A method, system, device and product for inverting chlorophyll a concentration in water

Abstract

The present invention belongs to the technical field of water quality monitoring, and its purpose is to provide a method, system, equipment and product for inverting chlorophyll a concentration in water bodies. The present invention is based on remote sensing data of domestic satellites and combines the characteristics of plateau lake water bodies to achieve rapid and accurate inversion of chlorophyll a concentration in plateau lake water bodies. It can not only make full use of the advantages of domestic satellite remote sensing data, reduce data acquisition costs, and enhance my country's autonomy and controllability in the field of water quality monitoring, but also improve the accuracy and reliability of chlorophyll a concentration inversion models in plateau lake applications. At the same time, the inversion method based on satellite remote sensing data has the advantages of real-time and large-scale monitoring, and can provide strong data support for real-time, continuous and large-scale monitoring and protection of plateau lake water quality.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality monitoring, and in particular relates to a method, system, equipment and product for inverting chlorophyll a concentration in water. Background Art

[0002] As an important freshwater resource in my country, the monitoring and protection of the water quality of plateau lakes is of great significance for maintaining ecological balance, ensuring the safety of people's drinking water and promoting sustainable development. Chlorophyll a is an important indicator reflecting the eutrophication of water bodies, and its concentration changes directly affect the stability of lake ecosystems. Therefore, monitoring the concentration of chlorophyll a in plateau lakes has become one of the important tasks of water quality monitoring.

[0003] The traditional method of measuring chlorophyll a concentration mainly relies on on-site sampling and laboratory analysis. Although this method is accurate, it has disadvantages such as high cost, long cycle and susceptibility to interference from human factors. At the same time, it cannot achieve real-time monitoring of large-scale lakes and it is difficult to meet the needs of rapid response to dynamic changes in lake water quality.

[0004] In recent years, with the rapid development of remote sensing technology, the use of remote sensing data to invert the concentration of chlorophyll a in water bodies has become a new monitoring method. Remote sensing technology has the advantages of wide coverage, fast data acquisition, and no restrictions on ground conditions, and can achieve continuous and dynamic monitoring of plateau lake water bodies.

[0005] The water chlorophyll a concentration inversion model is a statistical model used to study the relationship between chlorophyll a concentration in water and remote sensing data. In the process of using remote sensing technology for water quality testing, a water chlorophyll a concentration inversion model can be established based on remote sensing data, based on which the concentration trend of chlorophyll a in water can be predicted, and then the ecological status of the water body, the degree of water pollution and other issues can be analyzed to help decision makers take corresponding measures to improve water quality.

[0006] At present, some scholars at home and abroad have studied the chlorophyll a concentration inversion model based on satellite remote sensing data, but most of the models are mainly aimed at lakes or sea areas in plain areas, and have limited applicability to plateau lake water bodies. In addition, most inversion models rely on foreign satellite data, such as Landsat (a multi-series satellite jointly developed and operated by NASA and the U.S. Geological Survey), Sentinel (a high-resolution multispectral imaging satellite of the European Space Agency), etc. However, in the process of using the existing technology, the inventor found that there are at least the following problems in the existing technology:

[0007] The acquisition of foreign satellite data is restricted by many factors, such as data acquisition permissions and data update frequency, which not only increases the cost and difficulty of data acquisition, but may also be affected by international political and economic factors, limiting my country's autonomy and controllability in the field of water quality monitoring, making the application of chlorophyll a concentration inversion models have certain limitations; in addition, due to the special geographical location and complex climatic environment of plateau lakes, their water spectral characteristics are significantly different from those of plain lakes. Monitoring of chlorophyll a concentration in plateau lake water bodies based on foreign satellite data and general models is often difficult to accurately reflect the actual situation of plateau lake water bodies, resulting in large errors in the application of inversion models based on foreign satellite data in plateau lakes. Summary of the invention

[0008] The present invention aims to solve the above technical problems at least to a certain extent. The present invention provides a method, system, equipment and product for inverting chlorophyll a concentration in water.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] In a first aspect, the present invention provides a method for inverting chlorophyll a concentration in a water body, comprising:

[0011] Obtaining chlorophyll a concentration sampling data and remote sensing data of designated plateau lakes; wherein the remote sensing data is collected by designated domestic satellites;

[0012] Preprocessing the remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary;

[0013] Obtaining the reflectance of each band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data, and performing correlation analysis between the chlorophyll a concentration sampling data and the reflectance of each band, obtaining multiple reflectances of each band whose correlation with the chlorophyll a concentration sampling data is greater than a preset value, and then using the multiple reflectances of the bands whose correlation is greater than the preset value as characteristic parameters of the preprocessed remote sensing data;

[0014] Performing band combination processing on the characteristic parameters to obtain a plurality of band combination indexes, and performing correlation analysis on the chlorophyll a concentration sampling data and each band combination index to obtain the band combination index with the greatest correlation with the chlorophyll a concentration sampling data among the band combination indexes, and then using the band combination index with the greatest correlation as an independent variable and the chlorophyll a concentration sampling data as a dependent variable, and analyzing to obtain a chlorophyll a concentration inversion model of the designated plateau lake;

[0015] According to the chlorophyll a concentration inversion model and the pre-processed remote sensing data, remote sensing inversion of the chlorophyll a concentration in the designated plateau lake is achieved.

[0016] In a possible design, the remote sensing data includes first remote sensing data collected by a first designated domestic satellite and second remote sensing data collected by a second designated domestic satellite; correspondingly, the remote sensing data is preprocessed to obtain preprocessed remote sensing data with a water body as a boundary, including:

[0017] Performing radiation calibration processing on the remote sensing data, converting the digital quantization value of the remote sensing data into radiance, so as to obtain radiation-calibrated remote sensing data; wherein the radiation-calibrated remote sensing data is divided into first radiation-calibrated remote sensing data corresponding to the first remote sensing data and second radiation-calibrated remote sensing data corresponding to the second remote sensing data;

[0018] Performing atmospheric correction processing on the radiometrically calibrated remote sensing data to obtain atmospherically corrected remote sensing data; wherein the atmospherically corrected remote sensing data includes first atmospherically corrected remote sensing data corresponding to the first radiometrically calibrated remote sensing data and second atmospherically corrected remote sensing data corresponding to the second radiometrically calibrated remote sensing data;

[0019] Performing image mosaicking processing on the atmospherically corrected remote sensing data to obtain mosaicked remote sensing data; wherein the mosaicked remote sensing data includes first mosaicked remote sensing data corresponding to the first atmospherically corrected remote sensing data and second mosaicked remote sensing data corresponding to the second atmospherically corrected remote sensing data;

[0020] The first mosaicked remote sensing data is subjected to water body extraction processing to obtain water body boundary data, and the water body boundary data is used as a mask to perform water body boundary clipping on the second mosaicked remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary.

[0021] In one possible design, the radiance is:

[0022] Lt=Gain×DN+Bias;

[0023] Wherein, Gain is the gain read from the header file of the remote sensing data, DN is the digital quantization value, and Bias is the bias read from the header file of the remote sensing data.

[0024] In a possible design, when the correlation analysis is performed on the chlorophyll a concentration sampling data and the reflectance of each band, the correlation coefficient between the reflectance of any band and the chlorophyll a concentration sampling data is obtained as follows:

[0025] ;

[0026] In the formula, and Respectively represent sampling points Chlorophyll a concentration sampling data and sampling points The value of reflectivity in any band at and They represent the average value of chlorophyll a concentration sampling data at all sampling points and the average value of reflectance in any band at all sampling points. Represents the total number of sampling points, correlation coefficient The range is [-1,1], and the correlation coefficient The absolute value of | The closer | is to 1, the greater the correlation between the reflectance of any band and the chlorophyll a concentration sampling data.

[0027] In a possible design, when the characteristic parameters are subjected to band combination processing, a two-band method is adopted, and the obtained multiple band combination indexes are divided into ratio index, difference index and normalized difference index; wherein, the ratio index corresponding to any two characteristic parameters Bi and Bj is:

[0028] RI(Bi,Bj)=Bi / Bj;

[0029] The difference index corresponding to any two characteristic parameters Bi and Bj is:

[0030] DI(Bi,Bj)=Bi-Bj;

[0031] The normalized difference index corresponding to any two feature parameters Bi and Bj is:

[0032] NDI(Bi,Bj)=(Bi-Bj) / (Bi+Bj).

[0033] In a possible design, the band combination index with the largest correlation is used as an independent variable, and the chlorophyll a concentration sampling data is used as a dependent variable, and the chlorophyll a concentration inversion model of the specified plateau lake is obtained by analysis, including:

[0034] Taking the band combination index with the largest correlation as the independent variable and the chlorophyll a concentration sampling data as the dependent variable, a scatter plot is established, and based on the scatter plot, linear analysis, quadratic polynomial regression analysis, cubic polynomial regression analysis and exponential regression analysis are respectively performed to obtain a linear inversion model, a quadratic polynomial inversion model, a cubic polynomial inversion model and an exponential inversion model as well as the determination coefficient of each inversion model;

[0035] According to the determination coefficient of each inversion model, the inversion model with the largest determination coefficient is extracted from the linear inversion model, the quadratic polynomial inversion model, the cubic polynomial inversion model and the exponential inversion model, and the inversion model with the largest determination coefficient is used as the chlorophyll a concentration inversion model of the designated plateau lake.

[0036] In a possible design, the chlorophyll a concentration inversion model for the designated plateau lake is:

[0037] y'=-693.068x' 3 +329.581x' 2 -15.192x'+3.848;

[0038] Wherein, x' is the band combination index with the largest correlation, and y' is the inversion result of the chlorophyll a concentration of the designated plateau lake.

[0039] In a second aspect, the present invention provides a water body chlorophyll a concentration inversion system, which is used to implement a water body chlorophyll a concentration inversion method as described in any one of the above; the water body chlorophyll a concentration inversion system comprises:

[0040] A source data acquisition module is used to obtain chlorophyll a concentration sampling data and remote sensing data of a designated plateau lake; wherein the remote sensing data is collected by a designated domestic satellite;

[0041] A source data preprocessing module, which is in communication with the source data acquisition module and is used to preprocess the remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary;

[0042] A characteristic parameter extraction module is connected to the source data preprocessing module for obtaining the reflectance of each band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data, and performing a correlation analysis between the chlorophyll a concentration sampling data and the reflectance of each band, obtaining a plurality of reflectances of each band whose correlation with the chlorophyll a concentration sampling data is greater than a preset value, and then using the plurality of reflectances of the bands whose correlation is greater than the preset value as characteristic parameters of the preprocessed remote sensing data;

[0043] an inversion model construction module, which is in communication connection with the characteristic parameter extraction module, and is used for performing band combination processing on the characteristic parameters to obtain a plurality of band combination indexes, and performing correlation analysis between the chlorophyll a concentration sampling data and each band combination index to obtain the band combination index with the greatest correlation with the chlorophyll a concentration sampling data among the band combination indexes, and then using the band combination index with the greatest correlation as an independent variable and the chlorophyll a concentration sampling data as a dependent variable, and analyzing to obtain the chlorophyll a concentration inversion model of the specified plateau lake;

[0044] The remote sensing inversion module is communicatively connected with the inversion model building module, and is used to realize the remote sensing inversion of the chlorophyll a concentration in the designated plateau lake according to the chlorophyll a concentration inversion model and the pre-processed remote sensing data.

[0045] In a third aspect, the present invention provides an electronic device, comprising:

[0046] a memory for storing computer program instructions; and,

[0047] A processor is used to execute the computer program instructions to complete the operation of a water body chlorophyll a concentration inversion method as described in any one of the above.

[0048] In a fourth aspect, the present invention provides a computer program product, comprising a computer program or instructions, which, when executed by a computer, implements a method for inverting chlorophyll a concentration in a water body as described in any one of the above.

[0049] The beneficial effects of the present invention are:

[0050] The invention discloses a method, system, equipment and product for inverting chlorophyll a concentration in water bodies. The invention is based on remote sensing data of domestic satellites and combines the characteristics of plateau lake water bodies to achieve rapid and accurate inversion of chlorophyll a concentration in plateau lake water bodies. It can not only fully utilize the advantages of domestic satellite remote sensing data, reduce data acquisition costs, and enhance my country's autonomy and controllability in the field of water quality monitoring, but also improve the accuracy and reliability of chlorophyll a concentration inversion models in plateau lake applications. At the same time, the inversion method based on satellite remote sensing data has the advantages of real-time and large-scale monitoring, and can provide strong data support for real-time, continuous and large-scale monitoring and protection of plateau lake water quality.

[0051] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the method for inverting chlorophyll a concentration in water body in Example 1;

[0053] Figure 2 It is a schematic diagram of the distribution of rivers and lakes of the designated plateau lakes exemplified in Example 1;

[0054] Figure 3 is a schematic diagram of the distribution of sampling points corresponding to the chlorophyll a concentration sampling data exemplified in Example 1;

[0055] Figure 4 is a flow chart of performing atmospheric correction processing on remote sensing data after radiation calibration as exemplified in Embodiment 1;

[0056] Figure 5 is a schematic diagram of the water body boundary data exemplified in Example 1;

[0057] Figure 6 It is a schematic diagram of the correlation result between the reflectance of the corresponding bands from b1 to b6 in the pre-processed remote sensing data and the chlorophyll a concentration sampling data as exemplified in Example 1;

[0058] Figure 7 It is a schematic diagram of the correlation results between the band combination indexes RI, BI and NDI corresponding to the two bands b5 and b6 in Example 1 and the chlorophyll a concentration sampling data;

[0059] Figure 8 is a schematic diagram of the best fitting curve corresponding to the chlorophyll a concentration inversion model illustrated in Example 1;

[0060] Fig. 9 is a schematic diagram of the chlorophyll a concentration inversion result obtained based on the chlorophyll a concentration inversion model illustrated in Example 1;

[0061] Fig.10 is a module block diagram of the water body chlorophyll a concentration inversion system in Example 2;

[0062] Fig.11 This is a module block diagram of the electronic device in Example 3. DETAILED DESCRIPTION

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0064] Embodiment 1:

[0065] This embodiment discloses a method for inverting chlorophyll a concentration in a water body, which can be executed by, but is not limited to, a computer device or a virtual machine with certain computing resources, such as a personal computer, a smart phone, a personal digital assistant, or a wearable device, or by a virtual machine.

[0066] like Figure 1 As shown, a method for inverting chlorophyll a concentration in water may include but is not limited to the following steps:

[0067] S1. Obtain chlorophyll a concentration sampling data and remote sensing data of a designated plateau lake; wherein the remote sensing data is collected by a designated domestic satellite; it should be noted that, in this embodiment, in order to ensure that the sampling time is synchronized with the transit time of the remote sensing satellite to maximize the correlation of the data, the difference between the acquisition time of the chlorophyll a concentration sampling data and the time when the domestic satellite collects the remote sensing data of the designated plateau lake is less than a preset duration threshold. In this embodiment, under the premise of preliminary investigation, in order to ensure that the sampling time is synchronized with the transit time of the remote sensing satellite to maximize the correlation between the chlorophyll a concentration sampling data and the remote sensing data, the sampling activity is arranged near the satellite transit time.

[0068] To further illustrate the technical solution of the present invention, in this embodiment, the designated plateau lakes are taken as examples of the main rivers and lakes in the urban area of ​​Xichang City, Sichuan Province, including Donghe River, Xihe River, Nan'an River, Guanba River, Haihe River and Qionghai Lake. The schematic diagram of the distribution of rivers and lakes is shown in the figure. Figure 2 shown.

[0069] As an example, in view of the fact that there are many rivers in the study area, the vast area of ​​Qionghai Lake and the changeable weather conditions, sampling work will start on January 12, 2023 and continue until January 14, 2023, to ensure that when the satellite passes on January 13, ground sampling data that is highly matched with the remote sensing data can be obtained; the chlorophyll a concentration sampling data includes sampling data at multiple sampling points set in the designated plateau lake. When arranging the sampling points, the main focus is on the lake water dynamics conditions of each element type, lake area, lake basin morphology, recharge conditions, water discharge and water intake methods, sewage treatment equipment site selection and scale, and the flow and transmission laws of pollutants in the water environment. The final determined sampling points in the study area are 28 in total, and the distribution diagram of the sampling points is shown in the following figure. Figure 3 As shown, Figure 3Points HL1 and HL2 are multiple pre-arranged sampling points. When sampling, if the German SIGMA desktop high-speed refrigerated centrifuge 3-18KS and the German Jena Analytic Jena Specord® 210 PLUS UV-visible spectrophotometer are used to measure the chlorophyll a concentration in the water, the chlorophyll concentration sampling data and location data (including longitude and latitude) of each sampling point are collected as shown in Table 1 below.

[0070] Table 1 Chlorophyll concentration sampling data (μg / L) and location data for example

[0071]

[0072] In addition, in this embodiment, the domestic satellites use Jilin-1 Wide 01A (JL1-KF01A) and Jilin-1 Spectral 01 (JL1-GP01). The remote sensing data of these two domestic satellites are used as data sources, including 0.75-meter high-resolution images collected by the former and 5-meter resolution multispectral images collected by the latter. Specifically, Jilin-1 Wide 01, also known as Hongqi-1-H9 Satellite, is a commercial optical remote sensing satellite with extremely high resolution. It was launched at the Taiyuan Satellite Launch Center on January 15, 2020. It has the characteristics of high resolution, ultra-wide width, high-speed storage, and high-speed transmission. Jilin-1 Spectral 01, also known as Jilin Lincao-1 Satellite, is one of the Jilin-1 Spectral Satellites. It was successfully launched at the Jiuquan Space Launch Center on January 21, 2019. The satellite orbit altitude is 528 km and the return period is 28 days. The satellite is equipped with multispectral imagers (PMS1, PMS2), short-wave, medium-wave, and long-wave infrared cameras, and can obtain remote sensing data with a resolution of 5m, a width of 110km, and 26 spectral bands. The specific indicators are shown in Table 2. The remote sensing image band settings of the two domestic satellites are shown in Table 3:

[0073] Table 2 Technical indicators of Jilin-1 Spectrum 01

[0074]

[0075] Table 3 Remote sensing monitoring data band settings

[0076]

[0077] Specifically, in step S1, the remote sensing data includes first remote sensing data collected by a first designated domestic satellite and second remote sensing data collected by a second designated domestic satellite. Specifically, in this embodiment, the first designated domestic satellite uses Jilin No. 1 Wideband 01A Satellite (JL1-KF01A), and the second designated domestic satellite uses Jilin No. 1 Spectral 01 Satellite (JL1-GP01). It should be noted that the remote sensing data in this embodiment is set to two, which can facilitate the improvement of the accuracy of the water body boundary in the pre-processed remote sensing data subsequently obtained.

[0078] S2. Preprocessing the remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary; it should be noted that preprocessing the acquired remote sensing data can eliminate the influence of the sensor itself and atmospheric conditions on the remote sensing data.

[0079] In step S2 of this embodiment, the remote sensing data is preprocessed to obtain preprocessed remote sensing data with the water body as the boundary, including:

[0080] S201. Perform radiation calibration processing on the remote sensing data, convert the digital quantization value of the remote sensing data into radiant brightness to obtain radiation calibrated remote sensing data; wherein the radiation calibrated remote sensing data is divided into first radiation calibrated remote sensing data corresponding to the first remote sensing data and second radiation calibrated remote sensing data corresponding to the second remote sensing data; in this embodiment, performing radiation calibration processing on the remote sensing data can facilitate the subsequent acquisition of the band reflectance value of each sampling point in the remote sensing data.

[0081] Specifically, in step S201, the radiance is:

[0082] Lt=Gain×DN+Bias;

[0083] Wherein, Gain is the gain read from the header file of the remote sensing data, DN is the digital quantization value, that is, the satellite payload observation value, and Bias is the bias read from the header file of the remote sensing data. It should be noted that the gain and bias constitute the calibration coefficients for the radiation calibration process, wherein the gain Gain corresponds to the parameter AbsCalibrationGain in the header file, and the bias Bias corresponds to the parameter AbsCalibrationBias in the header file.

[0084] S202. Perform atmospheric correction processing on the radiation calibrated remote sensing data to obtain atmospheric corrected remote sensing data; wherein the atmospheric corrected remote sensing data includes first atmospheric corrected remote sensing data corresponding to the first radiation calibrated remote sensing data and second atmospheric corrected remote sensing data corresponding to the second radiation calibrated remote sensing data; in this embodiment, the FLAASH (an atmospheric correction module developed by the Institute of Spectral Sciences with the support of the U.S. Air Force Research Laboratory) model is used to implement atmospheric correction processing on the radiation calibrated remote sensing data.

[0085] Specifically, in the process of executing step S202 in this embodiment, if Figure 4 As shown, first, the input file is set to the remote sensing data after radiation calibration, and all bands use the same scale; then, the output file location and name are set, and the parameters of the FLAASH model are set. As an example, the parameters (CenterLatitude, CenterLongitude) in the header file of the remote sensing data are set to the field of view center parameters of the FLAASH model, the parameters CenterTime (in the format of year, month, day, hour, minute, and second) in the header file of the remote sensing data are set to the shooting time parameters of the FLAASH model, the flight parameters of the Jilin-1 spectral satellite sensor (such as a flight altitude of 528km) are set to the sensor altitude parameters of the FLAASH model, the terrain data of the designated plateau lake (such as a ground elevation of 1.921km) is set to the ground elevation parameter of the FLAASH model, and NONE (indicating that no special model needs to be set) is set to the aerosol parameter of the FLAASH model. retrieval parameters; finally, a suitable spectral library is selected to perform atmospheric correction processing on the remote sensing data after the radiometric calibration. As an example, the spectral library selects the spectral library of Jilin-1 spectral satellite, thereby obtaining the remote sensing data after the atmospheric correction. Based on this, this embodiment can further eliminate the color and brightness information distortion problem of the remote sensing data after the radiometric calibration caused by atmospheric factors such as atmospheric absorption, atmospheric scattering, atmospheric radiation, and atmospheric aerosols, and improve the accuracy and efficiency of subsequent remote sensing data processing.

[0086] S203. Perform image mosaic processing on the atmospherically corrected remote sensing data to obtain mosaicked remote sensing data; wherein the mosaicked remote sensing data includes first mosaicked remote sensing data corresponding to the first atmospherically corrected remote sensing data and second mosaicked remote sensing data corresponding to the second atmospherically corrected remote sensing data; as an example, the mosaicked remote sensing data in this embodiment can cover the entire study area of ​​the main rivers and lakes in the main urban area of ​​Xichang City. Specifically, since the image data all have geographic coordinates, in this embodiment, the Seamless Mosaic image seamless mosaicking tool is used to quickly mosaic the atmospherically corrected remote sensing data, and after selecting all atmospherically corrected remote sensing data, that is, multiple adjacent remote sensing images, the spliced ​​result can be quickly generated to complete the image mosaicking and obtain the mosaicked remote sensing data.

[0087] S204. Perform water body extraction processing on the first mosaicked remote sensing data to obtain water body boundary data, and use the water body boundary data as a mask to perform water body boundary clipping on the second mosaicked remote sensing data to obtain pre-processed remote sensing data with the water body as the boundary. It should be noted that the water body extraction process is used to eliminate interference from other land objects, and only the water body range is extracted for the rivers and lakes in the study area. In this embodiment, when performing water body boundary clipping on the second mosaicked remote sensing data, the mask extraction tool in the ArcGIS mapping tool is used as the clipping tool. Its input data is the second mosaicked remote sensing data and the water body boundary data. No other parameter settings are required to output the clipped image data, that is, the pre-processed remote sensing data with the water body as the boundary. As an example, the water body boundary data obtained after the water body extraction process on the first mosaicked remote sensing data is extracted based on the high-resolution image of the Jilin-1 wide-band 01A satellite on January 3, 2023. The example water body boundary data is as follows Figure 5 shown.

[0088] Specifically, in step S204, when the water body extraction processing is performed on the first mosaicked remote sensing data, the vector boundary extraction method is adopted. It should be noted that there are many water body extraction methods such as threshold segmentation, normalized difference water body index, and vector boundary extraction. When part of the study area is in the urban area and the terrain around the water body is complex, the extraction effect using the normalized water body difference index is not good, and it is impossible to completely distinguish the building from the water body. It contains many small spots. Therefore, the vector boundary extraction method is used in this embodiment to extract the water body. The vector boundary extraction method uses visual interpretation, manual interpretation and hand-drawing, and has higher precision and accuracy.

[0089] S3. Obtain the reflectance of each band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data, and perform correlation analysis on the chlorophyll a concentration sampling data and the reflectance of each band, to obtain multiple band reflectances whose correlation with the chlorophyll a concentration sampling data is greater than a preset value, and then use the multiple band reflectances whose correlation is greater than the preset value as characteristic parameters of the preprocessed remote sensing data; specifically, in this embodiment, when obtaining the reflectance of the band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data, the preprocessed remote sensing data and the sampling point position of the chlorophyll a concentration sampling data are first superimposed, and then the reflectance of the band corresponding to the sampling point position of the chlorophyll a concentration sampling data can be extracted from the superimposed remote sensing data.

[0090] In step S3, when the correlation analysis is performed on the chlorophyll a concentration sampling data and the reflectance of each band, the correlation coefficient between the reflectance of any band and the chlorophyll a concentration sampling data is obtained as follows:

[0091] ;

[0092] In the formula, and Respectively represent sampling points Chlorophyll a concentration sampling data and sampling points The value of reflectivity in any band at and They represent the average value of chlorophyll a concentration sampling data at all sampling points and the average value of reflectance in any band at all sampling points. Represents the total number of sampling points, correlation coefficient The range is [-1,1], and the correlation coefficient The absolute value of | The closer | is to 1, the greater the correlation between the reflectance of any band and the chlorophyll a concentration sampling data.

[0093] As an example, in this embodiment, in the b1 to b6 bands in the pre-processed remote sensing data, Figure 6 As shown, the two bands b5 and b6 passed the significance test, that is, the sensitive bands of chlorophyll a concentration in the pre-processed remote sensing data include the two bands b5 and b6.

[0094] S4. Perform band combination processing on the characteristic parameters to obtain multiple band combination indices, and perform correlation analysis on the chlorophyll a concentration sampling data and each band combination index to obtain the band combination index with the largest correlation with the chlorophyll a concentration sampling data among the band combination indices, and then use the band combination index with the largest correlation as the independent variable and the chlorophyll a concentration sampling data as the dependent variable to analyze and obtain the chlorophyll a concentration inversion model of the specified plateau lake; it should be noted that performing band combination calculation can effectively reduce the interference of environmental factors on spectral data, thereby improving the quality of effective spectral information and enhancing the sensitivity of spectral reflectance data to water quality parameter values.

[0095] Specifically, in step S4, when the characteristic parameters are subjected to band combination processing, a two-band method is adopted, and the obtained multiple band combination indexes are divided into a ratio index (Relative Index, RI), a difference index (Difference Index, DI) and a normalized difference index (Normalized Difference Index, NDI); wherein the ratio index corresponding to any two characteristic parameters Bi and Bj is:

[0096] RI(Bi,Bj)=Bi / Bj;

[0097] The difference index corresponding to any two characteristic parameters Bi and Bj is:

[0098] DI(Bi,Bj)=Bi-Bj;

[0099] The normalized difference index corresponding to any two feature parameters Bi and Bj is:

[0100] NDI(Bi,Bj)=(Bi-Bj) / (Bi+Bj).

[0101] In this embodiment, the correlation analysis between the chlorophyll a concentration sampling data and each band combination index is performed using SPSS software (a statistical analysis software). For example, in this embodiment, the band combination indexes RI (b5, b6), DI (b5, b6) and NDI (b5, b6) all passed the significance level test, such as Figure 7 As shown in FIG. 1 , it is a schematic diagram of the correlation results between the band combination index RI (b5, b6), DI (b5, b6) and NDI (b5, b6) corresponding to the two bands b5 and b6 and the chlorophyll a concentration sampling data. Figure 7It can be seen that NDI (b5, b6) has the greatest correlation with chlorophyll concentration, with a correlation coefficient of 0.568, which can be used to invert the chlorophyll concentration distribution in the study area. Based on this, the band combination index NDI (b5, b6) is used as the band combination index with the greatest correlation with the chlorophyll a concentration sampling data among all the band combination indices.

[0102] Among them, in step S4, the band combination index with the largest correlation is used as an independent variable, and the chlorophyll a concentration sampling data is used as a dependent variable, and the chlorophyll a concentration inversion model of the specified plateau lake is obtained by analysis, including:

[0103] S401. Taking the band combination index with the largest correlation as the independent variable and the chlorophyll a concentration sampling data as the dependent variable, a scatter plot is established, and linear analysis, quadratic polynomial regression analysis, cubic polynomial regression analysis and exponential regression analysis are performed based on the scatter plot to obtain a linear inversion model, a quadratic polynomial inversion model, a cubic polynomial inversion model and an exponential inversion model and the determination coefficient of each inversion model; as an example, the four inversion models and the determination coefficients obtained in this embodiment are shown in Table 4 below:

[0104] Table 4 Chlorophyll a concentration inversion model and determination coefficient

[0105]

[0106] S402. According to the determination coefficient of each inversion model, the inversion model with the largest determination coefficient is extracted from the linear inversion model, the quadratic polynomial inversion model, the cubic polynomial inversion model and the exponential inversion model, and the inversion model with the largest determination coefficient is used as the chlorophyll a concentration inversion model of the specified plateau lake. It should be noted that the larger the determination coefficient value, the higher the accuracy and reliability of the corresponding inversion model. As an example, in this embodiment, the cubic polynomial inversion model with the largest determination coefficient is used as the chlorophyll a concentration inversion model of the specified plateau lake, and the schematic diagram of the corresponding optimal fitting curve is shown as follows. Figure 8 shown.

[0107] In this embodiment, the chlorophyll a concentration inversion model of the designated plateau lake is obtained as follows:

[0108] y'=-693.068x' 3 +329.581x' 2 -15.192x'+3.848;

[0109] Wherein, x' is the band combination index with the largest correlation, and y' is the inversion result of the chlorophyll a concentration of the designated plateau lake.

[0110] It should be noted that in this embodiment, after obtaining the chlorophyll a concentration inversion model of the designated plateau lake, in order to verify the accuracy and reliability of the model, the model is verified by combining the ground measured spectral data and airborne remote sensing data, so as to ensure the accuracy and stability of the model in practical applications by comparing and analyzing the differences between the monitoring results and the measured data.

[0111] Specifically, in the implementation process of this embodiment, 80% of the measured data in the chlorophyll a concentration sampling data is used as a training set to perform step S4 to obtain the chlorophyll a concentration inversion model of the specified plateau lake, and the remaining 20% ​​of the measured data is used as a validation set, and the corresponding remote sensing band reflectivity is substituted into the chlorophyll a concentration inversion model to obtain the chlorophyll a concentration model estimation value corresponding to the point. The accuracy of the inversion model is evaluated by comparing the measured data with the model estimation value, and the results are shown in Table 5 below:

[0112] Table 5 Measured values ​​of chlorophyll a concentration and estimated values ​​from the inversion model

[0113]

[0114] By comparing the measured data with the model estimated values, it can be seen that the estimated values ​​of chlorophyll a concentration are not much different from the actual values, and are basically stable near the measured values. Among them, the relative errors of H1, HP3, and HP9 are within 10%, and the relative errors of HL5 and HP13 are greater than 10%, which is related to the number of sampling points, layout, and modeling accuracy. At the same time, the sampling work lasted for 3 days, and the synchronization between the field sampling points and the remote sensing images decreased, but in general, the chlorophyll concentration inversion model has high accuracy and is more reliable. It is feasible to use the obtained chlorophyll a concentration inversion model to invert the chlorophyll concentration of the main rivers and lakes in the urban area of ​​Xichang City.

[0115] S5. Based on the chlorophyll a concentration inversion model and the pre-processed remote sensing data, remote sensing inversion of the chlorophyll a concentration in the designated plateau lake is achieved.

[0116] It should be noted that, in this embodiment, the constructed water chlorophyll a concentration inversion model is applied to the remote sensing image processing software and output into a map, so that the water quality of plateau lakes can be intuitively understood, and the chlorophyll a concentration of plateau lakes can be quickly and accurately monitored. As an example, in this embodiment, the chlorophyll a concentration inversion result obtained is as follows: Fig. 9As shown. From the chlorophyll spatial distribution map in the chlorophyll a concentration inversion result, it can be seen that the inversion result is relatively reliable and can be consistent with the trend of the measured data. It can be seen that the chlorophyll a concentration in the entire Qionghai waters is relatively low, and the chlorophyll a concentration in the nearshore waters of the lake is higher than that in the farshore area. Among them, the chlorophyll a concentration in the water source protection area is below 5 mg / L, and the concentrations in the wetland park, Nan'an River and Xihe River at the mouth of the lake are slightly higher. Combining the measured results of the sampling points and the inversion results of the spectrum star, it can be seen that in terms of lakes, the chlorophyll a concentration in the Qionghai drinking water source protection area is low, and the chlorophyll concentration in the downstream wetland park is slightly higher; in terms of rivers, the chlorophyll a concentration of the Guanba River inversion data entering the lake is high, and the influence of soil erosion should be eliminated in combination with actual observations. The chlorophyll concentration in the upper reaches is low, and the water quality of the rivers entering the lake is good; the chlorophyll a concentration of some of the Haihe River out of the lake and the Anning River that merges into it is relatively high. The rivers in these areas flow through urban areas and are greatly affected by human factors.

[0117] In view of the limitations and error problems existing in the prior art of using foreign satellite data, the present invention proposes a method for inverting the chlorophyll a concentration of plateau lake water bodies based on domestic satellite remote sensing data. Specifically, during the implementation of this embodiment, the chlorophyll a concentration sampling data of the designated plateau lake and the remote sensing data collected by domestic satellites are first obtained, and the remote sensing data are preprocessed to obtain the preprocessed remote sensing data with the water body as the boundary; then, the reflectance of each band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data is obtained, and the correlation analysis of the chlorophyll a concentration sampling data and the reflectance of each band is performed to obtain multiple band reflectances whose correlation with the chlorophyll a concentration sampling data is greater than a preset value, and then the multiple band reflectances with the correlation greater than the preset value are used as the preprocessed remote sensing data. The characteristic parameters of the remote sensing data are obtained by band combination processing; then, the characteristic parameters are subjected to band combination processing to obtain multiple band combination indices, and correlation analysis is performed on the chlorophyll a concentration sampling data and each band combination index to obtain the band combination index with the largest correlation with the chlorophyll a concentration sampling data, and then the band combination index with the largest correlation is used as an independent variable, and the chlorophyll a concentration sampling data is used as a dependent variable to analyze and obtain the chlorophyll a concentration inversion model of the designated plateau lake; finally, according to the chlorophyll a concentration inversion model and the preprocessed remote sensing data, the remote sensing inversion of the chlorophyll a concentration in the designated plateau lake is realized. This embodiment is based on the remote sensing data of domestic satellites and combines the characteristics of plateau lake water bodies to achieve rapid and accurate inversion of chlorophyll a concentration in plateau lake water bodies. It can not only fully utilize the advantages of domestic satellite remote sensing data, reduce data acquisition costs, and enhance my country's autonomy and controllability in the field of water quality monitoring, but also improve the accuracy and reliability of the chlorophyll a concentration inversion model in the application of plateau lakes. At the same time, the inversion method based on satellite remote sensing data has the advantages of real-time and large-scale monitoring, and can provide strong data support for real-time, continuous and large-scale monitoring and protection of plateau lake water quality.

[0118] Embodiment 2:

[0119] This embodiment discloses a water body chlorophyll a concentration inversion system, which is used to implement the water body chlorophyll a concentration inversion method in embodiment 1; Fig.10 As shown, the water body chlorophyll a concentration inversion system includes:

[0120] A source data acquisition module is used to obtain chlorophyll a concentration sampling data and remote sensing data of a designated plateau lake; wherein the remote sensing data is collected by a designated domestic satellite;

[0121] A source data preprocessing module, which is in communication with the source data acquisition module and is used to preprocess the remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary;

[0122] A characteristic parameter extraction module is connected to the source data preprocessing module for obtaining the reflectance of each band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data, and performing a correlation analysis between the chlorophyll a concentration sampling data and the reflectance of each band, obtaining a plurality of reflectances of each band whose correlation with the chlorophyll a concentration sampling data is greater than a preset value, and then using the plurality of reflectances of the bands whose correlation is greater than the preset value as characteristic parameters of the preprocessed remote sensing data;

[0123] an inversion model construction module, which is in communication connection with the characteristic parameter extraction module, and is used for performing band combination processing on the characteristic parameters to obtain a plurality of band combination indexes, and performing correlation analysis between the chlorophyll a concentration sampling data and each band combination index to obtain the band combination index with the greatest correlation with the chlorophyll a concentration sampling data among the band combination indexes, and then using the band combination index with the greatest correlation as an independent variable and the chlorophyll a concentration sampling data as a dependent variable, and analyzing to obtain the chlorophyll a concentration inversion model of the specified plateau lake;

[0124] The remote sensing inversion module is communicatively connected with the inversion model building module, and is used to realize the remote sensing inversion of the chlorophyll a concentration in the designated plateau lake according to the chlorophyll a concentration inversion model and the pre-processed remote sensing data.

[0125] It should be noted that the working process, working details and technical effects of the water chlorophyll a concentration inversion system provided in this embodiment 2 can be found in embodiment 1 and will not be repeated here.

[0126] Embodiment 3:

[0127] Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a laptop computer, or a desktop computer. The electronic device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc. Fig.11 As shown, the electronic equipment includes:

[0128] a memory for storing computer program instructions; and,

[0129] A processor is used to execute the computer program instructions to complete the operation of a water body chlorophyll a concentration inversion method as described in any one of Example 1.

[0130] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen.

[0131] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the water body chlorophyll a concentration inversion method provided in Example 1 of the present application.

[0132] In some embodiments, the terminal may further optionally include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected via a bus or a signal line. Each peripheral device may be connected to the communication interface 303 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 304, a display screen 305 and a power supply 306.

[0133] The communication interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0134] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals.

[0135] The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof.

[0136] The power supply 306 is used to supply power to various components in the electronic device.

[0137] Embodiment 4:

[0138] Based on any one of Examples 1 to 3, this embodiment discloses a computer program product, including a computer program or instructions, which, when executed by a computer, implements a method for inverting chlorophyll a concentration in a water body as described in any one of Example 1.

[0139] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for inverting chlorophyll a concentration in water, characterized by: include: Obtaining chlorophyll a concentration sampling data and remote sensing data of designated plateau lakes; wherein the remote sensing data is collected by designated domestic satellites; Preprocessing the remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary; Obtaining the reflectance of each band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data, and performing correlation analysis between the chlorophyll a concentration sampling data and the reflectance of each band, obtaining multiple reflectances of each band whose correlation with the chlorophyll a concentration sampling data is greater than a preset value, and then using the multiple reflectances of the bands whose correlation is greater than the preset value as characteristic parameters of the preprocessed remote sensing data; Performing band combination processing on the characteristic parameters to obtain a plurality of band combination indexes, and performing correlation analysis on the chlorophyll a concentration sampling data and each band combination index to obtain the band combination index with the greatest correlation with the chlorophyll a concentration sampling data among the band combination indexes, and then using the band combination index with the greatest correlation as an independent variable and the chlorophyll a concentration sampling data as a dependent variable, and analyzing to obtain a chlorophyll a concentration inversion model of the designated plateau lake; According to the chlorophyll a concentration inversion model and the pre-processed remote sensing data, remote sensing inversion of the chlorophyll a concentration in the designated plateau lake is achieved; The remote sensing data includes first remote sensing data collected by a first designated domestic satellite and second remote sensing data collected by a second designated domestic satellite; correspondingly, the remote sensing data is preprocessed to obtain preprocessed remote sensing data with a water body as a boundary, including: Performing radiation calibration processing on the remote sensing data, converting the digital quantization value of the remote sensing data into radiance, so as to obtain radiation-calibrated remote sensing data; wherein the radiation-calibrated remote sensing data is divided into first radiation-calibrated remote sensing data corresponding to the first remote sensing data and second radiation-calibrated remote sensing data corresponding to the second remote sensing data; Performing atmospheric correction processing on the radiometrically calibrated remote sensing data to obtain atmospherically corrected remote sensing data; wherein the atmospherically corrected remote sensing data includes first atmospherically corrected remote sensing data corresponding to the first radiometrically calibrated remote sensing data and second atmospherically corrected remote sensing data corresponding to the second radiometrically calibrated remote sensing data; Performing image mosaicking processing on the atmospherically corrected remote sensing data to obtain mosaicked remote sensing data; wherein the mosaicked remote sensing data includes first mosaicked remote sensing data corresponding to the first atmospherically corrected remote sensing data and second mosaicked remote sensing data corresponding to the second atmospherically corrected remote sensing data; The first mosaicked remote sensing data is subjected to water body extraction processing to obtain water body boundary data, and the water body boundary data is used as a mask to perform water body boundary clipping on the second mosaicked remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary.

2. A method for inverting chlorophyll a concentration in water according to claim 1, characterized in that: The radiance is: Lt=Gain×DN+Bias; Wherein, Gain is the gain read from the header file of the remote sensing data, DN is the digital quantization value, and Bias is the bias read from the header file of the remote sensing data.

3. The method for inverting chlorophyll a concentration in water according to claim 1, characterized in that: When the correlation analysis is performed on the chlorophyll a concentration sampling data and the reflectance of each band, the correlation coefficient between the reflectance of any band and the chlorophyll a concentration sampling data is obtained as follows: In the formula, x i and i They represent the chlorophyll a concentration sampling data at sampling point i and the reflectance value of any band at sampling point i, respectively. and They respectively represent the average value of the chlorophyll a concentration sampling data at all sampling points and the average value of the reflectance of any band at all sampling points. n represents the total number of sampling points. The range of the correlation coefficient r is [-1,1]. The closer the absolute value of the correlation coefficient r |r| is to 1, the greater the correlation between the reflectance of any band and the chlorophyll a concentration sampling data.

4. The method for inverting chlorophyll a concentration in water according to claim 1, characterized in that: When the characteristic parameters are subjected to band combination processing, a two-band method is used, and the obtained multiple band combination indexes are divided into ratio index, difference index and normalized difference index; wherein, the ratio index corresponding to any two characteristic parameters Bi and Bj is: RI(Bi,Bj)=Bi / Bj; The difference index corresponding to any two characteristic parameters Bi and Bj is: DI(Bi,Bj)=Bi-Bj; The normalized difference index corresponding to any two feature parameters Bi and Bj is: NDI(Bi,Bj)=(Bi-Bj) / (Bi+Bj).

5. The method for inverting chlorophyll a concentration in water according to claim 1, characterized in that: The band combination index with the largest correlation is used as an independent variable, and the chlorophyll a concentration sampling data is used as a dependent variable, and the chlorophyll a concentration inversion model of the specified plateau lake is obtained by analysis, including: Taking the band combination index with the largest correlation as the independent variable and the chlorophyll a concentration sampling data as the dependent variable, a scatter plot is established, and based on the scatter plot, linear analysis, quadratic polynomial regression analysis, cubic polynomial regression analysis and exponential regression analysis are respectively performed to obtain a linear inversion model, a quadratic polynomial inversion model, a cubic polynomial inversion model and an exponential inversion model as well as the determination coefficient of each inversion model; According to the determination coefficient of each inversion model, the inversion model with the largest determination coefficient is extracted from the linear inversion model, the quadratic polynomial inversion model, the cubic polynomial inversion model and the exponential inversion model, and the inversion model with the largest determination coefficient is used as the chlorophyll a concentration inversion model of the designated plateau lake.

6. A method for inverting chlorophyll a concentration in water according to claim 5, characterized in that: The chlorophyll a concentration inversion model of the designated plateau lake is: y’=-693.068x’ 3 +329.581x’ 2 -15.192x’+3.848; Wherein, x' is the band combination index with the largest correlation, and y' is the inversion result of the chlorophyll a concentration of the designated plateau lake.

7. A water body chlorophyll a concentration inversion system, characterized by: Used to implement a water body chlorophyll a concentration inversion method as claimed in any one of claims 1 to 6; the water body chlorophyll a concentration inversion system comprises: A source data acquisition module is used to obtain chlorophyll a concentration sampling data and remote sensing data of a designated plateau lake; wherein the remote sensing data is collected by a designated domestic satellite; A source data preprocessing module, which is in communication with the source data acquisition module and is used to preprocess the remote sensing data to obtain preprocessed remote sensing data with the water body as the boundary; A characteristic parameter extraction module is connected to the source data preprocessing module for obtaining the reflectance of each band corresponding to the sampling point position of the chlorophyll a concentration sampling data in the preprocessed remote sensing data, and performing a correlation analysis between the chlorophyll a concentration sampling data and the reflectance of each band, obtaining a plurality of reflectances of each band whose correlation with the chlorophyll a concentration sampling data is greater than a preset value, and then using the plurality of reflectances of the bands whose correlation is greater than the preset value as characteristic parameters of the preprocessed remote sensing data; an inversion model construction module, which is in communication connection with the characteristic parameter extraction module, and is used for performing band combination processing on the characteristic parameters to obtain a plurality of band combination indexes, and performing correlation analysis between the chlorophyll a concentration sampling data and each band combination index to obtain the band combination index with the greatest correlation with the chlorophyll a concentration sampling data among the band combination indexes, and then using the band combination index with the greatest correlation as an independent variable and the chlorophyll a concentration sampling data as a dependent variable, and analyzing to obtain the chlorophyll a concentration inversion model of the specified plateau lake; The remote sensing inversion module is communicatively connected with the inversion model building module, and is used to realize the remote sensing inversion of the chlorophyll a concentration in the designated plateau lake according to the chlorophyll a concentration inversion model and the pre-processed remote sensing data.

8. An electronic device, characterized in that: include: a memory for storing computer program instructions; as well as, A processor is used to execute the computer program instructions to complete the operation of the water body chlorophyll a concentration inversion method as described in any one of claims 1 to 6.

9. A computer program product comprising a computer program or instructions, characterized in that: When executed by a computer, the computer program or the instruction implements a method for inverting chlorophyll a concentration in a water body as described in any one of claims 1 to 6.

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