A Multi-Source Satellite High Spatiotemporal Resolution Monitoring Method for Cyanobacterial Bloom in Lake Taihu

Abstract

The present invention relates to the field of environmental monitoring technology, and specifically to a multi-source satellite high-temporal-spatial-resolution monitoring method for cyanobacteria blooms in Lake Taihu, comprising a Rayleigh correction method based on a 6SV radiation transfer model and a high-temporal-spatial-resolution cyanobacteria bloom monitoring method based on data fusion. The present invention applies a flexible spatiotemporal-data fusion method to generate FAI index information with high spatiotemporal-resolution. Benefiting from the high temporal-resolution characteristics of geostationary satellites, it can provide timely early warnings and information on the diurnal changes of cyanobacteria in Lake Taihu to environmental monitoring departments and scientific research departments around Lake Taihu, which is beneficial for local departments to promptly carry out cyanobacteria bloom prevention and control and cyanobacteria bloom outbreak cause analysis. At the same time, based on the high-spatial-resolution cyanobacteria bloom distribution map generated by the flexible spatiotemporal-data fusion method, it is possible to timely observe small-scale cyanobacteria bloom outbreaks and reduce missed judgments of cyanobacteria bloom outbreaks.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a multi-source satellite high temporal and spatial resolution monitoring method for cyanobacteria blooms in Taihu Lake. Background Art

[0002] Cyanobacterial blooms often occur in the summer months of June to September, with distinct seasonality, and are affected by temperature, sunlight, and nutrients. When temperatures are above 20°C, the pH value of the water is high, and the light intensity is strong and prolonged, cyanobacteria form air sacs that rise to the surface and multiply rapidly, leading to the formation of cyanobacterial blooms. Cyanobacterial blooms have multiple harmful effects, such as foul odors, deteriorating water quality, reducing fish habitats, and severely consuming oxygen in the water, leading to the death of aquatic organisms. In my country, cyanobacterial blooms are usually monitored by satellites.

[0003] Remote sensing monitoring of cyanobacterial blooms primarily relies on single-satellite index methods. Commonly used indices include the Normalized Difference Vegetation Index (NDVI), the Enhanced Difference Vegetation Index (EDVI), and the Normalized Difference Vegetation Index (NDVI). These three techniques share a very similar processing flow: they combine and calculate atmospherically corrected remote sensing reflectance to produce an index representing the intensity of the cyanobacterial bloom. A threshold for the index is then set to determine if a cyanobacterial bloom has occurred.

[0004] Of these three indices, the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) are sensitive to thin clouds and water surface reflection, making them poor indicators of cyanobacterial blooms. Furthermore, the Normalized Difference Vegetation Index (FAI), because it uses red and near-infrared wavelengths, includes fluorescence signals from other phytoplankton in the water, making it prone to misjudgment. Therefore, the FAI is generally considered the best cyanobacterial bloom index. However, in actual cyanobacterial bloom monitoring, the short duration and rapid diurnal variability of cyanobacterial blooms, coupled with the limitations of single satellite performance, make it difficult to simultaneously monitor bloom dynamics with high temporal and spatial resolution using FAI-based single-satellite monitoring. Furthermore, the calculation of the FAI requires a complex Rayleigh correction process, which many geostationary satellites with high temporal resolution lack. Summary of the Invention

[0005] The present invention aims to provide a multi-source satellite high-temporal-spatial-resolution monitoring method for cyanobacteria blooms in Lake Taihu, in order to address the problems raised in the above-mentioned background art. To achieve the above-mentioned object, the present invention provides the following technical solutions: a multi-source satellite high-temporal-spatial-resolution monitoring method for cyanobacteria blooms in Lake Taihu, comprising a Rayleigh correction method based on the 6SV radiation transfer model and a high-temporal-spatial-resolution cyanobacteria bloom monitoring method based on data fusion;

[0006] The steps of the Rayleigh correction method based on the 6sv radiation transfer model are as follows:

[0007] The first step is to perform radiometric calibration on the downloaded meteorological satellite data to obtain the top atmospheric reflectivity R TOA ;

[0008] In the second step, according to the satellite azimuth and solar zenith angle information carried by the meteorological satellite, the 6SV radiation transfer model is used to simulate the radiation transfer for each pixel. The apparent reflectivity obtained at this time is the Rayleigh reflectivity R r ;

[0009] The third step is to convert the Rayleigh reflectivity R r Perform Rayleigh correction to obtain the reflectivity R after Rayleigh correction rc It can be obtained by removing the Rayleigh reflectivity from the top atmospheric reflectivity, that is, R rc =R TOA -R r， in:

[0010] R rc : reflectivity after Rayleigh correction;

[0011] R TOA : reflectivity of the top atmosphere;

[0012] R r : Rayleigh reflectivity;

[0013] The steps of the high spatiotemporal resolution cyanobacteria bloom monitoring method based on data fusion are as follows:

[0014] S1, select the normalized vegetation index calculation results of the high-resolution satellite and the low-resolution satellite at time t1 and the normalized vegetation index calculation results of the low-resolution satellite at time t2;

[0015] S2, using the ISODATA unsupervised classification algorithm, classifies the normalized vegetation index of the high spatial resolution satellite at time t1;

[0016] S3, based on the satellite images of low spatial resolution satellites at time t1 and time t2, the change of normalized vegetation index in each class is estimated using the least squares method;

[0017] S4, using the estimated NDVI change, assuming that the NDVI type of each pixel does not change, estimate the high-resolution NDVI at time t2;

[0018] S5, resample the estimated high-resolution satellite NDVI at time t2 to the resolution of the satellite image, calculate the residual, and use TPS interpolation to interpolate the residual to high spatial resolution;

[0019] S6, adding the resampled residual and the estimated high spatial resolution normalized vegetation index at time t2 to obtain the final prediction result.

[0020] Preferably, the Rayleigh correction method based on the 6SV radiation transfer model is applicable to meteorological satellites for which no suitable Rayleigh correction method is available.

[0021] Preferably, the high spatiotemporal resolution cyanobacteria bloom monitoring method based on data fusion is applicable to a single meteorological satellite that cannot achieve high spatiotemporal resolution cyanobacteria bloom monitoring.

[0022] Preferably, in the first step, the meteorological satellites are the Sunflower-8 meteorological satellite and the GK2A meteorological satellite.

[0023] Preferably, in the second step, the aerosol type is set to no aerosol and the ground reflectivity is set to 0 during the simulation process.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention provides a simple and easy technical solution for Rayleigh correction. This method has achieved good results on the Sunflower-8 satellite and the GK-2A satellite and can be applied to many aspects of inland and offshore water environment monitoring, such as algal bloom monitoring, water quality monitoring, and water pollution monitoring.

[0026] This paper applies a flexible spatiotemporal data fusion method to generate high-resolution FAI index information. Leveraging the high temporal resolution of geostationary satellites, this method can provide timely warnings and diurnal information on cyanobacteria in Taihu Lake to environmental monitoring and research agencies surrounding the lake, facilitating timely prevention and control of cyanobacterial blooms and analysis of their causes. Furthermore, the high-resolution cyanobacterial bloom distribution map generated by this flexible spatiotemporal data fusion method can promptly detect small-scale cyanobacterial blooms, reducing the risk of missed blooms. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the Rayleigh correction method based on the 6sv radiation transfer model of the present invention;

[0028] Figure 2 This is a flow chart of the high spatiotemporal resolution cyanobacteria bloom monitoring method based on data fusion of the present invention;

[0029] Figure 3 A true color image of a lake area captured by the MODIS satellite in an embodiment of the present invention;

[0030] Figure 4 is a normalized vegetation index map calculated in an embodiment of the present invention;

[0031] Figure 5 The following are true color composite images (upper row) and FAI index images (lower row) obtained based on the Sunflower-8 satellite in an embodiment of the present invention;

[0032] Figure 6 This is a normalized vegetation index map obtained by integrating the Sunflower-8 satellite and the MODIS satellite in an embodiment of the present invention;

[0033] Figure 7 This is a flow chart of the existing cyanobacteria bloom remote sensing monitoring process of the present invention. DETAILED DESCRIPTION

[0034] 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. All other embodiments obtained by ordinary technical personnel in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0035] See also Figures 1 to 7 , the present invention provides a technical solution: a multi-source satellite high temporal and spatial resolution monitoring method for cyanobacteria blooms in Taihu Lake, including a Rayleigh correction method based on the 6SV radiation transfer model and a high temporal and spatial resolution cyanobacteria bloom monitoring method based on data fusion;

[0036] The steps of the Rayleigh correction method based on the 6sv radiation transfer model are as follows:

[0037] The first step is to perform radiometric calibration on the downloaded meteorological satellite data to obtain the top atmospheric reflectivity R TOA ;

[0038] In the second step, according to the satellite azimuth and solar zenith angle information carried by the meteorological satellite, the 6SV radiation transfer model is used to simulate the radiation transfer for each pixel. The apparent reflectivity obtained at this time is the Rayleigh reflectivity R r ;

[0039] The third step is to convert the Rayleigh reflectivity R r Perform Rayleigh correction to obtain the reflectivity R after Rayleigh correction rc It can be obtained by removing the Rayleigh reflectivity from the top atmospheric reflectivity, that is, R rc =R TOA -R r， in:

[0040] R rc : reflectivity after Rayleigh correction;

[0041] R TOA : reflectivity of the top atmosphere;

[0042] Rr : Rayleigh reflectivity;

[0043] The steps of the high spatiotemporal resolution cyanobacteria bloom monitoring method based on data fusion are as follows:

[0044] S1, select the normalized vegetation index calculation results of the high-resolution satellite and the low-resolution satellite at time t1 and the normalized vegetation index calculation results of the low-resolution satellite at time t2;

[0045] S2, using the ISODATA unsupervised classification algorithm, classifies the normalized vegetation index of the high spatial resolution satellite at time t1;

[0046] S3, based on the satellite images of low spatial resolution satellites at time t1 and time t2, the change of normalized vegetation index in each class is estimated using the least squares method;

[0047] S4, using the estimated NDVI change, assuming that the NDVI type of each pixel does not change, estimate the high-resolution NDVI at time t2;

[0048] S5, resample the estimated high-resolution satellite NDVI at time t2 to the resolution of the satellite image, calculate the residual, and use TPS interpolation to interpolate the residual to high spatial resolution;

[0049] S6, adding the resampled residual and the estimated high spatial resolution normalized vegetation index at time t2 to obtain the final prediction result.

[0050] In this embodiment, the Rayleigh correction method based on the 6SV radiation transfer model is applicable to meteorological satellites that do not have a suitable Rayleigh correction method.

[0051] In this embodiment, the high spatiotemporal resolution cyanobacteria bloom monitoring method based on data fusion is applicable to a single meteorological satellite that cannot achieve high spatiotemporal resolution cyanobacteria bloom monitoring.

[0052] In this embodiment, in the first step, the meteorological satellites are the Sunflower-8 meteorological satellite and the GK2A meteorological satellite.

[0053] In this embodiment, in the second step, the aerosol type is set to no aerosol and the ground reflectivity is set to 0 during the simulation process.

[0054] The FAI index map and true color composite map obtained by the Sunflower satellite and MODIS satellite are as follows: Figure 4 and Figure 3 As shown, from Figure 4 and Figure 3It can be seen that a cyanobacterial bloom occurred that morning. Since the MODIS satellite is a polar-orbiting satellite, it only acquired one satellite image at 11:30 a.m. However, the image from the Sunflower-8 satellite shows that the algal bloom had already occurred around 9:30 a.m.

[0055] The fused results have a higher spatial resolution, allowing for a clearer view of the algal bloom process starting at 9:30 a.m. that day. The fused results also have a higher spatiotemporal resolution, making it easier for local environmental monitoring departments to issue early warnings in a timely manner, and for relevant scientific research departments to study the diurnal variations and effectiveness patterns of cyanobacterial blooms, which is conducive to further addressing the environmental pollution problem of cyanobacterial blooms.

[0056] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-source satellite high temporal and spatial resolution monitoring method for cyanobacteria blooms in Lake Taihu, characterized by: The method comprises: Rayleigh correction method based on 6sv radiation transfer model; High spatiotemporal resolution cyanobacteria bloom monitoring method based on data fusion; The steps of the Rayleigh correction method based on the 6sv radiation transfer model are as follows: The first step is to perform radiometric calibration on the downloaded meteorological satellite data to obtain the top-of-atmosphere reflectivity RTOA; The second step is to simulate the radiation transfer of each pixel using the 6sv radiation transfer model based on the satellite observation geometry information, including the satellite azimuth and the solar zenith angle, to obtain the Rayleigh reflectivity Rr; The third step is to perform Rayleigh correction by removing the Rayleigh scattering component to obtain the corrected reflectivity Rrc. The calculation formula is Rrc=RTOA-Rr, where: Rrc: Rayleigh-corrected reflectivity; RTOA: reflectivity of the top of the atmosphere; Rr: Rayleigh reflectivity; The high spatiotemporal resolution cyanobacteria bloom monitoring method based on data fusion comprises the following steps: S1: Select the Normalized Difference Vegetation Index (NDVI) data of the high-resolution satellite and the low-resolution satellite at time t1, and the NDVI data of the low-resolution satellite at time t2; S2: Use ISODATA unsupervised classification algorithm to classify high spatial resolution NDVI data at time t1; S3: Based on the low spatial resolution NDVI data at time t1 and t2, the least squares method is used to estimate the change in NDVI of each category; S4: Assuming that the vegetation category of each pixel remains unchanged, estimate the high spatial resolution NDVI at time t2; S5: Resample the estimated high spatial resolution NDVI at time t2 to the spatial scale of the low spatial resolution image and calculate the residual; use the thin plate spline (TPS) interpolation method to interpolate the residual to high spatial resolution; S6: Add the interpolation residual to the estimated high spatial resolution NDVI at time t2 to obtain the final prediction result.

2. The multi-source satellite high temporal and spatial resolution monitoring method for cyanobacteria blooms in Lake Taihu according to claim 1, characterized in that: The Rayleigh correction method based on the 6SV radiation transfer model is applicable to meteorological satellite data that lacks support from existing Rayleigh correction methods.

3. The multi-source satellite high temporal and spatial resolution monitoring method for cyanobacteria blooms in Lake Taihu according to claim 1, characterized in that: The high-temporal-spatial-resolution cyanobacteria bloom monitoring method based on data fusion is applicable to scenarios where a single satellite cannot achieve high-temporal-spatial-resolution cyanobacteria bloom monitoring.

4. The method for monitoring cyanobacteria blooms in Lake Taihu using multi-source satellites with high spatiotemporal resolution according to claim 1, characterized in that: The meteorological satellite used in the first step includes the Sunflower-8 meteorological satellite or the GK2A meteorological satellite.

5. The method for monitoring cyanobacteria blooms in Lake Taihu using multi-source satellites with high spatiotemporal resolution according to claim 1, characterized in that: When performing the radiation transfer simulation in the second step, set the aerosol type to "no aerosol" and the ground reflectivity to 0.

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