Method for Converting Algal Bloom Area Scale Based on Point Spread Function Simulation

Abstract

The present invention discloses a method for converting the scale of algal bloom area based on point spread function simulation. Satellite remote sensing data with two different spatial resolutions are obtained, and the point spread function is used to simulate the remote sensing reflectance of the higher-resolution remote sensing data at different spatial scales. Using the simulated point spread function, the simulated reflectance when the resolution is the same as that of the lower-resolution remote sensing data is deduced. With the goal of maximizing the correlation between the simulated reflectance and the remote sensing reflectance of the lower-resolution remote sensing data, the optimal solution of the point spread function parameters is obtained. The point spread function with determined parameters is used to simulate the remote sensing reflectance of two remote sensing data with different spatial resolutions. After extracting the algal bloom area based on the simulated remote sensing reflectance, the relationship between the algal bloom areas of the simulated data with different spatial resolutions and the change of spatial resolution is analyzed, and an area conversion formula is established. The method of the present invention can supplement and improve the method system of lake environment remote sensing and algal bloom prediction and early warning, and improve the collaborative monitoring of multi-source data of algal blooms in eutrophic lakes.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite remote sensing and water environment analysis, and particularly relates to a method for converting the area scale of algal blooms based on the simulation of the point spread function. Background Art

[0002] Lake eutrophication and harmful algal blooms are common water ecological environment problems faced worldwide. The frequent outbreak of cyanobacterial blooms caused by lake eutrophication is the main challenge faced by Chinese freshwater lakes at present. Driven by the coupling of various environmental factors (external factors), cyanobacteria, due to their unique physiological and ecological characteristics (internal factors), produce a huge biomass and dominate the phytoplankton community. Under suitable hydrometeorological conditions, a large number of algal particles gather on the water surface to form cyanobacterial blooms. Research shows that cyanobacterial blooms have strong spatio-temporal variability, and the area of algal blooms changes greatly in a short time.

[0003] Satellite remote sensing has the advantages of wide range, rapidity, and real-time. However, monitoring satellites have different temporal (hours, days, several days) and spatial resolutions (1 km, 750 m, 500 m, 30 m, 10 m, 1 m...), and the mixed pixel effect is obvious. In addition, for these satellites, different algal bloom extraction methods (single threshold, NDVI, EVI, FAI, etc.) have been developed, and the scale effect is obvious. Different operators using different methods obtain quite different algal bloom areas. It is necessary to develop a method for converting the area scale of algal blooms based on the remote sensing mechanism to make up for the inconsistency of algal bloom monitoring results of data with different spatial resolutions. The traditional linear interpolation method does not conform to the law of signals from surrounding pixels in the process of atmospheric-water body radiation transmission. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for converting the area scale of algal blooms based on the simulation of the point spread function.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] The method for converting the area scale of algal blooms based on the simulation of the point spread function includes:

[0007] Obtain two satellite remote sensing data with different spatial resolutions, namely the first remote sensing data with higher resolution and the second remote sensing data with lower resolution; use the point spread function to simulate the remote sensing reflectance of the first remote sensing data at different spatial scales;

[0008] Using the simulated point spread function, deduce the remote sensing reflectance of the first remote sensing data when the resolution is the same as that of the second remote sensing data, denoted as the simulated reflectance; take the highest correlation between the simulated reflectance of the first remote sensing data and the remote sensing reflectance of the second remote sensing data as the goal, and obtain the optimal solution of the point spread function parameters;

[0009] The remote sensing reflectance of the first remote sensing data and the second remote sensing data with different spatial resolutions is simulated using a point spread function with determined parameters.

[0010] The algal bloom areas are respectively extracted from the simulated first remote sensing data and second remote sensing data with different spatial resolutions, a relationship between the algal bloom areas of the simulated data with different spatial resolutions and the spatial resolution is established, an area conversion formula is established, and the conversion of the algal bloom areas extracted from satellite remote sensing data with different spatial resolutions is realized using the established area conversion formula.

[0011] As a preferred implementation manner, a point spread function in Gaussian form is used to simulate the remote sensing reflectance of the first remote sensing data at different spatial scales.

[0012] As a preferred implementation manner, the form of the point spread function is:

[0013]

[0014] In the formula, i and j are respectively the row and column numbers of the remote sensing data, σ is the diffusion radius, and x is the central point value, which is determined according to the size of the PSF operator.

[0015] As a preferred implementation manner, the screening of the synchronous data satisfies: the transit time is less than 1 h, the coefficient of variation CV within the range of 3*3 of the target pixel is <0.1, and there is no cloud.

[0016] As a preferred implementation manner, the FAI index is used to extract the algal bloom area; for satellite remote sensing data lacking the short-wave infrared band, the near-infrared band is used to replace the short-wave infrared band, and the AFAI index is used to extract the algal bloom area.

[0017] As a preferred implementation manner, the maximum gradient method is used to extract the algal bloom area.

[0018] As a preferred implementation manner, when the remote sensing reflectance of the first remote sensing data and the second remote sensing data with different spatial resolutions is simulated using a point spread function with determined parameters, the numerical range of the simulated resolution is [R1, R2], where R1 is the original spatial resolution of the second remote sensing data and R2 is the original spatial resolution of the first remote sensing data. Preferably, the simulated resolutions are selected at equal step lengths within [R1, R2].

[0019] The algal bloom area scale conversion method constructed by the method of the present invention is an important technology for improving the collaborative monitoring of multi-source data of algal blooms in eutrophic lakes, can supplement and improve the method system of lake environmental remote sensing and water bloom prediction and early warning, and promote its development, and has great scientific significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are not intended to be drawn to scale. In the accompanying drawings, each identical or nearly identical component shown in each figure may be denoted by the same reference numeral. For the sake of clarity, not every component is labeled in each figure. Now, embodiments of various aspects of the present invention will be described by way of example and with reference to the accompanying drawings, wherein:

[0021] Figure 1 is a comparison of OLI data and GOCI data images on May 3, 2020. The left figure is OLI and the right figure is GOCI.

[0022] Figure 2 is a schematic diagram of the point spread function.

[0023] Figure 3 is the optimized solution of R 2 and the average relative error.

[0024] Figure 4 is a scatter plot of FAI and AFAI of OLI and GOCI synchronous data.

[0025] Figure 5 is the change of the algal bloom area of OLI simulated data with different spatial resolutions, where (a)-(c) are the algal bloom areas extracted from the simulated reflectances of different resolutions of the OLI image on May 3, 2020, and (g) is the line graph of the corresponding date; (d)-(f) are the algal bloom areas extracted from the simulated reflectances of different resolutions of the OLI image on June 6, 2022, and (h) is the line graph of the corresponding date.

[0026] Figure 6 is a scatter plot of the algal bloom areas of OLI and GOCI synchronous data.

[0027] In the foregoing Figures 1-6, all coordinates, identifiers, or other representations in English form are well-known in the art and will not be elaborated herein. Detailed Embodiments

[0028] For a better understanding of the technical content of the present invention, specific embodiments are hereby given and described in conjunction with the accompanying drawings as follows.

[0029] In the present disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure do not necessarily define all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those concepts and embodiments described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed in the present invention are not limited to any embodiment. Additionally, some aspects of the present invention can be used alone, or in any suitable combination with any other aspects of the present invention.

[0030] Example 1

[0031] This example illustrates the method for converting the scale of algal bloom area based on the simulation of the point spread function of the present invention.

[0032] This example performs scale conversion of the algal bloom area based on remote sensing data with different spatial resolutions, and the implementation method is as follows:

[0033] Taking the high-spatial-resolution remote sensing data OLI and the medium-low-spatial-resolution remote sensing data GOCI as the research objects, using the point spread function to simulate the remote sensing reflectance Rrc of OLI at different spatial scales; then calculating the algal bloom identification index FAI of OLI data at different spatial scales, and using the maximum gradient method to extract the algal bloom area; analyzing the relationship between the threshold for algal bloom extraction and the algal bloom area and the spatial resolution.

[0034] As an exemplary description, the implementation of the foregoing method will be specifically described below with reference to the accompanying drawings.

[0035] 1) Simulate the point spread function of remote sensing data at different scales. Taking the medium-low-resolution remote sensing data as the benchmark, use the Gaussian-type point spread function PSF to simulate the remote sensing reflectance of high-spatial-resolution remote sensing data at different spatial scales.

[0036] Among them, the high-spatial-resolution remote sensing data is Landsat8 / 9 OLI, with a spatial resolution of 30m, simply referred to as OLI data; the medium-low-spatial-resolution satellite is GOCI, with a spatial resolution of 500m, simply referred to as GOCI data. Figure 1 It is a comparison of the OLI data and GOCI data images on May 3, 2020.

[0037] The form of the point spread function is:

[0038]

[0039] Among them, i and j are the row and column numbers of the OLI data respectively, σ is the diffusion radius, which needs to be determined by the optimization method, x is the central point value, which is determined according to the size of the PSF operator, and 23 is taken in this example. Specifically, the resolution of the GOCI remote sensing data is 500m, 3 pixels of GOCI are 1500m, 1500 / 30m = 50, but the point spread function is a convolution operation operator and must be an odd number, so 45 is selected as the operator size, that is, the PSF is an operator of 45*45 pixels, and the central point is the 23rd pixel. Figure 2 It is a schematic diagram of the point spread function.

[0040] 2) Establish the relationship between the remote sensing reflectance of OLI data and the remote sensing reflectance of GOCI data with medium and low spatial resolution. Use the method of optimization to determine the variable σ in the point spread function PSF. σ is related to wavelength and spatial resolution. Figure 3 R optimized under different σ conditions 2 and the average relative error.

[0041] Extract the synchronous data of OLI and GOCI, and calculate Rrc for different wavelength bands λ and different σ values according to the following formula (2). 500m Then, aiming at the highest correlation between the reflectance data of the OLI data simulated by PSF at a resolution of 500m and the remote sensing reflectance of GOCI data, obtain the optimal solution of σ, as shown in formula (3).

[0042] Rrc 500m (λ, σ λ ) = subsample(PSF(λ, σ λ ) * Rrc 30m (λ)) (2)

[0043] σ λ = argmaxR 2 (Rrc 500m (λ, σ λ ), G 500m (λ)) (3)

[0044] Among them, Rrc 500m (λ, σ λ ) is the reflectance data of the OLI data simulated by PSF at a resolution of 500m under the conditions of wavelength band λ and diffusion radius σ λ ; PSF(λ, σ λ ) is the point spread function under the conditions of wavelength band λ and diffusion radius σ λ ; Rrc 30m (λ) is the remote sensing reflectance value of the OLI data at the original resolution (30m); G 500m (λ) is the remote sensing reflectance of GOCI data under the condition of wavelength band λ.

[0045] The screening of the synchronous data of OLI and GOCI meets the following conditions: the transit time is less than 1h, the coefficient of variation CV (= standard deviation / mean) within the 3*3 range of the target pixel is <0.1, and there is no cloud.

[0046] 3) Use the optimal σ confirmed in 2) λSubstitute the value of the diffusion radius σ into Equation (1) to simulate the OLI remote sensing reflectance Rrc data with different spatial resolutions. Here, the different spatial resolutions range from 50 m to 500 m, with an interval of 50 m. For data with different spatial resolutions, calculate the floating algae index (FAI) of OLI, use the maximum gradient method to extract the algal bloom area, and analyze the variation law of the algal bloom area of OLI simulation data with different spatial resolutions with respect to the spatial resolution.

[0047] The floating algae index (FAI) of OLI data is not easily affected by changes in environmental and observational conditions such as aerosol type and thickness, solar altitude angle, and flares. It can more effectively penetrate thin clouds, and the threshold for extracting cyanobacterial blooms is also more stable (Hu 2009a).

[0048]

[0049] where λ RED = 655, λ NIR = 865, λ SWIR = 1609 nm. For GOCI data lacking shortwave infrared bands, the near-infrared band is used to replace the shortwave infrared band, and the AFAI index is proposed (Qi, Hu, Visser, and Ma 2018):

[0050]

[0051] For GOCI data, λ1 = 660, λ2 = 745, λ3 = 865 nm. Figure 4 Figure showing the scatter plot of the floating algae indices FAI and AFAI calculated from the synchronized OLI and GOCI data obtained using the point spread function.

[0052] Figure 5 Taking the Taihu Lake images on May 3, 2020, and June 6, 2021, as examples, the area I represents the algal bloom area obtained by determining the threshold using the maximum gradient method for data with different pixel resolutions. As the spatial resolution decreases, the mixed pixel effect increases, increasing the uncertainty of the algal bloom area. From 50 m to 500 m, the difference in the algal bloom area reaches 50%. Therefore, it is necessary to establish a functional relationship for the algal bloom area at different spatial scales to ensure the consistency of the extraction results of multi-source data.

[0053] 4) Established the relationship between the algal bloom extraction areas of synchronized OLI and GOCI data:

[0054] BA GOCI = BA OLI * 0.61 + 9.92 (6)

[0055] BA GOCIArea for GOCI data extraction, BA OLI Area for OLI data extraction, R 2 Is 0.96. Figure 6 It can be seen that when comparing the synchronous data of two time periods, after using the point spread function, the correlation of the algal bloom areas extracted from OLI and GOCI data is higher than that of the linear decomposition results.

Claims

1. A method for scale conversion of algal bloom area based on point spread function simulation, characterized in that, Including: Obtain satellite remote sensing data with two different spatial resolutions, namely the first remote sensing data with higher resolution and the second remote sensing data with lower resolution; Use the point spread function to simulate the remote sensing reflectance of the first remote sensing data at different spatial scales; Utilize the simulated point spread function to deduce the remote sensing reflectance of the first remote sensing data when its resolution is the same as that of the second remote sensing data, denoted as the simulated reflectance; aiming at the highest correlation between the simulated reflectance of the first remote sensing data and the remote sensing reflectance of the second remote sensing data, obtain the optimal solution of the point spread function parameters; Use the point spread function with determined parameters to simulate the remote sensing reflectance of the first remote sensing data and the second remote sensing data with different spatial resolutions; Utilize the simulated first remote sensing data and second remote sensing data with different spatial resolutions to extract the algal bloom area respectively, establish the relationship between the algal bloom area of the simulated data with different spatial resolutions and the spatial resolution, establish an area conversion formula, and use the established area conversion formula to realize the conversion of the algal bloom area extracted from satellite remote sensing data with different spatial resolutions.

2. The method according to claim 1, characterized in that, Use the point spread function in the form of Gaussian to simulate the remote sensing reflectance of the first remote sensing data at different spatial scales.

3. The method according to claim 1 or 2, characterized in that, The form of the point spread function is: ； In the formula, i and j are the row and column numbers of the remote sensing data respectively, σ is the diffusion radius, x is the value of the center point, which is determined according to the size of the PSF operator.

4. The method according to claim 1, characterized in that, Use the FAI index to extract the algal bloom area; for satellite remote sensing data lacking the shortwave infrared band, use the near-infrared band to replace the shortwave infrared band and use the AFAI index to extract the algal bloom area.

5. The method according to claim 1, characterized in that, Use the maximum gradient method to extract the algal bloom area.

6. The method according to claim 1, characterized in that, When using the point spread function with determined parameters to simulate the remote sensing reflectance of the first remote sensing data and the second remote sensing data with different spatial resolutions, the numerical range of the simulated resolution is [R1, R2], where R1 is the original spatial resolution of the second remote sensing data and R2 is the original spatial resolution of the first remote sensing data.

7. The method according to claim 6, characterized in that, Select the simulated resolutions at equal step lengths between [R1, R2].

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