CN111751342B - Method for inverting sunlight-induced chlorophyll fluorescence based on Fraunhofer dark line - Google Patents
Method for inverting sunlight-induced chlorophyll fluorescence based on Fraunhofer dark line Download PDFInfo
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Abstract
The invention discloses a method for inverting sunlight-induced chlorophyll fluorescence based on Fraunhofer dark lines, which comprises the following steps: step 1: selecting representative radiance spectrums which do not contain fluorescence information to form a training spectrum data set; step 2: carrying out singular vector decomposition on the training spectrum data set by using a singular vector decomposition technology; and step 3: setting a 0.05% threshold value, and determining the number N of usable singular vectors according to the threshold value; and 4, step 4: respectively and successively carrying out actual measurement spectrum reconstruction by using the previous M singular vectors (M is less than or equal to N), and determining the number M of the singular vectors when the reconstruction precision is highest (the residual error between the reconstructed spectrum and the actual measurement spectrum is minimum) as a final model input parameter; and 5: and solving the unknown number in the model by using a standard least square method, and inverting the chlorophyll fluorescence. The invention can invert the chlorophyll fluorescence from a wide band range, and reduces the requirement of inverting the chlorophyll fluorescence on the spectral resolution of the sensor.
Description
Technical Field
The invention belongs to the technical field of vegetation sunlight-induced chlorophyll fluorescence remote sensing inversion, and particularly relates to a sunlight-induced chlorophyll fluorescence remote sensing inversion method based on Fraunhofer dark lines.
Background
Chlorophyll fluorescence is red light and far-red light emitted in a visible light-near infrared region when chlorophyll molecules are converted from a high excitation state to a ground state after leaves are excited by light. Chlorophyll fluorescence is a byproduct of photosynthesis, is derived from absorbing photosynthetically active radiation APAR, is homologous with photosynthesis carbon fixation and heat dissipation of vegetation, and is closely related to the general primary GPP of the vegetation and the stressed state of the vegetation. With the development of the technology, the inversion of chlorophyll fluorescence is expanded from the ground scale to the satellite scale, and the possibility of monitoring the photosynthesis state of vegetation in a large range is provided, so that the development of chlorophyll fluorescence inversion research has great significance.
The satellite-scale chlorophyll fluorescence inversion method can be roughly divided into three categories: an algorithm based on an atmospheric radiation transfer equation, a simplified physical model algorithm and a data-driven algorithm. The algorithm based on the atmospheric radiation transmission equation is a method for extracting chlorophyll fluorescence along the near-surface, and on the basis, the absorption and scattering effects of the atmosphere are considered, but the atmosphere correction with high precision is required; the simplified physical model algorithm is an inversion method based on filling of fluorescence to the solar Fraunhofer dark line in an atmospheric window and neglecting the influence of the atmosphere, and the method only can utilize the solar Fraunhofer dark line in the atmospheric window and needs to acquire the solar irradiance spectrum at the top of the atmospheric layer; the data driving algorithm is to take the entrance pupil radiance observed by the sensor as the superposition of a non-fluorescence signal and a fluorescence signal, extract the spectrum of the non-fluorescence signal by using a training set formed by the non-fluorescence target spectrum, express the observed non-fluorescence signal by using a small amount of extracted features, and extract the fluorescence signal by using a simplified radiation transmission equation.
The data-driven algorithm is a mainstream fluorescence inversion algorithm at present, and is already used for producing various fluorescent products, such as GOSAT, OCO-2, TanSat and the like, and the algorithm can also be divided into two algorithms, namely an inversion algorithm based on principal component analysis and an inversion algorithm based on singular vector decomposition. The inversion algorithm based on singular vector decomposition is only applied to inversion in a narrow band range (2-3nm) at present, and the study on chlorophyll fluorescence inversion in a long band range is relatively weak.
Disclosure of Invention
In order to solve the defects in the technical problem, the invention provides a sunlight-induced chlorophyll fluorescence remote sensing inversion method based on Fraunhofer dark lines, which can be suitable for inverting chlorophyll fluorescence in a long-wavelength band range.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for inverting sunlight-induced chlorophyll fluorescence based on Fraunhofer dark line comprises the following steps:
step 1: selecting radiance spectrums which have enough representativeness and do not contain fluorescence information to form a training spectrum data set;
step 2: carrying out singular vector decomposition on the training spectrum data set by using a singular vector decomposition technology;
and step 3: setting a threshold value, and determining the number N of usable singular vectors according to the threshold value;
and 4, step 4: respectively and successively carrying out actual measurement spectrum reconstruction by using the previous M singular vectors, wherein M is less than or equal to N, and determining the number M of the singular vectors with highest reconstruction precision, namely when the residual error between the reconstructed spectrum and the actual measurement spectrum is minimum, as a final model input parameter;
and 5: and solving the unknown number in the model by using a standard least square method, and inverting the chlorophyll fluorescence.
In the method for inverting chlorophyll fluorescence, in the step 1, the selected training spectrum is a waveband which does not contain any atmospheric and water vapor absorption lines and only contains continuous Fraunhofer dark lines, and the atmospheric influence can not be considered in the waveband range.
In the method for inverting chlorophyll fluorescence, in the step 3, the threshold value is 0.05%, and when the information quantity explained by a single singular vector is greater than 0.05% of the total spectrum information, the singular vector is selected as an available singular vector to restore the fluorescence-free spectrum as true as possible.
In the method for inverting chlorophyll fluorescence, in the step 4, the process of determining the number M of singular vectors specifically includes the following steps:
step 401: respectively multiplying the first singular vector by the atmospheric low-frequency contribution polynomial and adding the linear combination of the first M-1 singular vectors (M is less than or equal to N) to reconstruct the measured spectrum;
step 402: and respectively comparing the reconstructed spectrum with the actually measured spectrum, and determining M singular vectors used when the residual error is minimum as the input of the optimal model.
In the method for inverting chlorophyll fluorescence, in step 5, the radiance is regarded as the superposition of the non-fluorescence part and the fluorescence part, the non-fluorescence part can be regarded as the combination of the singular vector and the atmospheric low-frequency contribution, and the radiation transmission equation is as follows:
wherein F represents radiance, aiλiRepresenting the low frequency contribution of the atmosphere, a being the coefficient, λ being the wavelength, viRepresenting singular vectors, ωiRepresenting the weight corresponding to the singular vector, hFIs a fluorescent wave function, FsIs the fluorescence intensity, npAnd nvRespectively representing the order of the polynomial and the number of singular vectors.
Generally, for window ranges less than 10nm, the atmospheric low frequency contribution is approximately expressed using a first order polynomial, and the fluorescence waveform function is represented by a gaussian function, i.e., the above equation can be simplified as:
in the formula, λ0Representing the peak position, σhThe standard deviation is generally based on a window range.
And solving the formula by using a standard least square method to obtain the unknown number, and performing inversion to obtain the chlorophyll fluorescence.
The invention mainly develops a method for inverting sunlight-induced chlorophyll fluorescence based on Fraunhofer dark lines, combines the first effective singular vector with atmospheric low-frequency contribution, and can be suitable for inversion of chlorophyll fluorescence in a long-wave band range. The method utilizes a data driving algorithm principle, the radiance is regarded as superposition of a non-fluorescence signal and a fluorescence signal, meanwhile, the non-fluorescence signal can be represented as atmosphere high-frequency information and low-frequency information, the high-frequency information is represented by a singular vector obtained after singular vector decomposition is carried out on a training spectrum, and the atmosphere low-frequency information is represented by a polynomial of wavelength. Selecting a spectrum without fluorescence information to form a training set, then carrying out singular vector decomposition on the training set, reconstructing an actually measured spectrum by using singular vectors obtained by decomposition, determining the number of the selected singular vectors according to reconstruction accuracy, finally combining the selected singular vectors with a wavelength polynomial, inputting a radiation transmission equation, solving an unknown number in the equation by using a standard least square method, and carrying out inversion to obtain chlorophyll fluorescence.
The invention has the following beneficial effects: a sunlight-induced chlorophyll fluorescence remote sensing inversion method based on Fraunhofer dark lines is provided. The method can invert chlorophyll fluorescence from a long wave band range, is not like a traditional singular vector decomposition inversion algorithm, can only be used in a narrow wave band range, and takes chlorophyll fluorescence signals in an inversion window range as fixed values. The method does not need to combine all singular vectors with atmosphere low-frequency information respectively like a principal component analysis algorithm, but selects the first singular vector to combine with an atmosphere low-frequency polynomial, and linearly superposes the other singular vectors to invert chlorophyll fluorescence according to a radiation transmission equation. The method reduces the requirement of the traditional singular vector decomposition algorithm on the spectral resolution of the sensor, and is easier to popularize and apply.
Drawings
FIG. 1 is a global distribution map of a spectral training set;
FIG. 2 shows a carbon satellite ACGS sensor O2-A wave band 771-778nm radiance spectral curve diagram;
FIG. 3 is the first 10 singular vectors;
FIG. 4 is a diagram illustrating a result of reconstructing a radiance spectrum;
FIG. 5 is a comparison graph of inversion results;
Detailed Description
Step 1: according to the carbon satellite data description file, pure bare soil, ice and snow and water pixels (figure 1) are selected in the global range, the central wavelength of each channel is extracted according to a wavelength extraction formula (formula 3) to obtain a radiance spectral curve, and a 771-778 nm-range spectral curve (figure 2) is intercepted to form a training spectral data set.
Wherein, λ represents wavelength, C represents dispersion coefficient, and P represents channel number;
step 2: and carrying out singular vector decomposition on the obtained training spectrum data set by using a singular vector decomposition technology to obtain each singular vector. The singular vector decomposition formula is as follows:
E＝USVT (4)
wherein E represents a matrix to be decomposed, the number of rows and columns respectively consists of the number of spectral strips and the number of wave bands of each spectrum, U represents an orthogonal matrix formed by left singular vectors, S represents a diagonal matrix formed by singular values, V represents an orthogonal matrix formed by right singular vectors, and T represents transposition;
and step 3: each singular vector obtained through singular vector decomposition can explain a certain amount of spectral information. We set an empirical threshold of 0.05% and determine the number of singular vectors that can be used for spectral reconstruction based on the threshold. It is finally determined that the information interpretable by 10 singular vectors is greater than 0.05% of the total information amount, and therefore the first 10 singular vectors are determined to be usable singular vectors. The singular vectors are shown in FIG. 3;
and 4, step 4: respectively utilizing the first 2 to the first 10 singular vectors as model input, reconstructing the radiance spectrum (figure 4), then utilizing a reconstructed spectrum curve to compare with a real spectrum curve, and finding that the number of the singular vectors used when the residual error is minimum is 5, so that the chlorophyll fluorescence is determined to be inverted by the first 5 singular vectors;
and 5: and solving the unknown number in the model by using a standard least square method, inverting the chlorophyll fluorescence, and comparing the inversion result with the corresponding carbon satellite chlorophyll fluorescence product. The comparison results show that the results obtained by the Fraunhofer dark line-based chlorophyll fluorescence inversion method disclosed herein have higher consistency with the carbon satellite chlorophyll fluorescence product, and the deviation (bias) between the two is-0.23 mW/m2Per/nm, relative Root Mean Square Error (RMSE) of 0.62mW/m2The/sr/nm shows that the method for inverting chlorophyll fluorescence disclosed herein is very reliable. The comparison results are shown in fig. 5.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (1)
1. A method for inverting sunlight-induced chlorophyll fluorescence based on Fraunhofer dark line is characterized by comprising the following steps:
step 1: selecting representative radiance spectrums which do not contain fluorescence information to form a training spectrum data set; the selected training spectrum is a wave band which does not contain any atmospheric and vapor absorption lines and only contains continuous Fraunhofer dark lines, and the atmospheric influence can be not considered in the range of the wave band;
step 2: carrying out singular vector decomposition on the training spectrum data set by using a singular vector decomposition technology;
and step 3: setting a threshold value, and determining the number N of usable singular vectors according to the threshold value; the threshold value is 0.05%, and when the information quantity explained by a single singular vector is more than 0.05% of the total spectrum information, the singular vector is selected as an available singular vector to restore the fluorescence-free spectrum as true as possible;
and 4, step 4: respectively and successively carrying out actual measurement spectrum reconstruction by using the previous M singular vectors, wherein M is less than or equal to N, and determining the number M of the singular vectors with highest reconstruction precision, namely when the residual error between the reconstructed spectrum and the actual measurement spectrum is minimum, as a final model input parameter;
the method specifically comprises the following steps:
step 401: respectively multiplying the first singular vector by the atmospheric low-frequency contribution polynomial and adding the linear combination of the first M-1 singular vectors (M is less than or equal to N) to reconstruct the measured spectrum;
step 402: respectively comparing the reconstructed spectrum with the actually measured spectrum, and determining M singular vectors used when the residual error is minimum as the input of an optimal model;
and 5: solving the unknown number in the model by using a standard least square method, and inverting the chlorophyll fluorescence;
considering the radiance as a superposition of the non-fluorescent part, which can be regarded as a combination of singular vectors and atmospheric low frequency contributions, and the fluorescent part, the radiation transfer equation used is as follows:
wherein F represents radiance, aiλiRepresenting the low frequency contribution of the atmosphere, a being the coefficient, λ being the wavelength, viRepresenting singular vectors, ωiRepresenting the weight corresponding to the singular vector, hFIs a fluorescent wave function, FsIs the fluorescence intensity, npAnd nvRespectively representing the order of the polynomial and the number of singular vectors;
a window range of less than 10nm, the atmospheric low frequency contribution is approximately expressed using a first order polynomial, and the fluorescence waveform function is represented by a gaussian function, i.e. the above equation can be simplified as:
in the formula, λ0Representing the peak position, σhTaking a value according to a window range as a standard deviation;
and solving the formula by using a standard least square method to obtain the unknown number, and performing inversion to obtain the chlorophyll fluorescence.
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