Method and system for quantitative analysis of water color three elements by coupling ultraviolet-visible absorption and reflection spectra

Abstract

A kind of water color three-element quantitative analysis method and system of UV-visible absorption and reflection spectrum coupling, comprising: obtaining the reflection signal of multiple sample water bodies, and calculating reflectivity based on the reflection signal;Obtain the total absorption spectrum of each sample water body in ultraviolet-visible band and the absorption spectrum and concentration of each component of water color three-element;Based on the reflectivity and total absorption spectrum of each sample water body, the total absorption spectrum inversion model of water body is constructed;Based on the absorption spectrum characteristics of each component in sample water body, the water color three-element absorption spectrum separation and concentration inversion model is constructed;The reflection signal of the water body to be measured is obtained, the total absorption spectrum of the water body to be measured is obtained based on the total absorption spectrum inversion model of water body, and the water color three-element absorption spectrum separation and concentration inversion of the water body to be measured are realized using water color three-element absorption spectrum and concentration inversion model.The present application overcomes the problem that CDOM and NAP visible band absorption spectrum is difficult to separate, and can realize high-precision remote sensing observation of water color three-element concentration.

Description

Technical Field

[0001] This invention belongs to the field of water body monitoring and analysis technology, specifically relating to a quantitative analysis method and system for the three elements of water color coupled with ultraviolet-visible absorption and reflectance spectroscopy. Background Technology

[0002] Water color, as an important bio-optical indicator, carries crucial information about the aquatic environment through its dynamic changes, holding core value for ecological research, resource management, and environmental safety. The optical characteristics of water bodies are jointly determined by stable water molecules and variable optically active components (OACs), among which phytoplankton, colored dissolved organic matter (CDOM), and non-algal particulate matter (NAP) constitute the three elements of water color. The optical properties and concentration changes of these three types of substances directly affect the spectral characteristics of water bodies, making them the core objects of water color remote sensing monitoring.

[0003] Current mainstream water color remote sensing algorithms can be divided into two categories: empirical models and semi-analytical models. Empirical models directly establish the remotely sensed reflectance R... rs The statistical relationship between OACs concentration and radiative transfer equations is simple to operate but exhibits significant regional dependence. Semi-analytical models achieve better universality through numerical solutions of the radiative transfer equations, with the quasi-analytical algorithm (QAA) being a typical example. This can be achieved through R... rs The total absorption coefficient a(λ) is inverted and separated into phytoplankton absorption coefficients a ph The sum of the absorption coefficients (λ), NAP, and CDOM, a dg (λ). However, because the algorithm assumes that NAP and CDOM have similar exponential decay spectral shapes, it cannot achieve accurate separation between the two.

[0004] The technical bottlenecks are mainly reflected in two aspects: First, existing satellite sensors generally lack the ultraviolet band (CDOM characteristic absorption region), making it difficult to effectively distinguish the spectral contributions of CDOM and NAP using only the visible light band; second, although laboratory / in-situ ultraviolet-visible absorption spectroscopy measurements can accurately obtain the absorption characteristics of water bodies, their spatiotemporal coverage is orders of magnitude different from that of remote sensing observation. This technical disconnect between "high-precision point measurement" and "large-scale area observation" severely restricts the accuracy and efficiency of dynamic water environment monitoring. Summary of the Invention

[0005] To address the difficulty in separating CDOM and NAP in existing water color remote sensing technologies, this invention proposes a quantitative analysis method and system for the three water color elements by coupling ultraviolet-visible absorption spectroscopy with remote sensing reflectance. This approach utilizes deep neural networks as a bridge to learn how to predict ultraviolet-visible absorption spectra from readily available reflectance data, effectively integrating the spatiotemporal observation capabilities of remote sensing with accurate spectral information provided by laboratory or in-situ spectroscopic measurements. Based on this, the absorption spectrum is decomposed using a model with clear physical meaning to achieve effective separation of the three water color elements, particularly CDOM and NAP. This invention aims to provide a new approach for the further accurate inversion of water body OACs components.

[0006] The present invention adopts the following technical solution.

[0007] In one aspect, this invention discloses a quantitative analysis method for the three elements of water color coupled with ultraviolet-visible absorption and reflectance spectroscopy, characterized in that the method includes the following steps:

[0008] Step 1: Use optical sensors to acquire reflection signals from various water samples, and calculate the reflectivity R based on the reflection signals. rs ;

[0009] Step 2: Use a UV-Vis absorption spectrometer to obtain the total UV-Vis absorption spectrum a(λ) and the absorption spectra and concentrations of each component of the three water color elements for each water sample;

[0010] Step 3: Based on the reflectance R of each water sample rs Using the total absorption spectrum a(λ) and the total absorption spectrum a(λ), a total absorption spectrum inversion model for water bodies is constructed.

[0011] Step 4: Based on the absorption spectral characteristics of each component in the sample water body, construct a model for the separation of absorption spectra and concentration inversion of the three elements of water color;

[0012] Step 5: Obtain the reflection signal of the water body to be tested, obtain the total absorption spectrum a(λ) of the water body to be tested based on the total absorption spectrum inversion model of the water body, and realize the separation of the three elements of water color absorption spectrum and concentration inversion of the water body to be tested using the three elements of water color absorption spectrum and concentration inversion model.

[0013] More preferably,

[0014] In step 1, the reflectivity R rs Calculate using the following formula:

[0015]

[0016] Where L t It is the measured water surface reflection signal of the sample water, L skyIt is the signal of skylight reflected off the water surface, L p This is the reflected signal of a standard whiteboard, and r is the reflectivity between the water and air interfaces.

[0017] More preferably,

[0018] In step 3, a total absorption spectrum inversion model for the water body is constructed based on machine learning;

[0019] Calculate R rs The logarithm of R, and R rs First derivative and R rs The first derivative of the logarithm; R rs R r The logarithm of s and the first derivative of both are used as input samples to train the total absorption spectrum inversion model of the water body.

[0020] More preferably,

[0021] The constructed absorption spectrum prediction model adopts a feedforward neural network architecture with multiple hidden layers. The network hyperparameters are automatically determined by Bayesian optimization combined with K-fold cross-validation. The training process uses the Adam optimizer to minimize the root mean square error (RMSE) between the predicted spectrum and the target spectrum.

[0022] More preferably,

[0023] Constructing a model for the separation of absorption spectra and concentration inversion of the three elements of water color, specifically including:

[0024] Subtract the absorption of pure water a from the total absorption spectrum a(λ) in sequence. w (λ) and chlorophyll absorption a ph (λ), the sum of the absorption coefficients of CDOM and NAP components, a dg (λ), constructing a using the UV-Vis absorption spectral characteristics of CDOM and NAP. dg (λ) separation model to achieve absorption spectral separation and concentration inversion of CDOM and NAP.

[0025] More preferably,

[0026] The total absorption spectral coefficient a(λ) of the water body is expressed as:

[0027] a(λ)=a w (λ)+a ph (λ)+a g (λ)+a d (λ)

[0028] Among them, a w (λ) is the absorption coefficient of pure water, a ph (λ) is the absorption coefficient of chlorophyll components, a g(λ) is the absorption coefficient of the CDOM component, a d (λ) is the absorption coefficient of the NAP component.

[0029] More preferably,

[0030] In step 4, a ph (λ) is calculated using the following formula:

[0031] a ph =C chl ·a ph *(λ)

[0032] C chl =f(R) rs (λ1),R rs (λ2),…,R rs (λn))

[0033] Among them, C chl It is the concentration of chlorophyll components, a ph * (λ) is the absorption spectrum of chlorophyll per unit concentration, R rs (λ1), R rs (λ2), R rs (λn) represents the remote sensing reflectance at wavelengths of λ1, λ2 and λn, respectively, and f is the chlorophyll concentration inversion model based on remote sensing reflectance. The range of f is OC2, OC3, OC4 and OCI models.

[0034] More preferably,

[0035] When f is the OC3 model

[0036]

[0037] λ1 is the wavelength covering the chlorophyll a-second absorption peak, λ2 is the wavelength of the transition region between the chlorophyll absorption valley and the enhanced scattering of suspended matter, and λ3 is the wavelength reflecting the backscattering of suspended matter; R rs (λ1), R rs (λ2), R rs (λ3) represents the remote sensing reflectance at wavelengths of λ1, λ2 and λ3, respectively. a and b are the first and second parameter values ​​of the model, respectively, which are obtained by coupling the measured chlorophyll concentration with the remote sensing reflectance.

[0038] More preferably,

[0039] In step 4, a dg The (λ) separation model is constructed using the following equation:

[0040] a dg (λ)=a g (λ)+a d(λ)

[0041] a g (λ)=C CDOM ·a g * (λ)

[0042]

[0043] a d (λ)=C NAP ·a d * (λ)

[0044]

[0045] Among them, a g (λ) is the absorption coefficient of the CDOM component, a d (λ) is the absorption coefficient of the NAP component, C CDOM This represents the concentration of CDOM in the water sample being tested, where λ is the wavelength. NAP It is the concentration of NAP in the water sample to be tested, a g * (λ) represents the concentration-normalized absorption spectrum of the CDOM component, a d * (λ) is the concentration-normalized absorption spectrum of the NAP component, λ0 is the reference wavelength, and a d * (λ0) is the absorption coefficient at the reference wavelength, S is the spectral slope of the NAP absorption spectrum, and A i Let W be the amplitude of the i-th Gaussian function. i Let E be the width of the i-th Gaussian function, and E be the photon energy. i Let be the center energy location of the i-th Gaussian function.

[0046] More preferably,

[0047] Each parameter to be determined in the absorption spectral separation and concentration inversion model of the three elements of water color is estimated by nonlinear least squares method.

[0048] In another aspect, this invention discloses a method and system for quantitative analysis of the three elements of water color coupled with ultraviolet-visible absorption and reflectance spectroscopy. The analysis system includes one or more processors and one or more programs, characterized in that:

[0049] One or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing the aforementioned quantitative analysis method for the three elements of water color.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] This invention proposes and verifies a novel method that can effectively separate the CDOM and NAP absorption contributions, which have highly overlapping spectral features, in water bodies. It also successfully constructs and preliminarily verifies a water body component calculation framework that combines deep neural networks and physical models with UV-Vis absorption and reflectance spectroscopy.

[0052] This invention utilizes a deep neural network to analyze remote sensing reflectance R rs The full-band absorption spectrum a(λ) containing key ultraviolet information was predicted, thus providing the necessary spectral basis for distinguishing CDOM and NAP, overcoming the limitation of traditional methods lacking ultraviolet information.

[0053] This invention applies the predicted absorption spectrum to a physically meaningful decomposition model, achieving high-precision inversion of chlorophyll a, CDOM, and NAP concentrations (chlorophyll aR). 2 =0.95, CDOMR 2 =0.97,NAPR 2 =0.99). This result highlights the significant advantages of this coupling method in solving the long-standing bottleneck problem of separating the three elements of water color (especially CDOM and NAP).

[0054] The physical model employed in this invention provides a differentiated description of the two: it uses a combination of Gaussian functions to finely characterize the CDOM, while employing exponential decay combined with scattering correction to describe the optical properties of the NAP. This differentiated parameterization approach, combined with the full-spectrum information including the ultraviolet band provided by the neural network, allows the model to more effectively "deconstruct" a dg (λ), thereby achieving the effect of a g (λ) and a d Further separation of (λ) contributions. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the process for quantitative analysis of the three elements of water color using the coupling of ultraviolet-visible absorption and reflectance spectroscopy according to the present invention.

[0056] Figure 2 This is a diagram of the experimental setup for measuring reflectance spectra in Example 1.

[0057] Figure 3 The total absorption coefficient spectra of some simulated water samples under different concentration gradients of NAP, chlorophyll a, and CDOM are shown. (a) Chlorophyll a is 0.2 μg·L⁻¹ -1 NAP is 1 mg·L -1 (b) Chlorophyll a is 1 μg·L -1 NAP is 5 mg·L -1(c) Chlorophyll a is 10 μg·L -1 NAP is 20 mg·L -1 .

[0058] Figure 4 The remote sensing reflectance spectra of partially simulated water samples under different concentration gradients of NAP, chlorophyll a, and CDOM are shown. (a) Chlorophyll a is 0.2 μg·L⁻¹. -1 NAP is 1 mg·L -1 (b) Chlorophyll a is 1 μg·L -1 NAP is 5 mg·L -1 (c) Chlorophyll a is 10 μg·L -1 NAP is 20 mg·L -1 .

[0059] Figure 5 Evaluation of neural network prediction results. (a) Comparison of the mean actual absorption coefficient and the mean predicted absorption coefficient; (b) Scatter plot of actual absorption coefficient and predicted absorption coefficient based on six typical wavelengths of the training set; (c) Scatter plot of actual absorption coefficient and predicted absorption coefficient based on six typical wavelengths of the test set.

[0060] Figure 6 To verify the performance of the coupling method inverting the concentrations of the three water color elements on an independent test set. (a) Chlorophyll a concentration; (b) CDOM concentration; (c) NAP (kaolin) concentration. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0062] like Figure 1 As shown, this invention discloses a quantitative analysis method for the three elements of water color using ultraviolet-visible online absorption and reflectance spectroscopy coupling. The method includes the following steps:

[0063] Step 1: Use optical sensors to acquire reflection signals from various water samples, and calculate the reflectivity R based on the reflection signals. rs ;

[0064] The reflection signal of the water sample to be tested was obtained by actual measurement, and the reflectivity R was calculated based on the reflection signal. rs ;

[0065]

[0066] Where L t It measures the water surface reflection signal of the water sample to be tested, L sky It is the signal of skylight reflected off the water surface, L p This is the reflection signal of a standard whiteboard, and r is the reflectivity between the water and air interfaces;

[0067] Step 2: Use a UV-Vis absorption spectrometer to obtain the total UV-Vis absorption spectrum a(λ) and the absorption spectra and concentrations of each component of the three water color elements for each water sample;

[0068] Step 3: Based on the reflectance R of each water sample rs Using (λ) and the total absorption spectrum a(λ), a total absorption spectrum inversion model for water bodies is constructed;

[0069] To achieve the mapping from reflectance spectra to absorption spectra, the inherent optical properties of water absorption spectra are obtained through apparent optical signals. A machine learning model simulating the reflectance and absorption spectra of water samples is constructed as the overall absorption spectrum inversion model for the water body. Using R... rs Spectrum, R rs The logarithm of the spectrum and its first derivative are used as input features. The input features are then normalized using z-score to achieve the calculation from the reflectance spectrum to the absorption spectrum.

[0070] The machine learning inversion models include, but are not limited to, random forests, support vector machines, and neural networks.

[0071] This invention preferably constructs an absorption spectrum prediction model based on a feedforward neural network model. The main body of the model adopts a feedforward neural network architecture with multiple hidden layers, integrating techniques such as batch normalization, ReLU activation, and Dropout, aiming to effectively learn the complex nonlinear mapping relationship between the reflectance spectrum and the absorption spectrum and improve generalization ability. The hyperparameters of the network are automatically determined through Bayesian optimization combined with K-fold cross-validation to ensure optimal model performance. The training process uses the Adam optimizer to minimize the root mean square error (RMSE) between the predicted spectrum and the target spectrum, and incorporates an early stopping strategy to prevent overfitting. The output standardized predicted spectrum is de-standardized and optionally Gaussian smoothed to obtain the final predicted absorption spectrum, which will be used for the absorption spectrum decomposition and component inversion described later.

[0072] Step 4: Based on the absorption spectral characteristics of each component in the sample water, construct a model for the separation of absorption spectra and concentration inversion of the three elements of water color; specifically including:

[0073] Subtract the absorption of pure water a from the total absorption spectrum a(λ) in sequence. w (λ) and chlorophyll absorption a ph (λ), the sum of the absorption coefficients of CDOM and NAP components, adg (λ), constructing a using the UV-Vis absorption spectral characteristics of CDOM and NAP. dg (λ) separation model to achieve absorption spectral separation and concentration inversion of CDOM and NAP.

[0074] Since the main optically active components of water include pure water, chlorophyll (i.e., phytoplankton components), CDOM, and NAP, the absorption spectrum of a water sample can be represented using an additive model of the absorption spectra of these four components:

[0075] a(λ)=a w (λ)+a ph (λ)+a g (λ)+a d (λ)

[0076] Where aw(λ) represents pure water absorption, aph(λ) represents chlorophyll component absorption, ag(λ) represents CDOM component absorption, and ad(λ) represents NAP component absorption.

[0077] The optically active substance absorption spectral coefficient α(λ) of the water sample is obtained by subtracting the baseline absorption of pure water from the total absorption coefficient α(λ) of the water sample absorption spectrum. nw (λ), denoted as:

[0078] a nw (λ)=a ph (λ)+a g (λ)+a d (λ)

[0079] Among them, a ph (λ) is the absorption coefficient of chlorophyll components, a g (λ) is the absorption coefficient of the CDOM component, a d (λ) is the absorption coefficient of the NAP component;

[0080] The absorption coefficient of chlorophyll components was calculated based on their absorption spectral characteristics, and the absorption spectral coefficient of optically active substances was obtained from the absorption spectral coefficient a. nw The sum of the absorption coefficients of CDOM and NAP components, a, is obtained by separating and subtracting the absorption contribution of chlorophyll components from (λ). dg (λ):

[0081] a dg (λ)=a g (λ)+a d (λ)

[0082] a ph (λ) is calculated using the following formula:

[0083] a ph (λ)=C chl ·a ph*(λ)

[0084] C chl =f(R) rs (λ1),R rs (λ2),…,R rs (λn))

[0085] Among them, C chl It is the concentration of chlorophyll components in the water sample to be tested, a ph * (λ) is the absorption spectrum of chlorophyll component per unit concentration, R rs (λ1), R rs (λ2), R rs (λn) represents the remote sensing reflectance at wavelengths λ1, λ2, and λn, respectively, and f is the chlorophyll concentration inversion model based on the remote sensing reflectance. The range of f can be OC2, OC3, OC4, OCI models, etc. Taking the OC3 model as an example:

[0086]

[0087] Among them, R rs (λ1), R rs (λ2), R rs (λ3) represents the remote sensing reflectance at wavelengths λ1, λ2, and λ3, respectively. a and b are the first and second parameter values ​​of the model, respectively, which are calculated by coupling the measured chlorophyll concentration with the remote sensing reflectance. The values ​​of λ1, λ2, and λ3 are usually selected from the bands around 660nm (the band where the absorption peak and fluorescence peak of chlorophyll a are located), 690-730nm (the band that is greatly affected by the absorption of CDOM and NAP), and 740-760nm (the band where water reflection is enhanced and affected by the backscattering of suspended matter), respectively. The specific values ​​are selected according to the actual situation.

[0088] a dg The (λ) separation model is constructed using the following equation:

[0089] a dg (λ)=a g (λ)+a d (λ)

[0090] a g (λ)=C CDOM ·a g * (λ)

[0091]

[0092] a d (λ)=C NAP ·a d * (λ)

[0093]

[0094] Among them, a g (λ) is the absorption coefficient of the CDOM component, a d (λ) is the absorption coefficient of the NAP component, C CDOM This represents the concentration of CDOM in the water sample being tested, where λ is the wavelength. NAP It is the concentration of NAP in the water sample to be tested, a g * (λ) represents the concentration-normalized absorption spectrum of the CDOM component, a d * (λ) is the concentration-normalized absorption spectrum of the NAP component, λ0 is the reference wavelength, and a d * (λ0) is the absorption coefficient at the reference wavelength, S is the spectral slope of the NAP absorption spectrum, and A i Let W be the amplitude of the i-th Gaussian function. i Let E be the width of the i-th Gaussian function, and E be the photon energy. i Let be the center energy location of the i-th Gaussian function.

[0095] In a preferred embodiment of the present invention, a g * The absorption spectrum of (λ) in the 200-800 nm range is precisely described by three Gaussian functions:

[0096]

[0097] Where E is the photon energy, measured in eV; λ is the wavelength, measured in nm; E i Let A be the center energy location of the i-th Gaussian function. i Let W be the amplitude of the i-th Gaussian function. i Let be the width of the i-th Gaussian function.

[0098] Each relevant parameter to be determined in the fitting formula is calculated using a continuous iterative nonlinear least squares method, which yields the ultraviolet-visible absorption spectrum information of the water body, thereby achieving the separation of CDOM and NAP.

[0099] Step 5: Obtain the reflection signal of the water body to be tested, obtain the total absorption spectrum a(λ) of the water body to be tested based on the total absorption spectrum inversion model of the water body, and realize the separation of the three elements of water color absorption spectrum and concentration inversion of the water body to be tested using the three elements of water color absorption spectrum and concentration inversion model.

[0100] The technical solution of this invention will be simulated in the laboratory to verify its technical effects.

[0101] 1. Simulation Experiment of Quantitative Analysis Method for Three Elements of Water Color Coupled with Ultraviolet-Visible Absorption Spectroscopy and Remote Sensing Reflectance

[0102] 1.1 Preparation of water samples for experimental simulation

[0103] To construct and verify the technical solution of this invention, simulated water samples with different concentration gradients of OACs representing a wide range of water body types, from clear to turbid and from oligotrophic to eutrophic, were prepared using laboratory simulation methods. Commercially available chlorophyll a standard, the internationally recognized Suwannee River Natural Organic Matter (SRNOM) standard, and analytical grade kaolin powder were selected as simulants for phytoplankton, colored dissolved organic matter (CDOM), and non-algal particulate matter (NAP), respectively. Using ultrapure water as a substrate, simulated water samples were prepared by precisely adding different amounts of chlorophyll a, SRNOM, and kaolin, and the concentrations were calibrated while their reflectance and UV-Vis absorption spectra were simultaneously measured. Kaolin and chlorophyll a were graded using concentration gradients ranging from 0.1 to 10 μg·L⁻¹. -1 and 1-20 mg·L -1 SRNOM uses its absorption coefficient at 443 nm (α) g (443)) Gradient classification is performed, with a gradient range of 0.05-1.6m. -1 .

[0104] 1.2 Reflectance and Absorption Spectrum Measurement and Processing

[0105] Set up a reflectance spectroscopy measurement device in the laboratory. Figure 2 Using a Newport 94061A simulated solar light source, a 1.4L black cylindrical container was used to hold the simulated water sample. The reflectance spectrum of the water sample was measured using a Marine Optics QE65-pro spectrometer. Ultimately, simulated water sample L with a spectral range of 350-800nm ​​and a spectral resolution of 1nm was obtained. t L skt L p The reflectance R of the water sample was calculated using equation (1). rs .

[0106]

[0107] Among them, L t The spectrometer probe receives the reflected signal from the simulated water sample surface, L sky It is the signal of skylight reflected off the water surface, L p This is the reflected signal of a standard whiteboard, and r is the reflectivity at the water-air interface.

[0108] The absorption spectra of the water samples were measured using a Perkin Elmer Lambda 850 UV-Vis spectrophotometer with a measurement range of 250-800 nm and a spectral resolution of 1 nm.

[0109] 1.3 Mapping method from reflectance spectrum to absorption spectrum

[0110] To achieve the mapping from reflectance spectra to absorption spectra, the inherent optical properties of water absorption spectra are obtained through apparent optical signals. A deep learning approach is used to construct a neural network model simulating the reflectance and absorption spectra of water samples. Using R... rs Spectrum, R rs The logarithm of the spectrum and its first derivative are used as input features. The input features are normalized by z-score and a machine learning prediction model for absorption spectra is constructed to realize the calculation from reflection spectrum to absorption spectrum.

[0111] The machine learning inversion model includes, but is not limited to, random forest, support vector machine, neural network, etc.

[0112] This invention preferably constructs an absorption spectrum prediction model based on a feedforward neural network model. The main body of the model adopts a feedforward neural network architecture with multiple hidden layers, integrating techniques such as batch normalization, ReLU activation, and Dropout, aiming to effectively learn the complex nonlinear mapping relationship between the reflectance spectrum and the absorption spectrum and improve generalization ability. The hyperparameters of the network are automatically determined through Bayesian optimization combined with K-fold cross-validation to ensure optimal model performance. The training process uses the Adam optimizer to minimize the root mean square error (RMSE) between the predicted spectrum and the target spectrum, and incorporates an early stopping strategy to prevent overfitting. The output standardized predicted spectrum is de-standardized and optionally Gaussian smoothed to obtain the final predicted absorption spectrum, which will be used for the absorption spectrum decomposition and component inversion described later.

[0113] 1.4 Absorption Spectral Analysis Method

[0114] The main optically active components of water include pure water, chlorophyll, CDOM, and NAP. Therefore, the absorption spectrum of a water sample can be represented using an additive model of the absorption spectra of these four components:

[0115] a(λ)=a w (λ)+a ph (λ)+a g (λ)+a d (λ) (2)

[0116] Among them, a w (λ) represents absorption by pure water, a ph (λ) represents the absorption of chlorophyll components, a g (λ) represents the absorption of CDOM components, ad (λ) represents the absorption of NAP components.

[0117] Since the experiment used a double-beam spectrophotometer to measure the absorption with ultrapure water as the background reference, the baseline absorption of pure water was subtracted before measuring the absorption of the solution. Therefore, the absorption spectrum of the water sample obtained in the experiment can be expressed as Equation (3):

[0118] a nw (λ)=a ph (λ)+a g (λ)+a d (λ) (3)

[0119] Chlorophyll a exhibits significant absorption peaks in the blue-violet and red light regions, while the absorption spectra of CDOM and NAP show similar characteristics, both exhibiting exponential decay with increasing wavelength. Based on the significant differences in absorption spectra between chlorophyll a and CDOM / NAP, the absorption contribution of chlorophyll a can be effectively separated and subtracted from the total absorption. ph (λ) can be expressed as equations (4)-(5):

[0120] a ph =C chl ·a ph * (λ) (4)

[0121]

[0122] Among them, C chl It is chlorophyll concentration; a ph * (λ) is the absorption spectrum of chlorophyll per unit concentration; R rs (λ1), R rs (λ2), R rs (λ3) represents the remote sensing reflectance at wavelengths of λ1, λ2, and λ3, where λ1, λ2, and λ3 are 660, 710, and 748 nm, respectively; a and b are model parameter values, which can be calculated by coupling measured chlorophyll concentration with remote sensing reflectance, where a and b are 289.2 and 31.57, respectively.

[0123] Absorption of CDOM components a g (λ) is expressed as in equation 6-7:

[0124] a g (λ)=C CDOM ·a g * (λ)·ε (6)

[0125]

[0126] Among them, CCDOM ε represents the concentration of CDOM in the laboratory solution; γ represents the interference correction term of NAP; γ is the maximum attenuation of the CDOM absorption spectrum, describing the maximum inhibition ratio of CDOM absorption by NAP; δ is the attenuation rate of the CDOM absorption spectrum, describing the rate of change in the inhibition of the CDOM absorption spectrum as the NAP concentration increases; C NAP This is the concentration of NAP (kaolin) in the laboratory solution. g * (λ) represents the concentration-normalized CDOM absorption spectrum. g * The absorption spectrum of (λ) in the 200-800 nm range can be accurately described by three Gaussian functions:

[0127]

[0128] Where E is the photon energy, measured in eV; λ is the wavelength, measured in nm; E i Let A be the center energy location of the i-th Gaussian function. i Let W be the amplitude of the i-th Gaussian function. i Let be the width of the i-th Gaussian function.

[0129] Absorption of NAP components a d The coefficient (λ) is expressed as in equation (10):

[0130] a d (λ)=C NAP ·a d * (λ) (10)

[0131] Among them, a d * (λ) is the concentration-normalized absorption spectrum, described by an exponential function. However, because quantitative filtration membrane technology was not used when measuring the UV-Vis absorption spectrum of simulated water samples in the laboratory, a d * The (λ) spectrum also includes the scattering component generated by NAP, which serves as the a... d * The modification term of (λ), therefore a in this invention d * (λ) can be expressed as:

[0132]

[0133] Where λ0 is the reference wavelength, typically taken as 440 nm; a d * (λ0) is the absorption coefficient at the reference wavelength; S is the spectral slope of the absorption spectrum, which determines the rate of absorption decay with wavelength.* (λ) represents a d * The scattering correction term for (λ) can be approximated by equation (12):

[0134]

[0135] Where m is the scattering correlation coefficient, which describes the scattering intensity; n is the wavelength exponent of scattering, which describes the relationship between scattering intensity and wavelength; and β is the saturation factor, which is used to simulate the saturation phenomenon of scattering under high concentrations of NAP.

[0136] Based on the UV-Vis absorption spectrum and concentration data of the simulated water sample in the laboratory, the model parameters are solved by combining formulas (3)-(12) and using the nonlinear least squares method.

[0137] 1.5 Method Accuracy Verification

[0138] To evaluate the proposed method for quantitative analysis of the three water color elements using online UV-Vis absorption and reflectance spectroscopy coupling, the accuracy of the method was verified using laboratory-measured reflectance, absorption spectra, and concentrations. First, the total absorption spectrum was calculated based on the laboratory-measured reflectance spectra, and the accuracy of the total absorption spectrum inversion was verified. Then, based on the inverted total absorption spectrum and the constructed absorption spectral analysis model, the inverted total absorption spectrum was decomposed into chlorophyll, CDOM, and NAP absorption spectra. The sum of the spectra of each decomposed component was compared with the measured total absorption spectrum to evaluate the spectral analysis accuracy of the absorption spectral analysis model for the three water color elements. Finally, the concentrations of the three components were solved using the constructed absorption spectral analysis model, and the accuracy of the inverted concentrations of the three components was evaluated.

[0139] 2. Analysis of Experimental Results

[0140] 2.1 Laboratory Reflectance and Absorption Spectroscopy Measurement Results

[0141] Figure 3 The total uptake coefficients of some simulated water samples under different NAP, chlorophyll a, and CDOM concentration gradients are shown. Figure 3 The three parts from left to right are: (a) Chlorophyll a is 0.2 μg·L -1 NAP is 1 mg·L -1 (b) Chlorophyll a is 1 μg·L -1 NAP is 5 mg·L -1 (c) Chlorophyll a is 10 μg·L -1 NAP is 20 mg·L -1 .

[0142] Depend on Figure 3It can be seen that the total absorption coefficient under different NAP, chlorophyll a, and CDOM concentration gradients exhibits an approximately exponential decay with increasing wavelength. With constant NAP and chlorophyll a concentrations, the total absorption coefficient systematically increases across the entire spectral range with increasing CDOM concentration, especially in the short-wavelength band. With constant chlorophyll a and CDOM concentrations, the total absorption coefficient shows an overall increase across the entire wavelength range with increasing NAP concentration. With constant NAP and CDOM concentrations, the total absorption coefficient changes accordingly with increasing chlorophyll a concentration, and the characteristic peaks in the blue and red light regions also increase accordingly, but the changes in absorption morphology are masked by the strong absorption of CDOM and NAP.

[0143] Figure 4 The remote sensing reflectance of some simulated water samples under different concentration gradients of NAP, chlorophyll a, and CDOM is shown. Figure 4 The three parts from left to right are: (a) Chlorophyll a is 0.2 μg·L -1 NAP is 1 mg·L -1 (b) Chlorophyll a is 1 μg·L -1 NAP is 5 mg·L -1 (c) Chlorophyll a is 10 μg·L -1 NAP is 20 mg·L -1 .

[0144] Figure 4 This indicates that NAP concentration primarily affects the overall intensity of reflectivity. As NAP concentration increases, backscattering also increases, and R... rs The concentration of CDOM increases across the entire visible light spectrum. CDOM concentration primarily affects the magnitude and spectral slope of remote sensing reflectance in the blue-green light band. With fixed background concentrations of NAP and chlorophyll a, as CDOM concentration increases, its strong absorption in the blue-violet region leads to a significant decrease in this portion of Rrs, resulting in a steeper spectral slope. Absorption valleys appear in the blue (approximately 440 nm) and red (approximately 675 nm) regions with increasing chlorophyll a concentration. Simultaneously, a reflectance peak that increases with increasing chlorophyll a concentration appears in the near-infrared band (approximately 700 nm).

[0145] 2.2 Remote Sensing Reflectance Analysis of Water Color Three-Component Concentration

[0146] Figure 5 The results of the inversion of the total absorption spectrum using a deep neural network model based on reflectance spectroscopy to estimate the absorption spectrum are presented. Among them, Figure 5The three parts from left to right are: (a) comparison of the mean actual absorption coefficient and the mean predicted absorption coefficient; (b) scatter distribution of the actual absorption coefficient and the predicted absorption coefficient based on six typical wavelengths of the training set; and (c) scatter distribution of the actual absorption coefficient and the predicted absorption coefficient based on six typical wavelengths of the test set.

[0147] Figure 5 Part (a) shows a comparison between the mean actual absorption coefficient and the mean predicted absorption coefficient in the wavelength range of 250-800nm. Figure 5 Sections (b) and (c) show scatter plots of predicted versus actual values ​​for six typical wavelengths in the training and test sets.

[0148] Depend on Figure 5 As shown in part (a), the model's predicted average absorption spectrum closely matches the actual measured average spectrum across the entire wavelength range of 250-800 nm. The standard deviation of the predicted results is also close to the standard deviation of the actual values, indicating that the waveform has good predictive ability regarding the dispersion of data around the mean. Figure 5 As shown in parts (b) and (c), the predicted coefficients and actual absorption coefficients of the six typical wavelengths all show good linear correlation; the scatter distribution of the training set and the test set is consistent, indicating that the model does not have obvious overfitting and has a certain generalization ability.

[0149] Table 1 further shows that, at typical wavelengths, the R values ​​for the training and test sets are... 2 The values ​​are generally high; meanwhile, the RMSE values ​​are generally low, and the RMSE values ​​on the test set are close to those on the training set, indicating that the model performance is stable and the prediction error is small. Overall, the model successfully learned and predicted the absorption coefficient spectrum, the results are reliable, and it has good potential for practical application.

[0150] Table 1. Evaluation of neural network prediction results for six typical bands.

[0151]

[0152] The concentrations of the three water color elements were inverted based on the constructed absorption spectral analysis model, and the accuracy was verified by comparing the results with the measured concentrations. The results are as follows: Figure 6 As shown. Figure 6 From left to right, the following values ​​are displayed: (a) chlorophyll a concentration; (b) CDOM concentration; and (c) NAP (kaolin) concentration. The results show that this method can achieve high-precision inversion of chlorophyll a, CDOM, and NAP concentrations, with a determination coefficient R0. 2The values ​​were 0.95, 0.97, and 0.99, respectively, and the root mean square error (RMSE) was 0.72 μg·L⁻¹. -1 0.25 mg·L -1 0.72 mg·L -1 .

[0153] The present invention also claims protection for a quantitative analysis system for the three elements of water color coupled with ultraviolet-visible online absorption and reflectance spectroscopy, the analysis system comprising one or more processors and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing the quantitative analysis method for the three elements of water color.

[0154] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0155] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0156] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0157] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A quantitative analysis method for the three elements of water color coupled with ultraviolet-visible absorption and reflectance spectroscopy, characterized in that, The method includes the following steps: Step 1: Use optical sensors to acquire reflection signals from various water samples, and calculate the reflectivity based on the reflection signals. ; Step 2: Obtain the total absorption spectrum in the UV-Vis band for each water sample using a UV-Vis absorption spectrometer. The absorption spectra of each component of the three water color elements were measured, and the concentrations of the three water color elements were measured simultaneously. Step 3: Based on the reflectance of each water sample With total absorption spectrum A machine learning model simulating the reflectance and absorption spectra of water samples was constructed as the total absorption spectrum inversion model for water bodies; spectrum, The logarithm of the spectrum and its first derivative are used as input features. The input features are normalized by z-score to realize the calculation from the reflectance spectrum to the absorption spectrum. Step 4: Based on the absorption spectral characteristics of each component in the sample water, construct a model for the separation of absorption spectra and concentration inversion of the three elements of water color; from the total absorption spectrum... Subtract pure water absorption in sequence and chlorophyll absorption The sum of the absorption coefficients of CDOM and NAP components was obtained. We constructed a system using the UV-Vis absorption spectral characteristics of CDOM and NAP. A separation model was developed to achieve absorption spectral separation and concentration inversion of CDOM and NAP; among which, The separation model is constructed using the following formula: ； ； ； in, The absorption coefficient of CDOM components. The absorption coefficient of the NAP component. This indicates the concentration of CDOM in the water sample being tested. For wavelength, It is the concentration of NAP in the water sample to be tested. This represents the concentration-normalized absorption spectrum of the CDOM components. It is the concentration-normalized absorption spectrum of the NAP component. It is the reference wavelength. It is the absorption coefficient at the reference wavelength. It is the spectral slope of the NAP absorption spectrum. For the first The amplitude of a Gaussian function, For the first The width of a Gaussian function, Photon energy, For the first The central energy location of a Gaussian function; Step 5: Obtain the reflection signal of the water body to be tested, and obtain the total absorption spectrum of the water body based on the total absorption spectrum inversion model of the water body. The absorption spectra and concentration inversion models of the three water color elements were used to achieve the separation of the three water color elements and the inversion of concentration in the water body to be tested.

2. The quantitative analysis method for the three elements of water color according to claim 1, characterized in that: In step 1, reflectivity Calculate using the following formula: in It is the water surface reflection signal obtained from the actual measurement of the sample water. It is the signal of skylight reflected off the water surface. It is the reflected signal of a standard whiteboard. It is the reflectivity between the water and air interfaces.

3. The quantitative analysis method for the three elements of water color according to claim 1, characterized in that: The constructed absorption spectrum inversion model adopts a feedforward neural network architecture with multiple hidden layers. The network hyperparameters are automatically determined by Bayesian optimization combined with K-fold cross-validation. The training process uses the Adam optimizer to minimize the root mean square error (RMSE) between the predicted spectrum and the target spectrum.

4. The quantitative analysis method for the three elements of water color according to claim 1, characterized in that: Total absorption spectral coefficient of water body , is represented as: in, The absorption coefficient of pure water is... The absorption coefficient of chlorophyll components. The absorption coefficient of the CDOM component. is the absorption coefficient of the NAP component.

5. The quantitative analysis method for the three elements of water color according to claim 4, characterized in that: In step 4, The following formula is used to calculate: in, It is the concentration of chlorophyll components. It is the absorption spectrum of chlorophyll per unit concentration. , , respectively wavelength at , and Remote sensing reflectance below, This is a chlorophyll concentration retrieval model based on remote sensing reflectance. The selection range is OC2, OC3, OC4, and OCI models.

6. The quantitative analysis method for the three elements of water color according to claim 5, characterized in that: when When using the OC3 model, To cover chlorophyll Secondary absorption peak wavelength To enhance the wavelength of the transition region between chlorophyll absorption valley and suspended matter scattering. To reflect the backscattering band of suspended matter; , , respectively wavelength at , and Remote sensing reflectance below, and These are the first and second parameter values ​​of the model, respectively, which were obtained by coupling measured chlorophyll concentration with remote sensing reflectance.

7. The quantitative analysis method for the three elements of water color according to claim 1, characterized in that: Each parameter to be determined in the absorption spectral separation and concentration inversion model of the three elements of water color is estimated by nonlinear least squares method.

8. A system for realizing a quantitative analysis method of the three elements of water color using ultraviolet-visible absorption and reflectance spectroscopy coupling, the system comprising one or more processors and one or more programs, characterized in that: One or more of the programs are stored in one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for executing the quantitative analysis method of the three elements of water color according to any one of claims 1-7.

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