A method and device for setting integration series and gain based on snowfall data

Abstract

The present invention discloses a method and device for setting an integral series and gain based on snowfall data. The method acquires historical snowfall data, multi-band reflectivity data and forecast data of a meteorological station, establishes a regression model to calculate the relationship between snowfall and reflectivity changes in different bands, and then uses the relationship to correct the integral series and gain growth model, and establishes a lookup table. Based on the forecast data, the correction value of the integral series and gain is dynamically calculated, and finally the adjusted integral series and gain setting are obtained by comprehensive calculation based on the basic reflectivity data and the correction value. The present invention improves the accuracy and reliability of remote sensing observation data, and is particularly suitable for remote sensing observation tasks under complex meteorological conditions such as snowfall.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing application, and in particular relates to a method and a device for setting an integral series and a gain based on snowfall data. Background Art

[0002] Optical satellite remote sensing technology has been widely used in meteorological observation, environmental monitoring, resource survey and other fields. The space optical camera payload is the most important payload for performing optical remote sensing monitoring and imaging tasks. The integral series and gain setting of the space optical camera payload have an important impact on the quality and accuracy of optical satellite remote sensing images. The traditional integral series and gain setting method usually sets fixed parameters for the space optical camera payload. Although this method is simple and easy, it often cannot meet the needs of precise measurement when facing complex and changeable surface and atmospheric conditions. For example, under different incident angle energy conditions, fixed parameters are difficult to adapt to energy changes, resulting in frequent oversaturation or undersaturation of observation data.

[0003] In order to solve the limitations of fixed settings, a floating adjustment method of the integral series and gain gear based on the satellite operating status was subsequently developed. This method uses the satellite's own operating status parameters and orbital parameters such as the satellite's pitch angle, the zenith angle of the sun and the satellite to dynamically adjust the integral series and gain settings to adapt to different observation conditions. This method overcomes the shortcomings of fixed settings to a certain extent, allowing the gain and integral series to be adjusted as the observation environment changes. However, although this method is more flexible than fixed settings, it mainly relies on satellite operating status parameters and does not fully consider changes in surface reflectivity, so it still has certain precision deficiencies in practical applications.

[0004] In summary, the prior art has the following two main disadvantages in terms of integration level and gain setting:

[0005] Failure to consider adjustments to clear sky surface reflectivity conditions in different regions and months: Traditional methods usually set fixed integration levels and gain parameters for satellite payloads, or simply rely on the satellite's operating status for floating adjustments, but do not fully consider the changes in clear sky surface reflectivity in different regions and months. Surface reflectivity can vary significantly in different geographic regions and seasons, and these changes have a direct impact on the integration level and gain settings. However, existing technologies often ignore these changes, resulting in insufficient accuracy and consistency of observation data, especially when the surface reflectivity varies greatly, and fixed or simple floating parameter settings are difficult to meet the needs of high-precision observations.

[0006] The impact of extreme weather conditions such as snowfall is not considered: Snowfall will significantly change the reflective properties of the surface, and traditional parameter setting methods are difficult to accurately reflect this change, resulting in insufficient quality and reliability of observation data under extreme weather conditions. In particular, fixed settings or methods that rely solely on satellite operating status for adjustment lack sufficient flexibility and adaptability in the face of extreme weather, and cannot be adjusted in time to cope with sudden environmental changes, resulting in increased errors in observation data. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a method and device for setting the integration level and gain based on snowfall data. By introducing surface reflectivity data and combining the working characteristics of the satellite payload, dynamic adjustment of the integration level and gain setting can be achieved; by real-time acquisition and analysis of clear sky surface reflectivity data in different regions and months, changes in surface and atmospheric conditions can be more accurately reflected, thereby improving the accuracy and reliability of observation data; by introducing real-time snowfall data, changes in surface reflectivity under extreme weather conditions can be more accurately predicted and reflected, thereby dynamically adjusting parameter settings to ensure the quality and consistency of observation data under various complex meteorological conditions, significantly improving the accuracy and reliability of satellite remote sensing observations, and being suitable for a wider range of application scenarios and more complex observation conditions.

[0008] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0009] In one aspect, the present invention provides a method for setting an integration level and gain based on snowfall data, the method comprising:

[0010] Step 1: Obtain historical snowfall data from the meteorological station, multi-band surface reflectivity data before and after snowfall, and forecast data, and establish a regression model to calculate the corresponding relationship between snowfall and changes in reflectivity in different bands;

[0011] Step 2: Correct the relationship between the integral series and the gain growth through the corresponding relationship between the snowfall and the reflectivity changes in different bands, obtain a model for setting the integral series and gain using the snowfall, and establish a lookup table;

[0012] Step 3: Obtain integral order correction value and gain correction value through forecast data and lookup table, and obtain adjusted integral order and gain settings through comprehensive calculation based on basic reflectivity data and obtained correction value.

[0013] In another aspect, the present invention provides an integration series and gain setting device based on snowfall data, comprising:

[0014] The regression model building unit is used to obtain the historical snowfall data of the meteorological station, the multi-band reflectivity data of the surface before and after snowfall, and the forecast data, and to build a regression model to calculate the corresponding relationship between the snowfall amount and the reflectivity changes in different bands;

[0015] A lookup table establishment unit is used to correct the integral series and gain growth relationship through the corresponding relationship between snowfall and reflectivity changes in different bands, obtain a model for setting the integral series and gain using snowfall, and establish a lookup table;

[0016] The adjustment value acquisition unit is used to obtain the integral series correction value and the gain correction value through the forecast data and the lookup table, and obtain the adjusted integral series and gain settings through comprehensive calculation based on the basic reflectivity data and the obtained correction value.

[0017] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method of setting the integration level and gain based on snowfall data.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for setting the integration level and gain based on snowfall data.

[0019] The beneficial effects of the present invention are:

[0020] Improved accuracy: The present invention realizes more accurate dynamic adjustment by combining surface reflectivity data, thereby greatly improving the accuracy of observation data; by comprehensively utilizing surface reflectivity data, snowfall data and meteorological forecast data, parameter adjustment is made more comprehensive and accurate, further improving the reliability and foresight of observation.

[0021] Enhanced flexibility: The present invention not only takes into account the operating status of the satellite, but also combines the real-time acquired surface reflectivity data and weather forecast data, making parameter settings more flexible and better adapted to actual observation needs;

[0022] Strong ability to cope with extreme weather: The present invention takes into account the impact of extreme weather such as snowfall on the surface reflectivity. Through detailed analysis and real-time adjustment, it ensures that high-quality observation data can still be obtained under extreme weather conditions;

[0023] Improved efficiency: By constructing a lookup table of reflectivity and integral series, the present invention can quickly find and apply pre-calculated parameter settings, thereby improving the efficiency of parameter adjustment in actual observations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a method for setting the integration level and gain based on snowfall data;

[0025] Figure 2is the snow spectral reflectance curve;

[0026] Figure 3 is the average extra-atmospheric solar irradiance curve in different bands;

[0027] Figure 4 Flowchart for obtaining final parameters based on the lookup table. DETAILED DESCRIPTION

[0028] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0029] like Figure 1 As shown, the present invention relates to a method and device for setting an integral series and gain based on snowfall data, which aims to adjust the integral series and gain correction value through snowfall data to improve the setting accuracy and consistency of the integral series and gain. Specifically, the following steps are included: first, the historical snowfall data, snowfall multi-band reflectivity data, non-snowfall multi-band reflectivity data and forecast data of the meteorological station are obtained, and a regression model is established to calculate the corresponding relationship between snowfall and the change of reflectivity in different bands; then, the integral series and gain growth relationship are corrected through the corresponding relationship between snowfall and the change of reflectivity in different bands, and a model for setting the integral series and gain using snowfall is obtained, and a lookup table is established; finally, the integral series correction value and gain correction value are obtained through forecast data, and the corrected integral series and gain are calculated. Through this method, the parameter settings of the satellite payload can be dynamically adjusted according to the actual observation conditions and forecast data, thereby improving the application effect of remote sensing data under various complex meteorological conditions. The present invention is introduced step by step below:

[0030] Step 1: Obtain historical snowfall data, multi-band reflectivity data of snowfall, multi-band reflectivity data of no snowfall (i.e., multi-band reflectivity data of the surface before and after snowfall), and forecast data from the meteorological station, and establish a regression model to calculate the corresponding relationship between snowfall and changes in reflectivity of different bands;

[0031] Step 1.1, obtain the historical snowfall data of the weather station;

[0032] Get NCEI's 2022-2023 global snowfall data to form snowfall data , where k represents the site.

[0033] Step 1.2, obtain the change value of multi-band reflectivity data of the surface before and after snowfall;

[0034] By obtaining the surface reflectance data of the PMS sensor carried by the GF-1 ABCD satellite in 2022-2023, 400 typical station pixels were selected from the station collection. Correspondingly, a typical surface reflectance sample of snowfall event was selected for each station, including reflectance sample data before snowfall. , sample data of reflectivity after snowfall , sample data of reflectivity changes before and after snowfall :

[0035]

[0036] The collected reflectivity change samples before and after snowfall And the snowfall data of the corresponding station at the corresponding time The corresponding data are formed into a data set AS. The data set AS (Archive of Snow) is as follows:

[0037] ,

[0038] like Figure 2 As shown in the figure, by analyzing the spectral reflectance curve of snow, it is found that its reflectance is relatively high in the visible light band, but drops significantly in the infrared band.

[0039] Therefore, the amount of snow is mainly related to the reflectivity of the visible light band and near-infrared band, but not much related to the reflectivity of the mid-infrared band and far-infrared band. Therefore, based on the reflectivity of the visible light band (bands 1, 2, and 3) and the near-infrared band (band 4), a relationship model between snowfall and the change of reflectivity in different bands is constructed. The specific method is as follows:

[0040] Assume that the reflectivity change sample data of each station is Snowfall data There is a linear relationship between:

[0041] ,

[0042] Among them, a and b are the slope and intercept of the regression model of snowfall and reflectivity changes in different bands, respectively.

[0043] Collect reflectance change sample data for all sites Snowfall data , perform linear regression analysis (linear regression method is the least squares method, and other regression methods such as LM method can be used as alternatives) to obtain the slope a and intercept b. The steps are as follows:

[0044] Among them, calculate the slope a:

[0045] ,

[0046] Where m is the total number of sites, here m=400.

[0047] Calculate the intercept b:

[0048] .

[0049] That is, we get the model:

[0050]

[0051] Among them, DS is the change of reflectivity in different bands, SD is the snowfall, a and b are the slope and intercept of the regression model of snowfall and the change of reflectivity in different bands, respectively.

[0052] Step 2: Correct the relationship between the integral series and the gain growth through the corresponding relationship between the snowfall and the reflectivity changes in different bands, obtain a model for setting the integral series and gain using the snowfall, and establish a lookup table;

[0053] After obtaining the snowfall and reflectivity changes in different bands, the load equation is used to apply the reflectivity changes to adjust the integration level and gain changes. The specific equation is as follows:

[0054] ,

[0055] in,

[0056] is the integral series and gain change;

[0057] is the gain amplification factor;

[0058] is the optional integral level;

[0059] is a reasonable value of DN, which can be specifically set to 0.6×2^Q, where Q is the number of quantization bits.

[0060] is the AD conversion coefficient, for example .

[0061] is the exposure time during one integration process;

[0062] is the relative aperture of the optical system, D is the effective aperture, and f is the focal length; is the camera’s surface occlusion coefficient;

[0063] is the equivalent spectral transmittance of the optical system;

[0064] is the spectral radiance before the pupil, Δ is the change in spectral radiance before and after entering the pupil;

[0065] is the spectral sensitivity coefficient of CCD;

[0066] and are the lower and upper limits of the spectrum integration;

[0067] in It is obtained from the following formula

[0068] ,

[0069] Where d is the distance between the sun and the earth in astronomical units, which is 1; is the average extra-atmospheric solar irradiance at band λ (unit: W·m-2·μm-1), see Table 1 and Figure 3 θ h is the solar altitude angle.

[0070] Table 1

[0071]

[0072] Combined with step 1, the relationship model of snowfall to integral series and gain change is obtained as follows:

[0073] ,

[0074] And according to the satellite payload type, different integration levels and gain adjustment modes need to be selected.

[0075] For the satellite payload type that can only adjust the integral series (payload type A), the relationship model is as follows:

[0076] ,

[0077] For satellite payload types that can only adjust gain (payload type B), the relationship model is as follows:

[0078] ,

[0079] Construct basic lookup tables for integral series and reflectivity: After constructing a complete model of the relationship between snowfall and the integral series and gain of each band and each solar altitude angle, in order to facilitate fast calculation, set the integral series and gain lookup tables for specific snowfall, bands, and satellite types. The snowfall is divided into sporadic light snow, light snow, moderate snow, heavy snow, and blizzard. The solar altitude angle is divided into 0-15°, 15°-30°, 30°-45°, 45°-60°, 60°-75°, 75°-90°. The bands are red band, green band, blue band, and near-infrared band. The lookup table is set in the following Table 2:

[0080] Table 2

[0081]

[0082]

[0083]

[0084] Where Δn and ΔG are the integral series correction value and gain correction value.

[0085] Depending on the situation, the corresponding integral series and gain settings are pre-calculated and stored. These calculation results are compiled into a lookup table for quick search and application in actual observation.

[0086] Step 3: Obtain integral series correction value and gain correction value through forecast data and lookup table, and obtain adjusted integral series and gain settings through comprehensive calculation based on basic reflectivity data and the obtained correction value;

[0087] like Figure 4 As shown, in this step, by comprehensively utilizing forecast data and lookup tables, combined with basic reflectivity data, the integration level and gain settings are dynamically adjusted to ensure the accuracy and consistency of observation data under various meteorological conditions. The specific process is as follows:

[0088] Step 3.1, obtain forecast data and basic imaging information: First, obtain snowfall forecast data PD, as well as band B and solar altitude angle θ h This information will be used to find matches in the table.

[0089] Step 3.2, match the lookup table: input the forecast data and basic imaging information into the lookup table, and determine the preliminary integral series and gain correction value Δn / ΔG according to the forecast data through the lookup table. These correction values ​​are calculated based on the relationship model of snowfall to the integral series and gain change. The details are as follows:

[0090] or ,

[0091] Where Δn is the integral series correction value, ΔG is the gain correction value, and the function It means that the corresponding correction value is matched in the lookup table according to the forecast data PD.

[0092] Step 3.3, obtain basic reflectivity data: At the same time, obtain basic reflectivity data RD, which is the surface reflectivity information calculated based on historical observations.

[0093] Step 3.4, determine the basic integration level and gain: According to the basic reflectivity data, determine the basic integration level and gain. These basic values ​​are theoretically the most suitable settings for the current surface reflectivity conditions. The details are as follows:

[0094] or

[0095] Among them, n 1 is the basic integral series, G 1 For the basic gain, function F can calculate the basic integration level and basic gain through the basic reflectivity data.

[0096] Step 3.5, integrate the final integral series and gain: add the integral series correction value and gain correction value to the basic integral series and basic gain value respectively, and calculate the final integral series and gain settings. The final setting value is obtained after comprehensive consideration of the forecast data and basic reflectivity data, ensuring the accuracy and consistency of the observed data. The details are as follows:

[0097] or

[0098] where n f is the final integral level after adjustment, G f is the final gain after adjustment.

[0099] Dynamic adjustment method based on snowfall data: The present invention acquires and analyzes snowfall and surface reflectivity data in real time, combines the working characteristics of satellite payloads, dynamically adjusts the integration series and gain settings, and improves the accuracy and reliability of observation data.

[0100] Linear relationship model between reflectivity change and snowfall and construction of lookup table: The present invention constructs a linear relationship model between reflectivity change and snowfall, and uses the model to pre-calculate and store the integral series and gain settings corresponding to specific snowfall, forming a lookup table for easy and quick search and application.

[0101] Comprehensive use of meteorological forecast data and historical data for forward-looking adjustments: The present invention combines snowfall forecast data and historical reflectivity change data to predict future surface reflectivity changes, dynamically adjusts the integration level and gain settings, and improves the forward-looking and adaptability of observations.

[0102] In another aspect, the present invention provides an integration series and gain setting device based on snowfall data, comprising:

[0103] The regression model building unit is used to obtain the historical snowfall data of the meteorological station, the multi-band reflectivity data of the surface before and after snowfall, and the forecast data, and to build a regression model to calculate the corresponding relationship between the snowfall amount and the reflectivity changes in different bands;

[0104] A lookup table establishment unit is used to correct the integral series and gain growth relationship through the corresponding relationship between snowfall and reflectivity changes in different bands, obtain a model for setting the integral series and gain using snowfall, and establish a lookup table;

[0105] The adjustment value acquisition unit is used to obtain the integral series correction value and the gain correction value through the forecast data and the lookup table, and obtain the adjusted integral series and gain settings through comprehensive calculation based on the basic reflectivity data and the obtained correction value.

[0106] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method of setting the integration level and gain based on snowfall data.

[0107] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for setting the integration level and gain based on snowfall data.

[0108] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for setting the integration series and gain based on snowfall data, characterized in that: The method comprises: Step 1: Obtain historical snowfall data from the meteorological station, multi-band surface reflectivity data before and after snowfall, and forecast data, and establish a regression model to calculate the corresponding relationship between snowfall and changes in reflectivity in different bands; Step 2: The relationship between the integral series and gain growth is corrected by the corresponding relationship between snowfall and the change of reflectivity in different bands, and a model for setting the integral series and gain using snowfall is obtained, and a lookup table is established; wherein, the change of reflectivity in different bands is applied to adjust the change of integral series and gain using the load equation: ， Combining the slope a and intercept b, we can obtain the relationship model between snowfall and the integral series and gain change: ， in, is the integral series and gain change; is the gain amplification factor; is the optional integral level; is a reasonable value of DN, DN represents the digital signal value, is the AD conversion coefficient, is the exposure time during one integration process; is the relative aperture of the optical system, D is the effective aperture, and f is the focal length; is the camera’s surface occlusion coefficient; is the equivalent spectral transmittance of the optical system; is the spectral radiance before the pupil, Δ is the change in spectral radiance before and after entering the pupil; is the spectral sensitivity coefficient of CCD; and are the lower and upper limits of the spectrum integration; d is the distance between the sun and the earth in astronomical units; is the average extra-atmospheric solar irradiance at band λ, is the solar altitude angle; SD represents the snowfall; Set up integration levels and gain lookup tables for specific snowfall, bands, and satellite types; Step 3: Obtain integral order correction value and gain correction value through forecast data and lookup table, and obtain adjusted integral order and gain settings through comprehensive calculation based on basic reflectivity data and obtained correction value.

2. The method for setting the integral series and gain based on snowfall data according to claim 1, characterized in that: The step 1 comprises: Step 1.1: Get historical snowfall data from the weather station , k represents the kth site; Step 1.2: Obtain multi-band surface reflectance data before and after snowfall, including reflectance sample data before snowfall , sample data of reflectivity after snowfall , sample data of reflectivity changes before and after snowfall : ， Sample data of reflectivity changes before and after snowfall at each station Snowfall data There is a linear relationship between: ， Among them, a and b are the slope and intercept of the regression model of snowfall and reflectivity changes in different bands, respectively; Collect reflectance change sample data for all sites Snowfall data , perform regression analysis, and obtain the slope a and intercept b, that is, the model is obtained: ， Among them, DS represents the change of reflectivity in different bands, and SD represents the snowfall.

3. The method for setting the integral series and gain based on snowfall data according to claim 2, characterized in that: The calculation formulas for slope a and intercept b include: ， ， Where m is the total number of sites.

4. The method for setting the integral series and gain based on snowfall data according to claim 3, characterized in that: According to the satellite payload type, different integration levels and gain adjustment modes are selected.

5. The method for setting the integral series and gain based on snowfall data according to claim 4, characterized in that: For satellite payload types that can only adjust the integral series, the relationship model is as follows: ， For satellite payload types that can only adjust gain, the relationship model is as follows: ， Where Δn is the integral series correction value, and ΔG is the gain correction value.

6. The method for setting the integral series and gain based on snowfall data according to claim 1, characterized in that: The step 3 comprises: Step 3.1: Obtain forecast data and basic imaging information, including snowfall forecast data PD, band B and solar altitude angle ; Step 3.2: Input the forecast data and basic imaging information into the integral series and gain lookup table, and determine the preliminary integral series and gain correction values ​​according to the forecast data through the integral series and gain lookup table: or , Among them, the function Indicates that the corresponding correction value is matched in the lookup table according to the forecast data PD; Step 3.3, obtaining basic reflectivity data RD, wherein the basic reflectivity data RD is surface reflectivity information calculated based on historical observation data; Step 3.4: Determine the basic integration level and basic gain according to the basic reflectivity data RD: or , Wherein, n1 is the basic integral series, G1 is the basic gain, and function F represents the calculation of the basic integral series n1 and the basic gain G1 through the basic reflectivity data RD; Step 3.5, add the integral series correction value, gain correction value, basic integral series, and basic gain value respectively, and calculate the final integral series and gain settings: or , where n f is the final integral level after adjustment, G f is the final gain after adjustment.

7. A device for setting the integration level and gain based on snowfall data, characterized in that: include: The regression model building unit is used to obtain the historical snowfall data of the meteorological station, the multi-band reflectivity data of the surface before and after snowfall, and the forecast data, and to build a regression model to calculate the corresponding relationship between the snowfall amount and the reflectivity changes in different bands; The lookup table establishment unit is used to correct the integral series and gain growth relationship through the corresponding relationship between snowfall and the reflectivity change in different bands, obtain the model for setting the integral series and gain using snowfall, and establish a lookup table; wherein, the reflectivity change in different bands is applied to adjust the integral series and gain change using the load equation: ， Combining the slope a and intercept b, we can obtain the relationship model between snowfall and the integral series and gain change: ， in, is the integral series and gain change; is the gain amplification factor; is the optional integral level; is a reasonable value of DN, DN represents the digital signal value, is the AD conversion coefficient, is the exposure time during one integration process; is the relative aperture of the optical system, D is the effective aperture, and f is the focal length; is the camera’s surface occlusion coefficient; is the equivalent spectral transmittance of the optical system; is the spectral radiance before the pupil, Δ is the change in spectral radiance before and after entering the pupil; is the spectral sensitivity coefficient of CCD; and are the lower and upper limits of the spectrum integration; d is the distance between the sun and the earth in astronomical units; is the average extra-atmospheric solar irradiance at band λ, is the solar altitude angle; SD represents the snowfall; Set up integration levels and gain lookup tables for specific snowfall, bands, and satellite types; The adjustment value acquisition unit is used to obtain the integral series correction value and the gain correction value through the forecast data and the lookup table, and obtain the adjusted integral series and gain settings through comprehensive calculation based on the basic reflectivity data and the obtained correction value.

8. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Wherein, when one or more programs are executed by the one or more processors, the one or more processors implement the integration level and gain setting method based on snowfall data as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the method for setting the integration level and gain based on snowfall data as described in any one of claims 1-6.

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