Simulation method, device and electronic equipment for characteristics of space-based remote sensing instrument

Abstract

The application provides a simulation method and device for characteristics of a space-based remote sensing instrument and electronic equipment, and the method comprises the following steps: based on current space-based remote sensing load parameters, simulating first spectral data into satellite entrance pupil radiance according to a TOA direct process; resampling the simulated entrance pupil radiance data, so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on entrance pupil radiance; adding the signal-to-noise ratio of the instrument to the resampled entrance pupil radiance data; simulating the entrance pupil radiance into second spectral data according to a TOA reverse process; inverting the material content in soil according to the second spectral data; and constructing an evaluation index based on the inverted material content and the real material content in soil. The application can reflect the coupling relationship between the instrument characteristic model and the inversion model, restore the influence of the real space-based remote sensing load parameters on inversion, and can be used for detecting whether the space-based remote sensing load parameters are suitable and selecting suitable space-based remote sensing load parameters.

Description

TECHNICAL FIELD

[0001] The present application mainly relates to the technical field of space-based remote sensing load monitoring, and particularly relates to a simulation method and device for space-based remote sensing instrument characteristics and an electronic device. BACKGROUND

[0002] An artificial satellite is a device built by humans, launched into space by a space flight vehicle such as a rocket, a space shuttle, etc., and revolving around the earth or other planets like a natural satellite. Among them, an earth observation satellite is an artificial earth satellite for observing the earth and its atmosphere in space. The earth observation satellite can effectively obtain various information such as the earth's surface, atmospheric clouds, ocean environment, vegetation coverage, human activities, etc. due to a high viewing angle and a wide observation range.

[0003] High-precision monitoring of the content of organic matter in space-based black soil is of great significance for the utilization and protection of black soil resources. Prediction of the content of soil organic matter based on space-based hyperspectral data is an effective means to realize high-spatial and temporal coverage monitoring of soil. Based on the quantitative relationship between soil organic matter and soil spectral reflectance, space-based remote sensing images can be used for spatial inversion and prediction of the content of organic matter. This provides scientific data support for further evaluating the impact of farming activities on black soil and measuring the effectiveness of regional soil protection policies.

[0004] The existing correlation analysis method is used to select the sensitive wavelength range of black soil organic matter, a stepwise regression modeling method is adopted to establish a black soil organic matter inversion model, a continuous wavelet transform method is used to extract a soil organic matter characteristic band, and a series of methods such as machine learning nearest neighbor method, bagging algorithm, multivariate perception, and random forest algorithm are used for modeling of hyperspectral data.

[0005] At present, the processing of black soil organic matter spectral data and the research on inversion algorithms are very mature, but the influence of real atmospheric effects and space-based remote sensing instrument characteristics is less considered from the aspect of the influence of instrument parameters on the inversion and prediction performance. Moreover, most of the research does not consider the coupling relationship between signal-to-noise ratio and spectral wavelength and spectral resolution, and the coupling between the instrument model and the inversion model is not enough, which cannot provide the most accurate scientific theoretical data support for the system design of the black soil organic matter monitoring satellite. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a simulation method and device for space-based remote sensing instrument characteristics and an electronic device, which can reflect the coupling relationship between the instrument characteristic model and the inversion model, restore the influence of real space-based remote sensing load parameters on inversion, and can be used for detecting whether the space-based remote sensing load parameters are appropriate and selecting appropriate space-based remote sensing load parameters.

[0007] To solve the above technical problems, in a first aspect, the present application provides a simulation method for characteristics of a space-based remote sensing instrument, comprising: based on current space-based remote sensing load parameters, simulating first spectral data into satellite entrance pupil radiance according to a TOA direct process; resampling the simulated entrance pupil radiance data so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance; adding the signal-to-noise ratio of the instrument to the resampled entrance pupil radiance data; simulating the entrance pupil radiance into second spectral data according to a TOA reverse process; inverting the material content in the soil according to the second spectral data, and constructing an evaluation index based on the inverted material content and the actual material content in the soil.

[0008] Optionally, the TOA direct process is represented by the following formula:

[0009]

[0010] In the formula, L is the total radiance received by the sensor at the entrance pupil, p is the surface reflectivity of the target pixel, S is the downward spherical albedo of the atmosphere, L a is the atmospheric path radiation, and A is a coefficient based on atmospheric conditions and geometric illumination conditions.

[0011] Optionally, the first spectral data uses laboratory hyperspectral data.

[0012] Optionally, the TOA direct process is simulated by the atmospheric transmission model MODTRAN.

[0013] Optionally, the resampling of the simulated entrance pupil radiance data comprises using a Gaussian function resampling method to resample the simulated entrance pupil radiance data.

[0014] Optionally, the signal-to-noise ratio is calculated in the following way: first, the radiation energy received by a single detector pixel at any position on the image plane is obtained, and the number of electrons n e generated by the single detector pixel is obtained according to the radiation energy. e The signal-to-noise ratio is the ratio of the number of electrons n total to the total number of noise electrons n

[0015] Optionally, the inversion of the material content in the soil according to the second spectral data comprises using a partial least squares regression inversion model to invert the material content in the soil.

[0016] Optionally, before inverting the material content in the soil according to the second spectral data, it further comprises removing noise bands in the absorption range of atmospheric gases and / or removing outliers in the second spectral data.

[0017] Optionally, the evaluation index comprises a coefficient of determination R2 :

[0018]

[0019] wherein: y i is the true value of the i-th soil sample, is the average value of all predicted soil samples, is the predicted value of the i-th soil sample, and n is the total number of soil samples.

[0020] Optionally, the evaluation index comprises a root mean square error (RMSE) of prediction:

[0021]

[0022] wherein: y i is the true value of the i-th soil sample, is the predicted value of the i-th soil sample, and n is the total number of soil samples.

[0023] In a second aspect, the present application provides a simulation device for characteristics of a space-based remote sensing instrument, comprising: a first simulation module, configured to simulate first spectral data into an entrance pupil radiance of a satellite according to a TOA direct process based on current space-based remote sensing load parameters; a resampling module, configured to resample the simulated entrance pupil radiance data, so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance; an adding module, configured to add a signal-to-noise ratio of the instrument into the resampled entrance pupil radiance data; a second simulation module, configured to simulate the entrance pupil radiance into second spectral data according to a TOA reverse process; and a construction module, configured to inverse material content in soil according to the second spectral data, and construct an evaluation index based on the inverted material content and the real material content in soil.

[0024] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the simulation method for characteristics of a space-based remote sensing instrument according to the first aspect.

[0025] In a fourth aspect, the present application provides a readable storage medium, wherein the readable storage medium stores programs or instructions, and the programs or instructions are executed by a processor to implement the steps of the simulation method for characteristics of a space-based remote sensing instrument according to the first aspect.

[0026] Compared with the prior art, the present application has the following advantages: firstly, based on the current space-based remote sensing load parameters, the first spectral data is simulated into the entrance pupil radiance of the satellite according to the TOA direct process; then the simulated entrance pupil radiance data is resampled, so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance; then the signal-to-noise ratio of the instrument is added to the resampled entrance pupil radiance data; then the entrance pupil radiance is simulated into the second spectral data according to the TOA reverse process; finally, the soil matter content is retrieved according to the second spectral data, the evaluation index is constructed based on the retrieved matter content and the real matter content in the soil, and then the coupling relationship between the instrument characteristic model and the retrieval model can be reflected, the influence of the real space-based remote sensing load parameters on the retrieval is restored, which can be used for detecting whether the space-based remote sensing load parameters are suitable and selecting suitable space-based remote sensing load parameters. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute apart of this application, illustrate embodiments of the present application, and together with the description serve to explain the principles of the present application. In the drawings:

[0028] Figure 1 is a flowchart of a simulation method of space-based remote sensing instrument characteristics according to an embodiment of the present application;

[0029] Figure 2 is a schematic diagram of the relationship between RMSE, spectral resolution and signal-to-noise ratio (two-dimensional) in an embodiment of the present application;

[0030] Figure 3 is a schematic diagram of the relationship between RMSE, spectral resolution and signal-to-noise ratio (three-dimensional) in an embodiment of the present application;

[0031] Figure 4 is a projection of the three-dimensional surface of Figure 3 on the RMSE and spectral resolution plane;

[0032] Figure 5 is a projection of the three-dimensional surface of Figure 3 on the RMSE and signal-to-noise ratio plane;

[0033] Figure 6 is a structural schematic diagram of a simulation device of space-based remote sensing instrument characteristics according to an embodiment of the present application;

[0034] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the technical solutions of the embodiments of the present application clearer, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can be applied to other similar scenarios without creative effort. Unless the context clearly indicates otherwise or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0036] As shown in the present application and claims, unless the context clearly indicates otherwise or otherwise stated, the words "one", "a", "an", and / or "the" do not specify a singular number, but can also include a plural number. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0037] In addition, it should be noted that the use of the words "first", "second", and the like to define elements is only for the convenience of distinguishing the corresponding elements, and the above words have no special meaning unless otherwise stated, and therefore cannot be understood as a limitation on the scope of protection of the present application. In addition, although the terms used in the present application are selected from commonly known terms, some terms mentioned in the specification of the present application can be selected by the applicant according to his or her judgment, and the detailed meanings thereof are described in the relevant part of the description. In addition, the present application is not only required to be understood by the actual terms used, but also by the meaning implied by each term.

[0038] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously. Meanwhile, or other operations are added to these processes, or one or more steps of operations are removed from these processes.

[0039] Embodiment one

[0040] Figure 1 is a flowchart of a simulation method of the characteristics of a space-based remote sensing instrument according to an embodiment of the present application, and reference is made to Figure 1As shown, the method 100 comprises: S110, based on the current space-based remote sensing payload parameter, simulating first spectral data into the satellite's entrance pupil radiance according to the TOA (Top of atmosphere) direct process; S120, resampling the simulated entrance pupil radiance data so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance; S130, adding the signal-to-noise ratio of the instrument to the resampled entrance pupil radiance data; S140, simulating the entrance pupil radiance into second spectral data according to the TOA reverse process; S150, inverting the soil material content according to the second spectral data, and constructing an evaluation index based on the inverted material content and the real material content in the soil.

[0041] The radiation transmission path of space-based remote sensing is generally as follows: solar radiation reaches the ground surface after being reflected, scattered and absorbed by the atmosphere; after being reflected by the ground object, the reflected radiation again passes through the atmosphere and is received by the satellite sensor together with the direct reflection of solar radiation by the atmosphere. The method of the embodiment simulates this process, considers the relationship between signal-to-noise ratio and spectral wavelength and spectral resolution, and also considers the influence of atmospheric effects and space-based remote sensing instrument characteristics.

[0042] In an example, the TOA direct process can be represented by the following formula:

[0043]

[0044] In the formula, L is the total radiance received by the sensor at the entrance pupil, p is the surface reflectivity of the target pixel, S is the downward spherical albedo of the atmosphere, L a is the atmospheric path radiance, and A is a coefficient based on atmospheric conditions and geometric illumination conditions.

[0045] In an example, the first spectral data uses laboratory hyperspectral data.

[0046] In this embodiment, laboratory hyperspectral data (i.e. first spectral data) is input, and starting from the transmission process of radiation between the ground surface and the atmosphere, the surface characteristics of soil can be simulated by using a Lambertian surface model based on surface homogeneity. The direct process can simulate the TOA entrance pupil radiance under certain atmospheric conditions and geometric illumination conditions. The direct process divides the total radiant energy incident on the sensor into two parts: 1) the energy from the solar radiation transmitted through the atmosphere to the target surface and reflected to the sensor; 2) the energy of the solar radiation scattered by the atmosphere multiple times without being reflected by the ground object, i.e. the atmospheric path radiance.

[0047] In an example, the TOA direct process is implemented by the atmospheric transmission model MODTRAN.

[0048] MODTRAN model, i.e., a medium spectral resolution atmospheric transmittance and radiative transfer algorithm and calculation model, is jointly developed by the U.S. Air Force Research Laboratory and Spectral Sciences, Inc. using FORTRAN language. MODTRAN is a medium resolution atmospheric radiative transfer model, which is built-in multiple atmospheric models and can perform radiative transfer calculation in a 1.0 cm -1 resolution range of 0 to 50,000 cm -1 wave number. MODTRAN uses a two-stream approximation, a discrete coordinate multiple scattering method to calculate transmittance, irradiance, etc. containing atmospheric absorption and multiple scattering effects. The forward method is used, i.e., known ground information is used to obtain the radiation information received by the remote sensor. Exemplarily, the MODTRAN model that can be used in the method of the embodiment can be MODTRAN 4.0 version.

[0049] In the embodiment, A, S and L a in the TOA direct process formula can be simulated and inversed by setting observation viewing angle, atmospheric model, aerosol model, visibility range, solar elevation angle, wave number range, etc. parameters by the atmospheric transmission model MODTRAN. The scattering radiance mode of MODTRAN is used, the ground albedo is set to 0, 0.5, 1 respectively, TOT (total radiation brightness obtained by the sensor), TGR (total ground reflection radiance) and PATH (multiple scattering radiation) are obtained from the output results, which are substituted into formula (2), and atmospheric transmission parameters S, A and L a are obtained by mathematical inversion, as follows:

[0050]

[0051] A = (1-s) ATGR

[0052] L a = PATH0 (2)

[0053] Further, S, A and L a are substituted into formula (1), and the entrance pupil radiance L (which can also be represented as L(λ) toa_1nm ) is simulated and obtained.

[0054] In an example, the simulated entrance pupil radiance data is resampled by using a Gaussian function resampling method.

[0055] For a space-based remote sensing load, the spectral response function of the waveband generally presents an approximation to a Gaussian function. Therefore, in the embodiment, the resampling can be performed by using a Gaussian function resampling method, and the resampled entrance pupil radiance L(λ) toa_xnm .

[0056]

[0057]

[0058]

[0059] In the formula, c is the center wavelength of a certain waveband, λ is the wavelength, L(λ) toa_xnm is the entrance pupil radiance at wavelength λ, Gs(λ) is a Gaussian function simulated spectral response function, t is the spectral resampling interval, FWHM is the full width at half maximum of the spectral response, subscript xnm refers to x nanometers, and 1nm refers to 1 nanometer.

[0060] In this embodiment, laboratory hyperspectral data is simulated into the entrance pupil radiance received by the sensor through an atmospheric transmission MODTRAN simulation model and a TOA direct process, the entrance pupil radiance is simulated through resampling to obtain the entrance pupil radiance under different spectral resolutions, and then the signal-to-noise ratio curve under different spectral resolutions is obtained by substituting into the instrument signal-to-noise ratio model. The signal-to-noise ratio curve obtained at this time is added to the spectral data through the TOA reverse process, and then the spectral data under different spectral resolutions and corresponding signal-to-noise ratios can be substituted into the inversion model.

[0061] In this embodiment, the signal-to-noise ratio can be calculated in the following manner: the radiation energy received by a single detector pixel at any position on the image plane is obtained, the number of electrons n e generated by the single detector pixel is obtained according to the radiation energy, and then the signal-to-noise ratio is the ratio of the number of electrons n e to the total number of noise electrons n total .

[0062] Specifically, the signal-to-noise ratio is an index for characterizing the radiation resolution performance of an imaging system, and is defined as the ratio between the target useful signal and the noise signal. The signal-to-noise ratio is too large or too small, which has an impact on imaging, and needs to be determined according to the specific application requirements, and the relationship between the indexes is well balanced. The signal-to-noise ratio analysis must comprehensively consider the characteristics of the radiation source, the atmospheric radiation transmission characteristics, the optical characteristics of the ground target, the transmittance of the wide-width camera optical system, the transmittance characteristics of the filter, and the response characteristics of the detector, and the like.

[0063] The optical system converges the radiance at the entrance pupil to the detector target surface, and the radiation energy received by a single detector pixel at any position on the image plane can be obtained as follows:

[0064]

[0065] wherein E img (λ) is the image plane illuminance, T filter (λ) is the transmittance of the filter, which specifically corresponds to different filter arrays, p is the load half-pitch, and K optThe optical system transmittance is T, the F / # of the optical system is F / #, and the optical axis inclination angle is θ. The detector converts the incident light into an electronic number after sampling integration and records it. The integration time is the exposure time of the detector. The photoelectric conversion process of the detector has conversion efficiency, and the quantum efficiency of the detector is η. Therefore, according to formula (5), the electronic number generated by a single detector pixel is:

[0066]

[0067] where hc / λ is the energy of a single photon, t int is the exposure time, and the integration range is the set spectral interval range.

[0068] Whether it is the generation of photoelectrons in the photoelectric conversion process or the transfer and readout of electrons in the detector, the amplification and polarization in the circuit will generate noise in the signal. The total noise electronic number is:

[0069]

[0070] In the formula, N e is the photoelectron number of the target signal, N d is the dark current electronic number of the detector, and n cir is the noise electronic number of the detector readout circuit.

[0071] Therefore, according to the definition of the signal-to-noise ratio, the signal-to-noise ratio SNR of each spectral segment of the spectral camera is:

[0072]

[0073] The in-pupil radiance value obtained by simulation is substituted into the signal-to-noise ratio calculation model to obtain the signal-to-noise ratio corresponding to the spectral resolution.

[0074] In this embodiment, in order to add the calculated instrument signal-to-noise ratio to the simulated spectral data, first, the signal-to-noise ratio SNR(λ)_ xnm is converted into noise equivalent radiance NeΔL:

[0075]

[0076] Then, the NeΔL is substituted into the TOA direct process by applying the calculation method of Gaussian statistics to obtain:

[0077]

[0078] The above formula is inversely deduced, that is, the reverse process of TOA is obtained:

[0079]

[0080] The simulated reflectance data ρ(λ) with the atmospheric effects and the characteristics of the space-based remote sensing instrument are finally obtained s_xnm .

[0081] In an example, the content of the substance in the soil is inversed by using a partial least squares regression inversion model.

[0082] The partial least squares regression (PLSR) is a multivariate model commonly used for soil property estimation. As a multivariate regression analysis method, it can effectively reduce the variable dimension, reduce the variable information redundancy, and improve the model stability by orthogonal transformation of the variables used for soil property prediction through principal component transformation. It is a widely used regression modeling method for soil property inversion such as organic matter.

[0083] In the present embodiment, the steps of using the partial least squares regression method to inverse the content of organic matter in black soil include: extracting principal components; obtaining the optimal number of principal components by cross-validation; and obtaining regression coefficients.

[0084] 1) Standardize the simulated spectral data and the soil organic matter content data. Define the soil organic matter content SOM as the response variable Y, and define the simulated spectral data ρ s_xnm as the characteristic independent variable X. Then, decompose the simulated spectral data matrix X and the soil organic matter content data matrix Y as shown in equations (11) and (12) to perform factor analysis.

[0085] X=TP T +E (11)

[0086] Y=UQ T +F (12)

[0087] In the equations, T and P are the score matrix and the loading matrix of X respectively, E is the error caused by fitting of X, U and Q are the score matrix and the loading matrix of Y respectively, and F is the error caused by fitting of Y.

[0088] 2) In the partial least squares regression prediction, the optimal number of principal components is obtained by using the cross-validation method.

[0089] 3) PLSR uses the characteristic matrix T and the characteristic component matrix U of the mutually orthogonal response of each column vector to regress:

[0090] U=TB (13)

[0091] The regression coefficient matrix B is obtained as:

[0092] B=(T T T) -1 T T U (14)

[0093] The score matrix T of the sample can be obtained from the spectral matrix X of the sample to be tested and P in the test and calibration models. Finally, we get:

[0094] Y = TBQ = XP T BQ (15)

[0095] In one example, noise bands within the atmospheric gas absorption range and / or outliers in the second spectral data are removed before retrieving the soil material content from the second spectral data to make the soil material content retrieval more accurate.

[0096] In one example, the evaluation metrics include the coefficient of determination R. 2 :

[0097]

[0098] In the formula: y i This represents the true value of the i-th soil sample. The average value predicted for all soil samples. Let be the predicted value of the i-th soil sample, and n be the total number of soil samples.

[0099] In one example, the evaluation metric may also include the root mean square error of prediction (RMSE):

[0100]

[0101] In the formula: y i This represents the true value of the i-th soil sample. Let be the predicted value of the i-th soil sample, and n be the total number of soil samples.

[0102] Using the above two indicators, the coefficient of determination R 2 The root mean square error (RMSE) of prediction is used to evaluate the effectiveness and predictive ability of the model, respectively. The coefficient of determination R0 is used to evaluate the model's effectiveness and predictive ability. 2 The higher the value, the higher the model's stability, the smaller the root mean square error (RMSE) of the prediction, the more accurate the model's prediction, and the higher the inversion accuracy.

[0103] This embodiment of the method is based on the MODTRAN atmospheric transport model, the instrument signal-to-noise ratio analysis model, and the partial least squares regression soil organic matter inversion model. It builds a full-link simulation framework of hyperspectral space-based remote sensing "instrument-observation-inversion" for black soil organic matter inversion. It can take into account the influence of atmospheric effects and the coupling relationship between spectral resolution and signal-to-noise ratio, thereby solving the coupling between the instrument characteristic model and the inversion model, so as to restore the influence of real space-based remote sensing payload parameters on inversion.

[0104] One use state of the method of the embodiment is used for simulating the influence of space-based remote sensing load parameters on black soil organic matter inversion. For example, the soil spectrum data is collected by using the cultivated land soil in Youyi County in Sanjiang Plain as the soil sample, the soil spectrum is measured by using an ASD FeildSpec Hi-Res object spectrum instrument, the spectrum range is 400-2450 nm, and the spectrum sampling interval is 1 nm. The collected soil spectrum data is taken as the input of the simulation process to simulate the influence of space-based remote sensing load parameters on black soil organic matter inversion.

[0105] The spectral data under different spectral resolutions is added to the noise corresponding to the signal-to-noise ratio curve under the weighted signal-to-noise ratio (WSNR, generally also referred to as signal-to-noise ratio), and the obtained organic matter RMSE in the inversion model changes with the WSNR and the spectral resolution SR as shown in the following figure. Figures 2 to 5 Figure 2 is a schematic diagram (two-dimensional) of the relationship among the RMSE, the spectral resolution and the signal-to-noise ratio in an embodiment of the present application, the horizontal coordinate represents the spectral resolution, the vertical coordinate represents the signal-to-noise ratio, and different depths in the figure represent different sizes of the RMSE; Figure 3 is a schematic diagram (three-dimensional) of the relationship among the RMSE, the spectral resolution and the signal-to-noise ratio in an embodiment of the present application, the surface graph X axis is the signal-to-noise ratio, the Y axis is the spectral resolution, the Z axis is the RMSE, and the black points are data samples, Figure 4 is Figure 3 a two-dimensional projection graph of the three-dimensional surface on the Y axis and the Z axis, that is, a two-dimensional projection graph of the three-dimensional surface on the RMSE and the spectral resolution, reflecting the change of the inversion root mean square error with the spectral resolution. Figure 5 is Figure 3 a two-dimensional projection graph of the three-dimensional surface on the X axis and the Z axis, that is, a two-dimensional projection graph of the three-dimensional surface on the RMSE and the signal-to-noise ratio, reflecting the change of the inversion root mean square error with the signal-to-noise ratio curve corresponding to the signal-to-noise ratio mean. The depth of the gray scale in the figure reflects the size of the RMSE, the lighter the gray scale, the larger the value of the RMSE at the point, and the darker the gray scale, the smaller the value of the RMSE at the point.

[0106] Table 1 is the minimum weighted signal-to-noise ratio parameter under different spectral resolutions to meet the inversion requirement. Table 2 is the organic matter inversion result before and after adding different noises under different spectral resolutions.

[0107] Table 1 is the minimum weighted signal-to-noise ratio parameter under different spectral resolutions to meet the inversion requirement.

[0108] Spectral resolution / nm Weighted signal to noise ratio (WSNR) 10 nm 494.07 15 nm 477.31 20 nm 462.16 60 nm 358.84 100 nm 275.63

[0109] Table 2 is the PLSR inversion model result.

[0110]

[0111] ​The simulation method for the characteristics of the space-based remote sensing instrument provided in the embodiment first simulates first spectral data into the entrance pupil radiance of a satellite according to a TOA direct process based on current space-based remote sensing load parameters; then resamples the simulated entrance pupil radiance data, so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance; further adds the signal-to-noise ratio of the instrument into the resampled entrance pupil radiance data; further simulates the entrance pupil radiance into second spectral data according to a TOA reverse process; finally, inverses the material content in soil according to the second spectral data, constructs an evaluation index based on the inverted material content and the real material content in soil, and thus can reflect the coupling relationship between the instrument characteristic model and the inversion model, restores the influence of the real space-based remote sensing load parameters, and can be used to detect whether the space-based remote sensing load parameters are appropriate and to select appropriate space-based remote sensing load parameters.

[0112] If it is necessary to determine whether the space-based remote sensing load parameters of the instrument are appropriate, the corresponding evaluation index (including the determination coefficient and the prediction root mean square error) is finally obtained according to the simulation method of the embodiment, and thus it can be known whether the space-based remote sensing load parameters of the instrument are appropriate. If it is necessary to select appropriate space-based remote sensing load parameters, different space-based remote sensing load parameters can be simulated by using the simulation method of the embodiment, and thus the evaluation index under the condition of different space-based remote sensing load parameters is obtained, and the appropriate space-based remote sensing load parameters are determined by comparing the evaluation index.

[0113] Embodiment two

[0114] Figure 6 is a structural schematic diagram of a simulation device for the characteristics of a space-based remote sensing instrument according to an embodiment of the present application, and the device 600 mainly comprises: Figure 6 The first simulation module 601 is configured to simulate first spectral data into the entrance pupil radiance of a satellite according to a TOA direct process based on current space-based remote sensing load parameters; the resampling module 602 is configured to resample the simulated entrance pupil radiance data, so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance; the adding module 603 is configured to add the signal-to-noise ratio of the instrument into the resampled entrance pupil radiance data; the second simulation module 604 is configured to simulate the entrance pupil radiance into second spectral data according to a TOA reverse process; and the constructing module 605 is configured to inverse the material content in soil according to the second spectral data, and construct an evaluation index based on the inverted material content and the real material content in soil.

[0115] In an example, the TOA direct process is represented by the following formula:

[0116]

[0117] In the formula, L is the total radiance received when the sensor enters the pupil, p is the surface reflectivity of the target pixel, S is the atmospheric downward spherical albedo, L a is the atmospheric path radiance, and A is a coefficient based on atmospheric conditions and geometric illumination conditions.

[0118] In an example, the first spectral data adopts laboratory hyperspectral data.

[0119] In an example, the TOA direct process is implemented by the atmospheric transmission model MODTRAN.

[0120] In an example, the resampling of the simulated pupil radiance data can be resampling of the simulated pupil radiance data by using a Gaussian function resampling method.

[0121] In an example, the signal-to-noise ratio can be calculated in the following manner: first, the radiation energy received by a single detector pixel at any position on the image plane is obtained, and the number of electrons n e generated by the single detector pixel is obtained according to the radiation energy. e The signal-to-noise ratio is the ratio of the number of electrons n total to the total number of noise electrons n .

[0122] In an example, the inversion of the content of the substance in the soil according to the second spectral data can be inversion of the content of the substance in the soil by using a partial least squares regression inversion model.

[0123] In an example, before the inversion of the content of the substance in the soil according to the second spectral data, noise bands in the range of atmospheric gas absorption are removed and / or outliers in the second spectral data are removed.

[0124] In an example, the evaluation index includes a coefficient of determination R 2 :

[0125]

[0126] In the formula, y i is the true value of the i-th soil sample, is the average value of the predicted values of all soil samples, is the predicted value of the i-th soil sample, and n is the total number of soil samples.

[0127] In an example, the evaluation index includes a root mean square error RMSE of prediction:

[0128]

[0129] In the formula, y i is the true value of the i-th soil sample, is the predicted value of the i-th soil sample, and n is the total number of soil samples.

[0130] The details of other operations performed by the modules in this embodiment can refer to the foregoing embodiments, which will not be expanded here.

[0131] The simulation device for the characteristics of the space-based remote sensing instrument provided in this embodiment firstly simulates first spectral data into the satellite's entrance pupil radiance according to the TOA direct process based on the current space-based remote sensing load parameters; then resamples the simulated entrance pupil radiance data, so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance; then adds the signal-to-noise ratio of the instrument to the resampled entrance pupil radiance data; then simulates the entrance pupil radiance into second spectral data according to the TOA reverse process; finally, inverts the material content in the soil according to the second spectral data, constructs an evaluation index based on the inverted material content and the real material content in the soil, and thus can reflect the coupling relationship between the instrument characteristic model and the inversion model, restore the influence of the real space-based remote sensing load parameters, and can be used to detect whether the space-based remote sensing load parameters are appropriate and select appropriate space-based remote sensing load parameters.

[0132] The present application also provides an electronic device, comprising: a memory for storing programs or instructions executable by a processor; and a processor for executing the above-mentioned programs or instructions to realize the various processes of the above-mentioned simulation method for the characteristics of the space-based remote sensing instrument, and can achieve the same technical effects, to avoid repetition, which will not be described here.

[0133] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present application. The electronic device 700 can include an internal communication bus 701, a processor (Processor) 702, a read-only memory (ROM) 703, a random access memory (RAM) 704, and a communication port 705. When applied to a personal computer, the electronic device 700 can also include a hard disk 706. The internal communication bus 701 can realize data communication between the components of the electronic device 700. The processor 702 can make judgments and issue prompts. In some embodiments, the processor 702 can be composed of one or more processors. The communication port 705 can realize the data communication between the electronic device 700 and the outside. In some embodiments, the electronic device 700 can send and receive information and data from the network through the communication port 705. The electronic device 700 can also include different forms of program storage units and data storage units, such as the hard disk 706, the read-only memory (ROM) 703 and the random access memory (RAM) 704, which can store various data files used by the computer processing and / or communication, and possible programs or instructions executed by the processor 702. The results processed by the processor 702 are transmitted to the user device through the communication port 705 and displayed on the user interface.

[0134] The simulation method for the characteristics of the space-based remote sensing instrument described above can be implemented as a computer program, stored in the hard disk 706 and executable by the processor 702 to implement any of the simulation methods for the characteristics of the space-based remote sensing instrument described above.

[0135] The embodiment of the application further provides a readable storage medium, and a program or instructions are stored on the readable storage medium. The program or instructions are executed by a processor to implement each process of the simulation method for the characteristics of the space-based remote sensing instrument described above, and the same technical effects can be achieved. To avoid repetition, details are not described herein.

[0136] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer readable memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0137] The above disclosure of the application is merely exemplary and does not limit the application. Although the above disclosure does not explicitly describe the application, those skilled in the art can make various modifications, improvements and corrections to the application. Such modifications, improvements and corrections are suggested in the application, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the application.

[0138] Meanwhile, specific words are used in the application to describe the embodiments of the application. For example, "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that the "one embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different places in the specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the application can be properly combined.

[0139] In some embodiments, numbers describing components, attributes and quantities are used. It should be understood that such numbers used in the description of the embodiments are modified by the adjectives "about", "approximately" or "generally" in some examples. Unless otherwise stated, "about", "approximately" or "generally" indicates that the number allows a ±20% variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can vary according to the characteristics required by individual embodiments. In some embodiments, numerical parameters should be considered to have a specified number of significant figures and be rounded off using the method of general figure retention. Although the numerical ranges and parameters used to define the scope of some embodiments of the application are approximate values, in specific embodiments, such numerical values are as precise as possible within the feasible range.

[0140] While the present application has been described with reference to the currently preferred embodiments, those skilled in the art will recognize various changes in form and equivalent substitutions without departing from the spirit of the application and the scope of the following claims.

Claims

1. A simulation method for the characteristics of a space-based remote sensing instrument, characterized in that, include: Based on the current space-based remote sensing payload parameters, the first spectral data is simulated as the satellite's entrance pupil radiance according to the TOA direct process; The simulated entrance pupil radiance data is resampled so that the resampled entrance pupil radiance data can reflect the effect of spectral resolution on the entrance pupil radiance. The instrument's signal-to-noise ratio is added to the resampled entrance pupil radiance data; The entrance pupil radiance is simulated as second spectral data according to the TOA reverse process; The soil material content is inverted based on the second spectral data, and an evaluation index is constructed based on the inverted material content and the actual material content in the soil.

2. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 1, characterized in that, The direct TOA process is represented by the following formula: In the formula, L is the total radiance received by the sensor at the entrance pupil, ρ is the surface reflectance of the target pixel, S is the spherical albedo facing downwards, and L a For atmospheric path radiation, A is a coefficient based on atmospheric conditions and geometric illumination conditions.

3. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 2, characterized in that, The first spectral data was obtained from laboratory hyperspectral data.

4. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 2, characterized in that, The direct TOA process was simulated using the atmospheric transport model MODTRAN.

5. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 1, characterized in that, The resampling of the simulated entrance pupil radiance data includes: resampling the simulated entrance pupil radiance data using a Gaussian function resampling method.

6. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 1, characterized in that, The signal-to-noise ratio is calculated as follows: First, the radiation energy received by a single detector pixel at any position on the image plane is obtained. Based on the radiation energy, the number of electrons n produced by a single detector pixel is calculated. e Then the signal-to-noise ratio is the number of electrons n. e With the total number of noise electrons n total The ratio of .

7. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 1, characterized in that, The process of retrieving soil material content based on the second spectral data includes: using a partial least squares regression inversion model to retrieve soil material content.

8. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 7, characterized in that, Before retrieving the soil material content based on the second spectral data, the process also includes: removing noise bands within the atmospheric gas absorption range and / or removing outliers from the second spectral data.

9. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 1, characterized in that, The evaluation indicators include the coefficient of determination R. 2 : In the formula: y i This represents the true value of the i-th soil sample. The average value predicted for all soil samples. Let be the predicted value of the i-th soil sample, and n be the total number of soil samples.

10. The simulation method for the characteristics of space-based remote sensing instruments as described in claim 1 or 9, characterized in that, The evaluation metrics include the root mean square error of prediction (RMSE). In the formula: y i This represents the true value of the i-th soil sample. Let be the predicted value of the i-th soil sample, and n be the total number of soil samples.

11. A simulation device for the characteristics of a space-based remote sensing instrument, characterized in that, include: The first simulation module is used to simulate the entrance pupil radiance of the satellite based on the current space-based remote sensing payload parameters and according to the TOA direct process. The resampling module is used to resample the simulated entrance pupil radiance data so that the resampled entrance pupil radiance data can reflect the influence of spectral resolution on the entrance pupil radiance. An addition module is used to add the instrument's signal-to-noise ratio to the resampled entrance pupil radiance data; The second simulation module is used to simulate the entrance pupil radiance as second spectral data according to the TOA reverse process; A construction module is used to invert the content of substances in the soil based on the second spectral data, and to construct evaluation indicators based on the inverted content of substances and the actual content of substances in the soil.

12. An electronic device, characterized in that, include: A processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions being executed by the processor to implement the steps of the simulation method for the characteristics of a space-based remote sensing instrument as described in any one of claims 1-10.

13. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the simulation method for the characteristics of a space-based remote sensing instrument as described in any one of claims 1-10.

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