S6: Perform coordinate system conversion on all the inversion images obtained in step S5 to convert them into a unified WGS84 geographic coordinate system, and splice all the inversion images after coordinate system conversion to obtain a spliced image.

[0057] S7: Render the inversion image obtained in step S5 or the spliced image obtained in step S6 according to the inorganic nitrogen concentration from low to high to generate a thematic map.

[0058] In this embodiment, the monitoring area is selected as the Beihai Bay in Guangxi. The Beihai Bay in Guangxi is relatively large, and it is difficult for a single remote sensing image to cover it completely. Therefore, it is necessary to splice the remote sensing images. The Sentinel-2 remote sensing images use the UTM projection. This projection method uses different projections in different regions of the earth's surface, which can minimize the distortion of shape and size. Since the projection zones of each image are different, it is necessary to convert the coordinate system of each scene image to a unified WGS84 geographic coordinate system.

[0059] As Figure 2 shown, in step S1, the preprocessing of water quality data includes:

[0060] S11: Remove outliers. The outliers include null values, values less than 0 mg / L, and values greater than 70 mg / L. Null values, too large or too small numerical values will all affect the inversion results.

[0061] S12: Eliminate irrelevant water quality monitoring indicators and only retain the inorganic nitrogen concentration data. Irrelevant water quality indicators include chlorophyll concentration, pH value, ammonia nitrogen concentration, etc. Deleting data irrelevant to the inversion reduces the operation and calculation amount.

[0062] S13: Calculate the daily average value of the retained inorganic nitrogen concentration data to obtain the measured inorganic nitrogen concentration data.

[0063] In step S2, the preprocessing of the Sentinel-2 image includes performing atmospheric correction and resampling through the Sen2Cor plugin. Atmospheric correction is to eliminate the influence of atmospheric scattering and absorption on the remote sensing image to obtain more accurate surface reflectance data. Resampling is to adjust the spatial resolution of the image data to be consistent for subsequent analysis and comparison.

[0064] In step S3, during the generation of the dataset, it is necessary to screen the reflectance values of each band and eliminate outliers. To avoid the influence of outliers on the inversion results, for the reflectance values in the overlapping area, just calculate the average value.

[0065] In step S3, the screening of the reflectance values of each band includes introducing the Normalized Difference Water Index (NDWI) to extract water bodies. The Normalized Difference Water Index, i.e., NDWI, performs normalized difference processing on specific bands of the remote sensing image to highlight the water body information in the image.

[0066] In step S4, the analysis methods for correlation analysis include the dual-band ratio method, the BP neural network model, the three-band factor method, and the four-band index method. By combining the reflectance values of each band and performing correlation analysis with the measured inorganic nitrogen concentration data, the effects of different combinations are evaluated to determine the band combination with the highest correlation.

[0067] In step S6, after all the inverted images that have undergone coordinate system transformation are cropped by the coastline vector and then stitched together. In order to obtain better-quality and accurate reflectance values, during the acquisition process, it is necessary to screen and judge the selected points to prevent interference from clouds, specular reflection on the sea, and whether the current sampling is water. After introducing the normalized difference water index in step S3, it is possible to avoid interference from non-water bodies such as clouds, bare land, and buildings, making the generated inverted images better fit the actual marine water boundary. However, there are still other water bodies in non-marine areas such as lakes, rivers, and irrigated farmland with water. Therefore, it is necessary to use the coastline vector to crop the remote sensing image to make the boundary of the generated inverted image more accurate. Finally, the cropped images are stitched together to obtain a complete inverted image of the inorganic nitrogen concentration in the Beihai Bay area of Guangxi. By stitching the inverted images, it is possible to cover a wide monitoring area and meet the usage requirements of different regions.

[0068] Figure 3 It shows the reflectance values of each band on the remote sensing images matched by some monitoring stations at a certain time and the corresponding inorganic nitrogen concentrations. The image data obtained through remote sensing technology provides spectral information on the water surface, and these data will be used for comparison and analysis with the measured inorganic nitrogen concentration data. Figure 3 It shows the reflectance values of each corresponding band on the remote sensing image of the selected monitoring stations at the corresponding time points and the corresponding inorganic nitrogen concentration values. By integrating the measured data and the remote sensing image data, a more comprehensive and accurate assessment of the water quality status of the monitoring area is provided. During the data processing, outliers and null values are processed to ensure the reliability and accuracy of the measured inorganic nitrogen concentration data. By combining the measured data and remote sensing technology, it is possible to better monitor and evaluate the water quality and provide support for protecting the marine ecosystem and sustainable development.

[0069] Figure 4 It shows the correlation results between some dual-band combinations and some triple-band combinations and the overall inorganic nitrogen concentration data. By analyzing the correlation between these band combinations and the inorganic nitrogen concentration data, it is possible to evaluate the effects of different combinations and determine the band combination with the highest correlation, which will provide guidance for subsequent research and further explore the changes in the inorganic nitrogen concentration and the most relevant band combination in the spatial and temporal dimensions. From Figure 4 it can be observed that the highest correlation value between the dual-band combination and the inorganic nitrogen concentration is -0.416, and the band combination is the ratio of B04 and B01, while the highest correlation value between the triple-band combination and the inorganic nitrogen concentration is 0.546, and its band combination is (B03 - B04) / (B03 - B06). Therefore, in this embodiment, the triple-band combination of B03, B04, and B06 is selected for inorganic nitrogen inversion, and an inversion model between this triple-band combination and the inorganic nitrogen concentration is established.

[0070] Figure 5 shows the relationship between the inorganic nitrogen concentration and the combination of three bands B03, B04, and B06 and R 2 , without removing any outliers, R 2 reached 0.4215. To better establish the relationship between the image reflectance and the ocean inorganic nitrogen concentration, during data processing, some relatively discrete data were removed. The relationship between the combination of the three bands after removal and the inorganic nitrogen concentration is as Figure 6 shown.

[0071] As Figure 6 shown, after removing the discrete values, the correlation between the inorganic nitrogen concentration and the combination of the three bands B03, B04, and B06 has been greatly improved, and R 2 also reached 0.7295. Therefore, in step S4, the finally established inversion model is: y = 3.9585e 3.0022*x , x = (B03 - B04) / (B03 - B06), where y is the predicted value and x is the combined value of the three bands B03, B04, and B06.

[0072] As Figure 7 shown, to verify the accuracy of the inversion model, the inorganic nitrogen concentration predicted by the inversion model was compared with the measured value at this point. It can be seen from the figure that the overall trends of the measured value and the predicted value of inorganic nitrogen are basically the same, and the error is small.

[0073] As Figure 8 shown, a preprocessed Sentinel-2 image was selected and input into the inversion model proposed by the present invention for inversion to generate an inversion image. In the inversion image, the level of the inorganic nitrogen concentration is presented by the shade of gray. It can be seen from this figure that the inorganic nitrogen concentration in this area can be roughly divided into 5 zones as the distance from the coast increases. First is the near-coast zone, where the inorganic nitrogen concentration is relatively low, with an average concentration of about 27 mg / L. The possible reason is that the water depth in the near-coast area is relatively shallow, which is not conducive to the reproduction of marine plankton, and the surface seawater is easily affected by tides. The second inorganic nitrogen transition zone appears at a distance of 15 km - 30 km from the coast, where the inorganic nitrogen concentration is relatively high, with an average concentration reaching about 30 mg / L. The third transition zone is located at a distance of 37 km - 55 km from the coast and is inclined at a 45-degree angle, where the inorganic nitrogen concentration is about 24 mg / L. The fourth transition zone is located relatively farther and has a larger area. The fifth inorganic nitrogen area is located in the open sea, far from the coast, where the inorganic nitrogen concentration is relatively low, about 21 mg / L.

[0074] As Figure 9As shown, after the inversion image is generated, in order to highlight the change trend and distribution of inorganic nitrogen, the inorganic nitrogen concentration in the image is color-rendered from low to high. Figure 9 In Figure 9 , although the distribution of inorganic nitrogen is presented in gray, it is actually presented in color, with low concentration being green, medium concentration being yellow, and high concentration being red.

[0075] Figure 9 The distribution of inorganic nitrogen concentrations at different times in the Beihai Bay area of Guangxi is shown. Analyzing by the distance from the coast: From the four inversion maps in the figure, it can be seen that the inorganic nitrogen concentration in the coastal zone of the Beihai Bay in Guangxi is relatively low, mainly because the water depth in this area is relatively shallow and it is greatly affected by the tide; while the inorganic nitrogen concentration in the offshore area is relatively high, mainly because the offshore area is greatly affected by human activities; the inorganic nitrogen concentration in the open sea area is relatively low. This area is less affected by human activities, has a relatively deep water depth, contains less eutrophic substances in the water body, and has relatively few marine plankton. Analyzing by season: From the image, it can be seen that the inorganic nitrogen concentration in the Beihai Bay area of Guangxi is relatively low in spring, and the inorganic nitrogen concentration is relatively high in other seasons compared to spring.

[0076] As Figure 10 shown, it is an overview of the inorganic nitrogen concentration in the monitoring area (Beihai Bay, Guangxi) finally generated by the present invention.

[0077] The present invention can automatically generate a thematic map based on the remotely sensed inversion data, and reflect the high and low of the inorganic nitrogen concentration through different colors, which is convenient for managers to intuitively understand the degree of inorganic nitrogen pollution. In addition, after the inversion is completed, it can display the change trend of the pollution status at different time periods on the map, which is conducive to the management of the coastal waters.

[0078] The present invention also provides an inorganic nitrogen concentration inversion system, including:

[0079] A water quality data collection module, connected to multiple water quality monitoring stations in the monitoring area, for collecting water quality data in the monitoring area and sending the water quality data to the water quality data processing module.

[0080] A water quality data processing module, connected to the water quality data collection module, for preprocessing the water quality data to obtain the measured data of the inorganic nitrogen concentration in the monitoring area and sending the measured data of the inorganic nitrogen concentration to the data set generation module.

[0081] A remote sensing image acquisition module, connected to a satellite, for acquiring Sentinel-2 images of the monitoring area and sending the Sentinel-2 images to the remote sensing image processing module.

[0082] A remote sensing image processing module, connected to the remote sensing image acquisition module, is used to preprocess the Sentinel-2 image to obtain remote sensing data of the monitoring area and send the remote sensing data to the dataset generation module; wherein, the remote sensing data are reflectance values of each band.

[0083] A dataset generation module, connected to the water quality data processing module and the remote sensing image processing module, is used to correspond the measured inorganic nitrogen concentration data with the remote sensing data based on the monitoring date and the longitude and latitude of the water quality monitoring site, generate a dataset, and send the dataset to the correlation analysis module.

[0084] A correlation analysis module, connected to the dataset generation module, is used to perform a correlation analysis on the measured inorganic nitrogen concentration data and the corresponding remote sensing data in the dataset and establish an inversion model based on the correlation analysis.

[0085] An inversion module, connected to the correlation analysis module and the remote sensing image processing module, is used to input the Sentinel-2 image processed by the remote sensing image processing module into the inversion model established by the correlation analysis module for inversion to generate an inversion image, and the inversion image is color-rendered to generate a thematic map.

[0086] The inversion module includes an inorganic nitrogen inversion unit, a coordinate system conversion unit, a coastal boundary cropping unit, an image stitching unit, and a thematic map generation unit. The inorganic nitrogen inversion unit is connected to the correlation analysis module and the remote sensing image processing module and is used to generate an inversion image. If the inversion image is a single image, the inorganic nitrogen inversion unit directly sends the single inversion image to the thematic map generation unit for color rendering. If the inversion image is multiple images, the inorganic nitrogen inversion unit first sends the multiple inversion images to the coordinate system conversion unit for coordinate system conversion; after the conversion is completed, the coordinate system conversion unit then sends the multiple inversion images to the coastal boundary cropping unit for coastal boundary cropping; after the cropping is completed, the coastal boundary cropping unit then sends the multiple inversion images to the image stitching unit for stitching to obtain a stitched image; after the stitching is completed, the image stitching unit then sends the stitched image to the thematic map generation unit for color rendering.

[0087] The present invention is applied to the monitoring and management of inorganic nitrogen in the nearshore sea area, and has a good early warning effect. The nearshore sea area is divided into grids, and then the historical data of each grid is compared with that of its surrounding grids and itself. If it is higher than a certain proportion of the surrounding grids and also higher than the historical data by a certain proportion, then the high-value grid is identified through a set algorithm, and an early warning is issued for the high-value grid. If adjacent grids are all high-value grids, they can form an area for early warning. Therefore, the inversion module may further include a high-value identification unit and an early warning unit. The high-value identification unit is used to identify high-value grids, and the early warning unit is used to push alarm information.

[0088] For the high-value areas warned, the tasks are distributed to the cities under their jurisdiction through the workflow for verification. During the verification process, if treatment is required, the treatment situation needs to be reported to the superior environmental protection department. For issues that need to be disposed of, after the issues are disposed of, they are uniformly transferred to the superior department for cancellation and acceptance. After the acceptance is completed, the later treatment effect is checked. Therefore, forming problem clues through the high-value areas and pushing them to the local environmental protection department for accurate problem verification and treatment can well complete the management work of the ocean on the basis of limited human and material resources.

[0089] The present invention predicts the concentration of inorganic nitrogen in seawater through remote sensing inversion technology, with intelligent monitoring, consuming less human and material resources, having a wide monitoring range and a high monitoring frequency, and can effectively solve the problems that the traditional inorganic nitrogen monitoring method requires a large amount of human and material resources and is limited in the monitoring range and frequency.

[0090] The above description is a detailed description of the preferred and feasible embodiments of the present invention, but the embodiments are not intended to limit the patent application scope of the present invention. Any equivalent changes or modifications made under the technical spirit disclosed by the present invention shall fall within the patent scope covered by the present invention.

Claims

1. An inorganic nitrogen concentration inversion method, characterized in that It includes the following steps: S1: Collect water quality data of the monitoring area through multiple water quality monitoring stations, and preprocess the water quality data to obtain the measured inorganic nitrogen concentration data of the monitoring area; S2: Download Sentinel-2 images of the monitoring area, and preprocess the Sentinel-2 images to obtain remote sensing data of the monitoring area; wherein, the remote sensing data are the reflectance values of each band; S3: Taking the monitoring date and the longitude and latitude of the water quality monitoring station as conditions, correspond the measured inorganic nitrogen concentration data obtained in step S1 with the remote sensing data obtained in step S2 to generate a data set; S4: Conduct a correlation analysis on the measured inorganic nitrogen concentration data and the corresponding remote sensing data in the data set generated in step S3, and establish an inversion model based on the correlation analysis; S5: Input the preprocessed Sentinel-2 images in step S2 into the inversion model established in step S4 for inversion to generate inversion images; if there is more than one inversion image, execute step S6, if there is only one inversion image, then execute step S7; S6: Perform coordinate system conversion on all the inversion images obtained in step S5 to convert them into a unified WGS84 geographic coordinate system, and splice all the inversion images after coordinate system conversion to obtain a spliced image; S7: Render the inversion images obtained in step S5 or the spliced images obtained in step S6 according to the inorganic nitrogen concentration from low to high to generate a thematic map.

2. The inorganic nitrogen concentration inversion method according to claim 1, wherein: In step S1, the preprocessing of the water quality data includes: S11: Remove outliers, where the outliers include null values, values less than 0 mg / L, and values greater than 70 mg / L; S12: Eliminate irrelevant water quality monitoring indicators and only retain the inorganic nitrogen concentration data; S13: Calculate the daily average value of the retained inorganic nitrogen concentration data to obtain the measured inorganic nitrogen concentration data.

3. The inorganic nitrogen concentration inversion method according to claim 1, characterized in that: In step S2, the preprocessing of the Sentinel-2 images includes performing atmospheric correction and resampling processing through the Sen2Cor plugin.

4. The inorganic nitrogen concentration inversion method according to claim 1, characterized in that: In step S3, during the generation of the data set, it is necessary to screen the reflectance values of each band and eliminate outliers.

5. The inorganic nitrogen concentration inversion method according to claim 4, wherein: In step S3, the screening of the reflectance values of each band includes introducing the normalized difference water index to extract water bodies.

6. The inorganic nitrogen concentration inversion method according to claim 5, characterized in that: In step S4, the analysis methods for correlation analysis include the two-band ratio method, the BP neural network model, the three-band factor method, and the four-band index method; by combining the reflectance values of each band and conducting a correlation analysis with the measured inorganic nitrogen concentration data, evaluate the effects of different combinations to determine the band combination with the highest correlation.

7. A method for inverting the inorganic nitrogen concentration according to claim 6, characterized in that: In step S4, the finally established inversion model is: y = 3.9585e 3.0022*x , x = (B03 - B04) / (B03 - B06), where y is the predicted value and x is the combined value of the three bands B03, B04, and B06.

8. The inorganic nitrogen concentration inversion method according to claim 1, wherein: In step S6, all the inversion images after coordinate system conversion are clipped by the coastline vector and then spliced.

9. An inorganic nitrogen concentration inversion system, characterized in that, It includes: A water quality data acquisition module, connected to multiple water quality monitoring stations in the monitoring area, for collecting water quality data of the monitoring area and sending the water quality data to the water quality data processing module; A water quality data processing module, connected to the water quality data acquisition module, for preprocessing the water quality data to obtain the measured inorganic nitrogen concentration data of the monitoring area and sending the measured inorganic nitrogen concentration data to the data set generation module; A remote sensing image acquisition module, connected to a satellite, is used to acquire Sentinel-2 images of the monitoring area and send the Sentinel-2 images to the remote sensing image processing module; A remote sensing image processing module, connected to the remote sensing image acquisition module, is used to preprocess the Sentinel-2 images to obtain remote sensing data of the monitoring area and send the remote sensing data to the dataset generation module; wherein, the remote sensing data are reflectance values of each band; A dataset generation module, connected to the water quality data processing module and the remote sensing image processing module, is used to correspond the measured inorganic nitrogen concentration data with the remote sensing data based on the monitoring date and the longitude and latitude of the water quality monitoring station, generate a dataset, and send the dataset to the correlation analysis module; A correlation analysis module, connected to the dataset generation module, is used to perform a correlation analysis on the measured inorganic nitrogen concentration data and the corresponding remote sensing data in the dataset and establish an inversion model based on the correlation analysis; An inversion module, connected to the correlation analysis module and the remote sensing image processing module, is used to input the Sentinel-2 images processed by the remote sensing image processing module into the inversion model established by the correlation analysis module for inversion to generate an inversion image, and the inversion image is color-rendered to generate a thematic map.

10. The inorganic nitrogen concentration inversion system according to claim 9, characterized in that: The inversion module includes an inorganic nitrogen inversion unit, a coordinate system conversion unit, a coastal boundary cropping unit, an image stitching unit, and a thematic map generation unit; The inorganic nitrogen inversion unit is connected to the correlation analysis module and the remote sensing image processing module and is used to generate an inversion image; If the inversion image is a single image, the inorganic nitrogen inversion unit directly sends the single inversion image to the thematic map generation unit for color rendering; if the inversion image is multiple images, the inorganic nitrogen inversion unit first sends the multiple inversion images to the coordinate system conversion unit for coordinate system conversion; after the conversion is completed, the coordinate system conversion unit then sends the multiple inversion images to the coastal boundary cropping unit for coastal boundary cropping; after the cropping is completed, the coastal boundary cropping unit then sends the multiple inversion images to the image stitching unit for stitching to obtain a stitched image; after the stitching is completed, the image stitching unit then sends the stitched image to the thematic map generation unit for color rendering.

Images