Optical high-component load cross calibration method based on site BRDF characteristics

Abstract

The invention provides an optical high-component load cross calibration method based on site BRDF (Bidirectional Reflection Distribution Function) characteristics. The method comprises the following steps: constructing a bidirectional reflectance distribution function characteristic library based on MODIS (Moderate Resolution Imaging Spectroradiometer) data; determining cross calibration data of the reference satellite load and the to-be-calibrated satellite load based on a preset calibration field; and performing field matching and information extraction on the cross calibration data by the model parameters, and determining a cross absolute radiometric calibration coefficient of the to-be-calibrated satellite load in a preset calibration field. Through a bidirectional reflection distribution function characteristic library, reflection characteristic differences under different observation geometric conditions can be corrected, and it is ensured that the consistency and comparability of data between different satellite loads are enhanced. Through the orbit parameters, the space-time consistency of the cross calibration data can be ensured. The spectral response difference and geometric difference between the reference satellite load and the to-be-calibrated satellite load are corrected through model parameters of the bidirectional reflectance distribution function characteristic library, the spectral characteristics of the reference satellite load and the to-be-calibrated satellite load are comprehensively matched, and the accuracy of the calibration result is ensured.

Description

Technical Field

[0001] This application relates to the field of remote sensing imaging technology, and in particular to an optical high-resolution payload cross-calibration method based on site BRDF characteristics. Background Technology

[0002] Radiometric calibration of optical payloads in satellite orbit is fundamental to the quantitative application of remote sensing data. Its main purpose is to establish a precise quantitative relationship between the payload's output signal and actual physical quantities, correcting for payload deviations and ensuring the comparability and consistency of data acquired at different times and from different cameras. With the emergence of large-scale optical satellite constellations, different sensors within the constellation may exhibit differences in radiometric response. Furthermore, the radiometric decay of different satellite sensors varies in their orbits, leading to poor comparability of data from different satellite sensors or long-term data from the same satellite sensor. Therefore, radiometric calibration and correction of satellite sensors within a constellation is a crucial issue that urgently needs to be addressed in current remote sensing imaging.

[0003] It should be noted that the above introduction to the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of this application and facilitating understanding by those skilled in the art. It should not be assumed that these technical solutions are known to those skilled in the art simply because they have been described in the background section of this application. Summary of the Invention

[0004] The purpose of this application is to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose a cross-calibration method for optical high-resolution payloads based on site BRDF characteristics.

[0006] The second objective of this application is to propose an optical high-resolution load cross-calibration device based on the site BRDF characteristics.

[0007] The third objective of this application is to propose an electronic device.

[0008] The fourth objective of this application is to provide a non-transitory computer-readable storage medium.

[0009] The fifth objective of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first aspect of this application proposes a cross-calibration method for optical high-resolution payloads based on site BRDF characteristics, comprising:

[0011] A bidirectional reflectance distribution function characteristic library based on MODIS data is constructed, wherein the MODIS data is acquired based on optical remote sensing equipment;

[0012] Based on the orbital parameters of the reference satellite payload and the satellite payload to be calibrated, determine the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field;

[0013] The cross-calibration data is matched and information is extracted using model parameters from the bidirectional reflectance distribution function characteristic library to determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field. The cross-absolute radiometric calibration coefficients are used to perform radiometric correction on the satellite payload to be calibrated.

[0014] To achieve the above objectives, a second aspect of this application provides an optical high-resolution payload cross-calibration device based on site BRDF characteristics, comprising:

[0015] The first acquisition module is used to construct a bidirectional reflectance distribution function characteristic library based on MODIS data, wherein the MODIS data is acquired based on optical remote sensing equipment.

[0016] The second acquisition module is used to determine the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field, according to the orbital parameters of the reference satellite payload and the satellite payload to be calibrated.

[0017] The third acquisition module is used to perform field-of-view matching and information extraction on the cross-calibration data using model parameters from the bidirectional reflectance distribution function characteristic library, and to determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field, wherein the cross-absolute radiometric calibration coefficients are used to perform radiometric correction on the satellite payload to be calibrated.

[0018] To achieve the above objectives, a third aspect of this application provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the site-based BRDF characteristic-based optical high-resolution payload cross-calibration method proposed in the first aspect of this application.

[0019] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium, wherein when the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is able to execute the optical high-resolution payload cross-calibration method based on site BRDF characteristics proposed in the first aspect of this application.

[0020] To achieve the above objectives, a fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor in a communication device, implements the optical high-resolution payload cross-calibration method based on site BRDF characteristics proposed in the first aspect of this application.

[0021] In this embodiment, MODIS data, with its high temporal and spatial resolution, provides rich information on surface reflectance. Furthermore, the high temporal resolution of MODIS data allows calibration to be performed within shorter time intervals, increasing the frequency of calibration. By constructing a bidirectional reflectance distribution function (BRF) characteristic library based on MODIS data, differences in reflectance characteristics under different observation geometries can be corrected, enhancing the consistency and comparability of data between different satellite payloads. By analyzing the orbital parameters of the reference satellite and the satellite to be calibrated, the observation time and location of both in the pre-defined calibration field can be accurately determined, ensuring the spatiotemporal consistency of the cross-calibration data. The model parameters of the BRF characteristic library can accurately correct the differences in spectral response and geometry between the reference satellite payload and the satellite payload to be calibrated, facilitating comprehensive matching of their spectral characteristics and ensuring the accuracy of the calibration results. The BRF characteristic library can cover multiple bands, meeting the calibration requirements of most optical remote sensing satellite payloads, making the calibration method more flexible and adaptable to different application scenarios.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 A schematic flowchart illustrating a cross-calibration method for optical high-resolution payloads based on site BRDF characteristics provided in this application embodiment;

[0025] Figure 2 A schematic flowchart illustrating another optical high-resolution payload cross-calibration method based on site BRDF characteristics provided in this application embodiment;

[0026] Figure 3 A schematic flowchart illustrating another optical high-resolution payload cross-calibration method based on site BRDF characteristics provided in this application embodiment;

[0027] Figure 4A schematic diagram of the structure of an optical high-resolution payload cross-calibration device based on site BRDF characteristics provided in an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application;

[0029] Figure 6 This is a schematic diagram of the structure of another electronic device provided according to an embodiment of this application. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0031] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a” and “the” as used in the embodiments of this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0032] It should be understood that although the terms first, second, third, etc., may be used to describe various information in the embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" and "suppose" as used herein can be interpreted as "when," "when," or "in response to a determination."

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] On-orbit radiometric calibration of optical payloads establishes a precise quantitative relationship between the payload's output signal and actual physical quantities, thereby correcting payload response deviations. This process is crucial for ensuring the comparability and consistency of data acquired at different times and from different cameras. Through radiometric calibration, the digital output of satellite payloads can be converted into actual physical quantities, such as radiance or reflectance, thus providing a solid foundation for the scientific analysis and practical application of remote sensing data.

[0035] In some application scenarios, the commonly used on-orbit absolute radiometric calibration methods for optical payloads, both domestically and internationally, mainly include on-board calibration and field calibration. On-board calibration uses a calibrator on the satellite to perform radiometric calibration, enabling the acquisition of high-precision calibration data; however, for large-aperture, high-resolution optical payloads, on-board calibration is limited by on-board space, power consumption, and field of view, resulting in its limited application. Field calibration uses a large-area radiometric correction field on the Earth's surface or an artificial target as a reference, performing full-field-of-view, full-optical-path calibration. This is currently the most commonly used on-orbit absolute radiometric calibration method; however, field calibration has the following problems: each calibration requires simultaneous measurement of surface and atmospheric parameters, resulting in high costs; and the calibration parameter update cycle is long, making it difficult to reflect the radiometric changes of the satellite payload in a timely manner.

[0036] In some applications, the emergence of large-scale optical satellite constellations can lead to differences in radiometric responses among different sensors within the constellation. During satellite orbit, the radiometric decay of different satellite sensors varies, resulting in poor comparability of data from different satellite sensors or long-term data from the same satellite sensor. This manifests as brightness and color deviations in images of the same target observed by different satellite sensors or even the same satellite sensor at different time phases, affecting the accuracy of target change detection and interpretation, as well as the inversion of Earth-atmosphere parameters. Therefore, radiometric calibration and correction of satellite sensors within a constellation are crucial for maintaining long-term consistency between different satellite sensors and within the same sensor.

[0037] In some applications, calibration can be performed using cross-radiometry with optical payloads (i.e., simultaneously observing the same target area with a reference satellite and the satellite to be calibrated, and using the high-precision calibration data from the reference satellite to correct the radiometric response of the satellite to be calibrated). However, when used with large-scale optical remote sensing satellite constellations, the following problems exist:

[0038] Traditional cross-radiometric calibration conditions are stringent. For example, the reference satellite and the satellite to be calibrated need to observe the same target area at almost the same time, typically with a time difference within a few minutes. For high-resolution satellites, due to orbital and observation time limitations, it is difficult to find a sufficient number of synchronous observation points. The observation zenith angle and relative azimuth angle of the reference satellite and the satellite to be calibrated need to be as close as possible, typically with an angle difference within a few degrees. However, different satellite orbital designs and observation geometry conditions make it difficult to find observation points that meet strict angle matching conditions. The calibration site needs to have stable reflection characteristics and a uniform surface type to reduce the surface BRDF (Bidirectional Reflectance Distribution) effect. The Two-Way Reflectance Distribution Function (BRDF) effect refers to the phenomenon where the surface reflectance characteristics change with the observation geometry. This BRDF effect directly affects the reflectance observed by satellite payloads. Geometric conditions (including solar zenith angle, solar azimuth angle, observation zenith angle, and observation azimuth angle) introduce errors. In practical applications, it is difficult to find a calibration field that perfectly matches these conditions, especially in mid- and low-latitude regions. Accurate atmospheric parameters (such as aerosol optical thickness and water vapor content) are needed to correct for the effects of atmospheric scattering and absorption. However, atmospheric conditions vary significantly in time and space, making the acquisition of high-precision atmospheric parameters challenging. The spectral response functions of the reference satellite and the satellite to be calibrated need to be matched as closely as possible to reduce errors caused by spectral differences. However, the spectral response functions of different satellite payloads may differ, requiring correction through complex spectral matching algorithms. Although a few studies have proposed cross-calibration based on site angle characteristics to reduce observation geometry matching errors, the constructed models are simple, the sites are limited, and it is difficult to meet the needs of rapid calibration and radiometric consistency correction for dozens or hundreds of satellite constellations.

[0039] The following describes, with reference to the accompanying drawings, an optical high-resolution payload cross-calibration method and apparatus based on site BRDF characteristics according to embodiments of this application.

[0040] Figure 1 This is a flowchart illustrating a cross-calibration method for optical high-resolution payloads based on site BRDF characteristics, provided in an embodiment of this application.

[0041] like Figure 1 As shown, the optical high-resolution payload cross-calibration method based on site BRDF characteristics includes, but is not limited to, the following steps:

[0042] S101, Construct a bidirectional reflectance distribution function characteristic library based on MODIS data, wherein the MODIS data is acquired based on optical remote sensing equipment.

[0043] In one feasible implementation, constructing a bidirectional reflectance distribution function (BRDF) feature library based on MODIS data is an important task in the field of optical remote sensing. Its purpose is to extract model parameters of surface reflectance characteristics from multi-angle MODIS data, providing a foundation for applications such as surface parameter inversion, vegetation monitoring, and soil moisture estimation.

[0044] Furthermore, MODIS data (Moderate-resolution Imaging Spectroradiometer, which is used in Earth science and environmental monitoring and covers multiple aspects such as atmosphere, land and ocean) falls under the category of multi-angle remote sensing data. In other application scenarios, multi-angle remote sensing data can also be obtained from UAV multi-angle observation data, which can be obtained from areas with large variations in surface reflectance characteristics.

[0045] Furthermore, models such as the Ross-Li model, Lambertian model, Minnaert model, and Kerr model can be used as models in the two-way reflectance distribution function characteristic library. Specifically, the Ross-Li model describes the reflectance characteristics of the land surface under different observation geometries; the Lambertian model describes a Lambertian surface whose reflectance does not change with observation geometries; the Minnaert model describes the effect of small surface imperfections on reflectance; and the Kerr model describes the anisotropic reflectance characteristics of the land surface. It should be noted that the model settings in the two-way reflectance distribution function characteristic library should be selected according to the specific scenario.

[0046] Furthermore, a clustering algorithm (such as the ISODATA clustering algorithm) can be used to obtain the class set of the bidirectional reflectance distribution function characteristic library from the anisotropic flat index of multi-angle remote sensing data. The anisotropic flat index (AFX) is used to characterize the anisotropy of surface reflectance and is defined by the ratio of daytime albedo to the isotropic parameter of the semi-empirical linear kernel driven model. Then, based on the class set, the least squares fitting error of each measurement group within the class is determined, and the mean of the least squares fitting error for each class is calculated to obtain the average fitting error. Then, by analyzing the decreasing law of the average fitting error, the model parameters of the bidirectional reflectance distribution function characteristic library are determined (the model parameters may include isotropic parameters, volume scattering parameters, and geometric optical parameters). Finally, the model parameters are stored in the characteristic library, which can be a database or file system and may include calibration field number, band information, month information, calibration field topography, climate conditions, etc.

[0047] S102, based on the orbital parameters of the reference satellite payload and the satellite payload to be calibrated, determine the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on the preset calibration field.

[0048] In one feasible implementation, the orbital parameters of the reference satellite payload, including orbital altitude, inclination, and transit time, are obtained. The orbital parameters of the satellite payload to be calibrated, including orbital altitude, inclination, and transit time, are also obtained. An orbital prediction model (such as the SPICE tool) can be used to predict the positions of the reference and calibrated satellite payloads at specific times based on their orbital parameters. Further, the nadir trajectories of the reference and calibrated satellite payloads in a predetermined calibration field are determined based on the orbital parameters. The transit time and position of the reference and calibrated satellite payloads in the predetermined calibration field can be determined based on the nadir trajectories. Combining the side-swing capability and field of view of the reference and calibrated satellite payloads, cross-calibration data based on the predetermined calibration field is then determined.

[0049] It should be noted that the preset calibration field is a carefully selected ground area with specific reflective characteristics used for cross-calibration of optical satellite payloads. The following criteria can be considered when selecting the preset calibration field:

[0050] The atmosphere is dry and clean, with low aerosol load and low cloud cover;

[0051] The surface is flat and uniform, with near-Lambertian reflectance characteristics, and the ground features cover a large area with a relatively flat spectrum.

[0052] It is used by international calibration organizations (such as CEOS and USGS) for cross-calibration and calibration.

[0053] S103, using model parameters from the bidirectional reflectance distribution function characteristic library, performs field-of-view matching and information extraction on the cross-calibration data to determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field, wherein the cross-absolute radiometric calibration coefficients perform radiometric correction on the satellite payload to be calibrated.

[0054] In one feasible implementation, field-of-view matching is a crucial operation to ensure spatial consistency between the observation data of the reference satellite payload and the satellite payload to be calibrated. Field-of-view matching identifies pixels or regions corresponding to a predetermined calibration field. The radiance information and grayscale values ​​of the pre-defined calibration field matching region are extracted from the pixels or regions of both the reference and satellite payloads. Model parameters from a bidirectional reflectance distribution function characteristic library are then used to correct the entrance pupil radiance of the reference satellite payload, ensuring consistency with the observation conditions of the satellite payload to be calibrated. Using the corrected entrance pupil radiance of the reference satellite payload and the grayscale values ​​of the satellite payload to be calibrated, the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated are calculated within the predetermined calibration field. These calculated cross-absolute radiometric calibration coefficients are then used to perform radiometric correction on the observation data of the satellite payload to be calibrated, ensuring its data quality.

[0055] It should be noted that the calculated cross-absolute radiometric calibration coefficients can be compared with known reference satellite payload data to verify their accuracy. Ground measurement data from a pre-defined calibration field can also be used for comparison with the cross-absolute radiometric calibration coefficients.

[0056] In summary, the optical high-resolution payload cross-calibration method based on site BRDF characteristics provided in this application leverages the high temporal and spatial resolution of MODIS data, which provides rich information on surface reflectance. Furthermore, the high temporal resolution of MODIS data allows calibration to be performed within shorter time intervals, increasing the frequency of calibration. By constructing a bidirectional reflectance distribution function characteristic library based on MODIS data, differences in reflectance characteristics under different observation geometries can be corrected, enhancing the consistency and comparability of data between different satellite payloads. Analyzing the orbital parameters of the reference satellite and the satellite to be calibrated allows for precise determination of their observation time and location within a pre-defined calibration field, ensuring the spatiotemporal consistency of the cross-calibration data. The model parameters of the bidirectional reflectance distribution function characteristic library accurately correct for differences in spectral response and geometry between the reference satellite payload and the satellite payload to be calibrated, facilitating comprehensive matching of their spectral characteristics and ensuring the accuracy of the calibration results. The bidirectional reflectance distribution function characteristic library can cover multiple bands, meet the calibration requirements of most optical remote sensing satellite payloads, make the calibration method more flexible, and adapt to different application scenarios.

[0057] Figure 2 This is a flowchart illustrating another optical high-resolution payload cross-calibration method based on site BRDF characteristics provided in an embodiment of this application.

[0058] like Figure 2As shown, the optical high-resolution payload cross-calibration method based on site BRDF characteristics includes, but is not limited to, the following steps:

[0059] S201, based on the preset wavelength range, obtain the trend of the bidirectional reflection distribution function characteristics in the preset standard field as a function of the preset sampling period from the MODIS data.

[0060] In one feasible implementation, multiple preset target fields are determined globally based on preset screening principles. For further details on the preset target fields, please refer to the relevant content in the above embodiments, which will not be repeated here.

[0061] Optionally, the bands in the MODIS data are matched with a preset wavelength range, and the model parameters corresponding to the preset wavelength range are extracted.

[0062] Furthermore, the preset wavelength range can cover the spectral range from visible light to short-wave infrared, and its specific wavelength orientation can be determined based on the actual spectral density of the preset standard field. For example, band 1: 400-450nm (ultraviolet-blue light); band 2: 450-500nm (blue light); band 3: 500-550nm (green light); band 4: 550-600nm (yellow light); band 5: 600-650nm (red light); band 6: 650-700nm (near-infrared); band 7: 700-750nm (near-infrared); band 8: 750-800nm ​​(near-infrared); band 9: 800-850nm (near-infrared); band 10: 850-900nm (near-infrared).

[0063] Furthermore, MODIS data typically includes multiple bands, each with its specific center wavelength and bandwidth. The bands in the MODIS data are matched against a preset wavelength range, and the closest match is discarded. For example, MODIS data may include the following bands: Band 1: 620-670nm; Band 2: 841-876nm; Band 3: 459-479nm; Band 4: 545-565nm; Band 5: 1230-1250nm; Band 6: 1628-1652nm; Band 7: 2105-2155nm; Band 8: 405-420nm; Band 9: 435-450nm; Band 10: 900-920nm.

[0064] As an example, band 1 in the preset wavelength range matches band 8 in the MODIS data, and band 2 in the preset wavelength range matches band 9 in the MODIS data.

[0065] Furthermore, BRDF model parameters for bands matching the preset wavelength range are extracted from MODIS data. If the MODIS data BRDF is set using the Ross-Li model, its model parameters include a scattering kernel, a geometric optics kernel, and a hybrid type, used to describe the reflectivity characteristics of the surface (e.g., a preset benchmark field) under different observation geometry conditions.

[0066] In one feasible implementation, time series analysis is performed on the model parameters to obtain the variation trend of the bidirectional reflection distribution function characteristics in the preset standard field with the preset sampling period.

[0067] Optionally, the characteristics of the bidirectional reflection distribution function of the preset calibration field under different observation geometry conditions can be determined based on the model parameters.

[0068] Furthermore, the predefined calibration field is obtained under different observation geometric conditions, including: solar zenith angle, solar azimuth angle, observed zenith angle, observed azimuth angle, and relative azimuth angle. Based on the model parameters, the reflectivity of the predefined calibration field under different observation geometric conditions is determined, and this reflectivity is used as the characteristic of the two-way reflectance distribution function.

[0069] Furthermore, time series analysis was performed on the characteristics of the bidirectional reflectance distribution function to obtain the changing trend of the bidirectional reflectance distribution function characteristics with the preset sampling period.

[0070] Furthermore, the characteristics of the bidirectional reflectance distribution function are arranged in chronological order to ensure the continuity of the time series. Linear regression is used to fit the monthly and annual rates of change of the bidirectional reflectance distribution function characteristics. These monthly and annual rates of change are then used as the trend.

[0071] S202, Based on the changing trends, construct a bidirectional reflection distribution function characteristic library based on MODIS data.

[0072] In one feasible implementation, the changing trend reflects the analysis results of the model parameters based on time series analysis. The model parameters at each time point are stored in a feature library, including band information, time information, geographical location information, observation geometry, and model parameters, to construct a two-way reflectance distribution function feature library based on MODIS data.

[0073] S203, based on the orbital parameters of the reference satellite payload and the satellite payload to be calibrated, determine the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field.

[0074] In one feasible implementation, the required reference satellite payload can be determined based on a preset wavelength band, and the orbital parameters of the payload to be calibrated and the reference satellite payload can be obtained by increasing the cross-frequency. Further, reference satellite payloads that can cover these wavelength ranges are selected based on a preset wavelength range, wherein the operating wavelength range of the payload to be calibrated is consistent with the operating wavelength range of the reference satellite payload. For example, if the preset wavelength bands include visible light and near-infrared, payloads from satellites such as the Landsat series and Sentinel-2 can be selected. These satellites are typically equipped with multispectral sensors capable of covering multiple wavelength bands.

[0075] In one feasible implementation, orbital parameters can be predicted to determine the cross-observation range between the reference satellite payload and the satellite payload to be calibrated. The cross-observation range refers to the ability of the two satellite payloads to simultaneously observe the same geographical area in both time and space. Its core elements include the spatial overlap area (the common geographical area covered by the reference satellite payload and the satellite payload to be calibrated) and temporal synchronization (planning the transit time difference between the reference satellite payload and the satellite payload to be calibrated to meet the cross-phase conditions, and planning and determining the calibration field and its transit time that meet the cross-phase conditions; for example, if the transit time of the reference satellite payload is 10:00 and the transit time of the satellite payload to be calibrated is 10:15, then the time difference is 15 minutes). Then, orbit prediction tools (such as STK and GMAT) can be used to predict the orbits and coverage areas of the reference satellite payload and the satellite payload to be calibrated over a future period. Combined with meteorological conditions (such as cloud cover prediction), the observation window can be optimized to improve the success rate of cross-observations. Next, ground trajectory maps of the reference satellite payload and the satellite payload to be calibrated are plotted to identify overlapping areas. Using GIS tools (such as ArcGIS), the area and geographic coordinates of the overlapping areas are calculated. Finally, based on the transit times of the reference satellite payload and the satellite payload to be calibrated within the overlapping areas, a common observation time window is determined, thereby defining the cross-observation range between the reference satellite payload and the satellite payload to be calibrated.

[0076] In one feasible implementation, cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field can be determined according to the cross-observation range. Further, based on the cross-observation range, synchronous observation data of the reference satellite payload and the satellite payload to be calibrated based on the preset calibration field are obtained; and preprocessing such as radiometric correction and geometric correction is performed on the synchronous observation data to obtain the cross-calibration data.

[0077] S204, using model parameters from the bidirectional reflectance distribution function characteristic library to perform field-of-view matching and information extraction on the cross-calibration data, and determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field, wherein the cross-absolute radiometric calibration coefficients perform radiometric correction on the satellite payload to be calibrated.

[0078] In one feasible implementation, the cross-calibration data can be matched with the field of view and information extracted to obtain the first radiance of the preset calibration field at the entrance pupil of the reference satellite payload.

[0079] In some embodiments, the region of the preset calibration field can be determined in both the reference satellite payload and the satellite payload to be calibrated based on the latitude and longitude of the preset calibration field. The pixel positions (X, Y) of the preset calibration field can be located in the images of both the reference satellite payload and the satellite payload to be calibrated; and the coordinate positions of the upper left and lower right pixels of the region can be determined based on the width and height (W, H) of the preset calibration field and the spatial resolution R of the payload.

[0080]

[0081] Among them, Y TopLeft Y TopLeft Represents the coordinates of the top-left pixel of the region; X BottomRight Y BottomRight This represents the coordinates of the bottom right pixel of the region.

[0082] Further, determine the first average gray value DNr of the satellite payload to be calibrated within the region. Further, traverse all pixels within the region, count the number of pixels with non-null values ​​(i.e., valid observations), sum the gray values ​​of all valid pixels, and divide by the number of valid pixels to obtain the first average gray value DNr.

[0083] In some embodiments, the slope Kr used to describe the radiance variation and the intercept Br used to correct background noise are extracted from the image metadata of the reference satellite payload. Based on the first average gray value DNr, the slope Kr and the intercept Br, the first radiance Lr1 of the preset calibration field at the entrance pupil of the reference satellite payload is determined, where Lr=Kr×DNr+Br.

[0084] In one feasible implementation, a comprehensive matching factor is determined based on model parameters and the geometric conditions of the observation direction to correct for radiation differences between the reference satellite payload and the satellite payload to be calibrated.

[0085] In some embodiments, the first equivalent reflectivity ρc of the reference satellite payload in a preset band and the second equivalent reflectivity ρr of the satellite payload to be calibrated in the preset band can be determined using model parameters and geometric conditions of the observation direction. The model parameters are model parameters from the bidirectional reflectance distribution function characteristic library. The preset band can be selected from several bands in the MODIS operating bands to cover the operating band range of the reference satellite payload and the satellite payload to be calibrated. The observation direction is obtained from a preset calibration field. The geometric conditions are based on the solar zenith angle, solar azimuth angle, observation zenith angle, and observation azimuth angle.

[0086] Furthermore, the expressions for the first equivalent reflectance ρc and the second equivalent reflectance ρr are:

[0087]

[0088] Where L is the number of MODIS bands within the operating band of the reference satellite payload; R is the number of MODIS bands within the operating band of the satellite payload to be calibrated; ρ Mc,i The reflectivity of the reference satellite payload in the MODIS band along the observation direction is given by ρ, where i represents the MODIS band number; Mr,i The reflectivity of the satellite payload to be calibrated in the MODIS band along the observation direction is represented by i, where i represents the MODIS band number; RSF c,i The spectral response function value of the reference satellite payload corresponding to the center wavelength of MODIS band i; RSF r,i The spectral response function value of the satellite payload to be calibrated, corresponding to the center wavelength of MODIS band i; Δ M,i This represents the bandwidth of MODIS band i.

[0089] In some embodiments, the first equivalent reflectivity ρc, the second equivalent reflectivity ρr, the aerosol optical thickness of the preset band and the preset calibration field are input into a preset radiative transfer model. The radiative transfer model outputs the first simulated entrance pupil radiance Radc of the reference satellite payload in the observation direction and the second simulated entrance pupil radiance Radr of the satellite payload to be calibrated in the observation direction. A convolutional neural network can be used as the framework of the radiative transfer model to obtain the radiative transfer model.

[0090] In some embodiments, a comprehensive matching factor f is determined based on the first simulated entrance pupil radiance Radc and the second simulated entrance pupil radiance Radr, for correcting the radiation difference between the reference satellite payload and the satellite payload to be calibrated, where f = Radc / Radr.

[0091] In one feasible implementation, the cross-absolute radiometric calibration coefficient Kc of the satellite payload to be calibrated in a preset calibration field is determined based on the first radiance Lr1 and the comprehensive matching factor f.

[0092] In some embodiments, a second radiance Lr2 of the satellite payload to be calibrated is determined based on a first radiance Lr1 and a comprehensive matching factor f, where Lr2 = Lr1 / f. Then, based on the region of the obtained pre-calibrated field, a second average gray value DNc of the reference satellite payload within that region is determined. Then, based on the second radiance Lr2, the second average gray value DNc, and the dark current Dark obtained from the satellite payload to be calibrated, a cross-absolute radiometric calibration coefficient Kc of the satellite payload to be calibrated in the pre-calibrated field is determined, where Kc = (DNc - Dark) / Lr2, and the cross-absolute radiometric calibration coefficient Kc performs radiometric correction on the satellite payload to be calibrated.

[0093] In summary, the optical high-resolution payload cross-calibration method based on site BRDF characteristics provided in this application leverages the high temporal and spatial resolution of MODIS data, which provides rich information on surface reflectance. Furthermore, the high temporal resolution of MODIS data allows calibration to be performed within shorter time intervals, increasing the frequency of calibration. By constructing a bidirectional reflectance distribution function characteristic library based on MODIS data, differences in reflectance characteristics under different observation geometries can be corrected, enhancing the consistency and comparability of data between different satellite payloads. Analyzing the orbital parameters of the reference satellite and the satellite to be calibrated allows for precise determination of their observation time and location within a pre-defined calibration field, ensuring the spatiotemporal consistency of the cross-calibration data. The model parameters of the bidirectional reflectance distribution function characteristic library accurately correct for differences in spectral response and geometry between the reference satellite payload and the satellite payload to be calibrated, facilitating comprehensive matching of their spectral characteristics and ensuring the accuracy of the calibration results. The bidirectional reflectance distribution function characteristic library can cover multiple bands, meet the calibration requirements of most optical remote sensing satellite payloads, make the calibration method more flexible, and adapt to different application scenarios.

[0094] Figure 3 This is a flowchart illustrating another optical high-resolution payload cross-calibration method based on site BRDF characteristics provided in an embodiment of this application.

[0095] like Figure 3 As shown, the optical high-resolution payload cross-calibration method based on site BRDF characteristics includes, but is not limited to, the following steps:

[0096] S301, Construct a bidirectional reflectance distribution function characteristic library based on MODIS data, wherein the MODIS data is acquired based on optical remote sensing equipment.

[0097] S302, based on the orbital parameters of the reference satellite payload and the satellite payload to be calibrated, determines the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field.

[0098] S303 performs field-of-view matching and information extraction on the cross-calibration data to obtain the first radiance of the preset calibration field at the entrance pupil of the reference satellite payload.

[0099] S304, based on the model parameters and the geometric conditions of the observation direction, determines the comprehensive matching factor used to correct the radiation differences between the reference satellite payload and the satellite payload to be calibrated.

[0100] S305, based on the first radiance and the comprehensive matching factor, determines the cross-absolute radiometric calibration coefficient of the satellite payload to be calibrated in the preset calibration field.

[0101] For further details on steps S301 to S305, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0102] In summary, the optical high-resolution payload cross-calibration method based on site BRDF characteristics provided in this application leverages the high temporal and spatial resolution of MODIS data, which provides rich information on surface reflectivity. Furthermore, the high temporal resolution of MODIS data allows calibration to be performed within shorter time intervals, increasing the frequency of calibration. By constructing a bidirectional reflectance distribution function characteristic library based on MODIS data, differences in reflectance characteristics under different observation geometries can be corrected, enhancing the consistency and comparability of data between different satellite payloads. Analyzing the orbital parameters of the reference satellite and the satellite to be calibrated allows for precise determination of their observation time and location within a pre-defined calibration field, ensuring spatiotemporal consistency of the cross-calibration data. Field-of-view matching of the cross-calibration data ensures complete spatial alignment of the observation data of the reference satellite payload and the satellite payload to be calibrated, thereby improving calibration accuracy. Information extraction from the cross-calibration data ensures long-term consistency of the observation data of the reference satellite payload and the satellite payload to be calibrated at different times, facilitating long-term series transformation analysis. By using a comprehensive matching factor, the differences in spectral response and geometry between the reference satellite payload and the payload to be calibrated can be accurately corrected, facilitating a comprehensive matching of their spectral characteristics and ensuring the accuracy of the calibration results. The cross-absolute radiometric calibration coefficients of the payload to be calibrated are determined using the first radiance and the comprehensive matching factor, ensuring data consistency between different satellite payloads. This allows for rapid response to changes in the radiometric response of the satellite payload, enabling timely correction and ensuring data real-time performance and accuracy. The bidirectional reflectance distribution function characteristic library covers multiple bands, meeting the calibration requirements of most optical remote sensing satellite payloads, making the calibration method more flexible and adaptable to different application scenarios.

[0103] Figure 4 This is a schematic diagram of the structure of an optical high-resolution payload cross-calibration device based on site BRDF characteristics, provided in an embodiment of this application. Figure 4 As shown, the optical high-resolution payload cross-calibration device 400 based on site BRDF characteristics includes:

[0104] The first acquisition module 401 is used to construct a bidirectional reflectance distribution function characteristic library based on MODIS data, wherein the MODIS data is acquired based on optical remote sensing equipment.

[0105] The second acquisition module 402 is used to determine the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field according to the orbital parameters of the reference satellite payload and the satellite payload to be calibrated.

[0106] The third acquisition module 403 is used to perform field matching and information extraction on the cross-calibration data using model parameters from the bidirectional reflectance distribution function characteristic library, and to determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field. The cross-absolute radiometric calibration coefficients are used to perform radiometric correction on the satellite payload to be calibrated.

[0107] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0108] like Figure 5 As shown, the electronic device 500 includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from memory 506 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0109] The following components are connected to I / O interface 505: memory 506 including hard disks, etc.; and communication section 507 including network interface cards such as LAN (Local Area Network) cards, modems, etc., which performs communication processing via a network such as the Internet; and driver 508 is also connected to I / O interface 505 as needed.

[0110] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 507. When the computer program is executed by processor 501, it performs the functions defined in the methods of this application.

[0111] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, which can be executed by the processor 501 of the electronic device 500 to perform the above-described method. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0112] In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0113] Figure 6 This is a schematic diagram of the structure of another electronic device provided according to an embodiment of this application. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application. Figure 6 As shown, the electronic device 600 includes a processor 601 and a memory 602. The memory 602 stores program code, and the processor 601 is connected to the memory 602 and reads the program code from the memory 602 to implement the optical high-resolution payload cross-calibration method based on site BRDF characteristics described in the above embodiments.

[0114] Alternatively, the number of processors 601 can be one or more.

[0115] Optionally, the electronic device may also include an interface 603, and there may be multiple interfaces 603. The interface 603 can be connected to an application and can receive data from external devices such as sensors.

[0116] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0117] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A cross-calibration method for optical high-resolution payloads based on site BRDF characteristics, characterized in that, include: A bidirectional reflectance distribution function characteristic library based on MODIS data is constructed, wherein the MODIS data is acquired based on optical remote sensing equipment. Based on the orbital parameters of the reference satellite payload and the satellite payload to be calibrated, determine the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field; The cross-calibration data is matched and information is extracted using model parameters from the bidirectional reflectance distribution function characteristic library to determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field. The cross-absolute radiometric calibration coefficients are used to perform radiometric correction on the satellite payload to be calibrated.

2. The method according to claim 1, characterized in that, The construction of the bidirectional reflection distribution function characteristic library based on MODIS data includes: Based on preset screening principles, multiple pre-defined benchmarking fields are identified globally; Based on the preset wavelength range, the variation trend of the bidirectional reflection distribution function characteristics in the preset calibration field with the preset sampling period is obtained from the MODIS data; Based on the aforementioned trend, a bidirectional reflection distribution function characteristic library is constructed based on the MODIS data.

3. The method according to claim 2, characterized in that, The step of obtaining the variation trend of the bidirectional reflection distribution function characteristics in the preset calibration field with a preset sampling period from MODIS data according to the preset wavelength range includes: Match the bands in the MODIS data with the preset wavelength range, and extract the model parameters corresponding to the preset wavelength range; Time series analysis of the model parameters is performed to obtain the variation trend of the bidirectional reflection distribution function characteristics in the preset standard field with the preset sampling period.

4. The method according to claim 3, characterized in that, The step of performing time series analysis on the model parameters to obtain the changing trend of the bidirectional reflection distribution function characteristics in the preset standard field with a preset sampling period includes: Based on the model parameters, determine the bidirectional reflection distribution function characteristics of the preset calibration field under different observation geometry conditions; Time series analysis was performed on the bidirectional reflectance distribution function characteristics to obtain the variation trend of the bidirectional reflectance distribution function characteristics with a preset sampling period.

5. The method according to claim 1, characterized in that, The step of determining the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field, according to the orbital parameters of the reference satellite payload and the satellite payload to be calibrated, includes: The required reference satellite payload is determined according to the preset wavelength range, and the orbital parameters of the satellite payload to be calibrated and the reference satellite payload are obtained. The orbital parameters are predicted to determine the cross-observation range between the reference satellite payload and the satellite payload to be calibrated; The reference satellite payload and the satellite payload to be calibrated are determined based on the cross-calibration data of the preset calibration field according to the cross-observation range.

6. The method according to any one of claims 1-5, characterized in that, The step of performing field-of-view matching and information extraction on the cross-calibration data using the model parameters to determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field includes: The cross-calibration data is subjected to field-of-view matching and information extraction to obtain the first radiance of the preset calibration field at the entrance pupil of the reference satellite payload; Based on the model parameters and the geometric conditions of the observation direction, a comprehensive matching factor is determined to correct the radiation difference between the reference satellite payload and the satellite payload to be calibrated; Based on the first radiance and the comprehensive matching factor, the cross-absolute radiometric calibration coefficient of the satellite payload to be calibrated in the preset calibration field is determined.

7. The method according to claim 6, characterized in that, The step of performing field-of-view matching and information extraction on the cross-calibration data to obtain the first radiance of the preset calibration field at the entrance pupil of the reference satellite payload includes: Based on the latitude and longitude of the preset calibration field, the region of the preset calibration field is determined among the reference satellite payload and the satellite payload to be calibrated, and the first average gray value of the satellite payload to be calibrated is determined within the region. From the image metadata of the reference satellite payload, extract the slope used to describe the radiance variation and the intercept used to correct background noise; Based on the first average gray value, the slope, and the intercept, the first radiance of the preset calibration field at the entrance pupil of the reference satellite payload is determined.

8. The method according to claim 6, characterized in that, The determination of the comprehensive matching factor for correcting the radiation differences between the reference satellite payload and the satellite payload to be calibrated, based on the model parameters and the geometric conditions of the observation direction, includes: Using the model parameters and the geometric conditions of the observation direction, the first equivalent reflectivity of the reference satellite payload in the preset band and the second equivalent reflectivity of the satellite payload to be calibrated in the preset band are determined. The observation direction is obtained from a preset calibration field. The geometric conditions are based on the solar zenith angle, solar azimuth angle, observation zenith angle, and observation azimuth angle. The first equivalent reflectance, the second equivalent reflectance, the preset band, and the aerosol optical thickness of the preset calibration field are input into a preset radiative transfer model. The radiative transfer model outputs the first simulated entrance pupil radiance of the reference satellite payload in the observation direction and the second simulated entrance pupil radiance of the satellite payload to be calibrated in the observation direction. Based on the first simulated entrance pupil radiance and the second simulated entrance pupil radiance, a comprehensive matching factor is determined to correct the radiation difference between the reference satellite payload and the satellite payload to be calibrated.

9. The method according to claim 6, characterized in that, The step of determining the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field based on the first radiance and the integrated matching factor includes: Based on the first radiance and the comprehensive matching factor, the second radiance of the preset calibration field on the payload of the satellite to be calibrated is determined; Based on the obtained pre-defined calibration field area, determine the second average gray value of the reference satellite payload within the area; Based on the second radiance, the second average gray value, and the dark current obtained through the payload of the satellite to be calibrated, the cross-absolute radiometric calibration coefficient of the payload of the satellite to be calibrated in the preset calibration field is determined.

10. An optical high-resolution payload cross-calibration device based on site BRDF characteristics, characterized in that, include: The first acquisition module is used to construct a bidirectional reflectance distribution function characteristic library based on MODIS data, wherein the MODIS data is acquired based on optical remote sensing equipment. The second acquisition module is used to determine the cross-calibration data of the reference satellite payload and the satellite payload to be calibrated based on a preset calibration field, according to the orbital parameters of the reference satellite payload and the satellite payload to be calibrated. The third acquisition module is used to perform field-of-view matching and information extraction on the cross-calibration data using model parameters from the bidirectional reflectance distribution function characteristic library, and to determine the cross-absolute radiometric calibration coefficients of the satellite payload to be calibrated in the preset calibration field, wherein the cross-absolute radiometric calibration coefficients are used to perform radiometric correction on the satellite payload to be calibrated.

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