Intelligent planning method and planning system for cross calibration of ocean water color observation satellites

Abstract

The invention belongs to the technical field of ocean science, and particularly discloses an intelligent planning method and planning system for cross calibration of ocean water color observation satellites, and the planning method comprises the following steps: S1, calculating a cross region of time-space matching of the ocean water color observation satellites and a reference satellite; s2, constructing a global atmosphere top radiance data set, and dividing a plurality of atmosphere top radiance range intervals; s3, after traversing each intersection area, counting pixel points in each atmospheric layer top radiance range interval in each intersection area, and sorting according to the number of the pixel points; s4, sequentially judging whether each atmospheric layer top radiance range interval meets a pixel proportion condition and a cloud score condition in each intersection region or not; and if all the conditions are met, taking the current intersection area as a calibration area of the current atmospheric layer top radiance range interval, and outputting information. According to the invention, the sensor is ensured to continuously provide high-precision observation results in different water body environments, and system errors and drifting are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of marine science and technology, and in particular to an intelligent planning method and a planning system for cross-calibration of ocean color observation satellites. Background Art

[0002] Ocean color observation satellites play an important role in global water environment monitoring, climate change research and marine resource management. The new generation of ocean color observation satellite (HY-1E) is equipped with payloads such as the second generation of water color and temperature scanner (COCTS2), the medium resolution programmable imaging spectrometer (PMRIS) and the second generation of coastal zone imager (CZI2). It has a variety of detection methods and can dynamically monitor the water color, water temperature and suspended matter of the global oceans, offshore, estuaries and inland waters, and provide timely remote sensing information services for coastal areas, major waterways, important ports and inland lakes. However, the accuracy and reliability of satellite remote sensing data largely rely on precise on-orbit calibration.

[0003] Among the satellite calibration methods, the commonly used methods include solar calibration, cross calibration and substitution calibration. However, solar calibration and substitution calibration methods have certain limitations, that is, they can only calibrate the top of the atmosphere radiance Lt within a specific range, resulting in the calibration coefficients being unable to be more accurately applied to areas with different Lt ranges. Cross calibration can manually select different water areas and carry out observation tasks when the satellite passes through these areas, which makes cross calibration more flexible than other calibration methods and can achieve calibration of areas with different top of the atmosphere radiance Lt ranges.

[0004] With the continuous increase in observation needs, how to more flexibly select the cross-calibration area that can cover the dynamic range of the sensor has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides an intelligent planning method and a planning system for cross-calibration of ocean color observation satellites, which solves the problems of low efficiency and inflexibility of existing cross-calibration area planning.

[0006] In order to achieve the purpose of solving the above technical problems, the present invention adopts the following technical solutions: The intelligent planning method for cross-calibration of ocean color observation satellites includes the following steps: S1. Calculate the intersection area of ​​the temporal and spatial matching between the ocean color observation satellite and the reference satellite; S2. Construct a global top-of-atmosphere radiance dataset and divide it into multiple top-of-atmosphere radiance ranges; S3, after traversing each intersection area, counting the pixels in each atmosphere top radiance range interval in each intersection area and sorting them according to the number of pixels; S4, sequentially judging whether each atmospheric top radiance range interval satisfies the pixel ratio condition and the cloud fraction condition in each intersection area; if both conditions are satisfied, the current intersection area is used as the calibration area of ​​the current atmospheric top radiance range interval, and the information is output; The step S4 specifically comprises the following steps: S41, determining whether the current top of atmosphere radiance range interval is an edge range interval, if so, when the pixel ratio in the intersection area A is greater than α%, it is determined that the current top of atmosphere radiance range interval satisfies the pixel ratio condition in the intersection area A; otherwise, when the pixel ratio in the intersection area A is greater than β%, it is determined that the current top of atmosphere radiance range interval satisfies the pixel ratio condition in the intersection area A; S42, after the current top of atmosphere radiance range interval meets the pixel ratio condition in the intersection area A, calculate the cloud fraction of the intersection area A, and determine whether the cloud fraction is less than C%, if so, the intersection area A is used as the calibration area of ​​the current top of atmosphere radiance range interval, and output the information of the current top of atmosphere radiance range interval and the intersection area A; S43, traversing the remaining intersection areas, and determining the calibration area of ​​the current top of atmosphere radiance range interval in the remaining intersection areas; S44. Repeat steps S41-S43 to determine the calibration area of ​​each top-of-atmosphere radiance range interval.

[0007] In some embodiments of the present invention, constructing a global top-of-atmosphere radiance dataset includes: The top-of-atmosphere radiance and quality flag in the reference satellite data are fused to obtain the top-of-atmosphere radiance dataset, wherein the quality flag is the data after cloud ice, land, flares and high-latitude areas are removed.

[0008] In some embodiments of the present invention, the sorting according to the number of pixels in step S3 is specifically as follows: According to the statistical results, the number of pixels in each intersection area of ​​each atmospheric top radiance range is obtained; The intersection areas are sorted in descending order of the number of pixel points, so as to obtain a sorted sequence of the intersection areas of each atmospheric top radiance range interval.

[0009] In some embodiments of the present invention, the cloud score is calculated by a cloud prediction model; the cloud prediction model adopts a time series multivariate linear regression equation OLS algorithm.

[0010] In some embodiments of the present invention, the calculation formula of the cloud prediction model is: ; Among them, cfc is the cloud fraction, a0-a7 are coefficients, T is temperature, P is pressure, rh is relative humidity, sh is specific humidity, u is the wind speed in u direction, v is the wind speed in v direction, and prate is the precipitation rate.

[0011] In some embodiments of the present invention, step S1 specifically includes the following steps: S11, collect two lines of report data from the reference satellite and the ocean color observation satellite, and input them into the SGP4 model to obtain the position and speed of the reference satellite and the ocean color observation satellite; S12. Use Greenwich true sidereal time to perform coordinate conversion to obtain the latitude and longitude of the sub-satellite points of the reference satellite and the ocean color observation satellite, and combine the orbital inclination and the satellite width in the two reports to obtain the coverage of the reference satellite and the ocean color observation satellite at each moment; S13. Compare the coverage of the ocean color observation satellite at time t with the reference satellite coverage area within t±Δt to find all overlapping areas.

[0012] In some embodiments of the present invention, an intelligent planning system for cross-calibration of ocean color observation satellites is provided, which includes: An orbit prediction module is used to calculate the coverage area of ​​the reference satellite and the ocean color observation satellite at each moment, and determine the intersection area of ​​the reference satellite and the ocean color observation satellite; A top-of-atmosphere radiance processing module is used to divide the top-of-atmosphere radiance and determine the order of the top-of-atmosphere radiance range intervals in each intersection area; The calibration area dynamic selection module is used to judge and select the cross calibration area of ​​each atmospheric top radiance range according to the pixel ratio condition and cloud parameter condition; The communication module is used to communicate with external devices.

[0013] In some embodiments of the present invention, there is provided an electronic device, comprising: A processor, and a memory and a transceiver communicatively connected to the processor; The memory stores computer-executable instructions; the transceiver is used to send and receive data; The processor executes the computer-executable instructions stored in the memory to implement the above-mentioned planning method.

[0014] In some embodiments of the present invention, a computer readable storage medium is provided. The computer-readable storage medium stores computer-executable instructions, which are used to implement the above-mentioned planning method when executed by a processor.

[0015] The technical solution of the present invention has the following technical effects compared with the prior art: The present invention uses the SGP4 orbit prediction model, combined with data fusion technology, to scientifically and dynamically select different calibration areas, overcoming the limitations of traditional reliance on fixed calibration sites. By calibrating the sensor dynamic range on-orbit, not only can the observation accuracy and data reliability be significantly improved, but also the sensor can be ensured to continue to provide consistent and high-precision observation results under different environmental conditions, reducing system errors and drifts caused by long-term use. This process is crucial to ensuring the radiation stability and accuracy of the sensor, especially in a changing space environment and complex observation conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a planning method according to an embodiment of the present invention; Figure 2 A schematic diagram of a calibration planning area in the 443nm band is shown for an embodiment of the present invention; Figure 3 A schematic diagram of the calibration planning area in the 551nm band is shown for an embodiment of the present invention; Figure 4 A schematic diagram of the calibration planning area in the 671nm band is shown for an embodiment of the present invention; Figure 5 Schematic diagram of the structure of the intelligent planning system for cross-calibration of ocean color observation satellites; Figure 6 It is a structural schematic diagram of the electronic device.

[0018] Figure numerals: 100, planning system; 110, orbit prediction module; 120, top of atmosphere radiance processing module; 130, calibration area dynamic selection module; 140, communication module; 200, electronic device; 210, processor; 220, memory; 230, transceiver. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0021] Example 1: Reference Figure 1 As shown, the intelligent planning method for cross-calibration of ocean color observation satellites includes the following steps: S1. Calculate the intersection area of ​​the temporal and spatial matching between the ocean color observation satellite and the reference satellite; S11, collect two lines of report data from the reference satellite and the ocean color observation satellite, and input them into the SGP4 model to obtain the position and speed of the reference satellite and the ocean color observation satellite; S12. Use Greenwich true sidereal time to convert coordinates to obtain the latitude and longitude of the subsatellite points of the reference satellite and the ocean color observation satellite (86,400), and combine the orbital inclination and satellite width in the two reports to obtain the coverage of the reference satellite and the ocean color observation satellite at each moment; Specifically, by inputting two lines of report data from the reference satellite into the SGP4 model, the geocentric inertial coordinate system of 86400 (24 hours × 60 minutes × 60 seconds) satellites is obtained; The specific data output by the SGP4 model usually includes: Position: The position of the satellite in three-dimensional space, usually expressed by three coordinate components X, Y, and Z, in kilometers (km).

[0022] Velocity: The speed of a satellite in three-dimensional space, usually expressed by three components VX, VY, and VZ, in kilometers per second (km / s).

[0023] The coordinates are converted using Greenwich true sidereal time to obtain the latitude and longitude of the satellite subsatellite point (86400), and then the coverage of the reference satellite at each moment is obtained by the orbital inclination and satellite width in the two-line report. Through these two steps, the coverage of the reference satellite is finally obtained for every second of the day.

[0024] Then, the same method is used to traverse and calculate the coverage of the ocean color observation satellite every second on the same day.

[0025] S13. Compare the coverage of the ocean color observation satellite at time t with the reference satellite coverage area within t±Δt to find all overlapping areas.

[0026] Specifically, the coverage of the ocean color observation satellite at the current time t can be compared with the reference satellite coverage area within half an hour before and after the current time t (ie, Δt=0.5 hours in this embodiment) to find all overlapping areas.

[0027] In addition, in this step, the time t does not include the time when the solar zenith angle is greater than 60° and the satellite zenith angle is greater than 40°, and the night time.

[0028] S2. Construct a global top-of-atmosphere radiance dataset and divide it into multiple top-of-atmosphere radiance ranges; The top-of-atmosphere radiance and quality flag in the reference satellite data are fused to obtain the top-of-atmosphere radiance dataset, wherein the quality flag is the data after cloud ice, land, flares and high-latitude areas are removed.

[0029] Specifically, in this embodiment, the accurate top-of-atmosphere radiance (Lt) in the L1B data of the reference satellite one month before the target planning time and the quality flag (12 flags) in the L2 data are fused, and after removing low pixels such as cloud ice, land, flares and high-latitude areas (above 60° north and south latitude) in the quality flags, they are fused into a global top-of-atmosphere radiance Lt dataset with a resolution of 0.05°.

[0030] In addition, when constructing a global top-of-atmosphere radiance data set, the accurate top-of-atmosphere radiance (Lt) in the L1B data of other satellites and the quality flag (12 flags) in the L2 data may also be used. The other satellites in this embodiment are satellites other than ocean color observation satellites and reference satellites.

[0031] To prevent the influence of low outliers, starting from the minimum value of the top-of-atmosphere radiance Lt, with a step size of 0.1, the total number of pixels in the entire top-of-atmosphere radiance Lt data set that belong to the range of 0.1 is counted until a critical value α1 is found, so that the number of pixels in the top-of-atmosphere radiance Lt in the range of α1 to α1+0.1 is greater than X (the current threshold is set to 10,000). At this time, the minimum value of the top-of-atmosphere radiance Lt is set to α1.

[0032] To prevent the influence of high outliers, starting from the median of the top of the atmosphere radiance, with a step size of 0.1, the total number of pixels in the top of the atmosphere radiance Lt data set that belong to the range of 0.1 is counted until a critical value α2 is found, so that the number of pixels in the top of the atmosphere radiance Lt in the range of 2α to α2+0.1 is less than X (the current threshold is set to 10,000), at which time the maximum value of the top of the atmosphere radiance Lt is set to α2. Finally, the overall range of the top of the atmosphere radiance Lt that needs to be divided is determined, and the specific method is to divide the minimum to maximum value of Lt into multiple small ranges. In this embodiment, the determined overall range of the top of atmosphere radiance Lt is divided into six small ranges. Taking the 443nm band as an example, the six ranges are 4.675629-5.542295, 5.542295-6.408961, 6.408961-7.275627, 7.275627-8.142293, 8.142293-9.008959, and 9.008959-9.875625, and the units are all mw / cm² / μm / sr.

[0033] S3, after traversing each intersection area, counting the pixels in the range of the top of atmosphere radiance Lt in each intersection area and sorting them according to the number of pixels; Specifically, each intersection area is traversed, and the number of pixels in different ranges of top-of-atmosphere radiance Lt in each area is counted.

[0034] Finally, each range interval of the top-of-atmosphere radiance Lt has pixel data in all intersection areas; That is to say, the number of pixels in each intersection area of ​​each top-of-atmosphere radiance range interval is obtained according to the statistical results; Then, the intersection areas are sorted in descending order of the number of pixel points, so as to obtain the sorted sequence of the intersection areas of each range of the top of atmosphere radiance Lt.

[0035] S4. Determine in turn whether each top-of-atmosphere radiance range interval satisfies the pixel ratio condition and the cloud fraction condition in each intersection area; if both conditions are satisfied, the current intersection area is used as the calibration area of ​​the current top-of-atmosphere radiance range interval, and the information is output.

[0036] The step S4 specifically comprises the following steps: S41, determining whether the current top of atmosphere radiance range interval is an edge range interval, if so, when the pixel ratio in the intersection area A is greater than α%, it is determined that the current top of atmosphere radiance range interval satisfies the pixel ratio condition in the intersection area A; otherwise, when the pixel ratio in the intersection area A is greater than β%, it is determined that the current top of atmosphere radiance range interval satisfies the pixel ratio condition in the intersection area A; S42, after the current top of atmosphere radiance range interval meets the pixel ratio condition in the intersection area A, calculate the cloud fraction of the intersection area A, and determine whether the cloud fraction is less than C%, if so, the intersection area A is used as the calibration area of ​​the current top of atmosphere radiance range interval, and output the information of the current top of atmosphere radiance range interval and the intersection area A; Specifically, taking the first range interval (4.675629-5.542295) of the top of the atmosphere radiance as an example, traverse all intersection areas involving the pixel points in the first range interval; First, determine whether the pixel proportion of the first range interval in the intersection area is greater than 70%. If it is an edge range (such as the first range interval 4.675629-5.542295 and the fifth range interval 9.008959-9.875625 mentioned above), it can be allowed to be greater than 20%.

[0037] If it is satisfied, the cloud fraction of the intersection area is predicted, and it is determined whether the cloud fraction of the current intersection area is less than 60%. If it is less than 60%, all information is output, and the information at least includes the time when the satellite passes through the current area, the latitude and longitude range, the pixel ratio and the cloud fraction. So far, it is determined whether the current intersection area is the calibration area of ​​the first range interval of the top of the atmosphere radiance.

[0038] The cloud score is calculated by a cloud prediction model; the cloud prediction model adopts an OLS algorithm of a time series multiple linear regression equation.

[0039] The calculation formula of the cloud prediction model is: ;

[0040] Among them, cfc is the cloud fraction, a0-a7 are coefficients, T is temperature, P is pressure, rh is relative humidity, sh is specific humidity, u is the wind speed in u direction, v is the wind speed in v direction, and prate is the precipitation rate.

[0041] S43, traversing the remaining intersection areas, and determining the calibration area of ​​the current top of atmosphere radiance range interval in the remaining intersection areas; Specifically, by repeating the above steps, all calibration areas of the first range interval of the top of atmosphere radiance can be obtained.

[0042] S44. Repeat steps S41-S43 to determine the calibration area of ​​each top-of-atmosphere radiance range interval.

[0043] Specifically, for the first range interval to the sixth range interval, the pixel ratio conditions and cloud fraction conditions in each intersection area are judged respectively, and finally the following results are obtained as the calibration planning use case results of the ocean color observation satellite and the reference satellite Sentinel-3A on September 23, 2024: Reference Figure 2 As shown, in the 443nm band: 1. The first range is 4.675629-5.542295: Ocean color satellite observation satellite time: 20240923T223933 Reference satellite time: 20240923T224842 Cross-region range: longitude 153.981°E-167.013°E, latitude 49.704°S-37.851°S The percentage of pixels in this range in the intersection area: 28.46% Cloud score share: 58.94%.

[0044] 2. The second range is 5.542295-6.408961: Ocean color satellite observation satellite time: 20240923T105435 Reference satellite time: 20240923T110009 Crossover area range: longitude -28.083°E-14.446°E, latitude 43.404°S-32.381°S The percentage of pixels in this range in the intersection area: 61.69% Cloud score share: 32.24%.

[0045] 3. The third range is 6.408961-7.275627: Ocean color satellite observation satellite time: 20240923T022940 Reference satellite time: 20240923T023241 Cross-region range: longitude 100.247°E-114.225°E, latitude 33.929°S-23.780°S The percentage of pixels in this range in the intersection area: 67.20% Cloud score share: 53.26%.

[0046] 4. The fourth range is 7.275627-8.142293: Ocean color satellite observation satellite time: 20240923T022514 Reference satellite time: 20240923T022816 Cross-region range: longitude 104.210°E-117.951°E, latitude 17.756°S-8.495°S The percentage of pixels in this range in the intersection area: 89.15% Cloud score share: 0.00%.

[0047] 5. The fifth range is 8.142293-9.008959: Ocean color satellite observation satellite time: 20240923T121607 Reference satellite time: 20240923T122202 Crossover area range: longitude -36.728°E-23.423°E, latitude 24.435°N-34.356°S The percentage of pixels in this range in the intersection area: 85.61% Cloud score share: 49.04%.

[0048] 6. The sixth range is 9.008959-9.875625: Ocean color satellite observation satellite time: 20240923T204306 Reference satellite time: 20240923T205143 Crossover area range: longitude -166.262°E-153.892°E, latitude 8.438°N-17.422°N The percentage of pixels in this range in the intersection area: 83.19% Cloud score share: 0.00%.

[0049] Reference Figure 3 As shown, in the 551nm band: 1. The first range is 2.097290-2.897284: Ocean color satellite observation satellite time: 20240923T123602 Reference satellite time: 20240923T124206 Crossover area range: longitude -54.393°E-40.723°E, latitude 47.084°S-35.580°S The percentage of pixels in this range in the intersection area: 72.18% Cloud score share: 58.34%.

[0050] 2. The second range is 2.897284-3.697278: Ocean color satellite observation satellite time: 20240923T022940 Reference satellite time: 20240923T023241 Cross-region range: longitude 100.247°E-114.225°E, latitude 33.929°S-23.780°S The percentage of pixels in this range in the intersection area: 90.29% Cloud score share: 53.26%.

[0051] 3. The third range is 3.697278-4.497272: Ocean color satellite observation satellite time: 20240923T121623 Reference satellite time: 20240923T122219 Crossover area range: longitude -36.972°E-23.697°E, latitude 23.526°N-33.373°S The percentage of pixels in this range in the intersection area: 93.52% Cloud score share: 58.24%.

[0052] 4. The fourth range is 4.497272-5.297266: Ocean color satellite observation satellite time: 20240923T154703 Reference satellite time: 20240923T155411 Crossover area range: longitude -95.152°E-82.459°E, latitude 10.654°S-1.713°S The percentage of pixels in this range in the intersection area: 55.81% Cloud score share: 51.97%.

[0053] 5. The fifth range is 5.297266-6.097261: Ocean color satellite observation satellite time: 20240923T053849 Reference satellite time: 20240923T054248 The intersection area ranges from longitude 59.842°E-73.351°E, latitude 8.514°N-17.677°N The percentage of pixels in this range in the intersection area: 15.91% Cloud score share: 49.78%.

[0054] 6. The sixth range is 6.097261-6.897255: Ocean color satellite observation satellite time: 20240923T004355 Reference satellite time: 20240923T004626 Cross-region range: longitude 130.024°E-143.869°E, latitude 14.712°S-5.519°S The percentage of pixels in this range in the intersection area: 3.42% Cloud score share: 54.10%.

[0055] Reference Figure 4 As shown, in the 671nm band: 1. The first range is 0.958000-1.591329: Ocean color satellite observation satellite time: 20240923T173652 Reference satellite time: 20240923T174429 Crossover area range: longitude -129.035°E-115.835°E, latitude 44.871°S-33.725°S The percentage of pixels in this range in the intersection area: 99.04% Cloud score share: 36.81%.

[0056] 2. The second range is 1.591329-2.224657: Ocean color satellite observation satellite time: 20240923T121348 Reference satellite time: 20240923T121937 Crossover area range: longitude -34.457°E-20.831°E, latitude 32.229°N-42.953°N The percentage of pixels in this range in the intersection area: 97.69% Cloud score share: 35.13%.

[0057] 3. The third range is 2.224657-2.857985: Ocean color satellite observation satellite time: 20240923T122358 Reference satellite time: 20240923T123003 Cross-region range: Longitude -43.219°E-30.279°E, Latitude 3.012°S-5.910°N The percentage of pixels in this range in the intersection area: 80.49% Cloud score share: 13.08%.

[0058] 4. The fourth range is 2.857985-3.491313: Ocean color satellite observation satellite time: 20240923T154709 Reference satellite time: 20240923T155417 Cross-region range: longitude -95.232°E-82.538°E, latitude 11.013°S-2.066°S The percentage of pixels in this range in the intersection area: 24.01% Cloud score share: 52.89%.

[0059] 5. Fifth range interval 3.491313-4.124641: Ocean color satellite observation Satellite time: 20240923T053849 Reference satellite time: 20240923T054248 The intersection area ranges from longitude 59.842°E-73.351°E, latitude 8.514°N-17.677°N The percentage of pixels in this range in the intersection area: 11.16% Cloud score share: 49.78%.

[0060] 6. The sixth range is 4.124641-4.757970: Ocean color satellite observation satellite time: 20240923T040000 Reference satellite time: 20240923T040330 The intersection area ranges from longitude 83.643°E to 97.233°E and latitude 2.669°N to 11.729°N. The percentage of pixels in this range in the intersection area: 1.60% Cloud score share: 28.07%.

[0061] The technical solution of the present invention has the following technical effects compared with the prior art: The planning method of the present invention is specially designed for the new generation of ocean color satellites (such as HY-1E), and can fully adapt to the observation requirements of its small-width payloads (MRI and CZI2) that are not turned on all day for complex water environment. The design of this system enables the new generation of satellites to achieve high-precision calibration and maintain the reliability and consistency of observation data in a dynamic environment. And setting an efficient calibration startup plan for payloads that are not turned on all day can extend the service life of the payload. And the cross-calibration area can cover different ranges of top of atmosphere radiance, and realize calibration of different top of atmosphere radiance characteristic ranges, which can not only significantly improve the observation accuracy and data reliability, but also ensure that the sensor continues to provide consistent and high-precision observation results under different environmental conditions.

[0062] It can effectively predict cloud coverage and intelligently select the appropriate calibration area. The protection point is that the system can cope with high cloud coverage, ensuring that the best calibration opportunity can still be found under adverse observation conditions, thereby ensuring the effectiveness of the calibration task.

[0063] Example 2: This example will be based on Figure 5 and Figure 6 An intelligent planning system 100 and an electronic device 200 for cross-calibration of ocean color observation satellites are described.

[0064] For planning system 100, refer to Figure 5 As shown, including: The orbit prediction module 110 is used to calculate the coverage area of ​​the reference satellite and the ocean color observation satellite at each moment, and determine the intersection area of ​​the reference satellite and the ocean color observation satellite; A top-of-atmosphere radiance processing module 120, which is used to divide the top-of-atmosphere radiance and determine the order of the top-of-atmosphere radiance range intervals in each intersection area; A calibration area dynamic selection module 130 is used to determine and select cross calibration areas of each atmospheric top radiance range interval according to pixel ratio conditions and cloud parameter conditions; The communication module 140 is used to communicate with external devices.

[0065] It should be understood that the planning system 100 here is embodied in the form of a functional module. The term "module" here may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, a merged logic circuit and / or other suitable components that support the described functions. In an optional example, those skilled in the art may understand that the planning system 100 may be specifically the electronic device 200 in the above embodiment, or the functions of the electronic device 200 in the above embodiment may be integrated in the planning system 100, and the planning system 100 may be used to execute the various processes and / or steps corresponding to the electronic device 200 in the above method embodiment, and to avoid repetition, they will not be described here.

[0066] The planning system 100 has the functions of implementing the corresponding steps performed by the electronic device 200 of the planning method in Example 1; the above functions can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions. For example, the above acquisition module can be a communication interface, such as a transceiver interface.

[0067] In the embodiments of the present application, Figure 5 The planning system 100 may also be a chip or a chip system, such as a system on chip (SoC).

[0068] Reference Figure 6 As shown, in this embodiment, an electronic device 200 is provided, including: A processor 210, and a memory 220 and a transceiver 230 communicatively connected to the processor; The memory 220 stores computer-executable instructions; the transceiver 230 is used to send and receive data; The processor 210 executes the computer-executable instructions stored in the memory 220 to implement the planning method in Example 1.

[0069] It should be understood that the electronic device 200 can be used to execute the corresponding steps and / or processes in the above method embodiments. Optionally, the memory 220 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory 220 may also include a non-volatile random access memory. For example, the memory 220 may also store information about the device type. The processor 210 may be used to execute the instructions stored in the memory 220, and when the processor 210 executes the instructions, the processor 210 may execute the corresponding steps and / or processes in the above method embodiments.

[0070] It should be understood that in the embodiment of the present application, the processor 210 may be a central processing unit (CPU), and the processor 210 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0071] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 210 or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in the processor 210 for execution. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor executes the instructions in the memory, and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0072] Embodiment 3: In this embodiment, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the planning method in Embodiment 1.

[0073] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0076] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0077] The technical solution of the present invention has the following technical effects compared with the prior art: The system framework of the present invention integrates multiple modules such as orbit prediction, cloud prediction, dynamic data fusion and mission planning to form an efficient and automated calibration task execution system. The system framework design is modular and scalable, supporting flexible integration and adjustment of different data sources and mission requirements. The design of the framework is the basis for ensuring that the system maintains high efficiency and flexibility in long-term operation.

[0078] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0079] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent planning method for cross-calibration of ocean color observation satellites, characterized in that: The following steps are involved: S1. Calculate the intersection area of ​​the temporal and spatial matching between the ocean color observation satellite and the reference satellite; S2. Construct a global top-of-atmosphere radiance dataset and divide it into multiple top-of-atmosphere radiance ranges; S3, after traversing each intersection area, counting the pixels in each atmosphere top radiance range interval in each intersection area and sorting them according to the number of pixels; S4, sequentially judging whether each atmospheric top radiance range interval satisfies the pixel ratio condition and the cloud fraction condition in each intersection area; if both conditions are satisfied, the current intersection area is used as the calibration area of ​​the current atmospheric top radiance range interval, and the information is output; The step S4 specifically comprises the following steps: S41, determining whether the current top of atmosphere radiance range interval is an edge range interval, if so, when the pixel ratio in the intersection area A is greater than α%, it is determined that the current top of atmosphere radiance range interval satisfies the pixel ratio condition in the intersection area A; otherwise, when the pixel ratio in the intersection area A is greater than β%, it is determined that the current top of atmosphere radiance range interval satisfies the pixel ratio condition in the intersection area A; S42, after the current top of atmosphere radiance range interval meets the pixel ratio condition in the intersection area A, calculate the cloud fraction of the intersection area A, and determine whether the cloud fraction is less than C%, if so, the intersection area A is used as the calibration area of ​​the current top of atmosphere radiance range interval, and output the information of the current top of atmosphere radiance range interval and the intersection area A; S43, traversing the remaining intersection areas, and determining the calibration area of ​​the current top of atmosphere radiance range interval in the remaining intersection areas; S44. Repeat steps S41-S43 to determine the calibration area of ​​each top-of-atmosphere radiance range interval.

2. The intelligent planning method for cross-calibration of ocean color observation satellites according to claim 1 is characterized in that: The construction of the global top-of-atmosphere radiance dataset includes: The monthly average global top-of-atmosphere radiance dataset is obtained by removing low-quality marker pixels from the top-of-atmosphere radiance in the reference satellite data, and then merging them. The low-quality marker pixels are the data after cloud ice, land, flares and high-latitude areas are removed.

3. The intelligent planning method for cross-calibration of ocean color observation satellites according to claim 1 is characterized in that: The descending sorting according to the number of pixels in step S3 is specifically as follows: According to the statistical results, the number of pixels in each intersection area of ​​each atmospheric top radiance range is obtained; The intersection areas are sorted in descending order of the number of pixel points, so as to obtain a sorted sequence of the intersection areas of each atmospheric top radiance range interval.

4. The intelligent planning method for cross-calibration of ocean color observation satellites according to claim 1 is characterized in that: The cloud score is calculated by a cloud prediction model; the cloud prediction model adopts an OLS algorithm of a time series multiple linear regression equation.

5. The intelligent planning method for cross-calibration of ocean color observation satellites according to claim 4 is characterized in that: The calculation formula of the cloud prediction model is: ； Among them, cfc is the cloud fraction, a0-a7 are coefficients, T is temperature, P is pressure, rh is relative humidity, sh is specific humidity, u is the wind speed in u direction, v is the wind speed in v direction, and prate is the precipitation rate.

6. The intelligent planning method for cross-calibration of ocean color observation satellites according to claim 1 is characterized in that: The step S1 specifically includes the following steps: S11, collect two lines of report data from the reference satellite and the ocean color observation satellite, and input them into the SGP4 model to obtain the position and speed of the reference satellite and the ocean color observation satellite; S12. Use Greenwich true sidereal time to perform coordinate conversion to obtain the latitude and longitude of the subsatellite points of the reference satellite and the ocean color observation satellite, and combine the orbital inclination and satellite width in the two reports to obtain the coverage of the reference satellite and the ocean color observation satellite at each moment; S13. Compare the coverage of the ocean color observation satellite at time t with the reference satellite coverage area within t±Δt to find all overlapping areas.

7. An intelligent planning system for cross-calibration of ocean color observation satellites, characterized in that: Used to implement the intelligent planning method according to any one of claims 1 to 6, the intelligent planning system comprises: An orbit prediction module is used to calculate the coverage area of ​​the reference satellite and the ocean color observation satellite at each moment, and determine the intersection area of ​​the reference satellite and the ocean color observation satellite; A top-of-atmosphere radiance processing module is used to divide the top-of-atmosphere radiance and determine the order of the top-of-atmosphere radiance range intervals in each intersection area; The calibration area dynamic selection module is used to judge and select the cross calibration area of ​​each atmospheric top radiance range according to the pixel ratio condition and cloud parameter condition; The communication module is used to communicate with external devices.

8. An electronic device, characterized in that: include: A processor, and a memory and a transceiver communicatively connected to the processor; The memory stores computer-executable instructions; the transceiver is used to send and receive data; The processor executes the computer-executable instructions stored in the memory to implement the intelligent planning method described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the intelligent planning method described in any one of claims 1 to 6.

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