47 results about "Radiative transfer" patented technology

The invention relates to the field of photovoltaic module detection, and discloses a module hot spot defect infrared and visible light fusion detection method, which comprises the following steps: multi-mode acquisition: multi-mode data of a photovoltaic module are synchronously acquired, and the multi-mode data comprise infrared temperature original data, visible light polarization images and environmental parameters; data preprocessing: preprocessing the multi-modal data based on a radiation transmission model and a polarization optical principle to obtain preprocessed data including an infrared temperature field and a visible light image; and dynamic feature extraction: extracting a space-time gradient tensor and a Riemannian curvature tensor of the infrared temperature field from the preprocessed data based on differential geometry and a computer vision algorithm. By synchronously acquiring multi-modal data, based on a radiation transmission model and a polarization optical principle, interference of atmospheric attenuation and environment temperature and humidity on infrared temperature measurement and influence of uneven illumination and fog effect on a visible light image are eliminated, and dynamic characteristics of an infrared temperature field and structural characteristics of visible light are mined.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention relates to the technical field of data assimilation processing, in particular to a WRFDA assimilation method, system and device based on GIIRS data. The method comprises the following steps: acquiring GIIRS data, extracting data from the GIIRS data, and merging and checking the data to obtain checked GIIRS observation information; performing deviation correction on the background field data of the numerical weather forecasting mode by using an RTTOV rapid radiation transmission mode to obtain simulation data after stable deviation correction; performing cloud pollution screening on the GIIRS observation information after inspection and the simulation data after deviation correction to obtain GIIRS observation information without cloud pollution; wRFDA numerical assimilation is carried out on the GIIRS observation information without cloud pollution, an initial field file is obtained, and the initial field file is used for adjusting and optimizing an output weather forecast result. According to the method, a more accurate initialization file is obtained, and the simulation accuracy of the typhoon precipitation area is more accurately improved.

The invention provides a dry-farming oat growth vigor monitoring method and system based on unmanned aerial vehicle hyperspectral remote sensing, and relates to the technical field of dry-farming oat growth vigor monitoring. The reflectivity of each image pixel under different spectral band conditions is calculated through the absolute radiation brightness and the known reflectivity of the reference plate, the average reflectivity of the unit grids in different spectral bands is obtained, a linear product is adopted to construct a growth score index, the growth score index of each grid unit is clustered by adopting a K-means clustering method, and the average reflectivity of each grid unit is calculated. And outputting the clustering condition of the growth vigor of the dry farming oats of the grid units, and setting a judgment threshold according to the clustering condition of the growth vigor of the dry farming oats to realize growth vigor classification. A reference plate is introduced to calibrate the reflectivity, the pixel-level absolute reflectivity is calculated through a radiation transfer equation, and the influence of illumination condition changes is eliminated; k-means clustering is adopted to automatically divide growth vigor levels, and a threshold value can be generated in a self-adaptive mode based on data distribution.

A weather prediction method includes generating radiance differences as a difference between measured radiances from satellites and forecast satellite radiances generated by a radiative transfer model and forecasted state profiles output by a numerical weather prediction (NWP) model. When the radiance differences exceed a noise threshold, the method includes generating updated state profiles by generating radiance-sensitivities using a Jacobian model and the forecasted state profiles; constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the radiance-sensitivities; generating filtered state-profile changes from the Kalman-gain matrix and the radiance differences; updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield updated state profiles.

This invention provides a method for predicting the hazard range of oil fires on the water surface based on interface tracking coupled combustion dynamics. The method includes: using a conserved two-dimensional shallow water equation to describe oil film diffusion; spatial discretization using the finite volume method; mitigating frictional rigidity using the Strang splitting method; calculating flux using the Roe scheme; suppressing spurious flow using the Well-Balanced scheme; controlling the time step using CFL conditions; and outputting the oil film thickness hourly. A mass evaporation rate conversion model based on energy balance is constructed to transform the oil film thickness field into a mass evaporation rate field per unit area. The mass evaporation rate field is used as the bottom boundary source term of the fire dynamics model and coupled to the three-dimensional low Mach number Navier-Stokes equations through a volume source term, achieving real-time coupled simulation of oil film transport and fire spread. The combustion dynamics control equations, including the conservation of mass, momentum, energy, and components, are solved. Combined with the radiative transfer equations, the spatiotemporal distributions of the temperature field, thermal radiation flux, and flue gas concentration are calculated to achieve dynamic prediction of the hazard range of oil fires on the water surface.

This invention discloses a method for regulating the thermal environment of urban morphology under the coupling of human and bird's-eye view perspectives. It is based on the radiative transfer equation method and Landsat-8 remote sensing imagery to retrieve land surface temperature; it integrates multimodal data for refined extraction of urban feature information; it constructs a three-dimensional comprehensive representation system of urban morphology under the coupling of bird's-eye and human perspective perspectives; it establishes urban land surface temperature prediction models at different spatial scales, integrating two-dimensional urban morphology, three-dimensional urban morphology from bird's-eye view, and three-dimensional urban morphology from human perspective; and it explores the optimal configuration method for regulating the surface thermal environment of urban morphology at both fine and coarse scales. This invention can provide a scientific basis for the quantitative regulation and spatial planning optimization of the urban thermal environment, achieving refined cooling and thermal environment improvement effects through urban spatial structure optimization and greening configuration adjustment.

This invention relates to the fields of atmospheric physics and remote sensing, and particularly to a method and system for obtaining upward atmospheric transmittance in the infrared absorption band. The method includes: directly measuring solar spectral data from the ground; simultaneously measuring atmospheric composition profiles in the directions of the sun and satellite; calculating the downlink measured transmittance based on the measured data; calculating the downlink and uplink theoretical transmittance based on the profiles using a radiative transfer model; comparing the downlink measured and theoretical transmittance to verify errors; calculating a correction coefficient based on the uplink and downlink theoretical transmittance; and using this correction coefficient to correct the downlink measured transmittance to obtain the final uplink atmospheric transmittance. The system includes spectral measurement, profile measurement, and data processing units for implementing the above method. This invention combines the high precision of direct measurement with the physical extrapolation capability of theoretical models, solving the problem of obtaining high-precision uplink atmospheric transmittance and providing a reliable technical solution for satellite remote sensing calibration.

This invention discloses a fully automated atmospheric and topographic radiometric correction method and device for high-resolution remote sensing imagery, belonging to the field of remote sensing science and technology. The method includes: constructing a fully automated batch processing framework; developing an atmospheric correction module based on the 6S radiative transfer model to automatically retrieve atmospheric parameters and eliminate their influence; and developing a topographic radiometric correction module based on the digital elevation model (DEM) and VECA model to compensate for radiometric distortion caused by complex terrain. The key innovation lies in constructing a joint correction framework and integrating a geomorphic feature recognition algorithm, which can automatically identify and select to execute either single atmospheric correction or a combined atmospheric-topographic correction process based on the degree of topographic relief in the image. This invention achieves fully automated batch processing from data input to result output, requiring no manual intervention, significantly improving the processing efficiency, intelligence level, and adaptability to different terrain scenarios in high-resolution image radiometric correction.

This invention discloses a method for inverting atmospheric N2O column concentration based on thermal infrared satellite remote sensing, belonging to the field of atmospheric environment remote sensing technology. The method includes: acquiring and preprocessing satellite observation data; selecting a specific spectral range as the inversion characteristic spectral region; introducing a fast radiative transfer model to construct a forward simulation operator to simulate the radiance of the top atmospheric layer; selecting N2O-sensitive characteristic bands with high signal-to-noise ratio and low interference based on a channel optimization strategy using the N2O / CO2 Jacobi intensity ratio; constructing an extended state vector, using surface temperature as an unknown parameter and performing joint optimal estimation iterative inversion with the N2O profile to correct the surface temperature in the reanalysis data, thereby achieving high-precision inversion of N2O atmospheric column concentration. This invention transforms surface temperature from a fixed input parameter into a state variable to be inverted, effectively decoupling surface temperature error from the N2O signal, eliminating systematic bias, and significantly improving the inversion accuracy of N2O column concentration.

The invention provides an atmospheric methane column weighting function sensitivity analysis method based on SCIATRAN (Standard Committee for International Traffic Radio Access Network). The method is characterized by comprising the following steps: by setting initial input parameters of SCIATRAN radiation transmission software, simulating to obtain methane radiation intensity and a column weight function in standard atmosphere; other parameters are kept unchanged, single parameters are changed one by one, a sensitivity parameter set is constructed, simulation calculation is carried out, and methane radiation intensity and column weight functions under different parameters are obtained; the methane spectrum change rule is analyzed by comparing the methane radiation intensity and the column weight function after each group of parameters are changed with an initial simulation result; and calculating the sensitivity of the methane column weighting function to different sensitivity parameters, constructing a sensitivity data set, drawing corresponding images, analyzing the influence of each parameter on methane detection, and further optimizing simulation parameters. The method can be used for determining accurate modeling of the SCIATRAN radiation transfer model on the actually measured spectrum, and provides support for algorithm optimization of inversion of atmospheric methane column concentration.

The application provides a kind of fast solver of radiation diffusion equation based on meshless discrete, it is related to the technical field of numerical simulation and calculation of radiation diffusion problem.It includes: a computing node configuration module for determining the solution threshold of radiation diffusion, and constructing a set of configuration points, a set of center points and a set of evaluation points within the solution region; a compactly supported kernel function definition module for configuring a radial basis function with a fixed support radius for each center point as a compactly supported kernel function; a solver execution module for rigidly decomposing the solution problem to obtain rigid sub-problem partial differential equations and non-rigid sub-problem partial differential equations; discretizing the rigid and non-rigid sub-problem partial differential equations using implicit and explicit formats respectively, solving the two partial differential equations using the radial basis function collocation method to obtain the coefficient vector of the radial basis function; and calculating the numerical solution of the evaluation point set using the coefficient vector.The scheme can achieve fast solution of the radiation diffusion partial differential equation using a meshless method.

The invention discloses an accelerated calculation method for geogas background light scattering, and belongs to the technical field of light scattering characteristic simulation calculation. The method comprises the following steps: firstly, pre-loading a parameterized model obtained by machine learning training and a meteorological database into a memory; tracking a plurality of detection light rays in the detection view field by using a pre-compiled radiation transfer algorithm, and extracting parameter data corresponding to each detection light ray from a pre-stored meteorological database according to a tracking result; transmitting the parameter data of all the detection light rays to the parameterized model at one time by utilizing a cross-platform calling interface to carry out radiance calculation to obtain a calculation result; the parameterized model and the radiation transfer algorithm are operated in different software environments, and the simulation precision and the calculation speed of the geogas light scattering distribution characteristics are remarkably improved.

The application discloses a kind of based on two-dimensional radiation transmission constraint's edge atmosphere profile inversion method, system, belongs to atmospheric remote sensing inversion technical field.The present application is with edge radiation data, auxiliary profile, observation geometric parameter and along track observation sequence as input, after pre-processing, it is sent into deep neural network;Network uses time series convolution network to extract along track feature, through multi-head attention mechanism fusion multi-source information, profile estimate value is decoded and output by full connection layer;Introduce differentiable two-dimensional radiation transmission model as physical constraint, the radiation value generated by forward simulation of inversion profile is compared with observation, and constraint loss is constructed;Training uses composite loss function, fuses mean square error, gradient loss, smooth regularization and two-dimensional radiation transmission constraint loss, and the network is optimized by end-to-end gradient backpropagation.The present application significantly improves the inversion accuracy of polar vortex edge, stratospheric jet and other high gradient regions, with the advantages of strong physical consistency and good generalization ability.

The invention relates to the technical field of satellite remote sensing, in particular to a multi-cloud system radiation transmission modeling method and device. The method comprises the following steps: acquiring cloud physical parameters and observation geometric parameters; the cloud physical parameters and the observation geometric parameters are combined to determine the cloud bidirectional reflectivity / transmissivity corresponding to each pixel; acquiring atmospheric background state data, and determining the solar radiation brightness of a cloud top layer according to the observation geometric parameters; wherein the atmospheric background state data comprises solar radiation data at different angles, atmospheric transmissivity at different angles and temperatures of underlying surfaces with different properties; according to the atmospheric background state data, determining underlying surface upper radiation data with different properties; and determining the radiation brightness of each pixel according to the cloud bidirectional reflectivity / transmissivity corresponding to each pixel, the solar radiation brightness, the underlying surface upper radiation data with different properties and the underlying surface reflectivity. According to the scheme, radiation characteristics of multilayer clouds can be simulated.

A weather prediction method includes generating radiance differences as a difference between measured radiances from satellites and forecast satellite radiances generated by a radiative transfer model and forecasted state profiles output by a numerical weather prediction (NWP) model. When the radiance differences exceed a noise threshold, the method includes generating updated state profiles by generating radiance-sensitivities using a Jacobian model and the forecasted state profiles; constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the radiance-sensitivities; generating filtered state-profile changes from the Kalman-gain matrix and the radiance differences; updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield updated state profiles.

This application discloses a remote sensing monitoring method, device, equipment, and medium for sulfur dioxide in the atmosphere, relating to the field of remote sensing detection technology. The method includes: indexing high-resolution satellite remote sensing spectra, pre-processed spatiotemporal data, and wavelength data from a target dataset obtained through spatiotemporal analysis of an initial dataset; spatially splitting and pixel labeling the spectral data of the CCD row and column range corresponding to the area to be inverted based on the spatiotemporal data, and processing multiple partitioned data blocks in parallel; spectral splitting of the observed spectra of each partitioned data block based on the wavelength data, and inputting the resulting multiple sub-band data blocks into a radiative transfer model in parallel to obtain the simulated spectrum corresponding to each sub-band data block; fitting and integrating the simulated and observed spectra, and correcting the initial inversion result of the sulfur dioxide concentration to obtain the target inversion result. This method can improve the computational efficiency of sulfur dioxide inversion.

This invention discloses a volumetric cloud rendering method and system based on 3D Gaussian splashing. The method includes: using the SfM algorithm to perform feature matching and structure reconstruction on the volumetric cloud image to obtain sparse point clouds in the scene, represented as anisotropic Gaussian ellipsoids; decomposing the illumination transmission process of the volumetric cloud into two parts, forward single scattering and internal multiple scattering, based on the radiative transfer equation, and embedding the illumination modeling formula into the volumetric cloud rendering process; determining the contribution of each 3D Gaussian ellipsoid to the rendering result based on its opacity index, and using a delayed deletion strategy to dynamically prune low-contribution 3D Gaussian ellipsoids to reduce redundancy; constructing a comprehensive loss function to focus the 3D Gaussian model on the volumetric cloud region and enhance the ability to restore structural details; and outputting the optimized volumetric cloud rendering result and the 3D Gaussian model. This invention effectively improves the efficiency and quality of volumetric cloud rendering while ensuring real-time rendering.

The application belongs to the technical field of remote sensing application, and provides a grassland biomass inversion method based on a radiation transmission model and machine learning, comprising the following steps: obtaining and processing remote sensing data, DEM data and measured biomass data of a target region; performing terrain correction by adopting an SCS+C method; performing global sensitivity analysis on input parameters in a PROSAIL model, and calibrating the input parameters according to the global sensitivity analysis; introducing a terrain factor into the model, and correcting a solar zenith angle and an observation zenith angle; generating simulated canopy reflectance data by using the model, and constructing a data set; constructing a lookup table LUT by combining the simulated canopy reflectance data and aboveground biomass data, and performing machine learning modeling according to a canopy spectral data set; verifying inversion precision, and outputting a grassland aboveground biomass spatial distribution map. The inversion method introduces a terrain factor on the basis of a traditional model, and improves adaptability and estimation precision of the model to a complex terrain region by combining a machine learning method.

This invention discloses a method and system for optimizing the spectral dispersion coefficient of a greenhouse gas spectrometer before launch. The method includes: acquiring the instrument's linear function, initial dispersion coefficient, and laboratory direct solar observation spectrum; simulating the atmospheric absorption spectrum based on a radiative transfer model and performing convolution processing using the instrument's linear function; screening for positionally robust reference absorption lines through environmental parameter sensitivity analysis; fitting the observed spectrum and simulated spectrum to find peaks, extracting the center wavelength, and calculating the wavelength shift; establishing a polynomial fitting relationship for the wavelength shift to obtain the wavelength shift fitting coefficient; performing a difference calculation between the initial dispersion coefficient and the wavelength shift fitting coefficient, keeping higher-order terms unchanged, and outputting the optimized dispersion coefficient. This invention can effectively improve the wavelength calibration accuracy of the spectrometer before launch.

This invention discloses a method for color balancing remote sensing images based on surface reflectance and surface type. The method first inputs the remote sensing image and its auxiliary parameters, performs image calibration, converts DN values ​​to apparent radiance, and then further converts them to apparent reflectance. Subsequently, based on aerosol parameters, atmospheric correction is performed using a radiative transfer model to obtain the surface reflectance. Next, orthorectification of the surface reflectance is performed based on RPC information, and combined with surface type data, surface type information within the image range is obtained through geometric location matching. Then, a reference image is selected, and the mean and variance of surface reflectance for each channel of both the reference image and the image to be sized are calculated for different surface types. Finally, the mean-variance method is used to color-balance the image to be sized, obtaining a color-balanced surface reflectance image. This invention can effectively eliminate color deviations between remote sensing images, improving the color consistency and ground feature realism of mosaic images.

This invention belongs to the field of water quality parameter inversion technology, specifically a machine learning-based remote sensing inversion method and system for suspended particulate matter (SPM) concentration. The method includes: acquiring satellite remote sensing imagery and synchronously measured SPM concentration data; obtaining a spatially gridded water body remote sensing reflectance matrix through radiometric calibration, atmospheric correction, and water body masking; denoising through wavelet decomposition reconstruction; and obtaining a spectral numerical sequence through spectral normalization and standardization. Based on this sequence, a multi-scale band combination and differential features are constructed, and sensitive features are selected using XGBoost. Physical constraints are constructed by combining sensitive features with water body radiative transfer laws, and intermediate inversion results are obtained through numerical iteration. Deep learning residuals are used to compensate for biases in the intermediate results, and finally, through spatial smoothing, consistency verification, and high-concentration saturation optimization, a high-precision, spatially continuous spatial distribution result of SPM concentration is obtained. This invention achieves efficient feature mining and deep integration of physical mechanisms, significantly improving the model's interpretability and generalization ability.

This application relates to a method for sea ice classification and concentration inversion using UAV optical and thermal infrared data. The method includes: first, obtaining registered UAV optical and thermal infrared images through preprocessing techniques such as orbit correction, radiometric calibration, and geometric correction, providing high-quality data for subsequent sea ice information extraction; second, constructing an Ice-Unet deep learning model to obtain high-precision sea ice classification results; third, selecting surface temperature values ​​corresponding to open water and fixed ice in the sea ice classification to construct coordinate point values ​​for the thermal infrared data, and constructing a linear inversion model based on a radiative transfer model to obtain sea ice concentration inversion results based on thermal infrared data. This solves the problems of existing technologies in achieving rapid reporting and on-site support of sea ice information in Arctic waters.

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.

The invention discloses an adaptive dimming and energy consumption optimization method and device for an architectural lighting system based on digital twinning, electronic equipment and a storage medium, and relates to the technical field of intelligent optimization, and the method comprises the steps: obtaining physical space data through a photon event sensor and an event camera; updating the digital twin dynamic photon flow field model based on compressed sensing, a radiation transfer equation and a bidirectional reflection distribution function; generating and screening an optimal dimming strategy by using a confrontation generation optimization architecture; personalized adaptation is realized through light environment fingerprint matching and transfer learning; based on a ternary closed-loop self-evolution mechanism, the model and architecture parameters are corrected, and the energy consumption and the lighting effect are dynamically optimized. The light environment modeling precision and the dimming adaptability are improved, the building lighting energy consumption is reduced, intelligent and energy-saving upgrading of the lighting system is achieved, and different user preferences and scene requirements are met.

This invention discloses a high-resolution spatialization method for near-surface air temperature in glacial regions. This method integrates measured data from high-altitude meteorological stations, satellite remote sensing data, and machine learning-optimized spatial interpolation techniques to construct a continuous temperature field under complex terrain conditions. First, by fusing station-measured and satellite remote sensing data, a data complementarity method suitable for sparsely observed areas is established. Based on a machine learning optimization algorithm, spatialized glacial temperature records from meteorological station observations are used as training data, while satellite remote sensing data with broader coverage is employed. Surface temperature retrieved through a radiative transfer model is used as input features to correct the surface temperature of the study area, generating a spatial distribution of air temperature that better matches ground observations. Finally, by fusing altitude and vertical temperature lapse rate as core constraint variables and combining benchmark verification based on station observation data, the accuracy of near-surface air temperature spatialization calculations in high-altitude complex terrain areas is improved.

This invention discloses a method and apparatus for evaluating on-board very long wave (VLW) spectral calibration based on a radiative transfer model, belonging to the field of remote sensing optics technology. This invention targets a wide-band VLW infrared hyperspectral sensor covering the wavelength range of 12-16 μm, analyzing the atmospheric absorption characteristics and sensor imaging spectral characteristics within this spectral range. The spectral calibration accuracy is evaluated through physical model simulation, including: simulation using the MODTRAN radiative transfer model; construction of a spectral drift model; solving for the center wavelength shift based on a spectral matching combined with an iterative optimization algorithm; and uncertainty analysis of the impact of factors such as the estimation error of the algorithm itself, sensor signal-to-noise ratio, inter-spectral response inconsistency, and atmospheric CO2 concentration on the estimation results. This invention is applied to evaluate the spectral calibration accuracy of on-orbit satellite data.

The invention discloses an emergent aquatic vegetation photosynthetically active radiation absorption ratio estimation method, and belongs to the technical field of ecological remote sensing and environment monitoring. In order to solve the problems that the vertical layered structure of the emergent aquatic plant is complex and the photosynthetically active radiation absorption ratio is difficult to accurately estimate by underwater light propagation characteristics, the method mainly adopts a technical means of combining layered modeling and a two-flow approximate radiation transmission model, and integrates multiple scattering calculation, interface refraction and reflection correction and stalk contribution compensation. And accurate estimation of the FAPAR value of each structural layer of the emergent aquatic vegetation is realized. The method can improve the quantitative monitoring precision of the photosynthetic activity and the ecological function of the emergent aquatic vegetation, and is suitable for remote sensing application scenes of typical aquatic vegetation areas such as wetlands and lakes.

The present application relates to the technical field of marine ecological remote sensing, and particularly relates to a modeling method for underwater coral canopy radiation transmission and fluorescence effect, comprising the following steps: establishing a three-dimensional model of an underwater scene, and constructing a three-dimensional gridding model of underwater coral targets and substrates; determining environmental parameters; forward simulating the light transmission process in the canopy, constructing an irradiance spatial distribution model of the underwater scene, and obtaining a fluorescence emission radiance distribution model; based on the fluorescence emission radiance distribution model, reversely simulating the coral fluorescence spectrum through spectral imaging; model verification and optimization, adjusting algorithm parameters, and improving the accuracy of the model. The present application provides a modeling method for underwater coral canopy radiation transmission and fluorescence effect, which can comprehensively consider different scales, coral morphologies, substrate types and water conditions by extending the BRDF model to contain a fluorescence term to simulate the propagation of fluorescent light and the special optical effects of the underwater environment, so as to realize high-precision underwater coral canopy modeling.

The present invention relates to the field of meteorological prediction technology, and particularly to a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. The system includes: The present invention determines the cloud base height of the target cloud cluster based on a ceilometer, collects the radiation corresponding to the cloud top height of the target cloud cluster based on a meteorological satellite, calculates the cloud top temperature of the target cloud cluster based on a radiative transfer model, and determines the cloud top height of the target cloud cluster based on the cloud top temperature, detects the precipitation particles of the labeled segmented cloud layer, infers the water content density of the labeled segmented cloud layer according to the precipitation particles, obtains the extinction coefficient and backscattering coefficient of the aerosol particles of the labeled segmented cloud layer, obtains the meteorological characterization value of the target cloud cluster based on the water content density, extinction coefficient and backscattering coefficient of the aerosol particles, and analyzes the rationality of the existence of the target cloud cluster based on the meteorological characterization value of the target cloud cluster, which can improve the accuracy of cloud meteorological prediction and improve the timeliness of meteorological prediction.