66 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 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 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 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 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 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 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 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 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 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.

The application focuses on the field of agricultural informatization and remote sensing application technology, and particularly relates to a kind of alfalfa yield estimation method based on multispectral and physical constraint sample enhancement. The method first acquires unmanned aerial vehicle multispectral image data corresponding to the alfalfa growth period, and lays out field quadrats with equal area in the image coverage area, collects measured data of alfalfa hay yield, leaf area index, chlorophyll content and leaf equivalent water thickness, and constructs an initial sample set. Then, input each parameter data within a reasonable range, generate predicted multispectral samples that meet the physical consistency constraint using the radiation transfer model, to expand the sample space. The measured samples and predicted samples are fused to construct an alfalfa hay yield estimation training data set. Based on the training data set, a mapping model between unmanned aerial vehicle multispectral features and alfalfa hay yield is established, and the alfalfa hay yield is estimated through machine learning.

The invention relates to the technical field of infrared thermal image detection and digital twinning of power equipment, and provides an infrared synthetic data and domain self-adaption method based on digital twinning, which comprises the following steps of: constructing a digital twinning body of the power equipment, and aligning parameters of the digital twinning body with statistical characteristics of a real infrared sample through reverse calibration; performing multi-physical field thermal simulation to generate a temperature field; performing radiation transfer and imaging chain synthesis based on the temperature field to generate a synthesized infrared image; automatically marking the synthesized infrared image to obtain synthesized data; performing multi-target domain adaptive training by using the synthetic data as a source domain and real infrared data as a target domain to obtain an adaptive model; using the adaptive model to generate a confidence pseudo label for the target domain data, and performing gradual self-training based on the confidence pseudo label to update the adaptive model; during testing, self-adaption is carried out when testing is carried out on data without target domains, and uncertainty is output; and adding an auditing label to the output result to form a traceable evidence chain.

The application discloses a kind of ocean polarization lidar multiple scattering correction methods based on machine learning, comprising: based on polarization Monte Carlo method, construct ocean polarization lidar radiation transfer numerical model, set multiple groups of lidar hardware and water body environment parameters, obtain a large number of simulation data, utilize machine learning method to construct the polarization multiple scattering correction model of different detection system, and use non-uniform water body to verify the effectiveness of model.According to the multiple scattering model of the effective attenuation coefficient of the measured signal and the lidar of different platforms, the signal depolarization ratio and water body parameter are calculated, and the polarization multiple scattering correction model is brought into the polarization multiple scattering correction model combined with the system parameters of lidar, to obtain seawater backscattering depolarization ratio.Compared with the direct seawater backscattering depolarization ratio of lidar signal depolarization ratio, the error between the seawater backscattering depolarization ratio corrected by the method and the in-situ value is significantly reduced.

The application provides a kind of atmospheric temperature and humidity profile retrieval method and device based on satellite hyperspectral data, comprising: constructing background temperature and humidity profile using numerical mode historical forecast data, and calculating the background error between it and actual observation;Substitute the background profile into the radiation transfer model to simulate satellite radiation value, and obtain radiation simulation error according to satellite hyperspectral observation;Calculate the nonlinear distance correlation coefficient of each channel radiation simulation error and each pressure layer background temperature and humidity error, and use the first M channels with the highest correlation coefficient as the retrieval preselected channel of the layer;The background error and related data are divided into first and second data sets.Use the first data set, for each pressure layer, use the radial basis function neural network model of 1 to M channel number to establish the relationship between radiation error and background temperature and humidity error;Use the second data set to evaluate the retrieval error under each channel combination, select the optimal channel combination as the standard of minimum error, and thus obtain the final optimized neural network retrieval model.

The application discloses a point source emission carbon dioxide dry air column concentration remote sensing inversion error estimation method, system, device, medium and product, relates to the atmospheric gas monitoring technical field, and the method comprises the following steps: determining observation scene parameters affecting carbon dioxide remote sensing observation based on radiation transfer theory; constructing a point source carbon emission observation scene, generating radiance simulation data set by using a radiation transfer model; using the step-by-step Sobol analysis method, the iteration combination of global sensitivity analysis and local sensitivity analysis is used to analyze the influence of observation scene parameters on carbon dioxide radiance, and the parameter sensitivity degree is determined; according to the maximum posterior probability estimation algorithm and the benchmark scene parameters, the column average kernel and the posterior error are calculated, the posterior inversion error of the point source emission carbon dioxide dry air column concentration is calculated, the inversion error estimation of the point source emission carbon dioxide dry air column concentration is completed, and the effective determination of the point source remote sensing inversion influencing factor type and the quantitative estimation of the influence degree are realized.

The present application relates to the technical field of confined space fire flashover prediction. A confined space fire flashover prediction method and system based on physical information neural network are provided. The steps are as follows: obtaining sensor observation data and initial fuel distribution data; inputting the space-time coordinate vector and the initial fuel distribution data into the Mamba neural network to obtain temperature, incident radiation and fuel mass fraction prediction values; calculating the space-time derivatives of the prediction values, constructing physical residual terms containing energy conservation, radiation transfer and fuel consumption equation residuals, and boundary residual terms and observation residual terms; combining the three types of residuals by weighting to construct a loss function, updating the network parameters and the physical parameters to be learned through an optimization algorithm; and determining the flashover occurrence time when the temperature reaches the flashover critical value based on the updated network prediction temperature field. The present application solves the problems of poor reliability and prediction deviation caused by the lack of physical law constraints in existing data-driven fire prediction models.

The invention provides an atmospheric temperature and humidity profile inversion method and device based on satellite hyperspectral data, and the method comprises the steps: constructing a background temperature and humidity profile through employing historical forecast data of a numerical mode, and calculating a background error between the background temperature and humidity profile and live observation; substituting the background profile into a radiation transmission model to simulate a satellite radiation value, and obtaining a radiation simulation error according to satellite hyperspectral observation; calculating a nonlinear distance correlation coefficient between the radiation simulation error of each channel and the background temperature and humidity error of each barometric layer, and taking the first M channels with the highest correlation coefficient as inversion preselected channels of the layer; the background error and related data are divided into first and second data sets. Establishing a relation from a radiation error to a background temperature and humidity error by using the first data set and a radial basis function neural network model with 1-M channels for each air pressure layer; and evaluating an inversion error under each channel combination by using the second data set, and selecting an optimal channel combination by taking the minimum error as a standard so as to obtain a final optimized neural network inversion model.

The invention discloses a satellite inversion method for the canopy chlorophyll content based on a row-sowing rice geometrical optics-radiation transfer composite model, and the method comprises the steps: 1, simulating the spectral reflectance of rice canopies at an interval of 1 nm in different planting scenes based on agricultural priori knowledge through the row-sowing rice geometrical optics-radiation transfer composite model; 2, mapping the simulated canopy spectral reflectivity data set into a satellite image broadband canopy spectral reflectivity data set by using a spectral response function; step 3, constructing red edge chlorophyll index characteristics based on the simulated broadband reflection spectrum data set; and 4, establishing a hybrid inversion model between the red edge chlorophyll index and the paired canopy chlorophyll content CCC, and carrying out canopy chlorophyll content satellite inversion by using the hybrid inversion model to realize large-range rice chlorophyll content space-time dynamic monitoring. According to the method, the precision of the rice canopy chlorophyll content hybrid inversion model is remarkably improved in the early stage of rice growth.

The invention relates to a rocket boosting section tail flame infrared radiation characteristic time sequence dynamic simulation method, and belongs to the technical field of infrared radiation characteristic simulation. The method for dynamically simulating the time series of the infrared radiation characteristics of the tail flame of the rocket boosting section comprises the following steps: firstly, acquiring dynamic input source data, then dynamically simulating a flow field of the tail flame boosting section of the rocket, and then calculating infrared radiation characteristic parameters and solving a radiation transfer equation. And finally, correcting the time sequence of the infrared radiation intensity of the tail flame based on an atmospheric radiation transmission model, and finally completing the dynamic simulation of the time sequence of the infrared radiation intensity of the boosting section of the tail flame of the rocket. According to the method, the coupling influence of multiple physical fields such as a flow field, chemical components, spectral absorption and atmospheric attenuation can be considered synchronously, and the accuracy of infrared characteristic simulation of the rocket tail flame is remarkably improved. The method adapts to flight working conditions and environmental condition changes at different moments, and keeps calculation effectiveness and stability, thereby effectively breaking through the limitation of a traditional tail flame infrared radiation characteristic simulation method in the aspect of dynamic nature.

The present application relates to the technical field of atmospheric remote sensing monitoring, and particularly relates to a three-dimensional aerosol tomography method based on a multi-angle imager, comprising: obtaining geometric parameters and kernel-driven model coefficients, constructing a surface BRDF parameter prior dataset based on space-time filtering and spectral conversion technology, and combining a semi-empirical kernel-driven model to estimate MISR surface reflectivity at each angle; obtaining MISR surface reflectivity estimation error at each angle, analyzing the estimation error to construct a surface reflectivity monthly dynamic error correction model, and optimizing the estimated MISR surface reflectivity at each angle; determining the AOD inversion result at each angle according to the optimized MISR surface reflectivity at each angle, combining a 6S radiation transfer model to construct a lookup table; constructing an aerosol tomography dataset; planning a tomography region and discretizing, establishing an aerosol tomography model, and iteratively solving and extracting aerosol three-dimensional distribution information. The three-dimensional aerosol tomography based on the multi-angle imager is realized, and the aerosol vertical distribution information is effectively extracted.

The application discloses a structured grid radiation simulation method based on optical path ordering, and belongs to the field of radiation transmission simulation. The method reads and analyzes a structured grid file, constructs a structured data set, discretizes a solid angle, calculates the projection value of the geometric center of all units on the unit vector of each discrete direction, sorts the units according to the projection value, and obtains the unit calculation sequence along the actual propagation path of the light. The radiation transfer equation in each unit is solved according to the sequence, the calculation result of the upstream unit is transmitted to the downstream unit through the connecting surface as the incident boundary condition, and the global input radiation is converged through iterative calculation. The application ensures that the calculation sequence is consistent with the real propagation path of the radiation energy by optical path physical ordering, eliminates the non-physical reverse radiation transmission, improves the calculation accuracy and convergence, and can directly process general engineering grid data, thereby being convenient for integrated application.

The application provides a leaf area index inversion uncertainty quantification method and system, and belongs to the technical field of remote sensing data processing, and comprises the following steps: extracting target spectral features according to a remote sensing image; generating a simulation sample set based on a radiation transfer model; combining model sensitivity and parameter prior calculation to obtain a first uncertainty degree derived from physical parameter transmission; repeatedly training and constructing a machine learning model set by using the simulation sample set; calculating a second uncertainty degree derived from algorithm randomness according to the statistical dispersion degree of model set prediction values; and obtaining a total uncertainty degree according to the first and second uncertainty degrees. By quantifying physical modeling errors and algorithm randomness errors respectively, end-to-end decoupling and comprehensive of the uncertainty are realized, so that the accuracy of the confidence evaluation of the leaf area index inversion result is improved.

The furnace temperature field reconstruction method based on the radiation transfer equation and the multi-layer feedforward neural network fusion comprises the following steps: calibrating the radiation detector by using a blackbody furnace to establish the relationship between the image intensity and the radiation intensity; establishing a detailed radiation transfer equation RTE considering the medium absorption, scattering and wall effect by using a DOM algorithm; generating a data set as a learning set of the neural network by smoothing the temperature field and the fluent simulation; constructing a multi-layer feedforward neural network; predicting the temperature of each grid by the learned model to obtain the furnace cross-section temperature field. The method couples the physical constraint of the radiation transfer equation with the feedforward neural network, avoids the shortcomings of the large amount of calculation of the pure physical constraint and the lack of physical theory support of the pure data driving, has small workload for replacing the fluent model simulation learning set for different sites, and can quickly obtain the temperature measurement result by calling the pre-learned model, and occupies less computing resources.

The application discloses a kind of soil-vegetation synergic interaction radiation transmission model construction method and system, belong to soil remote sensing monitoring technical field.The method includes: obtaining dynamic parameter, the parameter of target area crop is output soil state parameter and vegetation state parameter by running crop growth model;Parameter conversion is carried out by calling mechanism constraint transfer operator;Coupling radiation transmission model, and based on the sensor response function convolution method of radiation transmission mechanism, the hyperspectral reflectance data output by the canopy radiation transmission module is smoothed, and the equivalent reflectivity of each band is calculated by numerical integration;Output soil-crown complex mixed reflection characteristic data.The application breaks through the bottleneck problem of traditional radiation transmission model in soil organic carbon remote sensing monitoring under vegetation coverage scene, and finally achieves significant technical effect in spectral simulation accuracy, cross-platform applicability, scene generalization ability and application support value.

The invention belongs to the technical field of remote sensing data processing, and provides a remote sensing inversion method and system for the leaf area index of a coniferous and broad-leaved mixed forest and application, and the method comprises the steps: obtaining spectral data and observation geometric parameters of the coniferous and broad-leaved mixed forest in a research region; acquiring optical element characteristics of coniferous tree species and broad-leaved tree species matched with the remote sensing image wave band; based on observation geometric parameters, optical element characteristics and canopy structure parameters, forward simulation is carried out in combination with a forest heterogeneous canopy random radiation transfer model, a canopy reflectivity lookup table is generated, and the canopy structure parameters comprise a needle-broad mixing ratio with a plurality of discrete gradient values; screening an optimal solution set from the canopy reflectivity lookup table based on a dual-band differential error threshold and inverting a leaf area index; and performing inversion uncertainty and inversion precision evaluation on the inversion leaf area index by adopting a statistical index. The method is high in inversion precision and success rate, and the inversion result can be quantified and is high in reliability.

The invention relates to the technical field of solar heat utilization, in particular to a method for analyzing the influence of environmental factors on the heat efficiency of a parabolic trough type solar heat collector. Establishing a three-dimensional geometric model of the heat collector and carrying out grid division; setting boundary conditions and a physical model; based on a six-band radiation transmission model and a Bouger's law, calculating attenuation of the aerosol to solar radiation; constructing an aerosol two-dimensional classification method based on the optical thickness and the relative optical thickness; analyzing the influence of the operation parameters, the space-time environment, the aerosol type and the visibility on the thermal efficiency; and finally calculating and outputting thermal efficiency based on an energy balance formula. According to the method, action mechanisms of different environmental factors can be accurately analyzed, the accuracy of performance prediction of the heat collector in the complex atmospheric environment is remarkably improved, and a reliable theoretical basis is provided for system site selection, operation optimization and efficiency evaluation.

The embodiment of the application discloses a kind of chlorophyll a and suspended matter water surface spectrum synchronous iteration inversion methods, the simulated spectrum of Hydrolight radiation transfer model is verified by water surface spectrum;Two combination factors that normalized reflectance data are simultaneously significantly strong correlation with chlorophyll a concentration and suspended matter concentration are selected as method factor;With chlorophyll a and suspended matter as initial variable, the iteration relationship is constructed to obtain the synchronous iteration inversion model of water surface spectrum chlorophyll a and suspended matter;Synchronous iteration model is applied to water surface spectrum data, and the synchronous iteration inversion result of the chlorophyll a and suspended matter of water body is obtained, can provide theoretical reference for water quality monitoring and water environment protection, it has important significance and value.

The application belongs to the technical field of agricultural remote sensing, and particularly relates to a method and system for inverting total photosynthetic area index of rape, comprising: constructing MLMSTM of rape based on vector radiation transfer theory to obtain HH, VV, VH and HV total backscattering coefficients and plant layer backscattering coefficient; determining the corrected MLMSTM according to Sobol parameter sensitivity analysis; determining the microwave scattering ratio of each pixel in the Radarsat-2 image region based on spatial interpolation method according to the corrected MLMSTM; and inverting the total photosynthetic area index TPAI of rape by using the trained machine learning model based on the microwave scattering ratio of each pixel. The method and system focus on the vertical layering scattering characteristics of rape at different phenological stages, realize high-precision inversion of rape canopy parameters, and provide technical support for rape growth monitoring and yield estimation.

The application discloses a kind of measurement method and measurement system of the spectral distribution of PAR, including by the geometric calibration and radiation calibration of all-sky imager, the observation zenith angle and observation azimuth of each pixel of all-sky image and RGB waveband radiance value are acquired;Through cloud detection algorithm, all-sky image is divided into clear sky and cloudy condition;For clear sky all-sky image, combined with radiation transfer model, PAR spectral radiation distribution algorithm reconstruction is carried out to obtain the spectral distribution of PAR;For the image with cloud, a deep learning model is used to obtain the spectral distribution of PAR, and finally the spectral distribution of the whole PAR is obtained.The application has the advantages that: the spectral distribution of PAR is reconstructed using all-sky image, and by combining physical radiation model and deep learning model, the spectral irradiance of visible light band reaching the ground, i.e.PAR spectral distribution information, is obtained from the image captured by all-sky imager.