Water particle attenuation coefficient profile calculation method and system

Abstract

The invention provides a water body particle attenuation coefficient profile calculation method and system, and the method comprises the steps: firstly obtaining a corresponding relation between the chlorophyll concentration of a target water body surface layer and a water body particle attenuation coefficient; then, calculating the surface chlorophyll concentration corresponding to each pixel in the water body area by adopting a preset algorithm, and obtaining the surface water body particle attenuation coefficient of each pixel according to the corresponding relationship; and finally, identifying a water body particle attenuation coefficient profile type of each pixel, and calculating water body particle attenuation coefficient profile data of each pixel according to a preset mode corresponding to the profile type to which the water body particle attenuation coefficient profile type belongs. By adopting the method and the system disclosed by the invention, the profile data of the current water body particle attenuation coefficient of the target water body can be evaluated based on the remote sensing long-time sequence observation data, the three-dimensional field of the profile data can be constructed, and meanwhile, the profile data of the historical time period can be traced back.

Description

Technical Field

[0001] The invention belongs to the field of remote sensing technology, and in particular relates to a method and system for calculating a water body particle attenuation coefficient profile. Background Art

[0002] In recent years, the rapid development of observation technology has greatly expanded the observation range of the dynamic changes in the ocean. Satellite remote sensing of ocean color has become an important means of monitoring the marine environment because it can provide large-scale, continuous observation data for the global or regional areas. However, traditional ocean color remote sensing technology mainly relies on the optical parameters of water bodies, and the information it obtains is mainly limited to the upper 20% of the euphotic layer, which cannot effectively analyze the distribution of phytoplankton biomass in the subsurface layer and its dynamic changes.

[0003] Field observations in the ocean have shown that there is a subsurface maximum phytoplankton biomass layer in the upper ocean. This phenomenon has caused significant errors in the estimation of phytoplankton biomass and net primary productivity. Existing studies have shown that compared with the assumption that biomass is evenly distributed, combining dynamic water optical profile information can improve the remote sensing observation accuracy of phytoplankton biomass, light attenuation coefficient and primary productivity by about 5-70%, 36-300% and 20-54%, respectively. Therefore, improving the observation capability of water color satellite remote sensing for water optical profiles is crucial to improving the accuracy of marine environmental monitoring and model simulation capabilities.

[0004] The particle attenuation coefficient (cp) of water describes the attenuation ability of suspended particles in water to light beams. Its profile type refers to the distribution pattern of particle attenuation coefficient of water body with depth. Water bodies in different regions and seasons will show different structural types of attenuation coefficient profiles due to different particle concentrations, compositions and hydrodynamic environments.

[0005] Currently, ocean color satellite remote sensing still faces many challenges in analyzing the complete profile information of the ocean true light layer, including: (1) Existing statistical models have good applicability at the global scale, but lack accurate characterization of local dynamic processes at the regional scale; (2) Machine learning methods based on observational data have advantages in refined modeling, but their reliance on Argo float data limits their large-scale application; (3) The water particle attenuation coefficient (cp) is a more comprehensive optical parameter, and its dynamic change characteristics still need to be further studied.

[0006] The current classification method for water body particle attenuation coefficient profile types is mainly based on static identification of sea surface chlorophyll concentration and seasonal changes, ignoring the changing characteristics of the mixing of Class I and Class II seawater. At the same time, the static classification method leads to unnatural jumps in the profile structure, limiting its applicability in complex marine environments. Summary of the invention

[0007] The present invention provides a method and system for calculating the profile of a water body particle attenuation coefficient, so as to solve the problems in the prior art of ignoring the changing characteristics of seawater mixing and the static classification method leading to unnatural jumps in the profile structure. In order to solve the above technical problems, the embodiments of the present invention disclose the following technical solutions: One aspect of the present invention provides a method for calculating a water body particle attenuation coefficient profile, comprising: Extract the water area in the target water body remote sensing image; Obtain the corresponding relationship between the chlorophyll concentration on the surface of the target water body and the water particle attenuation coefficient; The surface chlorophyll concentration corresponding to each pixel in the water body area is calculated by using a preset algorithm, and the surface water particle attenuation coefficient of each pixel is obtained according to the corresponding relationship; Identify the water body particle attenuation coefficient profile type of each pixel, wherein the water body particle attenuation coefficient profile types include mixed uniform type, Gaussian type and exponential attenuation type; The water body particle attenuation coefficient profile data of each pixel is calculated according to a preset method corresponding to the profile type, wherein the water body particle attenuation coefficient profile data belonging to Gaussian-type pixels is calculated based on a Bayesian optimization method.

[0008] Optionally, the step of obtaining the corresponding relationship between the surface chlorophyll concentration of the target water body and the water particle attenuation coefficient includes: Collect historical data on surface chlorophyll concentration and water particle attenuation coefficient at multiple observation stations in the target water body; Based on the historical data of the above two, the following corresponding relationship is obtained: log(cp660)=B1 × log(Chla) 2 +B2×log(Chla) -INT Among them, cp660 is the attenuation coefficient of surface water particles at a wavelength of 660nm below the water surface; Chla is the surface chlorophyll concentration; B1, B2 and INT are the regression coefficients obtained by comparing the historical data of the above two through regression analysis.

[0009] Optionally, the using a preset algorithm to calculate the surface chlorophyll concentration corresponding to each pixel in the water body area includes: Preprocess the remote sensing images of the target water body; Obtaining the remote sensing reflectance of each pixel in the water body area; The preset chlorophyll concentration inversion algorithm is used to calculate the surface chlorophyll concentration corresponding to the remote sensing reflectance of each pixel.

[0010] Optionally, the identifying the water body particle attenuation coefficient profile type of each pixel includes: Calculate the true photosphere depth at the pixel based on the surface chlorophyll concentration; Obtaining the water depth and the mixed layer depth at the pixel; Calculate the LaRaD ratio of the pixel, LaRaD=water depth / mixed layer depth; Determine whether the true light layer depth at the pixel is greater than 50, When the true light layer depth at the pixel is greater than 50, it is determined whether the LaRaD ratio is less than 7.5. If so, determine that the water body particle attenuation coefficient profile type of the pixel is a mixed uniform type; If not, determining that the water body particle attenuation coefficient profile type of the pixel is a quasi-Gaussian type; When the true light layer depth at the pixel is not greater than 50, it is determined whether the LaRaD ratio is less than 2. If so, determine that the water body particle attenuation coefficient profile type of the pixel is a mixed uniform type; If not, it is determined that the water body particle attenuation coefficient profile type of the pixel is an exponential decay type.

[0011] Optionally, the true photosphere depth at the pixel is calculated using the following formula: Zeu=10^(1.35-0.4026×log10(Chla)+0.0375×log10(Chl)^2) Wherein, Zeu is the true light layer depth; Chla is the surface chlorophyll concentration of the pixel.

[0012] Optionally, the calculating of the water body particle attenuation coefficient profile data of each pixel according to a preset method corresponding to the profile type includes: When the water particle attenuation coefficient profile type of the pixel is exponential attenuation type, the following formula is used to calculate the profile data of the water particle attenuation coefficient: cp660 z =cp660 surf , (z <MLD); cp660 z =cp660 surf × exp (-k exp / (z-MLD)), (z≥MLD); Among them, k exp is a preset constant parameter; MLD is the mixed layer depth at the pixel; z is the currently calculated profile depth; cp660 surf is the surface particle attenuation coefficient corresponding to the pixel; cp660 zis the water particle attenuation coefficient of the pixel when the profile depth is z.

[0013] Optionally, the calculating of the water body particle attenuation coefficient profile data of each pixel according to a preset method corresponding to the profile type includes: When the water particle attenuation coefficient profile type of the pixel is Gaussian, the following formula is used to calculate the profile data of the water particle attenuation coefficient: cp660 z =cp660 surf × (A gau × exp (- (zU) 2 / D 2 )-B gau ×z+1); Where z is the depth of the currently calculated profile; cp660 z is the water particle attenuation coefficient of the pixel at the profile depth z; cp660 surf is the surface particle attenuation coefficient corresponding to the pixel; A gau , B gau , U, and D are constant parameters determined by the Bayesian optimization method.

[0014] Optionally, the initial values ​​of the constant parameters U and D are determined in the following manner: Based on the historical data of surface chlorophyll concentration, subsurface maximum depth and subsurface maximum width, the correlation between surface chlorophyll concentration Chla and subsurface maximum depth U is obtained: log(U)= 0.446 × log(Chla)+0.413; And obtain the correlation between the surface chlorophyll concentration Chla and the subsurface maximum width D: log(D)= 0.406 × log(Chla)-0.132; The above correlation is used to calculate the initial values ​​of the constant parameters U and D corresponding to the pixel.

[0015] Optionally, the method further includes: The observation constraints are determined using the following method: cp660 Mean-Zeu = M1 × exp (M2 × cp660 surf ); cp660 Mean-Zmax = N1 × exp (N2 × cp660 surf ); cp660 Mean-2*Zmax =P1 × cp660surf + P2; Among them, cp660 Mean-Zeu is the mean attenuation coefficient of water particles in the true light layer; cp660 Mean-Zmax It is the average attenuation coefficient of water particles from the surface to the maximum depth of the subsurface layer of the water body; cp660 Mean-2*Zmax It is the average attenuation coefficient of water particles from the water surface to twice the subsurface maximum depth; M1, M2, N1, N2, P1 and P2 are preset constant parameters.

[0016] Optionally, the constant parameter A corresponding to each pixel is determined in the following way: gau , B gau , U, D final value: The water particle attenuation coefficient cp660 between the maximum depth of the subsurface layer and the maximum depth of the preset water body is calculated according to the following formula Deep-Zmax : cp660 Deep-Zmax =cp660 surf × exp(-0.025 × (zz max )) Among them, z max is the subsurface maximum depth, obtained by multiplying U and Zeu; Construct the following objective function: Dif = Dif Zeu 2 +Dif Zmax 2 +Dif 2-Zmax 2 +Dif U 2 +(Dif D / 10) 2 +Dif model 2 ; Among them, Dif Zeu 、Dif Zmax and Dif 2-Zmax Respectively represent cp660 Mean-Zeu 、cp660 Mean-Zmax and cp660 Mean-2*Zmax The difference between the calculated result and the actual observed data; Dif U and Dif D Dif is the difference between the calculated results of U and D and the actual observed data; model For cp660 Deep-Zmax The difference between the calculated result and the actual observed data; The Bayesian optimization method is used to optimize the objective function and obtain A gau , B gau , the final value of U,D.

[0017] Another aspect of the present invention discloses a system for calculating a water body particle attenuation coefficient profile, wherein the system executes the method for calculating a water body particle attenuation coefficient profile described in the aforementioned aspect.

[0018] The present invention discloses a method and system for calculating a water body particle attenuation coefficient profile. First, the corresponding relationship between the surface chlorophyll concentration of the target water body and the water body particle attenuation coefficient is obtained. Then, a preset algorithm is used to calculate the surface chlorophyll concentration corresponding to each pixel in the water body area, and the surface water body particle attenuation coefficient of each pixel is obtained according to the corresponding relationship; finally, the water body particle attenuation coefficient profile type of each pixel is identified, and the water body particle attenuation coefficient profile data of each pixel is calculated according to the preset method corresponding to the profile type. Using the method and system disclosed in the present invention, the current water body particle attenuation coefficient profile data of the target water body can be evaluated based on remote sensing long-term observation data and a three-dimensional field of profile data can be constructed. At the same time, profile data of historical periods can also be traced back.

[0019] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the disclosure, nor is it intended to limit the scope of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present disclosure.

[0021] Figure 1 A schematic diagram of a flow chart of a method for calculating a water body particle attenuation coefficient profile provided by an embodiment of the present invention; Figure 2 An implementation provided for an embodiment of the present invention Figure 1 Schematic diagram of the process of step S300; Figure 3 An implementation provided for an embodiment of the present invention Figure 1 Schematic diagram of the process of step S400. DETAILED DESCRIPTION

[0022] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0024] Figure 1 A schematic diagram of a flow chart of a method for calculating a water body particle attenuation coefficient profile provided by an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps: Step S100: extracting the water body area in the target water body remote sensing image.

[0025] The water area is extracted from the remote sensing image of the target water body using existing methods, for example, the water area is calculated by the normalized difference water index (NDWI) or the modified normalized difference water index (MNDWI), and the threshold is set to segment the water body; or, machine algorithms such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN) are used to classify pixels to determine the water area.

[0026] Step S200: Obtain the corresponding relationship between the chlorophyll concentration of the surface layer of the target water body and the water particle attenuation coefficient.

[0027] In one embodiment disclosed in the present invention, step S200 is implemented in the following manner: (1) Collect historical data on surface chlorophyll concentration and water particle attenuation coefficient at the observation station in the target water body.

[0028] Multiple observation stations are set up in the target water body. For each observation station, surface chlorophyll concentration data and water particle attenuation coefficient data at multiple times in a historical period are collected to obtain multiple sets of historical data that match the two.

[0029] (2) Based on the historical data of the above two, the following corresponding relationship is obtained: log(cp660)=B1 × log(Chla) 2+B2×log(Chla) -INT Among them, cp660 is the surface water particle attenuation coefficient at a wavelength of 660nm (red light band) below the water surface. Since the absorption of the 660nm wavelength in the water body is strong, it can still be scattered by particulate matter. At the same time, this band is also the absorption band of phytoplankton chlorophyll. Therefore, cp660 is used as the required surface water particle attenuation coefficient in the disclosed embodiment of the present invention.

[0030] Chla is the surface chlorophyll concentration; B1, B2 and INT are regression coefficients obtained by comparing the above two historical data through regression analysis. In a specific embodiment disclosed in the present invention, B1=0.59039, B2=1.40173, INT=0.6381.

[0031] Step S300: Calculate the surface chlorophyll concentration corresponding to each pixel in the water area using a preset algorithm, and obtain the surface water particle attenuation coefficient of each pixel based on the corresponding relationship.

[0032] In one embodiment disclosed in the present invention, a preset algorithm is used to calculate the surface chlorophyll concentration corresponding to each pixel in the water body area, such as Figure 2 As shown, including: Step S301: Preprocessing the remote sensing image of the target water body: For example, remote sensing images can be preprocessed in the following ways.

[0033] (1) Radiation correction: convert the digital value (DN) of the image into physical units (such as radiance, reflectivity, etc.).

[0034] (2) Atmospheric correction: for example, Rayleigh scattering correction to remove the influence of atmospheric molecular scattering; or aerosol correction to reduce the interference of suspended particles on water reflectivity.

[0035] (3) Geometric correction: Project the image to a unified coordinate system (WGS84, UTM) to correct the geometric distortion caused by terrain and sensor tilt.

[0036] (4) Noise removal: For example, cloud removal, using a cloud mask to remove cloud-covered areas in the image; or water masking to exclude water body edges and areas severely affected by sediment.

[0037] Step S302: Obtain the remote sensing reflectivity of each pixel in the water area.

[0038] The remote sensing reflectance of each pixel in the water area is obtained by using the existing method, which will not be repeated here.

[0039] Step S303: Calculate the surface chlorophyll concentration corresponding to the remote sensing reflectance of each pixel using a preset chlorophyll concentration inversion algorithm.

[0040] The remote sensing reflectance of each pixel is input into a preset chlorophyll concentration inversion algorithm, for example, a single-band empirical model, a ratio index method or a machine learning model is used to calculate the corresponding surface chlorophyll concentration value.

[0041] Step S400: Identify the water body particle attenuation coefficient profile type of each pixel.

[0042] The water body particle attenuation coefficient profile types include mixed uniform type, Gaussian type and exponential decay type.

[0043] In one embodiment disclosed in the present invention, Figure 3 As shown in the figure, for each pixel, the water body particle attenuation coefficient profile type is determined in the following way: Step S401: Calculate the true photosphere depth at the pixel based on the surface chlorophyll concentration.

[0044] In one embodiment disclosed in the present invention, the true photosphere depth at a pixel is calculated using the following formula: Zeu=10^(1.35-0.4026×log10(Chla)+0.0375×log10(Chl)^2) The above formula is an empirical regression model established based on historical data of euphotic layer depth and surface chlorophyll concentration, where Zeu is the euphotic layer depth and Chla is the surface chlorophyll concentration of the pixel.

[0045] Step S402: Obtain the water depth and mixed layer depth at the pixel.

[0046] Among them, the water depth is obtained through the ETOPO1 global geomorphic dataset. ETOPO1 (Earth Topography OneArc-Minute Global Relief Model) is a dataset that records global topography and ocean depth, covering all land and ocean areas in the world.

[0047] The mixed layer depth is obtained through HYCOM model data. HYCOM (Hybrid Coordinate Ocean Model) is a global ocean model that can provide high-resolution ocean physical data such as temperature, salinity, ocean currents, mixed layer depth, etc.

[0048] According to the geographic coordinate information of the remote sensing image and the row and column numbers of the pixel in the image, the longitude and latitude corresponding to the pixel are determined, and then the water depth at the pixel is obtained through the ETOPO1 global landform dataset, and the mixed layer depth at the pixel is obtained through the HYCOM model data.

[0049] Step S403: Calculate the LaRaD ratio of the pixel.

[0050] LaRaD = water depth / mixed layer depth.

[0051] Step S404: determine whether the true light layer depth at the pixel is greater than 50. When the true photosphere depth at the pixel is greater than 50, step S405 is executed: determining whether the LaRaD ratio is less than 7.5.

[0052] Usually, in spring and summer, the depth of the true light layer of the water body is greater than 50. If the LaRaD ratio is less than 7.5, the water particle attenuation coefficient profile type of the pixel is determined to be mixed uniform type; If the LaRaD ratio is not less than 7.5, the water particle attenuation coefficient profile type of the pixel is determined to be Gaussian-like.

[0053] When the true photosphere depth at the pixel is not greater than 50, step S406 is executed: determining whether the LaRaD ratio is less than 2.

[0054] Usually, in autumn and winter, the depth of the true light layer of the water body is no more than 50. If the LaRaD ratio is less than 2, the water particle attenuation coefficient profile type of the pixel is determined to be mixed uniform type; If the LaRaD ratio is not less than 2, the water particle attenuation coefficient profile type of the pixel is determined to be exponential attenuation type.

[0055] Step S500: Calculate the water body particle attenuation coefficient profile data of each pixel according to a preset method corresponding to the corresponding profile type.

[0056] Among them, if the water body particle attenuation coefficient profile type corresponding to the pixel is a mixed uniform type, then the water body particle attenuation coefficient profile data of the pixel is the surface water body particle attenuation coefficient, that is, within the water column represented by the pixel, for example, at a depth of less than 125 meters, the water body particle attenuation coefficient is the surface water body particle attenuation coefficient.

[0057] In one embodiment disclosed in the present invention, when the water body particle attenuation coefficient profile type of the pixel is an exponential attenuation type, the following formula is used to calculate the profile data of the water body particle attenuation coefficient: cp660 z =cp660 surf , (z <MLD); cp660 z =cp660 surf × exp (-k exp / (z-MLD)), (z≥MLD); Among them, k expis a preset constant parameter. In a specific embodiment disclosed in the present invention, k exp =0.024; MLD is the depth of the mixed layer at the pixel; z is the current calculated profile depth, i.e., water depth; cp660 surf is the surface particle attenuation coefficient corresponding to the pixel; cp660 z is the water particle attenuation coefficient of the pixel at the profile depth of z.

[0058] After completing the calculation for different z in the entire profile of the pixel, the water particle attenuation coefficient profile data of the pixel is generated. Through the profile data, the water particle attenuation coefficient at each depth in the water column represented by the pixel can be obtained.

[0059] For example, cp660 is calculated every 1 meter. z , until z reaches 125 meters, these 125 calculated cp660 z The discrete data is interpolated into the entire profile by interpolation (other existing methods may also be used) so that the profile has a continuous and smooth water particle attenuation coefficient.

[0060] In the embodiment disclosed in the present invention, the water particle attenuation coefficient profile data belonging to Gaussian-type pixels is calculated based on the Bayesian optimization method.

[0061] When the water particle attenuation coefficient profile type of the pixel is Gaussian, the following formula is used to calculate the profile data of the water particle attenuation coefficient: cp660 z =cp660 surf × (A gau × exp (- (zU) 2 / D 2 )-B gau ×z+1); Where z is the depth of the currently calculated profile; cp660 z is the water particle attenuation coefficient of the pixel at the profile depth z; cp660 surf is the surface particle attenuation coefficient corresponding to the pixel; A gau , B gau , U, and D are constant parameters determined by the Bayesian optimization method.

[0062] In one embodiment disclosed in the present invention, the initial values ​​of the constant parameters U and D are determined in the following manner: (1) Obtain historical data on surface chlorophyll concentration, subsurface maximum depth, and subsurface maximum width. Surface chlorophyll concentration (Chla) refers to the chlorophyll concentration in the uppermost layer of the water body (generally 0–5 m); subsurface maximum depth (U) refers to the depth at which the chlorophyll concentration in the water body reaches the subsurface maximum, which is usually located near the thermocline and represents the main growth layer of phytoplankton; subsurface maximum width (D) refers to the distribution range of chlorophyll concentration around the subsurface maximum, which is the vertical range from the subsurface chlorophyll concentration gradually decreasing from the maximum value to the background level, describing the thickness of the high chlorophyll concentration area.

[0063] Based on the above historical data, the correlation between the surface chlorophyll concentration Chla and the subsurface maximum depth U is obtained: log(U)= 0.446 × log(Chla)+0.413; And obtain the correlation between the surface chlorophyll concentration Chla and the subsurface maximum width D: log(D)= 0.406 × log(Chla)-0.132; (2) Use the above correlation relationship to calculate the initial values ​​of the constant parameters U and D corresponding to the pixel.

[0064] In one embodiment disclosed in the present invention, the following method is required to determine the observation constraint condition: cp660 Mean-Zeu = M1 × exp (M2 × cp660 surf ); cp660 Mean-Zmax = N1 × exp (N2 × cp660 surf ); cp660 Mean-2*Zmax =P1 × cp660 surf + P2; Among them, cp660 Mean-Zeu is the mean attenuation coefficient of water particles in the true light layer; cp660 Mean-Zmax It is the average attenuation coefficient of water particles from the surface to the maximum depth of the subsurface layer of the water body; cp660 Mean-2*Zmax is the mean attenuation coefficient of water particles from the water surface to twice the subsurface maximum depth; M1, M2, N1, N2, P1 and P2 are constant parameters calculated based on the corresponding historical data; in a specific embodiment disclosed in the present invention, M1=0.0189, M2=21.989, N1=0.0216, N2=19.782, P1=0.9501, P2=0.0027.

[0065] In one embodiment disclosed in the present invention, the final values ​​of the constant parameters Agau, Bgau, U, and D corresponding to each pixel are determined in the following manner: The water particle attenuation coefficient cp660Deep-Zmax between the maximum depth of the subsurface layer and the preset maximum depth of the water body is calculated according to the following formula: cp660 Deep-Zmax =cp660 surf × exp(-0.025 × (zz max )) Among them, z max is the subsurface maximum depth, obtained by multiplying U and Zeu.

[0066] Construct the following objective function: Dif = Dif Zeu 2 +Dif Zmax 2 +Dif 2-Zmax 2 +Dif U 2 +(Dif D / 10) 2 +Dif model 2 ; Among them, Dif Zeu 、Dif Zmax and Dif 2-Zmax Respectively represent cp660 Mean-Zeu 、cp660 Mean-Zmax and cp660 Mean-2*Zmax The difference between the calculated result and the actual observed data; Dif U and Dif D Dif is the difference between the calculated results of U and D and the actual observed data; model For cp660 Deep-Zmax The difference between the calculated result and the actual observed data.

[0067] Before participating in Bayesian optimization, U and D need to be normalized using the true light layer depth to achieve standardization of U and D, for example, U=U / Zeu, D=D / Zeu.

[0068] The Bayesian optimization method is used to optimize the objective function and obtain the A corresponding to the objective function closest to zero. gau , B gau ,U,D,as the final parameter value.

[0069] According to the above method, the formula for calculating the water particle attenuation coefficient corresponding to each pixel is obtained: cp660 z =cp660 surf × (A gau × exp (- (zU) 2 / D 2 )-B gau ×z+1), and calculate the water particle attenuation coefficient at each depth in the water column represented by the pixel, and generate continuous and smooth profile data.

[0070] After obtaining the water particle attenuation coefficient profile data of each pixel in the water body area using the above method, a three-dimensional scene of the water particle attenuation coefficient of the entire water body area is constructed.

[0071] The embodiment of the present invention further discloses a water body particle attenuation coefficient profile calculation system, which executes the water body particle attenuation coefficient profile calculation method disclosed in the above embodiment.

[0072] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for calculating a water body particle attenuation coefficient profile, characterized in that: include: Extract the water area in the target water body remote sensing image; Obtain the corresponding relationship between the chlorophyll concentration on the surface of the target water body and the water particle attenuation coefficient; The surface chlorophyll concentration corresponding to each pixel in the water body area is calculated by using a preset algorithm, and the surface water particle attenuation coefficient of each pixel is obtained according to the corresponding relationship; Identify the water body particle attenuation coefficient profile type of each pixel, wherein the water body particle attenuation coefficient profile types include mixed uniform type, Gaussian type and exponential attenuation type; The water body particle attenuation coefficient profile data of each pixel is calculated according to a preset method corresponding to the profile type, wherein the water body particle attenuation coefficient profile data belonging to Gaussian-type pixels is calculated based on a Bayesian optimization method.

2. The method according to claim 1, characterized in that The obtaining of the corresponding relationship between the surface chlorophyll concentration of the target water body and the water body particle attenuation coefficient includes: Collect historical data on surface chlorophyll concentration and water particle attenuation coefficient at multiple observation stations in the target water body; Based on the historical data of the above two, the following corresponding relationship is obtained: log(cp660)=B1 × log(Chla) 2 +B2×log(Chla) -INT Among them, cp660 is the attenuation coefficient of surface water particles at a wavelength of 660nm below the water surface; Chla is the surface chlorophyll concentration; B1, B2 and INT are the regression coefficients obtained by comparing the historical data of the above two through regression analysis.

3. The method according to claim 2, characterized in that The method of using a preset algorithm to calculate the surface chlorophyll concentration corresponding to each pixel in the water body area includes: Preprocess the remote sensing images of the target water body; Obtaining the remote sensing reflectance of each pixel in the water body area; The preset chlorophyll concentration inversion algorithm is used to calculate the surface chlorophyll concentration corresponding to the remote sensing reflectance of each pixel.

4. The method according to claim 1, characterized in that: The identifying of the water body particle attenuation coefficient profile type of each pixel includes: Calculate the true photosphere depth at the pixel based on the surface chlorophyll concentration; Obtaining the water depth and the mixed layer depth at the pixel; Calculate the LaRaD ratio of the pixel, LaRaD=water depth / mixed layer depth; Determine whether the true light layer depth at the pixel is greater than 50, When the true light layer depth at the pixel is greater than 50, it is determined whether the LaRaD ratio is less than 7.

5. If so, determine that the water body particle attenuation coefficient profile type of the pixel is a mixed uniform type; If not, determining that the water body particle attenuation coefficient profile type of the pixel is a quasi-Gaussian type; When the true light layer depth at the pixel is not greater than 50, it is determined whether the LaRaD ratio is less than 2. If so, determine that the water body particle attenuation coefficient profile type of the pixel is a mixed uniform type; If not, it is determined that the water body particle attenuation coefficient profile type of the pixel is an exponential decay type.

5. The method according to claim 4, characterized in that The true photosphere depth at the pixel is calculated using the following formula: Zeu=10^(1.35-0.4026×log10(Chla)+0.0375×log10(Chl)^2) Wherein, Zeu is the true light layer depth; Chla is the surface chlorophyll concentration of the pixel.

6. The method according to claim 1, characterized in that The calculating of the water body particle attenuation coefficient profile data of each pixel according to a preset method corresponding to the profile type includes: When the water particle attenuation coefficient profile type of the pixel is exponential attenuation type, the following formula is used to calculate the profile data of the water particle attenuation coefficient: cp660 z = cp660 surf , (With <MLD) cp660 z = cp660 surf × exp (-k exp / (z-MLD)),(z≥MLD) Among them, k exp is a preset constant parameter; MLD is the mixed layer depth at the pixel; z is the currently calculated profile depth; cp660 surf is the surface particle attenuation coefficient corresponding to the pixel; cp660 z is the water particle attenuation coefficient of the pixel when the profile depth is z.

7. The method according to claim 1, characterized in that The calculating of the water body particle attenuation coefficient profile data of each pixel according to a preset method corresponding to the profile type includes: When the water particle attenuation coefficient profile type of the pixel is Gaussian, the following formula is used to calculate the profile data of the water particle attenuation coefficient: cp660 z =cp660 surf × (A gau × exp (- (z-U) 2 / D 2 )-B gau ×z+1) Where z is the depth of the currently calculated profile; cp660 z is the water particle attenuation coefficient of the pixel at the profile depth z; cp660 surf is the surface particle attenuation coefficient corresponding to the pixel; A gau , B gau , U, and D are constant parameters determined by the Bayesian optimization method.

8. The method according to claim 7, characterized in that The initial values ​​of the constant parameters U and D are determined in the following way: Based on the historical data of surface chlorophyll concentration, subsurface maximum depth and subsurface maximum width, the correlation between surface chlorophyll concentration Chla and subsurface maximum depth U is obtained: log(U)= 0.446 × log(Chla)+0.413 And obtain the correlation between the surface chlorophyll concentration Chla and the subsurface maximum width D: log(D)= 0.406 × log(Chla)-0.132 The above correlation is used to calculate the initial values ​​of the constant parameters U and D corresponding to the pixel.

9. The method according to claim 8, characterized in that The method further comprises: The observation constraints are determined using the following method: cp660 Mean-Zeu = M1 × exp (M2 × cp660 surf ) cp660 Mean-Zmax = N1 × exp (N2 × cp660 surf ) cp660 Mean-2*Zmax =P1 ×cp660 surf + P2 Among them, cp660 Mean-Zeu is the mean attenuation coefficient of water particles in the true light layer; cp660 Mean-Zmax It is the average attenuation coefficient of water particles from the surface to the maximum depth of the subsurface layer of the water body; cp660 Mean-2*Zmax It is the average attenuation coefficient of water particles from the water surface to twice the subsurface maximum depth; M1, M2, N1, N2, P1 and P2 are preset constant parameters.

10. The method according to claim 9, characterized in that The constant parameter A corresponding to each pixel is determined in the following way: gau , B gau , U, D final value: The water particle attenuation coefficient cp660 between the maximum depth of the subsurface layer and the maximum depth of the preset water body is calculated according to the following formula Deep-Zmax : cp660 Deep-Zmax =cp660 surf × exp(-0.025 × (z-z max )) Among them, z max is the subsurface maximum depth, obtained by multiplying U and Zeu; Construct the following objective function: Dif= Dif Zeu 2 +Dif Zmax 2 +Dif 2-Zmax 2 +Dif U 2 +(Dif D / 10) 2 +Dif model 2 Among them, Dif Zeu 、Dif Zmax and Dif 2-Zmax Respectively represent cp660 Mean-Zeu 、cp660 Mean-Zmax and cp660 Mean-2*Zmax The difference between the calculated result and the actual observed data; Dif U and Dif D Dif is the difference between the calculated results of U and D and the actual observed data; model For cp660 Deep-Zmax The difference between the calculated result and the actual observed data; The Bayesian optimization method is used to optimize the objective function and obtain A gau , B gau , the final value of U,D.

11. A water body particle attenuation coefficient profile calculation system, characterized in that: The system executes the water body particle attenuation coefficient profile calculation method according to any one of claims 1-10.

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