CN114863294A

CN114863294A - Water quality parameter monitoring method and device suitable for brackish water lake

Info

Publication number: CN114863294A
Application number: CN202210504010.3A
Authority: CN
Inventors: 姚建斌; 苏维; 吴银; 刘雯悦; 裴华; 孙达诚; 王嘉威
Original assignee: Third Geological Brigade Of Bureau Of Geology And Mineral Exploration And Development Of Xinjiang Uygur Autonomous Region; Bazhou Xinkuang Surveying And Mapping Co ltd
Current assignee: Third Geological Brigade Of Bureau Of Geology And Mineral Exploration And Development Of Xinjiang Uygur Autonomous Region; Bazhou Xinkuang Surveying And Mapping Co ltd
Priority date: 2022-05-10
Filing date: 2022-05-10
Publication date: 2022-08-05

Abstract

The invention discloses a water quality parameter monitoring method and device suitable for brackish water lake. The method comprises the following steps: collecting first hyperspectral data and water samples in a first brackish water lake to generate a general water quality parameter inversion model; when the monitored lake is a first brackish water lake, resampling and generating a corresponding target water quality parameter inversion model; when the monitored lake is a second brackish water lake, acquiring third high-spectrum data in the second brackish water lake, and resampling and generating a corresponding target water quality parameter inversion model; acquiring a hyperspectral image of the monitored lake through the hyperspectral satellite, and calculating a water quality parameter of each pixel in the hyperspectral image; and obtaining the overall water quality parameter condition of the monitored lake. The water quality parameter monitoring method provided by the invention is very suitable for brackish water lakes, integrates a plurality of models for the water quality monitoring result, is more accurate compared with the existing method which only uses a single model, and can greatly improve the efficiency of water quality monitoring.

Description

Water quality parameter monitoring method and device suitable for brackish water lake

Technical Field

The application relates to the technical field of water quality research, in particular to a water quality parameter monitoring method and device suitable for brackish water lake.

Background

Water resources are important natural resources for human beings, and are the basic conditions for human beings, animals, plants and other various lives to live and develop. In water resources, the saline water lake is a lake with water salinity more than 1 per thousand, wherein the lake with salinity between 1 per thousand and 24.7 per thousand is a slightly saline water lake.

With the development of society, industry and the like and the increase of population, a large amount of water resources are polluted. Therefore, as water resource pollution becomes more serious, water quality monitoring becomes more important as a basic work in water pollution control work.

The water quality condition of the lake is obtained by measuring the water quality of a lake water sample through a chemical method, and the lake water quality is monitored by utilizing a remote sensing monitoring technology; the detection process for detecting the water quality of the water sample by a chemical method is complex, and the required time is long, so that the time consumption and the efficiency are low, the water quality of the collected water sample cannot accurately reflect the water quality condition of the whole lake, and the environment is polluted to a certain extent; although some researches have been made on monitoring the lake water quality by using remote sensing monitoring technology, the inventor realizes that the researches on monitoring the water quality parameters by using remote sensing monitoring means in the case of brackish water are still less, and the models used by the existing monitoring methods are single.

Disclosure of Invention

Based on the technical problem, a water quality parameter monitoring method and device suitable for brackish water lakes are provided.

In a first aspect, a method for monitoring water quality parameters applicable to brackish water lakes comprises the following steps:

step S1, collecting corresponding first hyperspectral data and water samples respectively at a plurality of collection points preset in a first brackish water lake to obtain a plurality of first hyperspectral data and a plurality of corresponding water samples, and analyzing the water samples through a water body experiment to obtain a water quality parameter measured value of each water sample; generating a universal water quality parameter inversion model according to the plurality of first hyperspectral data and the measured water quality parameters of the corresponding water samples;

step S2, when the monitored lake is a first brackish water lake, resampling each first hyperspectral data according to the resolution ratio of a hyperspectral satellite based on the hyperspectral satellite to be utilized, and obtaining a plurality of second hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the plurality of second hyperspectral data and the water quality parameter measured values of the corresponding water samples;

step S3, when the monitored lake is a second brackish water lake, collecting corresponding third high-spectrum data at a plurality of collection points preset in the second brackish water lake respectively to obtain a plurality of third high-spectrum data, calculating a water quality parameter inversion value corresponding to each third high-spectrum data through the universal water quality parameter inversion model, and taking the water quality parameter inversion value corresponding to each obtained third high-spectrum data as a water quality parameter measured value corresponding to each third high-spectrum data; based on the hyperspectral satellite to be utilized, resampling each third hyperspectral data according to the resolution ratio of the hyperspectral satellite to obtain a plurality of fourth hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the water quality parameter measured values of the corresponding water samples;

step S4, acquiring a hyperspectral image of the monitored lake through the hyperspectral satellite, and calculating a water quality parameter of each pixel in the hyperspectral image by using a corresponding target water quality parameter inversion model;

step S25, calculating the average value of the corresponding water quality parameters of each pixel according to the water quality parameters of each pixel calculated by each water quality parameter inversion model in the target water quality parameter inversion model; and taking the average value of the water quality parameter of each pixel as the inversion value of the water quality parameter of the pixel, thereby obtaining the overall water quality parameter condition of the monitored lake.

Optionally, the step S1 of generating the universal water quality parameter inversion model according to the plurality of first hyperspectral data and the measured water quality parameters of the corresponding water samples specifically includes:

step S1A, preprocessing each first hyperspectral data to obtain a plurality of first preprocessed hyperspectral data;

S1B, carrying out correlation analysis on the plurality of first hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; carrying out correlation analysis on the plurality of first preprocessed hyperspectral data and corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models;

S1C, screening out a water quality parameter inversion model with the precision reaching preset conditions from each generated water quality parameter inversion model as a general water quality parameter inversion model;

in step S2, the generating of the corresponding target water quality parameter inversion model according to the plurality of second hyperspectral data and the measured water quality parameters of the corresponding water samples specifically includes:

S2A, preprocessing each second hyperspectral data to obtain a plurality of second preprocessed hyperspectral data;

S2B, carrying out correlation analysis on the plurality of second hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; performing correlation analysis on the plurality of second preprocessed hyperspectral data and corresponding measured water quality parameters, and generating corresponding water quality parameter inversion models;

S2C, screening out a water quality parameter inversion model with the precision reaching preset conditions from each generated water quality parameter inversion model as a target water quality parameter inversion model;

in step S3, the generating of the corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the measured water quality parameters of the corresponding water samples specifically includes:

S3A, preprocessing each fourth hyperspectral data to obtain a plurality of fourth preprocessed hyperspectral data;

S3B, carrying out correlation analysis on the fourth hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; carrying out correlation analysis on the fourth preprocessed hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models;

and S3C, screening out a water quality parameter inversion model with the precision reaching preset conditions from each generated water quality parameter inversion model as a target water quality parameter inversion model.

Further optionally, the step S1A specifically includes:

performing first-order derivation processing on each first hyperspectral data to obtain corresponding first-order hyperspectral data; performing second-order derivation processing on each first hyperspectral data to obtain corresponding first second-order hyperspectral data; performing third-order derivation processing on each first hyperspectral data to obtain corresponding first third-order hyperspectral data;

the step S2A specifically includes:

performing first-order derivation processing on each second hyperspectral data to obtain corresponding second-order hyperspectral data; performing second-order derivation processing on each second hyperspectral data to obtain corresponding second-order hyperspectral data; performing third-order derivation processing on each second hyperspectral data to obtain corresponding second third-order hyperspectral data;

the step S3A specifically includes:

performing first-order derivation processing on each fourth hyperspectral data to obtain corresponding fourth-order hyperspectral data; performing second-order derivation processing on each fourth hyperspectral data to obtain corresponding fourth second-order hyperspectral data; and performing third-order derivation processing on each fourth hyperspectral data to obtain corresponding fourth third-order hyperspectral data.

Further optionally, the step S1B specifically includes:

performing correlation analysis on the plurality of first hyperspectral data and the corresponding water quality parameter measured values, and performing unary linear fitting on a single waveband with the highest correlation in the plurality of first hyperspectral data and the corresponding water quality parameter measured values to obtain corresponding water quality parameter inversion models;

respectively carrying out correlation analysis on the plurality of first one-order high spectrum data, the plurality of first two-order high spectrum data and the plurality of first three-order high spectrum data and a water quality parameter measured value, and respectively carrying out unary one-time linear fitting on a single waveband with highest correlation in the first one-order high spectrum data, a single waveband with highest correlation in the first two-order high spectrum data and a single waveband with highest correlation in the first three-order high spectrum data and the water quality parameter measured value to obtain a corresponding water quality parameter inversion model; respectively carrying out unary one-time linear fitting on the wave band ratio with the highest correlation in the first-order high spectrum data, the wave band ratio with the highest correlation in the first second-order high spectrum data and the wave band ratio with the highest correlation in the first third-order high spectrum data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models;

the step S2B specifically includes:

performing correlation analysis on the plurality of second hyperspectral data and the corresponding water quality parameter measured values, and performing unary linear fitting on a single waveband with the highest correlation in the plurality of second hyperspectral data and the corresponding water quality parameter measured values to obtain corresponding water quality parameter inversion models;

respectively carrying out correlation analysis on a plurality of first-order hyperspectral data, a plurality of second-order hyperspectral data and a water quality parameter measured value, and respectively carrying out unary linear fitting on a single waveband with highest correlation in the first-order hyperspectral data, a single waveband with highest correlation in the second-order hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models; respectively carrying out unary linear fitting on the wave band ratio with the highest correlation in the second first-order hyperspectral data, the wave band ratio with the highest correlation in the second-order hyperspectral data and the wave band ratio with the highest correlation in the second third-order hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models;

the step S3B specifically includes:

performing correlation analysis on the fourth hyperspectral data and the corresponding water quality parameter measured values, and performing unary linear fitting on a single waveband with the highest correlation in the fourth hyperspectral data and the corresponding water quality parameter measured values to obtain corresponding water quality parameter inversion models;

respectively carrying out correlation analysis on a plurality of fourth first-order hyperspectral data, a plurality of fourth second-order hyperspectral data and a plurality of fourth third-order hyperspectral data and a water quality parameter measured value, and respectively carrying out unary linear fitting on a single waveband with highest correlation in the fourth first-order hyperspectral data, a single waveband with highest correlation in the fourth second-order hyperspectral data and a single waveband with highest correlation in the fourth third-order hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models; and performing unary one-time linear fitting on the wave band ratio with the highest correlation in the fourth first-order hyperspectral data, the wave band ratio with the highest correlation in the fourth second-order hyperspectral data and the wave band ratio with the highest correlation in the fourth third-order hyperspectral data and the water quality parameter measured value respectively to obtain corresponding water quality parameter inversion models.

Further optionally, the water quality parameter inversion models with the screened precision reaching the preset condition are all:

calculating the decision coefficient R of each generated water quality parameter inversion model ² Screening out the coefficient of determination R ² And (3) the target water quality parameter inversion model exceeding a preset decision coefficient threshold value.

Further optionally, the decision coefficient

Wherein n is the number of the collected hyperspectral data, t _i Measured value of water quality parameter, y, corresponding to each hyperspectral data _i In order to calculate the water quality parameter inversion value of each hyperspectral data by using the water quality parameter inversion model,

the average value of the measured values of the water quality parameters corresponding to all the hyperspectral data is obtained; the predetermined decision coefficient threshold is 0.65.

Further optionally, the step S3 of calculating the water quality parameter inversion value corresponding to each third hyperspectral data through the general water quality parameter inversion model specifically includes:

calculating the water quality parameter of each third high-spectrum data by using a general water quality parameter inversion model;

calculating the water quality parameter of each third high-spectrum data according to the water quality parameter of each water quality parameter inversion model in the universal water quality parameter inversion model, and calculating the average value of the water quality parameter corresponding to each third high-spectrum data; and taking the average value of the water quality parameter of each third high-spectrum data as the inversion value of the water quality parameter of the third high-spectrum data.

Further optionally, the step S2C and the step S3C each further include:

calculating the root mean square error RMSE and/or the average relative error RE of each screened water quality parameter inversion model, and removing the water quality parameter inversion model with the root mean square error RMSE and/or the average relative error RE exceeding the corresponding preset threshold value from the target water quality parameter inversion model; wherein:

root mean square error

Average relative error

Wherein n is the number of the collected hyperspectral data, t _i Measured value of water quality parameter, y, corresponding to each hyperspectral data _i The water quality parameter inversion value of each hyperspectral data is calculated by using a water quality parameter inversion model.

Further optionally, the water quality parameter is at least one of permanganate index, chemical oxygen demand, ammonia nitrogen content, total phosphorus content, total nitrogen content, suspended matter content, and chlorophyll a content; the hyperspectral satellite is a resource first satellite or a hyperspectral fifth satellite; the hyperspectral data are collected through a portable surface feature spectrometer; the correlation analysis was performed by using the method of the Rason correlation coefficient.

In a second aspect, a water quality parameter monitoring device suitable for brackish water lake comprises:

the general water quality parameter inversion model generation module is used for respectively acquiring corresponding first hyperspectral data and water samples at a plurality of acquisition points preset in a first brackish water lake to obtain a plurality of first hyperspectral data and a plurality of corresponding water samples, and analyzing the water samples through a water body experiment to obtain a water quality parameter measured value of each water sample; generating a universal water quality parameter inversion model according to the plurality of first hyperspectral data and the measured water quality parameters of the corresponding water samples;

the first target water quality parameter inversion model generation module is used for resampling each first hyperspectral data according to the resolution ratio of a hyperspectral satellite based on the hyperspectral satellite to be utilized when the monitored lake is a first brackish water lake to obtain a plurality of second hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the plurality of second hyperspectral data and the water quality parameter measured values of the corresponding water samples;

a second target water quality parameter inversion model generation module, configured to, when the monitored lake is a second brackish water lake, respectively acquire corresponding third high-spectrum data at a plurality of acquisition points preset in the second brackish water lake, to obtain a plurality of third high-spectrum data, and calculate a water quality parameter inversion value corresponding to each third high-spectrum data through the general water quality parameter inversion model, and use the water quality parameter inversion value corresponding to each obtained third high-spectrum data as a water quality parameter measured value corresponding to each third high-spectrum data; based on the hyperspectral satellite to be utilized, resampling each third hyperspectral data according to the resolution ratio of the hyperspectral satellite to obtain a plurality of fourth hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the water quality parameter measured values of the corresponding water samples;

the hyperspectral image acquisition module is used for acquiring a hyperspectral image of the monitored lake through the hyperspectral satellite and calculating the water quality parameter of each pixel in the hyperspectral image by using a corresponding target water quality parameter inversion model;

the inversion value determining module is used for calculating the average value of the corresponding water quality parameters of each pixel according to the water quality parameters of each pixel calculated by each water quality parameter inversion model in the target water quality parameter inversion model; and taking the average value of the water quality parameter of each pixel as the inversion value of the water quality parameter of the pixel, thereby obtaining the overall water quality parameter condition of the monitored lake.

The invention has at least the following beneficial effects:

based on further analysis and research on the problems in the prior art, the invention realizes that the research on monitoring the water quality parameters by using a remote sensing monitoring method under the condition of brackish water is less at present, and the model used by the existing monitoring method is single; the surface of the brackish water lake has less phytoplankton, so that a hyperspectral image of the brackish water lake acquired by a hyperspectral satellite can better reflect the condition of the water body, and therefore, by the water quality parameter monitoring method provided by the invention, the water quality parameter of each pixel in the hyperspectral image is more accurately inverted by using the acquired water quality parameter inversion model, so that the overall water quality parameter condition of the monitored brackish water lake can be more accurately obtained; that is, the water quality parameter monitoring method provided by the invention is very suitable for brackish water lakes.

Meanwhile, in the water quality parameter monitoring method provided by the invention, the generated target water quality parameter inversion model comprises a plurality of models, and the water quality parameter inversion value of each pixel is an average value calculated by a plurality of models in the target water quality parameter inversion model, namely, the water quality parameter monitoring method provided by the invention is obtained by integrating the calculation results of the plurality of models on the water quality monitoring result, and compared with the existing monitoring method, the water quality parameter monitoring method only uses a single model to monitor the water quality parameter more accurately.

In addition, by the water quality parameter monitoring method provided by the invention, after a plurality of first hyperspectral data and water quality parameter measured values of a plurality of water samples are obtained in one lake, when the lake to be monitored is changed, the water samples are not required to be collected in a new lake, only the hyperspectral data are required to be collected, and the corresponding water quality parameter measured values are obtained by the inversion values calculated by the universal water quality parameter inversion model, so that the monitoring efficiency can be greatly improved; when different hyperspectral satellites are used for monitoring the water quality parameters of the same lake, after hyperspectral data are collected in the lake, when the satellite to be utilized changes, the spectral data collected by the ASD portable surface feature spectrometer are resampled according to the corresponding resolution of the satellite, and then the subsequent modeling process is repeated; compared with the existing monitoring method, the method for monitoring the water quality parameters can greatly improve the efficiency and convenience of water quality monitoring because different hyperspectral satellites need to be sampled at the acquisition points repeatedly.

Drawings

FIG. 1 is a schematic flow chart of a method for monitoring water quality parameters of a brackish water lake according to an embodiment of the present invention;

FIG. 2 is a graph illustrating the results of monitoring the total nitrogen content of a lake Bos Teng provided by an embodiment of the present invention;

fig. 3 is a block diagram of a module architecture of a water quality parameter monitoring device suitable for brackish water lake according to an embodiment of the present invention;

FIG. 4 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

Example one

In this embodiment, as shown in fig. 1, a method for monitoring water quality parameters of brackish water lake is provided, which includes the following steps:

step S1, collecting corresponding first hyperspectral data and water samples respectively at a plurality of collection points preset in a first brackish water lake to obtain a plurality of first hyperspectral data and a plurality of corresponding water samples, and analyzing the water samples through a water body experiment to obtain a water quality parameter measured value of each water sample; and generating a universal water quality parameter inversion model according to the plurality of first hyperspectral data and the water quality parameter measured values of the corresponding water samples.

Specifically, the plurality of preset collection points in the first brackish water lake may be 50 collection points uniformly distributed in a certain area of the first brackish water lake, because the water depth on the bank is shallow, the collection points are arranged to avoid being close to the bank, a hyperspectral curve is collected at a distance of about 200cm from the water surface when each collection point is collected, and the measurement sequence is standard plate measurement (a probe is vertically downward and about 25cm from the standard plate), inclined water body measurement (a probe nadir angle is 40 degrees and downward), inclined sky light measurement (a probe is upward and a probe zenith angle is 40 degrees), standard plate measurement (a probe is vertically downward and about 25cm from the standard plate), inclined water body measurement (a probe is downward and a probe nadir angle is 40 degrees), inclined sky light measurement (a probe is upward and a probe zenith angle is 40 degrees), standard plate measurement (a probe is vertically downward and about 25cm from the standard plate), The measurement of inclined water body (the probe is downward, the bottom angle of the probe is 40 degrees), the measurement of inclined sky light (the probe is upward, the top angle of the probe is 40 degrees), and 50 acquisition points are respectively provided with a corresponding hyperspectral curve by calculation. The effective spectrum of the water body is 400-950nm, so that each high spectral curve also comprises a wave band which is correspondingly approximately between 400-950 nm. In addition, the collection point is used for collecting a spectrum curve through an ASD portable surface feature spectrometer, the working spectrum range of the ASD portable surface feature spectrometer is 350-2500nm, the spectrum resolution is 1nm, namely, the ASD portable surface feature spectrometer can identify the reflectivities of the wave bands of 450nm, 451nm, 452nm. In addition, the collected water sample is generally a mixed water body 20cm to 70cm below the water surface, after the water sample is collected, a water quality parameter measured value of the water sample needs to be obtained through water body experimental analysis, the water quality parameter can be at least one of permanganate index, Chemical Oxygen Demand (COD), ammonia nitrogen content (NH3-N), total phosphorus content (P), total nitrogen content (N), suspended matter content (SS) and chlorophyll a content, and which water quality parameter needs to be monitored, so that the water quality parameter in the water sample can be correspondingly analyzed. Of course, the water quality parameters are not limited to those listed above.

Further, in step S1, the generating a general water quality parameter inversion model according to the plurality of first hyperspectral data and the measured water quality parameters of the corresponding water samples specifically includes:

step S1A, preprocessing each first hyperspectral data to obtain a plurality of first preprocessed hyperspectral data; the method specifically comprises the following steps:

performing first order derivation processing on each first hyperspectral data to obtain corresponding first order hyperspectral data, namely, each first order hyperspectral data contains the relation between each wave band of the first hyperspectral data and a first order derivative of the corresponding reflectivity; performing second-order derivation processing on each first hyperspectral data to obtain corresponding first second-order hyperspectral data, namely, each first second-order hyperspectral data contains the relationship between each wave band of the first hyperspectral data and a second-order derivative of the corresponding reflectivity; performing third-order derivation processing on each first hyperspectral data to obtain corresponding first third-order hyperspectral data, namely, each first third-order hyperspectral data contains the relation between each wave band of the first hyperspectral data and the third-order derivative of the corresponding reflectivity;

S1B, carrying out correlation analysis on the plurality of first hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; carrying out correlation analysis on the plurality of first preprocessed hyperspectral data and corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; the method specifically comprises the following steps:

performing correlation analysis on the plurality of first hyperspectral data and the corresponding measured water quality parameter values by using a pearson correlation coefficient method, finding out a single wave band with the highest correlation in the plurality of first hyperspectral data, performing unary one-time linear fitting on the single wave band with the highest correlation in the plurality of first hyperspectral data and the corresponding measured water quality parameter values to obtain a corresponding water quality parameter inversion model, and generating a corresponding linear model: a is ₁ x+b ₁ Y is a water quality parameter, x is a reflectivity corresponding to a single band with the highest correlation, and a ₁ And b ₁ Is a constant;

carrying out correlation analysis on the first primary high-spectrum data and the water quality parameter measured values, finding out a single-waveband with highest correlation in the first primary high-spectrum data, carrying out unary one-time linear fitting on the single-waveband with highest correlation in the first primary high-spectrum data and the corresponding water quality parameter measured values to obtain a corresponding water quality parameter inversion model, and generating a corresponding linear model: a is ₂ x+b ₂ Y is a water quality parameter, x is an absolute value of a first derivative of the reflectivity corresponding to a single band with the highest correlation, a ₂ And b ₂ Is a constant; carrying out correlation analysis on the plurality of first second-order high-spectrum data and the water quality parameter measured values, finding out a single-waveband with highest correlation in the plurality of first second-order high-spectrum data, carrying out unary linear fitting on the single-waveband with highest correlation in the plurality of first second-order high-spectrum data and the corresponding water quality parameter measured values to obtain a corresponding water quality parameter inversion model, and generating a corresponding linear model: a is ₃ x+b ₃ Y is a water quality parameter, x is a reflectivity second derivative absolute value corresponding to a single waveband with the highest correlation, and a ₃ And b ₃ Is a constant; similarly, a plurality of first third-order hyperspectral data and the measured water quality parameter are subjected to correlation analysis to find out a plurality of first third-order hyperspectral dataThe single wave band with the highest correlation in the third-order hyperspectral data is subjected to unary one-time linear fitting on the single wave band with the highest correlation in the first-order hyperspectral data and the corresponding water quality parameter measured value to obtain a corresponding water quality parameter inversion model, and a corresponding linear model is generated: a is ₄ x+b ₄ Y is a water quality parameter, x is the absolute value of the third derivative of the reflectivity corresponding to the single band with the highest correlation, a ₄ And b ₄ Is a constant;

meanwhile, finding out the wave band ratio value with the highest correlation in the first-order high spectrum data, wherein the wave band ratio value in the first-order high spectrum data is the absolute value of the ratio of the first-order derivatives of the reflectivity corresponding to the two wave bands, and the wave band ratio value can be defined as QRVI (| B) ₁ /B ₂ I) or QNDVI (| (B) ₁ -B ₂ )/(B ₁ +B ₂ ) In which B) is ₁ And B ₂ The first derivative of the reflectivity corresponding to the two wave bands; carrying out unary one-time linear fitting on the wave band ratio with the highest correlation in the first-order high spectrum data and the corresponding water quality parameter measured value to obtain a corresponding water quality parameter inversion model, and generating a corresponding linear model: a is ₅ x+b ₅ Y is a water quality parameter, x is a wave band ratio with the highest correlation, a ₅ And b ₅ Is a constant; similarly, find out the most relevant band ratio in the first and second-order hyperspectral data, the band ratio in the first and second-order hyperspectral data is the absolute value of the ratio of the reflectivity second derivative corresponding to two bands, the band ratio can be defined as QRVI (| B) ₁ /B ₂ I) or QNDVI (| (B) ₁ -B ₂ )/(B ₁ +B ₂ ) In which B) is ₁ And B ₂ The second derivative of the reflectivity corresponding to the two wave bands; carrying out unary one-time linear fitting on the wave band ratio with the highest correlation in the first and second-order high spectrum data and the corresponding water quality parameter measured value to obtain a corresponding water quality parameter inversion model, and generating a corresponding linear model: a is ₆ x+b ₆ Y is a water quality parameter, x is a wave band ratio with the highest correlation, a ₆ And b ₆ Is a constant; similarly, the most relevant data in the first three-order hyperspectral data is foundHigh wave band ratio, the wave band ratio in the first three-order high spectrum data is the ratio absolute value of the reflectivity third-order derivative corresponding to the two wave bands, and the wave band ratio can be defined as QRVI (| B) ₁ /B ₂ I) or QNDVI (| (B) ₁ -B ₂ )/(B ₁ +B ₂ ) In which B) is ₁ And B ₂ The three derivatives of the reflectivity corresponding to the two wave bands; carrying out unary one-time linear fitting on the wave band ratio with the highest correlation in the first three-order hyperspectral data and the corresponding water quality parameter measured value to obtain a corresponding water quality parameter inversion model, and generating a corresponding linear model: a is ₇ x+b ₇ Y is a water quality parameter, x is a wave band ratio with the highest correlation, a ₇ And b ₇ Is a constant; that is, through step S1B, at least seven water quality parameter inversion models can be obtained;

specifically, the screening of the water quality parameter inversion model with the accuracy reaching the preset condition in step S1C specifically includes:

for each generated water quality parameter inversion model, calculating respective corresponding determination coefficient R ² ，

In the formula, n is the number of the hyperspectral data collected in the step S1, that is, the number of the water samples collected in the step S1; t is t _i A water quality parameter measured value corresponding to each hyperspectral data, namely a water quality parameter measured value of each water sample; y is _i Calculating a water quality parameter inversion value of each hyperspectral data by using a water quality parameter inversion model, namely calculating a water quality parameter inversion value of each water sample by using a water quality parameter inversion model;

the average value of the measured values of the water quality parameters corresponding to all the hyperspectral data, namely the measured values of the water quality parameters of all the water samplesAverage value;

determining the coefficient R ² For measuring accuracy, R, of generated water quality parameter inversion model ² The water quality parameter inversion model has the advantages that the regression fitting effect of the water quality parameter inversion model is better as the value is closer to 1. In the application, R in the water parameter inversion model generated at this time is screened by the invention ² And (3) taking the water quality parameter inversion model exceeding 0.65 as a general water quality parameter inversion model.

Step S2, when the monitored lake is a first brackish water lake, resampling each first hyperspectral data according to the resolution ratio of a hyperspectral satellite based on the hyperspectral satellite to be utilized, and obtaining a plurality of second hyperspectral data matched with the resolution ratio of the hyperspectral satellite; and generating a corresponding target water quality parameter inversion model according to the plurality of second hyperspectral data and the water quality parameter measured values of the corresponding water samples.

The step S2 is applicable to the situation that the lake to be monitored is the first brackish water lake in the step S1, and since the monitored lake is the same as the lake in which the hyperspectral data and the water sample are sampled by establishing the general water quality parameter inversion model in the step S1, the hyperspectral data and the water sample do not need to be sampled again in the monitored lake, and only the first hyperspectral data collected in the step S1 needs to be resampled and then modeled. The water quality parameters in the brackish water lake are monitored by a remote sensing monitoring method, a hyperspectral satellite is required to obtain a hyperspectral image of the monitored brackish water lake, and the hyperspectral satellite is low in spectral resolution which is usually 5nm, 10nm or 20nm and the like, so that the wide working spectral range and the resolution of 1nm of an ASD portable surface feature spectrometer cannot be achieved. That is to say, there are data of wave bands that many hyperspectral satellites can not identify in the spectral data that ASD portable surface feature spectrometer collected, consequently need carry out the resampling to every first hyperspectral data that ASD portable surface feature spectrometer collected for wave bands in every first hyperspectral data all can be discerned by hyperspectral satellite, thereby can guarantee to utilize hyperspectral satellite to carry out water quality monitoring's rate of accuracy. In other words, the resampling means that in each first hyperspectral data acquired by the ASD portable surface feature spectrometer, a band and a corresponding reflectivity that can be identified by a used hyperspectral satellite are intercepted and extracted, so that corresponding second hyperspectral data are obtained for each first hyperspectral data. In addition, the commonly utilized hyperspectral satellite can be, but is not limited to, resource one satellite or high-scoring five satellite.

Similarly to step S1, the generating of the corresponding target water quality parameter inversion model according to the plurality of second hyperspectral data and the measured water quality parameters of the corresponding water samples in step S2 specifically includes:

S2A, preprocessing each second hyperspectral data to obtain a plurality of second preprocessed hyperspectral data; the method specifically comprises the following steps:

S2B, carrying out correlation analysis on the plurality of second hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; carrying out correlation analysis on the plurality of second preprocessed hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; the method specifically comprises the following steps:

respectively carrying out correlation analysis on a plurality of first-order hyperspectral data, a plurality of second-order hyperspectral data and a water quality parameter measured value, and respectively carrying out unary linear fitting on a single waveband with highest correlation in the first-order hyperspectral data, a single waveband with highest correlation in the second-order hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models; respectively carrying out unary linear fitting on the wave band ratio with the highest correlation in the second first-order hyperspectral data, the wave band ratio with the highest correlation in the second-order hyperspectral data and the wave band ratio with the highest correlation in the second third-order hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models; similarly, through step S2B, at least seven water quality parameter inversion models can be obtained;

similarly, the screening of the water quality parameter inversion model with the accuracy reaching the preset condition in step S2C includes:

Wherein n is the number of the hyperspectral data acquired in the step S1, that is, the number of the water samples acquired in the step S1; t is t _i A water quality parameter measured value corresponding to each hyperspectral data, namely a water quality parameter measured value of each water sample; y is _i Calculating a water quality parameter inversion value of each hyperspectral data by using a water quality parameter inversion model, namely calculating a water quality parameter inversion value of each water sample by using a water quality parameter inversion model;

the average value of the water quality parameter measured values corresponding to all the hyperspectral data, namely the average value of the water quality parameter measured values of all the water samples;

determining the coefficient R ² For measuring accuracy, R, of generated water quality parameter inversion model ² The water quality parameter inversion model has the advantages that the regression fitting effect of the water quality parameter inversion model is better as the value is closer to 1. In bookIn the application, R in the water parameter inversion model generated at this time is screened by the invention ² And the water quality parameter inversion model exceeding 0.65 is used as a target water quality parameter inversion model for monitoring the monitored lake.

After the target water quality parameter inversion model is obtained, the target water quality parameter inversion model can be optimized again, and an excellent target water quality parameter inversion model is selected, and the method specifically comprises the following steps:

calculating the root mean square error RMSE and/or the average relative error RE of each screened water quality parameter inversion model, and removing the water quality parameter inversion model with the root mean square error RMSE and/or the average relative error RE exceeding the corresponding preset threshold value from the target water quality parameter inversion model; the preset threshold value of the average relative error RE may be 20%; the water quality parameter inversion model with the root mean square error RMSE and/or the average relative error RE exceeding the corresponding preset threshold is considered to be a less excellent model in the target water quality parameter inversion model; wherein:

root mean square error

Average relative error

Wherein n is the number of the collected hyperspectral data, t _i Measured value of water quality parameter, y, corresponding to each hyperspectral data _i Calculating a water quality parameter inversion value of each hyperspectral data by using a water quality parameter inversion model; n is the number of the hyperspectral data collected in the step S1, that is, the number of the water samples collected in the step S1; t is t _i A water quality parameter measured value corresponding to each hyperspectral data, namely a water quality parameter measured value of each water sample; y is _i Calculating a water quality parameter inversion value of each hyperspectral data by using a water quality parameter inversion model, namely calculating a water quality parameter inversion value of each water sample by using a water quality parameter inversion model; removing Root Mean Square Error (RMSE) and/or flattening in an inverse model of a target water quality parameterAfter the homogeneous relative error RE exceeds the water quality parameter inversion model of the corresponding preset threshold value, further optimization of the target water quality parameter inversion model is completed, and the selected excellent target water quality parameter inversion model is used for subsequent water quality monitoring; the optimized models meeting the quality control requirements can ensure the accuracy of water quality monitoring due to enough guarantee of the calculation precision and accuracy.

Step S3, when the monitored lake is a second brackish water lake, collecting corresponding third high-spectrum data at a plurality of collection points preset in the second brackish water lake respectively to obtain a plurality of third high-spectrum data, calculating a water quality parameter inversion value corresponding to each third high-spectrum data through the universal water quality parameter inversion model, and taking the water quality parameter inversion value corresponding to each obtained third high-spectrum data as a water quality parameter measured value corresponding to each third high-spectrum data;

based on the hyperspectral satellite to be utilized, resampling each third hyperspectral data according to the resolution ratio of the hyperspectral satellite to obtain a plurality of fourth hyperspectral data matched with the resolution ratio of the hyperspectral satellite; and generating a corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the water quality parameter measured values of the corresponding water samples.

The step S3 is applicable to the situation that the lake to be monitored is not the first brackish water lake in the step S1, and since the monitored lake is different from the lake in which the hyperspectral data and the water sample are sampled by establishing the general water quality parameter inversion model in the step S1, the sampling needs to be performed again on the monitored lake, and the preset collection points may also be 50 collection points uniformly distributed in a certain area of the monitored lake. Different from the sampling in the first brackish water lake, only 50 corresponding third high-spectrum data need to be acquired at 50 acquisition points in the monitored lake, then the water quality parameter inversion value corresponding to each third high-spectrum data is calculated by using the above obtained general water quality parameter inversion model, then the water quality parameter inversion value corresponding to each third high-spectrum data is used as the water quality parameter measured value of the water sample corresponding to each third high-spectrum data, and the water quality parameter measured value of the water sample is obtained through water body experimental analysis without measuring 50 water samples.

Specifically, the step S3 of calculating the water quality parameter inversion value corresponding to each third hyperspectral data by using the general water quality parameter inversion model specifically includes:

Similarly to step S2, the obtained third high spectrum data also needs to be resampled according to the resolution of the high spectrum satellite, and the waveband and the corresponding reflectivity that can be identified by the high spectrum satellite used are intercepted, so as to obtain the corresponding fourth high spectrum data for each third high spectrum data.

Further, similarly to steps S1 and S2, the generating a corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the measured water quality parameters of the corresponding water samples in step S3 specifically includes:

S3A, preprocessing each fourth hyperspectral data to obtain a plurality of fourth preprocessed hyperspectral data; the method specifically comprises the following steps:

performing first-order derivation processing on each fourth hyperspectral data to obtain corresponding fourth-order hyperspectral data; performing second-order derivation processing on each fourth hyperspectral data to obtain corresponding fourth second-order hyperspectral data; performing third-order derivation processing on each fourth hyperspectral data to obtain corresponding fourth third-order hyperspectral data;

S3B, carrying out correlation analysis on the fourth hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; carrying out correlation analysis on the fourth preprocessed hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; the method specifically comprises the following steps:

respectively carrying out correlation analysis on a plurality of fourth first-order hyperspectral data, a plurality of fourth second-order hyperspectral data and a plurality of fourth third-order hyperspectral data and a water quality parameter measured value, and respectively carrying out unary linear fitting on a single waveband with highest correlation in the fourth first-order hyperspectral data, a single waveband with highest correlation in the fourth second-order hyperspectral data and a single waveband with highest correlation in the fourth third-order hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models; respectively carrying out unary linear fitting on the wave band ratio with the highest correlation in the first-order fourth hyperspectral data, the wave band ratio with the highest correlation in the second-order fourth hyperspectral data and the wave band ratio with the highest correlation in the third-order fourth hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models; similarly, through step S3B, at least seven water quality parameter inversion models can be obtained.

Similarly, the screening of the water quality parameter inversion model with the accuracy reaching the preset condition in step S3C includes:

Wherein n is the number of the hyperspectral data acquired in the step S3; t is t _i A water quality parameter measured value corresponding to each hyperspectral data; y is _i For each high spectrum number calculated by using a water quality parameter inversion modelAccording to the water quality parameter inversion value;

the average value of the measured values of the water quality parameters corresponding to all the hyperspectral data is obtained; the R in the water quality parameter inversion model generated at this time is screened ² And the water quality parameter inversion model exceeding 0.65 is used as a target water quality parameter inversion model for monitoring the monitored lake.

root mean square error

Average relative error

Wherein n is the number of the hyperspectral data acquired in the step S3; t is t _i A water quality parameter measured value corresponding to each hyperspectral data; y is _i Calculating a water quality parameter inversion value of each hyperspectral data by using a water quality parameter inversion model; after the water quality parameter inversion model with the root mean square error RMSE and/or the average relative error RE exceeding the corresponding preset threshold value is removed from the target water quality parameter inversion model, further optimization of the target water quality parameter inversion model is completed, and the selected excellent target water quality parameter inversion model is used for optimizing the target water quality parameter inversion modelMonitoring the water quality later; the optimized models meeting the quality control requirements can ensure the accuracy of water quality monitoring due to enough guarantee of the calculation precision and accuracy.

The derivation work of the steps can be completed through ENVI5.6 remote sensing software.

The plurality of first hyperspectral data and the measured water quality parameters of the plurality of water samples acquired in the step S1, the plurality of third hyperspectral data and the measured water quality parameters acquired in the step S3 are stored as a master database, and when different hyperspectral satellites are used for monitoring the water quality parameters, the hyperspectral data or the water samples do not need to be repeatedly acquired at the acquisition point by the ASD portable surface feature spectrometer. For each hyperspectral satellite, the data needed for modeling is the data collected from the ASD portable surface spectrometer. That is, after the hyperspectral data are collected in a lake, when a satellite to be utilized changes, the spectral data collected by the ASD portable surface feature spectrometer are re-sampled according to the corresponding resolution of the satellite, and then the subsequent modeling process is repeated; in addition, after the water quality parameter measured values of the plurality of first hyperspectral data and the plurality of water samples are obtained in one lake through the step S1, when the lake to be monitored is changed, the water samples are not required to be collected in a new lake, only the hyperspectral data are required to be collected, and the corresponding water quality parameter measured values are the inversion values calculated by the general water quality parameter inversion model, so that the monitoring efficiency can be greatly improved.

And step S4, acquiring a hyperspectral image of the monitored lake through the hyperspectral satellite, and calculating the water quality parameter of each pixel in the hyperspectral image by using a corresponding target water quality parameter inversion model.

Specifically, the hyperspectral image acquired in step S4 may be a local hyperspectral image of the monitored lake acquired by the hyperspectral satellite, or may be a complete local hyperspectral image of the monitored lake. After the hyperspectral image is obtained through a resource first satellite (ZY102D) or a high-resolution fifth satellite (GF5), preprocessing work such as geometric correction, radiation correction and atmospheric correction is required to be carried out on the obtained hyperspectral image; and only extracting the water body part in the hyperspectral image for water quality monitoring.

Step S5, calculating the average value of the corresponding water quality parameters of each pixel according to the water quality parameters of each pixel calculated by each water quality parameter inversion model in the target water quality parameter inversion model; and taking the average value of the water quality parameter of each pixel as the inversion value of the water quality parameter of the pixel, thereby obtaining the overall water quality parameter condition of the monitored lake.

Specifically, each pixel in the hyperspectral image has an independent hyperspectral curve, so that the hyperspectral curve corresponding to each pixel in the hyperspectral image can be substituted into the obtained target water quality parameter inversion model, and the water quality parameter of each pixel is obtained through inversion. The water quality parameters of each pixel in the hyperspectral image are integrated, and the overall water quality parameter condition of the monitored lake in the hyperspectral image can be obtained. The target water quality parameter inversion models may be multiple, a value of a water quality parameter is inverted for each pixel, and an average value of a plurality of water quality parameters obtained for each pixel is used as a final water quality parameter inversion value of the pixel. When the average value of the corresponding water quality parameter of each pixel is calculated, obvious abnormal values, such as numerical values which are obviously too large or too small compared with the monitoring data of the past water quality parameters, can be removed, so that the calculation efficiency is improved. The ENVI5.6 remote sensing software can be used for carrying out first-order, second-order and third-order derivation processing on each independent hyperspectral curve, and when the hyperspectral curve is substituted into the obtained target water quality parameter inversion model to carry out inversion to obtain the water quality parameter, the optimum wave band or the absolute value of the ratio of the optimum wave band of each hyperspectral curve is obtained through the corresponding plug-in of the ENVI5.6 remote sensing software.

Furthermore, after the inversion value of the water quality parameter of each pixel is obtained, each pixel can be marked as a color corresponding to the interval range according to the interval range in which the inversion value of the water quality parameter of each pixel is located, so that the monitoring result of the water quality parameter of the lake can be displayed more visually. For example: when the total nitrogen content of each pixel is calculated, when the calculation result of the total nitrogen content (unit mg/L) of the pixel belongs to [0, 0.2], the pixel can be marked as type I, and the pixel is marked as red; the method comprises the steps of obtaining a total nitrogen content of a monitored lake, obtaining a total nitrogen content of the monitored lake, and marking the total nitrogen content of the monitored lake in a color space, wherein the total nitrogen content of the pixel belongs to the group II when the calculation result belongs to the group 0.2 and 0.5, and the pixel is marked as yellow, and the total nitrogen content of the pixel belongs to the group III when the calculation result belongs to the group 0.5 and 1, and the pixel is marked as red.

In addition, on two days or days before and after the hyperspectral satellite passes through the lake surface, namely two days or days before and after the hyperspectral image is acquired, a certain number of acquisition points can be selected from the monitored lake, corresponding hyperspectral data and water samples are acquired by using an ASD portable surface feature spectrometer, each water sample is analyzed, a water quality parameter measured value of each water sample is obtained, and then the acquisition points are used as quality control points. The number of the acquisition points at this time is only one tenth of the number of the acquisition points selected in the step S1 or the step S3, and the measured values of the water quality parameters of the acquisition points are compared with the water quality parameter inversion values of the acquisition points calculated by the target water quality parameter inversion model, so as to prove the accuracy of the target water quality parameter inversion model in calculating the water quality parameter inversion values, thereby proving that the accuracy of monitoring the water quality parameters of the monitored lake is guaranteed.

The brackish water lake can inhibit the growth of algae, so that the surface of the brackish water lake has less phytoplankton, and the hyperspectral image of the brackish water lake acquired by the hyperspectral satellite can better reflect the condition of the water body. Therefore, by the water quality parameter monitoring method provided by the invention, the water quality parameter of each pixel in the hyperspectral image is inverted by using the obtained water quality parameter inversion model, so that the whole water quality parameter condition of the monitored brackish water lake can be accurately obtained. That is to say, the water quality parameter monitoring method provided by the invention is very suitable for brackish water lakes.

In addition, by the water quality parameter monitoring method provided by the invention, after a plurality of first hyperspectral data and water quality parameter measured values of a plurality of water samples are obtained in one lake, when the lake to be monitored is changed, the water samples are not required to be collected in a new lake, only the hyperspectral data is required to be collected, and the corresponding water quality parameter measured values are obtained from the inversion values calculated by the universal water quality parameter inversion model, so that the monitoring efficiency can be greatly improved. When different hyperspectral satellites are used for monitoring the water quality parameters of the same lake, hyperspectral data do not need to be repeatedly acquired at an acquisition point through an ASD portable surface feature spectrometer, that is, after the hyperspectral data are acquired in one lake, when the satellite to be utilized changes, the spectral data acquired by the ASD portable surface feature spectrometer only need to be re-sampled according to the corresponding resolution of the satellite, and then the subsequent modeling process is repeated; compared with the existing monitoring method, the method for monitoring the water quality parameters can greatly improve the efficiency and convenience of water quality monitoring because different hyperspectral satellites need to be repeatedly sampled at the acquisition point.

It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

Example two

In this embodiment, the method provided in the first embodiment is exemplarily applied to the monitoring of total nitrogen content in bosteng lake:

step 1, collecting corresponding hyperspectral data and water samples at 50 collection points in the BossTeng lake by using an ASD portable surface feature spectrometer, and analyzing the 50 water samples to obtain the total nitrogen content of each water sample.

And 2, preprocessing The 50 resampled hyperspectral curves by using first-order Derivatives, second-order Derivatives and third-order Derivatives respectively by using The differences of Transform in a Modify module of The Unscrambler software to obtain 50 first-order spectra, second-order spectra and third-order spectra respectively, and preparing for correlation analysis.

And 3, performing correlation analysis on 50 hyperspectras and corresponding 50 total nitrogen contents by using correlated bivariates (B) in an analysis module of SPSS software to obtain an optimal single-waveband with the highest correlation of 798nm, taking the optimal single-waveband reflectivity as an independent variable and the total nitrogen content as a dependent variable, and performing unary linear fitting to obtain a monitoring model as follows: y is ₁ ＝0.0844*x ₁ +0.0102 corresponding to R ² ＝0.02。

Step 4, using codes to combine all the wave band ratios QRVI (| B) of the first-order spectrum, the second-order spectrum and the third-order spectrum in MATALB ₁ /B ₂ |)、QNDVI(|(B ₁ -B ₂ )/(B ₁ +B ₂ ) And |)) are respectively subjected to correlation analysis with the corresponding 50 total nitrogen contents, and the wave band ratio with the highest correlation is obtained through the magnitude of the correlation coefficient. The obtained wave band ratios with the highest correlation are respectively a first-order spectrum QNDVI (| (589-752)/(589+752) |), a first-order spectrum QRVI (|640/797|), and a second-order spectrum QNDVI (| (5 |)88-791)/(588+791) |), a second order spectrum QRVI (|702/774|), a third order spectrum QNDVI (| (470-865)/(470+865) |), a third order spectrum QRVI (|415/738 |);

then, by taking the wave band ratio as an independent variable and the total nitrogen content as a dependent variable, a monitoring model obtained by unitary linear fitting is respectively as follows:

first-order spectrum QNDVI: y is ₂ ＝-0.0027*x ₂ +0.7095 corresponding to R ² ＝0.82

First-order spectrum QRVI: y is ₃ ＝-0.0028*x ₃ +0.7119 corresponding R ² ＝0.87

Second-order spectrum QNDVI: y is ₄ ＝-0.0044*x ₄ +0.7101 corresponding to R ² ＝0.80

Second order spectrum QRVI: y is ₅ ＝-0.0008*x ₅ +0.7073 corresponding to R ² ＝0.89

Third-order spectrum QNDVI: y is ₆ ＝-0.0024*x ₆ +0.7075 corresponding to R ² ＝0.92

Third-order spectrum QRVI: y is ₇ ＝-0.0025*x ₇ +0.7112 corresponding to R ² ＝0.88

The accuracy summary of the above model is shown in table one:

table-model accuracy summary

Wherein R is satisfied ² Above 0.65, the model with RMSE below 0.02 and RE below 20.00% has y ₃ 、y ₅ 、y ₆ 、y ₇ 。

Step 5, mixing y ₃ 、y ₅ 、y ₆ 、y ₇ The total nitrogen content of the whole bosteng lake can be obtained by using the target detection model as a preferable target detection model for monitoring the total nitrogen content of the bosteng lakeThe method is described. When the target detection model is used for calculating the total nitrogen content of each pixel of the hyperspectral image of the Boskun lake, the calculation result of the total nitrogen content (unit mg/L) of the pixel belongs to [0, 0.2]]Marking the pixel as type I and marking the pixel as red; when the calculation result of the total nitrogen content of the pixel belongs to (0.2, 0.5)]Marking the pixel as type II and marking the pixel as yellow; when the total nitrogen content (unit mg/L) of the pixel belongs to the calculation result of (0.5, 1)]Marking the pixel as III type, and marking the pixel as red; the results of this monitoring of total nitrogen content in the lake of Bos Teng are shown in FIG. 2. it can be seen from FIG. 2 that the total nitrogen content in all regions of the lake of Bos Teng is (0.5, 1)]Within the interval (c).

EXAMPLE III

In this embodiment, as shown in fig. 3, there is provided a water quality parameter monitoring device suitable for brackish water lake, including:

the general water quality parameter inversion model generation module 301 is configured to collect corresponding first hyperspectral data and water samples at a plurality of collection points preset in a first brackish water lake, obtain a plurality of first hyperspectral data and a plurality of corresponding water samples, analyze the water samples through a water body experiment, and obtain a water quality parameter measured value of each water sample; generating a universal water quality parameter inversion model according to the plurality of first hyperspectral data and the measured water quality parameters of the corresponding water samples;

a first target water quality parameter inversion model generation module 302, configured to, when the monitored lake is a first brackish water lake, resample each first hyperspectral data according to a resolution of a hyperspectral satellite based on the hyperspectral satellite to be utilized, and obtain a plurality of second hyperspectral data matched with the resolution of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the plurality of second hyperspectral data and the water quality parameter measured values of the corresponding water samples;

a second target water quality parameter inversion model generation module 303, configured to, when the monitored lake is a second brackish water lake, respectively acquire corresponding third high-spectrum data at a plurality of acquisition points preset in the second brackish water lake, to obtain a plurality of third high-spectrum data, calculate a water quality parameter inversion value corresponding to each third high-spectrum data through the general water quality parameter inversion model, and use the water quality parameter inversion value corresponding to each obtained third high-spectrum data as a water quality parameter measured value corresponding to each third high-spectrum data; based on the hyperspectral satellite to be utilized, resampling each third hyperspectral data according to the resolution ratio of the hyperspectral satellite to obtain a plurality of fourth hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the water quality parameter measured values of the corresponding water samples;

the hyperspectral image acquisition module 304 is used for acquiring a hyperspectral image of the monitored lake through the hyperspectral satellite and calculating a water quality parameter of each pixel in the hyperspectral image by using a corresponding target water quality parameter inversion model;

an inversion value determining module 305, which calculates an average value of the water quality parameters corresponding to each pixel according to the water quality parameters of each pixel calculated by each water quality parameter inversion model in the target water quality parameter inversion model; and taking the average value of the water quality parameter of each pixel as the inversion value of the water quality parameter of the pixel, thereby obtaining the overall water quality parameter condition of the monitored lake.

For specific limitations of a water quality parameter monitoring device suitable for brackish water lake, refer to the limitations of the water quality parameter monitoring method suitable for brackish water lake in the above embodiment one, and are not described herein again. All modules in the water quality parameter monitoring device suitable for the brackish water lake can be completely or partially realized through software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

The technical effects of the present embodiment are consistent with those of the present embodiment, and are not described again.

Example four

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 4. The computer device comprises a processor, a memory, a communication interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like. The computer program is executed by a processor to perform the steps of:

acquiring a plurality of first hyperspectral data collected in a first brackish water lake and water quality parameter measured values of a plurality of water samples obtained through corresponding analysis; generating a universal water quality parameter inversion model according to the plurality of first hyperspectral data and the measured water quality parameters of the corresponding water samples;

when the monitored lake is a first brackish water lake, resampling each first hyperspectral data according to the resolution ratio of a hyperspectral satellite based on the hyperspectral satellite to be utilized, and obtaining a plurality of second hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the plurality of second hyperspectral data and the water quality parameter measured values of the corresponding water samples;

when the monitored lake is a second brackish water lake, acquiring third high-spectrum data collected in the second brackish water lake, calculating a water quality parameter inversion value corresponding to each third high-spectrum data through the universal water quality parameter inversion model, and taking the water quality parameter inversion value corresponding to each obtained third high-spectrum data as a water quality parameter measured value corresponding to each third high-spectrum data; based on the hyperspectral satellite to be utilized, resampling each third hyperspectral data according to the resolution ratio of the hyperspectral satellite to obtain a plurality of fourth hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the water quality parameter measured values of the corresponding water samples;

acquiring a hyperspectral image of the monitored lake through the hyperspectral satellite, and calculating a water quality parameter of each pixel in the hyperspectral image by using a corresponding target water quality parameter inversion model;

calculating the water quality parameter of each pixel according to the water quality parameter of each pixel calculated by each water quality parameter inversion model in the target water quality parameter inversion model, and calculating the average value of the corresponding water quality parameter of each pixel; and taking the average value of the water quality parameter of each pixel as the inversion value of the water quality parameter of the pixel, thereby obtaining the overall water quality parameter condition of the monitored lake.

It will be understood by those skilled in the art that the structure shown in fig. 4 is only a block diagram of a part of the structure related to the present application, and does not constitute a limitation to the computer device to which the present application is applied.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A water quality parameter monitoring method suitable for brackish water lakes is characterized by comprising the following steps:

2. The water quality parameter monitoring method according to claim 1, wherein the step S1 of generating a general water quality parameter inversion model according to the plurality of first hyperspectral data and the measured water quality parameters of the corresponding water samples specifically comprises:

S2B, carrying out correlation analysis on the plurality of second hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models; carrying out correlation analysis on the plurality of second preprocessed hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models;

S3B, performing correlation analysis on the fourth hyperspectral data and the corresponding measured water quality parameter values, and generating corresponding water quality parameter inversion models; carrying out correlation analysis on the fourth preprocessed hyperspectral data and the corresponding water quality parameter measured values, and generating corresponding water quality parameter inversion models;

3. The water quality parameter monitoring method according to claim 2, wherein the step S1A specifically comprises:

the step S2A specifically includes:

the step S3A specifically includes:

4. The water quality parameter monitoring method according to claim 3, wherein the step S1B specifically comprises:

the step S2B specifically includes:

performing correlation analysis on the plurality of second hyperspectral data and the corresponding measured water quality parameter values, and performing unary linear fitting on a single wave band with the highest correlation in the plurality of second hyperspectral data and the corresponding measured water quality parameter values to obtain a corresponding water quality parameter inversion model;

the step S3B specifically includes:

respectively carrying out correlation analysis on a plurality of fourth first-order hyperspectral data, a plurality of fourth second-order hyperspectral data and a plurality of fourth third-order hyperspectral data and a water quality parameter measured value, and respectively carrying out unary linear fitting on a single waveband with highest correlation in the fourth first-order hyperspectral data, a single waveband with highest correlation in the fourth second-order hyperspectral data and a single waveband with highest correlation in the fourth third-order hyperspectral data and the water quality parameter measured value to obtain corresponding water quality parameter inversion models; and respectively carrying out unary linear fitting on the wave band ratio with the highest correlation in the fourth first-order hyperspectral data, the wave band ratio with the highest correlation in the fourth second-order hyperspectral data and the wave band ratio with the highest correlation in the fourth third-order hyperspectral data and the measured water quality parameter to obtain corresponding water quality parameter inversion models.

5. The water quality parameter monitoring method according to claim 2, wherein the water quality parameter inversion models screened out with the accuracy reaching the preset condition are all:

6. The method of claim 5, wherein the determination factor is a function of the water quality parameter

7. The water quality parameter monitoring method according to claim 2, wherein the step of calculating the water quality parameter inversion value corresponding to each third high spectral data through the general water quality parameter inversion model in step S3 specifically comprises:

8. The method for monitoring the water quality parameter according to any one of the claims 2 to 7, wherein the steps S2C and S3C further comprise:

root mean square error

Average relative error

9. The method according to any one of claims 1 to 7, wherein the water quality parameter is at least one of permanganate index, chemical oxygen demand, ammonia nitrogen content, total phosphorus content, total nitrogen content, suspended matter content, and chlorophyll a content; the hyperspectral satellite is a resource first satellite or a hyperspectral fifth satellite; the hyperspectral data are collected through a portable surface feature spectrometer; the correlation analysis was performed by using the method of the Rason correlation coefficient.

10. A water quality parameter monitoring device suitable for brackish water lake, characterized by comprising:

a second target water quality parameter inversion model generation module, configured to, when the monitored lake is a second brackish water lake, respectively acquire corresponding third high-spectrum data at a plurality of acquisition points preset in the second brackish water lake, to obtain a plurality of third high-spectrum data, and calculate a water quality parameter inversion value corresponding to each third high-spectrum data through the general water quality parameter inversion model, and use the water quality parameter inversion value corresponding to each obtained third high-spectrum data as a water quality parameter measured value corresponding to each third high-spectrum data; based on the hyperspectral satellite to be utilized, resampling each third hyperspectral data according to the resolution ratio of the hyperspectral satellite to obtain a plurality of fourth hyperspectral data matched with the resolution ratio of the hyperspectral satellite; generating a corresponding target water quality parameter inversion model according to the fourth hyperspectral data and the measured water quality parameters of the corresponding water samples;