148 results about "Microwave remote sensing" patented technology

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention relates to the technical field of microwave remote sensing monitoring, in particular relates to microwave remote sensing monitoring applied to the internal temperature of a coal storage yard, and particularly discloses a coal pile multi-frequency-band microwave remote sensing monitoring method and a coal pile multi-frequency-band microwave remote sensing monitoring system. The system comprises a multi-band microwave transmitting and receiving module, a signal data processing module, a data fusion and inversion model, a data storage module, a visual early warning control system and the like. Microwave signals of the surface layer and / or the shallow layer of the coal pile are detected through high-frequency-band microwaves; temperature distribution data in the coal pile are analyzed through inversion calculation of a pre-established data fusion and inversion model, laser spectrum data of the coal pile are obtained through detection of laser equipment, and harmful gas concentration data and corresponding position data are obtained. The method and the system can be used for early warning and monitoring the internal temperature and the spontaneous combustion point of the coal pile of the coal storage yard, and can effectively and accurately monitor.

The invention discloses a method for determining a chlorophyll concentration value of phytoplankton based on ocean heat waves. The method comprises the following steps: acquiring ocean green ecological remote sensing image data and ocean heat wave data of a target sea area; and inputting the ocean heat wave data and the ocean green ecological remote sensing image data of the target sea area into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the ocean heat wave grade of the target sea area according to the ocean heat wave data, inputting the ocean green ecological remote sensing image data into a chlorophyll concentration determination sub-model according to the ocean heat wave grade, enabling the chlorophyll concentration determination sub-model to extract corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputting a phytoplankton chlorophyll concentration value of the target sea area; and transmitting the phytoplankton chlorophyll concentration value of the target sea area to a marine ecological monitoring system to assist the marine ecological monitoring system in sea area monitoring. The method can improve the accuracy of determining the chlorophyll concentration value of the phytoplankton and the application reliability.

The invention provides an active and passive combined microwave remote sensing detection method for earth surface air pressure and air pressure profile, which comprises the following steps: by combining a satellite-borne / airborne millimeter wave differential absorption radar and a satellite-borne / airborne microwave radiometer and utilizing oxygen differential absorption measurement, obtaining accurate land / ocean surface air pressure data; and the air pressure profile is further obtained by combining temperature and humidity profile data measured by a satellite-borne / airborne microwave radiometer. Compared with the prior art, the method has the advantages that active and passive microwaves are adopted for combined detection, detection information obtained by a microwave radiometer is complemented, calculation of the surface pressure is enhanced, and the surface pressure inversion precision is improved; according to the method, the surface pressure value can be obtained, and the atmospheric pressure profile can also be obtained. According to the method, accurate surface pressure detection can be realized, and the atmospheric pressure profile can be further deduced by combining the temperature profile and the humidity profile observed by the microwave radiometer.

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 relates to the technical field of spaceflight microwave remote sensing, and provides a spaceborne synthetic aperture radar in-orbit autonomous task planning method, device and equipment, and the method comprises the steps: obtaining imaging parameter information; the imaging parameter information is determined by the satellite imaging system based on task annotation information, orbit information and an imaging parameter preset table, and the task annotation information is obtained by the satellite imaging system by analyzing observation requirements of a user; the orbit information is determined based on satellite forecast information. According to the method, imaging parameter information is determined by a satellite imaging system based on task uploading information, orbit information and an imaging parameter preset table, partial parameter calculation and instruction generation processes of traditional satellite-borne SAR task planning are moved to a satellite, manual calculation and ground uploading steps are omitted, and dependence of the task planning process on ground calculation is reduced; the task uploading information is obtained by analyzing the observation demand of the user through the satellite imaging system, the observation demand can be uploaded in advance, satellite resources are fully utilized, and the task execution efficiency is greatly 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 provides a super-resolution reconstruction method based on a CNN (convolutional neural network)-Transform hybrid architecture. According to the method, aiming at the defects of generalization and performance of a traditional super-resolution reconstruction algorithm of satellite-borne passive microwave remote sensing data, accurate matching and dynamic fusion of orbit ascending and orbit descending data are realized through an interactive feature alignment module, and a double-branch network is constructed by means of a multi-scale space-time attention mechanism module; extracting and integrating spatio-temporal characteristics under different receptive fields; meanwhile, a long-distance dependency relationship is established by using a hybrid adaptive attention module, the global feature modeling capability is enhanced, and finally the spatial resolution of passive microwave remote sensing data is remarkably improved through an efficient up-sampling module. According to the invention, passive microwave remote sensing data with higher spatial resolution can be obtained, and passive microwave remote sensing data with higher quality can be provided for global scale earth surface parameter inversion and monitoring research.

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 invention discloses an anti-interference L-band microwave remote sensing radiometer, and the radiometer comprises a radio frequency receiving assembly which comprises a miniaturized array antenna, a single-pole three-throw switch, a band-pass filter, a low-noise amplifier, a frequency mixer, a local oscillation source, an intermediate-frequency filter, an intermediate-frequency amplifier, and a detector; a signal control and acquisition assembly; a calibration component; a data processing and transmitting component; the miniaturized array antenna is used for receiving a radio frequency noise signal in a target direction; the single-pole three-throw switch is used for respectively switching and receiving the radio frequency noise signal in the target direction, a radio frequency noise signal after heat source matching and a radio frequency noise signal after cold source matching; a radio frequency noise signal is processed through a band-pass filter, a low-noise amplifier, a frequency mixer, an intermediate-frequency filter, an intermediate-frequency amplifier and a detector in sequence, and a detection signal is combined with a calibration algorithm to complete noise temperature measurement in a target direction; the data processing and transmission assembly transmits the data to the platform. The radiometer provided by the invention has strong anti-interference capability and can be used for various scenes for retrieving environmental parameters.

The invention relates to the technical field of microwave remote sensing, and provides a passive microwave soil moisture product downscaling method fusing earth surface heterogeneity, which comprises the following steps: determining respective earth surface heterogeneity and earth surface environment data under a first spatial resolution and a second spatial resolution based on original earth surface environment data; inputting the surface environment data and the surface heterogeneity under the first spatial resolution into a downscaling relation model to obtain downscaling soil moisture data under the first spatial resolution output by the downscaling relation model; the scale of the second spatial resolution is thicker than that of the first spatial resolution; the downscaling relation model is obtained by training based on the earth surface environment data and earth surface heterogeneity under the second spatial resolution and the downscaling soil moisture data under the second spatial resolution, so that the defects of poor downscaling precision and reliability at present are overcome, the spatial resolution of the passive microwave soil moisture product is effectively improved, and the method is suitable for popularization and application. And the application requirement of the regional scale can be met.

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 present invention belongs to the field of microwave remote sensing and relates to a method and system for synchronous inversion of Doppler precipitation radar precipitation and three-dimensional wind field. The method includes: a method for synchronous inversion of Doppler precipitation radar precipitation and three-dimensional wind field, comprising: determining the reflectivity factor of the observation area through the echo power of the Doppler precipitation radar; determining the precipitation area and calculating the size of the precipitation particles based on the reflectivity factor; calculating the vertical falling velocity of the precipitation particles based on the precipitation particle size; calculating the radial velocity of the precipitation particles based on the echo phase of the Doppler precipitation radar; removing the vertical falling velocity of the precipitation particles to obtain the radial motion velocity of the precipitation particles caused by the wind field; and inverting the three-dimensional wind field of the precipitation area based on the radial motion velocity of the precipitation particles caused by the wind field, combining the radar observation geometry and the three-dimensional wind field inversion algorithm. The method of the present invention improves the accuracy of wind field inversion.

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 invention discloses a microwave remote sensing-based water ammonia nitrogen concentration differential absorption detection device, and relates to the technical field of water pollution detection, the microwave remote sensing-based water ammonia nitrogen concentration differential absorption detection device comprises a floating platform used for floating on a detected water body and a mounting plate arranged on the floating platform, and the mounting plate is provided with a microwave transmitting module and a microwave receiving module which are used for microwave remote sensing detection; the floating platform is provided with a signal processing and analyzing module used for information processing and a data storage and display module. In the detection process, through mutual cooperation of the swing assembly, the water body depth recognition assembly, the lifting assembly and the adjusting assembly, microwave signals emitted by the microwave emission module on the mounting plate can cover the water surface with a larger area, three-dimensional sampling of the water body is achieved, and the one-sidedness of single-point detection is avoided; the detection state can be adaptively adjusted according to the depth of the detected water body in the detection process, and flexible detection operation is facilitated.