CN117216480A - Near-surface ozone remote sensing estimation method for deep coupling geographic space-time information - Google Patents
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Abstract
The application relates to a near-surface ozone remote sensing estimation method of depth coupling geographic space-time information, which comprises the following steps: acquiring multi-source data of a research area and preprocessing the multi-source data; performing space-time matching on the preprocessed multi-source data, and constructing a space-time sample data set; constructing a deep learning model of coupling geographic space-time information; training the deep learning model by using a space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model; and carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model. The beneficial effects of the application are as follows: the method is used for deeply coupling geographic space-time information when estimating the near-surface ozone, improves the accuracy and space-time robustness of the remote sensing estimation model, and has important practical application value.
Description
Technical Field
The application relates to the technical field of environmental remote sensing monitoring methods, in particular to a near-surface ozone remote sensing estimation method of depth coupling geographic space-time information.
Background
Near-surface ozone is one of six air pollutants, and seriously threatens the safety of the global ecological system and the health of people. In recent years, with the implementation of a series of atmospheric pollution control policies, PM in China 2.5 The concentration has been significantly reduced, but at the same time, the near-surface ozone concentration has a fluctuating trend, and the polluted season gradually extends from summer to winter and spring, becoming a new primary pollutant. Therefore, the satellite remote sensing is utilized to carry out large-scale dynamic monitoring on the near-surface ozone so as to clarify the time-space distribution characteristics of the near-surface ozone, and the method has important practical significance for preventing and treating ozone pollution.
The total amount of the ozone column inverted by satellite remote sensing represents the ozone concentration condition of the whole atmosphere column, and is influenced by meteorological conditions, artificial activities and the like, the ozone is not uniformly distributed in the vertical direction and continuously changes at any time, the near-surface ozone concentration and the total amount of the ozone column are in a complex nonlinear relation, and a high-performance model needs to be established to accurately estimate the near-surface ozone concentration. The current correlation models can be divided into two main types, namely a traditional multiple linear regression model and an emerging artificial intelligence model. The traditional multiple linear regression model is difficult to describe a complex nonlinear relation, so that the estimation accuracy of the near-surface ozone concentration is relatively low. Emerging artificial intelligent models such as random forests, deep neural networks and the like rely on data mining and strong nonlinear fitting capability, relatively high precision is obtained on near-surface ozone concentration estimation, but the geographic space-time heterogeneity of the near-surface ozone is not considered, so that the space-time robustness of the model is generally poor, and the precision is required to be further improved. Therefore, there is an urgent need for an artificial intelligence model that can fully incorporate geographical spatiotemporal context information to enable accurate remote sensing estimation of near-surface ozone.
Disclosure of Invention
The application aims at overcoming the defects of the prior art, and provides a near-surface ozone remote sensing estimation method for deep coupling geographic space-time information.
In a first aspect, a near-surface ozone remote sensing estimation method for depth-coupled geographic spatiotemporal information is provided, including:
step 1, multi-source data of a research area are obtained, and the multi-source data are preprocessed;
step 2, performing space-time matching on the preprocessed multi-source data, and constructing a space-time sample data set;
step 3, constructing a deep learning model coupling geographic space-time information, wherein the deep learning model comprises a geographic space information coding network, a geographic time sequence information coding network and a geographic space-time feature decoding network;
step 4, training the deep learning model by utilizing the space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model;
and 5, carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model.
Preferably, in step 1, the multi-source data includes: the method comprises the steps of satellite remote sensing inversion of ozone column total amount data TO3, meteorological data, normalized vegetation index NDVI data, digital elevation model DEM data and ozone monitoring data of a ground air quality station;
the pretreatment comprises the following steps: uniformly resampling meteorological data, NDVI data and DEM data TO the same spatial resolution as TO3 data inverted by satellite remote sensing; and eliminating the missing value in the ozone monitoring data of the ground air quality station and calculating the average ozone concentration of 8 hours per day.
Preferably, in step 2, the space-time matching includes:
2.1, acquiring longitude and latitude LAT and LON of a ground air quality site on a spatial scale, taking a corresponding grid as a center, extracting grid pixel values in a 7X 7 range around TO3 data and meteorological data, and extracting only center grid pixel values for NDVI and DEM data;
2.2, calculating annual product day DOY of the target day on a time scale, and extracting data of 3 days before and after the target day from the total amount data of the ozone column and the meteorological data by taking the target day as a reference, wherein NDVI data are used for extracting current month data of the target day;
and 2.3, constructing a space-time sample data set, wherein the space-time sample data set consists of a three-dimensional space-time variable and a one-dimensional dummy variable.
Preferably, in step 3, the geospatial information coding network includes: a 1 x 1 convolutional layer for mapping input data to a high-dimensional feature space; a residual module of a 1×1 convolution kernel, configured to extract a coupling relationship between features and increase nonlinear expression; a residual module of a 3 x 3 convolution kernel for depth extraction of local geospatial dependencies of features; a 7 x 7 global convolution layer for identifying global geospatial dependencies of features; the structural calculation formula of the geospatial information coding network is as follows:
FS＝Conv_7(ResBlock_3(ResBlock_1(Conv_1(x))))
in the above formula, FS is an extracted geospatial feature, x is an input three-dimensional space-time variable, conv_1 and conv_7 represent convolution layers with convolution kernel sizes of 1×1 and 7×7, and resblock_1 and resblock_3 represent residual modules with convolution kernel sizes of 1×1 and 3×3, respectively;
the residual block calculation formula is expressed as follows:
x′＝ReLU(x+BN(Conv_i(ReLU(BN(Conv_i(x))))))
in the above formula, x' is the output result of the residual module, x is the input data, conv_i represents the convolution layer with the convolution kernel size of i×i, and ReLU and BN represent the linear rectification function and the batch normalization layer respectively.
Preferably, in step 3, the timing information encoding network is a modified transducer network, including: the position coding module is used for endowing the extracted geospatial information features with time sequence information which can be identified by the network; the multi-head self-attention module is used for extracting time sequence characteristics affecting near-surface ozone concentration estimation; a feed-forward network for further refining the spatiotemporal features; the structural calculation formula of the time sequence information coding network is as follows:
FST＝FFN(Multihead(Pos(FS)))
in the above formula, FST is the extracted geospatial feature, FS is the geospatial feature extracted by the geospatial information encoding network, pos represents the position encoding module, multihead represents the multi-head self-attention module, and FFN represents the feed-forward network.
Preferably, in step 3, the calculation formula of the position coding module is as follows:
in the above formula, p represents the p-th time point, i represents the i-th feature, and c is the total feature number;
the calculation formula of the multi-head self-attention module is expressed as follows:
MA(Q,K,V)＝Concat(head 1 ,…,head h )W O
where FS' is a position-coded geospatial feature,and->Weights of Query (Q), key (K) and Value (V) respectively representing ith self-attention head, softmax is normalized exponential function, h is multi-head self-attention number, W O For the weight of the output layer, concat represents a connection function, MA is a multi-head self-attention result;
the FFN calculation formula is expressed as follows:
FST＝LN(LN(x)+FC(ReLU(FC(LN(MA)))
in the above formula, LN represents layer normalization operation, and FC represents fully connected layers.
Preferably, in step 3, the geographic spatiotemporal feature decoding network includes: the connection module is used for connecting the geographic space-time characteristics extracted by the geographic space information coding network and the time sequence information coding network together with the one-dimensional Dummy variable so as to acquire more geographic space-time characteristics; the deep neural network DNN comprises a plurality of full-connection layers and a ReLU function, and is used for performing characteristic nonlinear conversion decoding and finally realizing near-surface ozone concentration estimation; the whole structural calculation formula is expressed as follows:
O 3 ＝DNN(Concat(FST,Dummy)
wherein DNN is represented as follows:
in the above-mentioned method, the step of,the ith neuron, k, representing the first layer l Represents the number of the total neurons of the first layer, W ji And b ji Respectively represent neuron->And->The weights and residuals between, L is the total number of layers.
Preferably, in step 4, the model super-parameters include the size of the geospatial neighborhood, the length of the neighborhood time window, the number of internal feature channels of the geospatial information coding network, the number of multi-head self-attentions of the time sequence information coding network, the number of hidden layers of the geospatial feature decoding network, and the optimal result is selected through 5-fold cross validation to determine the final parameter combination.
Preferably, in step 4, the overall accuracy of the model is verified by 5-fold cross verification based on samples, the time-based and site-based 5-fold cross verification are used for verifying the space-time robustness of the model, and the decision coefficient R is used for evaluating the verification accuracy 2 Root mean square error RMSE and absolute average error MAE.
In a second aspect, a near-surface ozone remote sensing estimation system for deep coupling geographic space-time information is provided, and the near-surface ozone remote sensing estimation method for deep coupling geographic space-time information in the first aspect is performed, and includes:
the acquisition module is used for acquiring multi-source data of the research area and preprocessing the multi-source data;
the matching module is used for carrying out space-time matching on the preprocessed multi-source data and constructing a space-time sample data set;
the construction module is used for constructing a deep learning model coupled with the geographic space-time information, and the deep learning model comprises a geographic space information coding network, a geographic time sequence information coding network and a geographic space-time feature decoding network;
the training module is used for training the deep learning model by utilizing the space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model;
and the estimation module is used for carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model.
The beneficial effects of the application are as follows: according to the method, firstly, the multi-source data are processed into a space-time sample data set composed of three-dimensional space-time variables and one-dimensional dummy variables according to the characteristics of the multi-source data, then a targeted geographic space information coding network and a time sequence information coding network are constructed to deeply extract geographic space-time context characteristics, and then a geographic space-time characteristic decoding network is utilized to carry out nonlinear conversion decoding of the geographic space-time characteristics, so that near-surface ozone high-precision estimation is finally realized. Compared with the existing mainstream model, the method has the advantages that geographic space-time information is deeply coupled when near-surface ozone is estimated, the accuracy and space-time robustness of the remote sensing estimation model are improved, and the method has important practical application value.
Drawings
FIG. 1 is a flow chart of a near-surface ozone remote sensing estimation method of depth coupling geographic space-time information provided by the application;
FIG. 2 is a schematic diagram of a geospatial information encoding network architecture provided by the present application;
FIG. 3 is a schematic diagram of a timing information encoding network architecture according to the present application;
FIG. 4 is a schematic diagram of a geographic spatiotemporal feature decoding network architecture provided by the present application;
FIG. 5 is a graph comparing the cross-validation results of the method provided by the present application with the mainstream method.
Detailed Description
The application is further described below with reference to examples. The following examples are presented only to aid in the understanding of the application. It should be noted that it will be apparent to those skilled in the art that modifications can be made to the present application without departing from the principles of the application, and such modifications and adaptations are intended to be within the scope of the application as defined in the following claims.
Example 1:
as shown in fig. 1, embodiment 1 of the present application provides a near-surface ozone remote sensing estimation method for deep coupling geographic space-time information, which includes:
step 1, multi-source data of a research area are obtained, and the multi-source data are preprocessed.
Specifically, the multi-source data includes: the method comprises the steps of satellite remote sensing inversion of ozone column total amount data TO3, meteorological data, normalized vegetation index NDVI data, digital elevation model DEM data and ozone monitoring data of a ground air quality station; the meteorological data comprise air temperature T2M at the position of 2 meters on the earth surface, dew point temperature D2M at the position of 2 meters on the earth surface, earth surface pressure SP, weft wind speed U10 at the position of 10 meters on the earth surface, warp wind speed V10 at the position of 10 meters on the earth surface, atmospheric layer top height BLH, relative humidity RH, O3 mass mixing ratio OMR, earth surface net solar radiation SSR, earth surface net heat radiation STR and earth surface downlink ultraviolet radiation UVB.
The pretreatment comprises the following steps: uniformly resampling meteorological data, NDVI data and DEM data TO the same spatial resolution as TO3 data inverted by satellite remote sensing; and eliminating the missing value in the ozone monitoring data of the ground air quality station and calculating the average ozone concentration of 8 hours per day.
And step 2, performing space-time matching on the preprocessed multi-source data, and constructing a space-time sample data set.
In step 2, the space-time matching includes:
2.1, acquiring longitude and latitude LAT and LON of a ground air quality site on a spatial scale, taking a corresponding grid as a center, extracting grid pixel values in a 7X 7 range around TO3 data and meteorological data, and extracting only center grid pixel values for NDVI and DEM data;
2.2, calculating annual product day DOY of the target day on a time scale, and extracting data of 3 days before and after the target day from the total amount data of the ozone column and the meteorological data by taking the target day as a reference, wherein NDVI data are used for extracting current month data of the target day;
and 2.3, constructing a space-time sample data set, wherein the space-time sample data set consists of a three-dimensional space-time variable and a one-dimensional dummy variable. In particular, three dimensional space-time variables include TO3, T2M, D M, SP, U10, V10, BLH, RH, OMR, SSR, STR and UVB, each variable having dimensions of 7 x 7, 7 x 7 neighborhood spatiotemporal information representing adjacent 7 days; one-dimensional dummy variables include NDVI, DEM, LAT, LON and DOY.
And 3, constructing a deep learning model coupling the geographic space-time information, wherein the deep learning model comprises a geographic space information coding network, a geographic time sequence information coding network and a geographic space-time feature decoding network.
And 4, training the deep learning model by using the space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model.
And 5, carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model.
Example 2:
based on embodiment 1, embodiment 2 of the present application provides a more specific near-surface ozone remote sensing estimation method for depth-coupled geographic spatiotemporal information, which comprises:
step 1, multi-source data of a research area are obtained, and the multi-source data are preprocessed.
And step 2, performing space-time matching on the preprocessed multi-source data, and constructing a space-time sample data set.
And 3, constructing a deep learning model coupling the geographic space-time information, wherein the deep learning model comprises a geographic space information coding network, a geographic time sequence information coding network and a geographic space-time feature decoding network.
In step 3, as shown in fig. 2, the geospatial information encoding network includes: a 1 x 1 convolutional layer for mapping input data to a high-dimensional feature space; a residual module of a 1×1 convolution kernel, configured to extract a coupling relationship between features and increase nonlinear expression; a residual module of a 3 x 3 convolution kernel for depth extraction of local geospatial dependencies of features; a 7 x 7 global convolution layer for identifying global geospatial dependencies of features; the structural calculation formula of the geospatial information coding network is as follows:
FS＝Conv_7(ResBlock_3(ResBlock_1(Conv_1(x))))
in the above formula, FS is an extracted geospatial feature, x is an input three-dimensional space-time variable, conv_1 and conv_7 represent convolution layers with convolution kernel sizes of 1×1 and 7×7, and resblock_1 and resblock_3 represent residual modules with convolution kernel sizes of 1×1 and 3×3, respectively;
the residual block calculation formula is expressed as follows:
x′＝ReLU(x+BN(Conv_i(ReLU(BN(Conv_i(x))))))
in the above formula, x' is the output result of the residual module, x is the input data, conv_i represents the convolution layer with the convolution kernel size of i×i, and ReLU and BN represent the linear rectification function and the batch normalization layer respectively.
In step 3, the timing information encoding network is an improved transform network, which eliminates the decoding module of the conventional transform network, simplifies the encoding module, and can effectively improve the estimation efficiency of the model, as shown in fig. 3, and specifically includes: the position coding module is used for endowing the extracted geospatial information features with time sequence information which can be identified by the network; the multi-head self-attention module is used for extracting time sequence characteristics affecting near-surface ozone concentration estimation; a feed-forward network for further refining the spatiotemporal features; the structural calculation formula of the time sequence information coding network is as follows:
FST＝FFN(Multihead(Pos(FS)))
in the above formula, FST is the extracted geospatial feature, FS is the geospatial feature extracted by the geospatial information encoding network, pos represents the position encoding module, multihead represents the multi-head self-attention module, and FFN represents the feed-forward network.
In step 3, the calculation formula of the position coding module is expressed as follows:
in the above formula, p represents the p-th time point, i represents the i-th feature, and c is the total feature number;
the calculation formula of the multi-head self-attention module is expressed as follows:
MA(Q,K,V)＝Concat(head 1 ,…,head h )W O
where FS' is a position-coded geospatial feature,and->Weights of Query (Q), key (K) and Value (V) respectively representing ith self-attention head, softmax is normalized exponential function, h is multi-head self-attention numberNumber W O For the weight of the output layer, concat represents a connection function, MA is a multi-head self-attention result;
the FFN calculation formula is expressed as follows:
FST＝LN(LN(x)+FC(ReLU(FC(LN(MA)))
in the above formula, LN represents layer normalization operation, and FC represents fully connected layers.
In step 3, the geographic space-time feature decoding network is as shown in fig. 4, and includes: the connection module is used for connecting the geographic space-time characteristics extracted by the geographic space information coding network and the time sequence information coding network together with the one-dimensional Dummy variable so as to acquire more geographic space-time characteristics; the deep neural network DNN comprises a plurality of full-connection layers and a ReLU function, and is used for performing characteristic nonlinear conversion decoding and finally realizing near-surface ozone concentration estimation; the whole structural calculation formula is expressed as follows:
O 3 ＝DNN(Concat(FST,Dummy)
wherein DNN is represented as follows:
in the above-mentioned method, the step of,the ith neuron, k, representing the first layer l Represents the number of the total neurons of the first layer, W ji And b ji Respectively represent neuron->And->The weights and residuals between, L is the total number of layers.
And 4, training the deep learning model by using the space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model.
In step 4, the model super parameters comprise the size of the geographic space neighborhood, the length of the neighborhood time window, the number of characteristic channels in the geographic space information coding network, the number of multi-head self-attentions of the time sequence information coding network and the number of hidden layers of the geographic space-time characteristic decoding network, and the optimal result is selected through 5-fold cross verification to determine the final parameter combination.
In step 4, the overall accuracy of the model is verified by adopting sample-based 5-fold cross verification, the time-based and site-based 5-fold cross verification are respectively adopted for verifying the space-time robustness of the model, and the decision coefficient R is adopted for evaluating the verification accuracy 2 Root mean square error RMSE and absolute average error MAE.
And 5, carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model.
The effect of the present application is further analyzed by specific experimental results as follows:
firstly, collecting multisource data of 1 month 1 day to 31 months 12 years in a long triangular region 2022, wherein the multisource data comprises ozone column total amount data of a sentinel No. 5 TROPOMI, analysis data meteorological data of a European middle weather forecast center ERA5, NDVI data of a MODIS of the national aviation and aerospace agency, DEM data of USGS of the U.S. geological survey agency and ground site monitoring data of a national environment monitoring center, and preprocessing and constructing a required space-time sample data set according to the method.
Then according to the method, a pytorch deep learning tool is used for constructing a deep learning model coupled with the geographic space-time information, training of the model is carried out by utilizing a space-time sample data set, super-parameters are determined, and overall accuracy and space-time robustness are verified. The results are shown in FIG. 5, and the sample-based five-fold cross-validation shows that the model of the application determines the coefficient R 2 The root mean square error RMSE and the absolute average error MAE are respectively 0.94 and 10.25 mu g/m 3 And 7.52. Mu.g/m 3 The model is higher than the mainstream random forest and the multiple linear regression model, and the model has excellent near-surface ozone estimation accuracy. R of cross verification result based on site and time by using model of the application 2 Up to 0.94 and 0.76 respectively, which are also significantly higher than the mainstream random forest and manyThe meta-linear regression model demonstrates the spatio-temporal robustness of the model of the application to near-surface ozone estimation.
In this embodiment, the same or similar parts as those in embodiment 1 may be referred to each other, and will not be described in detail in the present disclosure.
Example 3:
based on embodiments 1 and 2, embodiment 3 of the present application provides a near-surface ozone remote sensing estimation system for depth-coupled geographic spatial and temporal information, which comprises:
the acquisition module is used for acquiring multi-source data of the research area and preprocessing the multi-source data;
the matching module is used for carrying out space-time matching on the preprocessed multi-source data and constructing a space-time sample data set;
the construction module is used for constructing a deep learning model coupled with the geographic space-time information, and the deep learning model comprises a geographic space information coding network, a geographic time sequence information coding network and a geographic space-time feature decoding network;
the training module is used for training the deep learning model by utilizing the space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model;
and the estimation module is used for carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model.
Specifically, the system provided in this embodiment is a system corresponding to the method provided in embodiments 1 and 2, so that the portions in this embodiment that are the same as or similar to those in embodiments 1 and 2 may be referred to each other, and will not be described in detail in this disclosure.
Claims (10)
1. A near-surface ozone remote sensing estimation method of depth coupling geographic space-time information is characterized by comprising the following steps:
step 1, multi-source data of a research area are obtained, and the multi-source data are preprocessed;
step 2, performing space-time matching on the preprocessed multi-source data, and constructing a space-time sample data set;
step 3, constructing a deep learning model coupling geographic space-time information, wherein the deep learning model comprises a geographic space information coding network, a geographic time sequence information coding network and a geographic space-time feature decoding network;
step 4, training the deep learning model by utilizing the space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model;
and 5, carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model.
2. The method for estimating the near-surface ozone remote sensing of the deep coupled geographical spatiotemporal information of claim 1, wherein in step 1, the multi-source data comprises: the method comprises the steps of satellite remote sensing inversion of ozone column total amount data TO3, meteorological data, normalized vegetation index NDVI data, digital elevation model DEM data and ozone monitoring data of a ground air quality station;
the pretreatment comprises the following steps: uniformly resampling meteorological data, NDVI data and DEM data TO the same spatial resolution as TO3 data inverted by satellite remote sensing; and eliminating the missing value in the ozone monitoring data of the ground air quality station and calculating the average ozone concentration of 8 hours per day.
3. The method for estimating near-surface ozone remote sensing of deep coupled geographic spatiotemporal information of claim 2, wherein in step 2, the spatiotemporal matching comprises:
2.1, acquiring longitude and latitude LAT and LON of a ground air quality site on a spatial scale, taking a corresponding grid as a center, extracting grid pixel values in a 7X 7 range around TO3 data and meteorological data, and extracting only center grid pixel values for NDVI and DEM data;
2.2, calculating annual product day DOY of the target day on a time scale, and extracting data of 3 days before and after the target day from the total amount data of the ozone column and the meteorological data by taking the target day as a reference, wherein NDVI data are used for extracting current month data of the target day;
and 2.3, constructing a space-time sample data set, wherein the space-time sample data set consists of a three-dimensional space-time variable and a one-dimensional dummy variable.
4. The method for near-surface ozone remote sensing estimation of depth-coupled geospatial information according to claim 3, wherein in step 3, the geospatial information encoding network comprises: a 1 x 1 convolutional layer for mapping input data to a high-dimensional feature space; a residual module of a 1×1 convolution kernel, configured to extract a coupling relationship between features and increase nonlinear expression; a residual module of a 3 x 3 convolution kernel for depth extraction of local geospatial dependencies of features; a 7 x 7 global convolution layer for identifying global geospatial dependencies of features; the structural calculation formula of the geospatial information coding network is as follows:
FS＝Conv_7(ResBlock_3(ResBlock_1(Conv_1(x))))
in the above formula, FS is an extracted geospatial feature, x is an input three-dimensional space-time variable, conv_1 and conv_7 represent convolution layers with convolution kernel sizes of 1×1 and 7×7, and resblock_1 and resblock_3 represent residual modules with convolution kernel sizes of 1×1 and 3×3, respectively;
the residual block calculation formula is expressed as follows:
x′＝ReLU(x+BN(Conv_i(ReLU(BN(Conv_i(x))))))
in the above formula, x' is the output result of the residual module, x is the input data, conv_i represents the convolution layer with the convolution kernel size of i×i, and ReLU and BN represent the linear rectification function and the batch normalization layer respectively.
5. The method of claim 4, wherein in step 3, the time series information encoding network is a modified transducer network, comprising: the position coding module is used for endowing the extracted geospatial information features with time sequence information which can be identified by the network; the multi-head self-attention module is used for extracting time sequence characteristics affecting near-surface ozone concentration estimation; a feed-forward network for further refining the spatiotemporal features; the structural calculation formula of the time sequence information coding network is as follows:
FST＝FFN(Multihead(Pos(FS)))
in the above formula, FST is the extracted geospatial feature, FS is the geospatial feature extracted by the geospatial information encoding network, pos represents the position encoding module, multihead represents the multi-head self-attention module, and FFN represents the feed-forward network.
6. The method for estimating the near-surface ozone remote sensing of the depth-coupled geographical spatiotemporal information of claim 5, wherein in step 3, the calculation formula of the position coding module is expressed as follows:
in the above formula, p represents the p-th time point, i represents the i-th feature, and c is the total feature number;
the calculation formula of the multi-head self-attention module is expressed as follows:
MA(Q，K，V)＝Concat(head 1 ，...，head h )W O
where FS' is a position-coded geospatial feature,and->Weights of Query (Q), key (K) and Value (V) respectively representing ith self-attention head, softmax is normalized exponential function, h is multi-head self-attention number, W O Is the delivery ofThe weight of the out-layer, concat represents the connection function, MA is the multi-head self-attention result;
the FFN calculation formula is expressed as follows:
FST＝LN(LN(x)+FC(ReLU(FC(LN(MA)))
in the above formula, LN represents layer normalization operation, and FC represents fully connected layers.
7. The method for estimating the near-surface ozone remote sensing of the deep coupled geographical spatiotemporal information of claim 6, wherein in step 3, the geographical spatiotemporal feature decoding network comprises: the connection module is used for connecting the geographic space-time characteristics extracted by the geographic space information coding network and the time sequence information coding network together with the one-dimensional Dummy variable so as to acquire more geographic space-time characteristics; the deep neural network DNN comprises a plurality of full-connection layers and a ReLU function, and is used for performing characteristic nonlinear conversion decoding and finally realizing near-surface ozone concentration estimation; the whole structural calculation formula is expressed as follows:
O 3 ＝DNN(Concat(FST，Dummy)
wherein DNN is represented as follows:
in the above-mentioned method, the step of,the ith neuron, k, representing the first layer l Represents the number of the total neurons of the first layer, W ji And b ji Respectively represent neuron->And->The weights and residuals between, L is the total number of layers.
8. The method of claim 7, wherein in step 4, the model super-parameters include the size of the geospatial neighborhood, the length of the neighborhood time window, the number of intra-geospatial information encoding network feature channels, the number of multi-headed self-attentiveness of the time sequence information encoding network, the number of hidden layers of the geospatial information decoding network, and the optimal result is selected by 5-fold cross-validation to determine the final parameter combination.
9. The method for estimating near-surface ozone remote sensing of deep coupling geographic spatiotemporal information according to claim 8, characterized in that in step 4, 5-fold cross-validation based on samples is adopted for overall accuracy validation of a model, 5-fold cross-validation based on time and site is adopted for validation of model spatiotemporal robustness, and a decision coefficient R is adopted for evaluation of validation accuracy 2 Root mean square error RMSE and absolute average error MAE.
10. A near-surface ozone remote sensing estimation system for depth-coupled geographic spatiotemporal information, for performing the near-surface ozone remote sensing estimation method for depth-coupled geographic spatiotemporal information of any of claims 1 to 9, comprising:
the acquisition module is used for acquiring multi-source data of the research area and preprocessing the multi-source data;
the matching module is used for carrying out space-time matching on the preprocessed multi-source data and constructing a space-time sample data set;
the construction module is used for constructing a deep learning model coupled with the geographic space-time information, and the deep learning model comprises a geographic space information coding network, a geographic time sequence information coding network and a geographic space-time feature decoding network;
the training module is used for training the deep learning model by utilizing the space-time sample data set, optimizing and determining model super-parameters, and verifying the overall accuracy and space-time robustness of the model;
and the estimation module is used for carrying out large-scale remote sensing estimation of the near-surface ozone concentration by using the trained model.
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