CN117434011A - Inland lake water chlorophyll a concentration remote sensing inversion model and method based on linear SVR algorithm - Google Patents
Inland lake water chlorophyll a concentration remote sensing inversion model and method based on linear SVR algorithm Download PDFInfo
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Abstract
The invention provides an inland lake water chlorophyll a concentration remote sensing inversion model based on a linear SVR algorithm, which is a linear SVR model of Python language and further provides model parameters of the linear SVR model. The remote sensing inversion method for the chlorophyll a concentration of the inland lake water body based on the linear SVR algorithm is also provided. The inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm can reduce calculation result errors, improves inversion model precision of the water chlorophyll a concentration, is ingenious in design, simple and convenient to calculate, easy to realize and low in cost, and is suitable for large-scale popularization and application.
Description
Technical Field
The invention relates to the technical field of inland lake water environment monitoring, in particular to the technical field of inland lake water chlorophyll a concentration measurement, and particularly relates to an inland lake water chlorophyll a concentration remote sensing inversion model and method based on a linear SVR algorithm.
Background
Chlorophyll a is an important pigment for photosynthesis of phytoplankton (including algae and cyanobacteria). Chlorophyll a concentration is one of important parameters for estimating phytoplankton biomass and primary productivity of the lake ecosystem, and is also an important index for reflecting water quality of the lake water environment and eutrophication degree of the water body. The inland water body has complex optical characteristics, and the rapid quantitative estimation of the chlorophyll a concentration of the inland turbid water body by using hyperspectral and multispectral remote sensing technologies becomes a difficult point.
The current method for quantitatively inverting chlorophyll a by remote sensing mainly comprises an analysis method, a semi-empirical method and an empirical method, extracts chlorophyll a concentration information contained in various remote sensing data, and quantitatively calculates the chlorophyll a concentration. According to the analysis method, chlorophyll a concentration is quantitatively inverted through physical models such as biological optics, radiation transmission and the like, parameters such as apparent optical characteristics and inherent optical characteristics of a water body are required to be determined, the components and optical characteristics of an inland lake water body are complex, accurate measurement of various parameters is difficult, and therefore inversion result accuracy is generally low; the semi-empirical method is used for inverting chlorophyll a concentration by determining part of operation parameters by a statistical analysis method through reference to a physical model of the analysis method, so that the accuracy is good, and the application is wider, but the constructed inversion model has a certain space-time limitation; the empirical method quantitatively calculates the chlorophyll a concentration by establishing a statistical relationship between remote sensing data and the ground measured chlorophyll a concentration.
The former people have made a great deal of research on the reverse evolution of the chlorophyll a concentration of the water body, and have made active progress. In a plurality of research results, the wave band ratio model based on two wave bands is considered to have higher precision than other models, such as a single wave band model and a first-order differential model (Duan Hongtao, zhang Bai, liu Dianwei, and the like). In most studies, the two-band model was constructed using the ratio of the two bands to construct an exponential equation for chlorophyll a concentration (Jiao Gongbo. Taihu lake water chlorophyll a remote sensing optimal band selection and model research [ D ]. Nanjing university, 2006) based on the measured spectrum of the water surface. In actual situations, because the components of the inland lake water body are complex, characteristic wave bands of the components are mutually overlapped, so that the characteristic wave bands of chlorophyll a are difficult to determine. Therefore, when the chlorophyll a concentration is inverted by using a linear model (or a generalized linear model) constructed by the characteristic wave band, the calculation result diverges, the error is larger, and further improvement and perfection are required. In addition, for inland lake water with complex components, the high-precision spectrometer can accurately measure the remote sensing reflectivity of the water, and how to fully utilize the information contained in the remote sensing reflectivity of the water and accurately invert the chlorophyll a concentration in the water becomes the technical problem to be solved in the current remote sensing monitoring application and research of the chlorophyll a concentration of the water.
Therefore, it is desirable to provide an inland lake water chlorophyll a concentration remote sensing inversion model, which can reduce calculation result errors and improve the accuracy of the inversion model of the water chlorophyll a concentration.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide an inland lake water chlorophyll a concentration remote sensing inversion model based on a linear SVR algorithm, which can reduce calculation result errors, improve the inversion model precision of the water chlorophyll a concentration and is suitable for large-scale popularization and application.
The invention further aims to provide an inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm, which is ingenious in design, simple and convenient to calculate, easy to realize, low in cost and suitable for large-scale popularization and application.
The invention further aims to provide an inland lake water chlorophyll a concentration remote sensing inversion method based on a linear SVR algorithm, which can reduce calculation result errors, improve inversion accuracy of the water chlorophyll a concentration and is suitable for large-scale popularization and application.
The invention further aims to provide an inland lake water chlorophyll a concentration remote sensing inversion method based on the linear SVR algorithm, which is ingenious in design, simple and convenient to operate, low in cost and suitable for large-scale popularization and application.
In order to achieve the above object, in a first aspect of the present invention, an inland lake water chlorophyll a concentration remote sensing inversion model based on a linear svr algorithm is provided, which is characterized in that the inland lake water chlorophyll a concentration remote sensing inversion model based on the linear svr algorithm is a linear svr model in Python language, and model parameters of the linear svr model are as follows: 'C' 29.454540374164942, 'interference_scaling' 0.5446070238291971, 'loss' is 'squared_epsilon_insittive', 'max_iter' 305716, 'tol' 0.19594504249237796, 'epsilon' 0.43564649850770354, 'fit_interference' True.
Preferably, the linear SVR model is trained by adopting a data set of inland lake water, wherein the data set comprises water remote sensing reflectance and chlorophyll a concentration of m sample points of the inland lake water, the m sample points are uniformly distributed on the inland lake water, and the water remote sensing reflectance is that of n characteristic wave bands.
More preferably, m is 32, n characteristic bands are 751 characteristic bands, and 751 characteristic bands are from 350nm band to 1100nm band.
In a second aspect of the invention, a remote sensing inversion method for chlorophyll a concentration in inland lake water based on a linear SVR algorithm is provided, which is characterized by comprising the following steps:
(1) Measuring the water body remote sensing reflectance of the inland lake water body;
(2) Measuring the chlorophyll a concentration C of the inland lake water body Chla ：
(3) Calculating by using the remote sensing reflectance of the water body as input data and adopting a linear SVR model of Python language to obtain an inversion value, and calculating a determination coefficient R according to the inversion value and the chlorophyll a concentration 2 Altering the LinThe value of the model parameters of the earSVR model is R 2 The larger the change of the model parameters is, the larger the importance of the model parameters is, and the model parameters are arranged from big to small according to the importance to construct a model parameter tuning rank matrix;
(4) Training the LinearSVR model by taking the remote sensing reflectance of the water body as the input data and the chlorophyll a concentration as an output result, and sequentially tuning the model parameters according to the model parameter tuning rank order matrix to obtain tuning values of the model parameters;
(5) And training the LinearSVR model by taking the water body remote sensing reflectance as the input data and the chlorophyll a concentration as the output result and adopting the optimal value of the model parameter, obtaining an inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm after the training of the LinearSVR model is finished, storing the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm by using a save method, and loading the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm by using a load method if the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm is needed.
Preferably, in the step (1), the step of measuring the remote sensing reflectance of the inland lake water body specifically includes:
measuring water spectrum data L of inland lake water sw Sky diffuse scattered light L of inland lake water body sky Reflected light L of standard plate p Calculating the remote sensing reflectance of the water body:
Rrs＝(L sw -r sky *L sky )/(L p *π/ρ p )，
wherein Rrs is the water remote sensing reflectance; r is (r) sky The reflectivity of the air surface of the inland lake water body is 0.022 to 0.028, and r is the value when the inland lake water body is on calm water surface sky Take the value of 0.022, r at the wind speed of 5m/s sky The value is 0.025, and the value is 0.026-0.028 at the wind speed of 10m/s, ρ p Is the reflectivity of the standard plate.
More preferably, in the step (1), the measurement is performed by using a portable ground object spectrometer, the portable ground object spectrometer uses a lens with a field angle of 25 °, 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 (phi) v =135°, v=40°, the standard plate being a standard white plate with a reflectivity of 10%.
Preferably, in the step (2), the chlorophyll a concentration C of the inland lake water body is measured Chla The method specifically comprises the following steps:
collecting a water sample of the inland lake water body, filtering the water sample by adopting filter paper to obtain a filter paper sample, extracting the filter paper sample by adopting a chlorophyll a extraction solution, centrifuging, taking a supernatant, and measuring the absorbance E of the supernatant at 665nm and 750nm 665 And E is 750 Acid treatment is added, and then the absorbance A of the supernatant at 665nm and 750nm is measured 665 And A 750 Calculating chlorophyll a concentration C of the inland lake water body by using the following formula Chla ：
Wherein V is 1 Is the volume of the chlorophyll-a extraction solution, V 2 Is the volume of the water sample.
More preferably, in the step (2), the filter paper is Whatman GF/F filter paper, the chlorophyll-a extraction solution is 90% ethanol, the extraction is soaking and leaching for 8 hours at 4 ℃ in dark, the acid is 1mol/L hydrochloric acid solution, the adding amount of the hydrochloric acid solution is 1-2 drops, and the acid adding treatment time is 1 minute.
Preferably, in the step (3), the model parameter tuning rank order matrix is:
Params＝{'C','intercept_scaling','loss','max_iter','tol','epsilon','fit_intercept','dual'}。
more preferably, in the step (4), the tuning value of the model parameter is:
'C':29.454540374164942,'intercept_scaling':0.5446070238291971,'loss':'squared_epsilon_insensitive','max_iter':305716,'tol':0.19594504249237796,'epsilon':0.43564649850770354,'fit_intercept':True,'dual':True。
preferably, in the step (1), the step of measuring the water body remote sensing reflectance of the inland lake water body specifically measures the water body remote sensing reflectance of m sample points of the inland lake water body, the m sample 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 chlorophyll a concentration C of the inland lake water body is measured Chla Specifically, the chlorophyll a concentration C of the m spots is measured Chla 。
More preferably, in the step (1), m is 32, the n characteristic bands are 751 characteristic bands, and the 751 characteristic bands are from 350nm band to 1100nm band.
The beneficial effects of the invention are mainly as follows:
1. the remote sensing inversion model of the chlorophyll a concentration of the inland lake water body based on the linear SVR algorithm is a linear SVR model of Python language, and model parameters of the linear SVR model are as follows: the model is checked for ' C ' 29.454540374164942, ' interference_scaling ' 0.5446070238291971, ' loss ' square_epsilon_insittive ', ' max_iter ' 305716, ' tol ' 0.19594504249237796, ' epsilon ' 0.43564649850770354, ' fit_interference ' True, ' real ' True, R 2 The method is above 0.85, so that the calculation result error can be reduced, the accuracy of an inversion model of the chlorophyll a concentration of the water body is improved, and the method is suitable for large-scale popularization and application.
2. The remote sensing inversion model of the chlorophyll a concentration of the inland lake water body based on the linear SVR algorithm is a linear SVR model of Python language, and model parameters of the linear SVR model are as follows: 'C' 29.454540374164942, 'interference_scaling' 0.5446070238291971, 'loss' of 'squared_epsilon_insittive', 'max_iter' 305716, 'tol' 0.19594504249237796, 'epsilon' of 0.43564649850770354, 'fit_interval' True, 'real' True, the model is checked, R 2 Above 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 of the chlorophyll a concentration of the inland lake water body based on the linear SVR algorithm comprises the following steps: measuring the water body remote sensing reflectance of the inland lake water body; measurement of chlorophyll a concentration C in inland lake Water Chla : the remote sensing reflectance of water body is used as input data, and the linear SVR model of Python language is adopted for calculation so as to determine the coefficient R 2 Constructing a model parameter tuning rank matrix; training a linear SVR model by taking the remote sensing reflectance of the water body as input data and the chlorophyll a concentration as output result, and sequentially optimizing the model parameters according to the model parameter optimizing rank order matrix to obtain an optimizing value of the model parameters; taking the water body remote sensing reflectance as input data, taking the chlorophyll a concentration as output result, training a LinearSVR model by adopting the tuning value of model parameters to obtain an inland lake water body chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm, checking the model, and R 2 The method is more than 0.85, so that the calculation result error can be reduced, the inversion accuracy of the chlorophyll a concentration of the water body is improved, and the method is suitable for large-scale popularization and application.
4. The remote sensing inversion method of the chlorophyll a concentration of the inland lake water body based on the linear SVR algorithm comprises the following steps: measuring the water body remote sensing reflectance of the inland lake water body; measurement of chlorophyll a concentration C in inland lake Water Chla : the remote sensing reflectance of water body is used as input data, and the linear SVR model of Python language is adopted for calculation so as to determine the coefficient R 2 Constructing a model parameter tuning rank matrix; training a linear SVR model by taking the remote sensing reflectance of the water body as input data and the chlorophyll a concentration as output result, and sequentially optimizing the model parameters according to the model parameter optimizing rank order matrix to obtain an optimizing value of the model parameters; the remote sensing reflectance of the water body is used as input data, the chlorophyll a concentration is used as output result, the linear SVR model is trained by adopting the tuning value of model parameters, and the inland lake water body chlorophyll a concentration remote control based on the linear SVR algorithm is obtainedSense inversion model, test the model, R 2 Above 0.85, therefore, the device has smart design, simple and convenient operation and low cost, and 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 learned by the practice of the invention as set forth hereinafter, the apparatus and the combination thereof as set forth hereinafter.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the remote sensing inversion method of chlorophyll a concentration in inland lake water based on the LinearSVR algorithm.
Fig. 2 is a schematic view of the above-water observation geometry of the spectrometer of the embodiment shown in fig. 1.
FIG. 3 is a schematic diagram of a model building flow for the embodiment shown in FIG. 1.
FIG. 4 is a schematic diagram of the model test results for the embodiment shown in FIG. 1, wherein the units of predicted and measured values are μg/L.
Detailed Description
Aiming at the defects of the existing chlorophyll a concentration inversion model, the inventor provides an inland lake water chlorophyll a concentration remote sensing inversion model based on a linear SVR (Linear Support Vector Machine Regression ) algorithm, wherein the inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm is a linear SVR model of Python language, and model parameters of the linear SVR model are as follows: 'C' 29.454540374164942, 'interference_scaling' 0.5446070238291971, 'loss' is 'squared_epsilon_insittive', 'max_iter' 305716, 'tol' 0.19594504249237796, 'epsilon' 0.43564649850770354, 'fit_interference' True.
The linear SVR model can be trained by adopting any suitable data set, preferably, the linear SVR model is trained by adopting a data set of inland lake water, the data set comprises water remote sensing reflectance and chlorophyll a concentration of m sample points of the inland lake water, the m sample points are uniformly distributed on the inland lake water, and the water remote sensing reflectance is water remote sensing reflectance of n characteristic wave bands.
The m and the n are positive integers, and can be determined according to the need, more preferably, the m is 32, the n characteristic wave bands are 751 characteristic wave bands, and the 751 characteristic wave bands are from 350nm wave bands to 1100nm wave bands.
The invention also provides an inland lake water chlorophyll a concentration remote sensing inversion method based on the linear SVR algorithm, which comprises the following steps:
(1) Measuring the water body remote sensing reflectance of the inland lake water body;
(2) Measuring the chlorophyll a concentration C of the inland lake water body Chla ：
(3) Calculating by using the remote sensing reflectance of the water body as input data and adopting a linear SVR model of Python language to obtain an inversion value, and calculating a determination coefficient R according to the inversion value and the chlorophyll a concentration 2 Changing the value of the model parameter of the LinearSVR model, R 2 The larger the change of the model parameters is, the larger the importance of the model parameters is, and the model parameters are arranged from big to small according to the importance to construct a model parameter tuning rank matrix;
(4) Training the LinearSVR model by taking the remote sensing reflectance of the water body as the input data and the chlorophyll a concentration as an output result, and sequentially tuning the model parameters according to the model parameter tuning rank order matrix to obtain tuning values of the model parameters;
(5) And training the LinearSVR model by taking the water body remote sensing reflectance as the input data and the chlorophyll a concentration as the output result and adopting the optimal value of the model parameter, obtaining an inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm after the training of the LinearSVR model is finished, storing the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm by using a save method, and loading the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm by using a load method if the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm is needed.
In the step (1), the step of measuring the remote sensing 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 sensing reflectance of the inland lake water body specifically includes:
measuring water spectrum data L of inland lake water sw Sky diffuse scattered light L of inland lake water body sky Reflected light L of standard plate p Calculating the remote sensing reflectance of the water body:
Rrs＝(L sw -r sky *L sky )/(L p *π/ρ p )，
wherein Rrs is the water remote sensing reflectance; r is (r) sky The reflectivity of the air surface of the inland lake water body is 0.022 to 0.028, and r is the value when the inland lake water body is on calm water surface sky Take the value of 0.022, r at the wind speed of 5m/s sky The value is 0.025, and the value is 0.026-0.028 at the wind speed of 10m/s, ρ p Is the reflectivity of the standard plate.
In the step (1), the measurement may be performed by using any suitable spectrometer, the measurement may be performed by using any suitable standard board, and more preferably, in the step (1), the measurement is performed by using a portable ground object spectrometer, the portable ground object spectrometer uses a lens with an angle of view of 25 °, 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 (phi) v =135°, v=40°, the standard plate being a standard white plate with a reflectivity of 10%.
In the step (2), the chlorophyll a concentration C of the inland lake water body is measured Chla The step (2) may specifically comprise any suitable method, preferably, the step of measuring the chlorophyll a concentration C of the inland lake water body Chla The method specifically comprises the following steps:
collecting a water sample of the inland lake water body, filtering the water sample by adopting filter paper to obtain a filter paper sample, extracting the filter paper sample by adopting a chlorophyll a extraction solution, centrifuging, taking a supernatant, and measuring the absorbance E of the supernatant at 665nm and 750nm 665 And E is 750 Acid treatment is added, and then the absorbance A of the supernatant at 665nm and 750nm is measured 665 And A 750 Calculating chlorophyll a concentration C of the inland lake water body by using the following formula Chla ：
Wherein V is 1 Is the volume of the chlorophyll-a extraction solution, V 2 Is the volume of the water sample.
In the step (2), the filter paper may be any suitable filter paper, the chlorophyll-a extraction solution may be any suitable chlorophyll-a extraction solution, the acid may be any suitable acid, more preferably, in the step (2), the filter paper is Whatman GF/F filter paper, the chlorophyll-a extraction solution is 90% ethanol, the extraction is soaking and leaching for 8 hours at 4 ℃ in the dark, the acid is 1mol/L hydrochloric acid solution, the addition amount of the hydrochloric acid solution is 1 to 2 drops, and the acid adding treatment time is 1 minute.
In the step (3), the model parameter tuning rank order matrix is based on a decision coefficient R 2 Determining, preferably, in the step (3), the model parameter tuning rank matrix is:
Params＝{'C','intercept_scaling','loss','max_iter','tol','epsilon','fit_intercept','dual'}。
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:
'C':29.454540374164942,'intercept_scaling':0.5446070238291971,'loss':'squared_epsilon_insensitive','max_iter':305716,'tol':0.19594504249237796,'epsilon':0.43564649850770354,'fit_intercept':True,'dual':True。
in order to make the accuracy of the inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm higher, a plurality of sample points of the inland lake water can be selected, and the water remote sensing reflectance of a plurality of characteristic wave bands of the sample points and the chlorophyll a concentration of the sample points are measured, preferably, in the step (1), the step of measuring the water remote sensing reflectance of the inland lake water is specifically to measure the water remote sensing reflectance of m sample points of the inland lake water, and m sample points are uniformly distributed on the inland lake water, and the water remote sensing reflectance is the water remote sensing reflectance of n characteristic wave bands; in the step (2), the chlorophyll a concentration C of the inland lake water body is measured Chla Specifically, the chlorophyll a concentration C of the m spots is measured Chla 。
In the step (1), the m and the n are positive integers, which can be determined according to need, more preferably, in the step (1), the m is 32, the n characteristic wave bands are 751 characteristic wave bands, and the 751 characteristic wave bands are from 350nm wave bands to 1100nm wave bands.
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention.
Examples
The inland lake water chlorophyll a concentration remote sensing inversion method based on the linear SVR algorithm in the embodiment is based on actual measurement hyperspectral data, adopts water optical characteristics and water component information acquired by the Hongze lake, and has 43 sampling points which are uniformly distributed and completely cover the whole lake area of the Hongze lake. The Hongze lake is one of five large freshwater lakes in China, the average water depth is 5.62m, the re-suspension of the lake bottom sediment is greatly influenced by the wind and the wave on the lake surface, belongs to a high-turbidity water body, and is a typical inland second-class water body. The 43 sampling point data are divided into two parts by adopting a random method, wherein the 32 sampling point data are used for model construction, and the 11 sampling point data are used for model inspection. The flow of the inland lake water chlorophyll a concentration remote sensing inversion method based on the linear SVR algorithm is shown in a figure 1, and the method comprises the following steps:
1. and (5) measuring the spectrum of the water body.
And measuring the spectrum of the water body by using a portable ground object spectrometer and a standard plate, and obtaining the remote sensing reflectance Rrs. The observation geometry is shown in fig. 2 by adopting a measurement method above the water surface, and the typical observation geometry is adopted at present: Φv=135°, v=40°. In measuring the spectrum of a body of water, a single integral value of the spectrum is recorded, without taking an average value, and a plurality of spectral values, for example 15 tests, are recorded, the measurement time spanning the wave period (measurement time 2-5 minutes). The spectrometer uses a lens with a field angle of 25 deg. and a standard white board with a reflectivity of 10%. And respectively measuring water spectrum data of 350nm wave bands to 1100nm wave bands, sky diffuse scattered light and reflected light of a standard plate, and calculating a water remote sensing reflectance Rrs:
Rrs＝(L sw -r sky *L sky )/(L p *π/ρ p )
wherein Rrs is water remote sensing reflectance, L sw For the water spectrum data measured by the spectrometer, r sky For the reflectivity of the air-water surface, the value of the present case is 0.0245L sky Is diffuse scattered light of sky L p Is the reflection light of the standard plate, ρ p The reflectance of the standard plate used in this example was 10%.
2. And (5) measuring the chlorophyll a concentration of the water body.
When the spectral data of the water body are measured, the water sample is synchronously collected, whatman GF/F filter paper is used for filtering, the filter paper after the filtering is folded in half, and the filter paper is put into aluminum foil paper for packaging, and is frozen and stored in a refrigerator at the temperature of minus 20 ℃. Extracting filter paper sample with 90% hot ethanol solution at 75deg.C, soaking and extracting at 4deg.C for 8 hr, centrifuging, collecting supernatant, measuring with 722 spectrophotometer, and recording absorbance E at 665nm and 750nm 665 And E is 750 Then 1-2 drops of 1mol/L hydrochloric acid solution are added by using a common rubber head suction pipe, and absorbance A at 665nm and 750nm is recorded after 1 minute 665 And A 750 Chlorophyll a concentration was calculated using the formula:
wherein C is Chla Chlorophyll a concentration (ug/L), E 665 、E 750 Absorbance at 665 and 750nm before acid addition, A 665 、A 750 Absorbance at 665 and 750nm after acid addition, V 1 Is the volume (ml) of 90% hot ethanol solution, V 2 Is the volume (L) of the water sample.
3. Model construction
The model construction adopts a linear SVR model of Python language for construction, please refer to FIG. 3, and the model construction mainly comprises the following steps:
3.1 data verification
And checking the acquired water remote sensing reflectance data, and eliminating abnormal whole spectrum curve data. The abnormal spectrum in the invention refers to a spectrum value with a variation of more than 100% between adjacent spectrums and comprises a null value and a negative value.
3.2 preprocessing of data
And preprocessing the verified water body remote sensing reflectance data and chlorophyll a concentration data, wherein the preprocessing comprises removing the paired water body remote sensing reflectance data and chlorophyll a concentration data containing missing values and null values.
3.3 partitioning of data sets
To ensure reasonable evaluation of the model training and inversion results, a random method was used to divide the entire dataset into two parts, 75% of the data was used for model training and 25% of the data was used for effect evaluation after training. In this embodiment, data of 32 sampling points are used for model training, and data of 11 sampling points are used for model effect evaluation.
3.4 partitioning of training data sets
To ensure the model training effect, a random method is used, and the training data set is divided into 5 parts during each model training iteration, so that the model is trained.
3.5 construction of parameter tuning rank order matrix of model
In the present invention, the moldThe tuning of model parameters in the 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 model parameter tuning. The invention uses the decision coefficient R 2 (R 2 The closer to 1 the better) as a test parameter, a parameter rank matrix for evaluating the model parameter weights is constructed. Firstly, calculating by using a default value of a model parameter according to a training data set to obtain an inversion value, and calculating a decision coefficient R according to the inversion value and chlorophyll a concentration 2 Then changing the value of the model parameters, R 2 The larger the variation of the model parameters is, the larger the importance of the model parameters is, and the model parameters are arranged according to the importance from large to small to construct a model parameter tuning rank order matrix for subsequent calculation.
According to the water remote sensing reflectance data and the corresponding chlorophyll a concentration data in the training data set, the calculated model parameter tuning rank order matrix is as follows:
Params＝{'C','intercept_scaling','loss','max_iter','tol','epsilon','fit_intercept','dual'}。
3.6 model construction
According to the obtained model parameter tuning rank matrix, modeling data comprising actually measured water remote sensing reflectance data and corresponding actually measured chlorophyll a concentration data, taking the actually measured water remote sensing reflectance data as input data, taking the actually measured chlorophyll a concentration data as output result, training a linear SVR model, and sequentially tuning model parameters according to the model parameter tuning rank matrix to obtain the complete parameters and values of the model, wherein the method comprises the following steps:
'C':29.454540374164942,'intercept_scaling':0.5446070238291971,'loss':'squared_epsilon_insensitive','max_iter':305716,'tol':0.19594504249237796,'epsilon':0.43564649850770354,'fit_intercept':True,'dual':True。
after model training is finished, the save method is used for storing the model, and if the model is needed, the load method is operated for loading and using.
3.7 model inspection
Using 10 sampling points outside the constructed model to input hyperspectral data into the model, and using the optimized modelCalculating model parameters to obtain predicted value, analyzing relationship between predicted value and measured value (chlorophyll a concentration), and obtaining R of model as shown in figure 4 2 = 0.8840, using the default parameters of the model, R 2 Only 0.
The random partitioning of the training data and the test data and the construction, training and testing of the model used Matlab software (version: R2020a 9.8.0.1380330) and Python (version: 3.7.0) developed by MathWorks corporation, U.S. and called the Python's LinearSVR model by Matlab software.
Therefore, the invention provides a novel inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm and based on actual measurement hyperspectral remote sensing data, which can overcome the phenomenon of drift caused by the influence of factors such as complex optical characteristics of inland turbid water, suspended matters contained in the water, colored soluble organic matters and other complex components on chlorophyll a concentration characteristic wave bands, and fully utilizes chlorophyll a concentration information contained in each wave band data of high-resolution water remote sensing reflectivity, thereby improving the inversion model precision of inland lake chlorophyll a concentration.
Compared with the prior art, the invention has the following advantages:
(1) The method is based on the measured full-band data of the hyperspectral remote sensing data of the water body, fully utilizes the complete chlorophyll a concentration information contained in the hyperspectral data, and avoids the influence on the characteristic wave band of the chlorophyll a concentration caused by complex water body components of inland lakes, such as the phenomenon of characteristic wave band drift.
(2) According to the invention, the information about the chlorophyll a concentration contained in the high-resolution water remote sensing reflectivity is fully considered, the linear SVR model is used, the information about the chlorophyll a concentration contained in the high-band-resolution water remote sensing reflectivity can be fully utilized, the limitation of time consumption and slowness in calculation caused by the fact that the number of band data is greatly higher than that of sample data is avoided, the chlorophyll a concentration inversion model based on the linear SVR is constructed, the accuracy of chlorophyll a concentration inversion is improved, the training efficiency of the chlorophyll a concentration inversion model is improved, and the popularization and application of the chlorophyll a concentration inversion model are facilitated.
(3) The chlorophyll a concentration inversion method provided by the invention is simple and convenient to calculate, and is suitable for remote sensing quantitative inversion of chlorophyll a concentrations in inland lakes in different areas and in different seasons.
(4) The invention fully considers the independence of model training and model checking, uses a random segmentation method to divide the training data set and the checking data set, wherein the training data set is only used for model training, and the checking data set is only used for model checking, so as to ensure the rationality of model effect checking.
(5) Since the parameter tuning of the model is very important to the calculation accuracy of the model, a model parameter rank order matrix is constructed in the invention to determine the coefficient R 2 In order to evaluate parameters, a trial-and-error method is used for model parameter tuning, and on the basis of guaranteeing the parameter tuning effect, the speed of model training and parameter tuning is greatly improved.
In conclusion, the inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm can reduce calculation result errors, improves inversion model accuracy of the water chlorophyll a concentration, is ingenious in design, simple and convenient to calculate, easy to realize and low in cost, and is suitable for large-scale popularization and application.
It will thus be seen that the objects of the present invention have been fully and effectively attained. The functional and structural principles of the present invention have been shown and described in the examples and embodiments may be modified at will without departing from such principles. Therefore, this invention includes all modifications encompassed within the spirit of the following claims and the scope of the following claims.
Claims (12)
1. The inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm is characterized in that the inland lake water chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm is a linear SVR model of Python language, and model parameters of the linear SVR model are as follows: 'C' 29.454540374164942, 'interference_scaling' 0.5446070238291971, 'loss' is 'squared_epsilon_insittive', 'max_iter' 305716, 'tol' 0.19594504249237796, 'epsilon' 0.43564649850770354, 'fit_interference' True.
2. The inland lake water body chlorophyll a concentration remote sensing inversion model based on the linear SVR algorithm of claim 1, wherein the linear SVR model is trained by adopting a data set of inland lake water bodies, the data set comprises water body remote sensing reflectance and chlorophyll a concentration of m sample points of the inland lake water bodies, the m sample points are uniformly distributed on the inland lake water bodies, and the water body remote sensing reflectance is water body remote sensing reflectance of n characteristic wave bands.
3. The inland lake water body chlorophyll a concentration remote sensing inversion model based on the linear svr algorithm of claim 2, wherein m is 32, the n characteristic wave bands are 751 characteristic wave bands, and the 751 characteristic wave bands are from 350nm wave bands to 1100nm wave bands.
4. An inland lake water chlorophyll a concentration remote sensing inversion method based on a linear SVR algorithm is characterized by comprising the following steps:
(1) Measuring the water body remote sensing reflectance of the inland lake water body;
(2) Measuring the chlorophyll a concentration C of the inland lake water body Chla ：
(3) Calculating by using the remote sensing reflectance of the water body as input data and adopting a linear SVR model of Python language to obtain an inversion value, and calculating a determination coefficient R according to the inversion value and the chlorophyll a concentration 2 Changing the value of the model parameter of the LinearSVR model, R 2 The larger the change of the model parameters is, the larger the importance of the model parameters is, and the model parameters are arranged from big to small according to the importance to construct a model parameter tuning rank matrix;
(4) Training the LinearSVR model by taking the remote sensing reflectance of the water body as the input data and the chlorophyll a concentration as an output result, and sequentially tuning the model parameters according to the model parameter tuning rank order matrix to obtain tuning values of the model parameters;
(5) And training the LinearSVR model by taking the water body remote sensing reflectance as the input data and the chlorophyll a concentration as the output result and adopting the optimal value of the model parameter, obtaining an inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm after the training of the LinearSVR model is finished, storing the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm by using a save method, and loading the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm by using a load method if the inland lake water chlorophyll a concentration remote sensing inversion model based on the LinearSVR algorithm is needed.
5. The remote sensing inversion method for chlorophyll a concentration in inland lake water based on the linear svr algorithm as claimed in claim 4, wherein in the step (1), the step of measuring the remote sensing reflectance of the inland lake water specifically comprises:
measuring water spectrum data L of inland lake water sw Sky diffuse scattered light L of inland lake water body sky Reflected light L of standard plate p Calculating the remote sensing reflectance of the water body:
Rrs＝(L sw -r sky *L sky )/(L p *π/ρ p )，
wherein Rrs is the water remote sensing reflectance; r is (r) sky The reflectivity of the air surface of the inland lake water body is 0.022 to 0.028, and r is the value when the inland lake water body is on calm water surface sky Take the value of 0.022, r at the wind speed of 5m/s sky The value is 0.025, and the value is 0.026-0.028 at the wind speed of 10m/s, ρ p Is the reflectivity of the standard plate.
6. The remote sensing inversion method of chlorophyll a concentration in inland lake water based on the linear SVR algorithm of claim 5, wherein in said step (1), said measurement is takenThe method is characterized in that the method is carried out by a portable ground object spectrometer, the portable ground object spectrometer adopts a lens with a field 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 (phi) v =135°, v=40°, the standard plate being a standard white plate with a reflectivity of 10%.
7. The remote sensing inversion method of chlorophyll a concentration in inland lake water based on the linear SVR algorithm of claim 4, wherein in said step (2), said chlorophyll a concentration C in said inland lake water is measured Chla The method specifically comprises the following steps:
collecting a water sample of the inland lake water body, filtering the water sample by adopting filter paper to obtain a filter paper sample, extracting the filter paper sample by adopting a chlorophyll a extraction solution, centrifuging, taking a supernatant, and measuring the absorbance E of the supernatant at 665nm and 750nm 665 And E is 750 Acid treatment is added, and then the absorbance A of the supernatant at 665nm and 750nm is measured 665 And A 750 Calculating chlorophyll a concentration C of the inland lake water body by using the following formula Chla ：
Wherein V is 1 Is the volume of the chlorophyll-a extraction solution, V 2 Is the volume of the water sample.
8. The remote sensing inversion method of chlorophyll a concentration in inland lake water based on the linear SVR algorithm according to claim 7, wherein in the step (2), the filter paper is Whatman GF/F filter paper, the chlorophyll a extraction solution is 90% ethanol, the extraction is light-shielding soaking and leaching at 4 ℃ for 8 hours, the acid is 1mol/L hydrochloric acid solution, the adding amount of the hydrochloric acid solution is 1-2 drops, and the acid adding treatment time is 1 minute.
9. The remote sensing inversion method of chlorophyll a concentration in inland lake water based on the linear SVR algorithm of claim 4, wherein in the step (3), the model parameter tuning rank order matrix is:
Params＝{'C','intercept_scaling','loss','max_iter','tol','epsilon','fit_intercept','dual'}。
10. the remote sensing inversion method of chlorophyll a concentration in inland lake water based on the linear svr algorithm of claim 9, wherein in the step (4), the optimal values of the model parameters are:
'C':29.454540374164942,'intercept_scaling':0.5446070238291971,'loss':'squared_epsilon_insensitive','max_iter':305716,'tol':0.19594504249237796,'epsilon':0.43564649850770354,'fit_intercept':True,'dual':True。
11. the remote sensing inversion method for chlorophyll a concentration in inland lake water based on the linear svr algorithm of claim 4, wherein in the step (1), the step of measuring the remote sensing reflectance of the inland lake water is specifically to measure the remote sensing reflectance of the inland lake water at m sample points, wherein the m sample points are uniformly distributed on the inland lake water, and the remote sensing reflectance of the water is that of n characteristic bands; in the step (2), the chlorophyll a concentration C of the inland lake water body is measured Chla Specifically, the chlorophyll a concentration C of the m spots is measured Chla 。
12. The remote sensing inversion method of chlorophyll a concentration in inland lake water based on the linear svr algorithm of claim 11, wherein in the step (1), m is 32, the n characteristic bands are 751 characteristic bands, and the 751 characteristic bands are from 350nm to 1100 nm.
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