Rice growth process three-dimensional scene construction method based on crop growth model and rendering engine

Abstract

The invention relates to a rice growth process three-dimensional scene construction method based on a crop growth model and a rendering engine, and the method comprises the following steps: obtaining plant density, distribution, leaf inclination angle distribution, a leaf spectrum, a canopy spectrum and substrate reflectivity through a field experiment, and constructing a single-plant rice 3D model; coupling a crop growth model, and describing vegetation canopy reflection characteristics through canopy structure parameters and leaf BRDF to simulate canopy reflection factors; respectively calculating a water body scattering phase function and a water surface BRDF according to the water body biological optical model and the wave water surface reflection model; generating a three-dimensional water body volume model through an AVRM model; blender software is combined with a LuxCoreRender rendering engine to couple incident radiation, vegetation canopy reflection factors, water optical attributes and other related input parameters, and a real rice scene is constructed. According to the method, the high-precision three-dimensional rice scene model is established, important technical support is provided for improving the precision and efficiency of rice remote sensing radiation modeling and further for agricultural remote sensing and wetland monitoring, and the method has popularization and application prospects.

Description

(I) Technical field

[0001] The invention relates to a method for constructing a three-dimensional scene of a rice growth process based on a crop growth model and a rendering engine, belongs to the field of optical remote sensing, and has important significance in wetland ecological research and quantitative crop monitoring applications. (II) Background technology

[0002] Remote sensing monitoring plays a vital role in precision agriculture. It can not only monitor growth and estimate crop yields, but also monitor and warn agricultural disasters, assist agricultural decision-making, achieve precise management, and promote the process of agricultural modernization. However, remote sensing information reflects the instantaneous physical conditions and it is difficult to reveal the internal mechanism of crop growth and development. The crop growth model mathematically describes a series of physiological and biochemical processes in the process of crop growth, development, and yield formation, and can dynamically simulate the growth and development process of crops. Coupling remote sensing information with crop growth models can make full use of the macro-monitoring capabilities of remote sensing technology and the mechanism simulation capabilities of crop growth models, thereby improving the accuracy of crop monitoring and prediction.

[0003] The three-dimensional radiation transfer model can simulate the three-dimensional light field distribution within the crop canopy, so as to more accurately understand the absorption, reflection and transmission process of light by crop leaves, which is of great significance for studying crop photosynthesis, light energy utilization and other physiological processes. In this context, the three-dimensional radiation transfer model has become a core tool for accurately inverting remote sensing data due to its excellent performance in simulating complex and heterogeneous remote sensing scenes. The simulation of three-dimensional scenes can not only capture the subtle differences in the structures of different types of plants, but also reflect the effects of multiple scattering and changes in component proportions caused by specific regions and vegetation structures. This simulation method is closer to the actual situation and can provide a more accurate description of reflectance characteristics than traditional models, effectively supporting the monitoring and management of rice. Through the three-dimensional radiation transfer model, researchers can reproduce complex natural scenes in a virtual environment, so as to better understand the ecological information contained in remote sensing data. This simulation method significantly improves the accuracy of remote sensing monitoring and provides a solid foundation for scientific research and management decisions.

[0004] The reflective characteristics of an object are usually accurately described by a bidirectional reflectance distribution function (BRDF). The present invention couples a plant leaf spectral model, a rice canopy three-dimensional radiation transmission model, a wave water surface reflection model, a shallow water bio-optical model, and a physical rendering engine to construct a three-dimensional scene construction method for the rice growth process based on a crop growth model and a rendering engine, which improves the accuracy and efficiency of rice remote sensing modeling and provides important theoretical and practical references for agricultural remote sensing monitoring and ecological protection. (III) Summary of the invention

[0005] The present invention relates to a method for constructing a three-dimensional scene of a rice growth process based on a crop growth model and a rendering engine, and the steps are as follows: obtaining plant density, distribution, leaf inclination angle distribution, leaf spectrum, canopy spectrum and bottom reflectivity through field experiments to construct a single rice 3D model; coupling the crop growth model, describing the vegetation canopy reflectance characteristics through canopy structure parameters and leaf BRDF to simulate the canopy reflectance factor; calculating the water body scattering phase function and the water surface BRDF according to the water body bio-optical model and the wave water surface reflection model; generating a three-dimensional water body volume model through an AVRM model; and using Blender software combined with a LuxCoreRender rendering engine to couple relevant input parameters such as incident radiation, vegetation canopy reflectance factor, water body optical properties, etc. to construct a real rice scene, and the specific steps are as follows:

[0006] (1) Based on the statistical characteristics of rice plant density, two-dimensional spatial distribution and leaf inclination distribution, as well as leaf reflectance spectrum, canopy reflectance spectrum and substrate reflectance, a single rice plant 3D model was constructed. The reflectance characteristics of the vegetation canopy at different growth stages were described by canopy structure parameters and leaf BRDF in combination with the crop growth model, and the reflectance factor of the vegetation canopy was simulated.

[0007] (2) The water body scattering phase function and the water surface BRDF are calculated according to the water body bio-optical model number and the wave water surface reflection model, and the water body scattering phase function and the water surface BRDF are input into the aquatic vegetation radiosity model AVRM. The water body optical parameters and the observed geometric parameters are combined to generate a complete three-dimensional water volume model;

[0008] (3) Blender software was used in combination with the LuxCoreRender rendering engine to couple all input parameters, including incident radiation, vegetation canopy reflectance factor, leaf BRDF, water scattering phase function, and water surface BRDF, to construct realistic rice vegetation scenes under different canopy structures and observation geometries.

[0009] 5. A three-dimensional scene construction of rice growth process based on crop growth model and rendering engine according to claim 1

[0010] The method is characterized in that: the step (1) of "constructing a single rice plant 3D model based on field experimental measurements of rice plant density, two-dimensional spatial distribution and statistical characteristics of leaf inclination angle distribution, leaf reflectance spectrum, canopy reflectance spectrum and substrate reflectance, further combining the crop growth model, describing the reflectance characteristics of the vegetation canopy at different growth stages through canopy structure parameters and leaf BRDF, and simulating the reflectance factor of the vegetation canopy"; the specific construction method is as follows:

[0011] Through field experiments, the morphological parameters of the plants, including plant height, leaf area index and leaf inclination angle distribution, were measured. Multiple small facets were used to construct the curved surface of the leaves. Finite elements were used to approximate and replace the iterative process of the smooth curved surface to simulate the real 3D morphology of the leaves. Based on the measured plant height, leaf area index and leaf inclination angle distribution, a geometric structure model of the rice plant was established. The leaf spectrum data calculated by the leaf radiation transmission model was assigned to the leaves of the geometric model, so that the three-dimensional model had real optical properties and a 3D model of a single rice plant was constructed.

[0012] The three-dimensional structure of rice is generated through a rice spatial structure growth model based on measured data, including information such as leaf length, node position, leaf angle and tillering angle, while considering the impact of light cycle on growth; specify parameters such as the spatial position of rice in the scene and the growth stage of interest, and use rendering software to establish a three-dimensional structural model of rice in the field in combination with the three-dimensional structural model of a single rice plant; according to the plant density data, set the spatial distribution pattern of the single rice plant model in the entire field, and then calculate the reflectance factor of the vegetation canopy; obtain a high-precision rice canopy reflectance spectrum by simulating the canopy reflectance under different lighting and observation geometries.

[0013] 6. A three-dimensional scene construction of rice growth process based on crop growth model and rendering engine according to claim 1

[0014] The method is characterized in that: the specific method of "calculating the water body scattering phase function and the water surface BRDF according to the water body biological optical model number and the wave water surface reflection model respectively in step (2), inputting the water body scattering phase function and the water surface BRDF into the aquatic vegetation radiosity model AVRM, and comprehensively combining the water body optical parameters and the observed geometric parameters to generate a complete three-dimensional water body volume model" is as follows:

[0015] Step 1: Calculate the water scattering phase function according to the water bio-optical model: In the rice scene, since the scattering characteristics of the water element are anisotropic, the water body is regarded as a volume scattering medium, so it is necessary to pay attention to its three main properties: absorption coefficient, scattering coefficient and scattering phase function; the first thing to solve is the parameterization problem of the absorption coefficient and scattering coefficient; the water bio-optical model calculates the absorption and scattering coefficients by considering parameters such as chlorophyll, suspended matter and colored dissolved organic matter CDOM in the water body. This method splits the absorption and scattering coefficients of the water body according to the contribution of different components. The specific calculation method is as follows:

[0016]

[0017] Among them, a pw 、a ph 、a NAP with a CDOMare the absorption coefficients of pure water, phytoplankton, non-algae particles and CDOM, respectively, bw With b p denote the scattering coefficients of pure water and particulate matter, respectively, NAP The calculation method of the term is:

[0018] a NAP (λ) = a NAP (443)·0.75e -0.0123(λ-443)

[0019] In this formula, the absorption coefficient of non-algae particles at 443nm is used as the reference absorption coefficient, where λ is the wavelength to be solved, and a NAP (443) shows a linear relationship with the concentration of suspended particles SPM, satisfying a NAP (443) = 0.041m 2 ·g -1 ·SPM, the absorption coefficient contributed by CDOM components is:

[0020] a CDOM (λ) = a CDOM (375)e -0.0192(λ-375 )

[0021] Among them, a CDOM The value of (375) is 1.25m -1 , the unit absorption coefficient of phytoplankton is:

[0022]

[0023] Among them, C is a parameter related to the concentration of chlorophyll a, A(λ) and B(λ), and the scattering coefficient of pure water is expressed as:

[0024]

[0025] The scattering coefficient of particles is expressed as:

[0026] b p (λ)=EC 0.62 550 / λ

[0027] In the above formula, C is the concentration of chlorophyll a, and E is an empirical coefficient, which is taken as 1.0 in the model;

[0028] Step 2: Calculate the water surface BRDF according to the Cox-Munk model: The Cox-Munk model regards the water surface as the geometry of tiny surface elements with different slopes, and calculates the BRDF of the wavy water surface as:

[0029]

[0030] Where r(ω) is the Fresnel reflectivity at an incident angle of ω when the water surface is calm, and p(z x , z y ) is the probability distribution function of the wave surface slope, z x , z y is the microsurface coordinate system, θ n is the zenith angle direction of the normal line of the wave surface element where the specular reflection occurs, θ s is the solar zenith angle, θ o To observe the zenith angle, σ is the water surface roughness factor to describe the degree of water surface fluctuation, which replaces the wind speed W as the input of the water surface reflectivity model;

[0031] Step 3: Input the water scattering phase function and the water surface BRDF into the AVRM model, integrate the optical parameters and observation geometry parameters related to the water body, and generate a complete three-dimensional water volume model. The specific calculation process is as follows:

[0032] In the absence of inelastic scattering, the parameters used to calculate the radiative transfer through a volume medium are the spectral absorption coefficient a(λ, p) and the spectral volume scattering function VSFβ(λ, p, ψ), where λ is the wavelength and p = (p x , p y , p z ) is a point in space, ψ is the scattering angle, 0≤ψ≤π, and the scattering coefficient b(λ, p) and attenuation coefficient c(λ, p) are derived from the absorption coefficient and VSF:

[0033]

[0034] c(λ,p)=a(λ,p)+b(λ,p)

[0035] The radiation distribution L(λ, p, v) at a point p in space is scattered by the VSF, which in turn generates the path radiation at that point:

[0036]

[0037] Among them, dΩ(v in ) is the same as v in The relevant solid angle, integrated over it, Considering the radiation energy emitted from the inside of the medium, the radiation distribution L(λ, p, v) at point p is the radiation energy emitted from point p. S The sum of the received path attenuation radiation, the point p S The energy at the surface of the first voxel encountered when tracing backward from direction v, plus any radiant energy received on the way through the medium that is scattered or emitted from the volume:

[0038]

[0039] Among them, L EX-G (λ,p S , V) represents the position at point p S The outgoing radiation of the direction vector v of the surface in the global coordinate system, the function K(λ, p1, p2) represents the transmittance of the path radiation transmitted from point p1 to point p2 in the medium:

[0040]

[0041] Where d = |p1-p2| is the distance between points p1 and p2. Note that even if the first intersection surface is transparent, there is no need to consider the reverse path along the vector v, because the influence of the radiation source on the far side of the volume surface can be regarded as the outgoing radiation of the surface itself. The volume discretization is initiated by subdividing the model domain into cubic voxels, where the viewpoint of each voxel is located at the center of the voxel. Since the voxel has a cubic shape, the incident radiation at the center of the voxel has a voxel path length x(q) that depends on q. The distance x(q) can be numerically pre-calculated in advance on all voxels of a given size by taking the solid angle mean. If the emission is zero, the source function based on these path integrals is as follows:

[0042]

[0043] in

[0044]

[0045] In the formula, L′(λ, i, q in ) is the radiation distribution at the center of the voxel from the external source, that is, excluding the scattered radiation from the voxel itself, while the radiation at the center point is extrapolated forward and backward to the voxel boundary and integrated along the path length:

[0046]

[0047] This formula handles the internal scattering in the direction of the incident radiation, and integrates and attenuates the current voxel path radiation along the path in the voxel. Since the voxel average VSF is assumed, β(λ, i, q in ,q out ), is constant within the voxel, and the path integral can be derived analytically:

[0048]

[0049] For a given beam attenuation coefficient c(λ, i) and voxel size x(qi n ), in many model applications, the range of voxel sizes and beam attenuation coefficient values ​​will be very limited. It should be noted that although L *is cyclic, but the solution algorithm is iterative, so L * It is updated from the current value on each iteration.

[0050] 7. The method for constructing a three-dimensional scene of a rice growth process based on a crop growth model and a rendering engine according to claim 1, characterized in that: the step (3) of "using Blender software in combination with the LuxCoreRender rendering engine to couple all input parameters, including incident radiation, vegetation canopy reflectance factor, leaf BRDF, water body scattering phase function and water surface BRDF, to construct a real rice vegetation scene under different canopy structures and observation geometries" is specifically implemented as follows:

[0051] LuxCoreRender uses physical principles to calculate the interaction between light and materials, and adopts path tracing as its ray tracing integration algorithm. During the rendering process, light is emitted from the camera into the scene, and then interacts with objects in the scene until it finds a light source or is absorbed. When building a three-dimensional rice scene, 128 rays are generated for each camera pixel, including direct scattering and diffuse scattering. By setting the maximum number of bounces of the light path in the engine, single scattering and multiple scattering rendering results are obtained. It is assumed that the leaves and rhizomes are both Lambertian surfaces with the same reflectivity, and the rhizomes have no transmittance. The soil is set to be horizontal and flat, and the water body is set according to the water body voxel parameters in AVRM. The volume body in the Blender software; in order to To simulate a limited-size solar light source, a "sunlight" configuration was used, and a perspective camera with a 5° field of view was selected. When the camera was pointed at the center of the scene from a distance of 50 m, an area of ​​the 2m×2m experimental plot could be captured; at the same time, to avoid potential boundary effects, eight replications were performed around the central scene. By specifying parameters such as the spatial position of rice in the scene and the growth stage of interest, combined with a single rice plant model, a three-dimensional model of rice was established using Blender software. The dynamics of the canopy of the 3D rice growth model was driven by the number of growing days. From the two-leaf stage to the beginning of male heading, six growth stages were simulated every 100 ℃ d. By changing the six parameters of the model except the driving parameters, 36 rice canopy models at different periods can be simulated. .

[0052] The advantages of the present invention compared with the prior art are:

[0053] (1) In the field of vegetation remote sensing, remote sensing modeling research currently lacks knowledge of the spatiotemporal variations of key ground parameters, which limits the ability to simulate continuous time phases. For example, in large-scale crop planting areas in China, the growth process of crops causes significant changes in plant structure and biochemical parameters, thereby affecting the spatial distribution of crops. In order to achieve continuous time phase data simulation, the present invention introduces crop growth process knowledge and models, couples remote sensing information with crop growth models, and can fully utilize the macro-monitoring capabilities of remote sensing technology and the mechanism simulation capabilities of crop growth models. This method not only improves the understanding of the crop growth process, but also enhances the time-phase continuous simulation capabilities of remote sensing models, and can more accurately reflect the actual situation, thereby improving the accuracy of crop monitoring and prediction.

[0054] (2) The present invention uses the aquatic vegetation canopy three-dimensional radiation transfer model AVRM and the rendering engine LuxCoreRender as the technical framework, and tightly couples and integrates the water surface reflection and transmission process, the absorption and scattering process inside the vegetation canopy, the absorption and scattering process of the water body components, and the bottom reflection process into a model, constructs real rice scenes under different canopy structures and observation geometries, and calculates the vegetation canopy reflectivity. The model of the present invention has clear physical concepts, strong versatility, and reliable accuracy guarantee. (IV) Description of the drawings

[0055] Figure 1 The technical process of the present invention; Figure 2 Create a schematic diagram for the 3D blade; Figure 3 This is a single vegetation model in Blender; Figure 4 Rendering a scene for a 3D aquatic vegetation canopy. (V) Specific implementation methods

[0056] In order to better illustrate the method for constructing a three-dimensional scene of rice growth process based on a crop growth model and a rendering engine according to the present invention, the scene established by the present invention was tested and analyzed, and good results were achieved. The specific implementation method is as follows:

[0057] (1) Based on the field experimental measurements of rice plant density, two-dimensional spatial distribution, and leaf inclination distribution statistical characteristics, as well as leaf reflectance spectrum, canopy reflectance spectrum, and substrate reflectance, a single plant 3D model was constructed. At the same time, the reflectance characteristics of the vegetation canopy were described by canopy structure parameters and leaf BRDF in combination with the crop growth model, and the reflectance factor of the vegetation canopy was simulated;

[0058] (2) The water body scattering phase function and the water surface BRDF are calculated according to the water body bio-optical model number and the Cox-Munk model respectively, and the water body scattering phase function and the water surface BRDF are input into the aquatic vegetation radiosity model (Aquatic Vegetation Radiosity Model), namely the AVRM model, which integrates the basic water body parameters and the observed geometric parameters to form a complete three-dimensional water body volume model;

[0059] (3) Blender software was used in combination with the LuxCoreRender rendering engine to couple all input parameters, including incident radiation, vegetation canopy reflectance factor, leaf BRDF, water scattering phase function, and water surface BRDF, to construct realistic rice vegetation scenes under different canopy structures and observation geometries.

[0060] Figure 2 The 3D morphology modeling process of the leaf is shown. The leaf is naturally curved, so when maintaining the basic shape of the leaf, multiple small facets are needed to form the surface of the leaf. This is a process of using finite elements to approximate or replace smooth surfaces, such as Figure 2 -a, which can simulate a more realistic 3D shape of leaves. Figure 2 -b, the green curve represents the side edge of the actual naturally curved leaf. When the number of facets is gradually reduced when creating the leaf 3D object, the curvature of the leaf side edge gradually becomes less smooth, it changes from a green curve to a red segmented curve, and finally to a black segmented curve. This trend also applies to gradually simplified 3D objects, including the most simplified 3D objects.

[0061] Figure 3 Shows a single vegetation model in Blender, and Figure 4 The final rice scene rendered by LuxCoreRender is shown.

[0062] The invention establishes a real rice scene, which helps to explore the relationship between the rice canopy reflectance spectrum and solar radiation, vegetation structure parameters, water body component parameters, and bottom reflectance characteristics in a more in-depth and scientific manner. At the same time, the invention significantly improves the spatiotemporal continuous simulation capability of remote sensing modeling and significantly improves the simulation accuracy of wetland vegetation reflectance. At the same time, the invention adopts an advanced three-dimensional radiation transmission model and rendering engine to achieve high-precision rice scene modeling, ensuring the versatility and reliability of the model.

Claims

1. A method for constructing a three-dimensional scene of rice growth process based on a crop growth model and a rendering engine, characterized in that The following steps are involved: (1) Based on the statistical characteristics of rice plant density, two-dimensional spatial distribution and leaf inclination distribution, as well as leaf reflectance spectrum, canopy reflectance spectrum and substrate reflectance, a single rice plant 3D model was constructed. The reflectance characteristics of the vegetation canopy at different growth stages were described by canopy structure parameters and leaf BRDF in combination with the crop growth model, and the reflectance factor of the vegetation canopy was simulated. (2) The water body scattering phase function and the water surface BRDF are calculated according to the water body bio-optical model number and the wave water surface reflection model, and the water body scattering phase function and the water surface BRDF are input into the aquatic vegetation radiosity model AVRM. The water body optical parameters and the observed geometric parameters are combined to generate a complete three-dimensional water volume model; (3) Blender software was used in combination with the LuxCoreRender rendering engine to couple all input parameters, including incident radiation, vegetation canopy reflectance factor, leaf BRDF, water scattering phase function, and water surface BRDF, to construct realistic rice vegetation scenes under different canopy structures and observation geometries.

2. The method for constructing a three-dimensional scene of rice growth process based on a crop growth model and a rendering engine according to claim 1, characterized in that: The step (1) "based on the field experimental measurements of rice plant density, two-dimensional spatial distribution and leaf inclination distribution statistical characteristics, as well as leaf reflectance spectrum, canopy reflectance spectrum and substrate reflectance, construct a single rice plant 3D model, further combine the crop growth model, describe the reflectance characteristics of the vegetation canopy at different growth stages through canopy structure parameters and leaf BRDF, and simulate the reflectance factor of the vegetation canopy"; the specific requirements are as follows: Through field experiments, the morphological parameters of the plants, including plant height, leaf area index and leaf inclination angle distribution, were measured. Multiple small facets were used to construct the curved surface of the leaves. Finite elements were used to approximate and replace the iterative process of the smooth curved surface to simulate the real 3D morphology of the leaves. Based on the measured plant height, leaf area index and leaf inclination angle distribution, a geometric structure model of the rice plant was established. The leaf spectrum data calculated by the leaf radiation transmission model was assigned to the leaves of the geometric model, so that the three-dimensional model had real optical properties and a 3D model of a single rice plant was constructed. The three-dimensional structure of rice is generated through a rice spatial structure growth model based on measured data, including information such as leaf length, node position, leaf angle and tillering angle, while considering the impact of light cycle on growth; specify parameters such as the spatial position of rice in the scene and the growth stage of interest, and use rendering software to establish a three-dimensional structural model of rice in the field in combination with the three-dimensional structural model of a single rice plant; according to the plant density data, set the spatial distribution pattern of the single rice plant model in the entire field, and then calculate the reflectance factor of the vegetation canopy; obtain a high-precision rice canopy reflectance spectrum by simulating the canopy reflectance under different lighting and observation geometries.

3. The method for constructing a three-dimensional scene of rice growth process based on a crop growth model and a rendering engine according to claim 1, characterized in that: The specific method of step (2) of "calculating the water body scattering phase function and the water surface BRDF according to the water body bio-optical model number and the wave water surface reflection model, inputting the water body scattering phase function and the water surface BRDF into the aquatic vegetation radiosity model AVRM, and integrating the water body optical parameters and the observed geometric parameters to generate a complete three-dimensional water body volume model" is as follows: Step 1: Calculate the water scattering phase function according to the water bio-optical model: In the rice scene, since the scattering characteristics of the water element are anisotropic, the water body is regarded as a volume scattering medium, so it is necessary to pay attention to its three main properties: absorption coefficient, scattering coefficient and scattering phase function; the first thing to solve is the parameterization problem of the absorption coefficient and scattering coefficient; the water bio-optical model calculates the absorption and scattering coefficients by considering parameters such as chlorophyll, suspended matter and colored dissolved organic matter CDOM in the water body. This method splits the absorption and scattering coefficients of the water body according to the contribution of different components. The specific calculation method is as follows: Among them, a pw 、a ph 、a NAP with a CDOM are the absorption coefficients of pure water, phytoplankton, non-algae particles and CDOM, respectively, bw With b p denote the scattering coefficients of pure water and particulate matter, respectively, NAP The calculation method of the term is: the NAP (λ)=a NAP (443)·0.75e -0.0123(λ-443) In this formula, the absorption coefficient of non-algae particles at 443nm is used as the reference absorption coefficient, where λ is the wavelength to be solved, and a NAP (443) shows a linear relationship with the concentration of suspended particles SPM, satisfying a NAP (443) = 0.041m 2 ·g -1 ·SPM, the absorption coefficient contributed by CDOM components is: a CDOM (λ)=a CDOM (375)e -0.0192(λ-375) Among them, a CDOM The value of (375) is 1.25m -1 , the unit absorption coefficient of phytoplankton is: Among them, C is a parameter related to the concentration of chlorophyll a, A(λ) and B(λ), and the scattering coefficient of pure water is expressed as: The scattering coefficient of particles is expressed as: b p (λ)=EC 0.62 550 / λ In the above formula, C is the concentration of chlorophyll a, and E is an empirical coefficient, which is taken as 1.0 in the model; Step 2: Calculate the water surface BRDF according to the Cox-Munk model: The Cox-Munk model regards the water surface as the geometry of tiny surface elements with different slopes, and calculates the BRDF of the wavy water surface as: Where r(ω) is the Fresnel reflectivity at an incident angle of ω when the water surface is calm, and p(z x ,z y ) is the probability distribution function of the wave surface slope, z x ,z y is the microsurface coordinate system, θ n is the zenith angle direction of the normal line of the wave surface element where the specular reflection occurs, θ s is the solar zenith angle, θ o To observe the zenith angle, σ is the water surface roughness factor to describe the degree of water surface fluctuation, which replaces the wind speed W as the input of the water surface reflectivity model; Step 3: Input the water scattering phase function and the water surface BRDF into the AVRM model, integrate the optical parameters and observation geometry parameters related to the water body, and generate a complete three-dimensional water volume model. The specific calculation process is as follows: In the absence of inelastic scattering, the parameters used to calculate the radiative transfer through a volume medium are the spectral absorption coefficient a(λ,p) and the spectral volume scattering function VSFβ(λ,p,ψ), where λ is the wavelength and p = (p x ,p y ,p z ) is a point in space, ψ is the scattering angle, 0≤ψ≤π, and the scattering coefficient b(λ,p) and attenuation coefficient c(λ,p) are derived from the absorption coefficient and VSF: c(λ,p)=a(λ,p)+b(λ,p) The radiation distribution L(λ,p,v) at a point p in space is scattered by the VSF, which in turn generates the path radiation at that point: Among them, dΩ(v in ) is the same as v in The relevant solid angle, integrated over it, Considering the radiation energy emitted from the inside of the medium, the radiation distribution L(λ,p,v) at point p is the radiation energy emitted from point p. S The sum of the received path attenuation radiation, the point p S The energy at the surface of the first voxel encountered when tracing backward from direction v, plus any radiant energy received on the way through the medium that is scattered or emitted from the volume: Among them, L EX-G (λ,p S ,v) indicates the position at point p S The outgoing radiation of the direction vector v of the surface in the global coordinate system, the function K(λ,p1,p2) represents the transmittance of the path radiation transmitted from point p1 to point p2 in the medium: Where d = |p1-p2| is the distance between points p1 and p2. Note that even if the first intersection surface is transparent, there is no need to consider the reverse path along the vector v, because the influence of the radiation source on the far side of the volume surface can be regarded as the outgoing radiation of the surface itself. The volume discretization is initiated by subdividing the model domain into cubic voxels, where the viewpoint of each voxel is located at the center of the voxel. Since the voxel has a cubic shape, the incident radiation at the center of the voxel has a voxel path length x(q) that depends on q. The distance x(q) can be numerically pre-calculated in advance on all voxels of a given size by taking the solid angle mean. If the emission is zero, the source function based on these path integrals is as follows: in In the formula, L′(λ,i,q in ) is the radiation distribution at the center of the voxel from the external source, that is, excluding the scattered radiation from the voxel itself, while the radiation at the center point is extrapolated forward and backward to the voxel boundary and integrated along the path length: This formula handles the internal scattering in the direction of the incident radiation, and integrates and attenuates the current voxel path radiation along the path in the voxel. Since the voxel average VSF is assumed, β(λ,i,q in ,q out ), is constant within the voxel, and the path integral can be derived analytically: For a given beam attenuation coefficient c(λ,i) and voxel size x(q in ), in many model applications, the range of voxel sizes and beam attenuation coefficient values ​​will be very limited. It should be noted that although L * is cyclic, but the solution algorithm is iterative, so L * It is updated from the current value on each iteration.

4. The method for constructing a three-dimensional scene of rice growth process based on a crop growth model and a rendering engine according to claim 1, characterized in that: The specific implementation method of "using Blender software combined with LuxCoreRender rendering engine to couple all input parameters, including incident radiation, vegetation canopy reflectance factor, leaf BRDF, water scattering phase function and water surface BRDF, to construct a real rice vegetation scene under different canopy structures and observation geometries" in step (3) is as follows: LuxCoreRender uses physical principles to calculate the interaction between light and materials, and adopts path tracing as its ray tracing integration algorithm. During the rendering process, light is emitted from the camera into the scene, and then interacts with objects in the scene until it finds a light source or is absorbed; when establishing a three-dimensional rice scene, 128 rays are generated for each camera pixel, including direct scattering and diffuse scattering. By setting the maximum number of bounces of the light path in the engine, single scattering and multiple scattering rendering results are obtained, assuming that the leaves and rhizomes are both Lambertian surfaces and have the same reflection rate, and the rhizome has no transmittance, the soil is set to be horizontal and flat, and the water body is set in the Blender software according to the water body voxel parameters in AVRM; in order to simulate a finite-sized solar light source, the "sunlight" configuration is adopted, and a perspective camera with a 5° field of view is selected. When the camera is pointed to the center of the scene from a distance of 50m, an area of ​​2m×2m experimental plot can be captured; at the same time, to avoid potential boundary effects, 8 replications were made around the central scene. By specifying parameters such as the spatial position of rice in the scene and the growth stage of interest, combined with a single rice plant model, a three-dimensional model of rice was established using Blender software on this basis. The dynamics of the canopy of the 3D rice growth model is driven by the number of growing days. From the two-leaf stage to the beginning of male heading, six growth stages are simulated every 100℃d. By changing the six parameters of the model except the driving parameters, 36 rice canopy models at different periods can be simulated.

Images