Prediction method of cloud coverage area captured by remote sensing satellite based on multi-source meteorological model

Abstract

The present invention provides a method for predicting cloud coverage areas that can be photographed by remote sensing satellites based on a multi-source meteorological model, belonging to the field of remote sensing satellite photography technology. The method is as follows: obtaining multi-source meteorological model data and preprocessing; obtaining satellite orbit data, UTC time data and satellite photography range data according to a preset method; performing data mapping processing on the multi-source meteorological model data, satellite orbit data and satellite photography range data; generating multiple single-model cloud coverage data sets according to the data mapping results; wherein the single-model cloud coverage data sets are used to predict cloud coverage areas that can be photographed by remote sensing satellites. The present invention can predict the cloud cover of a remote sensing satellite photography area, thereby improving the success rate of satellite image acquisition and the cloud-free ratio of the overall satellite image area.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing satellite photography technology, and in particular to a method for predicting cloud coverage areas photographable by remote sensing satellites based on a multi-source meteorological model. Background Art

[0002] In the current advanced optical remote sensing satellite mission planning stage, satellite image acquisition often faces increasingly complex objective conditions and observation requirements. Among them, predicting future meteorological conditions has become a crucial and challenging issue. Cloud cover is one of the important factors affecting satellite image acquisition, which directly affects whether the satellite can successfully collect clear images, especially in areas with high cloud cover, such as the equatorial region. Due to the large cloud cover all year round, the satellite has fewer opportunities to collect clear images, which directly leads to serious waste of satellite observation resources. Therefore, it is essential to plan satellite shooting missions through efficient prediction of cloud cover. Summary of the invention

[0003] The present invention provides a method for predicting cloud coverage areas photographable by remote sensing satellites based on a multi-source meteorological model, which can predict the cloud cover in remote sensing satellite photographing areas, thereby improving the success rate of satellite image acquisition and the cloud-free ratio of the entire satellite image area.

[0004] It can be used in mission planning of optical satellites to improve the data acquisition success rate of satellite imagery and the overall cloud-free ratio of the area.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting cloud coverage areas that can be photographed by remote sensing satellites based on a multi-source meteorological model, the method comprising:

[0006] Obtain multi-source meteorological model data and pre-process it;

[0007] Obtain satellite orbit data, UTC time data and satellite shooting range data according to a preset method;

[0008] Perform data mapping processing on multi-source meteorological model data, satellite orbit data and satellite shooting range data;

[0009] Generate multiple single-model cloud coverage datasets based on the data mapping results;

[0010] Among them, the single-model cloud coverage dataset is used to predict the cloud coverage area that can be photographed by remote sensing satellites.

[0011] Optionally, obtain multi-source meteorological model data and perform preprocessing, including:

[0012] The multi-source meteorological model data are spatially aggregated at different resolutions and stored in a preset format;

[0013] Detect missing values ​​and outliers in cloud cover data from multi-source meteorological model data;

[0014] If outliers are detected, they are repaired using linear interpolation.

[0015] Optionally, obtaining satellite orbit data, UTC time data, and satellite shooting range data according to a preset method includes:

[0016] According to the satellite design characteristics, orbit calculation is performed with preset time accuracy to obtain satellite orbit data;

[0017] According to the matching of satellite orbit time and meteorological model time, the half-point split method is used to analyze and obtain UTC time data;

[0018] The maximum shooting range of the satellite at a preset time point is calculated by setting the maximum side swing angle of the satellite.

[0019] Optionally, data mapping processing is performed on the multi-source meteorological model data, the satellite orbit data and the satellite shooting range data, including:

[0020] Implementing data mapping between the multi-source meteorological model data and the satellite orbit data according to the relationship between the time corresponding to the multi-source meteorological model data and the time corresponding to the satellite orbit data;

[0021] The grid data within the shooting range is calculated based on the shooting range data of the satellite;

[0022] Implementing data mapping between satellite orbit data and satellite shooting range data according to the geographical location relationship between grid data and satellite orbit data;

[0023] The final data mapping relationship is: longitude and latitude grid-central longitude and latitude-UTC time-cloud cover;

[0024] The longitude and latitude grid is a grid with the center longitude and latitude as the center and the resolution as the length and width.

[0025] Optionally, performing data mapping processing on the multi-source meteorological model data, the satellite orbit data and the satellite shooting range data also includes:

[0026] The repeated grids in the grid data are merged, and the UTC time corresponding to the merged grid is taken as the latest time, and the cloud amount value is taken as the weighted average of the cloud amount values ​​of all merged grids.

[0027] Optionally, multiple single-model cloud coverage datasets are generated according to the data mapping results, including:

[0028] Get historical cloud cover data;

[0029] The cloud-free coefficient corresponding to the grid is obtained based on the analysis of historical cloud cover data;

[0030] The grids of global regions are graded according to the cloud-free factor and the influence factor is added;

[0031] Update the cloud cover in the data mapping results according to the influence coefficient;

[0032] Construct multiple single-model cloud coverage datasets based on the updated cloud cover data;

[0033] The single-model cloud coverage dataset is aggregated and calculated according to the preset cloud amount range to generate the cloud coverage area that can be photographed by remote sensing satellites.

[0034] Optionally, the remote sensing satellite photogenic cloud coverage area prediction method based on a multi-source meteorological model further includes:

[0035] The cloud coverage areas photographable by remote sensing satellites generated by multiple single-model cloud coverage datasets are aggregated and calculated, and the multi-model remote sensing satellite cloud coverage areas are generated by selecting completely overlapping areas.

[0036] The present invention provides a method for predicting cloud coverage areas that can be photographed by remote sensing satellites based on a multi-source meteorological model. The method is as follows: obtaining multi-source meteorological model data and preprocessing; obtaining satellite orbit data, UTC time data and satellite shooting range data according to a preset method; performing data mapping processing on the multi-source meteorological model data, satellite orbit data and satellite shooting range data; generating multiple single-model cloud coverage data sets according to the data mapping results; wherein the single-model cloud coverage data sets are used to predict cloud coverage areas that can be photographed by remote sensing satellites. The present invention can predict the cloud cover of the remote sensing satellite shooting area, thereby improving the success rate of satellite image acquisition and the cloud-free ratio of the overall satellite image area.

[0037] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements.

[0039] Figure 1 This is a flow chart of a method for predicting cloud coverage area photographable by a remote sensing satellite based on a multi-source meteorological model according to an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a meteorological model according to an embodiment of the present invention being aggregated by resolution;

[0041] Figure 3 A scatter plot of cloud amount values ​​according to an embodiment of the present invention;

[0042] Figure 4 A scatter plot of cloud amount values ​​after outlier repair in an embodiment of the present invention;

[0043] Figure 5 A schematic diagram of grid merging according to an embodiment of the present invention;

[0044] Figure 6 A schematic diagram of a grid of a cloud coverage area that can be photographed according to an embodiment of the present invention;

[0045] Figure 7 A schematic diagram of a photographable cloud coverage area according to an embodiment of the present invention;

[0046] Figure 8 is a schematic diagram of a photographable cloud coverage area according to another embodiment of the present invention;

[0047] Fig. 9 A schematic diagram of a photographable cloud coverage area according to another embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0049] It should be noted that the description of the embodiments of the present invention is only for more clearly illustrating the technical solutions of the embodiments of the present invention, and does not constitute a limitation on the technical solutions provided by the embodiments of the present invention.

[0050] Figure 1 FIG. 4 is a flow chart of a method for predicting cloud coverage area captured by remote sensing satellites based on a multi-source meteorological model according to an embodiment of the present invention; Figure 1 As shown, including:

[0051] S101, obtaining multi-source meteorological model data and performing preprocessing.

[0052] Optionally, obtain multi-source meteorological model data and perform preprocessing, including:

[0053] The multi-source meteorological model data are spatially aggregated at different resolutions and stored in a preset format;

[0054] Detect missing values ​​and outliers in cloud cover data from multi-source meteorological model data;

[0055] If outliers are detected, they are repaired using linear interpolation.

[0056] Exemplarily, meteorological model data are obtained from different sources to obtain more comprehensive and reliable cloud cover data, and the frequency and period of obtaining data are determined according to timeliness;

[0057] The meteorological model data obtained include but are not limited to meteorological model data made public on the Internet.

[0058] Optionally, the obtained meteorological model may be as follows:

[0059] GFS model data: The data comes from NOAA (National Oceanic and Atmospheric Administration), the data update frequency is 6 hours, and the prediction frequency is I = 1 hour;

[0060] GEM model data: The data comes from CMC (Canadian government), the data update frequency is 12 hours, and the prediction frequency is I=3 hours.

[0061] Exemplarily, according to different data update frequencies of meteorological models, downloading is performed using a download script, and the files are named by year, month, day and hour.

[0062] Exemplarily, the file storage format may be as follows:

[0063] A unified Grib2 suffix is ​​used, and the prediction starts on May 24, 2023. The file name stored at 0:00 on May 24, 2023 is 20230524000.grib2.

[0064] Exemplarily, the original files of the multi-source meteorological models are stored in a relational database, and the meteorological models are aggregated by resolution;

[0065] Optionally, since storing data at the original resolution will result in a large amount of spatial data, spatial aggregation storage optimization can be used and the values ​​can be normalized.

[0066] For example, Figure 2 As shown in Figure 2, the 16 grids with an original resolution of 0.15° of the GEM model are merged into a grid with a resolution of 0.6° by resolution aggregation.

[0067] For example, the normalization process may adopt the range transformation method, map the data to [0, 1] through linear transformation, and perform secondary normalization storage; the calculation formula is as follows:

[0068]

[0069] In the formula, x′ is the value obtained after normalization, x is the target value, and x min is the minimum value of the data to be normalized, x max is the maximum value in the data that needs to be normalized.

[0070] For example, if the results of missing value and outlier detection on cloud cover data in multi-source meteorological model data are as follows: Figure 3 As shown, outlier repair is required.

[0071] Optionally, outliers can be repaired by linear interpolation. The scattered coordinates of the outliers are taken as (x, y), the left side of the grid of the adjacent longitude is (x0, y0), and the right side is (x1, y1). The outlier calculation formula is as follows:

[0072]

[0073] In the formula, (x0, y0) and (x1, y1) as well as x can be calculated according to Figure 3 The cloud amount value scatter plot is calculated, and the y value can be calculated according to the above formula.

[0074] Optionally, after outlier repair, we can get Figure 4 Scatter plot of cloud cover values ​​shown.

[0075] Optionally, the above steps can improve the reliability of the cloud cover data obtained by analysis by acquiring multi-source meteorological model data and aggregating, managing and storing them, thereby improving the accuracy of subsequent cloud coverage area predictions.

[0076] S102: Obtain satellite orbit data, UTC time data, and satellite shooting range data according to a preset method.

[0077] Optionally, obtaining satellite orbit data, UTC time data, and satellite shooting range data according to a preset method includes:

[0078] According to the satellite design characteristics, orbit calculation is performed with preset time accuracy to obtain satellite orbit data;

[0079] According to the matching of satellite orbit time and meteorological model time, the half-point split method is used to analyze and obtain the Coordinated Universal Time (UTC) time data;

[0080] The maximum shooting range of the satellite at a preset time point is calculated by setting the maximum side swing angle of the satellite.

[0081] For example, it is determined whether the satellite is taken in an ascending orbit or a descending orbit according to the satellite design characteristics. When the satellite taken in an ascending orbit is calculated according to the orbital element (TLE) according to the SGP4 model, the descending orbit is ignored. The satellite taken in a descending orbit is the opposite. Then, the orbit is calculated with an accuracy of 0.1s, and the satellite orbit data set {(X1, Y1)(X2, Y2)…(X n ,Y n )}, where X represents longitude and Y represents latitude.

[0082] Optionally, the longitude of the grid line to which the coordinate belongs can be determined based on the longitude of the satellite orbit data, and then the UTC time dataset {T1 T2 … T n}.

[0083] For example, if the satellite passes the orbit at 2:30 on May 24, 2023, the UTC time corresponding to the meteorological model time is 20230524 02:00:00; if the satellite passes the orbit at 2:31 on May 24, 2023, the UTC time corresponding to the meteorological model time is 20230524 03:00:00.

[0084] Exemplarily, the maximum shooting range is stored according to the satellite orbit passing time, and the maximum roll angle C (C=35) is set, wherein if the pitch angle has a small effect on the time, it can be ignored.

[0085] S103: Perform data mapping processing on the multi-source meteorological model data, satellite orbit data and satellite shooting range data.

[0086] Optionally, data mapping processing is performed on the multi-source meteorological model data, the satellite orbit data and the satellite shooting range data, including:

[0087] Implementing data mapping between the multi-source meteorological model data and the satellite orbit data according to the relationship between the time corresponding to the multi-source meteorological model data and the time corresponding to the satellite orbit data;

[0088] The grid data within the shooting range is calculated based on the shooting range data of the satellite;

[0089] Implementing data mapping between satellite orbit data and satellite shooting range data according to the geographical location relationship between grid data and satellite orbit data;

[0090] The final data mapping relationship is: longitude and latitude grid-central longitude and latitude-UTC time-cloud cover;

[0091] The longitude and latitude grid is a grid with the center longitude and latitude as the center and the resolution as the length and width.

[0092] Exemplarily, based on the UTC time dataset obtained in step S102, the data mapping between the cloud cover data in the multi-source meteorological model data and the satellite orbit data can be completed.

[0093] Exemplarily, calculate the maximum shooting range at a certain time, and store the cloud cover into the grid that matches the maximum shooting range according to the satellite overpass time. At the same time, store all the corresponding grid data to realize the data mapping between the satellite orbit data and the satellite shooting range data, and finally form a cloud cover dataset stored according to UTC time every day.

[0094] Optionally, the time recorded when the data is stored in the database is determined according to the data acquisition time and the data acquisition frequency. If the time recorded when the data is stored in the database is TY, the data acquisition time is TR, and the data acquisition frequency is I, then TY, TR, and I satisfy the following formula:

[0095] 0 < TY - TR < I; where TY, TR, and I are all integers.

[0096] Exemplarily, if the data acquisition frequency is once every 3 hours, the data with the time recorded as 2023-05-24 02:00:00 when stored in the database can be sourced from the data obtained at 2023-05-24 00:00:00, such as the file recorded as 20230524000.grib2.

[0097] Optionally, the data mapping process for the multi-source meteorological model data, satellite orbit data, and satellite shooting range data further includes:

[0098] Merge the repeated grids in the grid data. For the merged grids, the UTC time corresponding to them takes the latest time, and the cloud cover value takes the weighted average of the cloud cover values of all the merged grids.

[0099] Exemplarily, as Figure 5 shown, the grids at this longitude in the figure will simultaneously receive data at 2023-05-24 02:00:00 and 2023-05-24 03:00:00. At this time, merge the different times of the same grid, and the time takes the latest time. Since the sizes of the same grid are the same, the value takes the average; specifically as shown in Table 1 below:

[0100] Table 1

[0101] time Cloud Cover % First Grid 2023-05-24 02:00:00 16 Second Grid 2023-05-24 03:00:00 24 Merged mesh 2023-05-24 03:00:00 20

[0102] Exemplarily, in the finally obtained dataset, the longitude and latitude are reserved to two decimal places, the UTC time is in whole hours, the cloud cover is in percentage values, and the longitude and latitude grid is a grid centered on the central longitude and latitude with the resolution as the length and width; as shown in Table 2 below:

[0103] Table 2

[0104] Latitude and longitude grid Center longitude and latitude UTC time Cloud Cover polygon(()) (X,Y) yyyy-mm-dd hh:00:00 C％ … … … …

[0105] S104. Generate multiple single-model cloud coverage data sets according to the data mapping results.

[0106] Among them, the single-model cloud coverage dataset is used to predict the cloud coverage area that can be photographed by remote sensing satellites.

[0107] Optionally, multiple single-model cloud coverage datasets are generated according to the data mapping results, including:

[0108] Get historical cloud cover data;

[0109] The cloud-free coefficient corresponding to the grid is obtained based on the analysis of historical cloud cover data;

[0110] The grids of global regions are graded according to the cloud-free factor and the influence factor is added;

[0111] Update the cloud cover in the data mapping results according to the influence coefficient;

[0112] Construct multiple single-model cloud coverage datasets based on the updated cloud cover data;

[0113] The single-model cloud coverage dataset is aggregated and calculated according to the preset cloud amount range to generate the cloud coverage area that can be photographed by remote sensing satellites.

[0114] For example, according to the VIRR global cloud cover daily product data of the Fengyun series meteorological satellites from 2017 to 2022, the world is divided into global grids at 0.5°*0.5°, the cloud cover of the grid is calculated according to the coverage and weighted average algorithm, the historical cloud cover data of the past five years are collected, and the number of days in which the cloud cover of a certain month in the five years of the grid is less than the cloud cover threshold value X=10 is calculated to obtain the cloud-free coefficient C x ;

[0115] For example, assuming that the number of days with cloud cover less than X=10 in a certain grid in the past five years is Z days, the global regional grids are graded according to the cloud-free coefficient, and the influencing coefficient on the prediction is added. The specific classification range is shown in Table 3 below:

[0116] Table 3

[0117] level Cloudless days Z Cloud-free factor Influence coefficient illustrate Ⅰ >130 >0.84 1 This area does not affect the forecast Ⅱ 100-130 0.65-0.84 1.2 Ⅲ 70-100 0.45-0.65 1.5 Ⅳ 40-70 0.26-0.45 2 Ⅴ <40 <0.26 2.5 This area affects the forecast

[0118] Exemplarily, the cloud cover in the data mapping result is updated according to the influence coefficient to obtain the data shown in Table 4 below:

[0119] Table 4

[0120] Grid The level corresponding to the number of cloudless days Z in May in the past five years Influence coefficient Initial cloud cover value Model final cloud cover value Grid a Ⅰ 1 45 45 Grid b Ⅱ 1.2 30 36 Grid c Ⅲ 1.5 30 45 Grid d Ⅳ 2 15 30 Grid Ⅴ 2.5 2 5

[0121] Exemplarily, multiple single-model cloud coverage data sets are constructed based on the data obtained in the above steps, and the range of cloud cover less than X in all grids of the model 1 data set on May 24, 2023 is aggregated and calculated, and X=10 is set. The grid data and cloud cover value of the data to be extracted at 00:00:00 on 2023-05-24 are shown in Table 5 below:

[0122] Table 5

[0123]

[0124] For example, the grid diagram corresponding to the grid in the above table can be as follows: Figure 6 shown.

[0125] Alternatively, if the satellite corresponding to model 1 is S, the aggregated data may be used to synthesize the cloudless or low cloud set NC1 of satellite S from 2023-05-24 00:00:00 to 2023-05-24 23:00:00, totaling all cloudless or low cloud sets within 24 hours.

[0126] For example, according to the set NC1, we can get Figure 7 The remote sensing satellite S shown can photograph cloud-covered areas.

[0127] Optionally, the method further comprises:

[0128] The cloud coverage areas photographable by remote sensing satellites generated by multiple single-model cloud coverage datasets are aggregated and calculated, and the multi-model remote sensing satellite cloud coverage areas are generated by selecting completely overlapping areas.

[0129] For example, the cloud-free range NC2 of model 2 is obtained according to the method of obtaining the cloud-free or low cloud set NC1. According to the set NC2, the following can be obtained: Figure 8 The remote sensing satellite S shown can photograph cloud-covered areas.

[0130] Then Figure 7 and Figure 8 The remote sensing satellites in the cloud coverage area can be photographed and aggregated to obtain the following Fig. 9 The multi-model remote sensing satellite shown can image cloud-covered areas.

[0131] Optionally, according to the method in step S104, a cloud coverage range dataset for each day may be formed, as shown in Table 6 below:

[0132] Table 6

[0133] time Dataset 2023-05-24 00:00:00 to 2023-05-24 23:00:00 <![CDATA[NC d ]]> 2023-05-25 00:00:00 to 2023-05-25 23:00:00 <![CDATA[NC d1 ]]> … …

[0134] Optionally, the remote sensing satellite photogenic cloud coverage area prediction method based on a multi-source meteorological model further includes:

[0135] Update cloud values ​​regularly every day to optimize cloud-free prediction results, and form a multi-satellite and multi-time cloud-free result set based on the characteristics of different satellites;

[0136] Generate a d+n-day cloud coverage data set of multiple different remote sensing satellites, and perform data iteration and cloud coverage update based on the data processing method in step S101; the final data set is shown in Table 7 below:

[0137] Table 7

[0138]

[0139] The embodiment of the present invention provides a method for predicting cloud coverage areas that can be photographed by remote sensing satellites based on multi-source meteorological models. By acquiring multi-source meteorological model data to analyze cloud cover, the reliability of data is improved compared to a solution using single cloud cover data, thereby improving the accuracy of subsequent cloud coverage area predictions. In addition, long-term predicted cloud cover data is used for real-time calculation and analysis, which can further increase the probability of satellites photographing in cloud-free areas. Furthermore, current data is analyzed and dynamically corrected by analyzing and calculating massive data and taking into account historical cloud cover and shooting area conditions. In summary, the success rate of satellite image acquisition and the cloud-free ratio of the overall satellite image area are greatly improved.

[0140] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) to form a technical solution.

Claims

1. A method for predicting cloud coverage area captured by remote sensing satellites based on a multi-source meteorological model, characterized in that: The method includes: Obtain multi-source meteorological model data and pre-process it; Obtain satellite orbit data, UTC time data and satellite shooting range data according to a preset method; Performing data mapping processing on the multi-source meteorological model data, the satellite orbit data and the shooting range data of the satellite; Generate multiple single-model cloud coverage data sets according to the data mapping results; the single-model cloud coverage data sets are used to predict the cloud coverage area that can be photographed by remote sensing satellites; The performing data mapping processing on the multi-source meteorological model data, the satellite orbit data and the shooting range data of the satellite includes: Implementing data mapping between the multi-source meteorological model data and the satellite orbit data according to the relationship between the time corresponding to the multi-source meteorological model data and the time corresponding to the satellite orbit data; Calculating grid data within the shooting range according to the shooting range data of the satellite; Implementing data mapping between the satellite orbit data and the shooting range data of the satellite according to the geographical location relationship between the grid data and the satellite orbit data; The final data mapping relationship is: longitude and latitude grid-central longitude and latitude-UTC time-cloud cover; The longitude and latitude grid is a grid with the central longitude and latitude as the center and the resolution as the length and width.

2. The method for predicting cloud coverage area captured by remote sensing satellites based on a multi-source meteorological model according to claim 1, characterized in that: The step of obtaining multi-source meteorological model data and preprocessing the data includes: Spatially aggregating the multi-source meteorological model data at a resolution and storing them in a preset format; Performing missing value and outlier detection on the cloud cover data in the multi-source meteorological model data; If outliers are detected, they are repaired using linear interpolation.

3. The method for predicting cloud coverage area captured by remote sensing satellites based on a multi-source meteorological model according to claim 1, characterized in that: The method of obtaining satellite orbit data, UTC time data and satellite shooting range data according to a preset method includes: Performing orbit calculation with preset time accuracy according to satellite design characteristics to obtain the satellite orbit data; According to the matching of the satellite orbit time and the meteorological model time, the half-point segmentation method is used to analyze and obtain the UTC time data; The maximum shooting range of the satellite at a preset time point is calculated by setting the maximum side swing angle of the satellite.

4. The method for predicting cloud coverage area captured by remote sensing satellites based on a multi-source meteorological model according to claim 1, characterized in that: The data mapping process of the multi-source meteorological model data, the satellite orbit data and the shooting range data of the satellite also includes: The repeated grids in the grid data are merged, the UTC time corresponding to the merged grids is the latest time, and the cloud amount value is the weighted average of the cloud amount values ​​of all merged grids.

5. The method for predicting cloud coverage area captured by remote sensing satellites based on a multi-source meteorological model according to claim 1, characterized in that: The generating of multiple single-model cloud coverage data sets according to the data mapping results includes: Get historical cloud cover data; Obtaining a cloud-free coefficient corresponding to a grid according to the historical cloud cover data analysis; According to the cloud-free factor, the grids of the global area are graded and an influence factor is added; updating the cloud cover in the data mapping result according to the influence coefficient; Construct multiple single-model cloud coverage datasets based on the updated cloud cover data; The single model cloud coverage data set is aggregated and calculated according to a preset cloud amount range to generate a cloud coverage area photographable by a remote sensing satellite.

6. The method for predicting cloud coverage area captured by remote sensing satellites based on a multi-source meteorological model according to claim 5, characterized in that: The method further includes: The cloud coverage areas photographable by remote sensing satellites generated by multiple single-model cloud coverage datasets are aggregated and calculated, and the multi-model remote sensing satellite cloud coverage areas are generated by selecting completely overlapping areas.

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