Radiation Performance Evaluation Method and Device for Satellite Sensors Based on Stable Targets

Abstract

The present application provides a method and device for evaluating the radiation performance of a satellite sensor based on a stable target. For the cross-radiometric calibration target of the top-of-atmosphere reflectance, the present application provides a novel cross-radiometric calibration mechanism, which can combine a high-precision second satellite sensor to perform high-precision radiation performance evaluation for the first satellite sensor in terms of the top-of-atmosphere reflectance, and further can perform high-precision correction for the top-of-atmosphere reflectance collected by the first satellite sensor.

Description

Technical Field

[0001] The present application relates to the field of satellites, and particularly to a method and device for evaluating the radiation performance of satellite sensors based on stable targets. Background Art

[0002] Currently, the methods for monitoring the radiation performance of satellite sensors on orbiting satellites mainly include on-board calibration and in-orbit replacement calibration. Among them, on-board calibrators often experience a certain degree of attenuation due to harsh space environments and strong solar radiation, etc., and cannot be used for radiation calibration; in-orbit replacement calibration can be further subdivided into field calibration method, cross-calibration method, etc.

[0003] Field calibration is based on measured data, which requires synchronous measurement between the ground and the satellite. It is greatly affected by natural conditions such as weather, consumes a large amount of manpower and material resources, and the calibration frequency is very limited.

[0004] Cross-calibration uses a sensor with high radiation calibration accuracy as a reference sensor to establish a relationship with the sensor to be calibrated, so as to track and correct the change of the radiation performance of the sensor to be calibrated. However, cross-calibration strictly restricts the observation geometry and transit time of the two sensors, resulting in too little remote sensing image data for a single scene.

[0005] That is to say, for satellite sensors, the existing cross-calibration schemes have problems of poor practicability and lack of processing accuracy.

[0006] Taking the Medium Resolution Spectral Imager II (MERSI-II), a satellite sensor, as an example, MERSI-II is one of the main payloads of the FY3D satellite. Before the satellite carrying MERSI-II is launched, radiation calibration will be accurately carried out in the laboratory. However, as the satellite goes from launch to in-orbit operation, during this process, its own characteristics will change. At the same time, the harsh space environment will cause the aging of sensor components and the decline of sensitivity, etc., affecting its radiation performance. Therefore, it is very necessary to monitor and calibrate the radiation performance of MERSI-II carried on the orbiting satellite.

[0007] Moreover, it is actually found that for the satellite carrying MERSI-II, its on-board calibration system is imperfect, mainly relying on field calibration, with a slow update frequency and being greatly affected by weather, and it cannot monitor the change of sensor performance in time. In addition, the satellite carrying MERSI-II has been in operation for more than five years, and its calibration performance has not been fully studied, restricting its extensive quantitative applications. Therefore, it is necessary to optimize the existing cross-calibration scheme for MERSI-II. Summary of the Invention

[0008] The present application provides a method and apparatus for evaluating the radiation performance of satellite sensors based on stable targets. For the cross-radiometric calibration target of the top-of-atmosphere reflectance, the present application provides a novel cross-radiometric calibration mechanism, which can combine a high-precision second satellite sensor to perform high-precision radiation performance evaluation of the first satellite sensor in terms of the top-of-atmosphere reflectance, and further can correct the top-of-atmosphere reflectance collected by the first satellite sensor with high precision.

[0009] In a first aspect, the present application provides a method for evaluating the radiation performance of satellite sensors based on stable targets, characterized in that the method includes:

[0010] Obtain the first radiation performance remote sensing data of the first satellite sensor to be evaluated for radiation performance in a historical time period, and obtain the second radiation performance remote sensing data of the second satellite sensor as a reference in the historical time period;

[0011] Extract the first top-of-atmosphere reflectance corresponding to the stable target from the first radiation performance remote sensing data, and extract the second top-of-atmosphere reflectance corresponding to the stable target from the second radiation performance remote sensing data, where the stable target is a selected geographical area;

[0012] Perform spectral adjustment of the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance through a spectral matching factor to reduce the difference between the spectral channels of the two sensors;

[0013] Perform bidirectional reflectance distribution function correction on the adjusted first top-of-atmosphere reflectance to reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in the viewing geometry on the time trend;

[0014] Calculate the ratio of the original first top-of-atmosphere reflectance to the first top-of-atmosphere reflectance after bidirectional reflectance distribution function correction as the normalized top-of-atmosphere reflectance;

[0015] Quantify the radiation performance of the first satellite sensor through a preset quantitative index based on the normalized top-of-atmosphere reflectance.

[0016] In a second aspect, the present application provides an apparatus for evaluating the radiation performance of satellite sensors based on stable targets, the apparatus includes:

[0017] An acquisition unit, configured to obtain the first radiation performance remote sensing data of the first satellite sensor to be evaluated for radiation performance in a historical time period, and obtain the second radiation performance remote sensing data of the second satellite sensor as a reference in the historical time period;

[0018] An extraction unit for extracting a first top-of-atmosphere reflectance corresponding to the first radiometric remote sensing data and a stable target, and extracting a second top-of-atmosphere reflectance corresponding to the second radiometric remote sensing data and the stable target, where the stable target is a selected geographical area;

[0019] An adjustment unit for spectroscopically adjusting the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance through a spectral matching factor to reduce the difference between the spectral channels of the two sensors;

[0020] A correction unit for performing bidirectional reflectance distribution function correction on the adjusted first top-of-atmosphere reflectance to reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in the viewing geometry on the time trend;

[0021] A calculation unit for calculating the ratio of the original first top-of-atmosphere reflectance to the first top-of-atmosphere reflectance after bidirectional reflectance distribution function correction as the normalized top-of-atmosphere reflectance;

[0022] A quantization unit for quantifying the radiometric performance of the first satellite sensor based on the normalized top-of-atmosphere reflectance through a preset quantitative index.

[0023] In a third aspect, the present application provides a processing device, including a processor and a memory. A computer program is stored in the memory, and when the processor calls the computer program in the memory, it executes the method provided in the first aspect of the present application or any possible implementation manner of the first aspect of the present application.

[0024] In a fourth aspect, the present application provides a computer-readable storage medium storing multiple instructions, and the instructions are suitable for being loaded by a processor to execute the method provided in the first aspect of the present application or any possible implementation manner of the first aspect of the present application.

[0025] From the above content, the following beneficial effects of the present application can be obtained:

[0026] For the cross-radiometric calibration target of the top-of-atmosphere reflectance, the present application first obtains the first radiometric performance remote sensing data of the first satellite sensor whose radiometric performance is to be evaluated in a historical time period, and obtains the second radiometric performance remote sensing data of the second satellite sensor as a reference in the historical time period. Then, the first top-of-atmosphere reflectance corresponding to the stable target is extracted from the first radiometric performance remote sensing data, and the second top-of-atmosphere reflectance corresponding to the stable target is extracted from the second radiometric performance remote sensing data. Here, the stable target is a selected geographical area. At this time, spectral adjustment of the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance is performed through a spectral matching factor to reduce the difference in spectral channels between the two sensors. Then, bidirectional reflectance distribution function correction is performed on the adjusted first top-of-atmosphere reflectance to reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in viewing geometry on the time trend. Next, the ratio of the original first top-of-atmosphere reflectance to the first top-of-atmosphere reflectance after bidirectional reflectance distribution function correction is calculated as the normalized top-of-atmosphere reflectance. At this time, based on the normalized top-of-atmosphere reflectance, the radiometric performance of the first satellite sensor is quantified through a preset quantitative index. In this way, the present application provides a novel cross-radiometric calibration mechanism, which can combine a high-precision second satellite sensor to perform high-precision radiometric performance evaluation of the first satellite sensor in terms of top-of-atmosphere reflectance, and further can perform high-precision correction on the top-of-atmosphere reflectance collected by the first satellite sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0028] Figure 1 It is a schematic flowchart of a method for evaluating the radiometric performance of a satellite sensor based on a stable target according to the present application;

[0029] Figure 2 It is a schematic structural diagram of a device for evaluating the radiometric performance of a satellite sensor based on a stable target according to the present application;

[0030] Figure 3 It is a schematic structural diagram of a processing device according to the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0032] The terms "first", "second", etc. in the description and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that shown or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules does not have to be limited to those steps or modules clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products, or devices. The naming or numbering of steps that appear in the present application does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The named or numbered process steps can be changed in the order of execution according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0033] The division of modules in the present application is a logical division. In actual implementation, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces. The indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the present application. And the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed to multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present application.

[0034] Before introducing the radiation performance evaluation method of the satellite sensor based on a stable target provided by the present application, the background content involved in the present application will be introduced first.

[0035] The radiation performance evaluation method, device, and computer-readable storage medium for satellite sensors based on stable targets provided by this application can be applied to processing devices. For the cross-radiometric calibration target of the top-of-atmosphere reflectance, this application provides a novel cross-radiometric calibration mechanism, which can combine a high-precision second satellite sensor to perform high-precision radiation performance evaluation for the first satellite sensor in terms of top-of-atmosphere reflectance, and then can correct the top-of-atmosphere reflectance collected by the first satellite sensor with high precision.

[0036] The execution subject of the radiation performance evaluation method for satellite sensors based on stable targets mentioned in this application can be a radiation performance evaluation device for satellite sensors based on stable targets, or different types of processing devices such as a server, a physical host, or a user equipment (UE) that integrates the radiation performance evaluation device for satellite sensors based on stable targets. Among them, the radiation performance evaluation device for satellite sensors based on stable targets can be implemented in a hardware or software manner. The UE can specifically be a terminal device such as a smart phone, a tablet computer, a laptop computer, a desktop computer, or a personal digital assistant (PDA). The processing device can be set up in the form of a device cluster.

[0037] It can be understood that the data processing involved in the radiation performance evaluation method for satellite sensors based on stable targets in this application is data processing carried out on the basis of the radiation performance remote sensing data processed during / after the data acquisition work of the satellite sensor carried on the on-orbit satellite. Therefore, for the processing device that executes the radiation performance evaluation method for satellite sensors based on stable targets provided by this application, or rather, the processing device that carries the corresponding application service of the radiation performance evaluation method for satellite sensors based on stable targets provided by this application, in actual situations, it only needs to have the required data processing capabilities.

[0038] If it is also necessary to involve the generation and processing of the analysis of radiation performance remote sensing data, it can be achieved by adjusting the composition structure of the processing device. For example, the processing device can specifically be a satellite control system with the ability to generate radiation performance remote sensing data. Another example is that the processing device can include two parts: a processing device body that executes the content of the solution of this application except for generating radiation performance remote sensing data, and a satellite control system that itself has the ability to generate radiation performance remote sensing data. In this way, the existing satellite control system can be linked to obtain the radiation performance remote sensing data required by the solution of this application.

[0039] Of course, in practical situations, the solution of the present application usually only needs to involve the existing photovoltaic power station images. There is no need to involve specific acquisition operations for the photovoltaic power station images, and the acquisition of the photovoltaic power station images can be handed over to the data source for processing.

[0040] Next, the method for evaluating the radiation performance of a satellite sensor based on a stable target provided by the present application will be introduced.

[0041] First, refer to Figure 1 , Figure 1 FIG. shows a schematic flowchart of a method for evaluating the radiation performance of a satellite sensor based on a stable target according to the present application. The method for evaluating the radiation performance of a satellite sensor based on a stable target provided by the present application may specifically include the following steps S101 to S106:

[0042] Step S101, obtain the first radiation performance remote sensing data of the first satellite sensor whose radiation performance is to be evaluated in a historical time period, and obtain the second radiation performance remote sensing data of the second satellite sensor as a reference in the historical time period;

[0043] It should be understood that the data processing performed in the present application is carried out within the time range of a preset historical time period, such as the historical time period from 2019 to 2021.

[0044] Based on the time range determined by the historical time period, obtain the radiation performance remote sensing data of the first satellite sensor whose radiation performance is to be evaluated and the second satellite sensor as a reference, and denote them as the first radiation performance remote sensing data and the second radiation performance remote sensing data respectively.

[0045] Among them, what the present application specifically does is to evaluate the radiation performance of the first satellite sensor in terms of the Top Of Atmosphere (TOA) reflectance. Thus, the radiation performance remote sensing data may directly include the data required for calculating the TOA reflectance, or may directly include the already calculated TOA reflectance, which is adjusted according to the specific data products of the data source.

[0046] As an example, the first satellite sensor involved in the present application may be the Medium Resolution Spectral Imager II (MERSI-II). In actual situations, specifically, it may be the MERSI-II on the Fengyun-3D (FY3D) series of satellites. Alternatively, it may also be other satellite sensors that need or can only be evaluated through cross-calibration methods, such as other polar-orbiting satellite sensors in the Fengyun series; the second satellite sensor may be a satellite sensor such as the Moderate-resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, which has high stability and accuracy.

[0047] In addition, it should be noted that starting from the remotely sensed radiation performance data obtained here, the series of data involved itself contains data content with variable factors such as different positions, different bands, and different times, rather than just one parameter.

[0048] Step S102: Extract the first top-of-atmosphere reflectance corresponding to the first remotely sensed radiation performance data and the stable target, and extract the second top-of-atmosphere reflectance corresponding to the second remotely sensed radiation performance data and the stable target, where the stable target is a selected geographical area.

[0049] It can be understood that corresponding to the different ways described above on how the remotely sensed radiation performance data obtained in the present application characterizes the top-of-atmosphere reflectance, an appropriate extraction method needs to be used here to obtain the corresponding top-of-atmosphere reflectance.

[0050] In addition, it should be noted that the top-of-atmosphere reflectance extracted here is specifically limited to the stable target focused on in the present application, that is, a specific geographical area. For this stable target, the present application believes that it can more accurately reflect the difference in the atmospheric reflectance collected by the first satellite sensor and the second satellite sensor. Therefore, the radiation performance of the first satellite sensor in terms of atmospheric reflectance can be more accurately evaluated through cross-radiometric calibration.

[0051] Specifically, as an exemplary embodiment, the stable target involved here may specifically include 4 targets, namely, a selected desert area, a glacier area, a deep convective cloud area, and an ocean current area.

[0052] That is to say, the present application believes that through 4 stable targets or 4 types of geographical areas, namely, the desert area, the glacier area, the deep convective cloud area, and the ocean current area, compared with other geographical areas and geographical area combinations, the radiation performance of the first satellite sensor in terms of atmospheric reflectance can be better evaluated.

[0053] Furthermore, as an exemplary embodiment, for the four targets focused on in this application, on the basis of the above specific types, this application also provides more specific configuration content to obtain better observation data, so as to further improve the solution effect to be achieved by this application focusing on the four stable targets.

[0054] Specifically, the content of the observation condition restrictions imposed on the four targets by this application are as follows:

[0055] 1) For the desert area (which can be denoted as DH), select a unified area of 20 km × 20 km centered on the corresponding first observation site, and limit the first solar zenith angle to be less than 65°, and the first observation zenith angle to be less than 40° to reduce data fluctuations at high solar angles. The standard deviation of the average value of the first top-of-atmosphere reflectance is less than 2% (used to check the spatial homogeneity of the stable target pixels extracted within the defined 20 km × 20 km area);

[0056] 2) For the glacier area (which can be denoted as DomeC), select a unified range of 20 km × 20 km centered on the corresponding second observation site, and limit the second solar zenith angle to be less than 70°. The standard deviation of the average value of the second top-of-atmosphere reflectance is less than 2% (used to eliminate the influence of clouds);

[0057] 3) For the deep convective cloud area (which can be denoted as DCC), select the threshold that the brightness temperature of the infrared channels at 10.78 μm - 11.28 μm (this range also corresponds to the 31st band of MODIS) and 10.8 μm (this range also corresponds to the 24th band of MERSI-II) is less than 205 K. As a prerequisite for the tropical region within the range of 20°S - 20°N and 85°E–125°E (this range is given in the standard latitude and longitude coordinate system) to identify potential deep convective cloud pixels, both the third solar zenith angle and the third observation zenith angle need to be less than 40°. In addition, in order to avoid the influence of thin clouds and the edge of clouds, improve the accuracy of deep convective cloud target recognition, and eliminate interference noise, the standard deviation of the 3×3 pixels in the visible - near-infrared channels is less than 3%, and the standard deviation of the brightness temperature in the infrared channels is less than 1 K;

[0058] 4) For the ocean current area (which can be denoted as TNP), select the circulation area within the range of 10°S–20°N and 150°E–160°E. Both the fourth observation zenith angle and the solar flare angle need to be less than 40°.

[0059] It can be seen that here a set of specific implementation supporting solutions is provided for how to configure the four stable targets, which has better practical significance.

[0060] Step S103: Perform spectral adjustment on the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance through the spectral matching factor to reduce the differences between the spectral channels of the two sensors.

[0061] After obtaining the two sets of data of the top-of-atmosphere reflectance of the first satellite sensor and the second satellite sensor, at this time, spectral adjustment operations based on the Spectral Band Adjustment Factor (SBAF) can be performed to reduce the differences between the two sets of data and the spectral channels of the two sensors, so as to better evaluate the radiation performance of the first satellite sensor in the subsequent process.

[0062] Specifically, as an exemplary embodiment, the spectral adjustment of the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance through the spectral matching factor may include:

[0063] 1) Interpolate the spectral sampling intervals of the spectral response functions of the first satellite sensor and the second satellite sensor to 1 nm.

[0064] It can be seen that the interpolation process here not only improves the resolution (accuracy), but also aligns the spectral sampling intervals (spectral resolutions) of the spectral response functions of the first satellite sensor and the second satellite sensor for subsequent processing.

[0065] 2) Resample the top-of-atmosphere reflectance of the third satellite sensor with a spectral sampling interval of 10 nm spectral response function (i.e., hyperspectral) to 1 nm.

[0066] It can be understood that here through resampling, the top-of-atmosphere reflectance of the third satellite sensor is resampled from the original 10 nm spectral resolution to 1 nm, and aligned with the spectral sampling intervals of the spectral response functions of the first satellite sensor and the second satellite sensor at 1 nm for subsequent processing.

[0067] Among them, the third satellite sensor may specifically be EO-1 Hyperion.

[0068] The top-of-atmosphere reflectance of the third satellite sensor is obtained by processing the third radiation performance remote sensing data of the third satellite sensor in the historical time period, and the processing involved can refer to the processing involved in the first radiation performance remote sensing data and the second radiation performance remote sensing data before.

[0069] 3) Convolve the top-of-atmosphere reflectance of the third satellite sensor at 1 nm with the spectral response functions of the corresponding channels of the first satellite sensor and the second satellite sensor respectively using the following formula, and fit the ratio of the two to obtain the spectral matching factor:

[0070]

[0071]

[0072] Among them, is the top-of-atmosphere reflectance of other satellite sensors simulated by using the third satellite sensor. In the same situation, the value range of λ is 1 or 2, corresponding to the first satellite sensor or the second satellite sensor. ρ represents the top-of-atmosphere reflectance of the third satellite sensor, and SRF λ represents the spectral response function of the first satellite sensor or the second satellite sensor corresponding to the channel of the top-of-atmosphere reflectance of the third satellite sensor. SBAF represents the spectral matching factor. represents the top-of-atmosphere reflectance of the second satellite sensor simulated by using ρ. represents the top-of-atmosphere reflectance of the first satellite sensor simulated by using ρ. represents the first top-of-atmosphere reflectance adjusted by using the spectral matching factor to match the second top-of-atmosphere reflectance.

[0073] It can be understood that here, in order to determine the spectral matching factor related to the first satellite sensor and the second satellite sensor, the top-of-atmosphere reflectance of the third satellite sensor is introduced and combined with interpolation processing to provide a specific implementation scheme.

[0074] Step S104, perform bidirectional reflectance distribution function correction on the adjusted first top-of-atmosphere reflectance to reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in the viewing geometry on the time trend.

[0075] It can be understood that after performing spectral adjustment to reduce the difference between the spectral channels of the two sensors, the bidirectional reflectance distribution function (BRDF) correction can be continued on the first top-of-atmosphere reflectance to further reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in the viewing geometry (angle parameters) on the time trend.

[0076] This application believes that due to the bidirectional reflectance distribution function effect and the surface type, there are corresponding bidirectional reflectance distribution function correction processes for the previous 4 objectives. Specifically, as an exemplary embodiment, the bidirectional reflectance distribution function correction model involved in performing bidirectional reflectance distribution function correction on the adjusted first top-of-atmosphere reflectance here can be:

[0077] 1) Corresponding to the desert area and the ocean current area, there is:

[0078] R(θ s ,θv , φ) = k 0 + k 1 f 1 (θ s , θ v , φ) + k 2 f 2 (θ s , θ v , φ),

[0079]

[0080] cosξ = cosθ s cosθ v + sinθ s sinθ v cosφ,

[0081] where R(θ s , θ v , φ) represents the top-of-atmosphere reflectance after model calibration, θ s represents the solar zenith angle, θ v represents the observation zenith angle, φ represents the relative azimuth angle, f 1 (θ s , θ v , φ) represents the volume scattering component, f 2 (θ s , θ v , φ) represents the surface scattering and geometric projection component, k 0 、k 1 and k 2 represent different model coefficients related to the surface type, cosξ represents an intermediate quantity;

[0082] 2) For the glacier area, there is:

[0083] R(θ s , θ v , φ) = c 1 + c 2 cos(π - φ) + c 3 cos[2(π - φ)],

[0084] c 1 = a 0 + a 1 (1 - cosθ v ), c 2 = a 2 (1 - cosθ v ), c 3 = a 3 (1 - cosθ v ),

[0085] a i = b 0i + b 1i cosθ s + b 2i cos 2 θ s , i = 0, 1, 2,

[0086] wherein, a i represents an intermediate quantity, and b 0i represents different model coefficients corresponding to the glacier region model;

[0087] 3) Corresponding to the deep convective cloud region, Clouds and the Earth’s Radiant Energy System (CERES) has established a deep convective cloud angular distribution model with different cloud amounts and different cloud optical thicknesses (cloud parameters, aerosols) based on the high-calibration-accuracy observation data of satellites such as Aqua, Terra, and TRMM. This model has been widely applied to the field of sensor radiometric calibration research based on deep convective cloud targets. This model is a lookup table for bidirectional reflectance distribution function calibration parameters. The bidirectional reflectance distribution function correction factor in each deep convective cloud pixel can be obtained by looking up a set of solar zenith angles, observation zenith angles, and relative azimuth angles given in this lookup table. Correspondingly, by selecting the lookup table with a cloud amount greater than 99.9% and a cloud optical thickness greater than 50, there can be:

[0088]

[0089] wherein, F BRDF (θ s , θ v , φ) represents the bidirectional reflectance distribution function correction factor of this geometric angle at a certain set of solar zenith angles, observation zenith angles, and relative azimuth angles, and ρ TOA (θ s , θ v , φ) represents the top-of-atmosphere reflectance of the said geometric angle.

[0090] It should be noted that a series of parameters of the three models here are provided by the data of the first top-of-atmosphere reflectance after the spectral adjustment performed previously.

[0091] In this way, a specific implementation solution is provided here for how this application realizes the bidirectional reflectance distribution function correction.

[0092] Step S105, calculate the ratio of the original first top-of-atmosphere reflectance to the first top-of-atmosphere reflectance after the bidirectional reflectance distribution function correction as the normalized top-of-atmosphere reflectance;

[0093] After the first top-of-atmosphere reflectance is adjusted spectrally and the bidirectional reflectance distribution function (BRDF) is corrected, the BRDF-corrected first top-of-atmosphere reflectance can be obtained.

[0094] At this time, for subsequent radiation performance evaluation processing, the ratio of the original first top-of-atmosphere reflectance (i.e., the first top-of-atmosphere reflectance before spectral adjustment and BRDF correction, and this observed value is the output value of step S102) to the BRDF-corrected first top-of-atmosphere reflectance can be calculated. That is, the former is used as the numerator and the latter as the denominator, and the obtained ratio is denoted as the normalized top-of-atmosphere reflectance.

[0095] For the convenience of understanding the ratio calculation process here, it can also be expressed by the following formula:

[0096]

[0097] Among them, R TOA represents the normalized top-of-atmosphere reflectance, ρ TOA represents the original first top-of-atmosphere reflectance, and R(θ s , θ v , φ) represents the BRDF-corrected first top-of-atmosphere reflectance.

[0098] After obtaining the normalized top-of-atmosphere reflectance, subsequent radiation performance quantification processing can be carried out with this as the input to achieve the radiation performance evaluation goal.

[0099] Step S106: Quantify the radiation performance of the first satellite sensor based on the normalized top-of-atmosphere reflectance through a preset quantitative index.

[0100] It can be understood that after obtaining the normalized top-of-atmosphere reflectance, the radiation performance of the first satellite sensor can be effectively quantified according to the preset quantitative index (corresponding to a specific index calculation formula / strategy) of this application.

[0101] Specifically, as an exemplary embodiment, quantifying the radiation performance of the first satellite sensor based on the normalized top-of-atmosphere reflectance through a preset quantitative index may include:

[0102] Quantify the radiation performance of the first satellite sensor based on the normalized top-of-atmosphere reflectance through three quantitative indexes: the total attenuation rate, the annual average attenuation rate, and the P-value verification;

[0103] 1) For the total attenuation rate, which characterizes the overall degradation degree of the radiation performance, it can be expressed by the following formula:

[0104]

[0105] Among them, the top-of-atmosphere reflectance on the 15th day of each month is used to represent the top-of-atmosphere reflectance of that month, x start represents the 15th day of the first month in the historical time period, x end represents the 15th day of the last month in the historical time period, R TOA (x start ) represents the normalized top-of-atmosphere reflectance on the 15th day of the first month, R TOA (x end ) represents the normalized top-of-atmosphere reflectance on the 15th day of the last month;

[0106] 2) For the annual average attenuation rate, which characterizes the annual average degradation degree of radiation performance, it can be expressed by the following formula:

[0107]

[0108] Among them, D year represents the annual average attenuation rate, D all represents the total attenuation rate;

[0109] 3) In order to characterize the stability of radiation performance, linear regression can be performed on the long-term trends of the reflectances of each channel. The slope in the regression line can indicate the stability of the sensor radiation calibration. Among them, the common method for testing the slope of the regression line is the P-value test, which can be expressed by the following formula:

[0110] R TOA (x i ) = β 0 +β 1 x i ,

[0111] H 0 : β 1 = 0,

[0112] H 1 : β 1 ≠0,

[0113] if β 1 = 0, R TOA (x i ) = β 0 ,

[0114] Among them, x i represents the i-th observation time, R TOA (x i ) represents the i-th normalized top-of-atmosphere reflectance, β 0 represents the intercept of the regression line, β 1 represents the slope of the regression line, H 0 represents the null hypothesis, H 1 represents the alternative hypothesis,

[0115] The null hypothesis states that the slope of the long-term trend of the radiation performance of each band (of the sensor) is 0, indicating that the radiation performance of the sensor in the current band is stable and has not degraded.

[0116] The alternative hypothesis indicates that the radiation performance in the current band is unstable and may have degraded.

[0117] Among them, the P-value is used to reject or explain the null hypothesis.

[0118] If the P-value < 0.05, the null hypothesis is rejected.

[0119] If the P-value ≥ 0.05, the null hypothesis cannot be rejected.

[0120] As an example, the calculated total attenuation rate and annual attenuation rate can be shown in the following table:

[0121] Table 1 - Example 1 of Quantitative Index

[0122]

[0123] Among them, band represents the band number, WV represents the specific band value, DH, DomeC, DCC, and TNP correspond to the 4 stable targets mentioned earlier, namely the desert area, glacier area, deep convective cloud area, and ocean current area. COM on the left represents the average normalized top-of-atmosphere reflectance of the 4 stable targets, and COM on the right also represents the average normalized top-of-atmosphere reflectance of the 4 stable targets.

[0124] As another example, the P-value verification results can be shown in the following table:

[0125] Table 2 - Example 2 of Quantitative Index

[0126]

[0127] Among them, band represents the band number, WV represents the specific band value, DH, DomeC, DCC, and TNP correspond to the 4 stable targets mentioned earlier, namely the desert area, glacier area, deep convective cloud area, and ocean current area, and Com represents the average normalized top-of-atmosphere reflectance of the 4 stable targets.

[0128] It can be understood that after determining the radiation indicators at different positions, different bands, and different times, the substandard bands and their corresponding data can be screened out by combining the preset radiation indicator thresholds or standard ranges. In the case where the precise radiation performance evaluation has been completed, the corresponding correction (calibration) processing can be continued.

[0129] In this regard, as an exemplary embodiment, after quantifying the radiation performance of the first satellite sensor based on a preset quantitative index on the basis of the normalized top-of-atmosphere reflectance, the method for evaluating the radiation performance of the satellite sensor based on a stable target according to the present application may further include:

[0130] For the bands whose radiation performance quantization results exceed the standard range, correction is performed by the following formula:

[0131]

[0132] where R TOAN represents the normalized top-of-atmosphere reflectance of the band whose radiation performance quantization result exceeds the standard range, A and D represent different fitting parameters, and t 0 represents the starting time, and t represents the time at which the data to be corrected is located (i.e., the time of the satellite image to be processed currently).

[0133] It can be seen that here the present application designs a time decay model, by which the corresponding data of the non-compliant bands can be corrected with better performance.

[0134] As an example, as shown in the following table:

[0135] Table 3 - Correction Example 1

[0136]

[0137] where band represents the band number, WV represents the specific band value, DH, DomeC, DCC, and TNP correspond to the 4 stable targets involved above, namely the desert area, the glacier area, the deep convective cloud area, and the ocean current area, and R 2 AD and RMSE are two commonly used evaluation indexes in the field of mathematical statistics. It can be seen that the numerical level is low, which means that the prediction result is very good.

[0138] Furthermore, the total decay rate and the annual decay rate involved above can be combined to more vividly understand the positive changes brought about by the correction process done here, as shown in another example below:

[0139] Table 4 - Correction Example 2

[0140]

[0141] band represents the band number, WV represents the specific band value, DH, DomeC, DCC, and TNP correspond to the 4 stable targets involved above, namely the desert area, the glacier area, the deep convective cloud area, and the ocean current area, and COM represents the average value of the normalized top-of-atmosphere reflectance of the 4 stable targets.

[0142] Meanwhile, for the processing of each of the above stages, the corresponding images of the processing process / results can also be presented in a visual interface by tabulating / graphing, so as to more vividly reflect the solution effects of the solution of the present application. Among them, it can also involve settings where the processing device itself has a display screen, the processing device uses an external display device, or the processing device is linked with other devices having a display screen.

[0143] Generally speaking, for the above solution content, aiming at the cross-radiometric calibration target of the top-of-atmosphere reflectance, the present application first obtains the first radiometric performance remote sensing data of the first satellite sensor to be evaluated for radiometric performance in a historical time period, and obtains the second radiometric performance remote sensing data of the second satellite sensor as a reference in the historical time period. Then, the first top-of-atmosphere reflectance corresponding to the stable target in the first radiometric performance remote sensing data is extracted, and the second top-of-atmosphere reflectance corresponding to the stable target in the second radiometric performance remote sensing data is extracted. Among them, the stable target is a selected geographical area. At this time, the spectral adjustment of the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance is carried out through the spectral matching factor to reduce the difference between the spectral channels of the two sensors. Then, the bidirectional reflectance distribution function correction is carried out on the adjusted first top-of-atmosphere reflectance to reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in the viewing geometry on the time trend. Then, the ratio of the original first top-of-atmosphere reflectance to the first top-of-atmosphere reflectance after the bidirectional reflectance distribution function correction is calculated as the normalized top-of-atmosphere reflectance. At this time, based on the normalized top-of-atmosphere reflectance, the radiometric performance of the first satellite sensor is quantified through a preset quantitative index. In this way, the present application provides a novel cross-radiometric calibration mechanism, which can combine the high-precision second satellite sensor to perform high-precision radiometric performance evaluation for the first satellite sensor in terms of top-of-atmosphere reflectance, and further can correct the top-of-atmosphere reflectance collected by the first satellite sensor with high precision.

[0144] Among them, the solution effects of the present application can be specifically reflected in the following four aspects:

[0145] 1) Through the setting of four stable targets, the radiometric performance of the first satellite sensor in the visible light, near-infrared, and short-wave infrared bands is comprehensively evaluated, and the bands with attenuated radiometric performance can be corrected accordingly, greatly improving its radiometric calibration accuracy and providing a basis for its quantitative application in global change detection.

[0146] 2) It provides a reference for the collaborative application of the first satellite sensor and the second satellite sensor (such as MERSI-II and MODIS).

[0147] 3) Four different stable targets (desert area, glacier area, deep convective cloud area, and ocean current area) specially designed for evaluation have good consistency and can all be well applied to the radiometric calibration of the first satellite sensor (such as MERSI-II).

[0148] 4) In actual situations, in addition to being applicable to the radiometric performance evaluation of MERSI-II, it is also applicable to the radiometric performance evaluation of other satellite sensors such as the polar-orbiting satellite sensors of the Fengyun series.

[0149] The above is an introduction to the method for evaluating the radiometric performance of a satellite sensor based on a stable target provided by this application. To facilitate the better implementation of the method for evaluating the radiometric performance of a satellite sensor based on a stable target provided by this application, this application also provides a device for evaluating the radiometric performance of a satellite sensor based on a stable target from the perspective of functional modules.

[0150] Refer to Figure 2 , Figure 2 which is a schematic structural diagram of a device for evaluating the radiometric performance of a satellite sensor based on a stable target in this application. In this application, the device 200 for evaluating the radiometric performance of a satellite sensor based on a stable target may specifically include the following structures:

[0151] An acquisition unit 201, configured to acquire first radiometric performance remote sensing data of a first satellite sensor to be evaluated for radiometric performance in a historical time period, and acquire second radiometric performance remote sensing data of a second satellite sensor as a reference in the historical time period;

[0152] An extraction unit 202, configured to extract a first top-of-atmosphere reflectance corresponding to the stable target from the first radiometric performance remote sensing data, and extract a second top-of-atmosphere reflectance corresponding to the stable target from the second radiometric performance remote sensing data, where the stable target is a selected geographical area;

[0153] An adjustment unit 203, configured to perform spectral adjustment on the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance through a spectral matching factor to reduce the difference between the spectral channels of the two sensors;

[0154] A correction unit 204, configured to perform bidirectional reflectance distribution function correction on the adjusted first top-of-atmosphere reflectance to reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in the viewing geometry on the time trend;

[0155] A calculation unit 205, configured to calculate the ratio of the original first top-of-atmosphere reflectance to the first top-of-atmosphere reflectance after bidirectional reflectance distribution function correction as the normalized top-of-atmosphere reflectance;

[0156] The quantization unit 206 is configured to quantify the radiation performance of the first satellite sensor based on the normalized top-of-atmosphere reflectance through a preset quantization index.

[0157] In yet another exemplary embodiment, the stable targets specifically include four targets: a selected desert area, a glacier area, a deep convective cloud area, and an ocean current area.

[0158] In yet another exemplary embodiment, the content of the observation condition restrictions imposed on the four targets respectively is as follows:

[0159] For the desert area, a unified area of 20 km × 20 km centered on the corresponding first observation site is selected, and it is restricted that the first solar zenith angle is less than 65°, the first observation zenith angle is less than 40°, and the standard deviation of the average value of the first top-of-atmosphere reflectance is less than 2%;

[0160] For the glacier area, a unified range of 20 km × 20 km centered on the corresponding second observation site is selected, and it is restricted that the second solar zenith angle is less than 70° and the standard deviation of the average value of the second top-of-atmosphere reflectance is less than 2%;

[0161] For the deep convective cloud area, a threshold where the brightness temperature in the 10.78 μm - 11.28 μm and 10.8 μm infrared channels is less than 205 K is selected as a prerequisite for identifying potential deep convective cloud pixels in the tropical region within the range of 20°S - 20°N and 85°E - 125°E. Both the third solar zenith angle and the third observation zenith angle need to be less than 40°, the standard deviation of the 3 × 3 pixels in the visible - near-infrared channels is less than 3%, and the standard deviation of the brightness temperature in the infrared channels is less than 1 K;

[0162] For the ocean current area, a circulation area within the range of 10°S - 20°N and 150°E - 160°E is selected, and both the fourth observation zenith angle and the solar flare angle need to be less than 40°.

[0163] In yet another exemplary embodiment, the bidirectional reflectance distribution function correction models involved in correcting the adjusted first top-of-atmosphere reflectance are as follows:

[0164] Corresponding to the desert area and the ocean current area, there is:

[0165] R(θ s ,θ v ,φ)=k 0 +k 1 f 1 (θ s ,θ v ,φ)+k 2 f 2 (θ s ,θ v, φ),

[0166]

[0167] cos ξ = cos θ s cos θ v + sin θ s sin θ v cos φ,

[0168] where R(θ s , θ v , φ) represents the top-of-atmosphere reflectance after model calibration, θ s represents the solar zenith angle, θ v represents the viewing zenith angle, φ represents the relative azimuth angle, f 1 (θ s , θ v , φ) represents the volume scattering component, f 2 (θ s , θ v , φ) represents the surface scattering and geometric projection component, k 0 、k 1 and k 2 represent different model coefficients related to the surface type, cos ξ represents an intermediate quantity;

[0169] For the glacier area, there is:

[0170] R(θ s , θ v , φ) = c 1 + c 2 cos(π - φ) + c 3 cos[2(π - φ)],

[0171] c 1 = a 0 + a 1 (1 - cos θ v ), c 2 = a 2 (1 - cos θ v ), c 3 = a 3 (1 - cos θ v ),

[0172] a i = b 0i + b 1i cos θ s + b 2i cos 2 θ s , i = 0, 1, 2,

[0173] where ai represents an intermediate quantity, b 0i represents different model coefficients of the model corresponding to the glacier area;

[0174] For the deep convective cloud area, there is:

[0175]

[0176] wherein, F BRDF (θ s , θ v , φ) represents the bidirectional reflectance distribution function correction factor of this geometric angle at a certain set of solar zenith angle, observation zenith angle and relative azimuth angle, and ρ TOA (θ s , θ v , φ) represents the top-of-atmosphere reflectance of the said geometric angle.

[0177] In another exemplary embodiment, the spectral matching factor includes the following processing contents:

[0178] 1) Interpolate the spectral sampling intervals of the spectral response functions of the first satellite sensor and the second satellite sensor to 1 nm;

[0179] 2) Resample the top-of-atmosphere reflectance of the third satellite sensor with a spectral sampling interval of 10 nm spectral response function to 1 nm;

[0180] 3) Convolve the top-of-atmosphere reflectance of the third satellite sensor at 1 nm with the spectral response functions of the corresponding channels of the first satellite sensor and the second satellite sensor respectively using the following formula, and fit the ratio of the two to obtain the spectral matching factor:

[0181]

[0182] ρ* 2 = ρ 1 ×SBAF,

[0183] wherein, is the top-of-atmosphere reflectance of other satellite sensors simulated by using the third satellite sensor. The value range of λ in the same case is 1 or 2, corresponding to the first satellite sensor or the second satellite sensor. ρ represents the top-of-atmosphere reflectance of the third satellite sensor, and SRF λ represents the spectral response function of the first satellite sensor or the second satellite sensor corresponding to the channel of the top-of-atmosphere reflectance of the third satellite sensor, and SBAF represents the spectral matching factor, represents the top-of-atmosphere reflectance of the second satellite sensor simulated by using ρ, represents the top-of-atmosphere reflectance of the first satellite sensor simulated by using ρ, Represents the first top-of-atmosphere reflectance adjusted using the spectral matching factor to match the second top-of-atmosphere reflectance.

[0184] In yet another exemplary embodiment, the quantization unit 206 specifically includes:

[0185] Based on the normalized top-of-atmosphere reflectance, quantify the radiation performance of the first satellite sensor through three quantitative indicators: the total attenuation rate, the annual average attenuation rate, and the P-value verification;

[0186] For the total attenuation rate, it is expressed by the following formula:

[0187]

[0188] Wherein, the top-of-atmosphere reflectance on the 15th day of each month is used to represent the top-of-atmosphere reflectance of that month, x start Represents the 15th day of the first month in the historical time period, x end Represents the 15th day of the last month in the historical time period, R TOA (x start ) represents the normalized top-of-atmosphere reflectance on the 15th day of the first month, R TOA (x end ) represents the normalized top-of-atmosphere reflectance on the 15th day of the last month;

[0189] For the annual average attenuation rate, it is expressed by the following formula:

[0190]

[0191] Wherein, D year Represents the annual average attenuation rate, D all Represents the total attenuation rate;

[0192] For the P-value verification, it is expressed by the following formula:

[0193] R TOA (x i ) = β 0 +β 1 x i ,

[0194] H 0 :β 1 = 0,

[0195] H 1 :β 1 ≠0,

[0196] ifβ 1 = 0, R TOA (x i ) = β 0 ,

[0197] Among them, x i represents the i-th observation time, and R TOA (x i ) represents the i-th normalized top-of-atmosphere reflectance, and β 0 represents the intercept of the regression line, and β 1 represents the slope of the regression line, H 0 represents the null hypothesis, and H 1 represents the alternative hypothesis.

[0198] The null hypothesis indicates that the slope of the long-term trend of the radiation performance in each band is 0, indicating that the radiation performance in the current band is stable and there is no degradation.

[0199] The alternative hypothesis indicates that the radiation performance in the current band is unstable and may degrade.

[0200] If the P-value < 0.05, then the null hypothesis is rejected.

[0201] If the P-value ≥ 0.05, then the null hypothesis cannot be rejected.

[0202] In yet another exemplary embodiment, the normalized top-of-atmosphere reflectance involves different bands, and the device further includes a correction unit 207 for:

[0203] For the bands whose radiation performance quantization results exceed the standard range, correct them through the following formula:

[0204]

[0205] Among them, R TOAN represents the normalized top-of-atmosphere reflectance of the band whose radiation performance quantization result exceeds the standard range, A and D represent different fitting parameters, and t 0 represents the starting point of time, and t represents the time at which the data to be corrected is located.

[0206] This application also provides a processing device from the perspective of the hardware structure. Refer to Figure 3 , Figure 3 which shows a schematic structural diagram of the processing device of this application. Specifically, the processing device of this application may include a processor 301, a memory 302, and an input / output device 303. The processor 301 is used to implement the steps of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment when executing the computer program stored in the memory 302; or, the processor 301 is used to implement the functions of each unit in the corresponding embodiment when executing the computer program stored in the memory 302. The memory 302 is used to store the above-mentioned Figure 1 The processor 301 is used to implement the steps of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment when executing the computer program stored in the memory 302; or, the processor 301 is used to implement the functions of each unit in the corresponding embodiment when executing the computer program stored in the memory 302. The memory 302 is used to store the above-mentioned Figure 2 The functions of each unit in the corresponding embodiment, and the memory 302 is used to store the above-mentioned Figure 1A computer program required for the method for evaluating the radiation performance of a satellite sensor based on a stable target in the corresponding embodiment.

[0207] Exemplarily, the computer program may be divided into one or more modules / units, and one or more modules / units are stored in the memory 302 and executed by the processor 301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0208] The processing device may include, but is not limited to, the processor 301, the memory 302, and the input / output device 303. Those skilled in the art can understand that the schematic diagram is only an example of the processing device and does not constitute a limitation on the processing device. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the processing device may further include a network access device, a bus, etc. The processor 301, the memory 302, the input / output device 303, etc. are connected through the bus.

[0209] The processor 301 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The processor is the control center of the processing device and connects various parts of the entire device through various interfaces and lines.

[0210] The memory 302 can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory 302, and by calling the data stored in the memory 302, the processor 301 realizes various functions of the computer device. The memory 302 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the processing device, etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0211] When the processor 301 is used to execute the computer program stored in the memory 302, the following functions can be specifically realized:

[0212] Obtain the first radiometric performance remote sensing data of the first satellite sensor whose radiometric performance is to be evaluated in a historical time period, and obtain the second radiometric performance remote sensing data of the second satellite sensor as a reference in the historical time period;

[0213] Extract the first top-of-atmosphere reflectance corresponding to the stable target from the first radiometric performance remote sensing data, and extract the second top-of-atmosphere reflectance corresponding to the stable target from the second radiometric performance remote sensing data, where the stable target is a selected geographical area;

[0214] Perform spectral adjustment on the first top-of-atmosphere reflectance and the second top-of-atmosphere reflectance through a spectral matching factor to reduce the difference between the spectral channels of the two sensors;

[0215] Perform bidirectional reflectance distribution function correction on the adjusted first top-of-atmosphere reflectance to reduce the influence of the change in the observed top-of-atmosphere reflectance caused by the difference in viewing geometry on the time trend;

[0216] Calculate the ratio of the original first top-of-atmosphere reflectance to the first top-of-atmosphere reflectance after bidirectional reflectance distribution function correction as the normalized top-of-atmosphere reflectance;

[0217] Quantify the radiometric performance of the first satellite sensor through a preset quantitative index based on the normalized top-of-atmosphere reflectance.

[0218] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described satellite sensor radiometric performance evaluation device, processing device, and their corresponding units based on stable targets can be referred to asFigure 1 For the description of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment, details are not elaborated herein.

[0219] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions or by controlling relevant hardware through instructions. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0220] Therefore, the present application provides a computer-readable storage medium storing multiple instructions that can be loaded by a processor to execute the steps of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment of the present application. For specific operations, reference can be made to Figure 1 the description of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment. Details are not elaborated herein. Figure 1 For the description of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment, details are not elaborated herein.

[0221] Among them, the computer-readable storage medium may include: Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk, optical disc, etc.

[0222] Since the instructions stored in the computer-readable storage medium can execute the steps of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment of the present application, the beneficial effects achievable by the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment of the present application can be realized. For details, refer to the previous description. Details are not elaborated herein. Figure 1 Since the instructions stored in the computer-readable storage medium can execute the steps of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment of the present application, the beneficial effects achievable by the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment of the present application can be realized. Figure 1 For the description of the radiation performance evaluation method of the satellite sensor based on a stable target in the corresponding embodiment, details are not elaborated herein.

[0223] The above has introduced in detail the radiation performance evaluation method, device, processing equipment, and computer-readable storage medium provided by the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application. At the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for evaluating the radiation performance of a satellite sensor based on a stable target, characterized in that: The method comprises: Acquire first radiation performance remote sensing data of a first satellite sensor for evaluating radiation performance in a historical time period, and acquire second radiation performance remote sensing data of a second satellite sensor for reference in the historical time period; Extracting a first top-of-atmosphere reflectivity corresponding to a stable target from the first radiation property remote sensing data, and extracting a second top-of-atmosphere reflectivity corresponding to the stable target from the second radiation property remote sensing data, wherein the stable target is a selected geographical area; Performing spectral adjustment of the first atmosphere top reflectance and the second atmosphere top reflectance by a spectral matching factor to reduce the difference between spectral channels of two sensors; Performing a bidirectional reflectance distribution function correction on the adjusted first top of atmosphere reflectivity to reduce the influence of the observed top of atmosphere reflectivity change on the time trend caused by the difference in observation geometry; Calculating a ratio of the original first atmosphere top reflectivity and the first atmosphere top reflectivity corrected by the bidirectional reflectance distribution function as a normalized atmosphere top reflectivity; Based on the normalized top of atmosphere reflectivity, quantifying the radiation performance of the first satellite sensor by a preset quantitative index; The stable targets specifically include four targets, namely, selected desert areas, glacier areas, deep convective cloud areas and ocean current areas; The observation condition restrictions imposed on the four targets are: For the desert area, a unified area of ​​20 km × 20 km centered on the corresponding first observation site is selected, and the first solar zenith angle is limited to be less than 65°, the first observation zenith angle is less than 40°, and the standard deviation of the average reflectance of the first top of the atmosphere is less than 2%; For the glacier area, a uniform range of 20 km × 20 km centered on the corresponding second observation site was selected, and the second solar zenith angle was restricted to be less than 70° and the standard deviation of the mean value of the second top of atmosphere reflectivity was less than 2%; For deep convective cloud regions, the threshold of brightness temperature less than 205 K in the 10.78 μm–11.28 μm and 10.8 μm infrared channels was selected as a prerequisite for identifying potential deep convective cloud pixels in the tropical region with a range of 20°S–20°N and 85°E–125°E. Both the third solar zenith angle and the third observation zenith angle needed to be less than 40°, the standard deviation of 3 × 3 pixels in the visible-near infrared channel was less than 3%, and the standard deviation of brightness temperature in the infrared channel was less than 1 K; For the ocean current region, the circulation region in the range of 10°S–20°N and 150°E–160°E was selected, and the fourth observation zenith angle and solar flare angle were both required to be less than 40°; The bidirectional reflectance distribution function correction model involved in performing bidirectional reflectance distribution function correction on the adjusted first atmosphere top reflectance includes: Corresponding to the desert area and the ocean current area, there are: R(θ s ,i v ,φ)=k0+k1f1(θ s ,i v ,φ)+k2f2(θ s ,i v ,φ), cosξ=cosθ s cosθ v +sinθ s sinθ v cosφ, Among them, R(θ s ,θ v ,φ) represents the atmospheric top reflectivity after model correction, θ s represents the solar zenith angle, θ v represents the observation zenith angle, φ represents the relative azimuth, f1(θ s ,θ v ,φ) represents the volume scattering component, f2(θ s ,θ v ,φ) represents the components of surface scattering and geometric projection, k0, k1 and k2 represent different model coefficients related to the surface type, and ξ represents the intermediate quantity; Corresponding to the glacier area, there are: R(θ s ,i v ,φ)=c1+c2cos(π―φ)+c3cos[2(π―φ)], c1=a0+a1(1―cosθ v ),c2=a2(1―cosθ v ),c3=a3(1―cosθ v ), a i =b 0i +b 1i cosθ s +b 2i cos 2 i s ,i=0,1,2, Among them, a i Indicates the intermediate quantity, b 0i represents the different model coefficients of the model corresponding to the glacier area; Corresponding to the deep convective cloud area, we have: Among them, F BRDF (θ s ,θ v ,φ) represents the bidirectional reflectance distribution function correction factor of this geometric angle at a certain set of solar zenith angle, observation zenith angle and relative azimuth angle, ρ TOA (θ s ,θ v ,φ) represents the reflectivity of the top of the atmosphere at the said geometric angle.

2. The method according to claim 1, characterized in that The spectral matching factor includes the following processing contents: 1) interpolating the spectral sampling intervals of the spectral response functions of the first satellite sensor and the second satellite sensor to 1 nm; 2) resample the top-of-atmosphere reflectance of the third satellite sensor with a spectral sampling interval of 10 nm spectral response function to 1 nm; 3) The atmospheric top reflectance of the third satellite sensor at 1 nm is convolved with the channel spectral response functions corresponding to the first satellite sensor and the second satellite sensor respectively using the following formula, and the ratio of the two is fitted to obtain the spectral matching factor: in, is the atmospheric top reflectance of other satellite sensors simulated and generated by the third satellite sensor. In the same case, the value range of λ is 1 or 2, corresponding to the first satellite sensor or the second satellite sensor. ρ represents the atmospheric top reflectance of the third satellite sensor. SRF λ represents the spectral response function of the first satellite sensor or the second satellite sensor of the channel corresponding to the atmospheric top reflectance of the third satellite sensor, SBAF represents the spectral matching factor, represents the top-of-atmosphere reflectivity of the second satellite sensor generated using ρ simulation, represents the top-of-atmosphere reflectivity of the first satellite sensor generated using ρ simulation, represents the first atmosphere top reflectance adjusted by the spectral matching factor to match the second atmosphere top reflectance.

3. The method according to claim 1, characterized in that The step of quantifying the radiation performance of the first satellite sensor based on the normalized top of atmosphere reflectivity by using a preset quantitative index includes: Based on the normalized top of atmosphere reflectivity, the radiation performance of the first satellite sensor is quantified by verifying three quantitative indicators, namely, total attenuation rate, annual average attenuation rate and P value; The total attenuation rate is expressed by the following formula: The atmospheric reflectance on the 15th of each month is used to represent the atmospheric reflectance of the month. start Indicates the 15th of the first month in the historical period, x end Indicates the 15th of the last month in the historical period, R TOA (x start ) represents the normalized atmospheric top reflectance on the 15th day of the first month, R TOA (x end ) represents the normalized top-of-atmosphere reflectance on the 15th day of the last month; The annual average decay rate is expressed by the following formula: Among them, D year represents the annual average decay rate, D all represents the total attenuation rate; The P value check is expressed by the following formula: R TOA (x i )=β0+β1x i , H0:β1＝0， H1:β1≠0， if β1=0,R TOA (x i )=β0, Among them, x i represents the i-th observation time, R TOA (x i ) represents the normalized atmospheric top reflectance of the ith, β0 represents the intercept of the regression line, β1 represents the slope of the regression line, H0 represents the null hypothesis, H1 represents the alternative hypothesis, The original hypothesis indicates that the slope of the long-term trend of the radiation performance in each band is 0, indicating that the radiation performance in the current band is stable and has not degraded. The alternative assumption states that the radiated performance is not stable and may be degraded in the current band described. If the P value is < 0.05, the null hypothesis is rejected. If the P value is ≥ 0.05, the null hypothesis cannot be rejected.

4. The method according to claim 1, characterized in that: The normalized top of atmosphere reflectance involves different bands. After quantifying the radiation performance of the first satellite sensor by a preset quantitative index based on the normalized top of atmosphere reflectance, the method further includes: For bands where the quantified results of radiation performance exceed the standard range, correction is performed using the following formula: Among them, R TOAN The normalized top-of-atmosphere reflectivity of the band where the quantified result of radiation performance exceeds the standard range, A and D represent different fitting parameters, t0 represents the starting time, and t represents the time at which the data is to be corrected.

5. A radiation performance evaluation device for a satellite sensor based on a stable target, characterized in that: The device comprises: An acquisition unit, configured to acquire first radiation performance remote sensing data of a first satellite sensor for evaluating radiation performance in a historical time period, and to acquire second radiation performance remote sensing data of a second satellite sensor for reference in the historical time period; an extraction unit, configured to extract a first top-of-atmosphere reflectivity corresponding to a stable target from the first radiation performance remote sensing data, and to extract a second top-of-atmosphere reflectivity corresponding to the second radiation performance remote sensing data, wherein the stable target is a selected geographical area; an adjustment unit, configured to perform spectral adjustments of the first atmosphere top reflectance and the second atmosphere top reflectance by using a spectral matching factor, so as to reduce a difference between spectral channels of two sensors; a correction unit, configured to perform a bidirectional reflectance distribution function correction on the adjusted first top of atmosphere reflectivity to reduce the influence of the observed top of atmosphere reflectivity change on the time trend caused by the difference in observation geometry; a calculation unit, used for calculating a ratio of an original first atmosphere top reflectance and the first atmosphere top reflectance corrected by the bidirectional reflectance distribution function as a normalized atmosphere top reflectance; a quantification unit, configured to quantify the radiation performance of the first satellite sensor by a preset quantitative index based on the normalized top of atmosphere reflectivity; The stable targets specifically include four targets, namely, selected desert areas, glacier areas, deep convective cloud areas and ocean current areas; The observation condition restrictions imposed on the four targets are: For the desert area, a unified area of ​​20 km × 20 km centered on the corresponding first observation site is selected, and the first solar zenith angle is limited to be less than 65°, the first observation zenith angle is less than 40°, and the standard deviation of the average reflectance of the first top of the atmosphere is less than 2%; For the glacier area, a uniform range of 20 km × 20 km centered on the corresponding second observation site was selected, and the second solar zenith angle was restricted to be less than 70° and the standard deviation of the mean value of the second top of atmosphere reflectivity was less than 2%; For deep convective cloud regions, the threshold of brightness temperature less than 205 K in the 10.78 μm–11.28 μm and 10.8 μm infrared channels was selected as a prerequisite for identifying potential deep convective cloud pixels in the tropical region with a range of 20°S–20°N and 85°E–125°E. Both the third solar zenith angle and the third observation zenith angle needed to be less than 40°, the standard deviation of 3 × 3 pixels in the visible-near infrared channel was less than 3%, and the standard deviation of brightness temperature in the infrared channel was less than 1 K; For the ocean current region, the circulation region in the range of 10°S–20°N and 150°E–160°E was selected, and the fourth observation zenith angle and solar flare angle were both required to be less than 40°; The bidirectional reflectance distribution function correction model involved in performing bidirectional reflectance distribution function correction on the adjusted first atmosphere top reflectance includes: Corresponding to the desert area and the ocean current area, there are: R(θ s ,i v ,φ)=k0+k1f1(θ s ,i v ,φ)+k2f2(θ s ,i v ,φ), cosξ=cosθ s cosθ v +sinθ s sinθ v cosφ, Among them, R(θ s ,θ v ,φ) represents the atmospheric top reflectivity after model correction, θ s represents the solar zenith angle, θ v represents the observation zenith angle, φ represents the relative azimuth, f1(θ s ,θ v ,φ) represents the volume scattering component, f2(θ s ,θ v ,φ) represents the components of surface scattering and geometric projection, k0, k1 and k2 represent different model coefficients related to the surface type, and ξ represents the intermediate quantity; Corresponding to the glacier area, there are: R(θ s ,i v ,φ)=c1+c2cos(π―φ)+c3cos[2(π―φ)], c1=a0+a1(1―cosθ v ),c2=a2(1―cosθ v ),c3=a3(1―cosθ v ), a i =b 0i +b 1i cosθ s +b 2i cos 2 i s ,i=0,1,2, Among them, a i Indicates the intermediate quantity, b 0i represents the different model coefficients of the model corresponding to the glacier area; Corresponding to the deep convective cloud area, we have: Among them, F BRDF (θ s ,θ v ,φ) represents the bidirectional reflectance distribution function correction factor of this geometric angle at a certain set of solar zenith angle, observation zenith angle and relative azimuth angle, ρ TOA (θ s ,θ v ,φ) represents the reflectivity of the top of the atmosphere at the said geometric angle.

6. A processing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the method according to any one of claims 1 to 4 when calling the computer program in the memory.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 4.

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