Intra-day high-frequency automatic monitoring method and system for cyanobacterial bloom

Abstract

The invention provides an intraday high-frequency automatic monitoring method and system for lake cyanobacterial bloom based on a GOCI-II satellite, and aims to solve the problems that an existing method is susceptible to interference of thin cloud, low in recognition precision, high in false positive rate, low in efficiency and the like, and the processing flow depends on manpower. The system is based on an improved AFAI index, fine cloud detection, cloud expansion processing and classification correction strategies are combined, misrecognition caused by thin clouds and shadows is effectively restrained, and the extraction accuracy and robustness are improved. The system has the whole-process unattended processing capacity from GOCI-II data automatic downloading, preprocessing, algal bloom recognition, thematic map making to report output, can generate a monitoring report within one hour after satellite imaging, and supports intra-day multi-temporal cyanobacterial bloom dynamic monitoring. The method has been successfully applied to lakes such as Taihu Lake, lakes, Chaohu Lake and Hongze Lake, is particularly suitable for monitoring and early warning cyanobacterial bloom in large and medium lakes, and has wide application prospects in the fields of water environment supervision, water quality risk control, ecological assessment and the like.

Description

Technical Field

[0001] This invention belongs to the field of water body detection technology, and in particular relates to a method and system for daily high-frequency automatic monitoring of cyanobacterial blooms. Background Technology

[0002] my country boasts abundant and widely distributed lake resources, playing a vital role in ensuring water supply, maintaining ecological balance, and supporting regional economic development. Currently, eutrophication of surface waters is becoming increasingly serious, with many lakes and reservoirs facing significant risks of water quality deterioration. Large and medium-sized lakes located in economically developed regions with relatively weak water exchange capacity are particularly affected by factors such as long-term population growth, urban expansion, and increased industrial and agricultural activities, resulting in particularly prominent cyanobacterial blooms characterized by high frequency, widespread distribution, and unpredictability.

[0003] Traditional monitoring of algal blooms relies on manual observation, but limitations in spatial coverage and temporal resolution make rapid response to bloom events difficult. In contrast, satellite remote sensing technology, with its advantages of wide coverage, periodic acquisition, and objectivity, has become an important tool for monitoring cyanobacterial blooms. Studies have shown that cyanobacterial aggregation can significantly alter the spectral reflectance characteristics of water bodies, particularly exhibiting a pronounced "steep slope effect" in the near-infrared band, which can serve as an important basis for remote sensing identification of algal blooms.

[0004] The distribution of cyanobacterial blooms exhibits high spatiotemporal instability, and their formation process is influenced by the interaction of multiple environmental factors, characterized by sudden onset and rapid evolution. Relying on low-frequency remote sensing or manual surveys makes it difficult to grasp their dynamic changes in a timely manner. Therefore, there is an urgent need to construct a high-temporal-resolution, stable, and reliable remote sensing monitoring system to achieve continuous, automatic, and rapid identification of cyanobacterial blooms.

[0005] To address these challenges, South Korea launched the world's first geostationary ocean color satellite, COMS (Communication Ocean and Meteorological Satellite), in 2010. Its onboard GOCI (Geostationary Ocean Color Imager) system can capture eight images daily, ushering in an era of hourly temporal resolution ocean color remote sensing. In 2020, the upgraded GOCI-II was launched aboard the GK-2B satellite. GOCI-II offers significant improvements over its predecessor in spatial resolution, spectral configuration, and observation capabilities. Its spatial resolution has increased to 250 meters, and the number of spectral bands has increased to 13, covering the visible to near-infrared range, significantly enhancing its ability to observe algal blooms, suspended matter, and water quality parameters. Furthermore, GOCI-II supports regional and global observation modes, can acquire up to 10 images daily, and possesses stronger event scheduling capabilities.

[0006] Currently, GOCI series data have been widely used in operational monitoring such as suspended solids concentration, chlorophyll a retrieval, and algal bloom identification. Related studies have shown that GOCI and GOCI-II exhibit good consistency and retrieval accuracy in eutrophic and optically complex water bodies. Furthermore, GOCI-II demonstrates excellent spatiotemporal response capabilities in monitoring harmful algal blooms, green tides, and sudden water quality events.

[0007] Therefore, developing an automated extraction and high-frequency operational monitoring method for cyanobacterial blooms based on GOCI-II data can fully leverage its advantages of hourly observation frequency, wide coverage, and multi-band information to achieve continuous monitoring of large and medium-sized lakes such as Taihu Lake and Chaohu Lake. This method can not only significantly improve the timeliness and accuracy of cyanobacterial bloom identification and reduce the cost of manual intervention, but also enhance the responsiveness of water environment management, demonstrating broad application prospects and promotional value. Summary of the Invention

[0008] This invention proposes a high-frequency automatic intraday monitoring method and system for cyanobacterial blooms. Leveraging the high temporal resolution and multispectral imaging capabilities of the GOCI-II satellite, it constructs a technical pathway for automatic identification and dynamic monitoring of cyanobacterial blooms in large and medium-sized lakes. The method integrates multiple processing modules, including Rayleigh scattering correction, cloud mask construction, vegetation frequency analysis, bloom index calculation, patch extraction, and area statistics, forming a highly automated remote sensing identification process. This invention can rapidly complete data processing and result output after acquiring GOCI-II imagery, achieving hourly dynamic response and significantly improving the efficiency and accuracy of cyanobacterial bloom monitoring. The system supports batch data processing and multi-temporal analysis, possesses excellent visualization capabilities and multi-source data collaborative processing capabilities, and is suitable for rapid identification and change monitoring of cyanobacterial blooms. Compared with existing methods, this invention has significant advantages in processing efficiency, identification accuracy, and system stability, and is particularly suitable for high-frequency remote sensing-driven cyanobacterial bloom monitoring, early warning, and water quality risk assessment, possessing broad engineering application value and promising prospects for widespread application.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A method and system for high-frequency automatic monitoring of cyanobacterial blooms during the day includes the following steps:

[0011] S1. Obtain data: Obtain the atmospheric apparent reflectance data of the area to be tested after radiometric calibration via satellite;

[0012] S2. Data preprocessing: Rayleigh scattering correction and projection transformation are performed on the S1 data;

[0013] S3. Construct an analysis mask to identify cloud-covered areas and, in conjunction with the vegetation frequency index, determine the spatial mask for extracting cyanobacterial blooms.

[0014] S4. Extract the cyanobacterial bloom region, calculate the relevant spectral indices, and identify the distribution of cyanobacterial blooms according to the set rules;

[0015] S5 generates monitoring results, outputs cyanobacterial bloom distribution maps, area statistics, and monitoring reports, and supports automated processing and rapid response.

[0016] Preferably, the satellite is a GOCI-II satellite;

[0017] Preferably, the Rayleigh scattering correction process in S2 includes the following steps:

[0018] S21. Implemented by calling the GOCI-II Toolbox (GTBX) plugin module and its RayleighCorrection processor in the SNAP software;

[0019] Preferably, the projection conversion process in S2 includes the following steps:

[0020] S22. Implemented by calling the GOCI-II Toolbox (GTBX) plugin module and its Reproject processor in the SNAP software;

[0021] Preferably, identifying the cloud coverage area in step S3 includes the following steps:

[0022] S31. By analyzing the reflectance of the Rrc (aerosol) and Rrc (red) bands in GOCI-II images, high reflectance areas are identified based on preset threshold combination conditions, and cloud-covered pixels are determined. The cloud mask area is then expanded by morphological dilation of three 3×3 structural elements.

[0023] Preferably, the spatial mask for determining the cyanobacterial bloom extraction using the vegetation frequency index in step S3 includes the following steps:

[0024] S32. Based on cloud masking, a decision tree model is constructed using NDVI and NDWI indices to classify pixels within the lake area as either vegetation or non-vegetation and perform binarization. Subsequently, the spatial distribution frequency method is used to calculate the vegetation frequency index SDFI. vegetation ;

[0025] Among them, NDWI, NDVI and SDFI vegetation The specific formula is:

[0026] NDWI=(Rrc(green)-Rrc(nir)) / (Rrc(green)+Rrc(nir)) (1)

[0027] NDVI=(Rrc(nir)-Rrc(red)) / (Rrc(nir)+Rrc(red)) (2)

[0028]

[0029] In the formula, Rrc(red), Rrc(green), and Rrc(nir) are the Rayleigh scattering corrected atmospheric apparent reflectances for the red, green, and near-infrared bands, respectively, corresponding to the corresponding bands of GOCI-II; R i_vegetation Let R be the value of the vegetation pixel in the binary vegetation map on day i. i_novegetation Given the non-vegetation raster values ​​in the binary vegetation map of day i, obtain the vegetation frequency (SDFI) over a specified time period in pixels. vegetation ;

[0030] Preferably, identifying the distribution of cyanobacterial blooms in step S4 includes the following steps:

[0031] S41. The improved Floating Algae Index (IFAI) and the Density Spectrum Indices (NDVI) are used for extraction in cyanobacterial bloom areas to eliminate false positives caused by cloud interference and high reflectivity areas outside of blooms. The formula for calculating the improved Floating Algae Index (IFAI) is as follows:

[0032] IFAI=Rrc(red2)-Rrc′(red2) (4)

[0033]

[0034] In the formula, Rrc(red1), Rrc(red2), and Rrc(red3) represent the apparent atmospheric reflectance of satellite image pixels after Rayleigh scattering correction in the three red-related bands, respectively, and λ represents the center wavelength of the corresponding band. IFAI is used to enhance the spectral response characteristics of floating algae, and NDVI is used to suppress vegetation background interference; the two are used together for algal bloom identification. By setting a combined threshold for IFAI and NDVI, it is used to assist in identifying cyanobacterial bloom pixels and suppress misjudgments caused by highly reflective non-target areas.

[0035] S42. Connectivity analysis is performed on the initially extracted cyanobacterial bloom mask image to identify continuously distributed patch regions, and small noise regions are removed based on area thresholds. Then, morphological processing methods are used to optimize the bloom boundary to improve the spatial integrity and edge smoothness of the extraction results.

[0036] Preferably, the steps in S5 to generate monitoring results and output cyanobacterial bloom distribution maps, area statistics, and monitoring reports include the following:

[0037] S51. Based on the mask image of cyanobacterial bloom, perform pixel-level area statistics on the extracted results, calculate the bloom coverage area and record the change information.

[0038] S52. Generate a thematic map of cyanobacterial blooms based on spatial distribution characteristics, and overlay elements such as the base map and time information for visualization.

[0039] S53. Generate a monitoring report based on a preset template, including monitoring time, algal bloom distribution map, area statistics, etc., to achieve automated output and timely response of monitoring results.

[0040] The present invention has at least the following beneficial effects: It realizes rapid and automated identification and dynamic monitoring of cyanobacterial blooms in large and medium-sized lakes within hours, significantly improving the efficiency of bloom extraction, the accuracy of identification, and the timeliness of response; it constructs a complete high-frequency remote sensing data processing workflow with good batch processing capabilities and result visualization effects; it is suitable for continuous monitoring, early warning, and water quality risk assessment of lake water bodies, and can provide strong technical support for water environment management and cyanobacterial bloom control, with high engineering application value and broad prospects for promotion. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A scatter plot of the area of ​​cyanobacterial blooms in Taihu Lake and the consultation results in this invention.

[0043] Figure 2 A schematic diagram of the hourly monitoring results of cyanobacterial blooms in Taihu Lake in this invention.

[0044] Figure 3 This invention presents a case study comparing the intraday variations of cyanobacterial blooms in the total area of ​​Taihu Lake and the water area of ​​Wuxi.

[0045] Figure 4 This invention presents a series of hourly remote sensing dynamic monitoring results of typical dates for cyanobacterial blooms in Taihu Lake.

[0046] Figure 5 This is a comparative example of the results of cyanobacterial bloom extraction based on satellite imagery in this invention. Detailed Implementation

[0047] This invention proposes a method and system for high-frequency automatic monitoring of cyanobacterial blooms throughout the day. To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0048] Based on the data characteristics of the GOCI-II geostationary water color satellite, this invention constructs an automated remote sensing processing workflow that supports hourly dynamic monitoring, addressing the rapid and diurnal variations in cyanobacterial blooms in lakes. This workflow integrates modules such as cloud detection and swelling processing, vegetation frequency mask construction, bloom identification index calculation, patch extraction and screening, area statistics, and result visualization, forming an integrated and efficient processing system from data acquisition to report output.

[0049] The system first automatically acquires reflectance data after GOCI-II radiometric calibration via an API interface, and then uses the GOCI-II Toolbox plugin on the SNAP platform to complete Rayleigh scattering correction and geographic projection conversion. Subsequently, cloud masks are constructed based on the Rrc (aerosol) and Rrc (red) bands, and morphological methods are used to expand high-reflectance areas, effectively eliminating interference from thin clouds, shadows, and solar flares. In the spatial analysis phase, the system uses NDVI and NDWI to construct vegetation frequency indices. By setting reasonable time windows and thresholds, it distinguishes between stable aquatic plant distribution areas and non-growing areas, thereby reducing interference from aquatic plants in cyanobacteria identification and improving identification accuracy.

[0050] In the cyanobacterial bloom identification stage, the system employs a joint discrimination algorithm combining IFAI and NDVI to extract high reflectance features in non-vegetated areas. It then removes noise patches through connected component analysis and area filtering to accurately identify the distribution areas of cyanobacterial blooms. Finally, the system outputs hourly distribution maps, area change maps, and automatically generated textual and graphic monitoring reports for cyanobacterial blooms. It supports visually displaying the spatial pattern and dynamic trends of cyanobacterial blooms through image sequences, time-based statistical curves, and frequency maps.

[0051] In summary, the intraday high-frequency automatic monitoring method and system for cyanobacterial blooms proposed in this invention are not only suitable for large lakes, but also particularly suitable for continuous dynamic monitoring of medium and large lakes during the peak period of cyanobacterial blooms. It has broad application value and can serve fields such as cyanobacterial early warning, water quality risk assessment and aquatic ecological protection, and has good prospects for promotion.

[0052] A method and system for daily high-frequency automatic monitoring of cyanobacterial blooms, the specific detection method including the following steps:

[0053] S1. Obtain data: Obtain radiometrically calibrated atmospheric apparent reflectance data of the area to be tested via satellite. In this embodiment, the GOCI-II LA product is used. The LA-level data product provides radiometrically calibrated atmospheric apparent reflectance data (Bottom-of-Atmosphere Corrected Reflectance Data) with a spatial resolution of 250m and a temporal resolution of 1 hour.

[0054] S2. Data processing: Rayleigh scattering correction and projection transformation are performed on the S1 data.

[0055] The data processing includes the following steps:

[0056] S21. Implemented by calling the GOCI-II Toolbox (GTBX) plugin module and its RayleighCorrection processor in the SNAP software.

[0057] S22, Implemented by calling the GOCI-II Toolbox (GTBX) plugin module and its Reproject processor in the SNAP software.

[0058] S3. Construct an analysis mask to identify cloud-covered areas and, in conjunction with the vegetation frequency index, determine the spatial mask for extracting cyanobacterial blooms.

[0059] The analysis and processing includes the following steps:

[0060] S31. By analyzing the reflectance of the Rrc (aerosol) and Rrc (red) bands in GOCI-II images, high reflectance areas are identified based on preset threshold combinations, and these areas are determined to be cloud-covered pixels. The cloud mask area is then expanded using three morphological dilation operations of 3×3 structural elements.

[0061] S32. Based on cloud masking, a decision tree model is constructed using NDVI and NDWI indices to classify pixels within the lake area as either vegetation or non-vegetation and perform binarization. Subsequently, the spatial distribution frequency method is used to calculate the vegetation frequency index SDFI. vegetation ;

[0062] Among them, NDWI, NDVI and SDFI vegetation The specific formula is:

[0063] NDWI=(Rrc(green)-Rrc(nir)) / (Rrc(green)+Rrc(nir)) (1)

[0064] NDVI=(Rrc(nir)-Rrc(red)) / (Rrc(nir)+Rrc(red)) (2)

[0065]

[0066] In the formula, Rrc(red), Rrc(green), and Rrc(nir) are the Rayleigh scattering corrected atmospheric apparent reflectances for the red, green, and near-infrared bands, respectively, corresponding to the corresponding bands of GOCI-II; R i_vegetationLet R be the value of the vegetation pixel in the binary vegetation map on day i. i_novegetation Given the non-vegetation raster values ​​in the binary vegetation map of day i, obtain the vegetation frequency (SDFI) over a specified time period in pixels. vegetation .

[0067] S33. Based on the phenological characteristics of lake aquatic plant growth, establish a vegetation frequency SDFI. vegetation The time interval and threshold were used to determine the area with low vegetation frequency as the extraction range for cyanobacterial blooms.

[0068] S4. Extract the cyanobacterial bloom region, calculate the relevant spectral indices, and identify the distribution of cyanobacterial blooms according to the set rules.

[0069] The analysis and processing includes the following steps:

[0070] S41. The improved Floating Algae Index (IFAI) and the Density Spectrum Indices (NDVI) are used for extraction in cyanobacterial bloom areas to eliminate false positives caused by cloud interference and non-bloom high reflectivity areas. The formula for calculating the improved Floating Algae Index (IFAI) is as follows:

[0071] IFAI=Rrc(red2)-Rrc′(red2) (4)

[0072]

[0073] In the formula, Rrc(red1), Rrc(red2), and Rrc(red3) represent the apparent atmospheric reflectance of satellite image pixels after Rayleigh scattering correction in the three red-related bands, respectively, and λ represents the center wavelength of the corresponding band. IFAI is used to enhance the spectral response characteristics of floating algae, and NDVI is used to suppress vegetation background interference; the two are used together for algal bloom identification. By setting a combined threshold for IFAI and NDVI, it is used to assist in identifying cyanobacterial bloom pixels and suppress misjudgments caused by highly reflective non-target areas.

[0074] S42. Connectivity analysis is performed on the initially extracted cyanobacterial bloom mask image to identify continuously distributed patch regions, and small noise regions are removed based on area thresholds. Subsequently, morphological processing methods are used to optimize the bloom boundary to improve the spatial integrity and edge smoothness of the extraction results.

[0075] S5 generates monitoring results, outputs cyanobacterial bloom distribution maps, area statistics, and monitoring reports, and supports automated processing and rapid response.

[0076] The analysis and processing includes the following steps:

[0077] S51. Based on the cyanobacterial bloom mask image, perform pixel-level area statistics on the extracted results, calculate the bloom coverage area, and record the change information.

[0078] S52, Combination Figure 4 As shown, a thematic map of cyanobacterial blooms is generated based on spatial distribution characteristics, and then overlaid with a base map, time information, and other elements for visualization.

[0079] S53, Combination Figure 2 , Figure 3 and Figure 4 As shown, a monitoring report containing monitoring time, algal bloom distribution map, area statistics and other information is generated based on a preset template, realizing automated output and timely response of monitoring results.

[0080] To verify the accuracy of the method described in this invention, relevant data comparisons are performed below.

[0081] like Figure 1 As shown, the Jiangsu Provincial Environmental Monitoring Center conducts routine monitoring of cyanobacterial blooms in Taihu Lake, combining MODIS, Fengyun-3 satellite imagery, and NDVI indicators, supplemented by manual visual interpretation. This monitoring result is considered authoritative data, having undergone multi-departmental discussion and manual revision, and can accurately depict the spatial distribution of cyanobacterial blooms. To evaluate the accuracy of the method proposed in this study, the area of ​​cyanobacterial blooms extracted at similar times was compared with the aforementioned official results. The comparison results for 2024-2025 show a high degree of consistency between the two, with a coefficient of determination (R²) of 100%. 2 The value was 0.8540, and the root mean square error (RMSE) was 34.49 km. 2 Currently, Jiangsu Province only conducts operational monitoring of cyanobacterial blooms in Taihu Lake. Therefore, the scatter plots drawn in this study are based on the area comparison results of cyanobacterial blooms in Taihu Lake. However, the method proposed in this study has good versatility and is applicable to the monitoring of cyanobacterial blooms in other inland lakes and water bodies worldwide, in addition to Taihu Lake.

[0082] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method and system for daily high-frequency automatic monitoring of cyanobacterial blooms, characterized in that, Includes the following steps: S1. Obtain data: Obtain the atmospheric apparent reflectance data of the area to be tested after radiometric calibration via satellite; S2. Data preprocessing: Rayleigh scattering correction and projection transformation are performed on the S1 data; S3. Construct an analysis mask to identify cloud-covered areas and, in conjunction with the vegetation frequency index, determine the spatial mask for extracting cyanobacterial blooms. S4. Extract the cyanobacterial bloom region, calculate the relevant spectral indices, and identify the distribution of cyanobacterial blooms according to the set rules; S5 generates monitoring results, outputs cyanobacterial bloom distribution maps, area statistics, and monitoring reports, and supports automated processing and rapid response.

2. The method and system for intraday high-frequency automatic monitoring of cyanobacterial blooms according to claim 1, characterized in that, The satellite in question is the GOCI-II satellite.

3. The intraday high-frequency automatic monitoring method and system for cyanobacterial blooms according to claim 2, characterized in that, The Rayleigh scattering correction process in S2 includes the following steps: S21. Implemented by calling the GOCI-II Toolbox (GTBX) plugin module and its Rayleigh Correction processor in the SNAP software.

4. The intraday high-frequency automatic monitoring method and system for cyanobacterial blooms according to claim 3, characterized in that, The projection conversion process in S2 includes the following steps: S22, Implemented by calling the GOCI-II Toolbox (GTBX) plugin module and its Reproject processor in the SNAP software.

5. The intraday high-frequency automatic monitoring method and system for cyanobacterial blooms according to claim 4, characterized in that, The step of identifying the cloud coverage area in S3 includes the following steps: S31. By analyzing the reflectance of the Rrc (aerosol) and Rrc (red) bands in GOCI-II images, high reflectance areas are identified based on preset threshold combinations, and these areas are determined to be cloud-covered pixels. The cloud mask area is then expanded using three morphological dilation operations of 3×3 structural elements.

6. The method and system for daily high-frequency automatic monitoring of cyanobacterial blooms according to claim 5, characterized in that, The determination of the spatial mask for cyanobacterial bloom extraction using the vegetation frequency index in S3 includes the following steps: S32. Based on cloud masking, a decision tree model is constructed using NDVI and NDWI indices to classify pixels within the lake area as either vegetation or non-vegetation and perform binarization. Subsequently, the spatial distribution frequency method is used to calculate the vegetation frequency index SDFI. vegetation ; Among them, NDWI, NDVI and SDFI vegetation The specific formula is: NDWI=(Rrc(green)-Rrc(nir)) / (Rrc(green)+Rrc(nir)) (1) NDVI=(Rrc(nir)-Rrc(red)) / (Rrc(nir)+Rrc(red)) (2) In the formula, Rrc(red), Rrc(green), and Rrc(nir) are the Rayleigh scattering corrected atmospheric apparent reflectances for the red, green, and near-infrared bands, respectively, corresponding to the corresponding bands of GOCI-II; R i_vegetation Let R be the value of the vegetation pixel in the binary vegetation map on day i. i_novegetation Given the non-vegetation raster values ​​in the binary vegetation map of day i, obtain the vegetation frequency (SDFI) over a specified time period in pixels. vegetation ; S33. Based on the phenological characteristics of lake aquatic plant growth, establish a vegetation frequency SDFI. vegetation The time interval and threshold were used to determine the area with low vegetation frequency as the extraction range for cyanobacterial blooms.

7. The intraday high-frequency automatic monitoring method and system for cyanobacterial blooms according to claim 6, characterized in that, The identification of cyanobacterial bloom distribution in S4 includes the following steps: S41. The improved Floating Algae Index (IFAI) and the Density Spectrum Indices (NDVI) are used for extraction in cyanobacterial bloom areas to eliminate false positives caused by cloud interference and high reflectivity areas outside of blooms. The formula for calculating the improved Floating Algae Index (IFAI) is as follows: IFAI=Rrc(red2)-Rrc′(red2) (4) In the formula, Rrc(red1), Rrc(red2), and Rrc(red3) represent the apparent atmospheric reflectance of satellite image pixels after Rayleigh scattering correction in the three red-related bands, respectively, and λ represents the center wavelength of the corresponding band. IFAI is used to enhance the spectral response characteristics of floating algae, and NDVI is used to suppress vegetation background interference; the two are used together for algal bloom identification. By setting a combined threshold for IFAI and NDVI, it is used to assist in identifying cyanobacterial bloom pixels and suppress misjudgments caused by highly reflective non-target areas. S42. Connectivity analysis is performed on the initially extracted cyanobacterial bloom mask image to identify continuously distributed patch regions, and small noise regions are removed based on area thresholds. Then, morphological processing methods are used to optimize the bloom boundary to improve the spatial integrity and edge smoothness of the extraction results.

8. The intraday high-frequency automatic monitoring method and system for cyanobacterial blooms according to claim 7, characterized in that, The steps in S5 to generate monitoring results and output cyanobacterial bloom distribution maps, area statistics, and monitoring reports include the following: S51. Based on the cyanobacterial bloom mask image, perform pixel-level area statistics on the extracted results, calculate the bloom coverage area, and record the change information. S52. Generate a thematic map of cyanobacterial blooms based on spatial distribution characteristics, and overlay elements such as the base map and time information for visualization. S53. Generate a monitoring report based on a preset template, including monitoring time, algal bloom distribution map, area statistics, etc., to achieve automated output and timely response of monitoring results.

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