61 results about "Microwave scattering" patented technology

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention discloses a soil water content estimation method fusing multi-source time sequence remote sensing data, and relates to the field of optical detection, and the method comprises the following steps: 1, obtaining an SAR remote sensing image of Sentinel-1 and an optical remote sensing image of Sentinel-2 in a designated area, carrying out the image fusion through a PCA algorithm, carrying out the feature set extraction of the fused images, and carrying out the feature set extraction of the feature set; a minimum analysis unit is generated through image segmentation, geometric shape features of the minimum analysis unit are extracted, a crop planting area recognition model is constructed through a convolutional neural network algorithm, and a crop coverage area is obtained; 2, introducing an MIMICS microwave scattering model, and constructing a crop multi-growth-period microwave scattering model; the SAR image and the optical image are fused through the PCA algorithm, the crop planting area recognition model based on multi-source remote sensing image fusion and the convolutional neural network algorithm is constructed, and the accuracy of crop planting area recognition is improved by utilizing complementary advantages of remote sensing images.

The invention belongs to the technical field of metamaterial sensors, and particularly relates to a metamaterial multi-parameter sensor for a complex environment, which comprises a dielectric substrate, a temperature sensor, a pressure sensor and a strain sensor, and is characterized in that the dielectric substrate is made of high-temperature co-fired alumina ceramic; the temperature sensor is arranged at the left end of the dielectric substrate; the pressure sensor is arranged in the middle of the medium substrate; and the strain sensor is arranged at the right end of the dielectric substrate and comprises a metal sheet structure capable of generating relative displacement. According to the invention, long-distance signal monitoring is realized by adopting a microwave scattering technology, and the problem that a traditional wired sensing transmission line is damaged at a high temperature is solved; the high-temperature-resistant aluminum oxide HTCC ceramic is used as the dielectric substrate, temperature measurement within the range of 25 DEG C to 1200 DEG C, pressure within the range of 0 kPa to 700 kPa and strain measurement within the range of-400 mu epsilon to + 400 mu epsilon are achieved, the sensor is smaller in size and higher in sensitivity, and application of a traditional sensor in the multi-metal environment is expanded.

The invention provides a wetland ecological safety early warning method and system based on remote sensing analysis, and the method comprises the steps: firstly obtaining a multi-dimensional remote sensing data set containing multi-temporal imaging, spectral radiation and microwave scattering data in a target wetland region, and carrying out the interpretation to obtain an ecological process signal set; establishing a mapping relation model of the ecological process signal and the wetland ecological safety state, and obtaining a current ecological safety state description; performing cross-process correlation analysis on the ecological process signal to form an ecological process abnormal conduction chain; constructing an early warning trigger node system and generating an early warning rule; and acquiring real-time multi-dimensional remote sensing data, interpreting the real-time multi-dimensional remote sensing data, matching the interpreted real-time multi-dimensional remote sensing data with an early warning rule, and generating wetland ecological safety early warning information containing an abnormal ecological process link, an abnormal conduction path and an early warning response node. The wetland ecological safety risk can be comprehensively and accurately pre-warned, and the stability of the wetland ecological system is guaranteed.

The invention provides a sea surface oil spill GNSS reflection signal simulation system and method, and the method comprises the steps: building an oil spill sea surface dielectric constant module, determining the effective dielectric constant of an oil spill sea surface, and obtaining a Fresnel reflection coefficient; establishing an oil spill sea surface rough spectrum module, obtaining a model of a sea wave spectrum of the oil spill sea surface according to a traditional model of the sea wave spectrum and the influence of oil spill on the sea wave spectrum, and obtaining a surface spectral density function and a surface root-mean-square height; establishing a microwave scattering module of the oil spill sea surface, and determining a microwave scattering coefficient according to the Fresnel reflection coefficient, the surface spectral density function and the surface root-mean-square height; a DDM simulation module is established, and a DDM waveform is obtained through simulation according to the GNSS signal parameters and the microwave scattering coefficients; and the DDM waveform is obtained through simulation of each module. According to the simulation system and method, a unified mathematical model is established to describe the relation between the oil film and the GNSS reflection signal, and accurate simulation of the influence of the oil film on the GNSS reflection signal is achieved.

The invention provides a simulation method for a GNSS scattering signal of a sea surface wind field. The simulation method comprises the following steps: establishing a simulation model of a delay-Doppler domain mapping waveform; a compact polarization model based on a microwave scattering model is established, a polarization component of a complete polarization scattering coefficient matrix is calculated by using the complete polarization microwave scattering model, and a scattering coefficient of a compact polarization signal is obtained according to the polarization component of the complete polarization scattering coefficient matrix and a Stokes sub-vector of the compact polarization signal. The coefficient is used as a bistatic radar scattering coefficient in a simulation model of a delay-Doppler domain mapping waveform; establishing a sea wave spectrum model for obtaining a sea wave spectrum density function; and simulating by using the model to obtain a delay-Doppler domain mapping waveform. According to the method provided by the invention, the full-polarization DDM waveform of the GNSS scattering signal can be generated in the compact polarization mode, so that the hardware complexity is reduced, and sufficient and comprehensive sea surface wind field inversion information is reserved.

The invention relates to the technical field of coal quality detection, in particular to an online detection method and system for the quality of coal as fired. Comprising the following steps: a multi-modal data acquisition step: respectively acquiring a microwave scattering parameter and a surface element characteristic spectrogram through a microwave chromatography sensor array and a surface spectrum detector; a physical field inversion and modeling step: reconstructing a complex dielectric constant distribution diagram based on scattering parameters, and establishing a three-dimensional physical distribution model containing density and moisture; a field-spectrum coupling deduction step: taking the characteristic spectrogram as a boundary constraint, and deducing internal chemical element distribution in combination with the complex dielectric constant distribution diagram; volume weighted quantization: executing volume weighted integration for the three-dimensional physical model and the chemical distribution, and outputting comprehensive coal quality parameters; according to the method, the detection blind area of a single technology is eliminated, and the representativeness and the safety of coal as fired total cross-section detection are remarkably improved.

A non-leaf green organ coupled crop photosynthetic area index radar inversion method comprises the steps that S1, a photosynthetic area index CAI is calculated, and the photosynthetic area index CAI is equal to the sum of a leaf area index LAI and a non-leaf green organ area index NFAI; s2, crop microwave scattering characteristics are analyzed, and polarization decomposition is carried out on radar remote sensing data; s3, screening out a polarization characteristic combination with the strongest correlation with a photosynthetic area index (CAI); and S4, on the basis of the selected feature combination, utilizing a machine learning algorithm to construct an inversion model of the photosynthetic area index CAI and the preferable polarization feature, and utilizing the inversion model to perform inversion on the photosynthetic area index of the regional crop to obtain a regional inversion result. The invention further provides a non-leaf green organ coupled crop photosynthetic area index radar inversion system. According to the method, the photosynthetic area index CAI is provided, the leaf area and the non-leaf green organ area are comprehensively considered, the mechanism process of crop growth change and yield formation is scientifically reflected, and the crop growth and yield can be accurately monitored.

The invention provides a method and a system for measuring soil moisture under crop coverage, a medium and electronic equipment. The soil moisture measurement method comprises the following steps: acquiring a microwave remote sensing image and field actual measurement data of a farmland to be monitored; preprocessing the microwave remote sensing image to obtain a farmland backscattering coefficient image; building a crop microwave scattering model based on the field measured data, and obtaining a farmland backscattering coefficient; and based on the farmland backscattering coefficient image, optimizing the farmland backscattering coefficient by using a Bayesian algorithm to obtain a farmland soil moisture estimation result. According to the method provided by the invention, the accuracy of soil moisture measurement under crop coverage can be improved.

The invention discloses a method and a device for correcting nonlinear deviation of a backscattering coefficient, relates to the technical field of active microwave remote sensing, and aims to solve the problem of poor measurement precision of the backscattering coefficient of a microwave scatterometer in the prior art. The method comprises the following steps: acquiring measurement data and truth value data of the satellite microwave scatterometer; performing data preprocessing on the measurement data and the truth value data to obtain an initial sample data set; constructing an azimuth angle uniform distribution calibration model data set by using a dynamic uniform distribution construction method and a random error elimination method based on the initial sample data set; correcting the back scattering coefficient nonlinear deviation of the satellite microwave scatterometer based on the azimuth angle uniform distribution calibration model data set; based on this, accurate correction of the non-linear deviation of the backscattering coefficient of the satellite microwave scatterometer is realized, and the measurement accuracy of the backscattering coefficient of the satellite microwave scatterometer is improved.

Disclosed is a diamond quantum sensor-based breast cancer detection apparatus. The apparatus comprises a fluorescence reading apparatus, a microwave generation control apparatus, a laser generation control apparatus, a calculation control system, and a medical diagnosis station. A diamond microwave sensing probe in the medical diagnosis station is configured for radiating detecting microwaves to a target to be tested, and a camera is configured for photographing an image of said target and sending the image to the calculation control system. The microwave generation control apparatus is configured for generating a control microwave input into the diamond microwave sensing probe. The laser generation control apparatus is configured for generating a laser input into the diamond microwave sensing probe. The calculation control system is configured for calculating the intensity of a microwave scattered by said target and solving for the distribution of dielectric properties in said target. In the present invention, the NV center in diamond is used as a probe to measure a microwave field, so that high-sensitivity and high-spatial-resolution microwave imaging of cancerous tissue in mammary tissue can be realized.

The invention relates to an SAR (Synthetic Aperture Radar) remote sensing image migration simulation method fusing ground object scattering characteristics, which is characterized in that an optical to SAR remote sensing image migration simulation network fusing the ground object scattering characteristics is designed based on a conditional generative adversarial network Pix2Pix, and the authenticity improvement of local scattering details in a simulated SAR remote sensing image and the interpretability enhancement of the migration network are realized. The invention provides an SAR remote sensing image migration simulation method aiming at the problems of low confidence coefficient and poor interpretability when an SAR remote sensing image is generated by an existing migration network technology by fully utilizing a heterogeneous ground feature microwave scattering mechanism. According to the method, by designing a multi-scale fine scattering discriminator integrated with ground object strong and weak scattering slice discrimination and a loss function driven by scattering characteristics, local scattering details are enhanced, and the overall simulation capability is improved, so that the purpose of performing high-quality simulation on an SAR remote sensing image is achieved. Experiments show that a large number of SAR remote sensing images more conforming to real ground object scattering characteristics can be generated, and data can be provided for training and verification of an optical and SAR bimodal remote sensing image intelligent processing algorithm.

The invention relates to the technical field of brain imaging, in particular to a microwave imaging detection method and device based on a fusion algorithm, electronic equipment and a medium, and the method comprises the steps: S1, collecting actual microwave scattering electric field distribution; s2, inputting the acquired actual microwave scattering electric field distribution into a Born approximation algorithm model, and performing inversion to obtain a first brain foreign body initial imaging result; s3, inputting the first brain foreign body initial imaging result into a deep learning neural network model to obtain first microwave scattering electric field distribution; s4, inputting the first microwave scattering electric field distribution into the Born approximation algorithm model again to continue iterative calculation to obtain a second brain foreign body initial imaging result, and inputting the second brain foreign body initial imaging result into the deep learning neural network model again to obtain second microwave scattering electric field distribution; s5, the microwave scattering electric field distribution is continuously iteratively updated in the step S4 until the difference value between the updated microwave scattering electric field distribution and the actual microwave scattering electric field distribution is smaller than a preset threshold value, and at the moment, the brain foreign body initial imaging result corresponding to the updated microwave scattering electric field distribution is a high-resolution brain foreign body imaging result; and S6, further judging whether the brain foreign body exists or not according to the high-resolution brain foreign body imaging result. According to the method, the microwave signal features are automatically extracted by using the deep learning model, and fine physical modeling is performed through the Born iteration method, so that the accuracy and reliability of detection are remarkably improved.

A concrete structure reinforcement corrosion detection method based on a random forest algorithm and microwave scattering parameters comprises the following steps: building a microwave detection platform, and carrying out fixed frequency sweep detection on a reinforced concrete structure to obtain S parameter data with reinforcement corrosion condition information; dividing the S parameter data into a training set and a test set; extracting frequency and S parameter data of the reinforcing steel bars under different corrosion degrees, and training the random forest algorithm model by taking the frequency and S parameter data as input features and the reinforcing steel bar corrosion depth as output features; and performing microwave detection on the reinforced concrete structure, and inputting the obtained frequency and S parameter data into the trained random forest algorithm model to obtain the corrosion depth of the reinforcing steel bar. According to the concrete structure reinforcement corrosion detection method based on the random forest algorithm and the microwave scattering parameters, prediction of the reinforcement corrosion depth is achieved, and the method has the advantage of being high in accuracy.

This invention relates to the field of coal quality testing technology, specifically to an online method and system for detecting coal quality entering the furnace; it includes: a multimodal data acquisition step: acquiring microwave scattering parameters and surface element characteristic spectra using a microwave tomography sensor array and a surface spectrometer, respectively; a physical field inversion and modeling step: reconstructing the complex permittivity distribution map based on the scattering parameters to establish a three-dimensional physical distribution model including density and moisture; a field-spectrum coupling deduction step: using the characteristic spectra as boundary constraints, and combining them with the complex permittivity distribution map to deduce the internal chemical element distribution; and a volume-weighted quantization step: performing volume-weighted integration on the three-dimensional physical model and chemical distribution to output comprehensive coal quality parameters; this invention eliminates the detection blind spots of single technologies and significantly improves the representativeness and safety of full-section detection of coal entering the furnace.

The application discloses a sea ice density inversion method and system based on physical information guidance and a medium. The method comprises the following steps: obtaining a corresponding spatial sea ice density map by using a pre-trained sea ice density inversion model; performing feature extraction of microwave scattering maps, microwave radiation maps and meteorological element maps in multiple scales; for each scale of microwave scattering features, microwave radiation features and meteorological element features, guiding microwave radiation feature correction by using meteorological element features and microwave scattering features to realize physical information guided cross-modal feature fusion; extracting spatial information maps and decoding the fused latent feature maps step by step to obtain corresponding spatial sea ice density maps. The application aims to fully model the physical dependence relationship among radiation, scattering and environmental driving factors, realize the spatio-temporal collaboration among features, improve the ability of learning the continuity and structural properties of sea ice distribution and the physical consistency and spatial generalization ability of sea ice density inversion results.

This invention discloses a joint inversion method for sea surface wind speed, comprising: jointly inverting observation data from a microwave scatterometer and a spaceborne GNSS-R over an overlapping observation area to obtain a first wind speed inversion result for that area; independently inverting the observation data from the microwave scatterometer to obtain a second wind speed inversion result; expanding the first wind speed inversion result to the second wind speed inversion result to obtain a third wind speed inversion result for the complete observation area of ​​the microwave scatterometer; independently inverting the observation data from a synthetic aperture radar to obtain a fourth wind speed inversion result; and expanding the fourth wind speed inversion result to the third wind speed inversion result to obtain a final joint inversion result. The technical solution provided by this invention performs joint inversion based on observation data from a microwave scatterometer, a spaceborne GNSS-R, and a synthetic aperture radar, thereby obtaining a final joint inversion result with a large observation area, high accuracy, and high resolution.

The present invention belongs to the technical fields of active microwave remote sensing, extraction of linear frequency modulation transmitted signals of scatterometers, and ocean data processing. Specifically, it relates to a method and device for extracting positive and negative slope linear frequency modulation transmitted signals of a spaceborne scatterometer. The method includes: using a spaceborne microwave scatterometer to perform conical scanning on an area to be observed within a swath through a fan beam, and obtaining in real time the scanning azimuth angle for this observed area; based on the scanning azimuth angle for this observed area obtained in real time, determining whether the antenna transmits a linear frequency modulation pulse signal with a negative slope or a linear frequency modulation pulse signal with a positive slope.

The invention provides a remote sensing and crop growth model-based field corn irrigation water demand calculation method. The method comprises the following steps of: constructing a microwave scattering model based on microwave remote sensing data, and constructing a thermal inertia model based on thermal infrared remote sensing data; learning a non-linear mapping relation between remote sensing data and ground actually-measured multi-layer soil moisture through a deep neural network, optimizing key parameters of a multi-source fusion model, and establishing calibration parameter values and soil moisture content parameter values of the multi-source fusion model under different hierarchies; on the basis of the difference of different soil layer moisture demands in different growth stages of the corn, the water amount needing irrigation is calculated in combination with a multi-layer soil moisture calculation result; according to the possible rainfall in the preset time period, combining the area of the region to obtain predicted rainfall; according to the intelligent irrigation decision-making system for the field corn, the remote sensing technology, the crop physiological model and meteorological prediction are organically combined, and a complete and practical intelligent irrigation decision-making system for the field corn is formed.

The present application belongs to the technical field of marine remote sensing, and particularly relates to a kind of full wind speed inversion methods based on spaceborne microwave active and passive combination physical statistics network, comprising the following steps: obtaining the brightness temperature data and backscattering coefficient of microwave radiometer and scatterometer, and performing data quality control;Construct sample data set;Load physical statistics constraint deep learning inversion algorithm model for training;Physical statistics constraint deep learning inversion algorithm model includes: input layer, network layer, physical layer, output layer;Network layer is composed of residual neural network, and physical layer is composed of Log formula of microwave radiometer and Log formula of microwave scatterometer to form microwave statistical matrix P matrix, which is a nonlinear matrix, and provides physical statistics constraint for the whole model;Output the wind speed of current full sea area.The present application effectively solves the problem of lack of physical support in the process of deep learning in marine parameter inversion, and provides high-quality and reliable wind speed products suitable for full wind speed range.

The application discloses a global average sea clutter near real-time estimation method, system, device and storage medium, including the following steps: obtaining multi-source satellite-borne radar historical backscattering coefficient data, and performing time and space matching and binning on the ERA5 wind field; an average sea clutter estimation model is established according to a geophysical model function fitting; the satellite-borne microwave scatterometer wind field is corrected in deviation and fused with a numerical weather prediction wind field, so that a high-coverage, continuous near real-time global sea surface wind field is obtained; the relative azimuth is calculated in combination with radar observation parameters and fused wind direction, and global average sea clutter data is obtained by using the model; the application improves the precision and application range of sea clutter estimation under the condition of medium and small incident angles, provides a high-quality input wind field through a multi-source wind field fusion technology, realizes rapid, stable and near real-time estimation of global average sea clutter, has high timeliness and business feasibility, and can meet the needs of near real-time sea clutter monitoring and ocean remote sensing application.

This invention discloses a space-ground coordinated radiometric characteristic calibration method for spaceborne active microwave sensors in the field of radar Earth observation technology. It constructs a space-ground coordinated calibration system consisting of a radiation reference source satellite and a ground calibration processing station. The method includes: determining the orbital parameters of the radiation reference source satellite; the satellite transmitting signals to the ground station in Earth observation mode, using a known RCS target on the ground to calibrate its own antenna gain and link gain; receiving signals from the satellite to be calibrated in Earth observation mode, accurately calculating and transmitting echo signals based on the calibrated parameters; and the satellite to be calibrated calibrating its own transmit / receive link gain based on the received echo signals. This invention can be widely applied to the on-orbit radiometric calibration of active microwave sensors such as synthetic aperture radar, altimeters, and microwave scatterometers, significantly improving calibration frequency and accuracy, supporting multi-band, high-stability quantitative remote sensing tasks, and providing reliable technical support for precise observation in fields such as ocean monitoring, weather forecasting, and topographic mapping.

The invention relates to the field of artificial intelligence, and provides a soil moisture inversion method and system based on a microwave scattering mechanism and a DNN model, and the method comprises the steps: obtaining field related data of a to-be-planted region, carrying out the preprocessing of the field related data, extracting a dual-polarization crop index, querying corresponding surface crop parameters to determine the growth periods of different plants, and carrying out the calculation of the growth periods. The method comprises the following steps: performing growth period back scattering simulation based on a preset coupling microwave scattering model to obtain back scattering simulation parameters and calculate an actual observation value, querying a time observation sequence therein, constructing a multi-source time sequence characteristic data set in combination with a preset DNN model, extracting multi-source characteristic data, analyzing plant position information, and inverting soil moisture content. And identifying content distribution characteristics, and finally generating an inversion report of the soil moisture of the to-be-planted area. According to the invention, the soil moisture inversion effect can be improved.

The invention relates to a plain region railway ground feature deformation scattering point coverage rate calculation method considering elevation compensation. The method comprises the steps of extracting railway ground feature scattering point positions based on microwave scattering features of railway ground features; determining a railway ground elevation value and a relative elevation of the position of the railway ground feature scattering point relative to the railway ground elevation value based on the digital elevation data of the area along the railway; fitting to obtain the relevance between the offset and the relative elevation; compensating the position of the railway ground feature scattering point according to the relevance; extracting railway ground object optical positions according to the optical remote sensing images in the same area; and matching the compensated railway ground feature scattering point position with the railway ground feature optical position to obtain and determine the coverage rate based on the railway ground feature deformation scattering point. According to the method, more comprehensive and accurate ground feature deformation information can be provided, and the extraction precision of railway ground feature deformation scattering points and the accuracy of coverage rate calculation can be remarkably improved.

The invention discloses a sea ice concentration inversion method and system based on physical information guidance, and a medium. The method comprises the following steps: obtaining a corresponding space sea ice concentration graph by using a pre-trained sea ice concentration inversion model; carrying out multi-scale feature extraction on a microwave scattering graph, a microwave radiation graph and a meteorological element graph; aiming at the microwave scattering characteristic, the microwave radiation characteristic and the meteorological element characteristic of each scale, guiding microwave radiation characteristic correction by utilizing the meteorological element characteristic and the microwave scattering characteristic so as to realize cross-modal characteristic fusion guided by physical information; and extracting a spatial information graph and carrying out step-by-step decoding on the spatial information graph and the fused potential feature graph to obtain a corresponding spatial sea ice concentration graph. The method aims at fully modeling the physical dependency relationship among radiation, scattering and environment driving factors, realizing space-time cooperation among features, and improving the ability of learning sea ice distribution continuity and structural properties and the physical consistency and space generalization ability of sea ice concentration inversion results.

A method and system for localized measurement of water-bearing porosity in multi-frequency broadband full-size core samples. The measurement method includes the following steps: (1) Preparation: Divide the core sample into m axial regions and p radial regions, each radial region being a coaxial ring from the inside out; (2) Measure the corresponding parameters: ① Measure the diameter and temperature of each axial region to obtain the diameter φ of the i-th axial region. i and temperature T i ; ② Pass the core through each sensor in sequence without contact and perform response tests on each sensor; (3) Local inversion of the complex dielectric constant of the core; (4) Local inversion of the water-bearing porosity and pore water salinity of the core. The measurement system includes a core propulsion device, a core diameter measuring device, a temperature recorder, a sensor group, a microwave multiplexer, a microwave scattering parameter sweep frequency measuring device, and a main control computer. This invention can realize localized, step-by-step fully automatic measurement of the dielectric properties of full-size cores, which can improve the accuracy and efficiency of core analysis at the well site.

The invention discloses a laser and microwave data receiving and processing system based on real-time perception, particularly relates to the technical field of traffic control, and comprises a laser data acquisition module, a microwave data acquisition module, a multi-source data fusion processing module and a real-time decision output module. The laser module dynamically matches a target motion state through a self-adaptive scanning frequency control unit, and generates anti-interference laser point cloud data; the microwave module receives the reflection signal and generates microwave imaging data; the fusion module adopts a space-time alignment convolution layer to extract laser motion features, applies a motion compensation attention mechanism to analyze microwave scattering characteristics, and fuses the features through a cross attention mechanism to generate a unified matrix; and the decision module analyzes the matrix through the FPGA and outputs a control instruction or an early warning signal according to a preset threshold value. According to the system, advantage complementation of laser and microwave data is realized, and the sensing precision and the response speed of a dynamic target in a complex environment are remarkably improved.

The present invention discloses an on-orbit directional pattern calibration method for a satellite-borne microwave scatterometer based on an active calibrator. The method comprises: obtaining the elevation angle and azimuth angle of the beam at different times within the scatterometer antenna system based on the position information of the active calibrator; obtaining the elevation angle and azimuth angle of the beam at different times within the active calibrator antenna system based on the pointing information and position information of the active calibrator; obtaining the data power received by the active calibrator at different times based on the power signal received by the active calibrator and the elevation angle and azimuth angle of the beam at different times within the active calibrator antenna system; and obtaining the antenna pattern of the scatterometer based on the data power received by the active calibrator at different times. The present invention solves the problem of directional pattern calibration under rapidly changing scatterometer antenna coordinate systems and compensates for the modulation effect of the active calibrator on the satellite-borne directional pattern, thereby improving the on-orbit test accuracy of the antenna pattern.

This invention relates to the field of artificial intelligence and proposes a method and system for soil moisture retrieval based on microwave scattering mechanism and DNN model. The method includes: acquiring relevant field data of the planting area; preprocessing the data to extract the bipolar crop index; querying corresponding surface crop parameters to determine the growth period of different plants; performing backscattering simulation of the growth period based on a preset coupled microwave scattering model; obtaining backscattering simulation parameters and calculating actual observed values; querying the time observation sequence; constructing a multi-source time-series feature dataset in conjunction with the preset DNN model; extracting multi-source feature data; analyzing plant location information; retrieving soil moisture content; identifying content distribution characteristics; and finally generating a soil moisture retrieval report for the planting area. This invention can improve the effectiveness of soil moisture retrieval.

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.