101 results about "Microwave remote sensing" patented technology

The invention discloses a soil humidity inversion method and device, a medium and equipment, and relates to the technical field of microwave remote sensing inversion. According to the method, the total backscattering coefficient, the normalized vegetation index, the leaf area index and the average leaf inclination angle of the electromagnetic waves of the target area pixel by pixel are obtained in combination with microwave remote sensing data and atmospheric apparent reflectance data of the target area; according to the normalized vegetation index, the leaf area index and the average leaf inclination angle, spatial clustering is carried out on vegetation, spatial heterogeneity of crop types is captured, and then based on the constraint that the surface roughness parameters and the vegetation parameters of the same vegetation type are the same and the soil humidity in the same surface space window is the same, the spatial heterogeneity of crop types is obtained. Through two-stage iteration inversion of the intra-class scale and the ground surface space window scale, the time dynamic state is adapted, the parameter calibration function of the in-situ soil humidity data is replaced, and the model parameters and the soil humidity can be determined without the in-situ soil humidity data. According to the invention, the accuracy and reliability of the soil humidity inversion method are improved.

The invention provides a crop yield estimation method and device, electronic equipment and a storage medium, and relates to the technical field of agricultural information technology, remote sensing monitoring and artificial intelligence crossing, and the method comprises the steps: carrying out VOD space-time fusion processing based on multi-source microwave remote sensing data through a VOD space-time fusion model, and obtaining VOD fusion data; simulating crop growth based on the environment data and the crop planting data through a target crop growth model to obtain crop physiological process data; constructing multi-source feature data based on the VOD fusion data, the crop physiological process data and the environmental data; and through a double-flow space-time deep learning model, carrying out space-time feature extraction and fusion on the multi-source feature data to obtain a biomass estimated value, and further calculating a crop yield estimated value. According to the method, the crop yield is estimated based on fusion of the multi-source microwave remote sensing data, the crop growth model and the deep learning model, the restriction of a single technical normal form is broken through, and the accuracy of an estimation result can be improved.

The invention relates to a satellite-borne microwave radiometer cold air calibration attitude planning method based on multi-objective optimization, and belongs to the field of satellite microwave remote sensing, and the method comprises the steps: building a satellite attitude kinematics model and an attitude dynamics model through a geocentric equator inertial coordinate system, an orbital coordinate system and a satellite body coordinate system; establishing a brightness temperature sum model received by the microwave radiometer in the cold air calibration process; according to the method, a microwave radiometer receiving brightness temperature sum, brightness temperature fluctuation, platform maneuvering capability, energy and a satellite sensor field of view are comprehensively considered, a multi-target optimization model of microwave radiometer cold air calibration consideration constraints is established, solving is performed, constraint conditions are established, and a solution or a solution set is obtained. According to the method, the brightness temperature sum and the brightness temperature fluctuation in the observation area of the microwave radiometer in the cold air calibration process are optimized by adopting multi-objective optimization, and the on-orbit cold air calibration effect and the system reliability of the satellite-borne microwave radiometer are improved.

The invention provides a farmland water demand accurate prediction method based on remote sensing data, and the method comprises the steps: firstly, synchronously collecting optical, thermal infrared, microwave remote sensing and ground data, carrying out the time-space alignment based on a dynamic time warping algorithm, and constructing a four-dimensional unified data set; then, an improved asymmetric automatic encoder integrating multi-branch encoding and a space attention mechanism is utilized to generate a deep fusion feature vector; and inputting the feature vector into an STCN-LSTM hybrid model, mining spatial features through multi-scale convolution, capturing a time evolution rule in combination with a bidirectional long short-term memory network, and embedding DSSAT crop physiological parameters to optimize the model weight. And finally, the uncertainty is quantized and predicted through Monte Carlo Dropout, and the error is further reduced by adopting a space-time layering correction strategy. The method breaks through the limitation of a single data source, the prediction error is smaller than 5%, a reliable basis is provided for precise irrigation, and the water resource utilization efficiency is remarkably improved.

The invention relates to the technical field of microwave remote sensing, in particular to a Ku, Ka and W three-frequency-band meteorological radar multi-source joint detection system and method and a storage medium, and the system comprises an antenna feeder subsystem, a main control computer, an integrated digital processor, a Ku / Ka integrated radio frequency machine, a Ku-frequency-band power amplifier, a Ka-frequency-band power amplifier, a W-frequency-band transmitter, a W-frequency-band power amplifier, a W-frequency-band receiver and a servo mechanism. The system is designed according to a working mode and working parameters of a main control computer, realizes multiple functions such as Ku / Ka / W single-frequency-band independent working or multi-frequency-band simultaneous working and the like, and has co-polarization and cross-polarization echo signal receiving capability; by controlling the servo mechanism, two modes of fixed-point detection and conical scanning detection are realized, and detection requirements of different meteorological elements are met; the detection capabilities of three frequency bands of Ku, Ka and W are integrated in the system, the detection mode is flexibly configured according to weather conditions and detection requirements, the system has the multi-frequency-band, multi-polarization and multi-element integrated detection capability, and the detection efficiency of the meteorological radar is improved.

The invention belongs to the technical field of atmospheric microwave remote sensing and monitoring, and relates to a rapid temperature and humidity profile inversion method for a multi-frequency atmospheric microwave radiometer. Acquiring a brightness temperature data set of the multi-frequency atmospheric microwave radiometer, performing space-time matching on the brightness temperature data set and corresponding ERA5 reanalysis or sounding balloon temperature and humidity profile data, constructing a matched data set, and dividing the matched data set into a test set, a training set and a verification set; establishing an atmosphere temperature and humidity profile inversion model based on a CNN-LSTM combined neural network; and training the model by using the training set, guiding the model optimization process by using the verification set, finally inputting multi-frequency brightness temperature data measured by the multi-frequency microwave radiometer into the trained model, and carrying out inversion to obtain an atmosphere temperature and humidity profile. According to observation data of the multi-frequency atmosphere microwave radiometer, regional atmosphere temperature and humidity profile data are rapidly and accurately obtained, and efficient inversion of the temperature and humidity profile is achieved; the vertical detection advantage of the multi-frequency microwave radiometer is exerted, the inversion efficiency is improved, and the method has a wide application prospect.

The invention belongs to the technical field of microwave radar image processing, and relates to a wind wave significant wave height inversion method based on a wind wave period, which comprises the following steps: firstly, establishing a significant wave height calculation model taking the wind wave period as input; then, acquiring a sea surface microwave remote sensing image, and performing coordinate transformation, such as preprocessing of a shore-based sea observation radar image, a shipborne navigation radar image, noise suppression and the like; thirdly, inverting a wind wave period from the preprocessed image through a spectrum analysis technology; and finally, substituting the wind wave period obtained by inversion into a pre-established model, and calculating to obtain the significant wave height. According to the method, the wind wave period is introduced to serve as a key intermediate variable, the image features and the sea wave physical parameters are closely associated, the defect that an existing direct inversion method is insufficient in precision and stability is overcome, and an effective wave height obtaining means which is clear in physical significance, high in inversion precision and easy to popularize in a business mode is provided.

The invention discloses a forest fire risk dynamic assessment and early warning system based on multi-source data fusion and artificial intelligence, and belongs to the technical field of forest fire prevention and disaster early warning. The system aims at solving the technical problems that in an existing forest fire monitoring technology, evaluation lags behind, data is single, and risk prospective prediction cannot be achieved. According to the technical scheme, satellite microwave remote sensing, optical remote sensing and near real-time meteorological and topographic data are integrated through a multi-source data acquisition module, and are fused and processed through a feature extraction module to generate a fire danger influence factor feature set containing the water content, the load capacity, the meteorological conditions and the topographic features of combustible materials; a dynamic risk distribution diagram in a future preset time period is output through a machine learning model of the dynamic risk assessment module, and finally automatic early warning is achieved through the early warning and decision making module. The system is mainly used for realizing scientific and accurate assessment and early warning of forest fire risks, and provides technical support for forest fire prevention decision making.

The application discloses a two-step method for accurately correcting GNSS-IR abnormal phase, and belongs to the technical field of microwave remote sensing. The method is characterized in that: the GNSS observation data is preprocessed, and the modulation term in the signal-to-noise ratio is extracted; then, the nonlinear least square method is used to fit the modulation term, and the phase of each satellite is obtained; finally, the abnormal phase is accurately corrected through the two-step method: in the first step, a signal-to-noise ratio period method is constructed to correct the phase of the whole period and half period jump caused by the limitation of the nonlinear least square method itself; in the second step, the standard fraction method is combined with the Savitzky-Golay filter to accurately detect and repair the phase of less than half period jump caused by the multipath environmental factors. The method can effectively improve the phase quality of each satellite, improve the shortcomings of the traditional phase solving method, and solve the problems in the prior art.

The invention provides a distributed InSAR sea surface two-dimensional flow velocity and elevation measurement method and device, and belongs to the technical field of ocean microwave remote sensing. According to the method, a dual-beam BiDi mode is introduced on a distributed InSAR platform, sea surface echoes are obtained at the same time through forward and backward squint angle beams, a multi-squint angle interference pair is constructed through beam separation and sub-aperture processing, and the sea surface two-dimensional flow velocity and elevation measurement is achieved. Image registration, interference, flat ground effect removal, phase unwrapping, interference suppression and Bragg wave phase velocity removal are sequentially completed, a multi-baseline phase equation set is finally established, and decoupling and synchronous inversion of the two-dimensional flow velocity and the elevation phase are achieved. According to the method, an additional antenna or a cooperative target is not needed, the sea surface flow velocity vector and the sea surface elevation can be obtained with high precision in one-time flight, and the ocean observation efficiency and the measurement precision of the distributed InSAR are remarkably improved.

The application discloses a water body ammonia nitrogen concentration differential absorption detection device based on microwave remote sensing, relates to the technical field of water pollution detection, and comprises a floating platform for floating on a detection water body and a mounting plate arranged on the floating platform. A microwave emission module and a microwave receiving module for microwave remote sensing detection are mounted on the mounting plate. A signal processing and analysis module for information processing and a data storage and display module are arranged on the floating platform. In the detection process, through the cooperation of a swing assembly, a water body depth identification assembly, a lifting assembly and an adjusting assembly, the microwave signal emitted by the microwave emission module on the mounting plate can cover a larger area of the water surface, three-dimensional sampling of the water body can be realized, one-sidedness of single-point detection can be avoided, the detection state can be adaptively adjusted according to the depth of the detection water body during the detection, and flexible detection operation is facilitated.

The application belongs to the technical field of microwave remote sensing, and relates to a three-dimensional swing ship target imaging reconstruction method under a staggered SAR system. The application solves the problems of serious imaging spectrum aliasing and serious defocusing of a three-dimensional swing ship target under the existing staggered SAR system. The application establishes a moving target signal echo model based on the staggered SAR system, detects echo signals of the moving target by using the moving target signal echo model, samples the detected echo signals of the moving target by using an optimal linear unbiased estimation interpolation method, obtains uniform target signals, converts the uniform target signals into a SAR image based on a two-dimensional spectrum by using a series inversion method, performs distance and azimuth inverse transformation on the SAR image to the echo domain to obtain equivalent ISAR echo signals, focuses the equivalent ISAR echo signals by using a distance-instantaneous Doppler time-frequency analysis method, and reconstructs three-dimensional swing ship target imaging. The application is suitable for three-dimensional swing ship target imaging.

This invention relates to the field of microwave remote sensing precision calibration technology, and particularly to a traceable microwave radiation calibration device and method based on the quantum coherence effect of Rydberg atoms with a wave-shaped structure. The device includes: a microwave standard radiation system, a dynamic ring track, a Rydberg atom electric field measurement system, a radiometer receiving antenna, a data acquisition and control system, and a data processing system. The device uses the dynamic ring track to move the microwave transmitting antenna, causing microwave electromagnetic signals to alternately irradiate the Rydberg atom electric field measurement system and the radiometer receiving antenna. The interaction between the microwave electric field and specific Rydberg energy levels induces the Euler-Townes effect, causing energy level splitting and resulting in peak position evolution in the electromagnetically induced transparency spectrum of the atomic laser system. The data processing system calculates the electric field intensity and converts it into the corresponding brightness temperature, thereby calibrating the radiometer receiving antenna.

The application relates to the technical field of microwave remote sensing, in particular to a star-borne millimeter wave and sub-millimeter wave detector brightness temperature calibration heat source, which comprises a metal shell, a radiation matrix, temperature measuring resistors, a heater, a heat insulation ring, a cover plate and an electric connector, wherein the radiation matrix is arranged above the inside of the metal shell; the temperature measuring resistors are multiple, and the multiple temperature measuring resistors are arranged in the inside of the radiation matrix; the heater is pasted on the back of the radiation matrix; the cover plate is fixed on the bottom of the metal shell; the heat insulation ring is arranged in the inside of the metal shell and located between the heater and the cover plate; the electric connector is a low-frequency electric connector and is arranged on the outer wall of the metal shell and electrically connected with the temperature measuring resistors and the heater. On the basis of meeting the anti-electromagnetic interference, the application effectively reduces the influence of the heat leakage caused by the heat transfer between the radiation matrix and the shell on the radiation surface temperature uniformity, and greatly improves the radiation surface uniformity.

This invention relates to the field of microwave remote sensing precision calibration technology, and particularly to a traceable microwave radiation calibration device and method based on the Rydberg atomic quantum coherence effect. The device includes: a microwave standard radiation system, a parabolic reflector (5), a Rydberg atomic electric field measurement system (1), a radiometer receiving antenna (3), a data acquisition and control system (6), and a data processing system (14). The microwave electromagnetic signal from the microwave transmitting antenna (4) is reflected by the parabolic reflector (5), so that the microwave electromagnetic signal alternately irradiates the Rydberg atomic electric field measurement system (1) and the radiometer receiving antenna (3). The interaction between the microwave electric field and a specific Rydberg energy level causes the Euler-Townes effect energy level splitting, resulting in a peak position evolution of the electromagnetically induced transparency spectrum in the atomic laser system. The data processing system (14) calculates the electric field strength and converts the electric field strength into the corresponding brightness temperature, thereby calibrating the radiometer receiving antenna (3).

1. The name of the design product: L-band microwave remote sensing radiometer. 2. The use of the design product: The design product is used for remote sensing measurement of microwave noise energy of atmosphere, ocean, vegetation and soil. 3. The design points of the design product: The shape and relative position of each module on the front view and the shape of the array antenna on the rear view. 4. The picture or photo that best indicates the design points: perspective view.

The present application relates to the technical field of agricultural drought information monitoring, and more particularly to an agricultural drought monitoring and prediction system based on unmanned aerial vehicle microwave remote sensing and deep learning, which comprises: a microwave remote sensing data fusion subsystem that carries multi-frequency microwave sensors on an unmanned aerial vehicle; dynamic adjustment of feature weights based on crop growth stages; elimination of geometric deviations between unmanned aerial vehicle flight strips through time series registration, and construction of a three-dimensional drought feature field by fusing multi-temporal data; a deep drought diagnosis subsystem that inputs the three-dimensional drought feature field into a space-time convolution network, extracts field patch-level anomalies in the spatial domain, and captures drought evolution patterns in the time domain; a drought response subsystem that divides drought levels based on the diagnosis results of the space-time convolution network; generation of differentiated irrigation schemes in combination with farmland Internet of Things data; and comparison of actual irrigation effects with prediction results. The present application realizes an upgrade of the agricultural drought prevention and control mode from passive monitoring to active prediction, and from extensive management to precise regulation.

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 invention belongs to the technical field of image recognition, and particularly relates to a Dai landform automatic recognition method based on remote sensing and DEM. The method comprises the following steps of: 1, synchronously acquiring an optical remote sensing image, a microwave remote sensing image and a public digital elevation model (DEM) of a covering target area through a multi-source remote sensing image acquisition operation; 2, performing heterogenous noise suppression processing on data of each dimension of the same-grid data cube of the remote sensing DEM to obtain a preprocessed same-grid data cube; and step 3, on the basis of preprocessing the same-grid data cube, under the condition of keeping the same-grid space consistency, outputting a Dai landform automatic identification result when the self-consistent differential change rate meets a termination criterion. According to the method, high-precision and physically consistent Dai landform recognition results can be automatically output under different resolutions and noise backgrounds, manual intervention is reduced, and the processing efficiency is improved.

The invention provides an all-weather high-temporal-spatial-resolution multi-source remote sensing atmospheric water vapor total amount data fusion method, which comprises the following steps of: performing temporal-spatial matching processing on acquired atmospheric water vapor total amount data to obtain equal latitude and longitude grid data of multi-source microwave remote sensing atmospheric water vapor total amount and equal latitude and longitude grid data of stationary satellite optical remote sensing atmospheric water vapor total amount; based on longitude and latitude grid data of geostationary satellite optical remote sensing atmospheric water vapor total amount and the like, performing deviation correction and fusion on the longitude and latitude grid data of multi-source microwave remote sensing atmospheric water vapor total amount and the like to obtain all-weather hour-by-hour coarse spatial resolution fused atmospheric water vapor total amount data; downscaling the coarse spatial resolution fused atmospheric water vapor total amount data to obtain all-weather hour-by-hour kilometer-level high spatial-temporal resolution fused atmospheric water vapor total amount data; and adaptive correction is carried out on the atmospheric water vapor total amount data under the cloudy day condition in the high-temporal-spatial-resolution fused atmospheric water vapor total amount data, and all-weather fusion of the high-temporal-spatial-resolution atmospheric water vapor total amount data is completed.

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 belongs to the technical field of metamaterials, and particularly relates to a liquid-based electromagnetic metamaterial with tunable emissivity, which comprises a plurality of periodically arrayed basic units, the basic unit comprises, from top to bottom, a dielectric layer, a dielectric slot layer, a substrate and a conductive film layer which are sequentially stacked and arranged, the dielectric slot layer is provided with a rice-shaped liquid cavity for storing liquid, the rice-shaped liquid cavity is formed by two cross-connected cross arms, the cross arm comprises two mutually perpendicular and equal-length straight arms, and the length ratio of the straight arms on different cross arms is 1-1.2:1. The application realizes flexible tuning of target emissivity under different backgrounds and reduces the visibility of the target in a microwave remote sensing system.

The invention relates to a method for detecting weak exogenous errors of a synthetic aperture radiometer based on mode brightness temperature, and belongs to the technical field of space microwave remote sensing. According to the method, the following steps are respectively executed on horizontal polarization microwave signals and vertical polarization microwave signals: carrying out grid division on the surface of the earth; for each grid point, establishing an L-band multi-incidence-angle microwave radiation mode brightness temperature database; gridding the brightness temperature data measured by the synthetic aperture radiometer to obtain measured brightness temperature data corresponding to each grid point; establishing a differential data box of each grid point; based on the differential data box of each grid point, three-time Sigma abnormal value detection is carried out, and data detection of external error interference in each grid point is realized; and after data interfered by the external source error in the grid points are eliminated, neighborhood brightness temperature consistency deviation detection is carried out on residual data in the grid points, so that data detection interfered by the external source error by taking the grid points as units is realized.

A passive remote sensing and reconnaissance integrated detection method based on a conical scanning system belongs to the technical field of space microwave remote sensing, and comprises the following steps: carrying out AD acquisition on an intermediate frequency signal output by a multi-band radiometer receiver; carrying out FFT (Fast Fourier Transform) change on the data acquired by the AD to divide the data into a plurality of channels, and carrying out RFI (Radio Frequency Identification) detection on each channel; if the RFI exists in the channel, IFFT and SFFT are sequentially carried out on a channel result with the RFI to obtain channel time-frequency information, parameterized measurement is carried out on the channel time-frequency information to obtain target pulse description word information, and then the target pulse description word information and channel data without RFI detection are packaged and output together. According to the invention, joint detection of satellite-borne microwave radiation detection and electronic reconnaissance is realized, the signal-to-noise ratio of satellite-borne electronic reconnaissance detection is improved, the detection capability is improved, the influence of RFI on the satellite-borne microwave radiometer is reduced, and the high-precision detection capability of a radiometer system is ensured.

This disclosure provides an optimization method for microwave remote sensing target classification. In one specific embodiment, the method includes acquiring multiple target sample images of a region to be detected via a ground-based center; extracting the boundary point coordinates of the multiple target sample images and calculating the centroid coordinates of the multiple target sample images; constructing a target sample dataset; receiving and storing the target sample dataset via a satellite; observing the region to be detected, acquiring satellite observation intervals and observation images, reading the target sample dataset, and filtering out multiple target sample images located within the observation interval; classifying the observation images to obtain at least one classification result; calculating the matching degree between each classification result and the multiple target sample images located within the observation interval, and obtaining the matching result. This implementation further optimizes microwave remote sensing target classification under low memory and computing power conditions, improves classification accuracy, and solves the false alarm problem.

The present application belongs to the field of remote sensing, and provides a microwave remote sensing ground calibration target installation device and a target installation method. When installing the target, the installation frame is supported on the ground, so that the plane where the azimuth pointer is located is parallel to the horizontal plane. The azimuth pointer is adjusted to an angle parallel to the designed flight path of the aircraft, and the pitch angle pointer is adjusted to a position parallel to the perpendicular line of the calibration point and the designed flight path. With the first reference component and the second reference component as references, the target is moved to a position where the azimuth reference line is attached to the first reference component, and at the same time, the target is moved to a position where the pitch angle reference surface is attached to the second reference component. The target installation device provided by the present application integrates the azimuth calibration component and the pitch angle calibration component into a whole structure, which can provide references for the azimuth and the pitch angle at the same time, and the problem that the first parameter changes when adjusting the second parameter after adjusting the first parameter does not occur.

A maneuvering target inverse synthetic aperture radar imaging method relates to the technical field of microwave remote sensing. The objective of the invention is to solve the problem of poor quality of an existing ISAR imaging method. According to the maneuvering target inverse synthetic aperture radar imaging method, the position of a particle swarm is generated randomly, the position of each particle is updated based on a particle swarm algorithm, and the particles are radar scattering points; dividing the particle population after position updating based on a hierarchical clustering method to obtain sub-populations; performing particle swarm optimization based on generalized Radon-Fourier transform on particles in each sub-population to obtain a global optimal position in each sub-population; and reconstructing echo signals of radar scattering points by using all global optimal positions of the population, and performing two-dimensional FFT processing on the reconstructed echo signals to obtain an inverse synthetic aperture radar imaging result of the maneuvering target.

The application discloses a physical information neural network optimized soil carbon storage precise evaluation system, relates to the cross field of artificial intelligence and geoscience, and comprises a multi-source heterogeneous data intelligent fusion module which is used for realizing multi-source heterogeneous data fusion from satellite hyperspectral images, unmanned aerial vehicle laser radar point clouds, microwave remote sensing, ground sensors, electromagnetic and thermal sensing, optical and acoustic monitoring, metagenomic sequencing, XRD mineralogy, hydrology and DEM through multi-modal space-time alignment, multi-physical field coupling perception, biological-mineral map construction and dynamic hydrological topology construction, and outputting consistency feature matrix, hydrological driving factor matrix and carbon stability biological index. In the application, key physical, chemical and biological kinetic equations of soil carbon cycle are embedded in a deep learning framework, so that the interpretability, generalization ability and prediction precision of the model in a complex environment are improved, and unified modeling and layered consistency prediction of surface and deep soil carbon cycle processes are realized.

The invention relates to the technical field of microwave remote sensing, in particular to a general soil dielectric constant prediction method based on random forest, which comprises the following steps: acquiring attributes of a soil sample to be detected; the attributes of the soil sample to be detected are input into a soil dielectric constant prediction model, dielectric constant real part and imaginary part prediction results of the soil sample to be detected are obtained, and the structure of the soil dielectric constant prediction model is determined through hyper-parameters of a Bayesian optimization random forest model. According to the method, the high-precision fitting capability of the random forest model to the dielectric properties of the soil is used, the soil dielectric constant prediction method which has good applicability within the large microwave frequency, soil texture and moisture and salt content range is constructed, and accurate prediction of the real part and the imaginary part of the dielectric constant of the soil medium can be achieved.

The application relates to a kind of MEO satellite machine BiSAR imaging methods based on improved equivalent single base, and relates to the field of microwave remote sensing imaging.The present application is to solve the problem of low precision and low efficiency of the existing satellite machine BiSAR imaging method.The present application comprises: establishing a non-stop MEO satellite machine BiSAR improved equivalent single base model based on the ground rectangular coordinate system;Using the non-stop MEO satellite machine BiSAR improved equivalent single base model to obtain the reflection echo of the MEO SAR transmitting linear frequency modulation signal, and then obtaining the closed expression of the echo signal two-dimensional spectrum;Using the compensation term in the closed expression of the echo signal two-dimensional spectrum to design a distance high-order compensation function, and correcting the distance error of the target;Using the compensation term in the closed expression of the echo signal two-dimensional spectrum to design a range-dependent compensation function, using the range-dependent compensation function and the echo corrected in step four to correct the phase error difference of the target at different azimuth positions, and obtaining a focused SAR image.The present application is used for satellite machine BiSAR imaging.