Cross-calibration method, system, electronic equipment and medium for geostationary satellite

Abstract

The present invention provides a cross-calibration method, system, electronic device, and medium for geostationary satellites, belonging to the field of remote sensing technology. The cross-calibration method includes: obtaining data to be calibrated of the satellite to be calibrated and calibration source data of a reference satellite; performing equal longitude and latitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data; performing spatiotemporal matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data; filtering the DN values ​​in the matching data to be calibrated and the radiance values ​​in the calibration source matching data to obtain a sample data set; and fitting samples in the sample data set to determine a radiometric calibration coefficient. Accurate cross-calibration is achieved for satellites that do not conform to international standards, such as the Indian geostationary satellite INSAT-3DR.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and in particular to a cross-calibration method, system, electronic equipment and medium for an Indian geostationary satellite INSAT-3DR. Background Art

[0002] Cross-calibration is one of the most widely used on-orbit alternative calibration and calibration verification methods. This method utilizes data from the intersecting (or adjacent) region of the sub-satellite trajectories of two satellites. Using high-precision satellite sensors as references, this method uses temporal, spatial, and spectral matching to create matching samples of the common observation area between the two satellites. This allows for on-orbit radiometric calibration of the target remote sensor and assessment of the original calibration deviation.

[0003] Among the existing satellites, not all are consistent with international standards, such as the Indian geostationary satellite INSAT-3DR. There is currently little research on cross-calibration methods for these satellites that are inconsistent with international standards. Therefore, a new cross-calibration method is urgently needed to achieve cross-calibration of satellites such as the Indian geostationary satellite INSAT-3DR that are inconsistent with international standards. Summary of the Invention

[0004] The purpose of the present invention is to provide a geostationary satellite cross-calibration method, system, electronic equipment and medium, which can be used to cross-calibrate Indian geostationary satellites such as INSAT-3DR that are inconsistent with international standards.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A cross-calibration method for a geostationary satellite, comprising:

[0007] Acquire the uncalibrated data of the uncalibrated satellite and the calibration source data of the reference satellite; the time period, regional range and channel of the uncalibrated data correspond to the time period, regional range and channel of the calibration source data;

[0008] Performing equal longitude and latitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data; the projection data to be calibrated and the calibration source projection data are both remote sensing images; the projection data to be calibrated includes a DN value of each pixel, and the calibration source projection data includes a radiance value of each pixel;

[0009] Performing spatiotemporal matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data;

[0010] Filtering the DN values ​​in the matching data to be calibrated and the radiance values ​​in the calibration source matching data to obtain a sample data set; the sample data set includes a plurality of samples; each sample includes a DN value and a corresponding radiance value;

[0011] Fitting is performed on samples in the sample data set to determine a radiation calibration coefficient; the radiation calibration coefficient is used to perform cross calibration on the data to be calibrated.

[0012] Optionally, the satellite to be calibrated is the Indian geostationary satellite INSAT-3DR.

[0013] Optionally, performing spatiotemporal matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data specifically includes:

[0014] Converting the time of the calibration source projection data into the time of the projection data to be calibrated to obtain calibration source time matching data;

[0015] If the resolution of the calibration source time matching data is greater than the resolution of the projection data to be calibrated, the projection data to be calibrated is the matching data to be calibrated, and the calibration source time matching data is resampled to obtain calibration source matching data, so that the resolution of the calibration source matching data is equal to the resolution of the matching data to be calibrated;

[0016] If the resolution of the projection data to be calibrated is greater than the resolution of the calibration source time matching data, the calibration source time matching data is used as the calibration source matching data, and the projection data to be calibrated is resampled to obtain the matching data to be calibrated, so that the resolution of the calibration source matching data is equal to the resolution of the matching data to be calibrated.

[0017] Optionally, filtering the DN values ​​in the to-be-calibrated matching data and the radiance values ​​in the calibration source matching data to obtain a sample data set specifically includes:

[0018] Calculating the correlation between the DN value of each pixel in the to-be-calibrated matching data and the radiance value of the corresponding pixel in the calibration source matching data at the same moment;

[0019] Eliminate the DN values ​​whose correlation with the radiance value is less than a set threshold in the matching data to be calibrated, and obtain filtered data to be calibrated;

[0020] In the filtered data to be calibrated, sample point windows are selected at equal intervals to obtain a first sample set; the first sample set includes a plurality of target area samples to be calibrated and a plurality of environment samples to be calibrated; the target area samples to be calibrated and the environment samples to be calibrated include DN values ​​in corresponding sample point windows;

[0021] Calculate the standard deviation of each target area sample to be calibrated and the standard deviation of each environmental sample to be calibrated;

[0022] The target area samples to be calibrated whose standard deviation is greater than the DN value target area uniformity threshold and the environmental samples to be calibrated whose standard deviation is greater than the DN value environment uniformity threshold in the first sample set are eliminated to obtain a second sample set; the second sample set includes multiple DN values; the sample data set includes the second sample set and the radiance values ​​corresponding to each DN value in the second sample set in the calibration source matching data.

[0023] Optionally, the sample point window of the target area sample to be calibrated is a grid of 3*3 in size;

[0024] The sample point window of the environmental sample to be calibrated is a grid with a size of 5*5.

[0025] Optionally, the cross-calibration method for a geostationary satellite further includes:

[0026] A scatter plot is drawn according to the radiation calibration coefficient and the sample data set.

[0027] To achieve the above object, the present invention also provides the following solution:

[0028] A cross-calibration system for a geostationary satellite, comprising:

[0029] A data acquisition unit is used to acquire the uncalibrated data of the uncalibrated satellite and the calibration source data of the reference satellite; the time period, regional range and channel of the uncalibrated data correspond to the time period, regional range and channel of the calibration source data;

[0030] a longitude and latitude projection unit connected to the data acquisition unit, configured to perform iso-longitude and latitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data; the projection data to be calibrated and the calibration source projection data are both remote sensing images; the projection data to be calibrated includes a DN value of each pixel, and the calibration source projection data includes a radiance value of each pixel;

[0031] a space-time matching unit connected to the longitude and latitude projection unit, configured to perform space-time matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data;

[0032] a filtering unit connected to the spatiotemporal matching unit, configured to filter the DN values ​​in the to-be-calibrated matching data and the radiance values ​​in the calibration source matching data to obtain a sample data set; the sample data set includes a plurality of samples; each sample includes a DN value and a corresponding radiance value;

[0033] A fitting unit is connected to the filtering unit and is used to fit the samples in the sample data set to determine a radiation calibration coefficient; the radiation calibration coefficient is used to perform cross calibration on the data to be calibrated.

[0034] To achieve the above object, the present invention also provides the following solution:

[0035] An electronic device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-mentioned cross-calibration method for geostationary satellites.

[0036] To achieve the above object, the present invention also provides the following solution:

[0037] A computer-readable storage medium stores a computer program, which implements the above-mentioned cross-calibration method for geostationary satellites when executed by a processor.

[0038] According to the specific embodiment provided by the present invention, the present invention discloses the following technical effects: first, the data to be calibrated of the satellite to be calibrated and the calibration source data of the reference satellite are obtained, then the data to be calibrated and the calibration source data are projected in equal longitude and latitude to obtain the projection data to be calibrated and the calibration source projection data, then the projection data to be calibrated and the calibration source projection data are spatiotemporally matched to obtain the matched data to be calibrated and the calibration source matching data, then the DN values ​​in the matched data to be calibrated and the radiance values ​​in the calibration source matching data are filtered to obtain a sample data set, and finally, the samples in the sample data set are fitted to determine the radiometric calibration coefficient. The radiometric calibration coefficient can be used to cross-calibrate the data to be calibrated, thereby achieving accurate cross-calibration of satellites that are inconsistent with international standards (such as the Indian geostationary satellite INSAT-3DR). BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 A flow chart of a cross-calibration method for a geostationary satellite according to the present invention;

[0041] Figure 2 This is the calibration diagram for the visible light (VIS) channel;

[0042] Figure 3 This is the test result diagram of the visible light (VIS) channel;

[0043] Figure 4 This is the calibration diagram for the shortwave infrared (SWIR) channel;

[0044] Figure 5 This is the short-wave infrared (SWIR) channel test result diagram;

[0045] Figure 6 This is the calibration diagram for the mid-wave infrared (MWIR) channel;

[0046] Figure 7 This is the result of the medium wave infrared (MWIR) test;

[0047] Figure 8 This is the calibration diagram of the water vapor (WV) channel;

[0048] Figure 9 This is the water vapor (WV) channel inspection result diagram;

[0049] Figure 10 This is the calibration diagram for the infrared split window (IR1) channel;

[0050] Figure 11 This is the test result diagram of the infrared split window (IR1) channel;

[0051] Figure 12 This is the calibration diagram for the infrared split window (IR2) channel;

[0052] Figure 13 This is the infrared split window (IR2) channel test result diagram;

[0053] Figure 14 Schematic diagram of the modules of the cross-calibration system for geostationary satellites of the present invention.

[0054] Explanation of symbols:

[0055] Data acquisition unit-1, longitude and latitude projection unit-2, time and space matching unit-3, filtering unit-4, fitting unit-5. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0057] The purpose of the present invention is to provide a geostationary satellite cross-calibration method, system, electronic equipment and medium, which can accurately cross-calibrate satellites that are inconsistent with international standards (such as the Indian geostationary satellite INSAT-3DR).

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] Currently, there is little research on cross-calibration methods for the Indian geostationary satellite INSAT-3DR. The present invention proposes a cross-calibration method with the Himawari 8 satellite, which can effectively fill the gap in current satellite calibration technology. That is, the present invention calibrates with the Himawari 8 satellite based on the characteristics of the Indian geostationary satellite.

[0060] Example 1

[0061] like Figure 1 As shown, the cross-calibration method of a geostationary satellite of the present invention includes:

[0062] S1: Acquire the data to be calibrated of the satellite to be calibrated and the calibration source data of the reference satellite. The time period, area range and channel of the data to be calibrated correspond to the time period, area range and channel of the calibration source data.

[0063] In this embodiment, the number of reference satellites can be one or more. The satellite to be calibrated is the Indian geostationary satellite INSAT-3DR. The INSAT-3DR calibration data includes data related to the VIS (visible light channel), SWIR (shortwave infrared channel), MWIR (medium wave infrared channel), IR1, IR2 (split window infrared channel), and WV (water vapor channel).

[0064] S2: Performing iso-latitude and longitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data. Both the projection data to be calibrated and the calibration source projection data are remote sensing images. The projection data to be calibrated includes the DN value of each pixel, and the calibration source projection data includes the radiance value of each pixel.

[0065] Specifically, we selected satellite data from the same time period and the same area. We created a lookup table of equal longitude and latitude based on this range and processed the data into equal longitude and latitude projections for the same area. The INSAT-3DR satellite data retained the DN value, while the reference satellite data was processed into radiance.

[0066] S3: performing spatiotemporal matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data.

[0067] Specifically, the time of the calibration source projection data is converted to the time of the projection data to be calibrated, obtaining calibration source time matching data. This means that the corresponding time data is matched based on the time information of the satellite data. The time information of the INSAT-3DR satellite is Beijing Time, while the time information of the reference satellite is Universal Time. Both time information is converted to Beijing Time for matching.

[0068] In this embodiment, time matching is to find data of the same target area within a suitable time interval (eg, 5 minutes) between two satellites, and the time is generally based on universal time.

[0069] If the resolution of the calibration source time matching data is greater than the resolution of the projection data to be calibrated, the projection data to be calibrated is the matching data to be calibrated, and the calibration source time matching data is resampled to obtain calibration source matching data.

[0070] If the resolution of the projection data to be calibrated is greater than the resolution of the calibration source time-matched data, the calibration source time-matched data becomes the calibration source matching data, and the projection data to be calibrated is resampled to obtain the matching data to be calibrated, so that the resolution of the calibration source matching data is equal to the resolution of the matching data to be calibrated. In other words, the high-resolution data is resampled to a lower-resolution data format.

[0071] In this embodiment, spatial matching is to unify the resolution based on the accurate positioning of the target satellite and the reference satellite, extract the appropriate same observation area (land or ocean), and match the extraction window samples.

[0072] S4: Filter the DN values ​​in the matching data to be calibrated and the radiance values ​​in the calibration source matching data to obtain a sample data set. The sample data set includes multiple samples. Each sample includes a DN value and a corresponding radiance value.

[0073] Furthermore, step S4 specifically includes:

[0074] S41: Calculating the correlation between the DN value of each pixel in the to-be-calibrated matching data and the radiance value of the corresponding pixel in the calibration source matching data at the same moment.

[0075] S42: Eliminate the DN values ​​whose correlation with the radiance value is less than a set threshold from the matching data to be calibrated, and obtain filtered data to be calibrated. In this embodiment, the threshold is adaptively adjusted according to different channels, such as the threshold of the IR1 channel is 0.8, and the threshold of the VIS channel is 0.85.

[0076] S43: Selecting sample point windows at equal intervals from the filtered data to be calibrated to obtain a first sample set. The first sample set includes a plurality of target area samples to be calibrated and a plurality of environment samples to be calibrated. The target area samples to be calibrated and the environment samples to be calibrated include DN values ​​in the corresponding sample point windows.

[0077] Specifically, the sample point window of the target area sample to be calibrated is a 3*3 grid. The sample point window of the environmental sample to be calibrated is a 5*5 grid. In this embodiment, from 9.5 degrees north latitude to 9.5 degrees south latitude, with an interval of 0.1 degrees, and from 90.5 degrees east longitude to 109.5 degrees east longitude, with an interval of 0.1 degrees, a 3*3 grid window is selected as the target area sample, and a 5*5 grid window is selected as the environmental sample. If there are invalid values ​​in the samples (including target area samples and environmental samples), the data in the corresponding window is filtered.

[0078] S44: Calculate the standard deviation of each target area sample to be calibrated and the standard deviation of each environment sample to be calibrated.

[0079] S45: Eliminate target area samples to be calibrated whose standard deviation exceeds the DN value target area uniformity threshold and environmental samples to be calibrated whose standard deviation exceeds the DN value environmental uniformity threshold from the first sample set, thereby obtaining a second sample set. The second sample set includes multiple DN values. Furthermore, if the target satellite's channel is MWIR and the average value of the target area samples corresponding to the reference satellite is greater than 1, the corresponding window is filtered. The sample data set includes the second sample set and the radiance values ​​corresponding to each DN value in the second sample set in the calibration source matching data.

[0080] In this example, data is first filtered according to a set threshold. Then, samples from multiple days of calibration analysis are accumulated and quality assessed based on metrics such as sample correlation and sample number, ultimately yielding samples suitable for cross-comparison. Furthermore, observation results under a uniform scenario are obtained by dually testing the uniformity of the environmental field and the equivalent observation field of view. Finally, anomalous observation samples are eliminated based on the effective physical range of the channel radiance.

[0081] S5: Fitting the samples in the sample data set to determine a radiation calibration coefficient. The radiation calibration coefficient is used to perform cross calibration on the data to be calibrated.

[0082] In order to more intuitively display the calibration results, the cross-calibration method for a geostationary satellite of the present invention further includes:

[0083] S6: Draw a scatter plot based on the radiation calibration coefficient and the sample data set.

[0084] In this example, the calibration and verification results are displayed in a scatter plot format. A curve is plotted using the selected DN values ​​calculated using the radiometric calibration coefficients. The scatter plots are plotted using the DN values ​​of the INSAT-3DR and the radiance values ​​of the reference satellite. A coordinate system is created based on the DN and radiance data ranges. Finally, a scatter plot is created using the radiometric calibration coefficients and the quality verification data.

[0085] The present invention provides a cross-calibration method for the Indian geostationary satellite INSAT-3DR, which can effectively fill the gap in current satellite calibration technology. In addition, the present invention has played an important role in the INSAT-3DR satellite mission and can provide technical support for the development of quantitative application algorithms for geostationary meteorological satellites in the INSAT-3DR satellite mission. In addition, the present invention adopts a cross-calibration method based on INSAT-3DR satellite data, cross-calibrates the 6 channel data of the INSAT-3DR satellite, and compares the calibration results with the same channels of HIMAWARI 8. The results show that the average deviation of the reflectance of the visible light channel is better than 10%, and the average deviation of the short-wave infrared radiance is better than 2W / (m 2 *Sr*μm), and the average deviation of infrared brightness temperature of other infrared channels is better than 2K, which proves the feasibility of using the present invention to perform cross-calibration of Indian geostationary satellites.

[0086] In order to better understand the solution of the present invention, it is further described below with reference to specific embodiments.

[0087] This embodiment performs cross calibration on the Indian geostationary satellite INSAT-3DR and the Himawari 8 satellite.

[0088] (1) Data analysis and preprocessing

[0089] The VIS, SWIR, MWIR, WV, IR1, and IR2 channels of the INSAT-3DR satellite correspond to the B03, B05, B07, B09, B13, and B15 channels of the Himawari 8 satellite, respectively.

[0090] Data from the INSAT-3DR satellite VIS and SWIR channels, along with corresponding Himawari 8 satellite channels, were collected daily from 11:00 AM to 3:00 PM Beijing Time (Beijing Time) from September 9, 2020, to September 13, 2020. Data from the INSAT-3DR satellite MWIR, WV, IR1, and IR2 channels, along with corresponding Himawari 8 satellite channels, were collected from January 1, 2021, to January 10, 2021, Beijing Time. The selected region covered longitude and latitude between 45°N and 5°S, and 65°E and 110°E. A lookup table for constant latitude and longitude projections was created based on the region and resolution, and the data were then projected using the lookup table for the corresponding resolution and satellite. The INSAT-3DR satellite data retained the DN value, while the Himawari 8 satellite data was processed to radiance.

[0091] (2) Space-time matching

[0092] Based on the satellite data's time information, the data of the corresponding channels at the corresponding times are matched. The time information of the INSAT-3DR satellite is Beijing Time. The time information of the HIMAWARI 8 satellite is Universal Time. The time information is converted to Beijing Time for matching.

[0093] Since the resolution of the Himawari 8 satellite channels is higher than that of the corresponding channels of the INSAT-3DR satellite, the resolution of the Himawari 8 satellite channels is resampled to the resolution of the corresponding channels of the INSAT-3DR satellite.

[0094] (3) Sample processing

[0095] Filter out the time data with the correlation between DN value and radiance less than 0.8 (VIS is 0.85) at the same time.

[0096] From 9.5 degrees north latitude to 9.5 degrees south latitude, with intervals of 0.1 degrees, and from 90.5 degrees east longitude to 109.5 degrees east longitude, with intervals of 0.1 degrees, a 3x3 grid window is selected as the target area sample, and a 5x5 grid window is selected as the environmental sample. If there are invalid values ​​in the sample, the data in this window is filtered out.

[0097] Calculate the mean, maximum, minimum, and standard deviation of the data in the target area samples and the standard deviation of the data in the environmental samples. If the standard deviation of the target area samples or environmental samples of the INSAT-3DR satellite exceeds the DN value target area uniformity threshold or the DN value environmental uniformity threshold (see Table 1), filter the corresponding window.

[0098] Table 1 Thresholds of each channel of INSAT-3DR satellite

[0099] VIS SWIR MWIR WV IR1 IR2 DN value target area uniformity 0.4 12 15 15 15 18 DN value environmental uniformity 6 12 15 15 15 18

[0100] If the channel is MWIR and the average value of the target area samples of the Himawari 8 satellite is greater than 1, filter the corresponding window.

[0101] The average value of the target area samples that pass the filtration is used as the data for calculating the calibration coefficient (sample data set).

[0102] (4) Calculation of calibration coefficients, testing and drawing

[0103] Fit the data obtained in the previous step (perform primary curve fitting for the VIS channel and quadratic curve fitting for other channels) to obtain the radiation calibration coefficient.

[0104] The INSAT-3DR satellite VIS and SWIR channels and the corresponding HIMAWARI 8 satellite channel data from 11:00 to 15:00 Beijing time from September 14, 2020 to September 17, 2020 are used. The INSAT-3DR satellite MWIR, WV, IR1, IR2 channels and the corresponding HIMAWARI 8 satellite channel data from January 11, 2021 to January 17, 2021 Beijing time are used to verify the radiometric calibration coefficients, such as Figure 2-13 shown.

[0105] in, Figure 2 This is the calibration diagram of the visible light (VIS) channel. Figure 3 This is the test result of the visible light (VIS) channel. Figure 2 The horizontal axis is the DN value of the INSAT-3DR satellite, and the vertical axis is the radiance value of the Himawari 8 satellite. In the figure, slope is the linear term coefficient of the calibration coefficient, intercept is the constant term coefficient, count is the number of data grid points used, R is the correlation coefficient, and STD is the standard deviation of the DN value of the sample selected from the INSAT-3DR satellite. Figure 3 The horizontal axis is the reflectivity of the INSAT-3DR satellite (calculated using the DN value through the calibration coefficient), and the vertical axis is the reflectivity of the Himawari 8 satellite. In the figure, slope is the linear term coefficient of the calibration coefficient, intercept is the constant term coefficient, count is the number of data grid points used, R is the correlation coefficient, STD is the standard deviation of the INSAT-3DR satellite reflectivity, and DP is the deviation percentage.

[0106] Figure 4 This is the calibration diagram for the shortwave infrared (SWIR) channel. Figure 5 This is the shortwave infrared (SWIR) channel inspection result diagram. Figure 4The horizontal axis is the DN value of the INSAT-3DR satellite, and the vertical axis is the radiance value of the Himawari 8 satellite. In the figure, a is the quadratic term coefficient of the calibration coefficient, b is the linear term coefficient of the calibration coefficient, c is the constant term coefficient, count is the number of data grid points used, and R is the correlation coefficient. Figure 5 The horizontal axis is the radiance value of the INSAT-3DR satellite (calculated using the DN value through the calibration coefficient), and the vertical axis is the radiance value of the Himawari 8 satellite. In the figure, slope is the linear term coefficient of the calibration coefficient, intercept is the constant term coefficient, count is the number of data grid points used, R is the correlation coefficient, RMSE is the root mean square error, DP is the bias percentage, and BIAS is the bias value.

[0107] Figure 6 This is the calibration diagram for the mid-wave infrared (MWIR) channel. Figure 7 This is the result of the medium-wave infrared (MWIR) test. Figure 6 The horizontal axis is the DN value of the INSAT-3DR satellite, and the vertical axis is the radiance value of the Himawari 8 satellite. In the figure, a is the quadratic term coefficient of the calibration coefficient, b is the linear term coefficient of the calibration coefficient, c is the constant term coefficient, count is the number of data grid points used, and R is the correlation coefficient. Figure 7 The horizontal axis is the infrared brightness temperature value of the INSAT-3DR satellite (calculated using the DN value through the calibration coefficient), and the vertical axis is the infrared brightness temperature value of the Himawari 8 satellite. In the figure, slope is the linear term coefficient of the calibration coefficient, intercept is the constant term coefficient, count is the number of data grid points used, R is the correlation coefficient, RMSE is the root mean square error, DP is the bias percentage, and BIAS is the bias value.

[0108] Figure 8 This is the calibration diagram of the water vapor (WV) channel. Figure 9 This is the water vapor (WV) channel inspection result diagram. Figure 8 The horizontal axis is the DN value of the INSAT-3DR satellite, and the vertical axis is the radiance value of the Himawari 8 satellite. In the figure, a is the quadratic term coefficient of the calibration coefficient, b is the linear term coefficient of the calibration coefficient, c is the constant term coefficient, count is the number of data grid points used, and R is the correlation coefficient. Figure 9 The horizontal axis is the infrared brightness temperature value of the INSAT-3DR satellite (calculated using the DN value through the calibration coefficient), and the vertical axis is the infrared brightness temperature value of the HIMAWARI 8 satellite. In the figure, slope is the linear term coefficient of the calibration coefficient, intercept is the constant term coefficient, count is the number of data grid points used, R is the correlation coefficient, RMSE is the root mean square error, DP is the bias percentage, and BIAS is the bias value.

[0109] Figure 10 This is the calibration diagram of the infrared split window (IR1) channel. Figure 11 This is the infrared split window (IR1) channel test result diagram. Figure 10 The horizontal axis is the DN value of the INSAT-3DR satellite, and the vertical axis is the radiance value of the HIMAWARE 8 satellite. In the figure, a is the quadratic term coefficient of the calibration coefficient, b is the linear term coefficient of the calibration coefficient, c is the constant term coefficient, count is the number of data grid points used, and R is the correlation coefficient. Figure 11 The horizontal axis is the infrared brightness temperature value of the INSAT-3DR satellite (calculated using the DN value through the calibration coefficient), and the vertical axis is the infrared brightness temperature value of the Himawari 8 satellite. In the figure, slope is the linear term coefficient of the calibration coefficient, intercept is the constant term coefficient, count is the number of data grid points used, R is the correlation coefficient, RMSE is the root mean square error, DP is the bias percentage, and BIAS is the bias value.

[0110] Figure 12 This is the calibration diagram for the infrared split window (IR2) channel. Figure 13 This is the infrared split window (IR2) channel test result diagram. Figure 12 The horizontal axis is the DN value of the INSAT-3DR satellite, and the vertical axis is the radiance value of the HIMAWARE 8 satellite. In the figure, a is the quadratic term coefficient of the calibration coefficient, b is the linear term coefficient of the calibration coefficient, c is the constant term coefficient, count is the number of data grid points used, and R is the correlation coefficient. Figure 13 The horizontal axis is the infrared brightness temperature value of the INSAT-3DR satellite (calculated using the DN value through the calibration coefficient), and the vertical axis is the infrared brightness temperature value of the Himawari 8 satellite. In the figure, slope is the linear term coefficient of the calibration coefficient, intercept is the constant term coefficient, count is the number of data grid points used, R is the correlation coefficient, RMSE is the root mean square error, DP is the bias percentage, and BIAS is the bias value.

[0111] The results show that the average deviation of the reflectance of the visible light channel is better than 10%, and the average deviation of the short-wave infrared radiance is better than 2W / (m 2 *Sr*μm), and the average deviation of infrared brightness temperature in other infrared channels is better than 2K.

[0112] Example 2

[0113] In order to execute the method corresponding to the above-mentioned embodiment 1 and achieve corresponding functions and technical effects, a cross-calibration system for geostationary satellites is provided below.

[0114] like Figure 14As shown, the cross-calibration system for geostationary satellites provided in this embodiment includes: a data acquisition unit 1, a latitude and longitude projection unit 2, a time-space matching unit 3, a filtering unit 4 and a fitting unit 5.

[0115] The data acquisition unit 1 is used to acquire the uncalibrated data of the uncalibrated satellite and the calibration source data of the reference satellite; the time period, regional range and channel of the uncalibrated data correspond to the time period, regional range and channel of the calibration source data.

[0116] A longitude and latitude projection unit 2 is connected to the data acquisition unit 1 and is configured to perform iso-longitude and latitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data. Both the projection data to be calibrated and the calibration source projection data are remote sensing images. The projection data to be calibrated includes the DN value of each pixel, and the calibration source projection data includes the radiance value of each pixel.

[0117] The space-time matching unit 3 is connected to the longitude and latitude projection unit 2 and is used to perform space-time matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data.

[0118] The filtering unit 4 is connected to the spatiotemporal matching unit 3 and is configured to filter the DN values ​​in the to-be-calibrated matching data and the radiance values ​​in the calibration source matching data to obtain a sample data set. The sample data set includes a plurality of samples. Each sample includes a DN value and a corresponding radiance value.

[0119] The fitting unit 5 is connected to the filtering unit 4 and is used to fit the samples in the sample data set to determine the radiation calibration coefficient. The radiation calibration coefficient is used to perform cross calibration on the data to be calibrated.

[0120] Example 3

[0121] This embodiment provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the cross-calibration method for geostationary satellites of the first embodiment.

[0122] Optionally, the above-mentioned electronic device may be a server.

[0123] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the cross-calibration method for geostationary satellites of the first embodiment is implemented.

[0124] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0125] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A cross-calibration method for a geostationary satellite, characterized in that: The cross calibration method of the geostationary satellite comprises: Acquiring data to be calibrated of a satellite to be calibrated and calibration source data of a reference satellite; wherein the time period, regional range, and channel of the data to be calibrated correspond to the time period, regional range, and channel of the calibration source data; and the satellite to be calibrated is a satellite that is inconsistent with international standards; Performing equal longitude and latitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data; the projection data to be calibrated and the calibration source projection data are both remote sensing images; the projection data to be calibrated includes a DN value of each pixel, and the calibration source projection data includes a radiance value of each pixel; Performing spatiotemporal matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data; Filtering the DN values ​​in the matching data to be calibrated and the radiance values ​​in the calibration source matching data to obtain a sample data set; the sample data set includes a plurality of samples; each sample includes a DN value and a corresponding radiance value; Fitting the samples in the sample data set to determine a radiation calibration coefficient; the radiation calibration coefficient is used to perform cross calibration on the data to be calibrated; The DN values ​​in the matching data to be calibrated and the radiance values ​​in the matching data of the calibration source are filtered to obtain a sample data set, which specifically includes: Calculating the correlation between the DN value of each pixel in the to-be-calibrated matching data and the radiance value of the corresponding pixel in the calibration source matching data at the same moment; Eliminate the DN values ​​whose correlation with the radiance value is less than a set threshold in the matching data to be calibrated, and obtain filtered data to be calibrated; In the filtered data to be calibrated, sample point windows are selected at equal intervals to obtain a first sample set; the first sample set includes a plurality of target area samples to be calibrated and a plurality of environment samples to be calibrated; the target area samples to be calibrated and the environment samples to be calibrated include DN values ​​in corresponding sample point windows; Calculate the standard deviation of each target area sample to be calibrated and the standard deviation of each environmental sample to be calibrated; The target area samples to be calibrated whose standard deviation is greater than the DN value target area uniformity threshold and the environmental samples to be calibrated whose standard deviation is greater than the DN value environment uniformity threshold in the first sample set are eliminated to obtain a second sample set; the second sample set includes multiple DN values; the sample data set includes the second sample set and the radiance values ​​corresponding to each DN value in the second sample set in the calibration source matching data.

2. The cross-calibration method for geostationary satellites according to claim 1, wherein: The satellite to be calibrated is the Indian geostationary satellite INSAT-3DR.

3. The cross-calibration method for geostationary satellites according to claim 1, wherein: The performing spatiotemporal matching on the projection data to be calibrated and the calibration source projection data to obtain the matched data to be calibrated and the calibration source matched data specifically includes: Converting the time of the calibration source projection data into the time of the projection data to be calibrated to obtain calibration source time matching data; If the resolution of the calibration source time matching data is greater than the resolution of the projection data to be calibrated, the projection data to be calibrated is the matching data to be calibrated, and the calibration source time matching data is resampled to obtain calibration source matching data, so that the resolution of the calibration source matching data is equal to the resolution of the matching data to be calibrated; If the resolution of the projection data to be calibrated is greater than the resolution of the calibration source time matching data, the calibration source time matching data is used as the calibration source matching data, and the projection data to be calibrated is resampled to obtain the matching data to be calibrated, so that the resolution of the calibration source matching data is equal to the resolution of the matching data to be calibrated.

4. The cross-calibration method for geostationary satellites according to claim 1, wherein: The sample point window of the target area sample to be determined is a grid of 3*3 in size; The sample point window of the environmental sample to be calibrated is a grid with a size of 5*5.

5. The cross-calibration method for geostationary satellites according to claim 1, wherein: The cross calibration method of the geostationary satellite also includes: A scatter plot is drawn according to the radiation calibration coefficient and the sample data set.

6. A geostationary satellite cross-calibration system, applied to the geostationary satellite cross-calibration method according to any one of claims 1 to 5, characterized in that: The cross-calibration system of the geostationary satellite comprises: A data acquisition unit is used to acquire the uncalibrated data of the uncalibrated satellite and the calibration source data of the reference satellite; the time period, regional range and channel of the uncalibrated data correspond to the time period, regional range and channel of the calibration source data; a longitude and latitude projection unit connected to the data acquisition unit, configured to perform iso-longitude and latitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data; the projection data to be calibrated and the calibration source projection data are both remote sensing images; the projection data to be calibrated includes a DN value of each pixel, and the calibration source projection data includes a radiance value of each pixel; a space-time matching unit connected to the longitude and latitude projection unit, configured to perform space-time matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data; a filtering unit connected to the spatiotemporal matching unit, configured to filter the DN values ​​in the to-be-calibrated matching data and the radiance values ​​in the calibration source matching data to obtain a sample data set; the sample data set includes a plurality of samples; each sample includes a DN value and a corresponding radiance value; A fitting unit is connected to the filtering unit and is used to fit the samples in the sample data set to determine a radiation calibration coefficient; the radiation calibration coefficient is used to perform cross calibration on the data to be calibrated.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the cross-calibration method for a geostationary satellite according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The device stores a computer program, which, when executed by a processor, implements the cross-calibration method for a geostationary satellite according to any one of claims 1 to 5.

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