CN111881870A - Remote sensing inversion model and method for phycocyanin concentration in inland lake water body - Google Patents

Remote sensing inversion model and method for phycocyanin concentration in inland lake water body Download PDF

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CN111881870A
CN111881870A CN202010772355.8A CN202010772355A CN111881870A CN 111881870 A CN111881870 A CN 111881870A CN 202010772355 A CN202010772355 A CN 202010772355A CN 111881870 A CN111881870 A CN 111881870A
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inland lake
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姜晓剑
吴莹莹
朱元励
李卓
任海芳
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Huaiyin Normal University
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Abstract

The invention provides an inland lake water body phycocyanin concentration remote sensing inversion model, which is an extreme random tree model of Python language, and the model parameters of the extreme random tree model are as follows: 'max _ features': log2',' split ': range', 'min _ samples _ leaf':1, 'max _ depth': None, 'min _ weight _ fraction _ leaf':0.0, 'criterion': fragment _ mse ',' min _ input _ split ': None,' ccp _ alpha ':0.0,' min _ input _ fraction ':0.000871292997015407 min _ samples _ split':2, 'max _ leaf _ node'. Also provides a remote sensing inversion method of the phycocyanin concentration in the inland lake water body. The remote sensing inversion model for the phycocyanin concentration in the inland lake water body can reduce the error of a calculation result and improve the accuracy of the inversion model for the phycocyanin concentration in the water body, and is ingenious in design, simple and convenient to calculate, easy to realize, low in cost and suitable for large-scale popularization and application.

Description

Remote sensing inversion model and method for phycocyanin concentration in inland lake water body
Technical Field
The invention relates to the technical field of inland lake water body environment monitoring, in particular to the technical field of inland lake water body phycocyanin concentration measurement, and specifically relates to an inland lake water body phycocyanin concentration remote sensing inversion model and method.
Background
The aggravation of the eutrophication of the water body of the inland lake often causes the abnormal growth of blue algae, thereby causing the outbreak of water bloom, polluting the water quality of the lake and seriously influencing the water safety of the surrounding population. The phycocyanin is an important pigment for the photosynthesis of blue algae and can be used as a marker pigment for monitoring the blue algae. Therefore, remote sensing inversion and model quantitative estimation of phycocyanin are the basis for enhancing high-frequency and rapid monitoring of phycocyanin in water (Maronghua, Confucian, slug, etc., and the phycocyanin content [ J ] in the outbreak period of blue-green algae in Taihu lake is estimated based on MODIS images [ Chinese environmental science, 2009(03): 254-. The optical characteristics of the inland turbid water body are complex, and the rapid quantitative estimation of the phycocyanin concentration of the inland turbid water body by using remote sensing technologies such as hyperspectrum, multispectral and the like becomes a difficulty (Von Longqing, Taihu lake water body phycocyanin and CDOM concentration estimation model research [ D ]. Nanjing agriculture university) based on hyperspectral remote sensing.
The existing remote sensing quantitative inversion method for the phycocyanin concentration mainly comprises an analysis method, a semi-empirical method and an empirical method, extracts phycocyanin concentration information contained in various remote sensing data, and quantitatively calculates the phycocyanin concentration. The analytical method quantitatively inverts the phycocyanin concentration through physical models such as bio-optics and radiation transmission, and the like, and needs to determine parameters such as apparent optical characteristics and inherent optical characteristics of water, while the water components and optical characteristics of inland lakes are complex, and various parameters are difficult to accurately measure, so that the accuracy of inversion results is generally low; the semi-empirical method is used for inverting the phycocyanin concentration by using a physical model of an analysis method and determining part of operation parameters by a statistical analysis method, the accuracy is good, the application is wide, but the constructed inversion model has certain space-time limitation; the empirical method quantitatively calculates the phycocyanin concentration by establishing a statistical relationship between the remote sensing data and the ground actual phycocyanin concentration, and an inversion model is greatly limited by space-time limitation and has poor transportability.
The three methods mostly adopt fixed-position wave band data in the process of constructing the model. The water body components and optical characteristics of inland lakes are complex, which causes the 'drifting' phenomenon of characteristic wave bands of the water body, so that the characteristic wave bands of the phycocyanin in different water bodies can not be accurately determined. The commonly used dual-band ratio model is used for a high-turbidity water body, saturation is easy to occur, three-band parameters are unstable, and the applicability is limited to a certain extent, so that the inversion model needs to be further improved aiming at the application of phycocyanin concentration inversion of high-turbidity water bodies such as inland lakes and the like. In addition, most of the inversion models used in the research are linear models or exponential models, and in practice, the phycocyanin concentration or the logarithm value of the phycocyanin concentration has a non-linear relationship with the characteristic wave band as well as a linear relationship. Therefore, further improvement and perfection are needed for the inversion method of the phycocyanin concentration.
Therefore, it is desirable to provide an inland lake water body phycocyanin concentration remote sensing inversion model, which can reduce calculation result errors and improve the accuracy of the inversion model of the water body phycocyanin concentration.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a remote sensing inversion model of the phycocyanin concentration in the water body of inland lakes, which can reduce the error of a calculation result, improve the accuracy of the inversion model of the phycocyanin concentration in the water body and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion model of the phycocyanin concentration in the inland lake water body, which has the advantages of ingenious design, simple and convenient calculation, easy realization and low cost and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion method for the phycocyanin concentration in the water body of the inland lake, which can reduce the error of a calculation result, improve the inversion accuracy of the phycocyanin concentration in the water body and is suitable for large-scale popularization and application.
The invention also aims to provide a remote sensing inversion method for the phycocyanin concentration in the inland lake water body, which has the advantages of ingenious design, simplicity and convenience in operation and low cost and is suitable for large-scale popularization and application.
In order to achieve the above object, in a first aspect of the present invention, there is provided an remote sensing inversion model of phycocyanin concentration in a water body of an inland lake, which is characterized in that the remote sensing inversion model of phycocyanin concentration in a water body of an inland lake is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: 'max _ features': log2',' split ': range', 'min _ samples _ leaf':1, 'max _ depth': None, 'min _ weight _ fraction _ leaf':0.0, 'criterion': fragment _ mse ',' min _ input _ split ': None,' ccp _ alpha ':0.0,' min _ input _ fraction ':0.000871292997015407 min _ samples _ split':2, 'max _ leaf _ node'.
Preferably, the extreme random tree model is trained by using a data set of an inland lake water body, the data set includes water body remote sensing reflectance ratios of m sampling points of the inland lake water body and 10-fold reduction values of phycocyanin concentration, the m sampling points are uniformly distributed on the inland lake water body, and the water body remote sensing reflectance ratio is a water body remote sensing reflectance ratio of n characteristic wave bands.
More preferably, m is 60, the n characteristic bands are 751 characteristic bands, and the 751 characteristic bands are from 350nm to 1100 nm.
In a second aspect of the invention, the invention provides a remote sensing inversion method for the phycocyanin concentration in a water body of an inland lake, which is characterized by comprising the following steps:
(1) measuring the remote sensing reflectance of the inland lake water body;
(2) measuring the phycocyanin concentration C of the inland lake water bodyPCThe phycocyanin concentration C is determinedPCDivision by 10 gives a 10-fold reduction in the phycocyanin concentration:
(3) calculating by using the water body remote sensing reflectance as input data and adopting an extreme random tree model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and a 10-fold reduction value of the phycocyanin concentration2Changing the value of a model parameter, R, of the extreme stochastic tree model2Is greater, the model parameters are describedThe greater the importance of the model parameter is, arranging the model parameters from large to small according to the importance of the model parameter to construct a model parameter tuning rank matrix;
(4) training the extreme random tree model by taking the water body remote sensing reflectance as the input data and taking the 10-fold reduction value of the phycocyanin concentration as an output result, and sequentially tuning the model parameters according to the model parameter tuning order matrix to obtain tuning values of the model parameters;
(5) and training the extreme random tree model by taking the water body remote sensing reflectance as the input data and taking a 10-time reduction value of the phycocyanin concentration as the output result, adopting the tuning value of the model parameter, obtaining an inland lake water body phycocyanin concentration remote sensing inversion model after the training of the extreme random tree model is finished, storing the inland lake water body phycocyanin concentration remote sensing inversion model by using a save method, and loading the inland lake water body phycocyanin concentration remote sensing inversion model for use by using a load method if the inland lake water body phycocyanin concentration remote sensing inversion model is required to be used.
Preferably, in the step (1), the step of measuring the remote sensing reflectance of the water body of the inland lake water body specifically includes:
measuring the water body spectrum data L of the inland lake water bodyswAnd the sky diffuse scattered light L of the inland lake water bodyskyAnd reflected light L of the standard platepAnd calculating the water body remote sensing reflectance:
Rrs＝(Lsw-rsky*Lsky)/(Lp*π/ρp)，
wherein Rrs is the remote sensing reflectance of the water body; r isskyThe air-water surface reflectivity of the inland lake water body is 0.022-0.028, and r is measured on a calm water surfaceskyThe value of r is 0.022, and r is obtained at the wind speed of 5m/sskyThe value is 0.025, and the value is 0.026-0.028 at the wind speed of 10m/s, rhopIs the reflectance of the standard plate.
More preferably, in the step (1), the measurement is performed by using a portable geophysical spectrometerThe portable type ground object spectrometer adopts a lens with a view angle of 25 degrees, the measurement adopts a measurement method above the water surface, and the observation geometry of the measurement method above the water surface is set as follows: phi (v135 °, v 40 °, the standard board is a standard white board with a reflectivity of 10%.
Preferably, in the step (2), the phycocyanin concentration C of the inland lake water body is measuredPCThe method specifically comprises the following steps:
collecting a water sample of the inland lake water body, filtering the water sample by using filter paper to obtain a filter paper sample, freezing and grinding the filter paper sample, adding a phycocyanin extract in the grinding process, refrigerating and centrifuging, taking supernatant to a constant volume, and measuring the absorbance A of the supernatant at 615nm, 652nm and 750nm615、A652And A750Calculating the phycocyanin concentration C of the inland lake water body by using the following formulaPC：
Figure BDA0002617124980000041
Wherein, V1Is the constant volume of the supernatant, V2Is the volume of the water sample, and L is the optical path of the cuvette.
More preferably, in the step (2), the filter paper is Whatman GF/F filter paper, the freezing is performed in a refrigerator at-20 ℃ for 8 hours, the phycocyanin extract is phosphate buffer, the cold storage is performed in the refrigerator, and the cold storage is performed for 30 minutes.
Preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'max_features','splitter','min_samples_leaf','max_depth','min_weight_fraction_leaf','criterion','min_impurity_split','ccp_alpha','min_impurity_decrease','min_samples_split','max_leaf_nodes'}。
preferably, in the step (4), the optimized values of the model parameters are:
'max_features':'log2','splitter':'random','min_samples_leaf':1,'max_depth':None,'min_weight_fraction_leaf':0.0,'criterion':'friedman_mse','min_impurity_split':None,'ccp_alpha':0.0,'min_impurity_decrease':0.000871292997015407,'min_samples_split':2,'max_leaf_nodes':None。
preferably, in the step (1), the step of measuring the water body remote sensing reflectance of the inland lake water body is specifically to measure the water body remote sensing reflectance of m sampling points of the inland lake water body, wherein the m sampling points are uniformly distributed on the inland lake water body, and the water body remote sensing reflectance is a water body remote sensing reflectance of n characteristic wave bands; in the step (2), the phycocyanin concentration C of the inland lake water body is measuredPCSpecifically, the phycocyanin concentration C of the m sampling points is measuredPC。
More preferably, in the step (1), the m is 60, the n characteristic bands are 751 characteristic bands, and the 751 characteristic bands are from 350nm band to 1100nm band.
The invention has the following beneficial effects:
1. the remote sensing inversion model of the phycocyanin concentration in the inland lake water body is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: 'max _ features' log2',' split 'range', min _ samples _ leaf '1,' max _ depth 'None,' min _ weight _ fraction _ leaf '0.0,' criterion 'Friedman _ me', 'min _ input _ split' None, 'ccp _ alpha' 0.0, 'min _ input _ fraction' 0.000871292997015407 min _ samples _ split '2,' max _ leaf _ None ', the model is examined, R _ samples _ gradient' is used2Above 0.85, therefore, the method can reduce the error of the calculation result, improve the accuracy of the inversion model of the phycocyanin concentration in the water body, and is suitable for large-scale popularization and application.
2. The remote sensing inversion model of the phycocyanin concentration in the inland lake water body is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: 'max _ features': log2',' split ': random', 'min _ samples _ leaf':1, 'max _ depth': None, 'min _ weight _ fraction _ leaf':0.0, 'criterion': fragment _ me ',' min _ graphics _ split ': None,' ccp _ al0.0, 'min _ input _ describe': 0.000871292997015407, 'min _ samples _ split':2, 'max _ leaf _ nodes': None, the model was examined, R2Above 0.85, therefore, the method has the advantages of ingenious design, simple and convenient calculation, easy realization and low cost, and is suitable for large-scale popularization and application.
3. The remote sensing inversion method for the phycocyanin concentration in the inland lake water body comprises the following steps: measuring the remote sensing reflectance of the inland lake water body; measuring the phycocyanin concentration C of inland lake water bodyPCAnd divided by 10 to obtain a 10-fold reduction in phycocyanin concentration: taking the water body remote sensing reflectance as input data, calculating by adopting an extreme random tree model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training an extreme random tree model by taking the water body remote sensing reflectance as input data and a 10-time reduction value of the phycocyanin concentration as an output result, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; training an extreme random tree model by taking the remote sensing reflectance of the water body as input data and the 10-time reduction value of the phycocyanin concentration as an output result and adopting the adjustment value of model parameters to obtain an inland lake water body phycocyanin concentration remote sensing inversion model, inspecting the model, and performing R2Above 0.85, therefore, the method can reduce the error of the calculation result, improve the inversion accuracy of the phycocyanin concentration in the water body, and is suitable for large-scale popularization and application.
4. The remote sensing inversion method for the phycocyanin concentration in the inland lake water body comprises the following steps: measuring the remote sensing reflectance of the inland lake water body; measuring the phycocyanin concentration C of inland lake water bodyPCAnd divided by 10 to obtain a 10-fold reduction in phycocyanin concentration: taking the water body remote sensing reflectance as input data, calculating by adopting an extreme random tree model of Python language to determine a coefficient R2Constructing a model parameter tuning order matrix; training an extreme random tree model by taking the water body remote sensing reflectance as input data and a 10-time reduction value of the phycocyanin concentration as an output result, and sequentially tuning model parameters according to a model parameter tuning order matrix to obtain tuning values of the model parameters; using the remote sensing reflectance of the water body as input data and the concentration of phycocyaninThe 10-time reduction value is an output result, an extreme random tree model is trained by adopting the adjustment value of model parameters to obtain an inland lake water body phycocyanin concentration remote sensing inversion model, the model is checked, and R2Above 0.85, therefore, the design is ingenious, the operation is simple and convenient, the cost is low, and the method is suitable for large-scale popularization and application.
These and other objects, features and advantages of the present invention will become more fully apparent from the following detailed description, the accompanying drawings and the claims, and may be realized by means of the instrumentalities, devices and combinations particularly pointed out in the appended claims.
Drawings
FIG. 1 is a schematic flow chart of a specific embodiment of the remote sensing inversion method for the phycocyanin concentration in the inland lake water body.
FIG. 2 is a schematic view of the above water surface observation geometry of the spectrometer of the embodiment shown in FIG. 1.
FIG. 3 is a schematic diagram of a model building process of the embodiment shown in FIG. 1.
FIG. 4 is a schematic diagram of the model test result of the embodiment shown in FIG. 1, wherein the measured value and the predicted value are both measured in units of 10 ug/L.
Detailed Description
The invention provides an inland lake water body phycocyanin concentration remote sensing inversion model aiming at the defects of the existing phycocyanin concentration inversion model, and provides the inland lake water body phycocyanin concentration remote sensing inversion model which is an extreme random tree model of Python language, wherein the model parameters of the extreme random tree model are as follows: 'max _ features': log2',' split ': range', 'min _ samples _ leaf':1, 'max _ depth': None, 'min _ weight _ fraction _ leaf':0.0, 'criterion': fragment _ mse ',' min _ input _ split ': None,' ccp _ alpha ':0.0,' min _ input _ fraction ':0.000871292997015407 min _ samples _ split':2, 'max _ leaf _ node'.
The extreme random tree model can be trained by any suitable data set, preferably, the extreme random tree model is trained by a data set of inland lake water, the data set comprises water body remote sensing reflectance ratios of m sampling points of the inland lake water and 10-time reduction values of phycocyanin concentration, the m sampling points are uniformly distributed on the inland lake water, and the water body remote sensing reflectance ratio is a water body remote sensing reflectance ratio of n characteristic wave bands.
M and n are positive integers, which can be determined according to needs, and more preferably, m is 60, the n characteristic bands are 751 characteristic bands, and the 751 characteristic bands are from 350nm to 1100 nm.
The invention also provides a remote sensing inversion method for the phycocyanin concentration in the inland lake water body, which comprises the following steps:
(1) measuring the remote sensing reflectance of the inland lake water body;
(2) measuring the phycocyanin concentration C of the inland lake water bodyPCThe phycocyanin concentration C is determinedPCDivision by 10 gives a 10-fold reduction in the phycocyanin concentration:
(3) calculating by using the water body remote sensing reflectance as input data and adopting an extreme random tree model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and a 10-fold reduction value of the phycocyanin concentration2Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the extreme random tree model by taking the water body remote sensing reflectance as the input data and taking the 10-fold reduction value of the phycocyanin concentration as an output result, and sequentially tuning the model parameters according to the model parameter tuning order matrix to obtain tuning values of the model parameters;
(5) and training the extreme random tree model by taking the water body remote sensing reflectance as the input data and taking a 10-time reduction value of the phycocyanin concentration as the output result, adopting the tuning value of the model parameter, obtaining an inland lake water body phycocyanin concentration remote sensing inversion model after the training of the extreme random tree model is finished, storing the inland lake water body phycocyanin concentration remote sensing inversion model by using a save method, and loading the inland lake water body phycocyanin concentration remote sensing inversion model for use by using a load method if the inland lake water body phycocyanin concentration remote sensing inversion model is required to be used.
In the step (1), the step of measuring the remote water body reflectance of the inland lake water body may specifically include any suitable method, and preferably, in the step (1), the step of measuring the remote water body reflectance of the inland lake water body specifically includes:
measuring the water body spectrum data L of the inland lake water bodyswAnd the sky diffuse scattered light L of the inland lake water bodyskyAnd reflected light L of the standard platepAnd calculating the water body remote sensing reflectance:
Rrs＝(Lsw-rsky*Lsky)/(Lp*π/ρp)，
wherein Rrs is the remote sensing reflectance of the water body; r isskyThe air-water surface reflectivity of the inland lake water body is 0.022-0.028, and r is measured on a calm water surfaceskyThe value of r is 0.022, and r is obtained at the wind speed of 5m/sskyThe value is 0.025, and the value is 0.026-0.028 at the wind speed of 10m/s, rhopIs the reflectance of the standard plate.
In the step (1), the measurement may be performed by any suitable spectrometer, the measurement may be performed by any suitable method, the standard plate may be any suitable standard plate, and preferably, in the step (1), the measurement is performed by a portable terrestrial object spectrometer, the portable terrestrial object spectrometer uses a lens with an angle of view of 25 °, the measurement is performed by a measurement method above the water surface, and an observation geometry of the measurement method above the water surface is set as: phi (v135 °, v 40 °, the standard board is a standard white board with a reflectivity of 10%.
In the step (2), the phycocyanin concentration C of the inland lake water body is measuredPCMay specifically include any suitable method, preferably at said stepIn the step (2), the phycocyanin concentration C of the inland lake water body is measuredPCThe method specifically comprises the following steps:
collecting a water sample of the inland lake water body, filtering the water sample by using filter paper to obtain a filter paper sample, freezing and grinding the filter paper sample, adding a phycocyanin extract in the grinding process, refrigerating and centrifuging, taking supernatant to a constant volume, and measuring the absorbance A of the supernatant at 615nm, 652nm and 750nm615、A652And A750Calculating the phycocyanin concentration C of the inland lake water body by using the following formulaPC：
Figure BDA0002617124980000081
Wherein, V1Is the constant volume of the supernatant, V2Is the volume of the water sample, and L is the optical path of the cuvette.
In the step (2), the filter paper may be any suitable filter paper, the freezing may be performed under any suitable conditions, the phycocyanin extract may be any suitable phycocyanin extract, the refrigerating may be performed under any suitable conditions, and more preferably, in the step (2), the filter paper is a Whatman GF/F filter paper, the freezing is performed at-20 ℃ in a refrigerator, the freezing time is 8 hours, the phycocyanin extract is a phosphate buffer solution, the refrigerating is performed in the refrigerator, and the refrigerating time is 30 minutes.
In the step (3), the model parameter tuning rank matrix is based on a decision coefficient R2Determining, preferably, in the step (3), that the model parameter tuning rank matrix is:
Params＝{'max_features','splitter','min_samples_leaf','max_depth','min_weight_fraction_leaf','criterion','min_impurity_split','ccp_alpha','min_impurity_decrease','min_samples_split','max_leaf_nodes'}。
in the step (4), the tuning values of the model parameters are sequentially determined according to the model parameter tuning rank matrix, and more preferably, in the step (4), the tuning values of the model parameters are:
'max_features':'log2','splitter':'random','min_samples_leaf':1,'max_depth':None,'min_weight_fraction_leaf':0.0,'criterion':'friedman_mse','min_impurity_split':None,'ccp_alpha':0.0,'min_impurity_decrease':0.000871292997015407,'min_samples_split':2,'max_leaf_nodes':None。
in order to make the precision of the remote sensing inversion model of the phycocyanin concentration in the inland lake water body higher, a plurality of sampling points of the inland lake water body can be selected, and the remote sensing reflectance of the water body of a plurality of characteristic bands of the plurality of sampling points and the phycocyanin concentration of the plurality of sampling points are measured, preferably, in the step (1), the step of measuring the remote sensing reflectance of the water body of the inland lake water body is specifically to measure the remote sensing reflectance of the water body of m sampling points of the inland lake water body, the m sampling points are uniformly distributed on the inland lake water body, and the remote sensing reflectance of the water body is the remote sensing reflectance of the water body of n characteristic bands; in the step (2), the phycocyanin concentration C of the inland lake water body is measuredPCSpecifically, the phycocyanin concentration C of the m sampling points is measuredPC。
In the step (1), m and n are positive integers, which can be determined according to requirements, and more preferably, in the step (1), m is 60, the n characteristic bands are 751 characteristic bands, and the 751 characteristic bands are from 350nm to 1100 nm.
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention.
Examples
The remote sensing inversion method for the phycocyanin concentration of the water body of the inland lake is based on actually measured hyperspectral data, the optical characteristics and the component information of the water body collected by the Hongze lake are adopted, and the total number of 80 sampling points are uniformly distributed and completely cover the whole lake area of the Hongze lake. The Hongze lake is one of five freshwater lakes in China, has the average water depth of 5.62m, has great influence on the resuspension of sediments at the bottom of the lake by the wind and waves on the surface of the lake, belongs to a high-turbidity water body, and is a typical inland class II water body. The 80 sampling point data are divided into two parts by a random method, wherein the data of 60 sampling points are used for model construction, and the data of 20 sampling points are used for model inspection. The process of the remote sensing inversion method of the phycocyanin concentration in the inland lake water body is shown in figure 1, and comprises the following steps:
1. and (4) measuring the water body spectrum.
And measuring the water body spectrum by using the portable surface feature spectrometer and the standard plate to obtain the remote sensing reflectance Rrs. Using the above water surface measurement method, the observation geometry is shown in fig. 2, using the current typical observation geometry: Φ v is 135 ° and v is 40 °. When measuring the spectrum of a body of water, a single integral value of the spectrum is recorded, without taking an average value, and the spectral values are recorded for a number of tests, for example 15, over a wave period (measurement time 2-5 minutes). The spectrometer uses a lens with a 25 degree field angle and a standard white board with a reflectivity of 10%. Respectively measuring water body spectral data of 350nm wave band to 1100nm wave band, sky diffuse scattering light and reflected light of a standard plate, and calculating a water body remote sensing reflectance Rrs:
Rrs＝(Lsw-rsky*Lsky)/(Lp*π/ρp)
wherein Rrs is the water body remote sensing reflectance, LswWater spectral data measured for a spectrometer, rskyThe reflectivity of the surface of the gas and water is 0.0245, LskyIs diffusely scattered light from the sky, LpIs the reflected light of the standard plate, ppThe reflectance of the standard plate used in this example was 10%.
2. And (4) measuring the concentration of the phycocyanin in the water body.
When the water body spectral data are measured, synchronously collecting a water sample, filtering the water sample by Whatman GF/C filter paper, folding the filtered filter paper in half, putting the folded filter paper into aluminum foil paper for wrapping, putting the wrapped filter paper into a sealing bag, and freezing and storing the wrapped filter paper in a refrigerator at the temperature of 20 ℃ below zero. After the sample is brought back to the laboratory, the sample is put into a refrigerator to be frozen for 8 hours; taking out the frozen sample for grinding (adding phosphate buffer solution in the grinding process), and putting the ground sample into a centrifuge tube; putting the ground sample into a refrigerator for refrigeration for 30 minutes; weighting the refrigerated samples in pairs, and placing the refrigerated samples into a centrifuge for centrifugation; taking supernatant and fixing the volume to a certain volume; and the volumized sample was measured with 722 visible spectrophotometer. Recording the absorbance at 615nm, 652nm and 750 nm; the phycocyanin concentration was calculated using the following formula:
Figure BDA0002617124980000111
wherein, CPCConcentration of phycocyanin (. mu.g/L), A650、A652、A750The absorbance values of the supernatant at 615nm, 652nm and 750nm, V1Is the volume of supernatant fluid with constant volume V2Volume of water sample, V1、V2In liters and L is the cell optical path length (cm).
3. Model construction
The model construction is implemented by using an extreme random tree model of Python language, please refer to fig. 3, and the model construction mainly includes the following steps:
3.1 data verification
And checking the acquired water body remote sensing reflectance data, and rejecting abnormal whole spectral curve data. The abnormal spectrum in the invention means that adjacent spectrum changes by more than 100%, and spectrum values including null values and negative values are included.
3.2 preprocessing of data
And preprocessing the verified water body remote sensing reflectance data and the phycocyanin concentration data, including removing paired water body remote sensing reflectance data and phycocyanin concentration data containing missing values and null values. In order to reduce the memory occupation and improve the data processing efficiency, the phycocyanin concentration data are processed, and the phycocyanin concentration value is converted into a value between 0 and 10 by dividing by 10, so that a 10-time reduction value of the phycocyanin concentration is obtained.
3.3 partitioning of data sets
In order to ensure reasonable evaluation of model training and inversion results, a random method is used for dividing the whole data set into two parts, wherein 80% of data is used for model training, and 20% of data is used for effect evaluation after training.
3.4 partitioning of training data sets
In order to ensure the effect of model training, a random method is used, and a training data set is divided into 5 parts to train the model when the model is trained and iterated every time.
3.5 construction of model parameter tuning rank matrix
In the invention, the tuning of the model parameters in the model training process is very important, and in order to ensure that the best model tuning is obtained as much as possible, a trial-and-error method is used for tuning the model parameters. The present invention uses the coefficient of determination R2(R2The closer to 1, the better) as the test parameter, a parameter rank matrix for evaluating the weight of the model parameter is constructed. According to the training data set, firstly, the default value of the model parameter is used for calculation to obtain an inversion value, and according to the inversion value and a 10-time reduction value of the phycocyanin concentration, a decision coefficient R is calculated2Then changing the value of the model parameter, R2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix for subsequent calculation.
According to the water body remote sensing reflectance data in the training data set and the corresponding 10-time reduction value data of the phycocyanin concentration, the model parameter tuning order matrix obtained by calculation is as follows:
Params＝{'max_features','splitter','min_samples_leaf','max_depth','min_weight_fraction_leaf','criterion','min_impurity_split','ccp_alpha','min_impurity_decrease','min_samples_split','max_leaf_nodes'}。
where a max _ leaf _ nodes change does not cause a change in the accuracy of the model.
3.6 model construction
Adjusting an order matrix according to the obtained model parameters, modeling data including actually measured water body remote sensing reflectance data and corresponding 10-time reduced value data of actually measured phycocyanin concentration, taking the actually measured water body remote sensing reflectance data as input data and the 10-time reduced value data of the actually measured phycocyanin concentration as output results, training an extreme random tree model, sequentially adjusting model parameters according to the model parameter adjustment order matrix to obtain complete parameters and values of the model, and the method comprises the following steps:
'max_features':'log2','splitter':'random','min_samples_leaf':1,'max_depth':None,'min_weight_fraction_leaf':0.0,'criterion':'friedman_mse','min_impurity_split':None,'ccp_alpha':0.0,'min_impurity_decrease':0.000871292997015407,'min_samples_split':2,'max_leaf_nodes':None。
after the model training is finished, the save method is used for saving the model, and if the model is required to be used, the load method is operated for loading and using.
For a data set containing m samples and n characteristic wave bands, the model construction calculation process of the extreme random tree model of the Python language is as follows:
(1) constructing a plurality of decision trees by using all the training samples;
(2) when the decision tree is constructed, constructing the decision tree by using the characteristics with the best scores according to the evaluation scores;
(3) when the decision tree is constructed, uniformly and randomly generating bifurcation values of the decision tree in a characteristic experience range, and selecting the division point with the highest score as a node from all random division points without limiting the depth of the decision tree;
(4) after training is complete, prediction of the unknown sample x can be achieved by averaging the predictions of all the individual regression trees on x:
Figure BDA0002617124980000131
wherein the content of the first and second substances,
Figure BDA0002617124980000132
for the final predicted value, B is the number of the constructed decision tree, fbTo construct a single decision tree, x is the sample data.
3.7 model test
Using 20 sampling points except the constructed model to input the hyperspectral data into the model, using the adjusted model parameters to calculate to obtain a predicted value, and analyzing the predicted valueThe relationship between the measured value and the actual measured value (10-fold reduction in the phycocyanin concentration) was found in FIG. 4, and the R of the model was found20.8848. And using a default parameter of the model, its R2Is only 0.
In the embodiment, Matlab software (version: R2020a 9.8.0.1380330) and Python (version:3.7.0) developed by MathWorks company in the United states are used for random division of training data and test data and construction, training and test of models, and the extreme random tree model of Python is called through the Matlab software.
Therefore, the invention provides a new remote sensing inversion model of the phycocyanin concentration in the inland lake water body based on the actually measured hyperspectral remote sensing data, and based on the actually measured water body spectral data and the phycocyanin concentration data collected on the spot, the 'drifting' phenomenon caused by the influence of factors such as the complex optical characteristics of the inland turbid water body, the complex components such as suspended matters, colored soluble organic matters and the like contained in the water body and the like on the characteristic wave band of the phycocyanin concentration can be overcome, and the accuracy of the inversion model of the phycocyanin concentration in the inland lake can be improved by fully utilizing the phycocyanin concentration information contained in the wave band data.
Compared with the prior art, the invention has the following advantages:
(1) according to the method, based on the measured full-waveband data of the hyperspectral remote sensing data of the water body, the complete phycocyanin concentration information contained in the hyperspectral data is fully utilized, and meanwhile, the influence on the characteristic waveband of the phycocyanin concentration, such as the characteristic waveband drifting phenomenon, caused by the complex optical characteristics and various components of the inland lake water body is avoided;
(2) the method fully considers various effects between the phycocyanin concentration and the characteristic wave band thereof, considers various relations between the phycocyanin concentration and the characteristic wave band, including common linear relations and nonlinear relations, uses an extreme random tree model (ET), avoids the limitation of using the linear model in the previous research work, and improves the accuracy of phycocyanin concentration inversion;
(3) the phycocyanin inversion method provided by the invention is simple and convenient to calculate, and is suitable for remote sensing quantitative inversion of phycocyanin concentrations in inland lakes in different regions and in different seasons.
(4) The independence of model training and model inspection is fully considered, the training data set and the inspection data set are divided by a random segmentation method, the training data set is only used for model training, and the inspection data set is only used for model inspection, so that the reasonability of model effect inspection is ensured.
(5) Since the parameter tuning of the model is very important to the calculation accuracy of the model, the invention constructs a model parameter rank matrix to determine the coefficient R2In order to evaluate the parameters, a trial and error method is used for model parameter tuning, and on the basis of ensuring the parameter tuning effect, the speed of model training and parameter tuning is greatly improved.
In conclusion, the remote sensing inversion model for the phycocyanin concentration in the inland lake water body can reduce the error of a calculation result and improve the accuracy of the inversion model for the phycocyanin concentration in the water body, and is ingenious in design, simple and convenient to calculate, easy to realize, low in cost and suitable for large-scale popularization and application.
It will thus be seen that the objects of the invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the embodiments, and the embodiments may be modified without departing from the principles. Therefore, this invention includes all modifications encompassed within the spirit and scope of the claims.

Claims (12)

1. The remote sensing inversion model of the phycocyanin concentration in the water body of the inland lake is characterized in that the remote sensing inversion model of the phycocyanin concentration in the water body of the inland lake is an extreme random tree model of Python language, and model parameters of the extreme random tree model are as follows: 'max _ features': log2',' split ': range', 'min _ samples _ leaf':1, 'max _ depth': None, 'min _ weight _ fraction _ leaf':0.0, 'criterion': fragment _ mse ',' min _ input _ split ': None,' ccp _ alpha ':0.0,' min _ input _ fraction ':0.000871292997015407 min _ samples _ split':2, 'max _ leaf _ node'.
2. The remote sensing inversion model of phycocyanin concentration in water body of inland lake of claim 1, wherein the extreme random tree model is trained by using a data set of water body of inland lake, the data set comprises water body remote sensing reflectance of m sampling points of the water body of inland lake and 10 times reduction value of phycocyanin concentration, the m sampling points are uniformly distributed on the water body of inland lake, and the water body remote sensing reflectance is water body remote sensing reflectance of n characteristic bands.
3. The remote sensing inversion model of phycocyanin concentration in water body of inland lake as claimed in claim 2, wherein said m is 60, said n characteristic bands are 751 characteristic bands, and said 751 characteristic bands are from 350nm band to 1100nm band.
4. A remote sensing inversion method for the phycocyanin concentration in a water body of an inland lake is characterized by comprising the following steps:
(1) measuring the remote sensing reflectance of the inland lake water body;
(2) measuring the phycocyanin concentration C of the inland lake water bodyPCThe phycocyanin concentration C is determinedPCDivision by 10 gives a 10-fold reduction in the phycocyanin concentration:
(3) calculating by using the water body remote sensing reflectance as input data and adopting an extreme random tree model of Python language to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and a 10-fold reduction value of the phycocyanin concentration2Changing the value of a model parameter, R, of the extreme stochastic tree model2The larger the change of the model parameter is, the greater the importance of the model parameter is, the model parameter is arranged from large to small according to the importance to construct a model parameter tuning rank matrix;
(4) training the extreme random tree model by taking the water body remote sensing reflectance as the input data and taking the 10-fold reduction value of the phycocyanin concentration as an output result, and sequentially tuning the model parameters according to the model parameter tuning order matrix to obtain tuning values of the model parameters;
(5) and training the extreme random tree model by taking the water body remote sensing reflectance as the input data and taking a 10-time reduction value of the phycocyanin concentration as the output result, adopting the tuning value of the model parameter, obtaining an inland lake water body phycocyanin concentration remote sensing inversion model after the training of the extreme random tree model is finished, storing the inland lake water body phycocyanin concentration remote sensing inversion model by using a save method, and loading the inland lake water body phycocyanin concentration remote sensing inversion model for use by using a load method if the inland lake water body phycocyanin concentration remote sensing inversion model is required to be used.
5. The remote sensing inversion method for the phycocyanin concentration in the water body of the inland lake of claim 4, wherein in the step (1), the step of measuring the remote sensing reflectance of the water body of the inland lake specifically comprises:
measuring the water body spectrum data L of the inland lake water bodyswAnd the sky diffuse scattered light L of the inland lake water bodyskyAnd reflected light L of the standard platepAnd calculating the water body remote sensing reflectance:
Rrs＝(Lsw-rsky*Lsky)/(Lp*π/ρp)，
wherein Rrs is the remote sensing reflectance of the water body; r isskyThe air-water surface reflectivity of the inland lake water body is 0.022-0.028, and r is measured on a calm water surfaceskyThe value of r is 0.022, and r is obtained at the wind speed of 5m/sskyThe value is 0.025, and the value is 0.026-0.028 at the wind speed of 10m/s, rhopIs the reflectance of the standard plate.
6. The remote sensing inversion method for phycocyanin concentration in water bodies of inland lakes according to claim 5, wherein in the step (1), the measurement is performed by using a portable geophysical spectrometer, the portable geophysical spectrometer uses a lens with an angle of view of 25 degrees, the measurement is performed by using a measurement method above the water surface, and the observation geometry of the measurement method above the water surface is set as follows: phi (v135 °, v 40 °, the standard board is a standard white board with a reflectivity of 10%.
7. The remote sensing inversion method for phycocyanin concentration in water body of inland lake of claim 4, wherein in the step (2), the phycocyanin concentration C in water body of inland lake is measuredPCThe method specifically comprises the following steps:
collecting a water sample of the inland lake water body, filtering the water sample by using filter paper to obtain a filter paper sample, freezing and grinding the filter paper sample, adding a phycocyanin extract in the grinding process, refrigerating and centrifuging, taking supernatant to a constant volume, and measuring the absorbance A of the supernatant at 615nm, 652nm and 750nm615、A652And A750Calculating the phycocyanin concentration C of the inland lake water body by using the following formulaPC：
Figure FDA0002617124970000021
Wherein, V1Is the constant volume of the supernatant, V2Is the volume of the water sample, and L is the optical path of the cuvette.
8. The remote sensing inversion method for phycocyanin concentration in water body of inland lake of claim 7, wherein in the step (2), the filter paper is Whatman GF/F filter paper, the freezing is performed in a refrigerator at-20 ℃, the freezing time is 8 hours, the phycocyanin extract is phosphate buffer, the cold storage is performed in the refrigerator, and the cold storage time is 30 minutes.
9. The remote sensing inversion method for phycocyanin concentration in water body of inland lake according to claim 4, wherein in the step (3), the model parameter tuning rank matrix is as follows:
Params＝{'max_features','splitter','min_samples_leaf','max_depth','min_weight_fraction_leaf','criterion','min_impurity_split','ccp_alpha','min_impurity_decrease','min_samples_split','max_leaf_nodes'}。
10. the remote sensing inversion method for phycocyanin concentration in water body of inland lake of claim 9, wherein in the step (4), the tuning values of the model parameters are as follows:
'max_features':'log2','splitter':'random','min_samples_leaf':1,'max_depth':None,'min_weight_fraction_leaf':0.0,'criterion':'friedman_mse','min_impurity_split':None,'ccp_alpha':0.0,'min_impurity_decrease':0.000871292997015407,'min_samples_split':2,'max_leaf_nodes':None。
11. the remote sensing inversion method of phycocyanin concentration in water body of inland lake according to claim 4, wherein in the step (1), the step of measuring the remote sensing reflectance of water body of inland lake water body is specifically to measure the remote sensing reflectance of water body of m sampling points of the inland lake water body, wherein the m sampling points are uniformly distributed on the water body of the inland lake, and the remote sensing reflectance of water body is the remote sensing reflectance of water body of n characteristic bands; in the step (2), the phycocyanin concentration C of the inland lake water body is measuredPCSpecifically, the phycocyanin concentration C of the m sampling points is measuredPC。
12. The remote sensing inversion method for phycocyanin concentration in water body of inland lake as claimed in claim 11, wherein in said step (1), said m is 60, said n characteristic bands are 751 characteristic bands, and said 751 characteristic bands are from 350nm band to 1100nm band.
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Citations (2)

* Cited by examiner, † Cited by third party
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CN106468658A (en) * 2016-09-05 2017-03-01 中国科学院南京地理与湖泊研究所 A kind of MODIS remote-sensing monitoring method of eutrophic lake phycocyanobilin
CN110598251A (en) * 2019-08-05 2019-12-20 中国科学院南京地理与湖泊研究所 Lake chlorophyll a concentration inversion method based on Landsat-8 data and machine learning

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106468658A (en) * 2016-09-05 2017-03-01 中国科学院南京地理与湖泊研究所 A kind of MODIS remote-sensing monitoring method of eutrophic lake phycocyanobilin
CN110598251A (en) * 2019-08-05 2019-12-20 中国科学院南京地理与湖泊研究所 Lake chlorophyll a concentration inversion method based on Landsat-8 data and machine learning

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