On-orbit cross-calibration method for micro-nano hyperspectral satellite sensor full spectral range

Abstract

The present application aims to provide a kind of micro-nano hyperspectral satellite sensor full spectral segment on-orbit cross radiation calibration method.The method of the present application comprises the following steps: a.selecting satellite-reference satellite image data pairs to be calibrated that meet the basic conditions of cross calibration;b.Gaussian analysis and parameter extraction of satellite sensor SRF;c.waveband matching and parameter calculation of sensor SRF;d.simulation of reference image matching the spectrum of satellite sensor to be calibrated;e.field BRDF simulation and image BRDF correction;f.cross calibration of target sensor is executed.The present application is applied to the field of remote sensing data processing method and application technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing data processing methods and application, and particularly relates to a micro-nano hyperspectral satellite sensor full-spectral on-orbit cross-radiometric calibration method, which is suitable for micro-nano hyperspectral satellite on-orbit cross-radiometric calibration and radiometric quality monitoring. BACKGROUND

[0002] Satellite sensor radiometric calibration is an important step in the process of satellite image data preprocessing to establish the mathematical relationship between the digital quantization value (DN) of remote sensing images and the actual physical quantity (TOA radiance), which has a significant impact on the quantitative accuracy of the sensor. According to the satellite launch and operation stage, the sensor radiometric calibration can be divided into pre-launch calibration (such as laboratory calibration, etc.) and post-launch calibration (such as on-board calibration, surrogate calibration, cross calibration, etc.). The latter is a necessary part of satellite sensor radiometric quality monitoring and calibration parameter adjustment. Among them, cross calibration is a method that uses internationally popular high-precision satellite sensors as a reference to shoot stable ground targets (such as PICS) under similar imaging conditions and perform radiometric reference transfer and comparative analysis, thereby realizing sensor quality monitoring and parameter optimization. For the current popular micro-nano satellite sensors, the characteristics of low economic cost and few space-carryable devices make it impossible to effectively deploy on-board calibration devices, while cross calibration can be used as a reference standard for large satellites carrying high-precision imaging instruments and on-board calibration devices, which to some extent makes up for the limitations of micro-nano satellite post-launch calibration, and is more abundant in reference data sources and imaging sites than surrogate calibration.

[0003] Due to the inherent differences in the relative spectral response (SRF) of different satellite sensors, the calculation of the spectral band matching factor (SBAF) between sensors is a common means of simulating the SRF of synchronous sensors in cross calibration. However, for hyperspectral satellite sensors with high spectral resolution, relatively narrow channels, and dimensional differences (such as between dozens of channels and hundreds of channels), the channel settings of the satellite sensor to be calibrated and the reference satellite sensor may not overlap or cover multiple bands, resulting in "single to multiple" (Single to Multiple) situations, etc. Therefore, the traditional "single to single" (Single to Single) band SBAF calculation method for multispectral satellite sensors cannot be used, and a method is needed that can overcome the above problems and achieve synchronization of sensor SRF reference. In addition, spectral synchronization between sensors also needs to consider the influence of the bidirectional reflectance distribution function (BRDF) of the site. Due to site distribution, there are few places where actual BRDF measurements can be carried out, and data applications are not easy. The data accumulation and engineering application effect of cross-radiometric calibration based on this reference are not good, and it is not conducive to the quantitative evaluation of the radiometric quality of the key band.

[0004] Based on the above key technical problems of hyperspectral satellite cross calibration engineering application, a cross calibration technology capable of adapting to the spectral channel setting difference between hyperspectral sensors and TOA BRDF correction of ground imaging site is crucial for micro-nano hyperspectral satellite launch radiation calibration and quality monitoring. SUMMARY

[0005] In view of the cross radiation calibration requirements of micro-nano hyperspectral satellites in orbit, the present application provides a micro-nano hyperspectral satellite sensor full-spectral on-orbit cross radiation calibration method which meets different dimensional spectral design.

[0006] The technical scheme adopted by the present application is a micro-nano hyperspectral satellite sensor full-spectral on-orbit cross radiation calibration method, which comprises the following steps:

[0007] a. Screening satellite-to-reference satellite image data pairs to be calibrated that meet the basic conditions of cross calibration;

[0008] b. Obtaining the spectral response function (SRF) of the satellite sensor to be calibrated and the reference satellite sensor, substituting it into the Gaussian curve model to evaluate the deviation, matching the SRF of the reference satellite sensor to the satellite sensor to be calibrated based on the principle of linear least squares, and resampling the reference satellite image data to adapt to the spectral channel of the satellite sensor to be calibrated;

[0009] c. Based on the MODIS global coverage data, a field TOA BRDF model is constructed based on the Ross-Li theoretical model, and key band field BRDF data is generated to correct the BRDF of the synthesized image after adaptation;

[0010] d. Geometric precise correction and spatial resolution adaptation are performed on the reference satellite sensor synthesized image and the satellite image to be calibrated;

[0011] e. Based on the radiance of the L1C level synthesized image and the DN quantization value of the original satellite image to be calibrated, the calibration coefficient is calculated by substituting it into the linear equation, and the cross calibration is completed.

[0012] Specifically, in step a, the cross calibration basic conditions include:

[0013] The site is a desert type pseudo-invariant calibration site, and the ground objects are stable;

[0014] The imaging time is close and there is no cloud layer interference;

[0015] The angle difference of the satellite imaging sun elevation angle and the observation elevation angle is less than 5°.

[0016] Specifically, in step b, the expression of the Gaussian curve model is:

[0017] ,

[0018] where x is the curve wavelength, μ is the center wavelength of the current band, σ is the standard deviation, and S is the scaling factor of the satellite sensor SRF and the theoretical amplitude of the Gaussian curve.

[0019] Specifically, in the b step, the specific process of matching the SRF of the reference satellite sensor to the satellite sensor to be calibrated is as follows:

[0020] With the band channel of the satellite sensor to be calibrated as the reference, the bands in which the FWHM wave parameters of the reference satellite sensor are located in the band range are detected, and the SRF matrix MAT of the reference satellite sensor is constructed. REF and the SRF matrix MAT of the satellite sensor to be calibrated. TAR The response weight array W is calculated through the following formula, and the simulated band SRF array STsimu is obtained based on W, where

[0021]

[0022]

[0023]

[0024] In the formula, SR and ST are the SRF values of the reference satellite sensor and the satellite sensor to be calibrated, respectively, x in the subscript is the number of SRF response sample points of each band of the reference satellite sensor under the condition that the FWHM wave parameters of the reference satellite sensor are located in the current band of the satellite sensor to be calibrated, y is the number of bands of the reference satellite sensor that meet the above condition, and W is the response weight array of each band of the reference satellite sensor calculated by the least square method.

[0025] Specifically, in the b step, when the reference satellite image data is resampled, the simulated image matching the radiation reference of the satellite sensor to be calibrated is generated based on the following formula:

[0026]

[0027] where b is , i.e. the band matching interval, λ is the band number, w λ is the response weight value of the reference satellite sensor band corresponding to the current band of the satellite sensor to be calibrated, and L λ is the radiation brightness value array of the reference satellite sensor band.

[0028] Specifically, in the c step, the expression of the Ross-Li model is as follows:

[0029]

[0030] where f​​​​​iso , f geo , f vol are the coefficients of isotropic kernel, L-R geometric scattering kernel and R-T volumetric scattering kernel respectively, K geo , K vol are the corresponding kernel functions.

[0031] Specifically, in the c step, the basic data source for constructing the site TOA BRDF model is the global observation TOA reflectivity observation product MYD02HKM and the corresponding observation condition parameter record product MYD03 in the MODIS product, and the kernel function coefficients are solved by screening the product, extracting the parameters, establishing a linear equation set and substituting into the LSF model.

[0032] Specifically, in the b step, when calculating the extraterrestrial solar spectral irradiance ESSI, the ISSSolar ESSI V2.0 version data published by the International Space Station (ISS) is adopted, and the integral sampling is obtained based on the matched SRF data.

[0033] Specifically, in the d step, the spatial resolution adaptation adopts the mode of downsampling the high-resolution image to adapt to the low-resolution image.

[0034] Specifically, in the e step, the following formula is adopted when calculating the calibration coefficient

[0035] ,

[0036] Wherein, L TOA_ref is the standard image converted from the reference satellite sensor, DN ori is the original DN image of the satellite sensor to be calibrated, Gain correct is the updated radiometric calibration parameter of the satellite sensor to be calibrated.

[0037] The beneficial effects of the present application are as follows:

[0038] The method is aimed at the "single-multi" channel difference problem of hyperspectral sensors (higher spectral resolution, relatively narrow channels and different dimensions (such as between dozens of channels and hundreds of channels)), first, the deviation of the sensor spectral response function (SRF) from the ideal distribution is evaluated through the Gaussian curve model, and the SRF is accurately simulated for the reference satellite sensor which does not provide detailed SRF data; then, based on the linear least square (LSF) principle, the SRF matrix of the reference satellite sensor and the SRF matrix of the satellite sensor to be calibrated are constructed, the response weight array W is calculated, and the simulated SRF matching the band of the satellite sensor to be calibrated is generated; further, the "multi-band weighted fitting" replaces the traditional multi-spectral satellite "single-single" band matching, which can make the multiple hyperspectral bands of the reference satellite sensor completely adapt to a single band of the satellite sensor to be calibrated, effectively solve the spectral synchronization problem caused by the non-overlapping or multiple coverage of the hyperspectral "single-multi" channels, and ensure the accurate transfer of the radiometric reference between different spectral dimension sensors, and improve the spectral synchronization accuracy.

[0039] To solve the influence of BRDF (bidirectional reflectance distribution function) on the calibration result, the method is based on the Ross-Li theoretical model, takes MODIS MYD02HKM (TOA reflectance) and MYD03 (observation parameters) as the basic data source, extracts the solar / satellite zenith angle, azimuth angle and TOA reflectance, constructs the kernel function matrix and the reflectance matrix, solves the kernel function coefficient by LSF, and finally generates the TOA BRDF model of the site; further, without relying on the scarce on-site BRDF measurement data (the traditional method is limited by the site), the BRDF simulation of any PICS site can be realized through the global coverage of MODIS data; by screening the to-be-corrected bands with FWHM overlap of more than 50%, the image BRDF correction is completed, the radiation distortion caused by the difference between illumination and observation angles is effectively eliminated, the calibration result is more in line with the real object radiation characteristics, and the reliability of the key band radiation quality evaluation is improved.

[0040] The method is based on on-orbit image data processing, and does not rely on the on-board calibration device which is difficult for micro / nano satellites to carry: first, the image pairs (satisfying the conditions of stable ground objects, similar time / angle) are obtained through screening historical data or cooperative shooting, avoiding hardware modification; then, the complete transfer of the radiometric reference is realized through the steps of converting the DN value to radiance, accurately sampling the ESSI parameter, converting the TOA reflectance / radiance, etc.; finally, the calibration coefficient is calculated after ensuring the spatial consistency through geometric fine correction and high-resolution image down-sampling adaptation. The present application solves the hardware limitation of micro / nano satellites "low cost, less carried devices" in a targeted manner, without additional investment in on-board calibration equipment cost; at the same time, each step of data processing is designed around "reducing error sources", and the calibration coefficient calculated through the linear equation can accurately establish the relationship between the DN value and the TOA radiance, which reduces the implementation threshold while ensuring the calibration accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is a simple flow chart of the method of the present application;

[0042] Figure 2 is a comparison of spectral response characteristics RSR of multispectral satellite sensors (a, for example Landsat-8 OLI and Sentinel-2A MSI) and hyperspectral satellite sensors (b, for example OHS-3C CMOS1 and EnMAP), with the display range limited between 400-1000 nm and 400-600 nm respectively;

[0043] Figure 3 is a simulated image map generated by matching the spectrum channels of the satellite sensor to be calibrated with reference satellite sensors;

[0044] Figure 4 is a comparison result of image and spectral characteristics before and after the simulated image is generated;

[0045] Figure 5 is a map of setting up the matrix channels for solving the field TOA BRDF. DETAILED DESCRIPTION

[0046] Definitions of terms:

[0047] SRF: spectral response function of satellite sensor;

[0048] LSF: linear least square;

[0049] DN: digital quantization value of remote sensing image;

[0050] TOA radiance: actual physical quantity (atmospheric top layer radiance);

[0051] SBAF: spectral band matching factor between sensors;

[0052] BRDF: bidirectional reflectance distribution function of field;

[0053] MODIS: moderate resolution imaging spectrometer;

[0054] PICS: pseudo-invariant calibration field, refers to a natural or artificial target area with highly stable surface reflectivity in a long time, which provides a reference for radiation calibration;

[0055] ESSI: extraterrestrial solar spectral irradiance;

[0056] FWHM: full width at half maximum of band.

[0057] As Figures 1-4As shown, the present application provides a micro-nano hyperspectral satellite sensor full-spectral on-orbit cross-radiometric calibration method, which comprises the following steps:

[0058] a. Screening satellite-reference satellite image data pairs to be calibrated that meet the basic conditions of cross-calibration;

[0059] b. Obtaining the spectral response functions (SRF) of the satellite sensor to be calibrated and the reference satellite sensor, substituting them into the Gaussian curve model to evaluate the deviation, matching the SRF of the reference satellite sensor to the satellite sensor to be calibrated based on the principle of linear least squares, and resampling the reference satellite image data to the spectral channel adapted to the satellite sensor to be calibrated;

[0060] c. Based on the Ross-Li theoretical model, a field TOA BRDF model is constructed based on the MODIS global coverage data, and key band field BRDF data is generated to perform BRDF correction on the adapted synthetic image;

[0061] d. Geometric precise correction and spatial resolution adaptation are performed on the reference satellite sensor synthetic image and the satellite image to be calibrated;

[0062] e. Based on the radiance of the L1C level synthetic image and the DN quantization value of the original satellite image to be calibrated, the calibration coefficient is calculated by substituting it into the linear equation, and the cross-calibration is completed.

[0063] Specifically, the present application respectively takes China OHS-3B CMOS2 (400-1000 nm, 32 bands) and Germany EnMAPHSI sensor (400-2500 nm, 224 bands, actually intercepting 400-1000 nm, i.e. 101 bands) as examples of satellite sensors to be calibrated and reference satellite sensors to implement and test the above-mentioned spectral matching and resampling method and related processing, and the specific steps are as follows:

[0064] (1) Construction of satellite inter-orbit imaging data pairs

[0065] Step S1: The following requirements are required for the imaging conditions of the on-orbit satellite cross-calibration: ① Stable PICS ground target. Desert and other PICS stable ground targets should be selected to eliminate the influence of changes in ground object properties; ② Similar imaging time and weather. The imaging should be performed at similar dates and times, and cloud interference should be excluded to ensure that the ground target receives similar solar energy; ③ Similar imaging geometric angles. Similar to ②, the satellite imaging solar elevation angle (SZA) and observation elevation angle (VZA) should be similar, and the difference should be less than 5°.

[0066] Since some satellites do not maintain the orbit according to the standard orbit model, therefore, in actual selection, the historical data of the satellite to be calibrated and the reference satellite at the PICS site can be selected, or satellite image data pairs can be obtained through collaborative shooting.

[0067] (II) Gaussian analysis and parameter extraction of satellite sensor SRF

[0068] Step S2: Sensor SRF data preprocessing. For satellite sensors that have provided detailed SRF data and central wavelength, FWHM parameters, the simulation process in this step can be skipped. For cases such as the EnMAP satellite sensor, which does not directly provide detailed SRF data but provides basic parameters, it is necessary to input the basic data to simulate the SRF curve under the premise of verifying the imaging quality of the sensor and its stability advantage. The theoretical formula of the Gaussian curve distribution of the satellite sensor is as follows:

[0069] ,

[0070] Where x is the curve wavelength, μ is the central wavelength of the current band, σ is the standard deviation, and S is the scaling factor of the satellite sensor SRF and the theoretical amplitude of the Gaussian curve.

[0071] (III) Band matching and parameter calculation of sensor SRF

[0072] Step S3: Reference satellite sensor SRF synthesis matching. Taking the band channel of the satellite sensor to be calibrated as the reference, the waveband in which the FWHM of the reference satellite sensor is located within the full wavelength range of each waveband is detected, and the SRF data MAT TAR of the current waveband of the satellite sensor to be calibrated is resampled based on the spectral resolution of the current waveband. After resampling, the SRF matrix MAT REF of the reference satellite sensor is constructed. The present application uses a SRF synthesis synchronization method based on the least squares method to fit the SRF of the current waveband of the reference satellite sensor and the satellite sensor to be calibrated. The detailed formula of this step is as follows:

[0073] ,

[0074] ,

[0075] ,

[0076] Where SR and ST are the SRF values of the reference satellite sensor and the satellite sensor to be calibrated, respectively, x in the subscript is the number of SRF response sample points of each waveband of the reference satellite sensor under the condition that the FWHM of the reference satellite sensor is located within the current waveband of the satellite sensor to be calibrated, y is the number of wavebands of the reference satellite sensor that meet the above condition, and W is the response weight array of each waveband of the reference satellite sensor calculated by the least squares method. For example, Figure 2The FWHM of several high spectral resolution continuous wave bands of the reference satellite sensor should be completely contained in the FWHM spectrum of the corresponding interval band of the satellite sensor to be calibrated, so as to be matched as a band, as shown in the following formula:

[0077]

[0078] Wherein, FWHM represents the full width of the half band, B ref represents the reference band, B tar represents the target band, b represents the matched band, and Band valid represents the matched band set.

[0079] After obtaining W, the reference satellite sensor simulation band SRF array ST corresponding to the current band of the satellite sensor to be calibrated is calculated according to the following formula: simu .

[0080]

[0081] Step S4: Sensor band ESSI parameter calculation. After the spectrum matching of the reference satellite sensor SRF in the foregoing steps is completed, the atmospheric layer outside solar spectral irradiance (ESSI, unit W / (m 2 * μm) of each band needs to be calculated for the subsequent calculation of the top-of-atmosphere reflectance (TOA reflectance). According to the ESSI data collected and used by the international industry in recent years, the present application selects the ESSI data of the International Space Station (ISS) collected and released in 2020 as the basis data for the sampling calculation of the satellite sensor band ESSI, and based on the SRF data integral sampling after the initial SRF spectrum matching of the reference satellite sensor, the ESSI parameters before and after the matching of each band are obtained. The calculation formula is as follows:

[0082]

[0083] Wherein, SRF(λ) is the SRF response data of the current band, λ1 and λ2 are the starting position and ending position of the spectrum range of the current band respectively, and ESSI value is the value of the ISS Solar ESSI V2.0 data in its range.

[0084] (Four) Reference image simulation matching the spectrum of the satellite sensor to be calibrated

[0085] Step S5: Reference satellite sensor image data L1 level preprocessing. For the reference satellite sensor image whose DN value is still assigned, the L1 level image data needs to be converted into radiance (L λ , unit W / (m2 The calculation process is shown in the following formula:

[0086]

[0087] Wherein, Gain and Offset are gain value and offset value of the wave band respectively (the offset value of OHS sensor image data is 0). If the reference satellite sensor initial data product has been assigned to the radiation brightness, the above calculation is not needed. In addition, this step extracts the imaging time, imaging range and central coordinates, solar elevation angle / azimuth angle, satellite observation elevation angle / azimuth angle and other parameters from the meta file of the reference satellite sensor data product, which are used for subsequent TOA reflectivity calculation and cross calibration processing.

[0088] Step S6: Matching simulation image generation of the satellite sensor to be calibrated. After the least square fitting calculation is performed in step S3, the effective wave band number and response weight value of the reference satellite sensor corresponding to each wave band of the satellite sensor to be calibrated are obtained, and based on this, the matching simulation processing is performed on the L1 level image data of the reference satellite sensor. Based on the L1 level radiation brightness image of the reference satellite sensor as the basic data, the matching simulation image of the reference satellite sensor is generated according to the following formula.

[0089] ,

[0090] Wherein, b is , that is, the wave band matching interval, λ is the wave band number, w λ is the response weight value of the reference satellite sensor wave band corresponding to the current wave band of the satellite sensor to be calibrated, L λ is the radiation brightness value array of the reference satellite sensor wave band.

[0091] Step S7: TOA reflectivity calculation of the simulation image. Since the TOA reflectivity can eliminate the influence of the solar zenith angle cosine, the reference satellite sensor image data is usually converted into the TOA reflectivity in the cross calibration, and the calculation method is shown in the following formula:

[0092] ,

[0093] Wherein, L is the radiation brightness value array of the reference satellite sensor (unit: W / (m 2 *μm*sr), ESSI is the atmospheric out-of-solar irradiance parameter of the current wave band (unit: W / (m 2 *μm), θ ele is the solar elevation angle corresponding to the imaging time of the satellite sensor, and d is the distance between the earth and the sun corresponding to the imaging time (unit: A.U.). The distance between the earth and the sun is calculated according to the following formula in the present application:

[0094] ,

[0095] ,

[0096] ,

[0097] where Year, Month and Date are the imaging year, month and day, respectively, and DOY is the ordinal number of the satellite sensor imaging day in the year.

[0098] In addition, the reference satellite sensor pre-ESSI parameter matched in step S5 and L λ is substituted into formula (9) to calculate the reference satellite sensor initial TOA reflectance image data.

[0099] Taking the area around the La Crau calibration site in France in the RadCalNet global calibration site dataset as an example, the comparison between the image after matching simulation and the image before simulation and the measured TOA reflectance of the site is shown in FIG. 6. Figure 3 As shown in FIG. 6, the results show that the simulated images of different types of ground objects all maintain consistent reflectance curve characteristics with the initial TOA image, and the quantitative differences in reflectance values at similar wavelength positions are also within the expected sensor difference tolerance range, that is, the images generated by spectrum matching and simulation realize the radiometric reference transmission and spectrum matching conversion from the reference satellite sensor to the satellite to be calibrated while maintaining the ground object reflectance characteristics.

[0100] (Five) Site BRDF simulation and image BRDF correction

[0101] Step S8: fitting site BRDF model parameters. BRDF correction helps to eliminate the influence of illumination-observation angle and true angle deviation on the radiation distortion of cross-calibration image data, and improves the calibration accuracy. However, since the global PICS calibration sites selected for cross-calibration are difficult to carry out normalized field BRDF measurement work, a method for simulating site BRDF model without relying on field measurement but using existing data is needed. The present application adopts a method for constructing a site BRDF model at the TOA level in a set time period based on the Ross-Li model to reduce the influence of radiation transfer model error while fitting the BRDF model corresponding to the cross-calibration image at the time.

[0102] The Ross-Li model, also known as the MODIS BRDF model, is composed of three kernel functions:

[0103] ,

[0104] Wherein, iso, geo, vol respectively represent the isotropic core (default 1) of Ross-Li model, L-R geometric scattering core and R-T volume scattering core, f corresponds to the coefficient of three core functions, that is, the model parameters to be fitted, K geo , K vol Respectively, the corresponding core function; ρ BRDF Represent the synthesis / pending calibration image after BRDF correction.

[0105] The selected fitting basic data source of the application is the global observation TOA reflectivity observation product MYD02HKM in the MODIS series product and the corresponding observation condition parameter record product MYD03 (the spatial resolution is 500 m, hereinafter referred to as "MYD TOA reflectivity product"), and the actual available bands are RGB and NIR four bands. For the remaining bands that cannot perform BRDF correction, on the one hand, the image data pairs of the reference satellite sensor and the satellite sensor to be calibrated can be screened to ensure that the sun / satellite geometry is close, on the other hand, PICS with small ground relief can be selected as the calibration field to eliminate the influence of BRDF as much as possible, and the bands performing BRDF correction can be used for quantitative evaluation of the radiation quality.

[0106] Firstly, according to the input time, latitude and longitude and other conditions, the corresponding product list is screened and downloaded and stored locally, and the local search index function is developed and deployed; then, the solar zenith angle / azimuth angle, satellite zenith angle / azimuth angle and TOA reflectivity and other parameters required for fitting Ross-Li model are extracted from the corresponding product, the linear equation group in the time period is established, and f iso , f geo And f vol Are solved by substituting LSF (least square) model. The solution matrix channel structure is shown in Figure 4 , and the mathematical process is shown in the following formula:

[0107] ,

[0108] ,

[0109] ,

[0110] ,

[0111] Wherein, MAT Kernel Is the core function matrix calculated based on the illumination-geometry parameters extracted from the cross-calibration area in N MYD TOA reflectivity products, K geo Indicates the geometric optical core value calculated by the sun and satellite angles, K vol Indicates the volume scattering core value calculated by the observation geometry; MATTOA is a reflectance result matrix of N MYD TOA reflectance products, Ref TOA denotes reflectance values; F local is a LSF fitting result matrix, f iso denotes an isotropic scattering component, f geo denotes a geometric optical scattering component, f vol denotes a volume scattering component. The above process is batch calculated for each pixel in the cross-calibration area, and the spatial resolution is 500 m.

[0112] Step S9: Synthesis of the image and BRDF correction of the image to be calibrated. The synthesis of the image and the image to be calibrated is converted into an array, and is spatially matched with the BRDF model array. Since the spatial resolution of the synthesis of the image and the image to be calibrated adopted by the present application is better than the BRDF model array, the assignment result of the corresponding BRDF correction value of the pixel is realized by the method that the main body of the image is positioned in the pixel of the BRDF array. The matching of the BRDF to be corrected band of the synthesis of the image and the image to be calibrated adopts the method that the FWHM overlap is more than one half (as shown in the following formula),

[0113]

[0114] wherein FWHM denotes the full width at half maximum of the band, B Sat denotes the band of the synthesis of the image, B BRDF denotes the BRDF model array, and b denotes the matched band, Band Sat denotes the matched band set.

[0115] Then, the correction is completed according to the following (six).

[0116] (Six) Cross-calibration execution of the target sensor

[0117] Step S10: Geometric matching between the image data pairs. After the simulation of the spectral matching and the BRDF correction of the conventional band, the TOA image data of the reference satellite sensor is converted into TOA radiance image data. Considering that the different data have spatial deviation, the geospatial matching under the visual condition should be firstly performed, so that the image of the reference satellite sensor and the image of the satellite sensor to be calibrated are basically overlapped in space, and then the high-resolution image is aligned and assigned with the low-resolution image data pixel as the reference. For OHS and similar sensors, the following formula is substituted:

[0118]

[0119] wherein L TOA_ref is the standard image converted from the reference satellite sensor, DN ori is the original DN image of the satellite sensor to be calibrated, Gain​correct That is, the pending satellite sensor radiometric calibration parameter is updated.

[0120] The application solves the technical problems of hyperspectral sensor "single-multi" channel adaptation and BRDF measured data dependence, improves the accuracy and engineering applicability of micro-nano hyperspectral satellite in-orbit radiometric calibration, and can be widely applied to micro-nano hyperspectral satellite in-orbit radiometric calibration and radiometric quality monitoring.

[0121] Finally, it should be emphasized that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor across the entire spectrum, characterized in that, The method includes the following steps: a. Select satellite-reference satellite imagery data pairs that meet the basic conditions for cross-calibration; b. Obtain the spectral response function (SRF) of the satellite sensor to be calibrated and the reference satellite sensor, substitute it into the Gaussian curve model to evaluate the deviation, match the SRF of the reference satellite sensor to the satellite sensor to be calibrated based on the principle of linear least squares, and resample the reference satellite image data to the spectral channel of the satellite sensor to be calibrated. c. Based on MODIS global coverage data, construct a site TOA BRDF model based on the Ross-Li theoretical model, generate key band site BRDF data, and perform BRDF correction on the adapted synthetic image. d. Perform geometrical fine correction and spatial resolution adaptation on the composite image from the reference satellite sensor and the image to be calibrated; e. Based on the radiance of the L1C-level composite image and the DN quantization value of the original image to be calibrated, the calibration coefficients are calculated by substituting them into the linear equation to complete the cross-calibration.

2. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step a, the basic conditions for cross-calibration include: The site is a desert-type pseudo-invariant calibration field with stable terrain features; The imaging times were similar and there was no cloud interference; The difference between the solar elevation angle and the observation elevation angle in satellite imaging is less than 5°.

3. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step b, the expression for the Gaussian curve model is: ， in x The wavelength of the curve, μ The center wavelength of the current band. σ Standard deviation, S is the scaling factor for the theoretical amplitude of the satellite sensor's SRF and Gaussian curve.

4. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step b, the specific process of matching the SRF of the reference satellite sensor to the satellite sensor to be calibrated is as follows: Using the band channels of the satellite sensor to be calibrated as a reference, the bands in the reference satellite sensor whose overall FWHM spectral parameters fall within that band range are detected, and the SRF matrix MAT of the reference satellite sensor is constructed. REF SRF matrix MAT of satellite sensors to be calibrated TAR The response weight array W is calculated using the following formula, and then the simulated band SRF array STsimu is obtained based on W, where, ， ， ， In the formula, SR , ST These are the SRF values ​​of the reference satellite sensor and the satellite sensor to be calibrated, respectively, in the subscripts. x The number of SRF response sample points in each band of the reference satellite sensor, provided that the overall FWHM spectral parameters of the reference satellite sensor are within the current band of the satellite sensor to be calibrated, is given by the subscript. y is the number of bands of the reference satellite sensor that meets the above conditions, and W is the response weight array of each band of the reference satellite sensor calculated by the least squares method.

5. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step b, when reacquiring reference satellite imagery data, a simulated image matching the radiometric reference of the sensor to be calibrated is generated based on the following formula. ， Where b is That is, the band matching interval, where λ is the band number. w λ The weighted value representing the contribution of the reference satellite sensor band to the current band of the satellite sensor to be calibrated. L λ This is an array of radiance values ​​for the reference satellite sensor band.

6. The full-spectrum on-orbit cross-radiometric calibration method for a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step c, the expression of the Ross-Li theoretical model is: ， in, ρ BRDF This represents a composite / uncalibrated image that has completed BRDF correction. f iso , f geo , f vol These are the coefficients of the isotropic kernel, the LR geometric scattering kernel, and the RT volume scattering kernel, respectively. K geo , K vol These are the corresponding kernel functions.

7. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step c, the basic data source for constructing the site TOA BRDF model is the global observation TOA reflectance observation result product MYD02HKM and its corresponding observation condition parameter record product MYD03 in MODIS products. The kernel function coefficients are solved by screening products, extracting parameters, establishing a system of linear equations, and substituting them into the LSF model.

8. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step b, when calculating the extra-atmospheric solar spectral irradiance (ESSI), the data from the International Space Station (ISS) ISSolarESSIV 2.0 version is used, and the data is obtained by integral sampling based on the matched SRF data.

9. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step d, spatial resolution adaptation is achieved by downsampling the high-resolution image to adapt to the low-resolution image.

10. The method for full-spectrum on-orbit cross-radiometric calibration of a micro / nano hyperspectral satellite sensor according to claim 1, characterized in that, In step e, the following formula is used to calculate the scaling factors. ， in, L TOA_ref It is a standard image converted from a reference satellite sensor. DN ori These are raw DN images from satellite sensors awaiting calibration. Gain correct For updating the radiometric calibration parameters of the satellite sensors to be calibrated.

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