36 results about "Polar orbit" patented technology

The invention provides a cloud condition adaptive geostationary satellite sea surface temperature inversion method based on deep learning, and relates to the technical field of sea surface temperature inversion, and the method specifically comprises the steps: obtaining geostationary satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, and analyzing the sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained stationary satellite observation data; constructing a cloud detection data set; a cloud detection model of deep learning is constructed and trained; constructing a clear sky data set; a clear sky inversion model of deep learning is constructed and trained; constructing a data set for under-cloud inversion model training; constructing an under-cloud inversion model of deep learning and training the under-cloud inversion model; and integrating the cloud detection model, the clear sky inversion model and the under-cloud inversion model, adaptively calling the corresponding model according to a real-time cloud detection result, and generating a final sea surface temperature product. According to the technical scheme, the problem that in the prior art, the accuracy of a reconstructed product on the spatial structure cannot be comprehensively reflected is solved.

The invention relates to a method and device for determining a lunar gravity field by using an inter-satellite frequency signal time-frequency link, and the method comprises the steps: connecting an earth observation station, a static satellite and a lunar polar orbit satellite which are provided with an atomic clock through a satellite carrying a clock system based on a generalized relativistic theory, and carrying out the time-frequency transmission through a satellite time-frequency transmission method, the gravity potential difference between the earth and the satellite and the gravity potential difference between the satellite and the moon polar orbit satellite are measured through a gravity frequency shift method, the gravity potential data of the moon polar orbit satellite are obtained through an inter-satellite time-frequency link according to the earth gravity potential data, and then the moon gravity field is determined through a physical geodetic measurement method. Therefore, the problems that the prior art is difficult to implement on the surface of the moon and the gravity potential of the moon and the earth cannot be connected are solved.

The invention discloses a power distribution network satellite meteorological data analysis method, system and device and a medium, and the method comprises the following steps: obtaining and preprocessing multi-source satellite meteorological data, and generating a time-space continuous meteorological element distribution diagram; extracting disaster associated features through the meteorological element distribution map; constructing a first calculation model to train the disaster associated features, and outputting a disaster distribution confidence interval; carrying out risk space mapping on the disaster distribution confidence interval and the topological structure of the power distribution network to generate a tower overturning risk assessment result; and establishing a multi-level early warning mechanism according to a tower overturning risk assessment result, generating a three-dimensional risk map and deploying a mobile terminal application. Through a multi-source satellite data fusion preprocessing technology, the problem of a space-time blind area caused by a traditional single data source is effectively solved, collaborative acquisition and space-time registration of data of a static meteorological satellite, a polar orbit meteorological satellite and a radar are achieved, and the problems of resolution difference and cloud noise interference of different satellite systems are solved.

The invention relates to the field of internal solitary wave prediction, and discloses an ocean internal solitary wave propagation prediction method based on an improved TimeXer model, and the method comprises the following steps: constructing the improved TimeXer model; remote sensing image data of a geostationary orbit satellite GK-2A and a polar orbit satellite are collected and preprocessed; extracting internal solitary wave parameters based on remote sensing image data, constructing an internal solitary wave prediction data set in combination with hydrological environment parameters, and training a model; and acquiring remote sensing image data of a geostationary orbit satellite GK-2A and a polar orbit satellite of a target sea area, preprocessing the remote sensing image data, inputting the remote sensing image data into the model which is tested to be qualified, and performing internal solitary wave prediction. According to the method disclosed by the invention, the precision and stability of propagation prediction of the solitary waves in the ocean can be effectively improved; by introducing an innovative module, the adaptability of the model to a complex marine environment is enhanced, and a reliable scheme is provided for accurate prediction of solitary waves in the ocean.

The invention belongs to the field of artificial intelligence, and particularly relates to a multi-source fusion perception air-space-ground integrated geological disaster identification method and system. According to the scheme, firstly, satellite images, collected by a stationary orbit satellite, a synthetic aperture radar satellite and a polar orbit satellite, of a target area are obtained in real time; performing time interpolation and spatial super-resolution reconstruction on the multi-source heterogeneous satellite image data to obtain three types of satellite images with consistent time and space and uniform resolution; synchronously inputting the three types of satellite images into a pre-trained coarse-grained disaster recognition model to obtain a macroscopic probability graph reflecting the geological disaster risk; after a high-risk local area is identified, a complete high-resolution image is obtained through mutual cooperation of ground monitoring equipment, an unmanned aerial vehicle and the like; and finally, inputting the high-resolution image into a pre-trained fine-grained disaster recognition model to obtain a geological disaster recognition result. According to the invention, the problems of the existing geological disaster monitoring scheme in the aspects of real-time performance and precision are solved.

In exemplary embodiments communication resiliency methods and systems for tribal ground stations may be described. An exemplary communication system may include a first ground station and a second ground station. The first ground station and the second ground station may be located on one or more tribal reservations. The first ground station and second ground station may be connected to a satellite. The satellite may be, for example but not limited to, geostationary (GEO) satellites, mid earth orbit (MEO) satellites, low earth orbit (LEO) satellites, polar orbiting satellites, and / or communication satellite constellations. In some embodiments the satellite may be a LEO satellite that provides direct communication between the first ground station and the second ground station.

This invention provides a method, apparatus, and device for identifying water bodies on a single orbit of a polar-orbiting meteorological satellite, comprising: acquiring first-band data, first-positioning information data, and second-band data, second-positioning information data, and product data at a first spatial resolution from a polar-orbiting meteorological satellite; sampling the data at the second spatial resolution to obtain second-band data, second-positioning information data, and product data at the first spatial resolution; acquiring high-quality binary classification data; training a decision tree model based on the first-band data, second-band data, normalized index data, band difference data, and band ratio data at the first spatial resolution corresponding to high-quality water body pixels and high-quality non-water body pixels within the target area, to obtain a target decision tree model; and obtaining the single-orbit water body identification area based on the target decision tree model. The solution of this invention accurately identifies water bodies in complex background conditions, improving monitoring accuracy and applicability.

The invention relates to the technical field of high-precision positioning, and discloses a GNSS and polar orbit remote sensing satellite combined troposphere delay product generation method, which comprises the following steps: calculating a difference value between a real-time GNSS true value point and a remote sensing satellite troposphere delay background field to generate a group of differential anchor points, and after reliability arbitration is carried out on the differential anchor points, generating a group of differential anchor points; a continuous troposphere difference correction field is generated through a topographic constraint interpolation algorithm, the weight basis of the interpolation algorithm is an effective path distance considering topographic obstacle punishment, and finally the correction field is superposed on a background field. According to the method, direct mathematical fitting of data of different sources in the prior art is replaced, a terrain constraint interpolation mode is utilized, so that spatial propagation of correction information can follow limitation of a real physical barrier, and a correction result which does not conform to reality is prevented from being generated in a complex terrain area.

The invention discloses a polar orbit satellite cloud phase state product quality inspection method, device and equipment and a medium. The method comprises the steps that to-be-inspected satellite data and inspection source satellite data are obtained, the to-be-inspected satellite data are polar orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, and the inspection source satellite data comprise MODIS data and CALIPSO data of other similar meteorological satellites. And respectively processing the to-be-inspected satellite data and the inspection source satellite data to obtain target to-be-inspected data and target inspection source data. And determining a quality inspection index corresponding to the to-be-inspected satellite data according to the target to-be-inspected data and the target inspection source data, and determining a quality inspection result corresponding to the to-be-inspected satellite data according to the quality inspection index. According to the technical scheme of the embodiment of the invention, the business real-time inspection system of the satellite cloud phase product is constructed, the inspection efficiency and the inspection accuracy of the data quality are improved, and a correction basis is provided for error adjustment of the satellite cloud phase product.

The application relates to the field of internal solitary wave prediction, and discloses a marine internal solitary wave propagation prediction method based on an improved TimeXer model, which comprises the following steps: constructing an improved TimeXer model; collecting and pre-processing remote sensing image data of a stationary orbit satellite GK-2A and a polar orbit satellite; extracting internal solitary wave parameters based on the remote sensing image data, combining with hydrological environment parameters to construct an internal solitary wave prediction data set, and training a model; obtaining remote sensing image data of the stationary orbit satellite GK-2A and the polar orbit satellite of a target sea area, pre-processing the remote sensing image data, inputting the remote sensing image data into a qualified model, and performing internal solitary wave prediction. The method disclosed by the application can effectively improve the precision and stability of marine internal solitary wave propagation prediction; the model adaptability to complex marine environments is enhanced by introducing an innovative module, and a reliable scheme is provided for accurate prediction of marine internal solitary waves.

The present invention discloses a forest fire monitoring method based on remote sensing satellite data and polar-orbiting satellite data. The method comprises: acquiring remote sensing data of a target area via a remote sensing satellite, acquiring first polar-orbit image data of the target area via a polar-orbiting satellite, processing the data to obtain standard data and polar-orbit data, extracting the regional forest area from the polar-orbiting data, removing flare pixel data to obtain second polar-orbiting image data, detecting and removing interference data from the second polar-orbiting image data to obtain third polar-orbiting image data, calculating a burning index to obtain the fire location, and masking the regional forest area with the fire location to obtain forest fire distribution information. By utilizing the high temporal resolution of remote sensing satellite data and the high spatial resolution of first polar-orbiting satellite image data to complement each other, the timeliness and fire identification of forest fire satellite monitoring can be improved. The method can be widely applied to the field of satellite data processing technology.

The application provides a cloud condition adaptive static satellite sea surface temperature retrieval method based on deep learning, relates to the technical field of sea surface temperature retrieval, and specifically comprises the following steps: obtaining static satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, reanalysis sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained static satellite observation data; constructing a cloud detection dataset; constructing a deep learning cloud detection model and training the same; constructing a clear sky dataset; constructing a deep learning clear sky retrieval model and training the same; constructing a dataset for cloud retrieval model training; constructing a deep learning cloud retrieval model and training the same; integrating the cloud detection model, the clear sky retrieval model and the cloud retrieval model, adaptively calling corresponding models according to real-time cloud detection results, and generating a final sea surface temperature product. The technical scheme of the application overcomes the problem in the prior art that the accuracy of the reconstructed product in the spatial structure cannot be comprehensively reflected.

The present application belongs to the field of artificial intelligence, and particularly relates to a space-air-ground integrated geological disaster identification method and system based on multi-source fusion perception. The scheme first acquires satellite images of a target area collected by stationary orbit satellites, synthetic aperture radar satellites and polar orbit satellites in real time; then performs time interpolation and spatial super-resolution reconstruction on multi-source heterogeneous satellite image data, and further obtains three types of satellite images with consistent time and space and unified resolution; the three types of satellite images are synchronously input into a pre-trained coarse-grained disaster identification model to obtain a macro probability graph reflecting geological disaster risk; after identifying the local area with high risk, ground monitoring equipment and unmanned aerial vehicles are used to obtain complete high-resolution images; finally, the high-resolution images are input into a pre-trained fine-grained disaster identification model to obtain the identification result of the geological disaster. The present application overcomes the problems of existing geological disaster monitoring schemes in real-time and precision.

The application relates to the field of high-precision positioning technology and discloses a troposphere delay product generation method combining GNSS and polar orbit remote sensing satellites, which comprises the following steps: calculating the difference between real-time GNSS true value points and a remote sensing satellite troposphere delay background field to generate a group of differential anchor points; after reliability arbitration is performed on the differential anchor points, a continuous troposphere differential correction field is generated through a terrain-constrained interpolation algorithm, the weight of the interpolation algorithm is based on the effective path distance considering the terrain obstacle penalty, and finally the correction field is superimposed on the background field; through generation and application of a dynamic correction field based on physical deviation, the method replaces the direct mathematical fitting of different source data in the prior art, the interpolation method of terrain constraint is used, space propagation of correction information can follow the restriction of real physical barriers, and unrealistic correction results in complex terrain areas are avoided.

A method implemented by a computer for maintaining a spacecraft near an orbit includes a step (3) of detecting that a distance from the spacecraft to the orbit is greater than a spacecraft threshold, and, in response thereto, a step (5) of linearizing the dynamics of the spacecraft from the current time over a certain time horizon with respect to a high-fidelity reference trajectory to generate a state transition matrix (STM) for uncontrollable motion of the spacecraft within the time horizon. The STM includes non-expanding eigenvectors having a magnitude of 1 or less and expanding eigenvectors having a magnitude greater than 1. The method further includes a step (7) of determining a control action that changes the next state of the spacecraft to a linear combination of non-expanding eigenvectors of the STM, and a step (9) of generating a control command to an actuator of the spacecraft.

The application discloses a time-varying gravity field model establishment method of a new generation of low-low satellite tracking satellite scheme based on Chinese regional enhancement, belongs to the field of satellite gravitation, and refers to selection indexes of a global optimal double-track four-star formation scheme to determine multiple double-track four-star formation schemes in the Chinese region, select an optimal scheme from the multiple double-track four-star formation schemes, independently solve low-order and high-order gravity potential spherical harmonic coefficients by using different data in the optimal scheme, weaken the negative influence of time domain aliasing effect on the precision of the time-varying gravity field model, reduce the error of the high-order gravity potential spherical harmonic coefficients to a certain extent, and finally obtain a Chinese regional gravity field model with higher precision. The final result shows that, compared with an independent polar orbit formation, the application not only greatly weakens the north-south strip error caused by the north-south tracking mode in the Chinese region, but also avoids problems such as gravity signal leakage caused by a post-processing process, and finally obtains a Chinese regional gravity field model with smaller error and higher signal-to-noise ratio.

The application provides a precipitation prediction method and device, electronic equipment and storage medium, the method comprises the following steps: obtaining a plurality of frequency polarization channel brightness temperature data and infrared channel brightness temperature data of a target area based on a polar orbit satellite and a geosynchronous orbit satellite; determining whether a target area has a precipitation event according to the obtained data; if so, filtering the target frequency polarization channel brightness temperature data according to the sea-land classification result of the target area; determining the precipitation prediction result of the target area according to the target frequency polarization channel brightness temperature data and the infrared channel brightness temperature data. By combining the meteorological monitoring data of the polar orbit satellite and the geosynchronous orbit satellite, the temporal and spatial resolution of the meteorological monitoring data is improved, and the passive microwave data, infrared data including water vapor channel data are integrated, thereby improving the accuracy of the precipitation prediction.

The present application relates to the technical field of spacecraft electromagnetic propulsion, and particularly relates to an electromagnetic propulsion device for polar orbit satellite and a propulsion control method, aiming at solving the technical problem of short service life of artificial satellite in the prior art. The electromagnetic propulsion device comprises a power supply assembly and a propulsion assembly; the power supply assembly is electrically connected with the propulsion assembly, and the two form a circuit loop; the propulsion assembly is used for shielding current passing through itself and is used for connecting with a spacecraft. The electromagnetic propulsion device does not need to use working medium such as liquid, gas and plasma, and obtains propulsive force through cooperation of the earth space magnetic field and the current, thereby reducing the cost of the propulsion device and greatly improving the service life of the on-orbit satellite.

The invention provides a domestic passive meteorological satellite cloud base height high-precision inversion method and system, and belongs to the field of atmospheric cloud parameter remote sensing inversion and quantitative remote sensing mechanisms. According to the method, re-analysis data such as satellite multi-channel atmospheric top reflectivity observation, brightness temperature observation and atmospheric temperature profile are fused, a complex nonlinear feature extraction and modeling method is developed, and the nonlinear mapping capability among the atmospheric top reflectivity, the brightness temperature and the cloud base height is improved; the night and all-day inversion capability is enhanced by combining all-time infrared channel characteristics and layer junction information; through a multi-source sample expansion and stack learning strategy, the generalization adaptability of the model is improved; and a sample expansion and auxiliary training mechanism is designed, so that the problem of insufficient high-quality training data is effectively relieved, and finally, high-precision inversion of the all-day cloud base height of a domestic static or polar orbit meteorological satellite is realized.

The invention provides a power grid mountain fire monitoring method and device, electronic equipment and a storage medium, and relates to the field of mountain fire monitoring. The method comprises the following steps: acquiring fire data corresponding to a target area based on a polar orbit satellite; determining power grid equipment influenced by the fire behavior according to the fire behavior data and power transmission line ledger data corresponding to the target region; determining a burning forbidden area corresponding to the target area according to burning forbidden area equipment corresponding to the target area; and displaying the fire data, the power grid equipment influenced by the fire and the burning prohibition area on a map in an overlapping manner. According to the method, high-precision positioning of the fire can be realized based on the polar orbit satellite, automatic identification of the fire influence equipment can be realized based on the fire data and the ledger data of the power transmission line, and linkage analysis between the fire and the burning prohibited area is realized through overlapped display of the fire data, the fire influence equipment and the burning prohibited area; and whether the fire relates to the burning prohibited area can be quickly judged, and the manual confirmation burden is reduced.

The present application belongs to the technical field of geophysics, and more particularly relates to a method and device for drawing a temperature-logarithmic pressure map by satellite joint observation, the method comprising: determining the position and altitude information of an image to be drawn, and converting the altitude into a ground pressure value; obtaining atmospheric temperature and humidity profile data of a polar orbiting meteorological satellite and a geostationary meteorological satellite respectively, and matching satellite orbit scanning points on the position of the image to be drawn; extracting vertical profile data information of temperature and specific humidity of the satellite orbit scanning points at the altitude according to the atmospheric temperature and humidity profile data, and drawing a temperature-logarithmic pressure map. The joint observation of the polar orbiting meteorological satellite and the geostationary meteorological satellite provides observation data with higher spatial coverage and time frequency, and the drawn temperature-logarithmic pressure map can serve as an effective supplement to conventional observation and play an important role in meteorological services.

The invention relates to the technical field of quantitative remote sensing, in particular to a global surface temperature monthly worse estimation method and system based on polar orbit satellite double-temporal data, equipment and a medium. According to the method, the two-phase continuous change model (DPCV) capable of describing two-day continuous change is established, so that the temperature dynamic state on the month scale is simulated more truly, and the precision is remarkably improved: the information dimension is expanded from 24 hours to 48 hours by constructing double-time-phase data, and richer constraint information is provided for the model. The DPCV model can better capture temperature fluctuation caused by factors such as weather system evolution in the month instead of simple averaging, so that the estimated monthly highest temperature and lowest temperature are closer to the real situation, and the precision of the monthly worse temperature is remarkably improved.

The application discloses a polar orbit satellite cloud phase state product quality inspection method, device, equipment and medium. The method comprises the following steps: acquiring satellite data to be inspected and inspection source satellite data, wherein the satellite data to be inspected is polar orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, and the inspection source satellite data comprises other similar meteorological satellite MODIS data and CALIPSO data. The satellite data to be inspected and the inspection source satellite data are subjected to data processing respectively to obtain target satellite data to be inspected and target inspection source data. According to the target satellite data to be inspected and the target inspection source data, a quality inspection index corresponding to the satellite data to be inspected is determined, and a quality inspection result corresponding to the satellite data to be inspected is determined according to the quality inspection index. The technical scheme of the embodiment of the application builds a business real-time inspection system of satellite cloud phase state products, improves the inspection efficiency and accuracy of data quality, and provides a correction basis for error adjustment of satellite cloud phase state products.

A low-overhead OSPF protocol improvement method for large-scale polar orbiting constellations utilizes the existing OSPF / OSPF+ protocol mechanism with the following improvements: 1) "Orbital Zone" division to reduce routing computational resource overhead; 2) a pre-configured link state database to reduce unnecessary message transmission; and 3) a new link "Test State" to ensure link reliability. In addition to the "Leave State" already present in the existing OSPF+ neighbor state machine, a new "Test State" is added. Regardless of whether a link is within or between "Orbital Zones," the link and its corresponding neighbors have four deterministic states: Connected, Leave, Test, and Closed. Predictable link interruptions and restorations are no longer communicated via Link State Update messages, accelerating route reconvergence in the event of predictable link interruptions and restorations and reducing unnecessary use of inter-satellite link resources.

The invention discloses a cloud motion vector estimation method and cloud motion vector estimation equipment based on polar orbit satellite remote sensing images, and relates to the crossing field of meteorological remote sensing and computer vision. The method comprises the steps of obtaining multi-temporal cloud image data based on a polar orbit satellite remote sensing image; calculating a global motion vector and a local motion vector based on the multi-temporal cloud image data; and estimating and obtaining a cloud motion vector based on the global motion vector and the local motion vector. According to the scheme, challenges of a traditional optical flow method in complex cloud layer motion and satellite image noise can be dealt with, and a more accurate and robust wind speed estimation result is provided.

This invention discloses a method, device, and medium for detecting polar daytime sea fog / low clouds using multi-source polar-orbiting satellites. The method includes: acquiring and preprocessing remote sensing image data collected by multi-source polar-orbiting satellites; calculating a sea surface identification index using two preset band data from the preprocessed remote sensing image data; then, based on the sea surface identification index and near-infrared band reflectance data from the remote sensing image data, jointly removing areas related to seawater and sea ice / snow from the preprocessed remote sensing image data; calculating the gray-level co-occurrence matrix for each pixel in a third preset band of the remote sensing image data after removing seawater, sea ice, and snow, and statistically analyzing several texture features based on the gray-level co-occurrence matrix; inputting the statistically analyzed texture feature values ​​corresponding to all remote sensing images from polar-orbiting satellites into a trained random forest model to identify and remove mid-to-high clouds, leaving the remaining clouds as sea fog / low clouds. This invention is less affected by satellite differences and variations in solar altitude angle.

To transmit flying object information to a coping system or a satellite integrated command center without interposing a satellite information transmission system.SOLUTION: In the polar orbital satellite system 442, the monitoring system has a group of six or more polar orbital satellites that fly in polar orbits having the same average orbital altitude on the same orbital plane. A polar orbital satellite constellation forms communication crosslinks with forward and aft flying polar orbital satellites. At least one or more polar orbit satellites form a communication crosslink with a countermeasure system or a satellite integrated command center provided with a communication route searching device for satellite information. The monitoring system transmits the flying object information to a coping system or a satellite integrated command center without interposing a satellite information transmission system.SELECTED DRAWING: Figure 11

The application provides a domestic passive meteorological satellite cloud bottom height high-precision inversion method and system, and belongs to the field of atmospheric cloud parameter remote sensing inversion and quantitative remote sensing mechanism. The application fuses satellite multi-channel atmospheric top reflectivity observation, brightness temperature observation and atmospheric temperature profile reanalysis data, develops a complex nonlinear feature extraction and modeling method, improves the nonlinear mapping capability among the atmospheric top reflectivity, the brightness temperature and the cloud bottom height, combines the whole period infrared channel characteristics and the stratification information, enhances the night and all-time inversion capability, improves the generalization adaptability of the model through the multi-source sample expansion and the stacking learning strategy, designs the sample expansion and the auxiliary training mechanism, effectively alleviates the problem of insufficient high-quality training data, and finally realizes the high-precision inversion of the all-time cloud bottom height suitable for the domestic stationary or polar orbit meteorological satellite.

The invention relates to the technical field of meteorological monitoring and forecasting, and discloses a convective cloud cluster prediction method, device and system based on a stationary meteorological satellite.The method comprises the steps that S1, continuous observation data of a polar orbit satellite and a stationary satellite are synchronously obtained, the polar orbit satellite collects high spatial resolution, and the stationary satellite has high time resolution; s2, fusing polar orbit satellite data and stationary satellite data through data fitting; s3, on the basis of a ConvLSTM-Transform hybrid model, extracting the spatial and temporal characteristics of the cloud cluster; s4, predicting the cloud cluster position within 2 hours to 10 minutes in the future; and S5, generating a structured strong convective cloud cluster product. According to the method, by extracting the multi-scale features of the data of the stationary satellites, the stationary satellites with low spatial resolution are fused to the spatial analysis advantages of the high-spatial-resolution and collaborative polar orbit satellites and the timeliness advantages of the stationary satellites, and convective cloud monitoring with high timeliness, hourly observation and high spatial resolution is achieved.

The application discloses a kind of based on Fengyun polar orbit meteorological satellite South China Sea summer monsoon double-index monitoring method, this method obtains 850hPa atmospheric temperature and specific humidity by polar orbit meteorological satellite FY-3D vertical detection instrument group VASS, 850hPa false equivalent potential temperature index is calculated;Ocean surface wind speed and wind direction are obtained by ocean surface wind field data of the wind field measurement radar WindRAD inversion of polar orbit meteorological satellite FY-3E, average zonal wind index is calculated;By evaluating the accuracy of FY-3D / VASS temperature and specific humidity relative to ERA5, and the accuracy of ocean surface wind of FY-3E / WindRAD relative to MetOp-C / ASCAT is evaluated index, the accuracy of the double-index monitoring South China Sea summer monsoon outbreak process is verified from the evaluation result.The application uses FY-3D to calculate false equivalent potential temperature and FY-3E ocean surface wind double-index monitoring South China Sea summer monsoon, double-index is well monitored 2022 South China Sea summer monsoon outbreak process temperature and humidity field and wind field conversion, so as to verify the application ability of two kinds of satellite data in South China Sea summer monsoon climate monitoring.