Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities, not necessarily corresponding to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
The scheme provided by the disclosure provides a satellite data processing scheme for receiving and/or detecting data according to preset priority by determining data for generating fusion satellite data and determining preset priority.
The scheme provided by the embodiment of the disclosure relates to the technical field, and is specifically described by the following embodiment.
An ecological detection system based on satellite data according to this embodiment of the present disclosure is described below with reference to fig. 1. The satellite data based ecological detection system shown in fig. 1 is only one example and should not be construed as limiting the functionality and scope of use of the disclosed embodiments.
As shown in fig. 1, the satellite data-based ecological detection system 100 of the present disclosure has the capability of receiving NOAA (National Oceanic and Atmospheric Administration, national oceanographic office) series, EOS (Earth Observation System, earth-observation system) series, NPP (National Polar-orbiting Partnership) satellites and compatible future satellites at home and abroad in real time.
The satellite data-based ecological detection system 100 of the present disclosure adopts a design concept of combining a service-oriented architecture and a table-driven architecture mode to meet the requirements of compatible reception of domestic and foreign meteorological satellites.
In the design of the remote sensing apparatus, hardware components such as AVHRR (Advanced Very High Resolution Radiometer, sensor mounted on NOAA-series meteorological satellite) and MODIS (mode-resolution Imaging Spectroradiometer, medium resolution imaging spectrometer) may be supported, but not limited thereto.
In terms of code Rate design, HRPT (High-resolution Picture transmission, high resolution image transmission), MPT (Moderate resolution Picture Transmit, medium resolution image transmission), MODIS, HRD (High Rate Data), and the like may be supported, for example, but not limited thereto.
On the channel design, the working frequency point is continuously adjustable, the receiving requirements of various data are met, and meteorological data are received and/or detected according to a preset priority.
The aerosol detection algorithm based on the present disclosure can distinguish cloud, clear sky and pixel with serious dust and smoke, and the remote sensing aerosol detection based on the infrared channel and the visible light channel of the multi-source satellite can be implemented on hardware such as AVHRR, MODIS, and the like.
The ecological detection method of the present disclosure is implemented based on the radiation characteristics of the aerosol. For example, particle spectral distribution, concentration, chemical composition, and location in the atmosphere vary as the aerosol is generated and consumed, and these physical characteristics change can affect the radiation characteristics of the primary aerosol and enable us to detect from satellite observations.
The inventors of the present disclosure found that: in principle, the radiation characteristics of an aerosol are determined by scattering and absorption characteristics (such as extinction efficiency factor, single scattering ratio and scattering phase function), and these are determined by three important particle physical parameters: refractive index, spectral distribution, and particle shape. Due to the characteristic difference of various aerosols and clouds in different wave bands, the detection of sand dust and smoke dust by using a spectral threshold method is possible.
As shown in fig. 1, the ecodetection system 100 of satellite data of the present disclosure may receive remote sensing data of a cloud No. 3 series satellite, a cloud No. 4 series satellite, and a sunflower No. eight series satellite, but is not limited thereto. The ecological detection system 100 for satellite data sequentially performs the processing steps of starting the data entry, starting the sub-packaging and unpacking, starting the sand and dust identification processing, generating the sand and dust climate products and the like, updates the data in the processing steps in real time and performs the product archiving processing so as to comprehensively improve the quality and reliability of the sand and dust climate products.
The generated dust climate product is sent to the client 102 or is accessed by the client 102 through an interface protocol.
The processing of the satellite data ecodetection system 100 shown in fig. 1 includes at least the following three stages:
first, the sand weather discrimination based on the multi-source satellite data including, but not limited to, FY-3D, FY-4A, sunflower number 8 data, etc.
And secondly, generating sand and dust climate products by adopting methods such as single-channel spectral clustering, bright temperature difference, a ratio method, a background field method, uniformity inspection and the like according to the input satellite data and auxiliary data.
Finally, based on man-machine interaction modification, output, sand and dust influence area statistical analysis and the like, the output of a sand and dust judgment result is realized.
Further, according to the determined identification criteria, the more criteria passed and the closer the spectrum is to the standard dust spectrum, the higher the dust score. The final dust climate product is determined based on the dust score, the higher the score the greater the likelihood of being dust.
Wherein, the generated dust climate product shown in fig. 1 at least comprises the following steps:
(1) Primary data in the multi-source satellite data, such as, but not limited to, infrared reflectivity, infrared transmissivity, and light temperature, are acquired.
(2) Assistance data in the multi-source satellite data, such as, but not limited to, earth surface coverage information, terrain, topography, posters, and the like, is acquired.
(3) And carrying out sand detection according to a judgment standard, wherein the judgment standard can be preset based on the ground surface information, namely, corresponding judgment standards are determined aiming at ground surface background information for different areas so as to improve the reliability and accuracy of sand climate products.
(4) The sand judgment is carried out according to the sand score, and the higher the score is, the more accurate the sand judgment is, the higher the reliability and the accuracy are, and the higher the image quality and the accuracy of sand weather products are.
The client 102 timely obtains the L1-level product data to be processed from the data storage device by means of the remote sensing detection analysis service subsystem interaction analysis platform, loads the L1-level product data into the local interaction analysis interface to perform remote sensing image data analysis, image adjustment, geographic vector matching and other works, generates dust climate products and related data, submits the dust climate products and related data to the product archiving process to perform unified data management, such as sorting according to the product generation time or classified archiving according to the region, and the like, but the application is not limited thereto.
The client 102 obtains a data retrieval interface by means of the product archiving management and retrieval subsystem retrieval function, and the interface mainly displays the data cataloging structure and archived data, so that a user can conveniently find and use various data. The search results may be selectively filtered and ranked based on identification of satellite, receiving station, product type, product name, resolution, start time, etc.
An ecological detection method based on satellite data according to this embodiment of the present application is described below with reference to fig. 2 to 6. The satellite data-based ecological detection method shown in fig. 2 to 6 is only one implementation, and should not be construed as limiting the functionality and scope of use of the embodiments of the present application.
As shown in fig. 2, the ecological detection method based on satellite data includes:
step S202, multi-source satellite data of an area to be detected is acquired.
In the above-described embodiment, the multi-source satellite data includes weather data of the cloud No. 3 series satellite, the cloud No. 4 series satellite, and the sunflower No. eight series satellite, but is not limited thereto.
In step S204, the first spectrum information and the first bright temperature information included in the multi-source satellite data are analyzed.
In the above embodiment, the first spectral information includes a spectrum of an aerosol in the air environment, and the first bright temperature information includes surface radiation amount information affected by the aerosol in the air environment.
Step S206, determining the surface information of the area to be detected.
Step S208, taking the earth surface information as a reference, and performing first dust judgment on the first spectrum information and the first bright temperature information.
In the above embodiment, the first spectral information and the first spectral information are necessary parameters for determining the dust climate, such as the aerosol reflectance under the 0.64um channel, the bright temperature under the 11um channel, the bright temperature under the 3.7um channel, the bright temperature difference between the bright temperature under the 11um channel and the bright temperature under the 12um channel, the bright temperature difference between the bright temperature under the 11um channel and the bright temperature under the 3.7um channel, and the like, but are not limited thereto.
Based on this, the topography, terrain, surface coverage, altitude, and the like are determined by referring to the surface information, but are not limited thereto. And performing a first dust identification based on the surface information to improve reliability and accuracy of the first dust identification process.
For example, different dust identification strategies are used for the marine and land areas to make dust climate decisions.
Step S210, if the first spectrum information and the first brightness temperature information pass through the first sand and dust judgment, generating sand and dust climate products of the area to be detected according to the multi-source satellite data.
In the above embodiment, the first dust discrimination is a discrimination condition set for the first spectrum information and the first bright temperature information, such as the wavelength bands and intensities of the plurality of atmospheric infrared channels, the radiation amount threshold of the region to be detected, and the like.
Based on the method, by performing first sand and dust judgment and determining that the first spectrum information and the first bright temperature information are all passed, sand and dust climate products are determined to be generated, and visual, accurate and reliable visualized sand and dust products are provided for observers in time.
Based on the steps shown in fig. 2, as shown in fig. 3, the multi-source satellite data further includes second spectral information, and the method further includes:
step S302, second sand and dust judgment is carried out on the second spectrum information and the ground surface information.
In the above embodiment, the second spectral information and the surface information are auxiliary parameters for judging the dust climate.
Wherein the second spectral information may be, for example, an aerosol reflectance at a 0.47um channel, an aerosol reflectance at a 1.38 channel, a reflectance ratio between an aerosol reflectance at a 0.47 channel and an aerosol reflectance at a 0.64 channel.
In addition, the surface information may be, for example, but not limited to, a dust intensity index, a snow cover index, a vegetation cover index, and a drought index.
Step S304, determining the grading result of the sand and dust climate products of the region to be detected according to the second sand and dust identification result of the second spectrum information and the ground surface information.
In the above embodiment, the second dust discrimination is a discrimination condition set for the second spectrum information and the surface information, such as the reflectivities of the plurality of atmospheric infrared channels, the surface parameters of the area to be detected, and the like.
Based on this, by performing the second dust judgment, the generation of the dust climate product is not affected, but the integration is performed when the dust judgment is passed, that is, the above-described scoring result, and the higher the integration, the higher the confidence. In addition, the strength of the sand climate is determined through the result of the second sand judgment so as to further improve the accuracy of detecting the sand climate.
Based on the steps shown in fig. 2, before acquiring the multi-source satellite data of the area to be detected, as shown in fig. 4, the method further includes:
step S402, obtaining background infrared data of a region to be detected.
In step S404, the difference calculation is performed on the infrared data and the background infrared data in the same period to generate an infrared differential sand dust index.
In the above embodiment, the principle of the infrared differential sand index (Infrared differential dust index, abbreviated as IDDI) is that the presence of the sand layer reduces the radiation of the sun to the ground on the one hand, so that the ground temperature decreases, and thus the ground emission radiation decreases, and on the other hand absorbs the ground emission radiation, and radiates outwards with the emissivity and temperature of the sand layer, further reducing the emission radiation. The combined effect of these processes is that the dust layer will result in a stronger attenuation of infrared radiation from the atmosphere.
Step S406, determining the sand intensity according to the infrared differential sand index, and writing the sand intensity into a sand climate product.
In the above embodiments, the satellite receives significantly less radiation from the region with the dust layer than from the region without the dust layer. This amount of attenuation is closely related to the concentration of the dust, as calculated by the radiation pattern, and can be used to characterize the magnitude of the dust intensity based thereon.
Step S402 shown in fig. 4, the acquiring background infrared data of the area to be detected, as shown in fig. 5, specifically includes:
step S5022, collecting historical infrared data of a region to be detected.
And step S5024, determining the surface temperature of the region to be detected according to the historical infrared data.
Step S5026, screening infrared data of the surface temperature belonging to a preset surface temperature range.
Step S5028, judging whether the screened infrared data meets the condition that the atmosphere dryness in a preset continuous period reaches the preset dryness.
In step S50210, if the determination is satisfied, the filtered infrared data is determined as the background infrared data.
If it is determined that the determination is not satisfied, step S5028 is repeatedly performed.
In the above embodiment, the dust discrimination technique due to IDDI is based on two basic assumptions:
(1) The surface temperature characteristics do not change over a period of time.
(2) At least one day out of the days taken as background is air-dry.
That is, the background infrared data of the present disclosure is a clear sky background field based on the IDDI technique to obtain the infrared bright temperature. In addition, the preset continuous period may be feedback adjusted based on the quality of the background infrared data.
For example, the method comprises the steps of obtaining L1 level data of a current period and L1 level data of a background field, calculating an infrared differential index through an infrared differential calculation formula, and outputting the infrared differential index as a sand dust intensity result. The background field L1 level data may be, for example, L1 level data within the same period of time within the first 10 days.
In one embodiment of the disclosure, the first spectral information comprises aerosol reflectivity at 0.64um channels.
In one embodiment of the disclosure, the first bright temperature information includes at least one of a bright temperature under an 11um channel, a bright temperature under a 3.7um channel, a bright temperature difference between the bright temperature under the 11um channel and the bright temperature under the 12um channel, and a bright temperature difference between the bright temperature under the 11um channel and the bright temperature under the 3.7um channel.
In one embodiment of the disclosure, the second spectral information includes at least one of an aerosol reflectance at 0.47um channel, an aerosol reflectance at 1.38um channel, a reflectance ratio between an aerosol reflectance at 0.47um channel and an aerosol reflectance at 0.64um channel.
In one embodiment of the disclosure, the surface information includes at least one of a dust intensity index, a snow cover index, a vegetation cover index, and a drought index.
As shown in fig. 6, an ecology detection method based on satellite data according to another embodiment of the present disclosure includes:
step S602, the L1-level data of the current time is acquired.
Step S604 includes the step of executing the first dust judgment.
The first dust judgment comprises judgment treatment such as 11um bright temperature, 0.66um reflectivity, 3.7um bright temperature, 11um and 12um bright temperature difference, 11um and 3.7um bright temperature difference and the like, and dust climate products can be generated only through judgment.
Step S606 includes an execution step of the second dust judgment.
The second sand dust judgment comprises judgment treatment such as 0.47um reflectivity, snow cover index, 1.38um reflectivity, sand dust intensity index, vegetation coverage index, drought index, ratio of 0.47um reflectivity to 0.64um reflectivity and the like.
Step S608, integrating the dust climate product.
Specifically, the main channels used for automatically judging the sand dust are 0.47um, 0.65um, 0.86um, 2.1um, 3.7um, 11um, 12um and the like.
The sand and dust identification method comprises single-channel spectral clustering, bright temperature difference, ratio method, background field method and uniformity inspection.
Firstly, multi-source meteorological data needs to be input, wherein the input data are as follows:
(1) 0.47um reflectivity, 0.66um reflectivity, 0.86um reflectivity, 1.6um reflectivity, 3.7um bright temperature, 11um bright temperature, 12um bright temperature.
(2) The auxiliary data includes: angle, earth surface type, elevation and background vegetation index, then checking whether the input data are normal, and carrying out different sand and dust detection strategies according to land and sea.
12 sets of identification criteria are determined herein, including 0.64um reflectivity, 11um bright temperature (or IDDI), 3.7um bright temperature, 11um and 12um bright temperature difference, 11um and 3.7um bright temperature difference, 0.47um reflectivity, dust intensity index, NDSI snow cover index, NDVI vegetation cover index, NDDI (Normalized Difference Drought Index ) index, ratio of 0.47um reflectivity to 0.64um reflectivity (R0.47/R0.64), 1.38um reflectivity, and standard deviation of reflectivity (for ocean only).
Among the above-mentioned criteria, the first 5 sets of criteria are necessarily passed, and the second 7 sets of criteria are selectively passed. If 1 group of the 5 groups of the identification standards which are passed through cannot be passed through, the pixel point cannot be identified as dust. And the more the passing judgment criteria and the closer the spectrum is to the standard dust spectrum, the higher the dust climate product score.
The final dust climate product is determined on the basis of the dust score, the higher the score the greater the likelihood of being dust, i.e. the higher the confidence.
Further, the sand and dust intensity function comprises the steps of obtaining L1 level data of the current time and L1 level data of a background field, calculating an infrared differential index through an infrared differential calculation formula, and outputting the infrared differential index as a sand and dust intensity result.
An ecological sensing apparatus 700 based on satellite data according to this embodiment of the present application is described below with reference to fig. 7. The satellite data based ecological sensing device 700 shown in fig. 7 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 7, the ecological sensing apparatus 700 based on satellite data includes: an acquisition module 702, a parsing module 704, a determination module 706, an identification module 708, and a generation module 710.
(1) The acquisition module 702 is configured to acquire multi-source satellite data of an area to be detected.
(2) The parsing module 704 is configured to parse the first spectrum information and the first bright temperature information included in the multi-source satellite data.
(3) The determining module 706 is configured to determine surface information of the area to be detected.
(4) The identification module 708 is configured to perform a first dust identification on the first spectrum information and the first bright temperature information with reference to the surface information.
(5) The generating module 710 is configured to generate a dust climate product of the area to be detected according to the multi-source satellite data if the first spectrum information and the first bright temperature information both pass through the first dust judgment.
An electronic device 800 according to such an embodiment of the application is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 8, the electronic device 800 is embodied in the form of a general purpose computing device. Components of electronic device 800 may include, but are not limited to: the processing unit 810, the storage unit 820, and a bus 830 that connects the different system components (including the storage unit 820 and the processing unit 810).
Wherein the storage unit stores program code that is executable by the processing unit 810 such that the processing unit 810 performs steps according to various exemplary embodiments of the present application described in the above section of the "exemplary method" of the present specification. For example, the processing unit 810 may perform all the steps as shown in fig. 2 to 6, as well as other steps defined in the satellite data based ecological detection method of the present disclosure.
The storage unit 820 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 8201 and/or cache memory 8202, and may further include Read Only Memory (ROM) 8203.
Storage unit 820 may also include a program/utility 8204 having a set of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 830 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 800 may also communicate with one or more external devices 870 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 850.
Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 over bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with an electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the aspects of the application may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the application as described in the "exemplary method" section of this specification, when the program product is run on the terminal device.
Referring to fig. 9, a program product 900 for implementing the above-described method according to an embodiment of the present application is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the various steps of the methods in the present disclosure are depicted in a particular order, this does not require or imply that the steps be performed in that particular order, or that all illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.