A method for reconstructing near-surface air temperature field with high temporal frequency from satellite remote sensing of the entire sky

Abstract

The present invention provides a method for reconstructing a near-surface temperature field of satellite remote sensing of the entire sky with high temporal frequency, and relates to the technical field of communication data processing. The method comprises: establishing an equation between the near-surface temperature field and the surface temperature field according to the surface energy balance equation; determining physical variables related to the estimation of the near-surface temperature field according to the equation between the near-surface temperature field and the surface temperature field; obtaining algorithm parameters for estimating the near-surface temperature field according to the physical variables, and preprocessing each parameter; establishing a temporal and spatial relationship between the algorithm parameters and the near-surface temperature field based on a geographic spatial relationship model, and solving the problem using a multi-scale geographic weighted regression method to obtain the near-surface temperature field. The present invention utilizes high temporal and frequency observations of geostationary satellites, and realizes accurate reconstruction of the near-surface temperature field of the entire sky with high temporal and frequency through physical equation modeling, spatiotemporal interpolation, and assimilation data fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of communication data processing, and in particular to a method for reconstructing a near-surface temperature field with high time-frequency and full-sky satellite remote sensing. Background Art

[0002] At present, satellite remote sensing research on near-surface temperature fields is mainly concentrated on polar-orbiting satellites. Polar-orbiting satellites have relatively high spatial resolution, but low temporal resolution. Polar-orbiting satellites usually observe the same location only twice a day, and this observation frequency is far from meeting the needs of obtaining high-frequency near-surface temperature fields. Geostationary satellites can conduct continuous observations of the same area hourly or even higher in frequency, and have obvious advantages in obtaining daily and diurnal changes in near-surface temperature fields. In addition, since thermal infrared remote sensing is greatly affected by water vapor in the atmosphere, it cannot penetrate clouds and cannot obtain surface radiation information under clouds. Therefore, the current remote sensing estimates of near-surface temperature fields are mostly limited to clear sky conditions, and it is impossible to obtain temperature under cloudy weather conditions. The reconstruction of the full-sky temperature field based on remote sensing usually requires the help of a large amount of auxiliary data, including some data that are difficult to obtain by remote sensing. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for reconstructing the near-surface temperature field of the entire sky with high time-frequency by satellite remote sensing, thereby realizing the reconstruction of the near-surface temperature field of the entire sky with high time-frequency.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] In a first aspect, a method for reconstructing a near-surface air temperature field with high temporal frequency and full sky remote sensing by satellite is provided, the method comprising:

[0006] The equation between the near-surface temperature field and the surface temperature field is established based on the surface energy balance equation;

[0007] Determine the physical variables related to the estimation of the near-surface air temperature field according to the equation between the near-surface air temperature field and the surface temperature field;

[0008] According to the physical variables, the algorithm parameters for estimating the near-surface air temperature field are obtained, and each parameter is preprocessed;

[0009] Based on the geospatial relationship model, the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field is established, and the multi-scale geographically weighted regression method is used to solve it to obtain the near-surface temperature field.

[0010] According to the algorithm parameters and meteorological station observation data, the near-surface temperature field under high-frequency clear sky conditions is calculated using the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field.

[0011] Based on the near-surface temperature field data of multiple times, sliding window spatiotemporal interpolation is performed to reconstruct the effective near-surface temperature field data and solve the near-surface temperature field under cloudy weather conditions within the window;

[0012] For the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, the all-sky high-frequency near-surface temperature field is reconstructed based on the spatiotemporal fusion method of assimilated data to obtain the reconstructed all-sky high-frequency near-surface temperature field.

[0013] Furthermore, the surface energy balance equation is:

[0014] ;

[0015] in, It represents the net solar radiation energy received by the surface; represents the sensible heat flux from the underlying surface to the atmosphere; represents soil heat flux; represents the latent heat flux from the underlying surface to the atmosphere; represents the downlink shortwave radiation, represents the surface albedo; It represents the effective emissivity of the atmosphere when there are no clouds; Indicates the reference altitude; represents the surface emissivity; is the real surface temperature field; represents the Stefan-Boltzmann constant;

[0016] The equations for the near-surface temperature field and the surface temperature field are:

[0017] ;

[0018] in, represents the wet-dry bulb constant; represents aerodynamic impedance; Represents the coefficient related to the surface vegetation coverage and leaf area index; It represents the slope of saturated water vapor pressure versus temperature; Indicates the air density; It represents the specific heat of air at constant pressure; Indicates temperature The current saturated water vapor pressure of the underlying surface; Indicates the water vapor pressure of the air at a reference altitude.

[0019] Furthermore, according to the physical variables, the algorithm parameters for estimating the near-surface temperature field are obtained, and each parameter is preprocessed, including:

[0020] Obtain hourly surface temperature field data from geostationary satellites, and parse the surface temperature field data to read the satellite observation time, longitude and latitude, cloud-covered pixels, and clear sky pixel surface temperature fields;

[0021] Obtain digital elevation model (DEM) data, parse the DEM data to read the altitude data, and resample the data to keep the spatial resolution consistent with the surface temperature field data;

[0022] The hourly surface temperature field data of the geostationary satellite and the digital elevation model DEM data are collected by Processing is performed to obtain preprocessed data, where represents the filtered surface temperature or elevation value at the coordinate (x, y); Ω represents the neighborhood range centered at the coordinate (x, y); Represents the original surface temperature or elevation value at coordinate (i, j); Represents the spatial domain weight, which is calculated based on the spatial distance between the pixel (i1, j1) and the center pixel (x1, y1). The closer the distance, the greater the weight.

[0023] ;

[0024] in, is the standard deviation of the Gaussian function in the spatial domain; Represents the pixel value range weight, which is calculated based on the pixel value difference between the pixel (i1, j1) and the center pixel (x1, y1), where:

[0025] ;

[0026] in, It is the standard deviation of the Gaussian function in the pixel value domain, which controls the decay speed of the pixel value weight; is the normalization factor.

[0027] Furthermore, the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field is established based on the geospatial relationship model, and the multi-scale geographically weighted regression method is used to solve it to obtain the near-surface temperature field, including:

[0028] The spatiotemporal relationship between algorithm parameters and near-surface temperature field is established based on the geospatial relationship model;

[0029] pass Solve the spatial and temporal relationship between the algorithm parameters and the near-surface temperature field to obtain the near-surface temperature field;

[0030] in, The near-surface air temperature field at the point; Indicates longitude; Indicates latitude; Indicates time; Indicates Algorithm parameters; represents random error; Represents the fitting solution coefficients of different algorithm parameters; the fitting solution coefficients are solved by the following formula:

[0031] ;

[0032] in, is a weight matrix whose elements are based on the distance between the estimated point j and the surrounding sample points k The specific formula is calculated as follows:

[0033] ;

[0034] in, is the adaptive bandwidth, whose value is determined by the golden section search method and the modified Akao information criterion is used As the bandwidth selection criterion; the calculation formula of AICc is:

[0035] ;

[0036] in, is the number of observations, is the standard deviation of the error term, is the hat matrix Trace of.

[0037] Furthermore, according to the algorithm parameters and the meteorological station observation data, the near-surface temperature field under high-frequency clear sky conditions is calculated by using the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field, including:

[0038] Conduct comprehensive quality inspection on the shutter box data observed at the meteorological station and eliminate data with abnormal quality;

[0039] The clear sky surface temperature field data are temporally and spatially matched with the station data, and the station data are matched with the average clear sky surface temperature field value of the 3×3 pixels around the pixel where the station data is located to obtain matching data;

[0040] 70% of the surface temperature field data and station data in the matching data are randomly selected as sample data and marked. The near-surface temperature field estimation model is used to build and calculate the high-frequency clear sky near-surface temperature field.

[0041] Furthermore, the effective near-surface temperature field data is reconstructed, and the near-surface temperature field under cloudy weather conditions within the window is solved, including:

[0042] For the pixels missing the near-surface temperature field under cloudy weather conditions at different times, a sliding window method with a size of 8×8 pixels is used for local spatial interpolation. When the proportion of near-surface temperature field pixels under clear sky conditions in the window is greater than 70%, a linear regression relationship between the temperature field and elevation of the clear sky pixels is established and extrapolated to the missing pixels of the temperature field in the window to solve the near-surface temperature field under cloudy weather conditions in the window.

[0043] Furthermore, for the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, the full-sky high-frequency near-surface temperature field is reconstructed based on the spatiotemporal fusion method of assimilated data to obtain the reconstructed full-sky high-frequency near-surface temperature field, including;

[0044] Obtain 2m temperature field data from the land surface data assimilation system CLDAS, and analyze and read time, longitude and latitude, and 2m temperature;

[0045] The bilinear interpolation method is used to resample the 2m temperature field data to a spatial resolution consistent with the satellite near-surface temperature field data;

[0046] For the missing pixels of the satellite near-surface temperature field, the 2m temperature field data of CLDAS at the corresponding time and geographical location are found according to the observation time and longitude and latitude information of the satellite near-surface temperature field, and the value is assigned to the missing pixels of the satellite near-surface temperature field to obtain the initial guess value of the temperature field of the missing pixels.

[0047] The difference between the non-missing pixel values ​​of the satellite near-surface temperature field within the 3×3 pixel range around the missing pixel and the 2m temperature field data of CLDAS, that is, the reconstruction error, is calculated. The initial guess value of the temperature field of the missing pixel is subtracted from the reconstruction error for secondary correction to obtain the reconstructed high-frequency near-surface temperature field of the entire sky.

[0048] Second, a satellite remote sensing all-sky high-frequency near-surface temperature field reconstruction system, including:

[0049] The acquisition module is used to pre-process the data uploaded to the cloud storage system to obtain pre-processed data; decompose the pre-processed data using the elliptic curve cryptography method to obtain decomposed data; analyze the characteristics of the decomposed data to obtain an analysis result, sort the decomposed data, and calculate a center point; sort the decomposed data according to the distance from each decomposed data to the center point, and evaluate the correlation between each decomposed data and the center point to obtain an evaluation result;

[0050] The processing module is used to store the decomposed data in the corresponding cloud storage node according to the analysis results and the evaluation results to form the data to be stored; use the encryption standard and the asymmetric encryption algorithm to encrypt the data to be stored to obtain the encrypted data; perform secondary construction on the encrypted data as the plaintext of the asymmetric encryption algorithm to form an encrypted data block; write the encrypted data block into the blockchain to form the blockchain encrypted data.

[0051] According to a third aspect, a computing device includes:

[0052] one or more processors;

[0053] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0054] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0055] The above solution of the present invention includes at least the following beneficial effects:

[0056] By using geostationary satellites for high-frequency continuous observations, it is possible to capture subtle changes in the near-surface temperature field and improve the accuracy of the data. At the same time, through the establishment of physical models and data interpolation, data loss is effectively reduced and data availability is improved. The sliding window spatiotemporal interpolation method based on multi-time near-surface temperature field data can effectively reconstruct a larger range of effective near-surface temperature field data, thereby expanding the coverage of the data and providing strong support for climate and environmental research in a wider area.

[0057] In view of the missing data of the near-surface temperature field under cloudy weather conditions, the spatiotemporal fusion method of assimilated data is used to reconstruct the near-surface temperature field with high temporal frequency throughout the sky, realizing all-weather observation capability and improving the integrity and continuity of the data. According to the surface energy balance equation, an equation with clear physical meaning between the near-surface temperature field and the surface temperature field is established, which provides a clear physical principle for satellite remote sensing estimation of the near-surface temperature field and enhances the scientificity and credibility of the data. This reconstruction method not only provides more accurate and comprehensive data support for research in the fields of climate, environment, agriculture, etc., but also helps to improve the cognition and understanding of the earth system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a method for reconstructing a satellite remote sensing all-sky high-time-frequency near-surface air temperature field provided by an embodiment of the present invention.

[0059] Figure 2It is a schematic diagram of a satellite remote sensing all-sky high time-frequency near-surface air temperature field reconstruction system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] like Figure 1 As shown, an embodiment of the present invention proposes a method for reconstructing a near-surface air temperature field with high time-frequency and full-sky satellite remote sensing, the method comprising the following steps:

[0062] Step 1, establishing an equation between the near-surface temperature field and the surface temperature field according to the surface energy balance equation;

[0063] Step 2, determining physical variables related to the estimation of the near-surface air temperature field according to the equation between the near-surface air temperature field and the surface temperature field;

[0064] Step 3: According to the physical variables, obtain the algorithm parameters for estimating the near-surface temperature field and pre-process each parameter;

[0065] Step 4: Based on the geospatial relationship model, the spatiotemporal relationship between the algorithm parameters and the near-surface temperature field is established, and the multi-scale geographically weighted regression method is used to solve the problem to obtain the near-surface temperature field;

[0066] Step 5, based on the algorithm parameters and the meteorological station observation data, the near-surface temperature field under high-frequency clear sky conditions is calculated using the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field;

[0067] Step 6: Perform sliding window spatiotemporal interpolation based on multi-time near-surface temperature field data, reconstruct effective near-surface temperature field data, and solve the near-surface temperature field under cloudy weather conditions within the window;

[0068] Step 7: For the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, the full-sky high-frequency near-surface temperature field is reconstructed based on the spatiotemporal fusion method of assimilated data to obtain the reconstructed full-sky high-frequency near-surface temperature field.

[0069] In an embodiment of the present invention, the mathematical relationship between the near-surface air temperature field and the surface temperature field is derived according to the surface energy balance equation. This equation is the basis for subsequent work, and it describes the physical connection between air temperature and surface temperature. By analyzing the above equation, physical variables directly related to the estimation of the near-surface air temperature field are identified. These variables are the key to understanding air temperature changes and influencing mechanisms. According to the determined physical variables, corresponding data are collected as algorithm parameters. These data may come from multiple sources such as satellite remote sensing and meteorological observation stations. The collected data is preprocessed, such as denoising, normalization, etc., to improve data quality and algorithm accuracy. Using the geospatial relationship model, the algorithm parameters are linked to the near-surface air temperature field to construct a spatiotemporal relationship model. The model is solved using a multi-scale geographical weighted regression method to obtain an estimated value of the near-surface air temperature field. Combining the algorithm parameters with the observation data of the meteorological station, the spatiotemporal relationship model established previously is used to calculate the near-surface air temperature field with high time frequency under clear sky conditions. The near-surface temperature field data of multiple times are interpolated in sliding windows in space and time to fill the missing data under cloudy weather conditions, so as to reconstruct more complete and effective near-surface temperature field data. For the near-surface temperature field data under cloudy weather conditions that are still missing, the space-time fusion method of assimilated data is used to complete them. Finally, the reconstructed near-surface temperature field data of the whole sky and high time frequency are obtained.

[0070] By establishing precise equations and spatiotemporal relationship models, the near-surface temperature field can be estimated more accurately and errors can be reduced. The spatiotemporal fusion method of sliding window spatiotemporal interpolation and assimilated data effectively fills the data gaps under cloudy weather conditions and enhances the integrity and continuity of the data. High-frequency observations and estimates enable changes in the near-surface temperature field to be captured and reflected more promptly, providing faster data support for relevant decisions. The full-sky, high-frequency near-surface temperature field reconstruction data has important application value in many fields such as climate research, environmental monitoring, and agricultural management.

[0071] In a preferred embodiment of the present invention, the formula of the surface energy balance equation is:

[0072] ;

[0073] in, It represents the net solar radiation energy received by the surface; represents the sensible heat flux from the underlying surface to the atmosphere; represents soil heat flux; represents the latent heat flux from the underlying surface to the atmosphere; represents the downlink shortwave radiation, represents the surface albedo; It represents the effective emissivity of the atmosphere when there are no clouds; Indicates the reference altitude; represents the surface emissivity; is the real surface temperature field; represents the Stefan-Boltzmann constant;

[0074] The equations for the near-surface temperature field and the surface temperature field are:

[0075] ;

[0076] in, represents the wet-dry bulb constant; represents aerodynamic impedance; Represents the coefficient related to the surface vegetation coverage and leaf area index; It represents the slope of saturated water vapor pressure versus temperature; Indicates the air density; It represents the specific heat of air at constant pressure; Indicates temperature The current saturated water vapor pressure of the underlying surface; Indicates the water vapor pressure of the air at a reference altitude.

[0077] In the embodiment of the present invention, the surface energy balance equation describes the income and expenditure of surface energy, that is, how the solar radiation energy received by the surface is distributed between the surface and the atmosphere. Specifically, the net solar radiation energy Rn obtained by the surface is equal to the downward shortwave radiation Subtract the part reflected by the surface (the product of the surface albedo α and the downward shortwave radiation), and then subtract the energy emitted by the surface longwave radiation (the surface emissivity , Stefan-Boltzmann constant σ and true surface temperature This net energy is converted into sensible heat flux (H, which is the heat transferred from the surface to the atmosphere), soil heat flux (G, which is the heat transferred into the ground), and latent heat flux (LE, which is the heat consumed by the evaporation of surface water).

[0078] The surface temperature Tg is a direct reflection of the surface energy balance. The surface receives solar radiation and exchanges heat with it. This energy is partially absorbed by the surface, partially reflected back to the atmosphere, and partially emitted as long-wave radiation. These processes directly affect the surface temperature.

[0079] Aerodynamic impedance ra reflects the resistance of air flow to the heat exchange between the surface and the atmosphere. The greater the impedance, the more difficult the heat transfer between the surface and the atmosphere, thus affecting the near-surface temperature Ta.

[0080] The slope Δ of the saturated water vapor pressure versus temperature describes the relationship between temperature and water vapor pressure. As the surface temperature changes, the water vapor pressure in the air will also change accordingly, thereby affecting the near-surface temperature. The air density ρ and the air constant pressure specific heat Cp jointly determine the air's heat storage capacity. The greater the air density, the higher the constant pressure specific heat, and the more heat the air can store, thus affecting the change in near-surface temperature. The air water vapor pressure ea at the reference height reflects the moisture content in the air. Water vapor is an important component of the atmosphere and has an important impact on the heat exchange and radiation balance between the surface and the atmosphere.

[0081] The wet-dry bulb constant γ is used to describe the dryness and wetness of the air. It affects the heat and water vapor exchange between the surface and the atmosphere.

[0082] Through these two equations, we can have a deeper understanding of the energy exchange process between the surface and the atmosphere, and how these processes affect the near-surface temperature. It helps scientists and researchers better understand the operating laws of the climate system. Based on these equations, more accurate prediction models can be constructed to predict near-surface temperature and other related climate variables. This is of great significance to many fields such as agriculture, energy, and transportation. Accurate predictions and in-depth understanding can help decision makers develop more effective response strategies to meet the challenges brought by climate change. For example, in the agricultural field, understanding the changes in near-surface temperature can help farmers choose appropriate sowing times and crop varieties.

[0083] In a preferred embodiment of the present invention, the algorithm parameters for estimating the near-surface air temperature field are obtained according to the physical variables, and each parameter is preprocessed, including:

[0084] Obtain hourly surface temperature field data from geostationary satellites, and parse the surface temperature field data to read the satellite observation time, longitude and latitude, cloud-covered pixels, and clear sky pixel surface temperature fields;

[0085] Obtain digital elevation model (DEM) data, parse the DEM data to read the altitude data, and resample the data to keep the spatial resolution consistent with the surface temperature field data;

[0086] The hourly surface temperature field data of the geostationary satellite and the digital elevation model DEM data are collected by Processing is performed to obtain preprocessed data, where Represents the filtered surface temperature or elevation value at the coordinate (x, y); Ω represents the neighborhood range centered on the coordinate (x, y), which can be adjusted as needed and is a rectangular or circular area; Represents the original surface temperature or elevation value at coordinate (i, j); Represents the spatial domain weight, which is calculated based on the spatial distance between the pixel (i1, j1) and the center pixel (x1, y1). The closer the distance, the greater the weight. This weight function is implemented using a Gaussian function, for example:

[0087] ;

[0088] in, It is the standard deviation of the Gaussian function in the spatial domain, which controls the decay rate of the spatial weight; Represents the pixel value range weight, which is calculated based on the pixel value difference between the pixel (i1, j1) and the center pixel (x1, y1). The closer the pixel value, the greater the weight. This weight function is also implemented using a Gaussian function, for example:

[0089] ;

[0090] in, It is the standard deviation of the Gaussian function in the pixel value domain, which controls the decay speed of the pixel value weight; It is a normalization factor, which is equal to the sum of the products of the spatial domain weights and the pixel value domain weights of all pixels in the neighborhood, and is used to ensure that the filtered value is within a reasonable range. Through this complex bilateral filtering formula, edge information can be better maintained while smoothing the data, because bilateral filtering takes into account both the spatial distance between pixels and the similarity of pixel values.

[0091] In an embodiment of the present invention, a geostationary satellite can provide continuous surface temperature observation data. By analyzing these data, the specific time of satellite observation, geographical location information (latitude and longitude), and the cloud coverage state of each pixel and the surface temperature under the clear sky state can be obtained. DEM data provides the elevation information of the surface, which is one of the important factors affecting the surface temperature. The DEM data is analyzed and the specific elevation value can be read. In order to ensure that the DEM data matches the surface temperature field data in space, it is necessary to resample the DEM data so that its spatial resolution is consistent with the surface temperature field data. The preprocessing step includes data cleaning, format conversion, spatial matching, etc. to ensure the accuracy and reliability of subsequent analysis. For the surface temperature field data, it is necessary to identify and process the cloud coverage pixels to avoid their interference with the estimation of the near-surface temperature field. For the DEM data, the resampling process can ensure its spatial alignment with the surface temperature field data, which is convenient for subsequent data fusion and analysis.

[0092] By acquiring high-temporal and spatial resolution geostationary satellite surface temperature field data, the dynamic changes of surface temperature can be captured more accurately. The introduction of DEM data provides a basis for considering the impact of terrain factors on surface temperature, thereby improving the accuracy of near-surface temperature field estimation. By resampling DEM data, its consistency with the surface temperature field data in spatial resolution is ensured, which facilitates subsequent data fusion and model construction. The preprocessing step can remove invalid data and outliers, reduce interference factors in subsequent analysis, and improve analysis efficiency.

[0093] In a preferred embodiment of the present invention, the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field is established based on the geospatial relationship model, and a multi-scale geographically weighted regression method is used to solve the near-surface temperature field, including:

[0094] The spatiotemporal relationship between algorithm parameters and near-surface temperature field is established based on the geospatial relationship model;

[0095] pass Solve the spatial and temporal relationship between the algorithm parameters and the near-surface temperature field to obtain the near-surface temperature field;

[0096] in, The near-surface air temperature field at the point; Indicates longitude; Indicates latitude; Indicates time; Indicates Algorithm parameters; represents random error; Represents the fitting solution coefficients of different algorithm parameters; the fitting solution coefficients are solved by the following formula:

[0097] ;

[0098] in, is a weight matrix whose elements are based on the distance between the estimated point j and the surrounding sample points k The specific formula is calculated as follows:

[0099] ;

[0100] in, is the adaptive bandwidth, whose value is determined by the golden section search method and the modified Akao information criterion is used As the bandwidth selection criterion; the calculation formula of AICc is:

[0101] ;

[0102] in, is the number of observations, is the standard deviation of the error term, is the hat matrix Trace of.

[0103] In the embodiment of the present invention, based on the geospatial relationship model, the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field is established by considering the geographical location (longitude and latitude) and time factors. Algorithm parameters Including surface temperature field, altitude, etc. These parameters have a significant impact on the near-surface temperature field. Construct a regression model to estimate the temperature at a specific time and space point. Near-surface air temperature field In the model, is the dependent variable, and is the independent variable, each of which is solved by a fitting coefficient is associated with the dependent variable. Introducing random error terms To represent all other influencing factors that are not explicitly considered in the model. Use statistical techniques (such as weighted least squares) to fit the model and determine the fitting coefficients of each algorithm parameter These coefficients reflect the influence of each algorithm parameter on the near-surface temperature field under a specific spatiotemporal background. And their corresponding fitting coefficients Substitute into the model and calculate at a given time and space point The predicted value of near-surface air temperature field The model was validated using an independent validation dataset and the accuracy of the model was assessed by comparing the predicted values ​​with the actual observed values.

[0104] Based on the validation results, the model can be adjusted, such as by adding or deleting certain algorithm parameters, or optimizing the fitting method to improve the model's prediction performance. The predicted near-surface temperature field data can be used in various application scenarios, such as climate model construction, agricultural management decision-making, and environmental monitoring. These data can also be combined with other environmental parameters to gain a more comprehensive understanding of regional climate conditions.

[0105] In a preferred embodiment of the present invention, according to the algorithm parameters and the meteorological station observation data, the near-surface temperature field under high time-frequency clear sky conditions is calculated by using the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field, including:

[0106] Conduct comprehensive quality inspection on the shutter box data observed at the meteorological station and eliminate data with abnormal quality;

[0107] The clear sky surface temperature field data are temporally and spatially matched with the station data, and the station data are matched with the average clear sky surface temperature field value of the 3×3 pixels around the pixel where the station data is located to obtain matching data;

[0108] 70% of the surface temperature field data and station data in the matching data are randomly selected as sample data and marked. The near-surface temperature field estimation model is used to build and calculate the high-frequency clear sky near-surface temperature field.

[0109] In an embodiment of the present invention, first, a comprehensive quality check is performed on the shutter box data observed by the meteorological station. By setting a reasonable data range, checking the continuity and consistency of the data, etc., erroneous or unreasonable data points are identified and removed. The clear sky surface temperature field data is matched with the meteorological station data in time and space. This step is to ensure the correspondence between the two in time and space, so that the relationship between them can be accurately analyzed. In the matching process, not only the correspondence between the clear sky surface temperature field data and the station data at the same observation time is considered, but also the station data is matched with the clear sky surface temperature field average of the 3×3 pixels around the pixel where it is located. This approach can more comprehensively reflect the surface temperature conditions in the area where the station is located and improve the accuracy of subsequent analysis. 70% of the matched data is randomly selected as sample data and marked. This not only ensures the representativeness of the sample, but also provides sufficient data support for the training and verification of the subsequent model. Modeling is performed using these sample data and the near-surface temperature field estimation model. By training the model, it can accurately capture the complex relationship between the algorithm parameters and the near-surface temperature field. Based on the trained model, the high-frequency clear sky near-surface temperature field is calculated. This step makes full use of the learning and predictive capabilities of the model to achieve a high-precision estimation of the near-surface temperature field.

[0110] Through a rigorous data quality inspection and cleaning process, the accuracy and reliability of the input data are ensured, laying a solid foundation for subsequent analysis. Through the time-space matching step, the clear sky surface temperature field data and the meteorological station data are accurately matched in time and space, improving the accuracy and credibility of the analysis. Using a large amount of sample data and advanced estimation models for modeling, the model can more accurately capture and predict the changing laws of the near-surface temperature field.

[0111] In a preferred embodiment of the present invention, reconstructing effective near-surface air temperature field data and solving the near-surface air temperature field under cloudy weather conditions within the window includes:

[0112] For the pixels missing the near-surface temperature field under cloudy weather conditions at different times, a sliding window method with a size of 8×8 pixels is used for local spatial interpolation. When the proportion of near-surface temperature field pixels under clear sky conditions in the window is greater than 70%, a linear regression relationship between the temperature field and elevation of the clear sky pixels is established and extrapolated to the missing pixels of the temperature field in the window to solve the near-surface temperature field under cloudy weather conditions in the window.

[0113] In an embodiment of the present invention, for pixels with missing near-surface temperature field data, especially under cloudy weather conditions, a sliding window of 8×8 pixel size is used for local spatial analysis. This window slides across the entire temperature field data, and each missing pixel is processed locally. In each 8×8 sliding window, the proportion of near-surface temperature field pixels under clear sky conditions is calculated. If this proportion is greater than 70%, it means that the temperature data of most pixels in the window are valid, and the next step of processing can be performed.

[0114] In the window where the proportion of clear sky pixels is greater than 70%, a linear regression relationship between the temperature field and elevation of the clear sky pixels is established. This linear regression model describes the trend of temperature change with elevation and is used to predict the temperature values ​​of missing pixels. Using the linear regression relationship established in the previous step, the temperature values ​​of the missing pixels in the temperature field in the window are extrapolated. In this way, the temperature data gaps under cloudy weather conditions can be filled, thereby reconstructing a complete near-surface temperature field.

[0115] The local spatial interpolation method can effectively fill in the missing near-surface temperature field data under cloudy weather conditions and improve the integrity and continuity of the entire temperature field data. The use of an 8×8 pixel sliding window and clear sky pixel ratio judgment ensures that interpolation is performed on the basis of sufficient valid data points, thereby improving the accuracy of the interpolation results. Complete and accurate temperature field data are of great significance to many fields such as climate research, environmental monitoring, and agricultural production. The temperature field data reconstructed by this method can provide more reliable support for applications in these fields.

[0116] In a preferred embodiment of the present invention, for the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, the all-sky high-frequency near-surface temperature field is reconstructed based on the spatiotemporal fusion method of assimilated data to obtain the reconstructed all-sky high-frequency near-surface temperature field, including:

[0117] Obtain 2m temperature field data from the land surface data assimilation system CLDAS, and analyze and read time, longitude and latitude, and 2m temperature;

[0118] The bilinear interpolation method is used to resample the 2m temperature field data to a spatial resolution consistent with the satellite near-surface temperature field data;

[0119] For the missing pixels of the satellite near-surface temperature field, the 2m temperature field data of CLDAS at the corresponding time and geographical location are found according to the observation time and longitude and latitude information of the satellite near-surface temperature field, and the value is assigned to the missing pixels of the satellite near-surface temperature field to obtain the initial guess value of the temperature field of the missing pixels.

[0120] The difference between the non-missing pixel values ​​of the satellite near-surface temperature field within the 3×3 pixel range around the missing pixel and the 2m temperature field data of CLDAS, that is, the reconstruction error, is calculated. The initial guess value of the temperature field of the missing pixel is subtracted from the reconstruction error for secondary correction to obtain the reconstructed high-frequency near-surface temperature field of the entire sky.

[0121] In an embodiment of the present invention, 2m temperature field data is obtained from the Land Data Assimilation System (CLDAS). These data are parsed to extract time, longitude and latitude, and 2m temperature information. The 2m temperature field data of CLDAS are resampled using a bilinear interpolation method to ensure that its spatial resolution is consistent with the satellite near-surface temperature field data. For missing pixels in the satellite near-surface temperature field, the 2m temperature value of the corresponding time and geographical location is found in the CLDAS data based on the observation time and longitude and latitude information. This temperature value is assigned to the missing pixel as the initial guess of its temperature field. The difference between the non-missing pixel value of the satellite near-surface temperature field within the 3×3 pixel range around the missing pixel and the corresponding CLDAS 2m temperature field data is calculated. This difference is called the reconstruction error. The initial guess of the temperature field of the missing pixel is subtracted from the reconstruction error, and a secondary correction is performed. Through this correction, the accuracy of the estimation of the temperature field of the missing pixel can be further improved.

[0122] By assimilating CLDAS data with satellite near-surface temperature field data, the fusion of multi-source data is achieved, thereby improving the accuracy and reliability of temperature field reconstruction. Resampling by bilinear interpolation ensures the consistency of spatial resolution between the reconstructed temperature field data and the original satellite data, providing support for high-precision applications. CLDAS data is used to provide initial guess values ​​for missing pixels, and corrections are made through the information of surrounding pixels, effectively filling in the missing data in the satellite near-surface temperature field. This method is not only applicable to clear sky conditions, but can also reconstruct the temperature field under cloudy weather conditions, realizing the reconstruction of the high-frequency near-surface temperature field of the entire sky, providing more comprehensive data support for climate monitoring and prediction.

[0123] like Figure 2 As shown, an embodiment of the present invention further provides a satellite remote sensing all-sky high time-frequency near-surface air temperature field reconstruction system 20, comprising:

[0124] The acquisition module 21 is used to pre-process the data uploaded to the cloud storage system to obtain pre-processed data; decompose the pre-processed data using the elliptic curve cryptography method to obtain decomposed data; analyze the characteristics of the decomposed data to obtain an analysis result, sort the decomposed data, and calculate a center point; sort the decomposed data according to the distance from each decomposed data to the center point, and evaluate the correlation between each decomposed data and the center point to obtain an evaluation result;

[0125] The processing module 22 is used to store the decomposed data in the corresponding cloud storage node according to the analysis results and the evaluation results to form the data to be stored; use the encryption standard and the asymmetric encryption algorithm to encrypt the data to be stored to obtain the encrypted data; perform secondary construction on the encrypted data as the plaintext of the asymmetric encryption algorithm to form an encrypted data block; write the encrypted data block into the blockchain to form the blockchain encrypted data.

[0126] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0127] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0128] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0129] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for reconstructing the near-surface temperature field of all-sky high-frequency satellite remote sensing, characterized in that: The method comprises: The equation between the near-surface temperature field and the surface temperature field is established based on the surface energy balance equation; Determine the physical variables related to the estimation of the near-surface air temperature field according to the equation between the near-surface air temperature field and the surface temperature field; According to the physical variables, the algorithm parameters for estimating the near-surface air temperature field are obtained, and each parameter is preprocessed; Based on the geospatial relationship model, the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field is established, and the multi-scale geographically weighted regression method is used to solve it to obtain the near-surface temperature field. According to the algorithm parameters and meteorological station observation data, the near-surface temperature field under high-frequency clear sky conditions is calculated using the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field. Based on the near-surface temperature field data of multiple times, sliding window spatiotemporal interpolation is performed to reconstruct the effective near-surface temperature field data and solve the near-surface temperature field under cloudy weather conditions within the window; For the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, the all-sky high-frequency near-surface temperature field is reconstructed based on the spatiotemporal fusion method of assimilated data to obtain the reconstructed all-sky high-frequency near-surface temperature field; According to the physical variables, the algorithm parameters for estimating the near-surface temperature field are obtained, and each parameter is preprocessed, including: Obtain hourly surface temperature field data from geostationary satellites, and parse the surface temperature field data to read the satellite observation time, longitude and latitude, cloud-covered pixels, and clear sky pixel surface temperature fields; Obtain digital elevation model (DEM) data, parse the DEM data to read the altitude data, and resample the data to keep the spatial resolution consistent with the surface temperature field data; The hourly surface temperature field data of the geostationary satellite and the digital elevation model DEM data are collected by Processing is performed to obtain preprocessed data, where represents the filtered surface temperature or elevation value at the coordinate (x, y); Ω represents the neighborhood range centered at the coordinate (x, y); Represents the original surface temperature or elevation value at coordinate (i, j); Represents the spatial domain weight, which is calculated based on the spatial distance between the pixel (i1, j1) and the center pixel (x1, y1). The closer the distance, the greater the weight. ； in, is the standard deviation of the spatial domain Gaussian function; represents the pixel value domain weight, which is calculated based on the pixel value difference between the pixel (i1, j1) and the center pixel (x1, y1); is the normalization factor; For the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, the all-sky high-frequency near-surface temperature field is reconstructed based on the spatiotemporal fusion method of assimilated data to obtain the reconstructed all-sky high-frequency near-surface temperature field, including; Obtain 2m temperature field data from the land surface data assimilation system CLDAS, and analyze and read time, longitude and latitude, and 2m temperature; The bilinear interpolation method is used to resample the 2m temperature field data to a spatial resolution consistent with the satellite near-surface temperature field data; For the missing pixels of the satellite near-surface temperature field, the 2m temperature field data of CLDAS corresponding to the time and geographical location are found according to the observation time and longitude and latitude information of the satellite near-surface temperature field, and the temperature field data are assigned to the missing pixels of the satellite near-surface temperature field to obtain the initial guess value of the temperature field of the missing pixels. The difference between the non-missing pixel values ​​of the satellite near-surface temperature field within the 3×3 pixel range around the missing pixel and the 2m temperature field data of CLDAS, that is, the reconstruction error, is calculated. The initial guess value of the temperature field of the missing pixel is subtracted from the reconstruction error for secondary correction to obtain the reconstructed high-frequency near-surface temperature field of the entire sky.

2. The method for reconstructing the near-surface air temperature field of satellite remote sensing all sky with high time-frequency according to claim 1, characterized in that: According to the algorithm parameters and meteorological station observation data, the near-surface temperature field under high-frequency clear sky conditions is calculated using the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field, including: Conduct comprehensive quality inspection on the shutter box data observed at the meteorological station and eliminate data with abnormal quality; The clear sky surface temperature field data are temporally and spatially matched with the station data, and the station data are matched with the average clear sky surface temperature field value of the 3×3 pixels around the pixel where the station data is located to obtain matching data; 70% of the surface temperature field data and station data in the matching data are randomly selected as sample data and marked. The near-surface temperature field estimation model is used to build and calculate the high-frequency clear sky near-surface temperature field.

3. The method for reconstructing the near-surface air temperature field of satellite remote sensing all sky with high time-frequency according to claim 2, characterized in that: Reconstruct the effective near-surface temperature field data and solve the near-surface temperature field under cloudy weather conditions within the window, including: For the pixels missing the near-surface temperature field under cloudy weather conditions at different times, a sliding window method with a size of 8×8 pixels is used for local spatial interpolation. When the proportion of near-surface temperature field pixels under clear sky conditions in the window is greater than 70%, a linear regression relationship between the temperature field and elevation of the clear sky pixels is established and extrapolated to the missing pixels of the temperature field in the window to solve the near-surface temperature field under cloudy weather conditions in the window.

4. A satellite remote sensing all-sky high-frequency near-surface temperature field reconstruction system, characterized in that: include: An acquisition module is used to establish an equation between the near-surface air temperature field and the surface temperature field according to the surface energy balance equation; Determine the physical variables related to the estimation of the near-surface air temperature field according to the equation between the near-surface air temperature field and the surface temperature field; According to the physical variables, the algorithm parameters for estimating the near-surface air temperature field are obtained, and each parameter is preprocessed; Based on the geospatial relationship model, the temporal and spatial relationship between the algorithm parameters and the near-surface temperature field is established, and the multi-scale geographically weighted regression method is used to solve it to obtain the near-surface temperature field. The processing module is used to calculate the near-surface temperature field under high-frequency clear sky conditions by using the spatial-temporal relationship between the algorithm parameters and the near-surface temperature field according to the algorithm parameters and the meteorological station observation data; perform sliding window spatial-temporal interpolation based on the near-surface temperature field data of multiple times, reconstruct the effective near-surface temperature field data, and solve the near-surface temperature field under cloudy weather conditions within the window; for the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, reconstruct the high-frequency near-surface temperature field of the whole sky based on the spatial-temporal fusion method of assimilated data, so as to obtain the reconstructed high-frequency near-surface temperature field of the whole sky; According to the physical variables, the algorithm parameters for estimating the near-surface temperature field are obtained, and each parameter is preprocessed, including: Obtain hourly surface temperature field data from geostationary satellites, and parse the surface temperature field data to read the satellite observation time, longitude and latitude, cloud-covered pixels, and clear sky pixel surface temperature fields; Obtain digital elevation model (DEM) data, parse the DEM data to read the altitude data, and resample the data to keep the spatial resolution consistent with the surface temperature field data; The hourly surface temperature field data of the geostationary satellite and the digital elevation model DEM data are collected by Processing is performed to obtain preprocessed data, where represents the filtered surface temperature or elevation value at the coordinate (x, y); Ω represents the neighborhood range centered at the coordinate (x, y); Represents the original surface temperature or elevation value at coordinate (i, j); Represents the spatial domain weight, which is calculated based on the spatial distance between the pixel (i1, j1) and the center pixel (x1, y1). The closer the distance, the greater the weight. ； in, is the standard deviation of the Gaussian function in the spatial domain; Represents the pixel value range weight, which is calculated based on the pixel value difference between the pixel (i1, j1) and the center pixel (x1, y1); is the normalization factor; For the missing data of the near-surface temperature field under cloudy weather conditions that have not been solved, the all-sky high-frequency near-surface temperature field is reconstructed based on the spatiotemporal fusion method of assimilated data to obtain the reconstructed all-sky high-frequency near-surface temperature field, including; Obtain 2m temperature field data from the land surface data assimilation system CLDAS, and analyze and read time, longitude and latitude, and 2m temperature; The bilinear interpolation method is used to resample the 2m temperature field data to a spatial resolution consistent with the satellite near-surface temperature field data; For the missing pixels of the satellite near-surface temperature field, the 2m temperature field data of CLDAS corresponding to the time and geographical location are found according to the observation time and longitude and latitude information of the satellite near-surface temperature field, and the temperature field data are assigned to the missing pixels of the satellite near-surface temperature field to obtain the initial guess value of the temperature field of the missing pixels. The difference between the non-missing pixel values ​​of the satellite near-surface temperature field within the 3×3 pixel range around the missing pixel and the 2m temperature field data of CLDAS, that is, the reconstruction error, is calculated. The initial guess value of the temperature field of the missing pixel is subtracted from the reconstruction error for secondary correction to obtain the reconstructed high-frequency near-surface temperature field of the entire sky.

5. A computing device, characterized in that: include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 3 when executed by a processor.

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