A Modeling Method for Reflectance Characteristics of Three-Dimensional Aquatic Vegetation Canopies Based on Real Scenarios

Abstract

This invention relates to a method for modeling the reflectance characteristics of three-dimensional aquatic vegetation canopy based on real-world scenarios. The steps are as follows: Leaf reflectance and transmission spectra are calculated using a plant leaf radiative transfer model; the canopy reflectance characteristics are described using canopy structure parameters and leaf BRDF (Brake-Based Flow Function) in conjunction with the actual aquatic vegetation scene; the model parameters are calibrated using the four-flow approximation and linear approximation, and then applied to approximate the solution of the canopy reflectance; finally, the LuxCoreRender physical rendering engine is used to couple incident radiation, canopy reflectance factor, water body optical properties, and observation geometry input parameters, combined with the aquatic vegetation scene, to calculate the canopy reflectance. This method establishes a high-precision three-dimensional aquatic vegetation canopy reflectance model, which helps improve the accuracy and application value of remote sensing radiation modeling. It provides an important tool platform for wetland remote sensing monitoring and ecological protection, as well as the assessment of the blue carbon sink function of coastal wetlands, and has promising prospects for widespread application.

Description

(I) Technical Field

[0001] This invention relates to a method for modeling the reflectance characteristics of three-dimensional aquatic vegetation canopy based on real-world scenes, belonging to the field of optical remote sensing, and also having significant implications for wetland ecological research and quantitative monitoring of crops. (II) Background Technology

[0002] Three-dimensional vegetation radiative transfer models can more accurately simulate the propagation and reflection of light in vegetation canopies, especially when considering details such as leaf angle, canopy structure, and light source location, thus improving the accuracy of remote sensing data retrieval. This is crucial for extracting parameters such as chlorophyll content, vegetation cover, and biomass. In agriculture, three-dimensional radiative transfer models can be used to simulate crop growth under different vegetation densities, structures, and irrigation conditions, helping to scientifically manage crops, optimize fertilizer and water use, increase yields, and improve agricultural management.

[0003] In 3D vegetation radiative transfer modeling, scene construction is a core step that directly impacts the model's accuracy and application effectiveness. Scene construction primarily involves detailed simulation of the realistic physical structures of vegetation, environment, lighting, and terrain. Realistic scenes can accurately describe vegetation structure; the vegetation structure in the 3D model should be as close to reality as possible, including leaf size, shape, and arrangement; canopy density and height distribution; and spatial distribution among different vegetation types. This detailed description effectively improves the accuracy of the radiative transfer model, making inversion parameters (such as LAI and chlorophyll content) more consistent with real data. Furthermore, realistic scene construction can simulate diverse lighting conditions. Realistic scenes often include diverse terrain and non-vegetated areas, factors that significantly affect the scattering, absorption, and reflection characteristics of radiation. By incorporating environmental background and terrain features into the 3D radiative transfer model, the radiative transfer paths in the realistic scene can be better reflected, reducing uncertainties in radiation modeling.

[0004] After constructing a realistic 3D scene, a simulation dataset can be generated by combining it with a radiative transfer model, providing a data calibration and validation benchmark for the development of remote sensing inversion algorithms. This simulated dataset plays a crucial role in algorithm development, model comparison, and error assessment, especially when actual observation data is scarce, serving as an ideal reference. 3D radiative transfer modeling based on real-world scenes supports multi-scale and multi-angle observation analysis. By constructing scenes at different scales and perspectives, the impact of observation angle and resolution on radiative transfer can be better studied. Particularly during multi-angle observations, the 3D scene can reveal the light reflection and transmission characteristics of different layers of the canopy, which is essential for developing multi-angle remote sensing inversion and correction algorithms. (III) Summary of the Invention

[0005] This invention relates to a method for modeling the reflectance characteristics of 3D aquatic vegetation canopy based on real-world scenes. The steps are as follows: Leaf spectra and canopy spectra are calculated using the PROSPECT 5 model; the canopy reflectance characteristics are described using canopy structure parameters and leaf BRDF in conjunction with the actual aquatic vegetation scene; the model parameters are calibrated using the four-flow approximation and linear approximation and applied to approximate the solution of the vegetation canopy reflectance; finally, the LuxCoreRender physical rendering engine is used to couple all input parameters, including incident radiation, vegetation canopy reflectance factor, and water optical properties, to calculate the vegetation canopy reflectance. The specific steps are as follows:

[0006] 1. A method for modeling the reflection characteristics of three-dimensional aquatic vegetation canopy based on real-world scenes, characterized by comprising the following steps:

[0007] (1) The leaf reflectance and transmission spectra were calculated by PROSPECT 5 plant leaf reflectance model, and the aquatic vegetation scene was constructed by combining the canopy spectrum based on measured data. The canopy structure parameters and leaf BRDF were used to describe the canopy reflectance characteristics of aquatic vegetation in a specific scene.

[0008] (2) The four-flow approximation and the linear approximation were used to calibrate the model parameters describing the reflectance of the vegetation canopy and the water-soil substrate and applied them to the approximate solution of the vegetation canopy reflectance.

[0009] (3) Calculate the vegetation canopy reflectivity by coupling all input parameters, including incident radiation, vegetation canopy reflectivity, and water optical property parameters using the LuxCoreRender graphics rendering engine.

[0010] 2. A method for modeling the reflection characteristics of a three-dimensional aquatic vegetation canopy based on a real scene, as described in claim 1, is characterized in that: the step (1) of "calculating the leaf reflectance and transmission spectra using the PROSPECT 5 plant leaf reflectance model, and constructing an aquatic vegetation scene by combining the canopy spectrum based on measured data, and describing the reflection characteristics of the aquatic vegetation canopy in a specific scene using canopy structure parameters and leaf BRDF" is as follows:

[0011] Based on the flat plate model theory, a single leaf is divided into several layers, and then the spectral reflectance and transmittance of a single leaf are calculated. Through field experiments, morphological parameters of the plant, including plant height, leaf area index, and leaf tilt angle distribution, are measured. Biochemical parameters of aquatic plant leaves, including mesophyll structure parameters, chlorophyll concentration, carotenoid concentration, water content, and dry matter concentration, are input into the PROSPECT 5 leaf radiative transfer model to calculate the reflectance and transmittance spectral data of a single leaf across the entire 400-2500 nm wavelength range, i.e., the leaf BRDF. The specific calculation method is as follows:

[0012] Assume the blade consists of N identical layers, which are separated by N-1 layers of air; the first layer receives incident light at an angle α, and assume the light wave is isotropic, using ρ α τ represents reflectivity. α Transmittance is expressed as ρ; inside the blade, it is expressed as ρ. 90 and τ 90 Let R represent the reflectance and transmittance of each layer of elements within the leaf, respectively; then the total reflectance R of layer N is... N,α and transmittance T N,α for:

[0013]

[0014] The scene model consists of a set of 3D models containing multiple randomly distributed individual plants within a certain spatial range, and a planar mesh representing the water-soil background (hereinafter collectively referred to as "substrate"). In nature, the growth and reproduction characteristics of plants require them to satisfy certain topological relationships in three-dimensional space. This means that the various components of two plants need to maintain a certain growth distance and there will be no complete three-dimensional spatial overlap, intersection, crossing, or sharing of the same three-dimensional spatial point. For example, the independent root parts of two plants will not completely overlap or intersect. At most, the branches, roots, or leaves of plants will be in close contact with each other, but will not occupy the same position in three-dimensional space at the same time. Based on the plant density data, the distribution of individual plant models in the entire field is set, and then the reflectance factor of the vegetation canopy is calculated. By simulating the canopy reflection under different lighting and observation geometry, a high-precision vegetation canopy reflectance spectrum is obtained.

[0015] 3. A method for modeling the reflectance characteristics of a three-dimensional aquatic vegetation canopy based on a real scene, as described in claim 1, is characterized in that: the specific implementation method of "calibrating the model parameters describing the reflectance of the vegetation canopy and the water-soil substrate using the four-flow approximation and the linear approximation and applying them to the approximate solution of the vegetation canopy reflectance" in step (2) is as follows: The model is calibrated for a given canopy structure ∑ and observation geometry Ω. It consists of two nested sub-modules. The first sub-module describes the relationship between the vegetation canopy and the substrate reflectance, which depends on a physical parameter that needs to be calibrated (called S). The second sub-module describes the correlation between the wavelength of the S term and the optical properties of the leaves. The required calibration parameter is called F. S ;

[0016] Step 1: Four-stream approximation calibration describes the relationship between vegetation canopy and substrate reflection. Parameter S:

[0017] Canopy BRF(R) is a bidirectional component The weighted sum in the direction of observation hemispherical direction reflection factor:

[0018]

[0019] Among them, E Sun E represents the solar irradiance on the ground. Sky This represents the ground-sky irradiance; to improve model rendering efficiency, diffuse light is not considered here, therefore E is assumed to be... Sky Zero:

[0020]

[0021]

[0022] Where r is assumed to be the substrate reflectance as a Lambert, and τ xy and ρ xy τ represents the transmittance and reflectance of the canopy, respectively. The incoming and outgoing directions are indicated by the subscripts x and y, which can be s (source direction), o (viewing direction), or d (diffusion direction downward or upward), respectively. ssoo It is the bidirectional gap probability, which, due to the correlated photon path, is greater than τ in the near-hotspot region. ss τ oo The formula can be expressed as:

[0023]

[0024] Where, T1=τ ssoo +τ ss τ do +τ sd τ do +τ sd τ oo And T2 = τ ssoo -τ ss τ oo , ρ so The terms correspond to the canopy reflectance simulated using different substrate types:

[0025] r = r0 = 0.0 => R0 = ρ so

[0026] ρ dd The terms T1 and T2 can be derived from the simulation of canopy reflectivity. [R1, R2, R3] correspond to the canopy reflectivity of the simulated scenarios under three different substrate types, and the substrate reflectivity is represented by r = [r1, r2, r3]. Then, the term ρ can be calculated. dd :

[0027]

[0028] Where M1=(R1-R0) / r1, M2=(R2-R0) / r2, M3=(R3-R0) / r3, the derived four-stream parameters S[S 4s ]=[ρ dd , ρ so [T1, T2] depends on the canopy structure, and only three of them [ρ so [T1,T2] also depends on the observation configuration defined by the viewpoint and illumination direction, which is due to ρ dd Corresponding to diffuse flux, in addition, [ρ dd ,ρ so [T1] also depends on the optical properties of the blades, while T2 should depend only on the gap fraction; therefore, the proposed solution method describing the correlation between canopy reflectivity and substrate reflectivity needs to be applied to different canopy structures, observation configurations, and blade optical properties [ρ]. l , τ l Given a combination under ], for each combination [∑, Ω, [ρ l , τ l The reference values ​​for reflectance of four different types of substrates were used: [r] ref = [r0, r1, r2, r3], which includes the typical range of variation in substrate reflectivity, and simultaneously calculates the four-stream parameter S[S 4s ];

[0029] Step 2: Linear approximation calibration of parameter S, which describes the relationship between vegetation canopy and substrate reflectance.

[0030] The S term can also be calculated using a linear approximation. By neglecting the nonlinear interaction terms T1 and T2 in the four-flow approximation, further simplifications can be introduced, resulting in a linear approximation of canopy reflection. This simplifies the S term to [S lin ]=[ρ so A).

[0031] R = ρ so +rA

[0032] A can be derived from ρ so And the analysis and calculation were performed by simulating the canopy reflection of another substrate type r1.

[0033]

[0034] Similar to the four-flow solution, it is necessary to process each set of [∑,Ω,[ρ] l , τ l Combinatorial calculation [S] lin Therefore, a previous set of canopy reflection simulations is needed to evaluate the accuracy of this linear approximation method: for each [∑,Ω,[ρ] term, l , τ l, calculate [S lin = [ρ so , A];

[0035] Step 3: Calibrate the correlation parameter F between the wavelength of the S term and the leaf optical properties S :

[0036] By calibrating Fs(K) to determine its correlation with the total leaf absorption coefficient K, the leaf optical properties are affected by many aspects, including surface characteristics, the internal structure of the mesophyll, the distribution of its biochemical components in the leaf volume, and the complex refractive indices of these components. The real part of the refractive index n that drives the light scattering process changes little in the 450 - 2200 nm band and is between 1.3 < n < 1.5. Therefore, the spectral changes in the leaf optical properties are mainly driven by the imaginary part of the refractive index, which corresponds to the absorption coefficient. The total absorption coefficient K is determined by the specific absorption coefficients k b (λ) of each biochemical component, namely chlorophyll, dry matter, water, and the corresponding component contents C b as follows:

[0037] K = ∑k b (λ)C b

[0038] The range of variation of the K value is calculated based on the minimum and maximum values of k b (λ) in PROSPECT5 and the upper and lower limits of the C b values of the main absorbing substances (chlorophyll, water, and dry matter) in the LOPEX and ANGERS datasets. For a given leaf mesophyll structure parameter N, refractive index n, canopy structure v, and observation configuration Ω, the spectral changes in S based on the four - flux solution [S 4s = [ρ dd , ρ so , T1, T2], or in the linear approximation solution [S lin = [ρ so , A]) can be described by a smooth and monotonically increasing function that depends on K. For each canopy structure, observation configuration, mesophyll structure index, and refractive index, the dependence of the S term on the K value can be approximately expressed as the function Fs(K):

[0039]

[0040] For the 450 - 2200 nm band, select six values [K] ref .

[0041] 4. A method for modeling the reflectance characteristics of three-dimensional aquatic vegetation canopy based on real-world scenes according to claim 1, characterized in that: the specific implementation method of "using the LuxCoreRender graphics rendering engine to couple all input parameters, including incident radiation, vegetation canopy reflectance factor, and water body optical property parameters, to calculate the vegetation canopy reflectance" in step (3) is as follows:

[0042] For typical aquatic vegetation canopy scenarios, the leaf area index (LAI) generally ranges from 0.5 to 5, the average leaf angle (ALA) ranges from 20° to 45°, and the coverage ranges from 5% to 80%. For each scenario, canopy reflectance, three substrate properties, and six sets of leaf optical properties (corresponding to the center wavelengths of the six bands of the Sentinel-2 satellite sensor) are simulated under different observation geometries (VZA = [0°, 45°] and SZA = [20°, 35°]). Reference canopy reflectance for 5184 scenarios (36 canopy structures × 4 observation geometries × 3 substrate properties × 6 leaf feature combinations × 2 bands) is calculated using LuxCoreRender, while also considering the spectral correlation of refractive index n(λ). The six leaf optical properties are based on the LOPEX dataset and input to PROSPECT. The model is used for calculations, while the water body sediment property parameters are calculated through the water body optical property parameterization step. Given the canopy structure ∑, observed geometry Ω, and leaf mesophyll structure index N, a calibrated model is applied to calculate canopy reflectance. First, specific absorption coefficients k of known leaf components are used. b (λ), given leaf biochemical component C b The total absorption coefficient K is calculated using the wavelength λ, and then the previously calibrated function F is used. S Calculate each S term for the K value considered; finally, for the actual substrate reflectance r, use the S terms to calculate the vegetation canopy reflectance to approximately reflect the correlation between canopy reflectance R and different substrate characteristics.

[0043] The advantages of this invention compared to the prior art are:

[0044] (1) Current research is usually based on the assumption of canopy homogeneity, and also assumes that a pixel contains only two components: leaves and soil. This simplified assumption is applicable to areas with high vegetation cover, but in aquatic vegetation in habitats such as wetlands, the sources of spectral reflectance signals are not limited to vegetation and soil, but also include factors such as water scattering. In this case, traditional inversion methods are no longer applicable. By introducing a spectral unmixing method, this invention can more accurately invert the spectral characteristics of leaves, thereby improving the accuracy of vegetation reflectance simulation in wetlands and similar environments.

[0045] (2) Background noise can affect the accurate measurement of vegetation biophysical parameters, while saturation effects may lead to a weakened response to parameter changes. In order to improve the accuracy of scene simulation, this invention effectively reduces the influence of background noise and saturation effects, ensuring higher sensitivity to vegetation biophysical parameters and significantly improving the model's performance under complex environmental conditions. (iv) Description of the attached drawings

[0046] Figure 1 The technical process of this invention; Figure 2 A comparison of LuxCoreRender reflection simulation values ​​and LESS simulation values; Figure 3 Comparison of LuxCoreRender BRF simulation and LESS model results under different VZA conditions. (V) Detailed Implementation

[0047] To better illustrate the three-dimensional aquatic vegetation canopy reflectance modeling method based on real-world scenarios proposed in this invention, tests and analyses were conducted using the model of this invention and the LESS model, achieving good results. The specific implementation method is as follows:

[0048] (1) The leaf reflectance and transmission spectra were calculated by PROSPECT 5 plant leaf reflectance model, and the aquatic vegetation scene was constructed by combining the canopy spectrum based on measured data. The canopy structure parameters and leaf BRDF were used to describe the canopy reflectance characteristics of aquatic vegetation in a specific scene.

[0049] (2) The four-flow approximation and the linear approximation were used to calibrate the model parameters describing the reflectance of the vegetation canopy and the water-soil substrate and applied them to the approximate solution of the vegetation canopy reflectance.

[0050] (3) Calculate the vegetation canopy reflectivity by coupling all input parameters, including incident radiation, vegetation canopy reflectivity, and water optical property parameters using the LuxCoreRender graphics rendering engine.

[0051] Single-scattering and multiple-scattering verifications were performed on the solar principal plane, and the results were compared with BRF simulations of a virtual photon model based on the LESS model extension in three commonly used terrestrial scenarios: HET10, HOM23, and HOM33. Figure 2 As shown, the results indicate that the LuxCoreRender data and the RAMI reference data are in excellent agreement, with root mean square errors of 0.0017 and 0.006 for single scattering and multiple scattering, respectively. 2 The scattering efficiency was 99.46% for single scattering and 92.61% for multiple scattering. Figure 3The figure compares the simulation results of LuxCoreRender and LESS from forward and backward viewing angles. The red line represents the BRF simulation results of LuxCoreRender, and the black line represents the LESS results, showing the comparison between single scattering and multiple scattering. The single scattering radiance simulated by LuxCoreRender is similar to that of the LESS model, both obtained using ray tracing sampling methods. Therefore, it avoids the issue of low vegetation hotspots, and the hotspots of the two methods match perfectly. Therefore, it can be concluded that the LuxCoreRender rendering simulation and its implementation in this study achieve the same level of accuracy as the state-of-the-art 3D models participating in the RAMI test.

[0052] This invention establishes a method for simulating realistic aquatic vegetation scenes and their canopy reflectance characteristics, which helps to explore more deeply and scientifically the relationship between the canopy reflectance spectrum of aquatic vegetation and solar radiation, vegetation structural parameters, and water-soil substrate reflectance characteristics. Simultaneously, this invention significantly improves the spatiotemporal continuous simulation capability of remote sensing modeling, accurately inverts leaf spectral characteristics, and significantly improves the simulation accuracy of wetland vegetation reflectance. By effectively reducing the interference of background noise and saturation effects, it enhances the sensitivity to vegetation biophysical parameters, ensuring the model's versatility and reliability.

Claims

1. A method for modeling the reflection characteristics of three-dimensional aquatic vegetation canopy based on real-world scenes, characterized in that... Includes the following steps: (1) The leaf reflectance and transmission spectra were calculated by PROSPECT 5 plant leaf reflectance model, and the aquatic vegetation scene was constructed by combining the canopy spectrum based on measured data. The canopy structure parameters and leaf BRDF were used to describe the canopy reflectance characteristics of aquatic vegetation in a specific scene. (2) The four-flow approximation and the linear approximation were used to calibrate the model parameters describing the reflectance of the vegetation canopy and the water-soil substrate, and then applied to the approximate solution of the vegetation canopy reflectance; the specific implementation method is as follows: The model is calibrated for a given canopy structure ∑ and observed geometry Ω. It consists of two nested submodules. The first submodule describes the relationship between vegetation canopy and substrate reflection, which depends on a physical parameter called S that needs to be calibrated. The second submodule describes the correlation between the wavelength of the S term and the optical properties of the leaves, and the required calibration parameter is called F. S ; Step 1: Four-stream approximation calibration describes the relationship between vegetation canopy and substrate reflection. Parameter S: The directional reflectivity factor (BRF) of the canopy is a two-way component. The weighted sum in the direction of observation hemispherical directional reflectance factor: Among them, E Sun E represents the solar irradiance received by the ground. Sky This represents the sky irradiance received by the ground; to improve model rendering efficiency, diffuse light is not considered here, therefore E is assumed to be... Sky Zero: Where r is assumed to be the substrate reflectance as a Lambert; τ xy and ρ xy τ represents the transmittance and reflectance of the canopy, respectively; the ingress and egress directions are represented by the subscripts x and y, respectively, which can be s (source direction), o (observation direction), or d (downward or upward diffusion direction); τ ssoo It is the bidirectional gap probability, which, due to the correlated photon path, is greater than τ in the near-hotspot region. ss τ oo The formula can be expressed as: Where, T1=τ ssoo +τ ss τ do +τ sd τ do +τ sd τ oo And T2 = τ ssoo -τ ss τ oo , ρ so The terms correspond to the canopy reflectance simulated using different substrate types: r=r0=0.0=>R0=ρ so ρ dd The terms T1 and T2 can be derived from the simulation of canopy reflectivity. [R1, R2, R3] corresponds to the canopy reflectivity of the simulated scenarios under three different substrate types. The substrate reflectivity is represented by r = [r1, r2, r3]. Then, the term ρ can be calculated. dd : Where M1=(R1-R0) / r1, M2=(R2-R0) / r2, M3=(R3-R0) / r3, the derived four-stream parameters S[S 4s ]=[ρ dd , ρ so [T1, T2] depends on the canopy structure, and only three of them [ρ so [T1,T2] also depends on the observation configuration defined by the viewpoint and illumination direction, which is due to ρ dd Corresponding to diffuse flux, in addition, [ρ dd ,ρ so [T1] also depends on the optical properties of the blades, while T2 should depend only on the gap fraction; therefore, the proposed solution method describing the correlation between canopy reflectivity and substrate reflectivity needs to be applied to different canopy structures, observation configurations, and blade optical properties [ρ]. l , τ l Given a combination under ], for each combination [∑, Ω, [ρ l , τ l The reference values ​​for reflectance of four different types of substrates were used: [r] ref = [r0, r1, r2, r3], which includes the typical range of variation in substrate reflectivity, and simultaneously calculates the four-stream parameter S[S 4s ]; Step 2: Linear approximation calibration of parameter S, which describes the relationship between vegetation canopy and substrate reflectance. The S term can also be calculated using a linear approximation. By neglecting the nonlinear interaction terms T1 and T2 in the four-flow approximation, further simplifications can be introduced, resulting in a linear approximation of canopy reflection. This simplifies the S term to [S lin ]=[ρ so [,A]; R=ρ so +rA A can be derived from ρ so And the analysis and calculation were performed by simulating the canopy reflection of another substrate type r1. Similar to the four-flow solution, it is necessary to process each set of [∑,Ω,[ρ] l , τ l Combinatorial calculation [S] lin Therefore, a previous set of canopy reflection simulations is needed to evaluate the accuracy of this linear approximation method: for each [∑,Ω,[ρ] term, l , τ l The combination of ]] is used to calculate [S] lin ]=[ρ so [,A]; Step 3: Calibrate the correlation parameter F between the wavelength of the S term and the optical properties of the blade. S : By calibrating Fs(K) to determine its correlation with the total leaf absorption coefficient K, the leaf optical properties are affected by many aspects, including surface characteristics, the internal structure of the mesophyll, the distribution of its biochemical components in the leaf volume, and the complex refractive index of these components. The real part of the refractive index n that drives the light scattering process changes little in the 450 - 2200 nm band and is between 1.3 < n < 1.

5. Therefore, the spectral changes in leaf optical properties are mainly driven by the imaginary part of the refractive index, which corresponds to the absorption coefficient. The total absorption coefficient K is determined by the specific absorption coefficients k b (λ) of each biochemical component, namely chlorophyll, dry matter, water, and the corresponding component contents C b as follows: K=∑k b (λ)C b The range of k values ​​is based on k in the PROSPECT 5 model. b The minimum and maximum values ​​of (λ) and the C values ​​of the main absorbents—chlorophyll, water, and dry matter—in the LOPEX and ANGERS datasets. b The upper and lower limits of the value are calculated to obtain, for a given leaf mesophyll structure parameter N, refractive index n, canopy structure ∑, and observation configuration Ω, the spectral variation of S based on the four-flow solution [S] is obtained. 4s ]=[ρ dd , ρ so [T1, T2], or solve [S] using a linear approximation. lin ]=[ρ so The term S can be described by a smooth and monotonically increasing function that depends on K. For each canopy structure, observation configuration, mesophyll structure index, and refractive index, the dependence of the S term on the value of K can be approximately expressed as the function Fs(K): For the 450-2200 nm wavelength range, six values ​​[K] that best represent the relationship between K and the changes in blade reflection and transmission were selected. ref ; (3) Calculate the vegetation canopy reflectivity by coupling all input parameters, including incident radiation, vegetation canopy reflectivity, and water optical property parameters using the LuxCoreRender physical rendering engine.

2. The method for modeling the reflection characteristics of a three-dimensional aquatic vegetation canopy based on a real scene according to claim 1, characterized in that: The step (1) described in the text, "Calculate the leaf reflectance and transmission spectra using the PROSPECT 5 plant leaf reflectance model, and construct an aquatic vegetation scene by combining the canopy spectrum based on measured data. Describe the canopy reflectance characteristics of aquatic vegetation in a specific scene using canopy structure parameters and leaf BRDF," is as follows: Based on the flat plate model theory, a single leaf is divided into several layers, and then the spectral reflectance and transmittance of a single leaf are calculated. Through field experiments, morphological parameters of the plant, including plant height, leaf area index, and leaf tilt angle distribution, are measured. Biochemical parameters of aquatic plant leaves, including mesophyll structure parameters, chlorophyll concentration, carotenoid concentration, water content, and dry matter concentration, are input into the PROSPECT 5 leaf radiative transfer model to calculate the reflectance and transmittance spectral data of a single leaf across the entire 400-2500 nm wavelength range, i.e., the leaf BRDF. The specific calculation method is as follows: Assume the blade consists of N identical layers, which are separated by N-1 layers of air; the first layer receives incident light at an angle α, and assume the light wave is isotropic, using ρ α τ represents reflectivity. α Transmittance is expressed as ρ; inside the blade, it is expressed as ρ. 90 and τ 90 Let R represent the reflectance and transmittance of each layer of elements within the leaf, respectively; then the total reflectance R of layer N is... N, α and transmittance T N, α is: The scene model consists of a set of 3D models of multiple randomly distributed individual plants within a certain spatial range, and a planar grid representing the water-soil background. The "water-soil background" will be referred to as the "substrate" in the following text. In nature, the growth and reproduction characteristics of plants require them to satisfy certain topological relationships in three-dimensional space. This means that the various components of two plants need to maintain a certain growth distance and there will be no complete overlap, intersection, crossing, or sharing of the same three-dimensional spatial point. For example, the independent root parts of two plants will not completely overlap or intersect. At most, the branches, roots, or leaves of plants will be in close contact with each other, but will not occupy the same position in three-dimensional space at the same time. Based on the plant density data, the spatial distribution pattern of the individual plant model in the entire field is set, and then the reflectance factor of the vegetation canopy is calculated. By simulating the canopy reflection under different lighting and observation geometry, a high-precision vegetation canopy reflectance spectrum is obtained.

3. The method for modeling the reflection characteristics of a three-dimensional aquatic vegetation canopy based on a real scene according to claim 1, characterized in that: The specific implementation method for "using the LuxCoreRender graphics rendering engine to couple all input parameters, including incident radiation, vegetation canopy reflectance, and water optical property parameters, to calculate vegetation canopy reflectance" in step (3) is as follows: For typical aquatic vegetation canopy scenarios, the leaf area index (LAI) generally ranges from 0.5 to 5, the average leaf angle (ALA) ranges from 20° to 45°, and the coverage ranges from 5% to 80%. For each scenario, canopy reflectance, three substrate properties, and six sets of leaf optical properties (corresponding to the center wavelengths of the six bands of the Sentinel-2 satellite sensor) are simulated under different observation geometries (VZA = [0°, 45°] and SZA = [20°, 35°]). Reference canopy reflectance for 5184 scenarios (36 canopy structures × 4 observation geometries × 3 substrate properties × 6 leaf feature combinations × 2 bands) is calculated using LuxCoreRender, while also considering the spectral correlation of refractive index n(λ). The six leaf optical properties are based on the LOPEX dataset and input to PROSPECT. The model is used for calculations, while the water body sediment property parameters are calculated through the water body optical property parameterization step. Given the canopy structure ∑, observed geometry Ω, and leaf mesophyll structure index N, a calibrated model is applied to calculate canopy reflectance. First, specific absorption coefficients k of known leaf components are used. b (λ), given leaf biochemical component C b The total absorption coefficient K is calculated using the wavelength λ, and then the previously calibrated function F is used. S Calculate each S term for the K value considered; finally, for the actual substrate reflectance r, use the S terms to calculate the vegetation canopy reflectance to approximately reflect the correlation between canopy reflectance R and different substrate characteristics.

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