Inland water body total absorption coefficient inversion method and system based on QAA classification algorithm

Abstract

The invention provides an inland water body total absorption coefficient inversion method and system based on a QAA classification algorithm. The method comprises the following steps: obtaining a first process parameter of a full wave band according to underwater remote sensing reflectivity; dividing the water body into a clean water body, a turbid water body and a highly turbid water body by taking the remote sensing reflectivity of the water body at the wavelength of 670nm as a discrimination standard; selecting different reference wavelengths for the three types of water bodies, and calculating absorption coefficients at the reference wavelengths, particulate matter backscattering coefficients and power parameters of the three types of water bodies; according to the backscattering coefficient and the power parameter of the reference wavelength, calculating the backscattering coefficient of the full wave band of the three types of water bodies; and according to the first process parameter of the full wave band and the backscattering coefficient of the full wave band, calculating the total absorption coefficient of the water body of the three types of water bodies. According to the scheme provided by the invention, the problem of insufficient precision of inland water body total absorption coefficient remote sensing inversion is solved, and the adaptability to complex optical characteristics is improved.

Description

Technical Field

[0001] The invention belongs to the field of remote sensing technology and water environment monitoring, and in particular relates to a total absorption coefficient inversion method and system for inland water bodies based on a QAA classification algorithm. Background Art

[0002] Inland water bodies, such as lakes, reservoirs and rivers, are important components of the global water cycle and regional water resources, and have a profound impact on ecological balance and human activities. With the increase of human activities, these water bodies are facing the threat of water quality degradation, including eutrophication, organic pollution and increased suspended matter. These changes not only affect the ecological health and biodiversity of water bodies, but are also directly related to the sustainable use of water resources.

[0003] Solar radiation plays a vital role in inland water ecosystems. It is not only the energy basis for phytoplankton photosynthesis, but also a key factor driving photochemical reactions in water bodies. The regulation of sunlight on the thermal balance of water bodies and the promotion of physical mixing in the upper water body are extremely important for maintaining the normal operation of the ecosystem. The transmission of light radiation inside the water body is mainly affected by the intrinsic optical quantities (IOPs), including the absorption and scattering processes of light, which are affected by suspended particles, dissolved substances, phytoplankton and their degradation products in the water. IOPs refer to optical properties that only depend on the characteristics of the water body itself, including but not limited to absorption coefficient, scattering coefficient and scattering phase function. These parameters are crucial to understanding and simulating the propagation of light in water bodies, because they determine the absorption and scattering ability of water bodies to light, which in turn affects the transparency, color and thermal radiation characteristics of water bodies. The IOPs of water bodies are the fundamental determinants of their apparent optical quantities (AOPs). Different water types and nutrient conditions lead to different characteristics of AOPs. Therefore, establishing the response relationship between water quality parameters and spectral characteristics is crucial for the development of water color remote sensing algorithms. Radiative transfer theory describes the propagation of light in water, involving IOPs and AOPs, and helps to more accurately understand and predict the propagation behavior of light in water.

[0004] IOPs include parameters such as absorption coefficient, scattering coefficient and scattering phase function, which can effectively describe the optical properties of phytoplankton, NAP and CDOM in water. AOPs are affected by the incident angle distribution of the light field, the type and concentration of substances in the water, and are related to the geometric structure and characteristics of the medium itself and the surrounding light field. The optical backscattering phenomenon is closely related to the substances in the water environment, making IOPs an important parameter for evaluating the characteristics of the water environment.

[0005] Optically active substances (OACs) in natural water bodies, such as phytoplankton, non-algae particles and colored soluble organic matter (CDOM), have a significant impact on the optical properties of water bodies. When sunlight enters a water body, part of the light is reflected on the water surface, and the rest is absorbed or scattered by OACs in the water body, providing energy for underwater ecosystems.

[0006] In the study of water optics, the total absorption coefficient of water a(λ) is one of the key parameters describing the inherent optical properties of water. The total absorption coefficient of water a(λ) describes the ability of water to absorb light and is a key indicator for assessing water quality and conducting water color remote sensing analysis. This parameter includes the absorption of all optically active components in the water, such as suspended particles, dissolved organic matter, chlorophyll, etc. This parameter is closely related to the nutrient status, particulate load and organic matter content of the water, and is an important indicator for assessing the health of water bodies and conducting water quality monitoring. Accurately obtaining a(λ) is of great significance for understanding the biogeochemical processes of water bodies, predicting water quality trends and formulating water environment management strategies.

[0007] Traditional a(λ) measurement methods rely on field sampling and laboratory analysis, which are costly, time-consuming, and difficult to achieve large-scale continuous monitoring. The development of remote sensing technology has made it possible to monitor a(λ) over a large area and at a high frequency. It has the advantages of wide coverage, fast response time, and low cost, and has become a key technology for monitoring the dynamic changes of water bodies. By analyzing the reflection and absorption characteristics of water bodies to solar radiation, remote sensing technology can indirectly invert IOPs parameters such as a(λ). However, the optical properties of inland water bodies are complex and changeable, and are affected by a variety of natural and human factors, all of which pose challenges to the accuracy of remote sensing inversion of a(λ). Existing intrinsic optical quantity inversion algorithms mainly include empirical algorithms, semi-analytical algorithms, and deep learning-assisted algorithms. The empirical algorithm establishes the relationship between the optical properties of water bodies and remote sensing reflectance through regression analysis, which is suitable for waters with single optical properties, but due to the lack of in-depth understanding of physical processes, it can only be applied in specific areas. Semi-analytical algorithms (such as QAA and GIOP) decompose the water absorption and backscattering properties through the radiative transfer equation and perform well in the open ocean, but have limited applicability to complex inland waters.

[0008] The problem of eutrophication of inland water bodies is usually caused by excessive input of nutrients such as nitrogen and phosphorus, which leads to excessive growth of aquatic plants such as algae. This overgrowth not only changes the optical properties of the water body and reduces the transparency of the water body, but also affects the photosynthesis of aquatic plants, thereby reducing the content of dissolved oxygen in the water body. The reduction of dissolved oxygen will interfere with the metabolic process of aquatic organisms, affect their survival and reproduction, and may even lead to a decline in biodiversity, causing serious impacts on the entire aquatic ecosystem. Therefore, timely and appropriate water quality monitoring is crucial for water quality assessment, water pollution control, and the formulation of effective water management measures.

[0009] However, the composition of inland and nearshore water bodies varies greatly, and the optical properties are complex and changeable, which brings challenges to water color and water quality monitoring. Although the existing QAA algorithm and its improved algorithm have achieved certain inversion effects in specific water bodies or water body types, they are often difficult to directly apply to multiple inland aquatic systems with large differences in optical properties. The main problems of these algorithms include insufficient model universality and low model accuracy, which limit their application in a wider range of water bodies. In view of the current situation, the remote sensing inversion of the total absorption coefficient of inland water bodies needs to be further explored and studied.

[0010] Disadvantages of existing technology:

[0011] First, the composition of inland and nearshore water bodies varies greatly, and the optical properties are complex and changeable. They are significantly affected by optically active substances (such as phytoplankton, non-algae particles, and colored soluble organic matter) and external factors (such as weather conditions and human activities). Existing methods are difficult to fully capture these changes, which poses a challenge to water color and water quality monitoring. Secondly, the traditional a(λ) measurement method relies on field sampling and laboratory analysis, which is time-consuming and costly, and cannot meet the needs of large-scale, continuous monitoring. Although remote sensing technology has advantages in large-scale and high-frequency monitoring, due to the complexity of the optical properties of inland water bodies, existing algorithms (such as the QAA algorithm) are not universal enough in water bodies with large differences in optical properties, resulting in low inversion accuracy. In addition, the eutrophication problem has exacerbated the difficulty of water quality monitoring, which not only affects the assessment of the health of water bodies, but also limits the effectiveness of water pollution control and management measures.

[0012] The existing intrinsic optical quantity inversion algorithms mainly include empirical algorithms, semi-analytical algorithms, etc. The empirical algorithm establishes the relationship between the optical properties of water bodies and remote sensing reflectivity through regression analysis. It is suitable for waters with single optical properties, but due to the lack of in-depth understanding of physical processes, it can only be applied in specific areas. Semi-analytical algorithms (such as QAA and GIOP) decompose the absorption and backscattering characteristics of water bodies through the radiation transfer equation. They perform well in open oceans, but have limited applicability to complex inland waters. First, empirical algorithms and semi-analytical algorithms are mostly based on data sets of clear waters, and it is difficult to adapt to the complex and heterogeneous optical properties of inland and coastal waters, such as different combinations of suspended particulate matter, CDOM and phytoplankton. In addition, the interaction between absorption and scattering in inland and coastal waters increases the difficulty of inverting the absorption coefficient and backscattering coefficient, resulting in the algorithm often having negative values ​​or large errors in high turbidity waters. Secondly, the uncertainty of atmospheric correction, especially the remote sensing reflectivity (R rs ) The correction accuracy decreases, which also affects the reliability of the inversion results

[0013] Although the existing QAA algorithms and their improved algorithms have achieved certain inversion effects in specific water bodies or water body types, they are often difficult to directly apply to multiple inland aquatic systems with large differences in optical properties. The main problems of these algorithms include insufficient model universality and low model accuracy, which limits their application in a wider range of water bodies. Summary of the invention

[0014] In order to solve the above technical problems, the present invention proposes a technical solution of a total absorption coefficient inversion method of inland water bodies based on a QAA classification algorithm to solve the above technical problems.

[0015] The first aspect of the present invention discloses a method for inverting the total absorption coefficient of an inland water body based on a QAA classification algorithm, the method comprising:

[0016] Step S1, bringing the water body remote sensing reflectivity containing multiple bands into the radiation transmission model formula to convert it into underwater remote sensing reflectivity; obtaining the first process parameter of the full band according to the underwater remote sensing reflectivity;

[0017] Step S2, using the remote sensing reflectance of the water body at a wavelength of 670nm as a discrimination criterion, the water body is divided into clean water body, turbid water body and highly turbid water body, and three types of water bodies are obtained;

[0018] Step S3, selecting reference wavelengths corresponding to the three types of water bodies, and calculating absorption coefficients and particle backscattering coefficients at the corresponding reference wavelengths;

[0019] Step S4, obtaining power parameters of three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength; calculating the full-band backscattering coefficients of the three types of water bodies through the backscattering coefficients and power parameters of the reference wavelengths of the three types of water bodies;

[0020] Step S5, calculating the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band and the backscattering coefficient of the full-band water body of the three types of water bodies.

[0021] According to the method of the first aspect of the present invention, in step S2, using the remote sensing reflectance of the water body at a wavelength of 670 nm as a discrimination criterion to classify the water body into clean water body, turbid water body and highly turbid water body includes:

[0022] When the remote sensing reflectivity of water at a wavelength of 670nm does not exceed 0.0015sr -1 When the remote sensing reflectance of water at 670nm wavelength is between 0.0015sr-1 and 0.0020sr-1, the water body is classified as clean water. -1 When the remote sensing reflectivity of water at a wavelength of 670nm exceeds 0.0020sr-1 When the water body is classified as highly turbid water body.

[0023] According to the method of the first aspect of the present invention, in step S3, selecting the reference wavelengths corresponding to the three types of water bodies includes:

[0024] For clean water, select 560nm as the reference wavelength;

[0025] For turbid water, the wavelength of 670nm is selected as the reference wavelength;

[0026] For highly turbid water, 705nm is selected as the reference wavelength;

[0027] Calculation of the absorption coefficient at a reference wavelength for clean water includes:

[0028]

[0029] Among them, a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; a(560) represents the total absorption coefficient of water at 560nm; a w (λ 01 ) represents the pure water absorption coefficient at the reference wavelength of clean water, h0, h1 and h2 represent empirical parameters; χ expresses the second process parameter; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1 (490) represents the underwater remote sensing reflectance of clean water at 490nm; r rs1 (560) represents the underwater remote sensing reflectance of clean water at 560nm; r rs1 (670) represents the underwater remote sensing reflectance of clean water at 670 nm;

[0030] Calculation of the absorption coefficient at a reference wavelength for turbid water includes:

[0031]

[0032] Among them, a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; a w (670) represents the absorption coefficient of pure water at 670 nm; R rs (670) represents the remote sensing reflectance of water at 670nm; R rs1 (443) shows the remote sensing reflectance of turbid water at 443nm; R rs1 (490) represents the remote sensing reflectance of turbid water at 490nm;

[0033] Calculation of the absorption coefficient at a reference wavelength for highly turbid water includes:

[0034] a(λ 03 )=a w (λ 03 )=a w (705)

[0035] Among them, a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; a w (λ 03 ) represents the pure water absorption coefficient at the reference wavelength of highly turbid water; a w (705) represents the absorption coefficient of pure water at a wavelength of 705 nm.

[0036] According to the method of the first aspect of the present invention, in step S3, calculating the particle backscattering coefficient at the reference wavelength of the clean water body includes:

[0037]

[0038] Among them, b bp (λ 01 ) represents the particle backscattering coefficient at the reference wavelength of clean water; b bp (560) represents the particle backscattering coefficient at 560nm; a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; u(λ 01 ) represents the first process parameter at the reference wavelength of the clean water body; b bw (560) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 560 nm;

[0039] Calculation of the particle backscattering coefficient at the reference wavelength of turbid water includes:

[0040]

[0041] Among them, b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; b bp (670) represents the particle backscattering coefficient at 670nm; a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; u(λ 02 ) represents the first process parameter at the reference wavelength of turbid water; b bw (670) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 670 nm;

[0042] Calculation of the particle backscattering coefficient at a reference wavelength for highly turbid water includes:

[0043]

[0044] Among them, b bp (λ 03 ) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; b bp (705) represents the particle backscattering coefficient at 705nm; a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; u(λ 03 ) represents the first process parameter at the reference wavelength of highly turbid water; b bw (705) represents the backscattering coefficient of pure water absorbed by particles at a wavelength of 705nm.

[0045] According to the method of the first aspect of the present invention, in step S4, obtaining the power parameters of three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength includes:

[0046] Calculation of power parameters for cleaning water include:

[0047]

[0048] Among them, η1 represents the power parameter of clean water; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1 (560) represents the underwater remote sensing reflectance of clean water at 560 nm;

[0049] Power parameters for calculating turbid water bodies include:

[0050]

[0051] Where η2 represents the power parameter of turbid water; r rs2 (443) represents the underwater remote sensing reflectance of turbid water at 443 nm; r rs2 (560) represents the underwater remote sensing reflectance of turbid water at 560 nm;

[0052] Power parameters for calculating turbid water bodies include:

[0053]

[0054] Where η2 represents the power parameter of turbid water; r rs2 (443) represents the underwater remote sensing reflectance of turbid water at 443 nm; r rs2 (560) represents the underwater remote sensing reflectance of turbid water at 560 nm;

[0055] Power parameters for calculating highly turbid waters include:

[0056]

[0057] Where η3 represents the power parameter of highly turbid water; r rs3 (443) represents the underwater remote sensing reflectance of highly turbid water at 443 nm; r rs3 (560) represents the underwater remote sensing reflectance of highly turbid water at 560nm.

[0058] According to the method of the first aspect of the present invention, in step S4, calculating the full-band backscattering coefficients of the three types of water bodies by using the backscattering coefficients and power parameters of the reference wavelengths of the three types of water bodies includes:

[0059] Calculation of the full-band backscatter coefficient for clean water includes:

[0060]

[0061] Among them, b bp (λ 1 ) represents the full-band backscatter coefficient of clean water; b bp (λ 01 ) represents the particle backscattering coefficient at the reference wavelength of clean water; λ 01 Indicates the reference wavelength of clean water; λ 1 Any wavelength of clean water; η1 represents the power parameter of clean water;

[0062] Calculation of the full-band backscatter coefficient of turbid water includes:

[0063]

[0064] Among them, b bp (λ 2 ) represents the backscattering coefficient of the whole band of turbid water; b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; λ 02 Indicates the reference wavelength of turbid water; λ 2 Any wavelength of turbid water; η2 represents the power parameter of turbid water;

[0065] Calculation of the full-band backscatter coefficient for highly turbid water includes:

[0066]

[0067] Among them, b bp (λ 3 ) represents the full-band backscattering coefficient of highly turbid water; b bp (λ 03) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; λ 03 Indicates the reference wavelength of highly turbid water; λ 3 Any wavelength of highly turbid water; η3 represents the power parameter of highly turbid water.

[0068] According to the method of the first aspect of the present invention, in step S5, the calculating of the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band and the backscattering coefficient of the full-band of the three types of water bodies comprises:

[0069] Calculation of the total absorption coefficient of clean water over the entire band includes:

[0070]

[0071] Among them, a(λ 1 ) represents the total absorption coefficient of clean water in the whole band; b bp (λ 1 ) represents the full-band backscatter coefficient of clean water; b bw (λ 1 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 1 ) represents the first process parameter of the full band of clean water;

[0072]

[0073] Among them, g 0 and g 1 represents a known empirical parameter; r rs1 (λ 1 ) represents the underwater remote sensing reflectivity of clean water;

[0074] Calculation of the total absorption coefficient of the whole band of turbid water includes:

[0075]

[0076] Among them, a(λ 2 ) represents the total absorption coefficient of the whole band of turbid water; b bp (λ 2 ) represents the backscattering coefficient of the whole band of turbid water; b bw (λ 2 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 2 ) represents the first process parameter of the full band of turbid water;

[0077]

[0078] Among them, r rs2(λ 2 ) represents the underwater remote sensing reflectivity of turbid water bodies;

[0079] Calculation of the total absorption coefficient of the water body for the entire band of highly turbid water bodies includes:

[0080]

[0081] Among them, a(λ 3 ) represents the total absorption coefficient of the water body in the whole band of highly turbid water body; b bp (λ 3 ) represents the full-band backscattering coefficient of highly turbid water; b bw (λ 3 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 3 ) represents the first process parameter of the full band of highly turbid water;

[0082]

[0083] Among them, r rs3 (λ 3 ) represents the underwater remote sensing reflectivity of highly turbid water bodies.

[0084] The second aspect of the present invention discloses a total absorption coefficient inversion system for inland water bodies based on a QAA classification algorithm, the system comprising:

[0085] The first processing module is configured to bring the water body remote sensing reflectivity containing multiple bands into the radiation transmission model formula to convert it into underwater remote sensing reflectivity; and obtain the first process parameter of the full band according to the underwater remote sensing reflectivity;

[0086] The second processing module is configured to use the remote sensing reflectance of the water body at a wavelength of 670nm as a discrimination criterion to classify the water body into clean water body, turbid water body and highly turbid water body, thereby obtaining three types of water bodies;

[0087] A third processing module is configured to select reference wavelengths corresponding to the three types of water bodies, and calculate the absorption coefficient and particle backscattering coefficient at the corresponding reference wavelengths;

[0088] The fourth processing module is configured to obtain power parameters of the three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength; calculate the full-band backscattering coefficients of the three types of water bodies through the backscattering coefficients and power parameters of the reference wavelengths of the three types of water bodies;

[0089] The fifth processing module is configured to calculate the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band and the backscattering coefficient of the full-band of the three types of water bodies.

[0090] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in any one of the methods for inverting the total absorption coefficient of inland water bodies based on the QAA classification algorithm in the first aspect of the present disclosure are implemented.

[0091] The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any one of the methods for inverting the total absorption coefficient of inland water bodies based on the QAA classification algorithm in the first aspect of the present disclosure are implemented.

[0092] In summary, the solution proposed in the present invention can effectively solve the problem of inverting the total absorption coefficient of water bodies in turbid and eutrophic water environments. By optimizing the reference wavelength selection and the calculation of scattering and absorption coefficients, the adaptability to different water types is improved. It is suitable for multi-source remote sensing data, covering a wide range of applications from clean water bodies to highly turbid water bodies, and further improves the accuracy of remote sensing inversion of the total absorption coefficient of water bodies. The consistency and reliability in water color and water quality monitoring have been enhanced, which is suitable for long-term and large-scale monitoring of water quality changes, and helps to improve the application efficiency and accuracy of multi-source data in water body monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0094] Figure 1 is a flow chart of a method for inverting the total absorption coefficient of inland water bodies based on a QAA classification algorithm according to an embodiment of the present invention;

[0095] Figure 2 A structural diagram of an inland water total absorption coefficient inversion system based on a QAA classification algorithm according to an embodiment of the present invention;

[0096] Figure 3 The figure is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0097] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0098] The first aspect of the present invention discloses a method for inverting the total absorption coefficient of inland water bodies based on a QAA classification algorithm. Figure 1 FIG. 4 is a flow chart of a method for inverting the total absorption coefficient of inland water bodies based on a QAA classification algorithm according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0099] Step S1, bringing the water body remote sensing reflectivity containing multiple bands into the radiation transmission model formula to convert it into underwater remote sensing reflectivity; obtaining the first process parameter of the full band according to the underwater remote sensing reflectivity;

[0100] Step S2, using the remote sensing reflectance of the water body at a wavelength of 670nm as a discrimination criterion, the water body is divided into clean water body, turbid water body and highly turbid water body, and three types of water bodies are obtained;

[0101] Step S3, selecting reference wavelengths corresponding to the three types of water bodies, and calculating absorption coefficients and particle backscattering coefficients at the corresponding reference wavelengths;

[0102] Step S4, obtaining power parameters of three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength; calculating the full-band backscattering coefficients of the three types of water bodies through the backscattering coefficients and power parameters of the reference wavelengths of the three types of water bodies;

[0103] Step S5, calculating the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band and the backscattering coefficient of the full-band water body of the three types of water bodies.

[0104] In step S1, the remote sensing reflectivity of a water body containing multiple bands is brought into a radiation transmission model formula to be converted into underwater remote sensing reflectivity; and a first process parameter of the entire band is obtained according to the underwater remote sensing reflectivity.

[0105] Specifically, the radiation transfer model formula is r rs (λ)=R rs (λ) / (0.52+1.7R rs (λ)), where the input value is the remote sensing reflectance of the water body, usually denoted as R rs (λ), unit is sr -1, which indicates the proportion of light reflected from the water surface back to the sensor. rs (λ) represents the reflectivity at different wavelengths, and the data comes from multi-band imaging of satellite remote sensors. Steps By applying a specific formula, the water body remote sensing reflectivity R rs (λ) is converted into underwater remote sensing reflectivity r through the radiation transfer formula rs (λ).

[0106] The first process parameter of obtaining the full band according to the underwater remote sensing reflectivity includes:

[0107] where g 0 and g 1 is a known empirical parameter. Since the light reflection and absorption in water are complex nonlinear relationships, it is not possible to directly use r rs (λ) is difficult to derive optical properties. Therefore, using the first process parameter u(λ) can simplify the problem, making it easier to estimate the absorption coefficient and scattering coefficient. The underwater remote sensing reflectance is converted into the process parameter u(λ), which reflects the relationship between the reflection and absorption behavior of light in the water body, simplifying the subsequent absorption and scattering calculations.

[0108] In step S2, the remote sensing reflectance of the water body at a wavelength of 670 nm is used as a discrimination criterion to divide the water body into clean water body, turbid water body and highly turbid water body, thereby obtaining three types of water bodies.

[0109] In some embodiments, in step S2, using the remote sensing reflectance of the water body at a wavelength of 670 nm as a discrimination criterion to classify the water body into clean water body, turbid water body and highly turbid water body includes:

[0110] When the water remote sensing reflectivity at 670nm wavelength does not exceed 0.0015sr -1 When the remote sensing reflectance of water at 670nm wavelength is between 0.0015sr-1 and 0.0020sr-1, the water body is classified as clean water. -1 When the remote sensing reflectivity of water at 670nm wavelength exceeds 0.0020sr -1 When the water body is classified as highly turbid water body.

[0111] This classification method targets water bodies with different water quality characteristics, and subsequently selects different reference wavelengths and absorption and scattering coefficient calculation methods, thereby optimizing the remote sensing monitoring and analysis strategies for different types of water bodies.

[0112] In step S3, corresponding reference wavelengths are selected for the three types of water bodies, and the absorption coefficients and particle backscattering coefficients at the corresponding reference wavelengths are calculated.

[0113] In some embodiments, in step S3, selecting the reference wavelengths corresponding to the three types of water bodies includes:

[0114] For clean water, select 560nm as the reference wavelength;

[0115] For turbid water, the wavelength of 670nm is selected as the reference wavelength;

[0116] For highly turbid water, 705nm is selected as the reference wavelength;

[0117] Calculation of the absorption coefficient at a reference wavelength for clean water includes:

[0118] When cleaning water, the total absorption coefficient of the water body is the total absorption coefficient at the reference wavelength of 560nm. The absorption coefficient of the reference wavelength is calculated by adding the correction term to the pure water absorption coefficient at the reference wavelength. The correction term is constructed by taking the logarithm of the underwater reflectivity of 443nm, 670nm, 490nm and 560nm. Formula:

[0119]

[0120] Among them, a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; a(560) represents the total absorption coefficient of water at 560nm; a w (λ 01 ) represents the pure water absorption coefficient at the reference wavelength of clean water, h0, h1 and h2 represent empirical parameters; χ expresses the second process parameter; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1 (490) represents the underwater remote sensing reflectance of clean water at 490nm; r rs1 (560) represents the underwater remote sensing reflectance of clean water at 560nm; r rs1 (670) represents the underwater remote sensing reflectance of clean water at 670 nm;

[0121] Calculation of the absorption coefficient at a reference wavelength for turbid water includes:

[0122] In turbid water, the reference wavelength is 670nm, and the absorption coefficient is the absorption coefficient of pure water at 670nm plus a correction term. The correction term is constructed by the remote sensing reflectance at 443nm, 490nm and 670nm; formula:

[0123]

[0124] Among them, a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; a w(670) represents the absorption coefficient of pure water at 670 nm; R rs (670) represents the remote sensing reflectance of water at 670nm; R rs1 (443) shows the remote sensing reflectance of turbid water at 443nm; R rs1 (490) represents the remote sensing reflectance of turbid water at 490nm;

[0125] Calculation of the absorption coefficient at a reference wavelength for highly turbid water includes:

[0126] In highly turbid water, the reference wavelength is 705nm, and the absorption coefficient is the absorption coefficient of pure water at a wavelength of 705nm. The formula is

[0127] a(λ 03 )=a w (λ 03 )=a w (705)

[0128] Among them, a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; a w (λ 03 ) represents the pure water absorption coefficient at the reference wavelength of highly turbid water; a w (705) represents the absorption coefficient of pure water at a wavelength of 705 nm.

[0129] Calculation of the particle backscatter coefficient at a reference wavelength for clean water includes:

[0130]

[0131] Among them, b bp (λ 01 ) represents the particle backscattering coefficient at the reference wavelength of clean water; b bp (560) represents the particle backscattering coefficient at 560nm; a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; u(λ 01 ) represents the first process parameter at the reference wavelength of the clean water body; b bw (560) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 560 nm;

[0132] Calculation of the particle backscattering coefficient at the reference wavelength of turbid water includes:

[0133]

[0134] Among them, b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; bbp (670) represents the particle backscattering coefficient at 670nm; a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; u(λ 02 ) represents the first process parameter at the reference wavelength of turbid water; b bw (670) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 670 nm;

[0135] Calculation of the particle backscattering coefficient at a reference wavelength for highly turbid water includes:

[0136]

[0137] Among them, b bp (λ 03 ) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; b bp (705) represents the particle backscattering coefficient at 705nm; a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; u(λ 03 ) represents the first process parameter at the reference wavelength of highly turbid water; b bw (705) represents the backscattering coefficient of pure water absorbed by particles at a wavelength of 705nm.

[0138] In step S4, power parameters of the three types of water bodies are obtained according to the underwater remote sensing reflectivity at predefined wavelengths; and full-band backscatter coefficients of the three types of water bodies are calculated through the backscatter coefficients and power parameters of the reference wavelengths of the three types of water bodies.

[0139] In some embodiments, in step S4, obtaining power parameters of three types of water bodies according to underwater remote sensing reflectivity at a predefined wavelength includes:

[0140] Calculation of power parameters for cleaning water include:

[0141]

[0142] Among them, η1 represents the power parameter of clean water; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1 (560) represents the underwater remote sensing reflectance of clean water at 560 nm;

[0143] Power parameters for calculating turbid water bodies include:

[0144]

[0145] Where η2 represents the power parameter of turbid water; r rs2(443) represents the underwater remote sensing reflectance at 443 nm of turbid water; r rs2 (560) represents the underwater remote sensing reflectance at 560 nm of turbid water;

[0146] Calculating the power parameters of turbid water includes:

[0147]

[0148] Among them, η2 represents the power parameter of turbid water; r rs2 (443) represents the underwater remote sensing reflectance at 443 nm of turbid water; r rs2 (560) represents the underwater remote sensing reflectance at 560 nm of turbid water;

[0149] Calculating the power parameters of highly turbid water includes:

[0150]

[0151] Among them, η3 represents the power parameter of highly turbid water; r rs3 (443) represents the underwater remote sensing reflectance at 443 nm of highly turbid water; r rs3 (560) represents the underwater remote sensing reflectance at 560 nm of highly turbid water.

[0152] Calculating the backscattering coefficient of the full wavelength band of the three types of water bodies through the backscattering coefficients and power parameters (spectral shape parameters) of the reference wavelengths of the three types of water bodies includes:

[0153] Calculating the backscattering coefficient of the full wavelength band of clean water includes:

[0154]

[0155] Among them, b bp (λ 1 ) represents the backscattering coefficient of the full wavelength band of clean water; b bp (λ 01 ) represents the particulate backscattering coefficient at the reference wavelength of clean water; λ 01 represents the reference wavelength of clean water; λ 1 Any wavelength of clean water; η1 represents the power parameter of clean water;

[0156] Calculating the backscattering coefficient of the full wavelength band of turbid water includes:

[0157]

[0158] Among them, b bp (λ 2) represents the backscattering coefficient of the whole band of turbid water; b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; λ 02 Indicates the reference wavelength of turbid water; λ 2 Any wavelength of turbid water; η2 represents the power parameter of turbid water;

[0159] Calculation of the full-band backscatter coefficient for highly turbid water includes:

[0160]

[0161] Among them, b bp (λ 3 ) represents the full-band backscattering coefficient of highly turbid water; b bp (λ 03 ) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; λ 03 Indicates the reference wavelength of highly turbid water; λ 3 Any wavelength of highly turbid water; η3 represents the power parameter of highly turbid water.

[0162] In step S5, the total absorption coefficients of the three types of water bodies in the full band are calculated based on the first process parameters of the full band and the backscattering coefficients of the three types of water bodies in the full band.

[0163] In some embodiments, in step S5, calculating the total absorption coefficient of the water body of the three types of water bodies in the full band according to the first process parameter of the full band and the backscattering coefficient of the full band of the three types of water bodies includes:

[0164] Calculation of the total absorption coefficient of clean water over the entire band includes:

[0165]

[0166] Among them, a(λ 1 ) represents the total absorption coefficient of clean water in the whole band; b bp (λ 1 ) represents the full-band backscatter coefficient of clean water; b bw (λ 1 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 1 ) represents the first process parameter of the full band of clean water;

[0167]

[0168] Among them, g 0 and g 1 represents a known empirical parameter; rrs1 (λ 1 ) represents the underwater remote sensing reflectivity of clean water;

[0169] Calculation of the total absorption coefficient of the whole band of turbid water includes:

[0170]

[0171] Among them, a(λ 2 ) represents the total absorption coefficient of the whole band of turbid water; b bp (λ 2 ) represents the backscattering coefficient of the whole band of turbid water; b bw (λ 2 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 2 ) represents the first process parameter of the full band of turbid water;

[0172]

[0173] Among them, r rs2 (λ 2 ) represents the underwater remote sensing reflectivity of turbid water bodies;

[0174] Calculation of the total absorption coefficient of the water body for the entire band of highly turbid water bodies includes:

[0175]

[0176] Among them, a(λ 3 ) represents the total absorption coefficient of the water body in the whole band of highly turbid water body; b bp (λ 3 ) represents the full-band backscattering coefficient of highly turbid water; b bw (λ 3 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 3 ) represents the first process parameter of the full band of highly turbid water;

[0177]

[0178] Among them, r rs3 (λ 3 ) represents the underwater remote sensing reflectivity of highly turbid water bodies.

[0179] In summary, the scheme proposed in the present invention can be applied to complex inland water environments, solving the problem that the traditional QAA algorithm is not accurate enough for inland waters. The remote sensing reflectance of multiple bands such as 443nm, 490nm, 560nm, 670nm and 705nm is used to enhance the adaptability and universality of the method to different types of inland waters. The power parameter η is calculated using the underwater remote sensing reflectance values ​​at wavelengths of 443nm and 560nm, accurately describing the trend of the backscattering coefficient of particulate matter with wavelength, and improving the accuracy of the inversion of full-band optical properties. The method calculates the absorption coefficient, backscattering coefficient and other optical parameters of the water body in steps, and gradually derives them from the basic remote sensing reflectance to ensure that the calculation results of each step accurately reflect the optical properties of the water body, significantly improving the accuracy and reliability in the field of water quality monitoring, especially in the long-term monitoring of water quality in inland waters.

[0180] The inversion accuracy in highly turbid water bodies has been improved, and the adaptability to highly turbid water bodies has been enhanced. Based on the traditional QAA method, the application of the 705nm band has been added, the processing strategy for highly turbid water bodies has been optimized, and the problem of low inversion accuracy of existing algorithms for eutrophic water bodies has been solved.

[0181] Applicable to long-term and large-scale monitoring, this method provides consistent and reliable inversion results for inland water bodies with different water quality characteristics, and is suitable for long-term and large-scale monitoring of water quality changes.

[0182] The second aspect of the present invention discloses a total absorption coefficient inversion system for inland water bodies based on a QAA classification algorithm. Figure 2 FIG. 4 is a structural diagram of an inland water total absorption coefficient inversion system based on a QAA classification algorithm according to an embodiment of the present invention; Figure 2 As shown, the system 100 includes:

[0183] The first processing module 101 is configured to bring the water body remote sensing reflectivity containing multiple bands into the radiation transmission model formula to convert it into underwater remote sensing reflectivity; and obtain the first process parameter of the full band according to the underwater remote sensing reflectivity;

[0184] The second processing module 102 is configured to use the remote sensing reflectance of the water body at a wavelength of 670nm as a discrimination criterion to classify the water body into clean water body, turbid water body and highly turbid water body, thereby obtaining three types of water bodies;

[0185] The third processing module 103 is configured to select reference wavelengths corresponding to the three types of water bodies, and calculate the absorption coefficient and particle backscattering coefficient at the corresponding reference wavelengths;

[0186] The fourth processing module 104 is configured to obtain power parameters of the three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength; calculate the full-band backscattering coefficients of the three types of water bodies through the backscattering coefficients and power parameters of the reference wavelengths of the three types of water bodies;

[0187] The fifth processing module 105 is configured to calculate the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band and the backscattering coefficient of the full-band water body of the three types of water bodies.

[0188] According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured as follows: the radiation transmission model formula is r rs (λ)=R rs (λ) / (0.52+1.7R rs (λ)), where the input value is the remote sensing reflectance of the water body, usually denoted as R rs (λ), unit is sr -1 , which indicates the proportion of light reflected from the water surface back to the sensor. rs (λ) represents the reflectivity at different wavelengths, and the data comes from multi-band imaging of satellite remote sensors. Steps By applying a specific formula, the water body remote sensing reflectivity R rs (λ) is converted into underwater remote sensing reflectivity r through the radiation transfer formula rs (λ).

[0189] The first process parameter of obtaining the full band according to the underwater remote sensing reflectivity includes:

[0190] where g 0 and g 1 is a known empirical parameter. Since the light reflection and absorption in water are complex nonlinear relationships, it is not possible to directly use r rs (λ) is difficult to derive optical properties. Therefore, using the first process parameter u(λ) can simplify the problem, making it easier to estimate the absorption coefficient and scattering coefficient. The underwater remote sensing reflectance is converted into the process parameter u(λ), which reflects the relationship between the reflection and absorption behavior of light in the water body, simplifying the subsequent absorption and scattering calculations.

[0191] According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to use the remote sensing reflectance of the water body at a wavelength of 670nm as a discrimination criterion to divide the water body into clean water body, turbid water body and highly turbid water body, including:

[0192] When the water remote sensing reflectivity at 670nm wavelength does not exceed 0.0015sr -1When the remote sensing reflectance of water at 670nm wavelength is between 0.0015sr-1 and 0.0020sr-1, the water body is classified as clean water. -1 When the remote sensing reflectivity of water at a wavelength of 670nm exceeds 0.0020sr -1 When the water body is classified as highly turbid water body.

[0193] This classification method targets water bodies with different water quality characteristics, and subsequently selects different reference wavelengths and absorption and scattering coefficient calculation methods, thereby optimizing the remote sensing monitoring and analysis strategies for different types of water bodies.

[0194] According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured as follows: the selecting of the reference wavelengths corresponding to the three types of water bodies comprises:

[0195] For clean water, select 560nm as the reference wavelength;

[0196] For turbid water, 670nm is selected as the reference wavelength;

[0197] For highly turbid water, 705nm is selected as the reference wavelength;

[0198] Calculation of the absorption coefficient at a reference wavelength for clean water includes:

[0199] When cleaning water, the total absorption coefficient of the water body is the total absorption coefficient at the reference wavelength of 560nm. The absorption coefficient of the reference wavelength is calculated by adding the correction term to the pure water absorption coefficient at the reference wavelength. The correction term is constructed by taking the logarithm of the underwater reflectivity of 443nm, 670nm, 490nm and 560nm. Formula:

[0200]

[0201] Among them, a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; a(560) represents the total absorption coefficient of water at 560nm; a w (λ 01 ) represents the pure water absorption coefficient at the reference wavelength of clean water, h0, h1 and h2 represent empirical parameters; χ expresses the second process parameter; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1 (490) represents the underwater remote sensing reflectance of clean water at 490nm; r rs1 (560) represents the underwater remote sensing reflectance of clean water at 560nm; r rs1 (670) represents the underwater remote sensing reflectance of clean water at 670 nm;

[0202] Calculation of the absorption coefficient at a reference wavelength for turbid water includes:

[0203] In turbid water, the reference wavelength is 670nm, and the absorption coefficient is the absorption coefficient of pure water at 670nm plus a correction term. The correction term is constructed by the remote sensing reflectance at 443nm, 490nm and 670nm; formula:

[0204]

[0205] Among them, a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; a w (670) represents the absorption coefficient of pure water at 670 nm; R rs (670) represents the remote sensing reflectance of water at 670nm; R rs1 (443) shows the remote sensing reflectance of turbid water at 443nm; R rs1 (490) represents the remote sensing reflectance of turbid water at 490nm;

[0206] Calculation of the absorption coefficient at a reference wavelength for highly turbid water includes:

[0207] In highly turbid water, the reference wavelength is 705nm, and the absorption coefficient is the absorption coefficient of pure water at a wavelength of 705nm. The formula is

[0208] a(λ 03 )=a w (λ 03 )=a w (705)

[0209] Among them, a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; a w (λ 03 ) represents the pure water absorption coefficient at the reference wavelength of highly turbid water; a w (705) represents the absorption coefficient of pure water at a wavelength of 705 nm.

[0210] Calculation of the particle backscatter coefficient at a reference wavelength for clean water includes:

[0211]

[0212] Among them, b bp (λ 01 ) represents the particle backscattering coefficient at the reference wavelength of clean water; b bp (560) represents the particle backscattering coefficient at 560nm; a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; u(λ01 ) represents the first process parameter at the reference wavelength of the clean water body; b bw (560) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 560 nm;

[0213] Calculation of the particle backscattering coefficient at the reference wavelength of turbid water includes:

[0214]

[0215] Among them, b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; b bp (670) represents the particle backscattering coefficient at 670nm; a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; u(λ 02 ) represents the first process parameter at the reference wavelength of turbid water; b bw (670) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 670 nm;

[0216] Calculation of the particle backscattering coefficient at a reference wavelength for highly turbid water includes:

[0217]

[0218] Among them, b bp (λ 03 ) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; b bp (705) represents the particle backscattering coefficient at 705nm; a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; u(λ 03 ) represents the first process parameter at the reference wavelength of highly turbid water; b bw (705) represents the backscattering coefficient of pure water absorbed by particles at a wavelength of 705nm.

[0219] According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured to obtain the power parameters of the three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength, including:

[0220] Calculation of power parameters for cleaning water include:

[0221]

[0222] Among them, η1 represents the power parameter of clean water; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1(560) represents the underwater remote sensing reflectance of clean water at 560 nm;

[0223] Power parameters for calculating turbid water bodies include:

[0224]

[0225] Where η2 represents the power parameter of turbid water; r rs2 (443) represents the underwater remote sensing reflectance of turbid water at 443 nm; r rs2 (560) represents the underwater remote sensing reflectance of turbid water at 560 nm;

[0226] Power parameters for calculating turbid water bodies include:

[0227]

[0228] Where η2 represents the power parameter of turbid water; r rs2 (443) represents the underwater remote sensing reflectance of turbid water at 443 nm; r rs2 (560) represents the underwater remote sensing reflectance of turbid water at 560 nm;

[0229] Power parameters for calculating highly turbid waters include:

[0230]

[0231] Where η3 represents the power parameter of highly turbid water; r rs3 (443) represents the underwater remote sensing reflectance of highly turbid water at 443 nm; r rs3 (560) represents the underwater remote sensing reflectance of highly turbid water at 560nm.

[0232] The backscattering coefficients of the reference wavelengths of the three types of water bodies and the power parameters (spectral shape parameters) are used to calculate the backscattering coefficients of the full bands of the three types of water bodies, including:

[0233] Calculation of the full-band backscatter coefficient for clean water includes:

[0234]

[0235] Among them, b bp (λ 1 ) represents the full-band backscatter coefficient of clean water; b bp (λ 01 ) represents the particle backscattering coefficient at the reference wavelength of clean water; λ 01 Indicates the reference wavelength of clean water; λ 1 Any wavelength of clean water; η1 represents the power parameter of clean water;

[0236] Calculation of the full-band backscatter coefficient of turbid water includes:

[0237]

[0238] Among them, b bp (λ 2 ) represents the backscattering coefficient of the whole band of turbid water; b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; λ 02 Indicates the reference wavelength of turbid water; λ 2 Any wavelength of turbid water; η2 represents the power parameter of turbid water;

[0239] Calculation of the full-band backscatter coefficient for highly turbid water includes:

[0240]

[0241] Among them, b bp (λ 3 ) represents the full-band backscattering coefficient of highly turbid water; b bp (λ 03 ) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; λ 03 Indicates the reference wavelength of highly turbid water; λ 3 Any wavelength of highly turbid water; η3 represents the power parameter of highly turbid water.

[0242] According to the system of the second aspect of the present invention, the fifth processing module 105 is specifically configured to calculate the total absorption coefficient of the full-band water body of the three types of water bodies based on the first process parameter of the full-band and the backscattering coefficient of the full-band of the three types of water bodies, including:

[0243] Calculation of the total absorption coefficient of clean water over the entire band includes:

[0244]

[0245] Among them, a(λ 1 ) represents the total absorption coefficient of clean water in the whole band; b bp (λ 1 ) represents the full-band backscatter coefficient of clean water; b bw (λ 1 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 1 ) represents the first process parameter of the full band of clean water;

[0246]

[0247] Among them, g 0 and g 1 represents a known empirical parameter; r rs1 (λ 1 ) represents the underwater remote sensing reflectivity of clean water;

[0248] Calculation of the total absorption coefficient of the whole band of turbid water includes:

[0249]

[0250] Among them, a(λ 2 ) represents the total absorption coefficient of the whole band of turbid water; b bp (λ 2 ) represents the backscattering coefficient of the whole band of turbid water; b bw (λ 2 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 2 ) represents the first process parameter of the full band of turbid water;

[0251]

[0252] Among them, r rs2 (λ 2 ) represents the underwater remote sensing reflectivity of turbid water bodies;

[0253] Calculation of the total absorption coefficient of the water body for the entire band of highly turbid water bodies includes:

[0254]

[0255] Among them, a(λ 3 ) represents the total absorption coefficient of the water body in the whole band of highly turbid water body; b bp (λ 3 ) represents the full-band backscattering coefficient of highly turbid water; b bw (λ 3 ) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ 3 ) represents the first process parameter of the full band of highly turbid water;

[0256]

[0257] Among them, r rs3 (λ 3 ) represents the underwater remote sensing reflectivity of highly turbid water bodies.

[0258] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm in any one of the first aspects of the present invention are implemented.

[0259] Figure 3 is a structural diagram of an electronic device according to an embodiment of the present invention, such as Figure 3 As shown, the electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile 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 the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, near field communication (NFC) or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covered on the display screen, or a button, a trackball or a touch pad set on the housing of the electronic device, or an external keyboard, touch pad or mouse, etc.

[0260] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the technical solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0261] The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method for inverting the total absorption coefficient of an inland water body based on a QAA classification algorithm in any one of the first aspects of the present invention are implemented.

[0262] Please note that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all belong to the scope of protection of the present application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.

Claims

1. A method for inverting the total absorption coefficient of inland water bodies based on the QAA classification algorithm, characterized in that: The method comprises: Step S1, bringing the water body remote sensing reflectivity containing multiple bands into the radiation transmission model formula to convert it into underwater remote sensing reflectivity; obtaining the first process parameter of the full band according to the underwater remote sensing reflectivity; Step S2, using the remote sensing reflectance of the water body at a wavelength of 670nm as a discrimination criterion, the water body is divided into clean water body, turbid water body and highly turbid water body, and three types of water bodies are obtained; Step S3, selecting reference wavelengths corresponding to the three types of water bodies, and calculating absorption coefficients and particle backscattering coefficients at the corresponding reference wavelengths; Step S4, obtaining power parameters of three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength; calculating the full-band backscattering coefficients of the three types of water bodies through the backscattering coefficients and power parameters of the reference wavelengths of the three types of water bodies; Step S5, calculating the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band and the backscattering coefficient of the full-band water body of the three types of water bodies.

2. The inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm according to claim 1 is characterized in that: In step S2, the water body remote sensing reflectance at a wavelength of 670 nm is used as a discrimination criterion to classify the water body into clean water body, turbid water body and highly turbid water body, including: When the water remote sensing reflectivity at 670nm wavelength does not exceed 0.0015sr -1 When the remote sensing reflectance of water at 670nm wavelength is between 0.0015sr-1 and 0.0020sr-1, the water body is classified as clean water. -1 When the remote sensing reflectivity of water at a wavelength of 670nm exceeds 0.0020sr -1 When the water body is classified as highly turbid water body.

3. The inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm according to claim 1 is characterized in that: In step S3, selecting the reference wavelengths corresponding to the three types of water bodies includes: For clean water, select 560nm as the reference wavelength; For turbid water, the wavelength of 670nm is selected as the reference wavelength; For highly turbid water, 705nm is selected as the reference wavelength; Calculation of the absorption coefficient at a reference wavelength for clean water includes: Among them, a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; a(560) represents the total absorption coefficient of water at 560nm; a w (λ 01 ) represents the pure water absorption coefficient at the reference wavelength of clean water, h0, h1 and h2 represent empirical parameters; χ expresses the second process parameter; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1 (490) represents the underwater remote sensing reflectance of clean water at 490nm; r rs1 (560) represents the underwater remote sensing reflectance of clean water at 560nm; r rs1 (670) represents the underwater remote sensing reflectance of clean water at 670 nm; Calculation of the absorption coefficient at a reference wavelength for turbid water includes: Among them, a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; a w (670) represents the absorption coefficient of pure water at 670 nm; R rs (670) represents the remote sensing reflectance of water at 670nm; R rs1 (443) shows the remote sensing reflectance of turbid water at 443nm; R rs1 (490) represents the remote sensing reflectance of turbid water at 490nm; Calculation of the absorption coefficient at a reference wavelength for highly turbid water includes: a(λ 03 )=a w (l 03 )=a w (705) Among them, a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; a w (λ 03 ) represents the pure water absorption coefficient at the reference wavelength of highly turbid water; a w (705) represents the absorption coefficient of pure water at a wavelength of 705 nm.

4. The inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm according to claim 1 is characterized in that: In step S3, calculating the particle backscattering coefficient at the reference wavelength of the clean water body includes: Among them, b bp (λ 01 ) represents the particle backscattering coefficient at the reference wavelength of clean water; b bp (560) represents the particle backscattering coefficient at 560nm; a(λ 01 ) represents the absorption coefficient of clean water at the reference wavelength; u(λ 01 ) represents the first process parameter at the reference wavelength of the clean water body; b bw (560) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 560 nm; Calculation of the particle backscattering coefficient at the reference wavelength of turbid water includes: Among them, b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; b bp (670) represents the particle backscattering coefficient at 670nm; a(λ 02 ) represents the absorption coefficient of turbid water at the reference wavelength; u(λ 02 ) represents the first process parameter at the reference wavelength of turbid water; b bw (670) represents the backscattering coefficient of pure water absorbing particles at a wavelength of 670 nm; Calculation of the particle backscattering coefficient at a reference wavelength for highly turbid water includes: Among them, b bp (λ 03 ) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; b bp (705) represents the particle backscattering coefficient at 705nm; a(λ 03 ) represents the absorption coefficient of highly turbid water at the reference wavelength; u(λ 03 ) represents the first process parameter at the reference wavelength of highly turbid water; b bw (705) represents the backscattering coefficient of pure water absorbed by particles at a wavelength of 705nm.

5. The inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm according to claim 1 is characterized in that: In step S4, obtaining the power parameters of three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength includes: Calculation of power parameters for cleaning water include: Among them, η1 represents the power parameter of clean water; r rs1 (443) represents the underwater remote sensing reflectance of clean water at 443 nm; r rs1 (560) represents the underwater remote sensing reflectance of clean water at 560 nm; Power parameters for calculating turbid water bodies include: Where η2 represents the power parameter of turbid water; r rs2 (443) represents the underwater remote sensing reflectance of turbid water at 443 nm; r rs2 (560) represents the underwater remote sensing reflectance of turbid water at 560 nm; Power parameters for calculating turbid water bodies include: Where η2 represents the power parameter of turbid water; r rs2 (443) represents the underwater remote sensing reflectance of turbid water at 443 nm; r rs2 (560) represents the underwater remote sensing reflectance of turbid water at 560 nm; Power parameters for calculating highly turbid waters include: Where η3 represents the power parameter of highly turbid water; r rs3 (443) represents the underwater remote sensing reflectance of highly turbid water at 443 nm; r rs3 (560) represents the underwater remote sensing reflectance of highly turbid water at 560nm.

6. The inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm according to claim 1 is characterized in that: In step S4, calculating the full-band backscatter coefficients of the three types of water bodies by using the backscatter coefficients and power parameters of the reference wavelengths of the three types of water bodies includes: Calculation of the full-band backscatter coefficient for clean water includes: Among them, b bp (λ1) represents the full-band backscattering coefficient of clean water; b bp (λ 01 ) represents the particle backscattering coefficient at the reference wavelength of clean water; λ 01 represents the reference wavelength of clean water; λ1 represents any wavelength of clean water; η1 represents the power parameter of clean water; Calculation of the full-band backscatter coefficient of turbid water includes: Among them, b bp (λ2) represents the full-band backscattering coefficient of turbid water; b bp (λ 02 ) represents the particle backscattering coefficient at the reference wavelength of turbid water; λ 02 represents the reference wavelength of turbid water; λ2 represents the arbitrary wavelength of turbid water; η2 represents the power parameter of turbid water; Calculation of the full-band backscatter coefficient for highly turbid water includes: Among them, b bp (λ3) represents the full-band backscattering coefficient of highly turbid water; b bp (λ 03 ) represents the particle backscattering coefficient at the reference wavelength of highly turbid water; λ 03 represents the reference wavelength of highly turbid water; λ3 represents the arbitrary wavelength of highly turbid water; η3 represents the power parameter of highly turbid water.

7. The inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm according to claim 1 is characterized in that: In step S5, the calculating of the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band water body and the backscattering coefficient of the full-band water body of the three types of water bodies includes: Calculation of the total absorption coefficient of clean water over the entire band includes: Where a(λ1) represents the total absorption coefficient of clean water in the whole band; b bp (λ1) represents the full-band backscattering coefficient of clean water; b bw (λ1) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ1) represents the first process parameter of the full band of clean water; Among them, g0 and g1 represent known empirical parameters; r rs1 (λ1) represents the underwater remote sensing reflectivity of clean water; Calculation of the total absorption coefficient of the whole band of turbid water includes: Where a(λ2) represents the total absorption coefficient of the turbid water body in the whole band; b bp (λ2) represents the full-band backscattering coefficient of turbid water; b bw (λ2) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ2) represents the first process parameter of turbid water in the full band; Among them, r rs2 (λ2) represents the underwater remote sensing reflectivity of turbid water bodies; Calculation of the total absorption coefficient of the water body for the entire band of highly turbid water bodies includes: Where a(λ3) represents the total absorption coefficient of the water body in the full band of highly turbid water; b bp (λ3) represents the full-band backscattering coefficient of highly turbid water; b bw (λ3) represents the backscattering coefficient of pure water absorbing particles in the full band; u(λ3) represents the first process parameter of the full band of highly turbid water; Among them, r rs3 (λ3) represents the underwater remote sensing reflectivity of highly turbid water bodies.

8. A system for inverting the total absorption coefficient of inland water bodies based on the QAA classification algorithm, characterized in that: The system comprises: The first processing module is configured to bring the water body remote sensing reflectivity containing multiple bands into the radiation transmission model formula to convert it into underwater remote sensing reflectivity; and obtain the first process parameter of the full band according to the underwater remote sensing reflectivity; The second processing module is configured to use the remote sensing reflectance of the water body at a wavelength of 670nm as a discrimination criterion to classify the water body into clean water body, turbid water body and highly turbid water body, thereby obtaining three types of water bodies; A third processing module is configured to select reference wavelengths corresponding to the three types of water bodies, and calculate the absorption coefficient and particle backscattering coefficient at the corresponding reference wavelengths; The fourth processing module is configured to obtain power parameters of the three types of water bodies according to the underwater remote sensing reflectivity at the predefined wavelength; calculate the full-band backscattering coefficients of the three types of water bodies through the backscattering coefficients and power parameters of the reference wavelengths of the three types of water bodies; The fifth processing module is configured to calculate the total absorption coefficient of the full-band water body of the three types of water bodies according to the first process parameter of the full-band and the backscattering coefficient of the full-band of the three types of water bodies.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps in the inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps in the inversion method of the total absorption coefficient of inland water bodies based on the QAA classification algorithm according to any one of claims 1 to 7 are implemented.

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