CN112378828B - Method and device for inverting concentration of atmospheric fine particulate matters based on satellite remote sensing data - Google Patents
Method and device for inverting concentration of atmospheric fine particulate matters based on satellite remote sensing data Download PDFInfo
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Abstract
The invention discloses a method and a device for inverting PM2.5 concentration based on satellite remote sensing data. The method includes acquiring raw data sets of a plurality of satellites; preprocessing the acquired original data set to obtain a variable data set of a unified geographical block; performing data fusion of the same type of variable data on the preprocessed variable data sets by using a data fusion model; and respectively matching the geographical position information of the variable data with the geographical position information of the land cover type aiming at various land cover types, monitoring the atmospheric fine particulate matter concentration according to the satellite remote sensing spatial variation coefficient regression model corresponding to the land cover type, and obtaining the atmospheric fine particulate matter concentration of the geographical area where the land cover type is located according to the variable data matched with the geographical position information. The method and the device consider that the PM2.5 concentration is greatly influenced by the underlying surface, and respectively establish an inversion model aiming at different underlying surfaces, so that a PM2.5 data set with high space-time distribution can be obtained.
Description
Technical Field
The invention relates to the technical field of atmospheric satellite remote sensing, in particular to a method and a device for inverting the concentration of atmospheric fine particles based on satellite remote sensing data.
Background
The atmospheric fine particulate matter (PM2.5) refers to particles with aerodynamic diameters of less than or equal to 2.5 μm, is an important component of atmospheric pollutants, and is also a main cause of haze. The PM2.5 has small particle size, wide distribution range and strong particle activity, is easy to attach toxic and harmful substances such as heavy metals and microorganisms, is easy to stay and gather in the atmosphere for a long time, and has great influence on the health of a human body and the quality of the atmospheric environment.
The PM2.5 concentration monitoring mode mainly comprises ground monitoring and satellite remote sensing monitoring. Recently, China has built a plurality of PM2.5 concentration monitoring networks to monitor fine particulate matters in real time. Although ground monitoring is accurate, due to the fact that observation stations are discrete and uneven in distribution, the data coverage degree is still low, and enough data cannot be acquired to study the PM2.5 pollution condition of the whole area.
At present, how to acquire PM2.5 observation data with high resolution, consistent quality and wide coverage range becomes a research focus. The high time resolution characteristic of the satellite remote sensing data can effectively make up the defects of the ground station. However, a method for systematically and comprehensively monitoring the concentration of PM2.5 particles by using a remote sensing means is not popularized yet. The most studied method in recent years is to invert the distribution situation of the ground PM2.5 through the atmospheric Aerosol Optical Depth (AOD) of a satellite remote sensing data product. The data for inverting PM2.5 is divided into the following steps: obtaining AOD data, establishing an inversion ground PM2.5 concentration model and optimizing the model. The most critical of which is the selection of AOD source data.
Research shows that the optical thickness of the aerosol obtained by satellite inversion is closely related to the concentration of particulate matters with the particle size of 0.1-2 microns, and most of PM2.5 is in the particle size range, so that the satellite AOD product is an effective tool for inverting the concentration of the PM2.5 theoretically.
Considering that a satellite sensor can obtain an Optical characteristic parameter of the atmospheric Aerosol, namely the Optical Aerosol thickness (AOD), remote sensing monitoring of air quality in a large range by using a satellite becomes a feasible technical means and also becomes a hotspot for research of broad students. In order to estimate the aerosol property under air pollution by using the optical thickness parameter measured by the satellite, the university scholars of NASA and Maryland (Kaufman and Fraser, 1983) constructs a relation between the mass concentration of aerosol particles and the satellite remote sensing AOD through theoretical analysis and observation experiments.
Successful emission from moderate resolution spectral imager MODIS sensors (mounted on Terra, emitted in 1999, and Aqua satellites, emitted in 2002) provides a wealth of data for the application of satellite telemetry to aerosol monitoring. The scholars of the university of yalaba, usa (wang and Christopher, 2003) have constructed a relation between the optical thickness of a satellite-measured aerosol and the mass concentration of near-surface aerosol particles on the basis of a study of the optical properties of aerosol particles:
τ＝f(RH)Qdext(0)Mdaer(0)Heff
wherein τ represents the optical thickness of the whole layer of the aerosol; (rh) denotes an aerosol hygroscopy growth factor; RH represents ambient air relative humidity; qdext(0) Denotes the extinction coefficient in m of the near-surface unit mass concentration dry (RH is less than or equal to 40%) aerosol particles2/μg；Mdaer(0) Denotes the mass concentration of the near-surface "dry" aerosol particles in μ g/m3；HeffThe equivalent aerosol elevation is expressed in m; beta is aext(z) represents the extinction coefficient of the aerosol at the height z in m-1(ii) a TOA represents the height of the Top of the Atmosphere (Top of Atmosphere) in m.
The formula provides a theoretical basis for satellite remote sensing monitoring of near-ground aerosol particle concentration, and a subsequent near-ground particle mass concentration estimation method is developed on the basis. On the basis of the theory, three parameters of moisture absorption growth factors, extinction coefficients of aerosol particles with unit mass concentration and aerosol elevation must be obtained for quantitatively obtaining the mass concentration of the aerosol particles near the ground from data of satellite remote sensing spectrum.
The statistical model mainly comprises a univariate simple linear model, a multivariate linear regression model, a multivariate nonlinear statistical model, a multivariate spatial statistical model and the like. The empirical physical mechanism model mainly comprises an empirical double-correction model and a numerical simulation correction model.
Currently used satellite products mainly include medium resolution imaging spectrometers (MODIS) AODs, multi-angle imaging spectrometers (MISR) AODs, Visible Infrared Imaging Radiometers (VIIRS) AODs, and Hiwari-8 AODs.
The PM2.5 remote sensing inversion statistical model mainly uses three parameters of aerosol optical thickness (AOD), boundary layer height HPBL and specific humidity RH to obtain the near-ground PM2.5 concentration. However, historical data of boundary layer height HPBL and specific humidity RH are mostly acquired by ground station observation, and the method has the defect of low data coverage degree and limits PM2.5 remote sensing inversion in a large-range area.
In the prior art, the most common AOD data product of the method for inverting the PM2.5 concentration is AOD data monitored by a MODIS (model Resolution Imaging spectrometer) sensor carried on a Terra & Aqua polar orbit satellite, and a stationary satellite is rarely researched as a data source. Through the development of the last 10 years, the advantages of a geostationary satellite that the same target area is observed at high frequency and a rapidly changing weather system is captured can be utilized, the geostationary satellite AOD data product is used as source data, polar orbit satellite AOD data is updated and supplemented, and the method for efficiently and accurately monitoring the PM2.5 concentration in the specified monitoring area can be provided. When the ground PM2.5 concentration is inverted, satellite AOD data with various resolutions and sources can be selected for use, AOD data can be selected from space and time scales or various AOD data can be combined for use.
Disclosure of Invention
The invention innovatively provides a method and a device for inverting the concentration of atmospheric fine particulate matters based on satellite remote sensing data, and solves the problem that the conventional satellite PM2.5 inversion needs to depend on a large amount of ground station observation data.
In order to achieve the technical purpose, on one hand, the invention discloses a method for inverting the concentration of atmospheric fine particles based on satellite remote sensing data. The method for inverting the concentration of the atmospheric fine particulate matters based on the satellite remote sensing data comprises the following steps: acquiring an original data set of a plurality of satellites; preprocessing the acquired original data set to obtain a variable data set of a unified geographical block; performing data fusion of the same type of variable data on the preprocessed variable data set by using a data fusion model of each type of variable data; respectively matching the geographical position information of variable data with the geographical position information of the land cover type aiming at various land cover types, monitoring a space change coefficient regression model of the atmosphere fine particulate matter concentration according to satellite remote sensing corresponding to the land cover types, and obtaining the atmosphere fine particulate matter concentration of a geographical area where the land cover type is located according to the variable data matched with the geographical position information.
Further, for the method for inverting the concentration of the atmospheric fine particulate matter based on the satellite remote sensing data, the regression model of the spatial variation coefficient of the satellite remote sensing monitoring atmospheric fine particulate matter concentration is obtained through the following processes: collecting hourly data of the concentration of the fine atmospheric particulates, data of all factors influencing the concentration of the fine atmospheric particulates and land coverage type data, and matching geographical position information of the concentration data of the fine atmospheric particulates, geographical position information of the data of all factors influencing the concentration of the fine atmospheric particulates and geographical position information of the land coverage type; and aiming at each land cover type, establishing a training data set on the basis of historical data of the concentration of the fine air particles and historical data of various factors influencing the concentration of the fine air particles, and training by adopting a neural network algorithm to obtain a spatial variation coefficient regression model applicable to different land cover types.
Further, for the method for inverting the concentration of the atmospheric fine particulate matter based on the satellite remote sensing data, the preprocessing is performed on the obtained original data set, and the method comprises the following steps: and matching the variable data to a unified geographical block by at least one interpolation method of linear interpolation, polynomial interpolation and spline curve interpolation.
Further, for the method for inverting the concentration of the atmospheric fine particulate matters based on the satellite remote sensing data, the geographic area is a longitude and latitude pixel grid, and the geographic position information is longitude and latitude information.
Further, for the method for inverting the concentration of the atmospheric fine particulate matter based on the satellite remote sensing data, the preprocessing is performed on the obtained original data set, and the method comprises the following steps: and performing quality control on the variable data according to at least one quality control index in the uncertainty and the reliability, and eliminating data which do not meet the quality control standard.
Further, for the method for inverting the concentration of the atmospheric fine particulate matters based on the satellite remote sensing data, the data fusion model is obtained through the following processes: and distributing proportional weights to the variable data of the same type of different satellites, and establishing a fusion relation model between the variable data of the same type of different satellites.
Further, for the method for inverting the concentration of the atmospheric fine particulate matter based on the satellite remote sensing data, the raw data set comprises aerosol optical thickness data, cloud cover data, atmospheric pollutant data and land cover type data.
In order to achieve the technical purpose, the invention discloses a device for inverting the concentration of atmospheric fine particles based on satellite remote sensing data. The device for inverting the concentration of the atmospheric fine particulate matters based on the satellite remote sensing data comprises: a data acquisition unit for acquiring raw data sets of a plurality of satellites; the preprocessing unit is used for preprocessing the acquired original data set to obtain a variable data set of a unified geographic area; the data fusion unit is used for performing data fusion of the same type of variable data on the preprocessed variable data sets by using the data fusion model of each type of variable data; and the atmosphere fine particle concentration calculation unit is used for matching the geographical position information of variable data with the geographical position information of the land cover type respectively aiming at various land cover types, monitoring a space change coefficient regression model of the atmosphere fine particle concentration according to satellite remote sensing corresponding to the land cover type, and obtaining the atmosphere fine particle concentration of a geographical area where the land cover type is located according to the variable data matched with the geographical position information.
To achieve the above technical object, in yet another aspect, the present invention discloses a computing device. The computing device includes: one or more processors, and a memory coupled with the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the above-described method.
To achieve the above technical objects, in yet another aspect, the present invention discloses a machine-readable storage medium. The machine-readable storage medium stores executable instructions that, when executed, cause the machine to perform the above-described method.
The invention has the beneficial effects that:
the method and the device for inverting the concentration of the atmospheric fine particulate matters based on the satellite remote sensing data provided by the embodiment of the invention adopt observation data of a plurality of satellites, and an algorithm uses a neural network to establish a correlation relation between influencing factors and considers distinguishing different land cover types. Independent variable factors are comprehensive and easy to obtain, and in order to identify regional clouds, fog and high-reflectivity ground objects, the embodiment of the invention considers the variables which are not considered in the past and can be obtained by satellite remote sensing, such as clouds and sulfur dioxide (SO)2) And/or nitrogen dioxide (NO)2) And adding PM2.5 inversion model. The problem that the conventional satellite PM2.5 inversion needs to depend on a large amount of ground station observation data is solved.
The method and the device for inverting the concentration of the atmospheric fine particulate matters based on the satellite remote sensing data provided by the embodiment of the invention provide a new method for establishing a PM2.5 inversion model by using satellite data, and a PM2.5 data set with high space-time distribution can be obtained. Considering that the PM2.5 concentration is greatly influenced by the underlying surface, the method respectively establishes an inversion model for different underlying surfaces.
Drawings
In the figure, the position of the upper end of the main shaft,
FIG. 1 is a flowchart of a method for inverting the concentration of atmospheric fine particulate matter based on satellite remote sensing data according to an embodiment of the present invention;
FIG. 2 is a flow chart of a multi-source satellite remote sensing monitoring PM2.5 concentration calculation according to an example of the present invention;
FIG. 3 is a flowchart of the preprocessing method of step S120 in FIG. 1 according to an exemplary embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for inverting the concentration of atmospheric fine particulate matter based on satellite remote sensing data according to embodiment 3 of the present invention;
fig. 5 is a block diagram of a computing device for inversion processing of atmospheric fine particulate matter concentration based on satellite remote sensing data according to an embodiment of the invention.
Detailed Description
The method and the device for inverting the concentration of the atmospheric fine particulate matters based on the satellite remote sensing data provided by the invention are explained and explained in detail below with reference to the attached drawings of the specification.
In order to solve the problem that the conventional satellite PM2.5 inversion needs a large amount of observation data depending on ground stations, the embodiment of the invention integrates the advantages of all satellite detectors and an algorithm based on multi-source satellite data information to obtain satellite AOD data, satellite cloud cover (cloud cover) data and atmospheric pollutant data (which can include SO)2Concentration and/or NO2Concentration), water vapor and/or land cover type and the like are used as source data, modeling is carried out by utilizing observation station historical data and using a neural network algorithm, and satellite remote sensing monitoring PM2.5 concentration spatial data are obtained through calculation.
Fig. 1 is a flowchart of a method for inverting the concentration of atmospheric fine particulate matters based on satellite remote sensing data according to an embodiment of the present invention. Fig. 2 is a flow chart of calculating the concentration of PM2.5 by remote sensing monitoring using a multi-source satellite according to an example of the present invention.
As shown in fig. 1 and 2, raw data sets of a plurality of satellites are acquired at step S110. The raw data set may include, among other things, aerosol optical thickness data, cloud cover data, atmospheric pollutants data, and land cover type data. Atmospheric pollutantsThe data may include sulfur dioxide (SO)2) Concentration and/or nitrogen dioxide (NO)2) Concentration data. As an alternative, the plurality of satellites herein may be a plurality of different types of satellites.
As a more specific example, aerosol optical thickness (AOD) data and cloud (clouded cover) data may be obtained from Himapari-8/9 geostationary satellite, sunflower satellite, SuomiNPP-VIIRS polar orbit, and atmospheric pollutants (which may include SO) data from Sentinel-5P TROPOMI polar orbit2Concentration and/or NO2Concentration, etc.) from Terra&The Aqua/MODIS polar orbit satellite acquires aerosol optical thickness (AOD) data, cloud cover data, land cover type data, and the like, in order to identify regional clouds, fog, and high reflectivity terrain.
In step S120, the obtained original data set is preprocessed to obtain a variable data set of a unified geographic region. The geographic zone may be a grid of latitude and longitude pixels.
Specifically, as shown in fig. 3, step S120 may include the steps of:
and step S122, performing quality control on the variable data according to at least one quality control index in the uncertainty and the credibility, and removing the data which do not accord with the quality control standard. Qualifying satellite data may include: screening quality control factors according to quality control indexes such as Uncertainty (uncertaintiy) and/or credibility (QA flag), and the like, and carrying out aerosol optical thickness (AOD) data, cloud cover (cloud cover) data and nitrogen dioxide (NO) on a plurality of satellites2) Concentration, sulfur dioxide (SO)2) And (3) performing quality control on the variable data such as the concentration and the like, and rejecting the data with poor quality interfered by factors such as cloud and/or surface topography and the like.
And step S124, matching the variable data to a unified geographical block by at least one interpolation method of linear interpolation, polynomial interpolation and spline curve interpolation. And (5) unifying longitude and latitude projections. As a more specific example, the plurality of satellites obtained in step S110 or step S122 can be interpolated by linear interpolation, polynomial interpolation, and/or spline curve interpolationAerosol optical thickness (AOD) data, cloud cover (clouded cover) data, nitrogen dioxide (NO)2) Concentration, sulfur dioxide (SO)2) Matching the variable data such as concentration and the like to a uniform longitude and latitude pixel grid to obtain AOD, cloud cover and NO in the research area2Concentration and SO2And (4) concentration.
In step S130, the data fusion model of each type of variable data is used to perform data fusion of the same type of variable data on the preprocessed variable data set. The data fusion model can be obtained through the following processes: and distributing proportional weights to the variable data of the same type of different satellites, and establishing a fusion relation model between the variable data of the same type of different satellites. In addition, the land cover type may be three-dimensional data composed of land categories and longitude and latitude information, and different land categories may be represented by different values, such as 0, 1, 2, 3, etc., which respectively represent different land categories. When a fusion relation model between variable data of the same type of different satellites is established, areas belonging to the same land coverage type are extracted, and a fusion relation model corresponding to the land coverage type is established in the area.
As a more specific example, for the aerosol optical thickness (AOD) data, the AOD data of a sunflower Hiwari-8/9 satellite, a SuomiNPP-VIIRS satellite and a Terra & Aqua/MODIS polar orbit satellite can be subjected to fusion processing, proportional weights are distributed, a relation model between the three types of satellite aerosol optical thicknesses is established, and the fusion AOD data of uniform longitude and latitude pixel grid points is obtained through inversion. The formula is as follows:
AOD＝f(AOD1,AOD2,AOD3)＝a1AOD1+a2AOD2+a3AOD3
a1+a2+a3＝1
wherein, AOD1Aerosol optical thickness (AOD) for sunflower Hiwari-8/9 satellite, AOD2AOD for SuomiNPP-VIIRS satellites, AOD3Is Terra&AOD of Aqua/MODIS polar orbit satellite, a1、a2And a3Are weight coefficients.
As a more specific example, for the cloud amount data, the weight proportion can be distributed to the cloud amount data of a sunflower Hiwari-8/9 satellite and a SuomiNPP-VIIRS satellite, a relation model between the two satellite cloud amounts is established, and the unified longitude and latitude pixel grid point fusion cloud amount is obtained through inversion. The formula is as follows:
CLOUD＝f(CLOUD1,CLOUD2)＝b1CLOUD1+b2CLOUD2
b1+b2＝1
wherein CLOUD1Is CLOUD, CLOUD of Hiwari-8/9 satellite2Cloud cover for SuomiNPP-VIIRS satellites, b1And b2Are weight coefficients.
In step S140, the geographical position information of the variable data is matched with the geographical position information of the land cover type for each land cover type, the atmospheric fine particle concentration of the geographical area in which the land cover type is located is obtained from the variable data matched with the geographical position information according to the spatial variation coefficient regression model for monitoring the atmospheric fine particle concentration by satellite remote sensing corresponding to the land cover type. The geographic location information may be latitude and longitude information.
The space variation coefficient regression model for monitoring the concentration of the atmospheric fine particulate matters through satellite remote sensing can be obtained through the following processes: collecting the time-by-time data of the concentration of the fine atmospheric particulates, the data of each factor influencing the concentration of the fine atmospheric particulates and the data of the land coverage type, and matching the geographical position information of the concentration data of the fine atmospheric particulates, the geographical position information of the data of each factor influencing the concentration of the fine atmospheric particulates and the geographical position information of the land coverage type; and aiming at each land cover type, establishing a training data set on the basis of historical data of the concentration of the atmospheric fine particulate matters and historical data of various factors influencing the concentration of the atmospheric fine particulate matters, and training by adopting a neural network algorithm to obtain a spatial variation coefficient regression model applicable to different land cover types. After a preset time, the space change coefficient regression model for monitoring the concentration of the fine atmospheric particulates by satellite remote sensing can be updated according to newly acquired concentration data of the fine atmospheric particulates, data of various factors influencing the concentration of the fine atmospheric particulates and/or land cover type data.
As a more specific example, the method comprises the steps of collecting the PM2.5 hourly data of the ground stations of the China region, the data of various factors influencing the concentration of the atmospheric fine particulate matters and the Terra of the China region published by the central environmental monitoring station in the last 3 years of 2017 and 2019&And matching the data of the land coverage type of the Aqua/MODIS polar orbit satellite with the longitude and latitude information of the observation station, the longitude and latitude information of the data of all factors influencing the concentration of the atmospheric fine particulate matters and the longitude and latitude information of the land coverage type, and classifying the land coverage type of the position of the observation station. The MODIS land cover types can be classified using a variety of criteria. For example, the schemes of extracting leaf area index/photosynthetically active radiation component (LAI/fPAR) according to satellite remote sensing can be divided into 9 types, which are water area, cereal and herbaceous crops, shrubs, broadleaf crops, spars grassland, broadleaf forest, coniferous forest, vegetation-free coverage area and cities respectively. The types of land cover may also be classified into 17 categories as defined by the international terrestris biosphere (IGBP) program. Aiming at each type of land cover type, a training data set is established on the basis of historical data of an observation station and data of various factors influencing the concentration of the atmospheric fine particulate matter, and a neural network algorithm is adopted to obtain a function relation applicable to different land cover types through training. As an alternative embodiment, the data for various factors affecting the concentration of atmospheric fine particulate matter may include aerosol optical thickness (AOD) data, cloud cover (clouded cover) data, NO2Concentration, and SO2Satellite data of concentration. The AOD data, cloud cover data, and NO can be obtained by Principal Component Analysis (PCA)2Concentration and SO2The weight coefficients of the influence of the concentration on PM2.5 are respectively c1、c2、c3And c4. Specifically, the formula of the functional relationship may be as follows:
PM2.5L＝fL(AOD,CLOUD,NO2,SO2)＝c1f(AOD)+c2f(CLOUD)+c3f(NO2)+c4f(SO2)
c1+c2+c3+c4＝1
wherein L is a land cover type, AOD and CLOOD are respectively AOD concentration and CLOUD cover amount obtained by fusing in step S130, NO2 and SO2 are respectively NO2 concentration and SO2 concentration obtained by preprocessing in step S120, and c1、c2、c3And c4Are weight coefficients.
And matching longitude and latitude geographic position information of the data according to each land coverage type, and fitting the PM2.5 concentration at each longitude and latitude pixel grid point according to the spatial variation coefficient regression model of the PM2.5 concentration monitored by satellite remote sensing obtained in the step S140.
Fig. 4 is a schematic structural diagram of an apparatus for inverting the concentration of atmospheric fine particulate matters based on satellite remote sensing data according to another embodiment of the present invention. As shown in fig. 4, the apparatus 400 for inverting the concentration of atmospheric fine particulate matter based on satellite remote sensing data provided by this embodiment includes a data acquisition unit 410, a preprocessing unit 420, a data fusion unit 430, and an atmospheric fine particulate matter concentration calculation unit 440.
The data acquisition unit 410 is used to acquire raw data sets for a plurality of satellites. The operation of the data acquisition unit 410 may refer to the operation of step S110 described above with reference to fig. 1.
The preprocessing unit 420 is configured to preprocess the acquired original data set to obtain a variable data set of a unified geographic area. The operation of the preprocessing unit 420 may refer to the operation of step S120 described above with reference to fig. 1.
The data fusion unit 430 is configured to perform data fusion of the same type of variable data on the preprocessed variable data sets by using a data fusion model of each type of variable data. The operation of the data fusion unit 430 may refer to the operation of step S130 described above with reference to fig. 1.
The atmosphere fine particle concentration calculation unit 440 is configured to match geographical position information of the variable data with geographical position information of the land cover type for each land cover type, obtain the atmosphere fine particle concentration of a geographical area where the land cover type is located from the variable data matched with the geographical position information according to a spatial variation coefficient regression model for monitoring the atmosphere fine particle concentration by satellite remote sensing corresponding to the land cover type. The operation of the atmospheric fine particulate matter concentration calculation unit 440 may refer to the operation of step S140 described above with reference to fig. 1.
Fig. 5 is a block diagram of a computing device for inversion processing of atmospheric fine particulate matter concentration based on satellite remote sensing data according to an embodiment of the invention.
As shown in fig. 5, computing device 500 may include at least one processor 510, memory 520, memory 530, communication interface 540, and internal bus 550, and at least one processor 510, memory 520, memory 530, and communication interface 540 are connected together via bus 550. The at least one processor 510 executes at least one computer-readable instruction (i.e., an element described above as being implemented in software) stored or encoded in a computer-readable storage medium (i.e., memory 520).
In one embodiment, stored in the memory 520 are computer-executable instructions that, when executed, cause the at least one processor 510 to: acquiring an original data set of a plurality of satellites; preprocessing the acquired original data set to obtain a variable data set of a unified geographical block; performing data fusion of the same type of variable data on the preprocessed variable data set by using a data fusion model of each type of variable data; respectively matching the geographical position information of variable data with the geographical position information of the land cover type aiming at various land cover types, monitoring a space change coefficient regression model of the atmosphere fine particulate matter concentration according to satellite remote sensing corresponding to the land cover types, and obtaining the atmosphere fine particulate matter concentration of a geographical area where the land cover type is located according to the variable data matched with the geographical position information.
It should be understood that the computer-executable instructions stored in the memory 520, when executed, cause the at least one processor 510 to perform the various operations and functions described above in connection with fig. 1-4 in the various embodiments of the present disclosure.
In the present disclosure, computing device 500 may include, but is not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile computing devices, smart phones, tablet computers, cellular phones, Personal Digital Assistants (PDAs), handheld devices, messaging devices, wearable computing devices, consumer electronics, and so forth.
According to one embodiment, a program product, such as a non-transitory machine-readable medium, is provided. A non-transitory machine-readable medium may have instructions (i.e., elements described above as being implemented in software) that, when executed by a machine, cause the machine to perform various operations and functions described above in connection with fig. 1-4 in various embodiments of the present disclosure.
Specifically, a system or apparatus may be provided which is provided with a readable storage medium on which software program code implementing the functions of any of the above embodiments is stored, and causes a computer or processor of the system or apparatus to read out and execute instructions stored in the readable storage medium.
In this case, the program code itself read from the readable medium can realize the functions of any of the above-described embodiments, and thus the machine-readable code and the readable storage medium storing the machine-readable code form part of the present invention.
Examples of the readable storage medium include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or from the cloud via a communications network.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the claims, and all equivalent structures or equivalent processes that are transformed by the content of the specification and the drawings, or directly or indirectly applied to other related technical fields are included in the scope of the claims.
Claims (9)
1. A method for inverting the concentration of atmospheric fine particles based on satellite remote sensing data is characterized by comprising the following steps:
acquiring an original data set of a plurality of satellites;
preprocessing the acquired original data set to obtain a variable data set of a unified geographical block;
performing data fusion of the same type of variable data on the preprocessed variable data set by using a data fusion model of each type of variable data;
respectively matching the geographical position information of variable data with the geographical position information of the land cover type aiming at various land cover types, monitoring a space change coefficient regression model of the concentration of the atmospheric fine particulate matters according to satellite remote sensing corresponding to the land cover types, and obtaining the concentration of the atmospheric fine particulate matters in a geographical area where the land cover type is located according to the variable data matched with the geographical position information; the space variation coefficient regression model for monitoring the concentration of the atmospheric fine particulate matters through satellite remote sensing is obtained through the following processes:
collecting hourly data of the concentration of the fine atmospheric particulates, data of all factors influencing the concentration of the fine atmospheric particulates and land coverage type data, and matching geographical position information of the concentration data of the fine atmospheric particulates, geographical position information of the data of all factors influencing the concentration of the fine atmospheric particulates and geographical position information of the land coverage type;
and aiming at each land cover type, establishing a training data set on the basis of historical data of the concentration of the fine air particles and historical data of various factors influencing the concentration of the fine air particles, and training by adopting a neural network algorithm to obtain a spatial variation coefficient regression model applicable to different land cover types.
2. The method for inverting the concentration of the atmospheric fine particulate matter based on the satellite remote sensing data according to claim 1, wherein preprocessing is performed on the acquired raw data set, and comprises:
and matching the variable data to a unified geographical block by at least one interpolation method of linear interpolation, polynomial interpolation and spline curve interpolation.
3. The method for inverting the concentration of the atmospheric fine particulate matter based on the satellite remote sensing data according to any one of claims 1-2, wherein the geographic area is a latitude and longitude pixel grid, and the geographic position information is latitude and longitude information.
4. The method for inverting the concentration of atmospheric fine particulate matter based on satellite remote sensing data according to claim 1 or 2, wherein preprocessing is performed on the acquired raw data set, and comprises:
and performing quality control on the variable data according to at least one quality control index in the uncertainty and the reliability, and eliminating data which do not meet the quality control standard.
5. The method for inverting the concentration of the atmospheric fine particulate matter based on the satellite remote sensing data according to claim 1, wherein the data fusion model is obtained through the following processes:
and distributing proportional weights to the variable data of the same type of different satellites, and establishing a fusion relation model between the variable data of the same type of different satellites.
6. The method for inverting the concentration of atmospheric fine particulate matter based on satellite remote sensing data according to claim 1, wherein the raw data set comprises aerosol optical thickness data, cloud cover data, atmospheric pollutant data, and land cover type data.
7. A device based on thin particulate matter concentration of satellite remote sensing data retrieval, its characterized in that includes:
a data acquisition unit for acquiring raw data sets of a plurality of satellites;
the preprocessing unit is used for preprocessing the acquired original data set to obtain a variable data set of a unified geographic area;
the data fusion unit is used for performing data fusion of the same type of variable data on the preprocessed variable data sets by using the data fusion model of each type of variable data;
the atmosphere fine particle concentration calculation unit is used for matching geographical position information of variable data with geographical position information of various land cover types respectively according to various land cover types, monitoring a space change coefficient regression model of the atmosphere fine particle concentration according to satellite remote sensing corresponding to the land cover types, and obtaining the atmosphere fine particle concentration of a geographical area where the land cover type is located according to the variable data matched with the geographical position information;
the space variation coefficient regression model for monitoring the concentration of the atmospheric fine particulate matters through satellite remote sensing is obtained through the following processes:
collecting hourly data of the concentration of the fine atmospheric particulates, data of all factors influencing the concentration of the fine atmospheric particulates and land coverage type data, and matching geographical position information of the concentration data of the fine atmospheric particulates, geographical position information of the data of all factors influencing the concentration of the fine atmospheric particulates and geographical position information of the land coverage type;
and aiming at each land cover type, establishing a training data set on the basis of historical data of the concentration of the fine air particles and historical data of various factors influencing the concentration of the fine air particles, and training by adopting a neural network algorithm to obtain a spatial variation coefficient regression model applicable to different land cover types.
8. A computing device, comprising:
one or more processors, and
a memory coupled with the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-6.
9. A machine-readable storage medium having stored thereon executable instructions that, when executed, cause the machine to perform the method of any one of claims 1 to 6.
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