125 results about "Radiation transfer" patented technology

The invention relates to an organic matter near infrared spectrum inversion method and device for removing moisture based on radiation. The method comprises the steps that after a soil sample is pretreated, multiple moisture content gradients are regulated and controlled, the natural moisture permeation state is simulated, visible light-near infrared spectrum data and the organic matter content are collected, and a soil spectrum library is constructed; quantifying the interference weight of moisture on the spectrum by using a physical radiation transmission model to obtain a preliminary correction spectrum; moisture and organic matter features are extracted through a deep learning multi-branch architecture, a moisture characterization index is constructed, cross-domain feature alignment is realized in combination with a domain adversarial network, and an accurate correction spectrum is obtained; all the modules are integrated to construct a soil moisture elimination reconstruction spectrum model, after layered verification and iterative optimization, a to-be-detected soil spectrum is input into the model, organic matter content near infrared spectrum inversion is completed, and a result is output. According to the method, moisture interference is removed through combination of physical radiation and deep learning, the inversion precision is improved, and the method is suitable for rapid detection of organic matters in various moisture-containing soil.

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention relates to a rice canopy optical and microwave integrated radiation modeling method, which comprises the following steps of: acquiring and uniformly processing rice canopy and environmental parameters, and constructing a same physical parameter system shared by optical and microwave models; the optical part is used for calculating bidirectional reflection factors and optical reflectivity of the rice canopy under different observation conditions based on an aquatic vegetation radiation transmission model; the microwave part is based on a vector radiation transfer equation, adopts a matrix multiplication method to calculate canopy multiple scattering, and simulates underlying surface scattering in combination with an improved integral equation model to obtain a multi-polarization backscattering coefficient; the optical reflectivity and the microwave backscattering coefficient are output under a unified parameter system, and optical and microwave collaborative forward modeling is achieved. According to the invention, a rice canopy optical and microwave integrated collaborative simulation framework is established based on a radiation transfer principle, a physical consistent basis is provided for crop parameter inversion, multi-source remote sensing data application and farmland environment monitoring, and the application prospect is wide.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention relates to an interstellar medium radiation transfer calculation method and device based on ray tracing. The method comprises the following steps: discretizing a three-dimensional space model of an interstellar medium by using a Cartesian grid to obtain a plurality of grid units, and emitting a plurality of uniformly distributed light rays at the center of each grid unit to construct a discretized radiation transfer equation; for each of the light rays, screening out the grid units through which the light rays penetrate by adopting an octree algorithm; calculating the length of a path where each light ray intersects with the grid units when the light ray passes through the grid units by adopting a flat plate method; and solving a discretized radiation transfer equation according to the path length to obtain a parameter equation of the interstellar medium. By adopting the method, the calculation amount of the ray tracing numerical integration in the radiation transfer equation can be reduced.

The invention relates to the technical field of infrared measurement, in particular to an intelligent calibration method and system for temperature measurement of an infrared sensor, and the method comprises the steps: enabling an infrared camera to align with a blackbody radiation source to form a calibration scene, collecting the output of a plurality of groups of pixels under different radiation temperatures and different internal part temperature states, fitting a temperature signal response model containing an exponential term and a bias term according to least square to obtain amplitude, exponential, temperature offset and output bias parameters; during measurement, a thermal image containing a target object and two reference blackbody sources is obtained, and parameters such as emissivity, environment equivalent radiation temperature, transmission medium and optical transmission, camera temperature and the like are synchronously obtained; performing non-uniformity and offset correction and abnormal pixel replacement on the thermal image; the initial temperature is inverted based on radiation transfer decomposition and compensation of internal radiation of the camera; and carrying out statistical averaging on the temperatures of the two blackbody reference regions to obtain a double-point correction coefficient, and outputting correction temperature or temperature distribution. According to the method, the problems that temperature inversion model parameters are difficult to adaptively update along with working conditions and on-site temperature measurement errors are difficult to effectively suppress when an existing infrared sensor array is in a long-distance non-contact temperature measurement scene can be solved.

The invention relates to an intelligent design method of a radiation shielding material based on cross-scale error feedback lock step, which comprises the following steps: determining a material component set and acquiring an atomic-scale graph structure of the material component set, and extracting intrinsic performance parameters such as heat conductivity and attenuation coefficient through an atomic-scale prediction module; and then, constructing a design variable vector in combination with a multi-physical field condition, calculating equivalent performance parameters of the composite material, establishing a cross-scale lock step mechanism, and triggering a molecular dynamics and Monte Carlo recalculation module to carry out correction when the deviation between a predicted value and a mesoscopic recalculation result exceeds a limit. Based on the correction parameters, a candidate material scheme is generated by using a multi-objective optimization algorithm; and performing gamma-ray and neutron shielding performance simulation on the candidate schemes through a Monte Carlo radiation transfer model accelerated by a deep neural network, and comparing with an experimental result to realize self-adaptive material design driven by cross-scale errors. According to the method, the design precision and efficiency of the radiation shielding material are remarkably improved.

The invention belongs to the technical field of water quality parameter inversion, and particularly relates to a machine learning-based suspended matter concentration remote sensing inversion method and system, and the method comprises the steps: collecting a satellite remote sensing image and synchronous actual measurement suspended matter concentration data, and obtaining a space gridding water body remote sensing reflectivity matrix through radiometric calibration, atmospheric correction and water body mask. Reconstructing and denoising through wavelet decomposition, and normalizing and standardizing the spectrum to obtain a spectrum numerical sequence; multi-scale waveband combination and differential features are constructed based on the sequence, and sensitive features are screened out by using XGBoost. A physical constraint term is constructed in combination with sensitive characteristics and a water body radiation transmission rule, and an intermediate inversion result is obtained through numerical iteration. And performing deviation compensation on an intermediate result by using a deep learning residual error, and finally performing spatial smoothing, consistency verification and high-concentration saturation optimization to obtain a high-precision and spatially continuous suspended matter concentration spatial distribution result. According to the method, efficient feature mining and physical mechanism deep fusion are realized, and the explanatory and generalization ability of the model is greatly improved.

The invention discloses a coal mine gas cloud contour infrared spectrum inversion method. The method comprises the following steps: 1, simulating an underground gas cloud formation environment; 2, acquiring infrared image data containing gas cloud by using an infrared spectrum detection device; step 3, carrying out de-noising processing on the infrared image data; 4, performing temperature drift correction and radiation calibration on the denoised infrared image, and converting a DN value into radiation brightness; 5, based on the multi-frame de-noised image, performing target detection and contour extraction on the gas cloud by using a Methane-YOLO model, performing multi-view geometric matching of feature points, performing adjustment optimization of three-dimensional coordinates in combination with a bundle method, generating a point cloud model, and reconstructing a curved surface; 6, based on the three-layer radiation transmission model and the Lambert-Beer law, inverting the gas concentration; and step 7, based on the CEEMDAN-VMD secondary decomposition and CNN-LSTM hybrid network, carrying out joint training on the space contour and time sequence data, and carrying out three-dimensional space concentration distribution prediction.

The invention provides an atmospheric aerosol particle parameter inversion method and device and electronic equipment. The inversion method comprises the following steps: performing parametric description on non-spherical aerosol particles to obtain an aerosol parameter vector; constructing and obtaining a trained physical guidance neural network model; applying the trained physical guidance neural network model to radiation transmission model forward modeling, calculating and outputting a derivative of the input aerosol parameter, and determining and outputting a Jacobian matrix relative to the aerosol parameter through automatic differentiation; and performing inversion iteration based on the Jacobian matrix and the to-be-inverted atmospheric top observation quantity, and determining a target aerosol parameter corresponding to the to-be-inverted atmospheric top observation quantity, thereby solving the problems that a traditional scattering model is non-derivable and a derivative is difficult to obtain, and improving aerosol remote sensing inversion precision and efficiency.

The invention discloses a remote sensing multi-parameter integrated inversion normal form method, system and equipment based on AI-Agent. According to the method, a deep learning neural network is dynamically driven through AI-Agent, a refining mechanism (RM)-Transformer-MoE size nested model, a physical method, a statistical method and expert knowledge are coupled, a DL-C-PSK normal form is constructed, a high-precision multi-source database is established based on the normal form, an appropriate radiation transfer equation is constructed through geophysical logical reasoning, and a high-precision multi-source database is established. And inversion of parameters such as surface temperature, surface emissivity, atmospheric water vapor content and near-surface air temperature is realized. According to a causal relationship between an input wave band and an output parameter, a direct synchronous inversion or iterative inversion mode is adopted to ensure multi-parameter high-precision synchronous inversion. Wherein the core of the deep learning neural network comprises RM logic derivation, SHAP model interpretation, Transform model architecture and a Transform-MoE size nested model, so that the interpretability, the adaptability and the precision of the model are improved. Through an AI-Agent driven RM-Transform-MoE nested model, deep coupling of physics-statistics-knowledge is realized, compared with a traditional SW method, the inversion precision is greatly improved, and verification shows that the technology is suitable for the fields of global climate observation, environment monitoring and the like.

The invention provides an offshore area atmosphere and ocean parameter collaborative inversion method and system, and relates to the technical field of satellite remote sensing, and the method comprises the steps: obtaining to-be-inverted parameters which comprise an atmosphere parameter and an ocean parameter, calling a multi-parameter physical information neural network model to carry out the ocean water-leaving radiation calculation of the ocean parameter, and calculating the ocean water-leaving radiation of the ocean parameter; an atmospheric aerosol model is called to carry out parameterization calculation on atmospheric parameters, and scattering parameters and absorption coefficients of atmospheric aerosol are obtained; inputting the ocean water-leaving radiation quantity and the scattering parameter and the absorption coefficient of the atmospheric aerosol into a forward radiation transfer model, and carrying out radiation transfer calculation to obtain entrance pupil radiation brightness of the top of the atmospheric layer; and finally, performing parameter inversion by combining a satellite measurement radiation signal to obtain an inversion parameter corresponding to the to-be-inverted parameter. According to the invention, problems existing in an atmospheric correction algorithm for ocean water color remote sensing in the prior art are solved.

The invention relates to a system deviation correction method for improving the salinity inversion precision of an interference type microwave radiometer, and relates to the technical field of satellite ocean remote sensing data processing. The method comprises the following steps of: acquiring an area, strictly purifying and screening observed brightness temperature data of a preset time window in the area to eliminate the influence of radio frequency interference, sun and moon pollution and the like, acquiring high-purity original data, calculating simulated brightness temperature by utilizing a radiation transmission model, and subtracting the simulated brightness temperature from the purified observed brightness temperature to obtain a brightness temperature deviation sample; and then a robust statistical method based on a sliding window and a median is adopted to carry out statistical calculation on the deviation sample to obtain a stable and reliable deviation correction value, and finally a two-dimensional lookup table is generated and applied to sea surface salinity inversion. According to the method, the observation brightness temperature for salinity inversion can be efficiently and accurately corrected from the source, and the contradiction between stability and adaptability in the prior art is solved.

The embodiment of the invention provides a remote sensing image atmospheric correction method and system for an ocean near-shore scene, and relates to the technical field of ocean remote sensing image atmospheric correction. According to the method, after remote sensing data is obtained, solar flare correction is executed by extracting a target mask and using a secondary simulation model of a satellite signal in a solar spectrum. And then a multi-band darkness index is calculated, and an optimal aerosol optical thickness value is inverted through a radiation transmission model. And calculating the atmospheric correction coefficient of each wave band according to the optimal aerosol optical thickness value so as to generate correction result data. According to the method, on the basis of robust flare correction of the SWIR proxy, scene-adaptive flare proxy is constructed by utilizing water body reflection and path radiation physical characteristics of the SWIR under a deepwater condition, and flare deduction is realized. Inversion is carried out through multiband combined darkness indexes, and the spectral characteristics of the deep water body reflected in the visible spectrum are utilized, so that single-band random noise is smoothed, and the accuracy of an AOT estimation result is improved.

The invention relates to a tail flame polarization state simulation method and device considering solid-phase particle size distribution, a medium and equipment. The method comprises the following steps: constructing a particle size distribution function; determining a cumulative distribution function based on the particle size distribution function, discretizing the cumulative distribution function, and calculating the representative particle size of each region; optical factors of the solid-phase particles in all the areas are calculated; calculating optical parameters of the particle swarm; taking each representative particle size and number density as particle phase input of an N-S equation of fluid mechanics to obtain two-phase flow tail flame flow field data and nozzle outlet data under different particle size distribution conditions; the method comprises the following steps of: taking a plurality of grids as input of a tail flame polarization radiation transfer model, calculating each solid phase parameter of each grid, taking an extinction coefficient and a single scattering coefficient of each cell as input of a two-phase flow tail flame vector radiation transfer equation, and outputting a polarization state of the tail flame. And the tail flame polarized radiation closer to the reality is obtained through simulation.

The invention provides a cloud atmosphere high-fidelity vector radiation simulation method based on physical constraints. The method comprises the following steps: A1, generating cloud layer micro-physical parameters by a WRF model; a2, integrating simulation parameters generated in the step A1, and inputting the simulation parameters into an MYSTIC model for three-dimensional radiation transfer simulation; a3, constructing a PC-GAN (U-Net + + + differentiable radiation layer + double-branch discriminator) physical constraint generative adversarial correction network, and defining adversarial loss, physical loss and KL divergence loss; a4, simulation data are input into PC-GAN, CALIPSO, PLODER3 and other actual measurement data are compared with the simulation data to calculate loss, physical loss is fed back, and cloud micro physical parameters are iteratively optimized until RMSElt is radiated; 5%; a5, backfilling the cloud layer micro-physical parameters corrected in the step A4, carrying out MYSTIC model radiation transfer simulation again, and carrying out satellite / foundation verification; and A6, outputting high-fidelity radiation simulation data. The method can be widely applied to meteorological satellite data verification, climate model development, cloud characteristic inversion and remote sensor design, and has the technical advantages of high precision and high efficiency.

The invention relates to the technical field of limited space fire hazard flashover prediction. The invention provides a limited space fire hazard flashover prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring sensor observation data and initial fuel distribution data; inputting the space-time coordinate vector and the initial fuel distribution data into a Mamba neural network to obtain predicted values of temperature, incident radiation and fuel mass fraction; calculating a space-time derivative of the predicted value, and constructing a physical residual term containing energy conservation, radiation transfer and fuel consumption equation residual, a boundary residual term and an observation residual term; weighted combination is carried out on the three types of residual errors to construct a loss function, and network parameters and to-be-learned physical parameters are updated through an optimization algorithm; and predicting a temperature field based on the updated network, and judging the time of detonation when the temperature reaches a detonation critical value. The problems of poor reliability and prediction deviation caused by lack of physical rule constraints of an existing data-driven fire prediction model are solved.

The invention belongs to the technical field of quantitative remote sensing, and relates to a mid-infrared hyperspectral emission and reflection characteristic integrated inversion method, which comprises the following steps: acquiring mid-infrared hyperspectral satellite remote sensing data, and optimizing sensing entrance pupil radiance data to obtain optimized entrance pupil radiance data; the intermediate infrared hyperspectral satellite remote sensing data comprises sensing entrance pupil radiance data; performing atmospheric correction on the optimized entrance pupil radiance data by using a radiation transfer model to obtain surface brightness temperature and atmospheric downward radiation, and calculating solar irradiance reaching the surface; constructing a mid-infrared hyperspectral earth surface bi-directional reflectivity inversion framework, and performing earth surface bi-directional reflectivity inversion based on the mid-infrared hyperspectral earth surface bi-directional reflectivity inversion framework to obtain earth surface bi-directional reflectivity and earth surface equivalent radiance; constructing a scene self-adaptive framework, and performing inversion based on the scene self-adaptive framework to obtain the surface temperature and the surface emissivity; therefore, accurate separation of surface emission radiation and surface reflection radiation is completed.

The application discloses a complex lunar surface scattering coefficient image simulation method and system based on multiple data jointing, and the method comprises the following steps: firstly, terrain camera data is used to acquire terrain height data of a special geological structure of the moon as model geometric information input; secondly, a lunar soil weathering layer electromagnetic scattering model is established by using a vector radiation transfer theory and a lunar surface stone double-component method to simulate the interaction between electromagnetic waves and lunar rocks, the lunar surface, weathering layer particles and the subsurface; finally, a radar imaging mechanism is simulated based on a mapping projection algorithm, the scattering coefficient on each scattering facet is calculated by using the electromagnetic scattering model, and the scattering coefficient is mapped to an imaging area by using a ray tracing method, a shadow judgment method and the like, so that the scattering echo of each region of the complex lunar surface is rapidly estimated, and the fast scattering coefficient image simulation is realized.

The application provides a polarization remote sensing multi-layer cloud identification method based on radiation simulation data. The process comprises the following steps: obtaining accurate atmospheric top radiation simulation data as a training sample of machine learning based on a vector radiation transfer model; building a multi-layer cloud identification algorithm based on a K-nearest neighbor model, training polarization remote sensing simulation data by using the K-nearest neighbor model based on multi-dimensional information such as intensity radiation, polarization radiation and multi-angle, and realizing identification of multi-layer clouds. The application can be used for multi-layer cloud identification of polarization remote sensing cloud pixels, and provides support for data application of polarization remote sensing.

The invention discloses a method for regulating and controlling a thermal environment by an urban form under coupling of human eyes and a bird's-eye view angle. The method comprises the following steps of: inverting a surface temperature based on a radiation transfer equation method and a Landsat-8 remote sensing image; carrying out fine extraction on urban ground feature information fused with multi-modal data; constructing a three-dimensional comprehensive representation system of the urban form under the coupling of a bird's-eye view angle and a human eye view angle; establishing an urban surface temperature prediction model fusing an urban two-dimensional form, an urban three-dimensional form in a bird's-eye view angle and an urban three-dimensional form in a human eye view angle under different spatial scales; and an optimal configuration method for regulating and controlling the surface thermal environment by the urban form under the fine scale and the rough scale is explored. According to the method, a scientific basis can be provided for quantitative regulation and control and space planning optimization of the urban thermal environment, and the effects of refined cooling and thermal environment improvement under urban space structure optimization and greening configuration adjustment are achieved.

The application discloses an infrared image adaptive atmospheric correction method and device based on a radiation transmission model, and belongs to the technical field of remote sensing image radiation correction. The method first extracts the spatial range, imaging time and observation geometry parameters of an image scene, constructs a scene-based atmospheric profile grid by distance inverse ratio weighted interpolation based on a reanalysis atmospheric profile, and combines a digital elevation model and a view zenith angle to correct and apply terrain and observation geometry constraints to generate control node input parameters. Then, the atmospheric radiation transmission model is called in batches to obtain the hyperspectral results of each control node, and the transmittance and path radiance parameters of the infrared target band are extracted by spectral response function convolution. Finally, the control node parameters are reprojected to the original image coordinates, the four-neighbor-domain bilinear interpolation and nine-neighbor-domain weighted interpolation results are adaptively fused for each pixel, the full-resolution atmospheric parameter field is recovered, and the observed radiance at the ground is inversed.

The invention discloses a RadCalNet data application range expansion method oriented to radiation calibration of a satellite-borne large-view-field sensor. The method comprises the following steps: 1) constructing a RadCalNet site earth surface BRDF model; (2) RadCalNet data missing at the transit moment of the satellite-borne large-field-of-view sensor are simulated; 3) simulating MODIS five-waveband equivalent surface reflectance under observation geometry and vertical angle of a sensor to be calibrated, and calculating a BRDF correction factor; 4) weakening the relative spectral response function difference of the sensor; 5) calculating a full-spectrum global correction factor to obtain the equivalent surface reflectance of the to-be-calibrated sensor; and 6) calculating the radiometric calibration coefficient of the to-be-calibrated sensor after obtaining the equivalent apparent reflectance of the to-be-calibrated sensor by using a 6S radiation transfer equation. The method disclosed by the invention well solves the problem that the high-frequency radiometric calibration of the satellite-borne large-view-field sensor cannot be realized due to angle limitation and data missing of RadCalNet data.

The invention provides a construction method, a prediction method and a system of a cooling coating property prediction model, and is suitable for the technical field of cooling coating optical property prediction. The construction method of the prediction model comprises the following steps: correcting an absorption efficiency factor and a scattering efficiency factor of scattering particles in a cooling coating to obtain a non-independent absorption efficiency factor and a non-independent scattering efficiency factor of the scattering particles; constructing a radiation transfer equation of non-independent scattering; based on the semi-isotropic strength of the cooling coating, constructing a double-flux model about forward and reverse radiation intensities; obtaining a forward radiation energy calculation formula and a reverse radiation energy calculation formula according to the double-flux model; determining an unknown coefficient in a radiation energy calculation formula based on the boundary condition; constructing a cooling coating property prediction model; the method has the beneficial effects that the actual physical process under the high-concentration condition can be accurately reflected, and the calculation precision of the properties of the cooling coating can be remarkably improved.

The invention discloses a beet nutrition detection system based on unmanned aerial vehicle multispectral data, and relates to the technical field of agricultural remote sensing and precision agriculture, and the beet nutrition detection system comprises an unmanned aerial vehicle multispectral acquisition module used for obtaining multispectral reflectivity data of beet canopies; according to the beet nutrition detection system based on the unmanned aerial vehicle multi-spectral data, the precision and the real-time performance of beet nutrition detection are remarkably improved through dynamic spectrum-physiological coupling modeling and multi-dimensional feature decoupling technologies; based on a differentiable PROSAIL radiation transport sub-network, the system fuses a physical mechanism and deep learning, and dynamically corrects spectrum deviation caused by canopy structure change, so that the root-mean-square error of nitrogen content detection is reduced, and the precision is improved.

The invention discloses an aerodynamic radiation transfer calculation method based on equivalent Lambert body surface element tracing, and the method comprises the steps: simplifying a conventional radiation transfer equation into a form based on the Kirchhoff's law based on the optically thin and non-scattering characteristics of a hypersonic velocity external flow field; the brightness spectrum of the equivalent Lambert area light source is obtained by integrating the emission coefficient along the observation sight line; then, enabling the transmission process of aerodynamic radiation of an aircraft external flow field to the star sensor to be equivalent to a Lambert surface radiation source located at the top end of a light shield, discretizing a light source surface and a detector image surface into infinitesimal elements, and performing tracing calculation on irradiance contributions generated by all the light source surface infinitesimal elements to a single detector image element; finally, the irradiance distribution of the pneumatic radiation on the star sensor detector is efficiently and accurately obtained; according to the method, the transmission process of aircraft flow field pneumatic radiation to the star sensor is reasonably simplified, the calculation efficiency of pneumatic radiation transmission is remarkably improved, and the method is suitable for engineering prediction and evaluation.

The present application relates to a kind of improved radiation transmission model mangrove chlorophyll estimation method, comprising: the measured data of the tree height, crown width and leaf area index of mangrove are collected;Mathematical relationship between leaf area index and tree height, crown width is constructed;Simulated spectral data is generated as training set using improved radiation transmission model;The sensitive feature screening of mangrove functional traits is carried out;Random forest inversion model based on physical constraint is constructed;The present application estimates the chlorophyll content of mangrove using the random forest inversion model based on physical constraint.The beneficial effects of the present application are: the present application can effectively improve the precision of chlorophyll inversion, provide scientific basis for the fine inversion of mangrove chlorophyll, provide data support and decision-making reference for the protection and scientific management of mangrove.

The invention provides a brightness temperature simulation calculation method conforming to a radiation transfer theory. The method comprises the following steps: establishing a radiation transfer model based on a radiance Stokes vector in a (Il, Ir, U, V) T form; collecting radiance simulation parameters; if the radiation transmission model is a scalar radiation transmission model, the atmospheric thermal radiation is a vector radiation transmission model, and if the radiation transmission model is a vector radiation transmission model, the atmospheric thermal radiation is obtained by calculating atmospheric particle scattering phase function information, atmospheric single scattering albedo, surface reflectance information and surface emissivity according to radiance simulation parameters; substituting the radiance simulation parameter, the atmospheric thermal radiation, the atmospheric particle scattering phase function information, the atmospheric single scattering albedo, the surface reflectance information and the surface emissivity into a radiation transmission model for calculation to obtain a radiance simulation result; and converting the simulation result of the radiance into the brightness temperature. The invention further provides a brightness temperature analog calculation device and equipment and a storage medium. The brightness temperature simulated by the method is consistent with a theoretical value.

The application discloses a lunar-based GNSS-R signal simulation and DDM generation method based on a full polarization layered scattering model, and belongs to the technical field of deep space exploration and microwave remote sensing. The method first constructs a full polarization bistatic scattering coefficient model based on a vector radiation transfer theory, comprehensively considers surface scattering, subsurface interface scattering, buried particle body scattering and multiple scattering mechanisms, and calculates the polarization bistatic scattering coefficient of each point on the lunar surface through a Mueller matrix summation method. Then, the obtained scattering coefficient is taken as an input parameter, a Z-V model is combined with an orbital geometric relationship between a GNSS satellite and a lunar-based receiver, delay and Doppler mapping is performed on each spatial grid point in a scintillation area, and finally, high-fidelity DDM waveforms are generated through integral operation. The application realizes full-link forward modeling from a microscopic physical scattering mechanism to macroscopic observation data, and has strong physical interpretability and high simulation accuracy.

The application discloses an aurora brightness inversion method based on satellite remote sensing data, and comprises the following steps: constructing an atmospheric transmittance lookup table based on VIIRS night light data, inverting DNB band surface reflectivity and calculating moonlight radiation brightness; establishing a radiation transfer equation containing aurora uplink radiation, surface reflected aurora radiation and surface reflected moonlight radiation, and solving aurora real brightness pixel by pixel; and removing abnormal high brightness signals and urban artificial light interference, and obtaining spatially continuous and stable annual aurora brightness distribution through annual scale synthesis and spatial interpolation smoothing. The application can realize wide-range, quantitative and long-time sequence aurora brightness monitoring, effectively overcome the shortcomings of ground-based observation and numerical simulation, and provide reliable technical means for spatial weather research and polar environment monitoring.