A synchronous iterative inversion method for chlorophyll-a and suspended substance water surface spectrum

Abstract

The embodiment of the application discloses a kind of chlorophyll a and suspended matter water surface spectrum synchronous iteration inversion methods, the simulated spectrum of Hydrolight radiation transfer model is verified by water surface spectrum;Two combination factors that normalized reflectance data are simultaneously significantly strong correlation with chlorophyll a concentration and suspended matter concentration are selected as method factor;With chlorophyll a and suspended matter as initial variable, the iteration relationship is constructed to obtain the synchronous iteration inversion model of water surface spectrum chlorophyll a and suspended matter;Synchronous iteration model is applied to water surface spectrum data, and the synchronous iteration inversion result of the chlorophyll a and suspended matter of water body is obtained, can provide theoretical reference for water quality monitoring and water environment protection, it has important significance and value.

Description

Technical Field

[0001] This invention relates to the field of chlorophyll a and suspended matter concentration inversion, and more particularly to the field of synchronous substitution inversion of chlorophyll a and suspended matter concentration. Background Technology

[0002] Variations in the optical signals of water components affect the apparent optical properties of water bodies. The main factors influencing water reflectance include chlorophyll a, suspended solids, and colored soluble organic matter (CDOM). Chlorophyll a and suspended solids, as important optical substances in water, exhibit complex optical properties. CDOM has relatively simple optical properties; backscattering is negligible, and it exhibits absorption characteristics in the ultraviolet and blue light bands of the visible light region. Furthermore, the absorption spectrum decreases exponentially with increasing wavelength. Therefore, it is advisable to select locations with weaker absorption characteristics to avoid the absorption effect of CDOM in the water.

[0003] Current methods for chlorophyll a and suspended matter inversion mainly focus on single-element inversion, with conventional single-element inversion methods considering only the characteristic bands of a single element. Since the absorption and scattering effects of chlorophyll a and suspended matter vary independently yet influence each other, a better inversion method is obtained by utilizing the common sensitive bands of chlorophyll a and suspended matter through synchronous iteration to simultaneously obtain inversion results for both.

[0004] Therefore, how to construct a synchronous iterative inversion method for chlorophyll a and suspended matter has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method for simultaneous iterative inversion of chlorophyll a and suspended matter water surface spectra, which simultaneously retrieves chlorophyll a concentration and suspended matter concentration, providing a theoretical reference for water quality monitoring and water environment protection.

[0006] In a first aspect, the present invention provides a method for constructing a synchronous iterative model of chlorophyll a and suspended matter surface spectra, the method comprising:

[0007] Obtain measured water quality parameter data and water quality environmental parameters of the study water body. The measured water quality parameter data includes parameters such as chlorophyll a concentration, suspended solids concentration, and colored soluble organic matter.

[0008] The measured water quality parameters and the water quality environmental parameters of the study water body are input into the Hydrolight radiative transfer model to obtain the simulated spectral data of the study water body.

[0009] The 490nm and 660nm band data in the simulated spectral data were processed and correlated with the parameter data of chlorophyll a concentration, suspended matter concentration and colored soluble organic matter. The combined reflectance of the 490nm band and the combined reflectance of the 660nm band were screened out to show a significant strong correlation with chlorophyll a concentration and suspended matter concentration.

[0010] The measured chlorophyll a concentration and suspended matter concentration were input into the Hydrolight radiative transfer model to simulate and obtain the reflectance of chlorophyll a and suspended matter as single components.

[0011] A first multiple regression model was constructed based on the combined reflectance of the 490nm band and the single-component reflectance of chlorophyll a and suspended matter; a second multiple regression model was constructed based on the combined reflectance of the 660nm band and the single-component reflectance of chlorophyll a and suspended matter.

[0012] Using chlorophyll a concentration as the initial variable, a first iterative relationship between chlorophyll a concentration and suspended matter concentration was established based on the first and second multiple regression models. Using suspended matter concentration as the initial variable, a second iterative relationship between chlorophyll a concentration and suspended matter concentration was established based on the first and second multiple regression models. A synchronous iterative model of chlorophyll a concentration and suspended matter surface spectral density was constructed based on the first and second iterative relationships.

[0013] In some possible embodiments, methods for obtaining measured water quality parameter data include:

[0014] Chlorophyll a concentration was determined by a spectrophotometric method based on hot ethanol extraction.

[0015] The concentration of suspended solids was determined by the filtration-drying-calcination-weighing method.

[0016] Colored soluble organic compounds were determined using a spectrophotometer method.

[0017] The chlorophyll a concentration, suspended solids concentration, and colored soluble organic matter concentration are expressed using the absorption coefficient at the 440 nm wavelength band.

[0018] In some possible embodiments, methods for obtaining water surface spectral data include:

[0019] Water surface spectral data were acquired using an ASD spectrometer via above-water measurement.

[0020] Among them, the water surface remote sensing spectrum in the water surface spectral data is obtained by calculation based on wavelength λ, water surface spectral value Lt(λ), sky light spectral value Lsky(λ), gray board spectral value Lp(λ), and gray board reflectance ρp(λ) and sky light-air-water interface reflectance ρsky(λ) calibrated in the laboratory.

[0021] In some possible embodiments, the combined reflectance at 490 nm and 660 nm bands, which is significantly strongly correlated with chlorophyll a concentration and suspended matter concentration, includes:

[0022] Simultaneously, the combined reflectance of the 490nm and 660nm bands showed a significant and strong correlation with chlorophyll a concentration and suspended matter concentration, with absolute values ​​of correlation coefficients greater than 0.7 (P<0.01). and ).

[0023] In some possible embodiments, the coefficients of determination between the reflectance of a single component and the concentrations of chlorophyll a and suspended matter are all greater than 0.99.

[0024] On the other hand, the present invention provides a method for synchronous iteration of chlorophyll a and suspended matter surface spectra, the method comprising:

[0025] Obtain measured water quality parameter data for the studied water body;

[0026] The concentrations of chlorophyll a and suspended matter in the measured water quality parameters were input into the synchronous iterative model of chlorophyll a and suspended matter surface spectra and iterated repeatedly to obtain the synchronous iterative inversion results of chlorophyll a and suspended matter in the studied water body.

[0027] On the other hand, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, characterized in that the processor, when executing the computer program, implements the above-described evaluation method for algorithm implementation in chip design.

[0028] On the other hand, embodiments of the present invention provide a computer-readable medium having processor-executable non-volatile program code, characterized in that the program code causes the processor to execute the above-described evaluation method for algorithm implementation in chip design.

[0029] Compared with existing technologies, the synchronous iterative inversion method for chlorophyll a and suspended matter water surface spectra provided in this invention verifies the simulated spectrum of the Hydrolight radiative transfer model through water surface spectra; selects two combined factors that are significantly and strongly correlated with both normalized reflectance data and chlorophyll a concentration and suspended matter concentration as method factors; uses chlorophyll a and suspended matter as initial variables to construct iterative relationships to obtain a synchronous iterative inversion model for chlorophyll a and suspended matter in water surface spectra; applies the synchronous iterative model to water surface spectral data to obtain synchronous iterative inversion results for chlorophyll a and suspended matter in water bodies, which can provide theoretical reference for water quality monitoring and water environment protection, and has important significance and value. Attached Figure Description

[0030] Figure 1A flowchart of the method for constructing a synchronous iterative model of chlorophyll a and suspended matter on the water surface spectrum;

[0031] Figure 2 This is a schematic diagram comparing simulated and measured spectra.

[0032] Figure 3 A schematic diagram illustrating the correlation between normalized reflectance and the three factors with 490nm as the normalization base, provided for an embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram illustrating the correlation between normalized reflectance and the three factors with 660nm as the normalization base, provided in an embodiment of the present invention.

[0034] Figure 5 The reflectance contribution rate of chlorophyll a single component obtained using the Hydrolight model is provided in this embodiment of the invention. A schematic diagram illustrating the relationship between chlorophyll a concentration and chlorophyll a concentration;

[0035] Figure 6 The reflectance contribution of a single component of suspended matter obtained using the Hydrolight model is provided in this embodiment of the invention. A schematic diagram illustrating the relationship between [the concentration of suspended solids and the concentration of suspended solids].

[0036] Figure 7 A schematic diagram illustrating the inversion results of ground spectral chlorophyll a concentration and suspended matter concentration in the Taihu Lake region provided in an embodiment of the present invention;

[0037] Figure 8 This is a flowchart of a method for synchronous iterative analysis of chlorophyll a and suspended matter surface spectra. Detailed Implementation

[0038] To provide a theoretical reference for water quality monitoring and water environment protection, this invention validates the simulated spectrum of the Hydrolight radiative transfer model using water surface spectra. Two combined factors, both showing a significant and strong correlation between normalized reflectance data and chlorophyll-a concentration and suspended matter concentration, are selected as method factors. Using chlorophyll-a and suspended matter as initial variables, an iterative relationship is constructed to obtain a synchronous iterative inversion model for chlorophyll-a and suspended matter in water surface spectra. This synchronous iterative model is applied to water surface spectral data to obtain the synchronous iterative inversion results for chlorophyll-a and suspended matter in the water body. The embodiments provided in this invention disclose a synchronous iterative inversion method for chlorophyll-a and suspended matter in water surface spectra.

[0039] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0040] On the one hand, this invention provides a method for constructing a synchronous iterative model of chlorophyll a and suspended matter surface spectra. Figure 1 This is a flowchart of a method for constructing a synchronous iterative model of chlorophyll a and suspended matter surface spectra. The method includes:

[0041] S110: Obtain measured water quality parameters and water surface spectral data for the studied water body.

[0042] Measured water quality parameters and surface spectral data were obtained through field sampling. The measured water quality parameters included chlorophyll a concentration, suspended solids concentration, and colored soluble organic matter concentration, also known as the three-element concentrations.

[0043] Chlorophyll a concentration was determined using a spectrophotometric method based on hot ethanol extraction.

[0044] The total suspended solids concentration was determined by the filtration, drying, calcination, and weighing method.

[0045] Colored soluble organic matter (CDOM) was determined using a spectrophotometer, and the concentration can be expressed using the absorption coefficient at 440 nm. Water surface remote sensing reflectance was collected using an ASD spectrometer via a surface-to-surface measurement method, and the formula for calculating the water surface remote sensing reflectance is as follows:

[0046]

[0047] R rs (λ) represents the remote sensing spectral reflectance of the water surface, and λ is the wavelength; L t (λ), L sky (λ), L p (λ) represents the measured spectral values ​​of the water surface, sky light, and gray panel, respectively; ρ p (λ) is the reflectance of the gray board calibrated in the laboratory;

[0048] ρ sky (λ) represents the reflectivity of the sky-air-water interface.

[0049] The total absorption coefficient of a water body can be considered as the sum of the absorption coefficients of pure water, chlorophyll a, suspended solids, and CDOM in the water body. The total backscattering coefficient can be considered as the sum of the backscattering coefficients of pure water, chlorophyll a, and suspended solids in the water body. The remote sensing reflectance R of the water body rs The mathematical expression relating the water absorption coefficient and the water backscattering coefficient is shown below:

[0050]

[0051] Where f is an empirical parameter, f≈0.32-0.37; Q is the light field distribution parameter, affected by the solar altitude angle and observation angle; t is the interface transmission coefficient from water to atmosphere, usually taken as 0.98; n is the refractive index of water, usually taken as 1.34; a w (λ) is the absorption coefficient of pure water, b w (λ) is the scattering coefficient of pure water, b b,w (λ) is the backscattering coefficient of pure water; a chl (λ), a tsm (λ), a cdom (λ) represents the absorption coefficients of chlorophyll a, suspended matter, and CDOM, respectively; b b,chl (λ), b b,tsm (λ) are the backscattering coefficients of chlorophyll a and suspended matter, respectively.

[0052] S120: Simulated spectral data of the studied water body were obtained using the Hydrolight radiative transfer model.

[0053] Based on the principle of water radiative transfer, the Hydrolight radiative transfer model was used to obtain simulated spectral data of the lake water by considering the absorption and scattering effects of four components in the water body: pure water, chlorophyll a, suspended matter, and CDOM, combined with the water quality environment of the lake. Figure 2 This is a schematic diagram comparing simulated and measured spectra.

[0054] In one possible embodiment, the Hydrolight radiative transfer model is used to obtain simulated spectral data of the water body based on the absorption and scattering characteristics of water components and the water environment conditions.

[0055] S130: Screening for band combinations of reflectance that show a significant and strong correlation between chlorophyll a concentration and suspended matter concentration.

[0056] Normalized reflectance was obtained by using the 490nm and 660nm band data from the simulated spectral data as normalization bases. Correlation analysis was then performed between the normalized reflectance and water quality parameters to identify the 490nm and 660nm band reflectance combinations that showed a significant and strong correlation with chlorophyll a concentration and suspended solids concentration. The specific implementation is as follows:

[0057] First, the simulated spectral data were normalized using 490nm and 660nm as normalization bases.

[0058] Then, correlation analysis was performed on the normalized reflectance with chlorophyll a concentration, suspended solids concentration, and colored soluble organic matter (CDOM). Since the optical properties of water are the result of the combined effects of suspended solids, chlorophyll a, and CDOM, water reflectance can be considered as the sum of the contributions from these three components. Therefore, band combinations with weaker CDOM correlation can be selected and treated as a mixed reflectance of chlorophyll a and suspended solids concentration.

[0059] Finally, the reflectance of band combinations that showed a significant and strong correlation with chlorophyll a concentration and suspended matter concentration were selected. Figure 3 This is a schematic diagram illustrating the correlation between normalized reflectance and the three factors, with 490nm as the normalization base, provided in an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the correlation between normalized reflectance and the three elements, with 660nm as the normalization base, provided in an embodiment of the present invention. As shown in the figure, by comparing the correlation between the concentrations of the three elements and the normalized reflectance, The correlation coefficients with chlorophyll a concentration and suspended matter concentration were greater than 0.7 (P<0.01), showing a significant strong correlation, while there was basically no correlation with CDOM (r<0.2).

[0060] The core of constructing the iterative inversion model is to establish an iterative relationship that converges. This is based on the relationship between two band combinations that are significantly correlated with both suspended matter and chlorophyll a. These two band combinations can be regarded as the mixed reflectance composed of two components: chlorophyll a and suspended matter.

[0061] S140: Single-component reflectance obtained using the Hydrolight radiative transfer model

[0062] The reflectance contributed by chlorophyll a and suspended matter as individual components can be obtained through Hydrolight simulation. In one possible embodiment, the measured chlorophyll a concentration and suspended matter concentration are input into the Hydrolight radiative transfer model to simulate and obtain the reflectance of chlorophyll a as a single component. Suspended matter contributes to reflectance

[0063] Figure 5 This is the reflectance contribution rate of chlorophyll a single component obtained using the Hydrolight model, as provided in this embodiment of the invention. A schematic diagram illustrating the relationship between chlorophyll a concentration and chlorophyll a concentration; Figure 6This invention provides an embodiment of the reflectance contribution of a single component of suspended matter obtained using the Hydrolight model. The relationship between reflectance and concentration of suspended solids is shown in the figure. The coefficient of determination (R²) between the reflectance contributed by a single component and the concentration is greater than 0.99. A multiple regression model is constructed by using the relationship between mixed reflectance and reflectance contributed by a single component.

[0064] S150: Constructing the first and second multiple regression models

[0065] Multiple regression analysis aims to decompose the effects of chlorophyll a and suspended matter on mixed reflectance, thereby achieving the goal of inverting the concentration of individual components.

[0066] In one possible embodiment, based on the combined reflectivity of the 490nm band And the reflectance of chlorophyll a and suspended matter as a single component Construct the first multivariate regression model; based on the combined reflectance of the 660nm band. And the reflectance of chlorophyll a and suspended matter as a single component Construct a second multiple regression model.

[0067] The first and second multiple regression models use chlorophyll a concentration and suspended matter concentration as variables, respectively. The mathematical expression for the multiple regression analysis of the first multiple regression model is:

[0068]

[0069] The mathematical expression for the multiple regression analysis of the second regression model is:

[0070]

[0071] in, For the combined reflectivity of the 490nm band, The reflectance of a single component of chlorophyll a concentration in the 490nm band. For the concentration of suspended matter and the reflectance of a single component in the 490nm band, For the combined reflectivity of the 660nm band, The reflectance of a single component of chlorophyll a concentration in the 660nm band. denoted as the single-component reflectance of suspended matter concentration in the 660nm band, where a, b, and c are the model parameters of the first multiple regression model, and d, e, and f are the model parameters of the second multiple regression model.

[0072] S160: Establish the first and second iterative relationships, and construct a synchronous iterative model for the chlorophyll a and suspended matter surface spectra based on the first and second iterative relationships.

[0073] The model can use chlorophyll a concentration (chl) and suspended solids concentration (tsm) as initial variables, respectively. The initial concentration values ​​can be set arbitrarily, based on... Chlorophyll a contributes to reflectance Suspended matter contributes to reflectivity The relationship, and Chlorophyll a contributes to reflectance Suspended matter contributes to reflectivity By iterating through the relationship, the chlorophyll a concentration and suspended matter concentration of the ground spectrum in the Taihu Lake area can be obtained.

[0074] Using chlorophyll a concentration as the initial variable, a first iterative relationship between chlorophyll a concentration and suspended matter concentration was established based on the first and second multiple regression models. Using suspended matter concentration as the initial variable, a second iterative relationship between chlorophyll a concentration and suspended matter concentration was established based on the first and second multiple regression models. A synchronous iterative model of chlorophyll a concentration and suspended matter surface spectral density was constructed based on the first and second iterative relationships.

[0075] The mathematical expression for the first iterative relation is:

[0076]

[0077] tsm=((Rrs(785 / 490)-0.12068-(chl*0.00408+0.0078)*0.78882) / 0.81477-

[0078] 0.00411) / 0.00701)

[0079] Where chl is the initial variable representing the chlorophyll a concentration. The combined reflectivity of the 660nm band is [missing information]. The combined reflectivity of the 490nm band;

[0080] The mathematical expression for the second iterative relation is:

[0081]

[0082]

[0083] Where tsm is the initial variable representing the suspended solids concentration. The combined reflectivity of the 660nm band is [missing information]. The combined reflectance is 490nm.

[0084] Figure 7This is a schematic diagram of the inversion results of chlorophyll a concentration and suspended matter concentration in the ground spectrum of the Taihu Lake region provided by an embodiment of the present invention. As shown in the figure, since both combined reflectance and single component reflectance are directly related to concentration, the multiple regression model can be converted into a representation of suspended matter and chlorophyll a concentration, establishing a connection between the two multiple regression models. This completes the construction of a synchronous iterative inversion model for chlorophyll a and suspended matter in the ground spectrum of the Taihu Lake region.

[0085] On the other hand, the present invention provides a method for synchronous iteration of chlorophyll a and suspended matter surface spectra. Figure 8 This is a flowchart of a method for synchronous iterative analysis of chlorophyll a and suspended matter surface spectra. The method includes:

[0086] S810: Obtain measured water quality parameter data for the studied water body;

[0087] S820: Input the chlorophyll a concentration and suspended matter concentration from the measured water quality parameter data into the synchronous iterative model of chlorophyll a and suspended matter surface spectrum and iterate repeatedly to obtain the synchronous iterative inversion results of chlorophyll a and suspended matter in the studied water body.

[0088] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0090] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing a synchronous iterative model of chlorophyll a and suspended matter surface spectra, the method comprising: Obtain measured water quality parameter data and water surface spectral data of the study water body. The measured water quality parameter data includes parameters such as chlorophyll a concentration, suspended solids concentration, and colored soluble organic matter. The measured water quality parameters and the surface spectral data of the water body under study are input into the Hydro ight radiative transfer model to obtain the simulated spectral data of the water body under study. The 490nm and 660nm band data in the simulated spectral data were processed and correlated with the parameter data of chlorophyll a concentration, suspended matter concentration and colored soluble organic matter. The combined reflectance of the 490nm band and the combined reflectance of the 660nm band were screened out to show a significant strong correlation with chlorophyll a concentration and suspended matter concentration. The measured chlorophyll a concentration and suspended matter concentration were input into the Hydrolight radiative transfer model to simulate and obtain the reflectance of chlorophyll a and suspended matter as single components. The first multiple regression model was constructed based on the combined reflectance of the 490nm band and the single component reflectance of chlorophyll a and suspended matter. A second multiple regression model was constructed based on the combined reflectance of the 660nm band and the single component reflectance of chlorophyll a and suspended matter. Using chlorophyll a concentration as the initial variable, a first iterative relationship between chlorophyll a concentration and suspended matter concentration was established based on the first and second multiple regression models. Using suspended matter concentration as the initial variable, a second iterative relationship between chlorophyll a concentration and suspended matter concentration was established based on the first and second multiple regression models. A synchronous iterative model of chlorophyll a concentration and suspended matter concentration on the water surface spectrum was constructed based on the first and second iterative relationships. The mathematical expression for the first iterative relation is: tsm=((Rrs(785 / 490)-0.12068-(chl*0.00408+0.0078)*0.78882) / 0.81477-0.00411) / 0.00701 Where chl is the initial variable representing the chlorophyll a concentration. The combined reflectivity of the 660nm band is... The combined reflectivity of the 490nm band; The mathematical expression for the second iterative relation is: Where tsm is the initial variable representing the suspended solids concentration. The combined reflectivity of the 660nm band is... The combined reflectance is 490nm.

2. The model construction method according to claim 1, characterized in that, The methods for obtaining the measured water quality parameter data include: Chlorophyll a concentration was determined by a spectrophotometric method based on hot ethanol extraction. The concentration of suspended solids was determined by the filtration-drying-calcination-weighing method. Colored soluble organic compounds were determined using a spectrophotometer method. The chlorophyll a concentration, suspended solids concentration, and colored soluble organic matter concentration are expressed using the absorption coefficient at the 440 nm wavelength band.

3. The model construction method according to claim 1, characterized in that, The method for obtaining the water surface spectral data includes: Water surface spectral data were acquired using an ASD spectrometer via above-water measurement. Among them, the water surface remote sensing spectrum in the water surface spectral data is obtained by calculation based on wavelength λ, water surface spectral value Lt(λ), sky light spectral value Lsky(λ), gray board spectral value Lp(λ), and gray board reflectance ρp(λ) and sky light-air-water interface reflectance ρsky(λ) calibrated in the laboratory.

4. The model construction method according to claim 1, characterized in that, The combined reflectance of the 490nm and 660nm bands, which is significantly strongly correlated with chlorophyll a concentration and suspended matter concentration, includes: Simultaneously, the combined reflectance of the 490nm and 660nm bands showed a significant and strong correlation with chlorophyll a concentration and suspended matter concentration, with absolute values ​​of correlation coefficients greater than 0.7 (P<0.01). and 5. The model construction method according to claim 1, characterized in that, The coefficients of determination between the reflectance of the single component and the concentrations of chlorophyll a and suspended matter are all greater than 0.

99.

6. The model construction method according to claim 1, characterized in that, The first and second multiple regression models use chlorophyll a concentration and suspended matter concentration as initial variables, respectively. The mathematical expression for the multiple regression analysis of the first multiple regression model is as follows: The mathematical expression for the multiple regression analysis of the second regression model is: in, For the combined reflectivity of the 490nm band, The reflectance of a single component of chlorophyll a concentration in the 490nm band. For the concentration of suspended matter and the reflectance of a single component in the 490nm band, For the combined reflectivity of the 660nm band, The reflectance of a single component of chlorophyll a concentration in the 660nm band. denoted as the single-component reflectance of suspended matter concentration in the 660nm band, where a, b, and c are the model parameters of the first multiple regression model, and d, e, and f are the model parameters of the second multiple regression model.

7. A method for simultaneous iterative analysis of chlorophyll a and suspended matter surface spectra, characterized in that, The method includes: Obtain measured water quality parameter data for the studied water body; The concentrations of chlorophyll a and suspended matter in the measured water quality parameters are input into the synchronous iterative model of chlorophyll a and suspended matter surface spectra and iterated repeatedly to obtain the synchronous iterative inversion results of chlorophyll a and suspended matter in the studied water body; the synchronous iterative model is obtained based on the model construction method described in any one of claims 1-6.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 6.

9. A computer-readable medium having processor-executable non-volatile program code, characterized in that, The program code causes the processor to execute the method according to any one of claims 1 to 6.

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