Satellite radiation product correction and spatial downscaling method

Abstract

The invention discloses a satellite radiation product correction and spatial downscaling method, and relates to the technical field of satellite radiation product correction, and the method comprises the following steps: carrying out the first correction of to-be-corrected radiation data through a first correction coefficient, and obtaining second radiation data; judging whether the target grid is shielded by the terrain based on a digital elevation model, and performing adjacent grid weighting correction on the second radiation data of the shielded grid to generate third radiation data; establishing a linear regression model of the ground radiation value and the altitude, and calculating a second correction coefficient based on the relative distance between the grid altitude and the regional average altitude; and performing secondary correction on the third radiation data through the second correction coefficient, and outputting the target radiation data, thereby solving the problems of insufficient precision and low spatial resolution of a satellite radiation product.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite radiation product correction, and more specifically, to a method for satellite radiation product correction and spatial downscaling. Background Art

[0002] Due to the absorption and scattering of solar radiation by the atmosphere, there are significant differences in surface solar radiation at different times and in different regions, which are mainly related to changes in atmospheric composition, cloud cover, water vapor content, and atmospheric suspension content. Therefore, it is particularly important to reasonably and accurately determine the spatio-temporal distribution of solar radiation resources.

[0003] In the process of solar energy resource assessment, in order to improve the problem of inaccurate assessment caused by sparse stations, existing satellite radiation product correction methods generally use climatological methods to increase radiation data samples, that is, to establish a relationship between the total horizontal radiation (SSI) at radiation stations, sunshine percentage, and extraterrestrial solar radiation (or clear-sky radiation). For stations without radiation observations, this relationship is used in combination with the local sunshine percentage and extraterrestrial solar radiation (or clear-sky radiation) to estimate SSI.

[0004] Traditional satellite radiation product correction methods mainly rely on sparse ground station observation data and historical climate statistical relationships, lacking the ability to dynamically respond to real-time changes in atmospheric conditions. In complex terrain areas, the distribution of solar radiation is affected by multiple factors such as cloud changes, terrain shading, and aerosol concentration, and the radiation intensity may fluctuate significantly on hourly or even minute-level time scales. However, existing climatological methods are difficult to capture these transient changes in a timely manner and perform spatial refinement characterization, resulting in a deviation between the assessment results and the actual radiation distribution.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for satellite radiation product correction and spatial downscaling, which fuses ground observation data and satellite remote sensing data, and combines terrain feature analysis to solve the problems of insufficient accuracy and low spatial resolution of satellite radiation products.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for correcting satellite radiation products and spatial downscaling, comprising the following steps: performing a first correction on the radiation data to be corrected through a first correction coefficient to obtain second radiation data; judging whether the target grid is blocked by terrain based on the digital elevation model, and performing adjacent grid weighted correction on the second radiation data of the blocked grid to generate third radiation data; establishing a linear regression model between the ground radiation value and the altitude, and calculating the second correction coefficient based on the relative anomaly between the grid altitude and the regional average altitude; performing a second correction on the third radiation data through the second correction coefficient to output the target radiation data.

[0008] In a preferred embodiment, the first correction includes data quality control based on a number of abnormal radiation values. Specifically, a sliding window and standard deviation method is used to preliminarily screen the radiation data to be corrected, and potential abnormal values deviating from the statistical characteristics are identified; multi-source auxiliary information is introduced to judge the rationality of the potential abnormal values, including analyzing the cloud amount and atmospheric transmittance changes in combination with the synchronous meteorological background and verifying the spatial consistency of the pixel neighborhood; the abnormal values determined to be reasonable are retained, the invalid abnormal values are eliminated, and time series interpolation is used to fill the gaps.

[0009] In a preferred embodiment, the step of performing a first correction on the radiation data to be corrected through the first correction coefficient is specifically as follows: interpolating the radiation data to be corrected through the inverse distance weighting method to generate a correction coefficient to be corrected; correcting the correction coefficient to be corrected based on the first data to obtain the first correction coefficient, where the first data includes the radiation relative ratio coefficient under different cloud states and the combination of the ground radiation and satellite radiation correction coefficient and the diurnal variation correction coefficient; using the first correction coefficient to perform the first correction of the radiation data.

[0010] In a preferred embodiment, the step of correcting the correction coefficient to be corrected based on the first data to obtain the first correction coefficient is specifically as follows: performing spatial interpolation on the ground radiation and satellite radiation correction coefficient and the diurnal variation correction coefficient to generate an initial correction coefficient for the full grid of satellite radiation data; obtaining the theoretical cloud detection value of each grid point based on the satellite cloud detection data of the ground radiation observation site; calculating the difference between the actual satellite cloud detection data and the theoretical cloud detection value of each grid point; using the radiation relative ratio coefficient under different cloud states to compensate and correct the difference of the initial correction coefficient to generate the first correction coefficient of the grid point with the original resolution.

[0011] In a preferred embodiment, the method for determining whether a target grid is blocked by terrain based on a digital elevation model is as follows: Obtain the occlusion impact data of the target radiation data, where the occlusion impact data includes the grid point slope and aspect derived from the digital elevation model, as well as the hourly solar altitude angle and azimuth angle within a year; for each grid point, determine whether it is blocked by terrain based on the angle between the current solar azimuth angle and the aspect, the slope, and the solar altitude angle; count the proportion of unblocked grids within the target grid range during a preset period to generate an index of the proportion of the effective radiation area.

[0012] In a preferred embodiment, the specific analysis method for establishing the linear regression model between the ground radiation value and the altitude is as follows: Extract the altitude data of all ground radiation observation stations within the target area; with each observation station as the center, gradually increase the analysis radius until at least N other radiation stations are covered within the radius; for each station, use the dataset of stations within the radius to solve the linear regression equation between the ground radiation value and the altitude by the least squares method.

[0013] In a preferred embodiment, the establishment of the linear regression model between the ground radiation value and the altitude further includes quantitatively correcting the linear relationship based on pre-obtained additional variables, specifically: Obtain the additional variable set of the target area; use the radiation-altitude linear regression residual as the dependent variable and perform multiple regression with the additional variables to generate a comprehensive correction equation.

[0014] In a preferred embodiment, the calculation of the second correction coefficient based on the relative anomaly between the grid altitude and the regional average altitude includes: Extract the altitude data from high-precision terrain data, obtain the average altitude data of each satellite radiation data by averaging, as well as the altitude anomaly of each grid point relative to the average altitude of the corresponding grid point, and perform spatial smoothing processing on the average altitude anomaly using a sliding window algorithm. The specific processing method is: Combine the spatial distance relationship between the target grid point and its surrounding adjacent grid points, introduce a weight function to construct a spatial weighted model, and the weight function is constructed using a Gaussian function.

[0015] In a preferred embodiment, the specific method for obtaining the second correction coefficient is: Based on the solution of the linear relationship equation, calculate the percentage change in the radiation data observed at the ground radiation observation stations that changes with altitude, and calculate in combination with the average altitude anomaly to obtain the required second correction coefficient.

[0016] The technical effects and advantages of a satellite radiation product correction and spatial downscaling method of the present invention: 1. The present invention constructs a multi-dimensional radiation correction coefficient system, comprehensively considering cloud status, solar altitude angle, and diurnal variation factors, significantly improving the accuracy of satellite radiation products. By using the method of directly correcting satellite data with ground observation data, it effectively overcomes the measurement errors caused by changes in atmospheric conditions, making the corrected radiation data closer to the true surface value.

[0017] 2. The present invention ensures the physical rationality and spatial continuity of high-resolution products through cloud cover ratio analysis and altitude correction. Compared with traditional climatological methods, this solution can provide more refined and accurate solar radiation spatial distribution data, providing reliable data support for fields such as solar energy resource assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of a method for correcting and spatially downscaling satellite radiation products of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment 1 Figure 1 A method for correcting and spatially downscaling satellite radiation products of the present invention is given, including the following steps: S1, perform a primary correction on the radiation data to be corrected through a first correction coefficient to obtain second radiation data; In this embodiment, the primary correction includes data quality control based on the retention of several abnormal radiation values. Specifically: Use the sliding window and standard deviation method to preliminarily screen the radiation data to be corrected, and identify potential abnormal values that deviate from the statistical characteristics; Introduce multi-source auxiliary information to judge the rationality of potential abnormal values, including analyzing the cloud amount and atmospheric transmittance changes in combination with the synchronous meteorological background and verifying the spatial consistency of the pixel neighborhood; Retain the abnormal values determined to be reasonable, eliminate the abnormal values confirmed to be invalid, and use time series interpolation to fill the gaps.

[0021] The use of the sliding window and standard deviation method to preliminarily screen the radiation data to be corrected and identify potential abnormal values that deviate from the statistical characteristics is specifically: Traverse the time series of the radiation data to be corrected with a sliding time window of a set length, and calculate the local average value and standard deviation within each sliding window; Compare each radiation data point in the current window with the local average value within the window. If its deviation exceeds the preset standard deviation multiple threshold, mark this data point as a potential outlier; The length of the sliding window and the standard deviation multiple threshold can be adaptively adjusted according to surface type, seasonal characteristics, or data stability; Perform a first correction on the radiation data to be corrected using the first correction coefficient. Specifically: Interpolate the radiation data to be corrected using the inverse distance weighting method to generate a correction coefficient to be corrected; Based on the first data, correct the correction coefficient to be corrected to obtain the first correction coefficient. The first data includes the radiation relative ratio coefficient under different cloud states and the combination of the ground radiation and satellite radiation correction coefficient and the diurnal variation correction coefficient; Use the first correction coefficient to perform the first correction of the radiation data; The radiation relative ratio coefficient under different cloud states is calculated based on the radiation data to be corrected and the second data. The second data includes the radiation data observed at the ground radiation observation station, the satellite cloud detection data, and the atmospheric relative thickness correction coefficient; The ground radiation and satellite radiation correction coefficient and the diurnal variation correction coefficient are obtained by constructing a linear regression equation with the second data.

[0022] Furthermore, based on the first data, correct the correction coefficient to be corrected to obtain the first correction coefficient. Specifically: Perform spatial interpolation on the ground radiation and satellite radiation correction coefficient and the diurnal variation correction coefficient to generate the initial correction coefficient for the full grid of satellite radiation data; Based on the satellite cloud detection data of the ground radiation observation station, obtain the theoretical cloud detection value of each grid point; Calculate the difference between the actual satellite cloud detection data and the theoretical cloud detection value of each grid point; Use the radiation relative ratio coefficient under different cloud states to compensate and correct the initial correction coefficient for the difference to generate the first correction coefficient for the grid points at the original resolution.

[0023] Furthermore, perform a first correction on the radiation data to be corrected using the first correction coefficient to obtain the second radiation data.

[0024] Use time series interpolation to fill in the gaps. The specific calculation formula is as follows:

[0025] The specific calculation formula for the atmospheric relative thickness correction coefficient is as follows:

[0026] The radiation relative ratio coefficient under different cloud states has the following specific calculation formula:

[0027] The ground radiation, satellite radiation correction coefficient, and diurnal variation correction coefficient are obtained by constructing a linear regression equation from the second data. The specific calculation formula is:

[0028]

[0029]

[0030]

[0031] The first correction coefficient has the following specific calculation formula:

[0032] The second radiation data has the following specific calculation formula:

[0033] In the formula, , where h is the time window size, is the radiation value, is the atmospheric relative thickness correction coefficient, is the solar altitude angle, is the radiation data to be corrected after quality control processing, is the relative ratio coefficient of radiation under different cloud states, is the time variable representing the observation time, is the cloudy time, is the clear sky time, is the satellite cloud monitoring data, is The value variable of can take 0, 1, 2, and 3, representing cloudy, possibly cloudy, possibly clear sky, and clear sky respectively; is the radiation data observed at the ground radiation observation site, is under different cloud cover states , is under different cloud cover states , The linear regression slope coefficient is used to correct the deviation between satellite and ground observations under different cloud states, is the linear regression intercept coefficient used to correct the systematic deviation, is different Correction coefficient; is the at the hth hour, is the at the hth hour , is the hourly linear regression slope coefficient, used to correct the radiation deviation in different periods, is the hourly linear regression intercept coefficient, used to correct the systematic deviation, is the daily variation correction coefficient; is the correction coefficient to be corrected, is the inverse distance weighted interpolation operation, for each grid point , is the first correction coefficient; is the second radiation data.

[0034] It should be noted that the radiation data to be corrected described in this article can be sourced from the surface shortwave radiation products provided by geostationary meteorological satellites (such as FY-4B, GOES, etc.), which have high temporal coverage and spatial consistency and are suitable for large-scale radiation estimation and analysis. The satellite cloud detection data is obtained by the remote sensing imager carried by the meteorological satellite and is the cloud cover feature information inverted by the image recognition algorithm, including cloud top height, cloud category, cloud amount distribution, etc., and is used to determine the radiation influencing factors. The radiation data observed at the ground radiation observation stations is the solar shortwave radiation value measured by ground radiometers (such as total radiation meters, four-component radiation meters), which has high-precision characteristics and is often used as the correction benchmark for satellite radiation products. The atmospheric relative thickness correction coefficient reflects the adjustment effect of the atmosphere on the solar radiation path length at different solar elevation angles and is often obtained by calculating the solar zenith angle or the atmospheric mass index to improve the consistency of radiation values between different time periods.

[0035] It should be noted that the second data refers to the basic data set containing the above-mentioned ground radiation data, cloud detection data, and atmospheric correction factors and is used to construct the subsequent correction model. The radiation relative ratio coefficient under different cloud states is the relative ratio coefficient reflecting the change in radiation intensity under different cloud states and is used to dynamically adjust the radiation error caused by different cloud conditions; the correction relationship coefficient and the daily variation trend correction coefficient between satellite radiation and ground radiation are usually obtained by constructing a linear regression model with the second data and are used to perform secondary correction on the initial interpolation result.

[0036] It should be noted that the inverse distance weighted interpolation is an interpolation algorithm based on spatial distance for weight assignment, and its power parameter and search radius should be flexibly set according to the geographical characteristics and site density of the target area; the linear regression equation should use the least squares method for parameter estimation to ensure that the obtained correction coefficients have strong statistical stability and explanatory power.

[0037] It should be noted that the original spatial resolution used for the target area should be clearly set, for example, 4 km × 4 km, as the unified data basis for radiative correction and spatial downscaling processing.

[0038] S2, based on the digital elevation model, determine whether the target grid is blocked by the terrain, and perform adjacent grid weighted correction on the second radiation data of the blocked grid to generate the third radiation data; The method for determining whether the target grid is blocked by the terrain based on the digital elevation model is as follows: Obtain the occlusion influence data of the target radiation data, where the occlusion influence data includes the grid point slope and aspect derived from the digital elevation model, as well as the hourly solar altitude angle and azimuth angle within one year; For each grid point, based on the included angle between the solar azimuth angle and the aspect at the current moment, the slope, and the solar altitude angle, determine whether it is blocked by the terrain; Statistically calculate the proportion of unblocked grids within the target grid range during the preset period to generate the effective radiation area proportion index.

[0039] The specific calculation formulas for the hourly solar altitude angle and azimuth angle are as follows:

[0040]

[0041] The specific calculation formula for determining whether the sun in each grid of the target radiation data is blocked by the decision is as follows:

[0042] The specific calculation formula for the effective radiation area proportion index is as follows:

[0043] The specific calculation formula for performing adjacent grid weighted correction on the second radiation data of the blocked grid to generate the third radiation data is as follows:

[0044] In the formula, is the solar azimuth angle, is the local geographical latitude, is the solar declination of the current day, is the solar hour angle at that time, is the grid point coordinate The variable of whether it is blocked, is the slope, The proportion of unblocked grids within the grid, and are respectively and The number of grid points; is the third radiation data.

[0045] It should be noted that both the grid point slope and aspect data can be obtained from the digital elevation model.

[0046] The advantages of analyzing whether the sun is blocked for correction are as follows: By judging the relationship between the solar altitude angle and the terrain slope hour by hour, the local influence of terrain shadow on solar radiation can be accurately quantified, so as to distinguish the true radiation distribution of the blocked and unblocked areas during the downscaling process; this step avoids the radiation overestimation or homogenization error caused by ignoring terrain occlusion in traditional methods. Especially in complex terrain areas such as mountains and hills, it can significantly improve the spatial rationality of grid radiation correction at the target scale and ensure that the final inversion result is more in line with the dynamic change of solar radiation received by the actual ground surface.

[0047] S3. Establish a linear regression model between the ground radiation value and altitude, and calculate the second correction coefficient based on the relative anomaly of the grid altitude and the regional average altitude; Furthermore, the specific construction method of the linear regression model between the ground radiation value and altitude is as follows: Extract the altitude data of all ground radiation observation stations in the target area; Taking each observation station as the center, gradually increase the analysis radius until at least N other radiation stations are covered within the radius; For each station, use the station data set within the radius to solve the linear regression equation between the ground radiation value and altitude by the least squares method.

[0048] Furthermore, establishing the linear regression model between the ground radiation value and altitude also includes quantitatively correcting the linear relationship based on the pre-acquired additional variables. Specifically: Obtain the additional variable set of the target area, and the additional variable set includes terrain complexity indexes such as undulation and terrain change; Take the radiation-altitude linear regression residual as the dependent variable and perform multiple regression with the additional variables to generate a comprehensive correction equation; The calculation of the second correction coefficient based on the relative anomaly of the grid altitude and the regional average altitude includes: Extract altitude data from high-precision terrain data, obtain the average altitude data of each satellite radiation data by averaging, as well as the altitude anomaly of each grid point relative to the average altitude of the corresponding grid point, and perform spatial smoothing processing on the average altitude anomaly using the sliding window algorithm.

[0049] Furthermore, the specific processing method of performing spatial smoothing processing on the average altitude anomaly using the sliding window algorithm is as follows: Combined with the spatial distance relationship between the target grid point and its surrounding neighboring grid points, a weight function is introduced to construct a spatial weighting model, and the weight function is constructed using a Gaussian function.

[0050] Further, the specific method for obtaining the second correction coefficient is as follows: Based on the solution of the linear relationship equation, calculate the percentage change of the radiation data observed at the ground radiation observation station that changes with altitude, and calculate in combination with the average altitude anomaly to obtain the required second correction coefficient.

[0051] The specific calculation formula for the undulation degree is as follows:

[0052] The specific calculation formula for the terrain change rate is as follows:

[0053] The linear relationship equation for the altitude of the ground radiation data at each station is as follows:

[0054] The specific calculation formula for the average relative altitude anomaly is as follows:

[0055] The sliding window algorithm is used to smooth the obtained altitude anomaly data, and the specific calculation formula is as follows:

[0056] The percentage change of the radiation data observed at the ground radiation observation station that changes with altitude, the specific calculation formula is as follows:

[0057] The specific calculation formula for the second correction coefficient is as follows:

[0058] In the formula, and are the maximum and minimum altitude values in the neighborhood around the observation point respectively, is the spatial gradient of altitude, is the variance function, is the altitude of the target radiation observation station, 、 、 and are the solutions of the above linear relationship equation, is the altitude change scale, is the undulation degree change scale, is the slope change scale, is the relative anomaly of the average altitude, is the Gaussian weight function, is the distance from point (i, j) to the center point (x, y), is the smoothing radius, is the percentage change in radiation data, is the second correction coefficient.

[0059] It should be noted that the set of additional variables includes terrain complexity indicators such as undulation and terrain change. Introducing them into the linear relationship equation can improve the adaptability of the linear relationship equation to complex terrain areas.

[0060] The advantages of analyzing the percentage change of the observed data of the ground radiation observation station with altitude for correction are as follows: It can quantify the direct impact of altitude change on solar radiation. By establishing a linear relationship between the observed data of the ground radiation observation station and altitude and calculating the percentage change in radiation with altitude change, the radiation values of different altitude grid points can be accurately adjusted during the downscaling process, especially suitable for areas with large terrain undulations, ensuring that the corrected radiation data not only reflects the attenuation effect of clouds and the atmosphere but also can accurately capture the radiation gradient change caused by altitude differences, ultimately improving the physical consistency and spatial accuracy of high-resolution radiation products under complex terrain.

[0061] S4, perform secondary correction on the third radiation data through the second correction coefficient, and output the target radiation data; The specific calculation formula for performing secondary correction on the third radiation data through the second correction coefficient is as follows:

[0062] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0063] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0064] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0065] In addition, in each embodiment of the present application, each functional module may be integrated into one processing module, may exist separately as individual physical modules, or two or more modules may be integrated into one module.

[0066] As described above, this is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0067] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for satellite radiation product correction and spatial downscaling, characterized in that It includes the following steps: Perform a first correction on the radiation data to be corrected using a first correction coefficient to obtain second radiation data, where the first correction includes data quality control based on a number of abnormal radiation values; Judge whether the target grid is blocked by terrain based on the digital elevation model, and perform weighted correction of adjacent grids on the second radiation data of the blocked grid to generate third radiation data; Establish a linear regression model between the ground radiation value and the altitude, and calculate the second correction coefficient based on the relative anomaly between the grid altitude and the regional average altitude; Perform a second correction on the third radiation data using the second correction coefficient and output the target radiation data.

2. The satellite radiation product correction and spatial downscaling method according to claim 1, wherein The first correction includes data quality control based on a number of abnormal radiation values, specifically: Use a sliding window and standard deviation method to perform a preliminary screening on the radiation data to be corrected, and identify potential abnormal values that deviate from the statistical characteristics; Introduce multi-source auxiliary information to perform a rationality discrimination on the potential abnormal values, including analyzing the changes in cloud amount and atmospheric transmittance in combination with the synchronous meteorological background and verifying the spatial consistency of the pixel neighborhood; Retain the abnormal values determined to be reasonable, eliminate the abnormal values confirmed to be invalid, and use time series interpolation to fill in the gaps.

3. The satellite radiation product correction and spatial downscaling method according to claim 2, characterized in that The specific method for performing a first correction on the radiation data to be corrected using the first correction coefficient is: Interpolate the radiation data to be corrected using the inverse distance weighting method to generate a correction coefficient to be corrected; Correct the correction coefficient to be corrected based on the first data to obtain the first correction coefficient, where the first data includes the radiation relative ratio coefficient under different cloud states and the combination of the ground radiation and satellite radiation correction coefficient and the diurnal variation correction coefficient; Use the first correction coefficient to perform the first correction of the radiation data.

4. The satellite radiation product correction and spatial downscaling method according to claim 3, characterized in that The specific method for correcting the correction coefficient to be corrected based on the first data to obtain the first correction coefficient is: Perform spatial interpolation on the ground radiation and satellite radiation correction coefficients and the diurnal variation correction coefficient to generate the initial correction coefficient of the entire grid of satellite radiation data; Based on the satellite cloud detection data of the ground radiation observation sites, obtain the theoretical cloud detection values of each grid point; Calculate the difference between the actual satellite cloud detection data and the theoretical cloud detection value of each grid point; Use the radiation relative ratio coefficient under different cloud states to compensate and correct the initial correction coefficient for the difference to generate the first correction coefficient of the original resolution grid points.

5. The satellite radiation product correction and spatial downscaling method according to claim 4, wherein The specific method for judging whether the target grid is blocked by terrain based on the digital elevation model is: Obtain the occlusion influence data of the target radiation data, where the occlusion influence data includes the grid point slope and aspect derived from the digital elevation model, as well as the hourly solar altitude angle and azimuth angle within one year; For each grid point, judge whether it is blocked by terrain based on the included angle between the current solar azimuth angle and the aspect, the slope, and the solar altitude angle; Statistically calculate the proportion of unblocked grids within the target grid range during the preset period to generate an effective radiation area occupancy index.

6. The satellite radiation product correction and spatial downscaling method according to claim 5, characterized in that The specific analysis method for establishing the linear regression model between the ground radiation value and the altitude is: Extract the altitude data of all ground radiation observation sites within the target area; Taking each observation site as the center, gradually increase the analysis radius until at least N other radiation sites are covered within the radius range; For each site, using the site dataset within the said radius, solve the linear regression equation of the ground radiation value and the altitude by the least squares method.

7. The satellite radiation product correction and spatial downscaling method according to claim 6, characterized in that The establishment of the linear regression model of the ground radiation value and the altitude further includes quantitatively correcting the linear relationship based on the additional variables obtained in advance, specifically: Obtain the additional variable set of the target area; Take the radiation-altitude linear regression residual as the dependent variable and conduct multiple regression with the additional variables to generate a comprehensive correction equation.

8. The satellite radiation product correction and spatial downscaling method according to claim 7, wherein The calculation of the second correction coefficient based on the relative anomaly between the grid altitude and the regional average altitude includes: Extract the altitude data from the high-precision terrain data, obtain the average altitude data of each satellite radiation data by averaging, and the altitude anomaly of each grid point relative to the average altitude of the corresponding grid point, and perform spatial smoothing processing on the average altitude anomaly by using the sliding window algorithm.

9. The satellite radiation product correction and spatial downscaling method according to claim 8, wherein The specific processing method of performing spatial smoothing processing on the average altitude anomaly by using the sliding window algorithm is: Combined with the spatial distance relationship between the target grid point and its surrounding adjacent grid points, introduce a weight function to construct a spatial weighted model, and the weight function is constructed by using a Gaussian function.

10. The satellite radiation product correction and spatial downscaling method according to claim 9, characterized in that The specific obtaining method of the second correction coefficient is: Based on the solution of the linear relationship equation, calculate the percentage change of the radiation data observed at the ground radiation observation site that changes with the altitude, and calculate in combination with the average altitude anomaly to obtain the required second correction coefficient.

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