A method and device for remotely sensing and extracting seawater light transmittance based on ocean optical classification

Abstract

The present application provides a method and device for remotely sensing and extracting seawater transmittance based on ocean optical classification. The method includes: for the target water body, combining measured data and remote sensing image data, performing classification processing and calculating the seawater transmittance. First, by acquiring measured data and remote sensing image data, a measured data set and a remote sensing data set are constructed. Based on these two data sets, the target water body is classified to distinguish clean water bodies and turbid water bodies. On this basis, for different target types, a remote sensing estimation model of the light attenuation coefficient is constructed using the measured data set, and the seawater transmittance of the target water body is calculated using this model; the present application can improve the calculation efficiency of the seawater transmittance.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular, to a method and device for remotely sensing and extracting seawater transmittance based on ocean optical classification. Background Art

[0002] Seawater transmittance (Fr) is the ratio of the light flux transmitted through the seawater body to the incident light flux, which reflects the variation of the apparent optical quantity and inherent optical quantity of the water body with depth, and directly determines the distribution and intensity of the underwater light field in the ocean. Light attenuates rapidly with depth in seawater. When the water body transmittance is small, it may cause benthic organisms to die due to insufficient light for photosynthesis. Seawater transmittance can be used for the classification of water body grades (for example, Jerlov divides seawater into 10 categories, the maximum transmittance of clear seawater is about 98%, and that of turbid coastal seawater is 56%), the identification of the distribution ranges of planktonic and benthic plants (for example, seagrass generally grows well in areas with a transmittance of not less than 20%; seaweeds are vertically distributed according to the light intensity, and the lower limit of transmittance for some multicellular algae is 0.05 - 0.1%), etc. At the same time, it can also be used for the calculation of the euphotic layer depth (generally the water depth at which the photosynthetically active irradiance drops to 1% of the surface value). The size of the euphotic layer depth directly determines the size of marine primary productivity, and is an important parameter for studying the photosynthesis of phytoplankton, the change of global carbon flux, and marine primary productivity. It is also an important reference for water mass and current system identification, water body cleanliness assessment, and fish activity range judgment.

[0003] Due to the vast sea area, large latitude span, and complex water body properties, the seawater transmittance varies significantly at different spatial and temporal scales. In the clear ocean area, the depth of the transparent layer (seawater transmittance is 1%) can exceed 150 m, while in the coastal area, it can be reduced to 20 m or even less. Studying, monitoring, and mastering the spatio-temporal variation of seawater transmittance can better understand the biological and water chemical properties of the water body, and provide basic data for the assessment of the marine ecological status and ecological protection and restoration, etc. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a method, device, electronic device, and storage medium for remotely sensing and extracting seawater transmittance based on ocean optical classification, which can improve the calculation efficiency of seawater transmittance.

[0005] The technical solution of the embodiments of the present application is implemented as follows:

[0006] In a first aspect, the embodiments of the present application provide a method for remotely sensing and extracting seawater transmittance based on ocean optical classification, including the following steps:

[0007] Obtain measured data and remote sensing image data for the target water body, construct a measured data set based on the measured data, and construct a remote sensing data set based on the remote sensing image data;

[0008] Classify the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type, where the target type includes clean water body or turbid water body;

[0009] Construct a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculate the seawater transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient.

[0010] In a second aspect, an embodiment of the present application further provides a device for remotely sensing and extracting seawater transmittance based on ocean optical classification, and the device includes:

[0011] An acquisition module, configured to acquire measured data and remote sensing image data for a target water body, construct a measured data set based on the measured data, and construct a remote sensing data set based on the remote sensing image data;

[0012] A classification module, configured to classify the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type, where the target type includes clean water body or turbid water body;

[0013] A calculation module, configured to construct a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculate the seawater transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to execute the method for remotely sensing and extracting seawater transmittance based on ocean optical classification according to any item in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the method for remotely sensing and extracting seawater transmittance based on ocean optical classification according to any item in the first aspect.

[0016] The embodiments of the present application have the following beneficial effects:

[0017] (1) According to the relative effects of three components, such as phytoplankton, yellow substances, and suspended substances, that affect the optical properties of seawater, a classification method based on remote sensing reflectance is established, a classification threshold is determined, and the water body is divided into clean water bodies and turbid water bodies, realizing a large-scale remote sensing water body classification method based on the optical properties of seawater.

[0018] (2) For clean water bodies and turbid water bodies, relationship models between remote sensing reflectance and light attenuation coefficient based on remote sensing basic parameters are established respectively, which can meet the needs of different water bodies, have higher portability and wider applicability. The directly adopted remote sensing reflectance avoids the use of any intermediate optical variables, thereby effectively controlling the error sources of the model. The method is simple and easy to operate, more suitable for actual satellite remote sensing applications, and easy to promote.

[0019] (3) A method for calculating seawater transmittance based on radiative transfer theory is established. From remote sensing reflectance to light attenuation coefficient, and then to seawater transmittance, there is only one intermediate optical variable, with less error. At the same time, through seawater transmittance, the euphotic layer and water depth surfaces at different seawater transmittance ratios such as Z_(0.5%), Z_(10%), and Z_(20%) can be further deduced, which can meet the selection and delineation of suitable illumination areas for various plants such as seagrass and seaweed, is more suitable for actual satellite remote sensing applications, and has a wider application range.

[0020] (4) The inversion method is simple and easy to operate. The algorithm involves fewer intermediate variables, the determination method of the optimal spectral characterization factor is simple, making full use of existing public and standardized data, with convenient operation and easy promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 is a schematic flowchart of steps S101 - S103 provided by an embodiment of the present application;

[0023] Figure 2 is a schematic flowchart of steps S201 - S202 provided by an embodiment of the present application;

[0024] Figure 3 is a schematic diagram of the principle of a method for remotely sensing and extracting seawater transmittance based on ocean optical classification provided by an embodiment of the present application;

[0025] Figure 4 is a schematic structural diagram of a device for remotely sensing and extracting seawater transmittance based on ocean optical classification provided by an embodiment of the present application;

[0026] Figure 5 is a schematic diagram of the composition structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. It should be understood that the accompanying drawings in this application are only for the purpose of illustration and description, and are not used to limit the protection scope of this application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of this application. It should be understood that the operations in the flowchart may not be implemented in sequence, and steps without logical context may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of this application.

[0028] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0029] In addition, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. The components of the embodiments of this application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but only represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of this application.

[0030] In the following description, the terms "first / second / third" involved are only used to distinguish similar objects, and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when allowed, so that the embodiments of this application described here can be implemented in an order other than that illustrated or described here.

[0031] It should be noted that the term "including" will be used in the embodiments of this application to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application and do not limit this application.

[0033] When implementing the embodiments of this application, the applicant found the following problems in the prior art:

[0034] The related research mainly focuses on directly solving the water body diffuse attenuation coefficient (Kdpar) and the euphotic layer (Zeu). The seawater transmittance is generally used as an intermediate quantity for the euphotic layer or the water body diffuse attenuation coefficient, and there is less research on it.

[0035] The remote sensing inversion methods of the diffuse attenuation coefficient are mainly divided into three types: the empirical algorithm based on water body component concentration, the band ratio algorithm, and the semi-analytical algorithm based on the radiative transfer model. ① For the empirical algorithm based on water body component concentration, the relationship between the optical characteristic spectrum of seawater and the water body component concentrations such as chlorophyll concentration and suspended matter is established by means of regression analysis, and then the attenuation characteristics of seawater are estimated. For example, Morel established an empirical equation between the diffuse attenuation coefficient and chlorophyll concentration; ② The band ratio algorithm is mainly based on the ratio of the water-leaving radiance or remote sensing reflectance between different bands, and the diffuse attenuation coefficient is retrieved by statistical methods. It is usually used to retrieve the diffuse attenuation coefficient at a wavelength of 490 nm (K 490 ). For example, "A Model Method for Estimating the Water Body Diffuse Attenuation Coefficient Using OLI Data" (CN106248601A) proposed to calculate K using the remote sensing reflectance of the green band and the near-infrared band of Landsat 8 OLI data 49 0. "A Method for Inverting the Vertical Variation of the PAR Diffuse Attenuation Coefficient Based on Remote Sensing Data" (CN114199827A) proposed to calculate the surface diffuse attenuation coefficient using the remote sensing reflectance at wavelengths of 488 nm and 555 nm; ③ The semi-analytical method is based on the radiative transfer theory. By solving the empirical models of inherent optical quantity parameters such as the absorption coefficient (a) and the backscattering coefficient (b), the quantitative relationship between them and the diffuse attenuation coefficient is established to achieve inversion. For example, "A Method for Inverting the Optical Attenuation Coefficient of the Global Ocean Based on Remote Sensing Reflectance" uses the remote sensing reflectance at 443 nm, 490 nm, 555 nm, 645 nm, and 667 nm to invert a and b and then solve the diffuse attenuation coefficient. "Remote Sensing Inversion Method and System for Water Body Diffuse Attenuation Coefficient" (CN113324952A) established a relationship model between the diffuse attenuation coefficient and a, b for different water body types, and used the remote sensing reflectance at 443 nm, 490 nm, 550 nm, and 670 nm to solve a, b. "A Method for Inverting the Water Body Diffuse Attenuation Coefficient Based on the Two-Stream Method Model" uses the two-stream method to establish a relationship formula between the irradiance reflectance and a, b, and introduces four undetermined parameters related to the solar zenith angle to establish a diffuse attenuation coefficient inversion model.

[0036] Based on the remote sensing inversion of the water body diffuse attenuation coefficient, there are mainly two methods for remotely sensing and extracting the depth of the euphotic layer. One is to calculate using the radiative transfer model according to the relationship between the depth of the euphotic layer and the diffuse attenuation coefficient For example, the relationship between suspended matter concentration and attenuation coefficient is used to calculate the diffuse attenuation coefficient and then further calculate the true light layer depth; the second is to directly establish a relationship model between the true light layer depth and remote sensing reflectance and water component concentration, ignoring the intermediate variable diffuse attenuation coefficient. For example, the exponential relationship between the true light layer depth and chlorophyll concentration is used to establish the model Zeu=m(chl) n "Satellite remote sensing method for monitoring the depth of the true light layer in coastal waters" (CN111460681A) establishes a model for the relationship between the true light layer and remote sensing reflectance

[0037] There are two main methods for calculating seawater transmittance. One is based on the definition, which is obtained by measuring the light flux through the seawater and the incident light flux on site; the other is based on the radiation transmission theory, which is solved through its relationship with the diffuse attenuation coefficient. The true light layer is the depth of seawater when the seawater transmittance = 1%. According to the formula Fr = e -h·kdpar (Fr = 1%, h = Zeu) Backward, (h is the depth of sea water), at this time it is still necessary to solve based on the diffuse attenuation coefficient.

[0038] At present, relevant research mainly focuses on the study of diffuse attenuation coefficient and true light layer. The seawater transmittance is generally directly measured by the ratio of the light flux through the seawater body to the incident light flux, and there is a lack of remote sensing inversion methods. Although the on-site measurement method is more accurate, it is usually time-consuming and labor-intensive, and it is expensive. It is impossible to obtain seawater transmittance synchronously over a large area, and it is difficult to cover all of my country's sea areas. There are obvious "breakpoints" in time and space, and it cannot objectively reflect the characteristics of temporal and spatial changes.

[0039] Second, according to the radiation transmission theory, the transmittance of seawater decays exponentially with the increase of seawater depth, which can be indirectly calculated through the diffuse attenuation coefficient of water body. There are three methods for calculating the diffuse attenuation coefficient of water body. ① The method based on the estimation of chlorophyll concentration and suspended solids concentration is simple to calculate, but it belongs to the regional algorithm. On the one hand, it is difficult to promote and apply due to the limitations of applicable sea areas and time. Second, it is only applicable to clean water bodies. For turbid water bodies, its optical properties are complex and changeable, with many influencing factors and poor accuracy. ② Based on the band ratio algorithm, K490 is obtained through the water-free radiance or remote sensing reflectance ratio of fixed wavelength, and Kdpar is estimated from K490 experience; ③ The semi-analytical method solves the absorption coefficient (a) and backscattering coefficient (b) through the remote sensing reflectance of fixed wavelength, and then calculates Kdpar. The latter two methods both require two-level intermediate variables (such as K490, a, b, and Kdpar) to calculate seawater transmittance. The optical properties are complex and changeable. When estimating intermediate variables using remote sensing reflectance, there are often errors. These errors will be further transmitted to the calculation of seawater transmittance, affecting the inversion quality. In addition, these two methods involve multiple parameter calculations, and the inversion process is complicated and difficult to operate.

[0040] See alsoFigure 1 , Figure 1 is a schematic flowchart of steps S101 - S103 of the seawater light transmittance remote sensing extraction method based on ocean optical classification provided by an embodiment of the present application, and will be described in combination with Figure 1 the steps S101 - S103 shown.

[0041] Step S101: Obtain measured data and remote sensing image data for the target water body, construct a measured data set based on the measured data, and construct a remote sensing data set based on the remote sensing image data;

[0042] Step S102: Classify the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type, where the target type includes clean water body or turbid water body;

[0043] Step S103: Construct a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculate the seawater light transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient.

[0044] The above - mentioned seawater light transmittance remote sensing extraction method based on ocean optical classification has the following beneficial effects:

[0045] (1) According to the relative roles of three components, such as phytoplankton, yellow substance, and suspended substances, which affect the optical properties of seawater, a classification method based on remote sensing reflectance is established, the classification threshold is determined, and the water body is divided into clean water body and turbid water body, realizing a large - scale remote sensing water body classification method based on the optical properties of seawater.

[0046] (2) Relationship models between remote sensing basic parameter remote sensing reflectance and light attenuation coefficient are established for clean water bodies and turbid water bodies respectively, which can meet the requirements of different water bodies, have higher portability and wider applicability. The directly adopted remote sensing reflectance avoids using any intermediate optical variables, thereby effectively controlling the model error sources. The method is simple and easy to operate, more suitable for actual satellite remote sensing applications, and is easy to promote.

[0047] (3) A method for calculating seawater light transmittance based on the radiative transfer theory is established. From remote sensing reflectance to light attenuation coefficient, and then to seawater light transmittance, there is only one intermediate optical variable, and the error is small. At the same time, through the seawater light transmittance, the euphotic layer and the water depth surfaces at different proportions of seawater light transmittance, such as Z_(0.5%), Z_(10%), Z_(20%), etc., can be further deduced, which can meet the selection of light - suitable areas for various plants such as seagrass and seaweed, is more suitable for actual satellite remote sensing applications, and has a wider application range.

[0048] (4) The inversion method is simple and easy to operate. The algorithm involves fewer intermediate variables, the determination method of the optimal spectral characterization factor is simple, and it makes full use of the existing public and standardized data, with convenient operation and easy popularization.

[0049] The above exemplary steps of the embodiments of the present application will be described separately below.

[0050] In step S101, measured data and remote sensing image data are obtained for the target water body, a measured data set is constructed based on the measured data, and a remote sensing data set is constructed based on the remote sensing image data.

[0051] Here, the present application needs to accurately and efficiently extract remote sensing data of seawater transmittance, especially for clean and turbid water bodies. For this purpose, we need to prepare the following data:

[0052] Measured data: These data include the actual measurement data of reflectance, light attenuation coefficient, light absorption coefficient of non-pigment particles, light absorption coefficient of yellow substances, light absorption coefficient of phytoplankton pigments, and total water absorption coefficient collected in the target water area.

[0053] Remote sensing image data: These are high-resolution images of the target water area obtained by satellites or aircraft.

[0054] Based on the measured data, we construct a measured data set, which will provide us with detailed information about the target water body. At the same time, based on the remote sensing image data, we construct a remote sensing data set for subsequent remote sensing analysis.

[0055] In step S102, the target water body is classified based on the measured data set and the remote sensing data set to obtain the target water body of the target type, where the target type includes clean water body or turbid water body.

[0056] Here, a comprehensive analysis of the measured data set and the remote sensing data set is carried out to achieve the classification of the target water body. The final classification result divides the target water body into two categories: clean water body and turbid water body. This requires analyzing parameters such as the light absorption coefficient of non-pigment particles, the light absorption coefficient of yellow substances, the light absorption coefficient of phytoplankton pigments, and the total water absorption coefficient in the measured data set.

[0057] In step S103, a remote sensing estimation model of the light attenuation coefficient for the target type is constructed based on the target type and the measured data set, and the seawater transmittance of the target water body is calculated based on the remote sensing estimation model of the light attenuation coefficient.

[0058] Here, based on the target types (clean or turbid water bodies) and the measured dataset, we construct remote sensing estimation models of the light attenuation coefficient specific to these target types. These models relate the remote sensing reflectance to the light attenuation coefficient, providing specific model formulas for each type of water body. According to the radiative transfer theory, combined with the depth information and light attenuation coefficient of the target water body, we can calculate the seawater transmittance of this water body.

[0059] In some embodiments, the method further includes:

[0060] Applying the remote sensing estimation model of the light attenuation coefficient to a long - time - series satellite remote sensing dataset to obtain the spatio - temporal distribution of the seawater transmittance of the water body in the target sea area.

[0061] In the above - mentioned method, applying the remote sensing estimation model of the light attenuation coefficient to a long - time - series satellite remote sensing dataset to obtain the spatio - temporal distribution of the seawater transmittance of the water body in the target sea area is a further extension and practical application of the whole method.

[0062] Data preparation: The long - time - series satellite remote sensing dataset is a vast and complex data collection, containing satellite remote sensing images of multiple time phases, multiple bands, and resolutions. These datasets are usually used to monitor the changes and trends of the marine environment.

[0063] Model application: Applying the previously constructed remote sensing estimation model of the light attenuation coefficient to the long - time - series satellite remote sensing dataset. This step involves issues such as model parameter settings, pre - processing of input data (such as radiometric calibration, atmospheric correction, etc.), and efficient calculation of the model on large datasets. Calculation and analysis: Based on the satellite remote sensing data and the remote sensing estimation model of the light attenuation coefficient, calculate the seawater transmittance of the water body in the target sea area for each pixel or region. This usually involves complex radiative transfer models and parameter inversion, and requires comprehensive analysis combining physical oceanography, optics, and remote sensing technologies.

[0064] Spatio - temporal analysis: By analyzing the long - time - series satellite remote sensing data, the spatio - temporal distribution of the seawater transmittance of the water body in the target sea area can be obtained. This helps to understand the optical properties of the water body, water quality conditions, changes in the ecosystem, and the impact of human activities, etc.

[0065] Visualization and publication: Visualize the calculated seawater transmittance results, such as making thematic maps, dynamic maps, etc., to facilitate researchers, decision - makers, and the public to understand the water quality conditions and change trends of the target sea area. At the same time, these data can also be published to relevant data sharing platforms or Geographic Information Systems (GIS) for multi - party use and reference.

[0066] Verification and Accuracy Evaluation: To ensure the accuracy and reliability of the calculation results, it is necessary to verify and evaluate the results using actual sampling data or verification data. This can be carried out by comparing the calculation results with actual measured values or the prediction results of other models.

[0067] Feedback and Improvement: According to the results of verification and evaluation, necessary adjustments and improvements are made to the remote sensing estimation model of the light attenuation coefficient to improve the prediction accuracy and application scope of the model. At the same time, according to the requirements and feedback of actual applications, the process and technical details of the entire method are continuously improved.

[0068] Through the above steps, applying the remote sensing estimation model of the light attenuation coefficient to the long-term satellite remote sensing data set, the spatio-temporal distribution of the seawater transmittance of the water body in the target sea area can be obtained, providing important scientific basis and technical support for marine environmental monitoring, ecological protection, resource development and sustainable utilization.

[0069] In some embodiments, refer to Figure 2 , Figure 2 is the schematic flow chart of steps S201 - S202 provided by the embodiments of the present application. The measured data set can be constructed through steps S201 - S202, and each step will be described in combination.

[0070] In step S201, the target water body is measured based on a digital optical quantum meter and an underwater radiometer to obtain the remote sensing reflectance, light attenuation coefficient, non - pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body. Among them, the measurement wavelength range is 380 - 710 nm.

[0071] In step S202, the measured data set is constructed based on the measured remote sensing reflectance, light attenuation coefficient, non - pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body.

[0072] Here, the measuring devices are selected as a digital optical quantum meter (used to measure the light attenuation coefficient in the water body, which can accurately measure the absorption and scattering effects of light in water) and an underwater radiometer (used to measure the reflectance of the water body and the spectral characteristics of different wavelengths).

[0073] Through the above devices, parameters such as the remote sensing reflectance, light attenuation coefficient, non - pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body can be measured. These parameters can reflect the optical characteristics and water quality conditions of the water body.

[0074] Based on parameters such as measured remote sensing reflectance, light attenuation coefficient, light absorption coefficient of non - pigment particles, light absorption coefficient of yellow substances, light absorption coefficient of phytoplankton pigments, and total water absorption coefficient, a measured data set is constructed. This data set will serve as the basis for subsequent classification processing and model construction.

[0075] During the entire measurement process, strict quality control is also required, including equipment calibration, repeated measurements, data cleaning, etc., to ensure the accuracy and reliability of the data.

[0076] The constructed measured data set needs to be properly stored and backed up regularly to prevent data loss or damage. At the same time, for the convenience of subsequent processing and analysis, it is recommended to format the data set into a common data format (such as CSV, Excel, etc.).

[0077] The above - mentioned method can construct an accurate measured data set, providing a reliable data basis for subsequent classification processing of the target water body and remote sensing extraction of seawater transmittance. The quality of the measured data set directly affects the accuracy and reliability of the entire method. Therefore, special attention needs to be paid to the accuracy and integrity of the data when constructing the measured data set.

[0078] In some embodiments, the remote sensing data set is constructed in the following manner:

[0079] Satellite remote sensing images that are consistent with the time window and space window of the measured data set are selected, and the satellite remote sensing images are pre - processed to obtain a remote sensing data set, where the pre - processing includes at least one of radiometric calibration, atmospheric correction, and normalization processing of remote sensing reflectance.

[0080] Here, images that are consistent with the time window and space window of the measured data set are selected from a large number of satellite remote sensing images. The matching of the time window is to ensure that the acquisition time of the remote sensing image is close to the time of measured data collection, and the matching of the space window is to ensure that the coverage range of the remote sensing image is consistent with the actual range of the target water body.

[0081] The selected satellite remote sensing images are pre - processed to improve the quality and usability of the images. The pre - processing includes, but is not limited to, radiometric calibration, atmospheric correction, and normalization processing of remote sensing reflectance.

[0082] Radiometric calibration: Converting the original radiance value obtained by the satellite sensor into reflectance or other physical quantities to better reflect the light reflection characteristics of the ground objects.

[0083] Atmospheric correction: Removing the atmospheric interference in the remote sensing image, such as the influence of aerosol, cloud cover, etc. on the light scattering and absorption, so that the remote sensing reflectance is closer to the true value.

[0084] Normalization processing: Normalize the remote sensing reflectance to eliminate the radiation differences between different times and different sensors, making the data comparable.

[0085] The preprocessed satellite remote sensing images are constructed into a remote sensing data set. This data set will serve as an auxiliary data source for subsequent classification processing and model construction.

[0086] During the preprocessing process, quality control is also required, including parameter setting, algorithm verification, etc., to ensure the accuracy and reliability of the preprocessing results. The constructed remote sensing data set needs to be properly stored and backed up regularly to prevent data loss or damage. At the same time, for the convenience of subsequent processing and analysis, it is recommended to format the data set into a common data format (such as GeoTIFF, ENVI, etc.).

[0087] The above method can construct an accurate remote sensing data set, providing reliable data support for subsequent target water body classification processing and remote sensing extraction of seawater transmittance. The quality of the remote sensing data set directly affects the accuracy and reliability of the entire method. Therefore, special attention needs to be paid to the accuracy and integrity of the data when constructing the remote sensing data set. At the same time, the preprocessing step is also a very important link, which can improve the quality and usability of satellite remote sensing images and lay a good foundation for subsequent data analysis and model construction.

[0088] In some embodiments, the classification processing of the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type includes:

[0089] Based on the distribution characteristics of remote sensing reflectance at different wavelengths and the band settings of the remote sensing image data, screen the sensitive wavelengths of water body suspended matter, yellow substances, and phytoplankton, and establish a water body classification criterion, where the water body classification criterion is the ratio of remote sensing reflectance λ1 and λ2 respectively represent different wavelengths, and R rs represents the remote sensing reflectance. When the remote sensing image data is Sentinel-3 remote sensing image, λ1 = 560 nm, λ2 = 412 nm, and when R < 1, it is the clean water body, and when R > 1, it is the turbid water body;

[0090] Based on the water body classification criterion, classify the remote sensing data set to determine the target type of the target water body.

[0091] Here, first, based on the distribution characteristics of remote sensing reflectance at different wavelengths and the band settings of remote sensing image data, screen the sensitive wavelengths of water body suspended matter, yellow substances, and phytoplankton. These sensitive wavelengths are of great significance for distinguishing different types of water bodies.

[0092] Secondly, based on the selected sensitive wavelengths, a water body classification criterion is established. This criterion usually takes the form of the ratio of remote sensing reflectance and is used to distinguish different types of water bodies. For example, when the ratio of remote sensing reflectance is less than 1, it is judged as a clean water body; when the ratio of remote sensing reflectance is greater than 1, it is judged as a turbid water body. When the remote sensing image data is Sentinel-3 image, specific wavelengths (such as λ1 = 560 nm, λ2 = 412 nm) can be set to calculate the ratio of remote sensing reflectance; when the remote sensing image data is MODIS image, specific wavelengths (such as λ1 = 555 nm, λ2 = 412 nm) can be set to calculate the ratio of remote sensing reflectance. Such settings can improve the accuracy and reliability of classification.

[0093] Then, based on the established water body classification criterion, the remote sensing data set is classified. This step classifies each pixel or region in the remote sensing image as a clean water body or a turbid water body. Through the classification process, the target type of the target water body, that is, a clean water body or a turbid water body, can be determined. These classification results will be used for subsequent model construction and seawater light transmittance calculation.

[0094] Finally, the results of the classification process are output to form a classification map of the target water body. This classification map can visually display the distribution and scope of different types of water bodies.

[0095] To ensure the accuracy and reliability of the classification results, the classification results need to be verified and the accuracy evaluated. This can be done by comparing the classification results with actual sampling data or with the prediction results of other models.

[0096] According to the results of verification and evaluation, the water body classification criterion is adjusted and improved as necessary to improve the classification accuracy and application scope. At the same time, according to the actual application requirements and feedback, the process and technical details of the entire method are continuously improved.

[0097] In the above manner, by classifying the target water body based on the measured data set and the remote sensing data set, the target water body of the target type can be obtained. This classification result is crucial for subsequent model construction and seawater light transmittance calculation, and helps to better understand and monitor the water quality and light conditions of the ocean. At the same time, the method can also be flexibly adjusted and applied according to actual needs to adapt to different data sources and actual scenarios.

[0098] In some embodiments, constructing a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculating the seawater light transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient includes:

[0099] Constructing the remote sensing estimation model of the light attenuation coefficient Kdpar = aX based on the relationship between remote sensing reflectance and the light attenuation coefficient 2+bX + c, where Kdpar is the light attenuation coefficient with the unit of m -1 , X is the spectral characterization factor of remote sensing reflectance. The model coefficients include the first model coefficient a, the second model coefficient b, and the third model coefficient c, and the values of the model coefficients match the target type of the target water body;

[0100] Based on the distribution characteristics of remote sensing reflectance at different wavelengths in the measured data set, combined with the remote sensing data band settings, sensitive wavelengths are selected, and single-band models R rs (λ1), double-band models R rs (λ1) - R rs (λ2), band ratio models logarithmic model ln R rs (λ1), normalization models where λ1 and λ2 respectively represent different wavelengths, and R rs represents the remote sensing reflectance;

[0101] Based on the measured data set, obtain the accuracy data of the single-band model, the double-band model, the band ratio model, the logarithmic model, and the normalization model under different wavelength combinations, and determine the optimal spectral characterization factor based on the highest accuracy data, and determine the model coefficients;

[0102] Based on the radiation transfer principle, obtain the seawater transmittance at different depths of the target water body, where Fr is the seawater transmittance, h is the depth from the sea surface to the measurement point with the unit of m, and E h is the photosynthetically active radiation intensity at depth h, and E0 is the photosynthetically active radiation intensity under the water surface with the unit of μmol·m -2 ·s -1 .

[0103] Here, first select the measured points. According to the classification results of the water bodies, select the measured points from clean water bodies and turbid water bodies respectively. These measured points will be used for subsequent model construction and verification.

[0104] Secondly, construct a remote sensing estimation model of the light attenuation coefficient, analyze the internal relationship between the remote sensing reflectance and the light attenuation coefficient of different types of water bodies, and construct a remote sensing estimation model of the light attenuation coefficient for clean water bodies and turbid water bodies respectively. This model uses the spectral characterization factor X of remote sensing reflectance and uses the method of linear regression to determine the model coefficients a, b,

[0105] Then, sensitive wavelengths are screened and multi-spectral models are constructed to analyze the distribution characteristics of the remote sensing reflectance of two types of water bodies in the measured dataset. Combining with the band settings of remote sensing data, the wavelengths sensitive to water body types are screened out. On this basis, different remote sensing estimation models such as single-band, double-band, band ratio, logarithm, and normalization models are constructed in sequence. Using the measured dataset, the estimation accuracies of these models under different wavelength combinations are evaluated, and the model with the highest estimation accuracy is selected as the optimal spectral characterization factor, and the coefficients of the model are further determined.

[0106] Finally, the seawater transmittance is calculated. According to the radiative transfer theory, using the constructed remote sensing estimation model of the light attenuation coefficient and the screened sensitive wavelengths, the seawater transmittance at different water depths of the two types of water bodies is calculated. The formula involves the depth h from the sea surface to the measurement point, the photosynthetically active radiation intensity E at depth h h and the photosynthetically active radiation intensity E0 under the water surface. In particular, when the seabed water depth data is brought in, the seawater transmittance at the seabed can be calculated.

[0107] The above method not only provides a basis for the classification of ocean water bodies, but also provides a practical tool for the estimation of seawater transmittance. It has important scientific significance and practical value for ocean environmental monitoring, ecological protection, resource development and sustainable utilization.

[0108] In some embodiments, the method further includes:

[0109] Based on the measured dataset, the accuracy of the inversion results of the remote sensing estimation model of the light attenuation coefficient is compared, and based on the remote sensing dataset, the light attenuation coefficients corresponding to all satellite remote sensing reflectances in the remote sensing dataset are estimated by the remote sensing estimation model of the light attenuation coefficient, and then compared with the light attenuation coefficients in the measured dataset to determine the error of the remote sensing estimation model of the light attenuation coefficient.

[0110] Here, based on the measured dataset, the accuracy of the inversion results of the remote sensing estimation model of the light attenuation coefficient is compared. By comparing the light attenuation coefficient estimated by the model with the light attenuation coefficient in the measured dataset, the estimation accuracy of the model is evaluated.

[0111] Based on the remote sensing dataset, the light attenuation coefficients corresponding to all satellite remote sensing reflectances in the remote sensing dataset are estimated using the remote sensing estimation model of the light attenuation coefficient, and then these estimated light attenuation coefficients are compared with the light attenuation coefficients in the measured dataset. By comparison, the error of the remote sensing estimation model of the light attenuation coefficient can be determined.

[0112] The above method can evaluate the accuracy and error of the remote sensing estimation model of the light attenuation coefficient, which helps to further optimize the model and improve the accuracy of seawater transmittance estimation. This error analysis has important guiding significance for the improvement and practical application of the model.

[0113] Please refer to Figure 3 , Figure 3 which is the schematic diagram of the method for remotely sensing and extracting seawater transmittance based on ocean optical classification provided by the embodiments of the present application. As Figure 3 shown, in a complete processing process, the following steps are included:

[0114] (1) Data acquisition and preprocessing.

[0115] In the first step, the measured remote sensing reflectance, light attenuation coefficient, non - pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water absorption coefficient of water bodies in China's sea areas are directly measured using a digital light quantum meter and an underwater radiometer. The wavelength range is 380 - 710 nm, and a measured data set is constructed.

[0116] In the second step, satellite remote sensing images consistent with the time window and space window of the measured data set are selected, and preprocessing such as radiometric calibration and atmospheric correction is carried out. The remote sensing reflectance is normalized to form a remote sensing data set.

[0117] In the third step, global ocean bathymetry data is obtained through the NOAA official website.

[0118] (2) Water body type classification.

[0119] In the first step, according to the water body composition, according to the morel and prieur (1977) two - way classification method, ocean water bodies are divided into type - I water bodies (clean water bodies, where the proportion of phytoplankton pigment light absorption coefficient in the total absorption coefficient is more than 2 / 3) and type - II water bodies (turbid water bodies, where the proportion of phytoplankton pigment light absorption coefficient is less than 2 / 3, and is jointly determined by phytoplankton, suspended matter, yellow substances, etc.).

[0120] In the second step, according to the measured values of the non - pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water absorption coefficient in the measured data set, these measured water bodies are divided into clean water bodies and turbid water bodies.

[0121] In the third step, analyze the distribution characteristics of the remote sensing reflectance ratio of the two types of water bodies at different wavelengths in the measured data set, and combine with the remote sensing data band settings to screen the sensitive wavelengths of water body suspended matter, yellow substances, and phytoplankton, and establish a water body classification criterion - the remote sensing reflectance ratio Evaluate the between-class distance and within-class distance of samples under different wavelength combinations, and finally determine the optimal band combination and threshold. (For example, in MODIS images, when Rrs(555) / Rrs(412) < 1, it is a clean water body; when Rrs(555) / Rs(412) > 1, the water body is a turbid water body).

[0122] In the fourth step, according to the water body classification formula established above, classify the remote sensing data set to determine the water body type.

[0123] (3) Calculation of seawater transmittance.

[0124] In the first step, based on the water body classification results, respectively select the measured points of clean water bodies and turbid water bodies, and according to the requirements of model construction, randomly divide the measured points into two parts, which are used for model construction and model verification respectively.

[0125] In the second step, analyze the internal relationship between the remote sensing reflectance and the light attenuation coefficient of different types of water bodies, and respectively construct the remote sensing estimation models of the light attenuation coefficient for clean water bodies and turbid water bodies:

[0126] Kdp a r=aX 2 +bX+c.

[0127] In the formula, Kdpar is the light attenuation coefficient (m -1 ), X is the spectral characterization factor of the remote sensing reflectance ratio, and a, b, and c are all model coefficients.

[0128] In the third step, analyze the distribution characteristics of the remote sensing reflectance ratio of the two types of water bodies at different wavelengths in the measured data set, and combine the band settings of the remote sensing data to screen the sensitive wavelengths, and successively construct the single-band model R rs (λ1), the double-band model R rs (λ1)-R rs (λ2), the band ratio model the logarithmic model ln R rs (λ1), the normalization model Use the measured data set to evaluate the estimation accuracy of the above remote sensing estimation models under different wavelength combinations, take the one with the highest estimation accuracy as the optimal spectral characterization factor, and further determine the model coefficients of the above remote sensing estimation models accordingly.

[0129] In the fourth step, according to the radiation transfer theory, the seawater transmittance at different water depths of the two types of water bodies can be obtained.

[0130]

[0131] In the formula, Fr is the seawater transmittance, h is the depth from the sea surface to the measurement point (m), E his the photosynthetically active radiation intensity at a depth of h, and E0 is the photosynthetically active radiation intensity under the water surface (μmol·m －2 ·s －1 ).

[0132] (4) Precision verification.

[0133] Based on the measured data set, the inversion results of the model are evaluated for precision from two aspects: the mean absolute error (MAE) and the mean relative error (MRE); meanwhile, based on the image data set, the light attenuation coefficient corresponding to all satellite remote sensing reflectances in the image data set is estimated by using the remote sensing estimation model, and then compared with the measured light attenuation coefficient in the measured data set to evaluate the practicability of the remote sensing estimation model.

[0134] (5) Production of seawater transmittance distribution data products.

[0135] Apply the remote sensing estimation model to the long-term satellite remote sensing data set to obtain the spatio-temporal distribution of seawater transmittance in the sea area water body. In particular, when the seawater transmittance Fr is 1%, the water depth h at this time is the euphotic layer depth; when the seawater transmittance Fr at the seabed ≥ 20%, it can be judged that the area meets the seagrass light requirement at this time.

[0136] In summary, the embodiments of the present application have the following beneficial effects:

[0137] (1) According to the relative effects of three components affecting the optical properties of seawater, such as phytoplankton, yellow substances, and suspended substances, a classification method based on remote sensing reflectance is established, the classification threshold is determined, and the water body is divided into clean water bodies and turbid water bodies, realizing a large-scale remote sensing water body classification method based on the optical properties of seawater.

[0138] (2) Relationship models between remote sensing reflectance, a basic remote sensing parameter, and the light attenuation coefficient are established for clean water bodies and turbid water bodies respectively, which can meet the needs of different water bodies, have higher portability and wider applicability. The directly used remote sensing reflectance avoids the use of any intermediate optical variables, thereby effectively controlling the model error source. The method is simple and easy to operate, more suitable for actual satellite remote sensing applications, and easy to promote.

[0139] (3) A method for calculating seawater transmittance based on the radiative transfer theory is established. From remote sensing reflectance to light attenuation coefficient, and then to seawater transmittance, there is only one intermediate optical variable, and the error is small. At the same time, through seawater transmittance, the euphotic layer and the water depth surface at different seawater transmittance ratios such as Z_(0.5%), Z_(10%), Z_(20%) can be further deduced, which can meet the selection and delineation of the light suitable areas for various plants such as seagrass and seaweed, is more suitable for actual satellite remote sensing applications, and has a wider application range.

[0140] (4) The inversion method is simple and easy to operate. The algorithm involves fewer intermediate variables, the method for determining the optimal spectral characterization factor is simple, and it makes full use of the existing publicly available and standardized data, with convenient operation and easy promotion.

[0141] Based on the same inventive concept, an embodiment of the present application further provides a device for remotely sensing and extracting seawater light transmittance based on ocean optical classification corresponding to the method for remotely sensing and extracting seawater light transmittance based on ocean optical classification in the first embodiment. Since the principle of solving problems by the device in the embodiment of the present application is similar to that of the above-mentioned method for remotely sensing and extracting seawater light transmittance based on ocean optical classification, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be elaborated.

[0142] As Figure 4 shown, Figure 4 FIG. is a schematic structural diagram of a device 400 for remotely sensing and extracting seawater light transmittance based on ocean optical classification provided by an embodiment of the present application. The device 400 for remotely sensing and extracting seawater light transmittance based on ocean optical classification includes:

[0143] An acquisition module 401, configured to acquire measured data and remote sensing image data for a target water body, construct a measured data set based on the measured data, and construct a remote sensing data set based on the remote sensing image data;

[0144] A classification module 402, configured to perform classification processing on the target water body based on the measured data set and the remote sensing data set to obtain the target water body of a target type, where the target type includes a clean water body or a turbid water body;

[0145] A calculation module 403, configured to construct a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculate the seawater light transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient.

[0146] Those skilled in the art should understand that Figure 4 the implementation functions of the various units in the device 400 for remotely sensing and extracting seawater light transmittance based on ocean optical classification shown can be understood with reference to the relevant descriptions of the above-mentioned method for remotely sensing and extracting seawater light transmittance based on ocean optical classification. Figure 4 The functions of the various units in the device 400 for remotely sensing and extracting seawater light transmittance based on ocean optical classification shown can be implemented by a program running on a processor or by specific logic circuits.

[0147] In a possible implementation manner, the calculation module 403 further includes:

[0148] Applying the remote sensing estimation model of the light attenuation coefficient to a long-term satellite remote sensing data set to obtain the spatio-temporal distribution of the seawater light transmittance of the water body in the target sea area.

[0149] In a possible implementation, the acquisition module 401 constructs the measured data set in the following manner:

[0150] Measure the target water body based on a digital optical quantum meter and an underwater radiometer to obtain the remote sensing reflectance, light attenuation coefficient, non-pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body, wherein the measurement wavelength range is 380 - 710 nm;

[0151] Construct the measured data set based on the measured remote sensing reflectance, light attenuation coefficient, non-pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body.

[0152] In a possible implementation, the acquisition module 401 constructs a remote sensing data set in the following manner:

[0153] Screen satellite remote sensing images that are consistent with the time window and space window of the measured data set, and preprocess the satellite remote sensing images to obtain a remote sensing data set, wherein the preprocessing includes at least one of radiometric calibration, atmospheric correction, and normalization processing of the remote sensing reflectance.

[0154] In a possible implementation, the classification module 402 classifies the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type, including:

[0155] Based on the distribution characteristics of the remote sensing reflectance at different wavelengths and the band settings of the remote sensing image data, screen the sensitive wavelengths of water suspended matter, yellow substances, and phytoplankton, and establish a water body classification criterion, wherein the water body classification criterion is the ratio of remote sensing reflectance λ1 and λ2 respectively represent different wavelengths, and R rs represents the remote sensing reflectance. When the remote sensing image data is Sentinel-3 image, λ1 = 560 nm, λ2 = 412 nm, and when R < 1, it is the clean water body, and when R > 1, it is the turbid water body;

[0156] Classify the remote sensing data set based on the water body classification criterion to determine the target type of the target water body.

[0157] In a possible implementation, the calculation module 403 constructs a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculates the seawater transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient, including:

[0158] Construct the remote sensing estimation model of the light attenuation coefficient Kdpar = aX based on the relationship between the remote sensing reflectance and the light attenuation coefficient 2 +bX + c, where Kdpar is the light attenuation coefficient with the unit of m -1 , X is the spectral characterization factor of the remote sensing reflectance ratio, and the model coefficients include the first model coefficient a, the second model coefficient b, and the third model coefficient c, and the values of the model coefficients match the target type of the target water body;

[0159] Based on the distribution characteristics of the remote sensing reflectance ratio at different wavelengths of different water bodies in the measured data set, combined with the remote sensing data band settings, screen the sensitive wavelengths, and sequentially construct the single-band model R rs (λ1), the double-band model R rs (λ1)-R rs (λ2), the band ratio model the logarithmic model ln R rs (λ1), the normalization model where λ1 and λ2 respectively represent different wavelengths, and R rs represents the remote sensing reflectance;

[0160] Based on the measured data set, obtain the accuracy data of the single-band model, the double-band model, the band ratio model, the logarithmic model, and the normalization model under different wavelength combinations, and determine the optimal spectral characterization factor based on the highest accuracy data, and determine the model coefficients;

[0161] Based on the radiation transfer principle, obtain the seawater light transmittance at different depths of the target water body, where Fr is the seawater light transmittance, h is the depth from the sea surface to the measurement location with the unit of m, and E h is the photosynthetically active radiation intensity at the depth h, and E0 is the photosynthetically active radiation intensity under the water surface with the unit of μmol·m -2 ·s -1 .

[0162] In a possible implementation manner, the calculation module 403 further includes:

[0163] Based on the measured data set, perform a comparison process on the accuracy of the inversion result of the remote sensing estimation model of the light attenuation coefficient, and based on the remote sensing data set, estimate the light attenuation coefficient corresponding to all satellite remote sensing reflectances in the remote sensing data set through the remote sensing estimation model of the light attenuation coefficient, and then perform a comparison process with the light attenuation coefficient in the measured data set to determine the error of the remote sensing estimation model of the light attenuation coefficient.

[0164] In a possible implementation, when Fr is 1%, the water depth h is directly determined as the euphotic layer depth; when Fr ≥ 20%, it is determined that the area meets the seagrass light requirement.

[0165] The above-mentioned remote sensing extraction device for seawater transmittance based on ocean optical classification has the following beneficial effects:

[0166] (1) According to the relative roles of three components affecting the optical properties of seawater, such as phytoplankton, yellow substances, and suspended substances, a classification method based on remote sensing reflectance is established, and classification thresholds are determined to divide water bodies into clear water bodies and turbid water bodies, realizing a large-scale remote sensing water body classification method based on seawater optical properties.

[0167] (2) Relationship models between remote sensing basic parameters, remote sensing reflectance, and light attenuation coefficient are established for clear water bodies and turbid water bodies respectively, which can meet the needs of different water bodies, have higher portability and wider applicability. The directly adopted remote sensing reflectance avoids the use of any intermediate optical variables, thereby effectively controlling the model error sources. The method is simple and easy to operate, more suitable for actual satellite remote sensing applications, and easy to promote.

[0168] (3) A method for calculating seawater transmittance based on radiative transfer theory is established. From remote sensing reflectance to light attenuation coefficient, and then to seawater transmittance, there is only one intermediate optical variable, with relatively small errors. At the same time, through seawater transmittance, the euphotic layer and water depth surfaces at different proportions of seawater transmittance such as Z_(0.5%), Z_(10%), and Z_(20%) can be further inverted, which can meet the selection of suitable illumination areas for various plants such as seagrass and seaweed, is more suitable for actual satellite remote sensing applications, and has a wider application range.

[0169] (4) The inversion method is simple and easy to operate. The algorithm involves fewer intermediate variables, the determination method of the optimal spectral characterization factor is simple, making full use of existing public and standardized data, with convenient operation and easy promotion.

[0170] As Figure 5 shown, Figure 5 is a schematic structural diagram of the electronic device 500 provided by the embodiment of the present application. The electronic device 500 includes:

[0171] A processor 501, a storage medium 502, and a bus 503. The storage medium 502 stores machine-readable instructions executable by the processor 501. When the electronic device 500 runs, the processor 501 communicates with the storage medium 502 through the bus 503, and the processor 501 executes the machine-readable instructions to perform the steps of the method for remote sensing extraction of seawater transmittance based on ocean optical classification described in the embodiment of the present application.

[0172] In practical applications, the various components in the electronic device 500 are coupled together through a bus 503. It can be understood that the bus 503 is used to realize the connection and communication between these components. In addition to the data bus, the bus 503 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 5 the various buses are all labeled as the bus 503.

[0173] The above-mentioned electronic device has the following beneficial effects:

[0174] (1) According to the relative effects of three components, namely phytoplankton, yellow substance, and suspended matter, which affect the optical properties of seawater, a classification method based on remote sensing reflectance is established, and classification thresholds are determined to divide water bodies into clear water bodies and turbid water bodies, realizing a large-scale remote sensing water body classification method based on the optical properties of seawater.

[0175] (2) Relationship models based on remote sensing basic parameters, i.e., the relationship between remote sensing reflectance and light attenuation coefficient, are established for clear water bodies and turbid water bodies respectively, which can meet the needs of different water bodies, have higher portability and wider applicability. The directly adopted remote sensing reflectance avoids the use of any intermediate optical variables, thereby effectively controlling the model error sources. The method is simple and easy to operate, more suitable for actual satellite remote sensing applications, and easy to promote.

[0176] (3) A method for calculating the seawater transmittance based on the radiative transfer theory is established. From remote sensing reflectance to light attenuation coefficient, and then to seawater transmittance, there is only one intermediate optical variable, and the error is small. At the same time, through the seawater transmittance, the euphotic layer and the water depth surfaces at different proportions of seawater transmittance such as Z_(0.5%), Z_(10%), and Z_(20%) can be further inversely deduced, which can meet the selection and delineation of the light suitable areas for various plants such as seagrass and seaweed, is more suitable for actual satellite remote sensing applications, and has a wider application range.

[0177] (4) The inversion method is simple and easy to operate. The algorithm involves fewer intermediate variables, the determination method of the optimal spectral characterization factor is simple, and it makes full use of the existing publicly available and standardized data, with convenient operation and easy promotion.

[0178] The embodiment of the present application also provides a computer-readable storage medium, and the storage medium stores executable instructions. When the executable instructions are executed by at least one processor 501, the method for remotely sensing and extracting the seawater transmittance based on ocean optical classification described in the embodiment of the present application is realized.

[0179] In some embodiments, the storage medium may be a ferromagnetic random access memory (FRAM), read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, magnetic surface memory, optical disc, or compact disc read only memory (CD ROM), etc.; it may also be various devices including one or any combination of the above memories.

[0180] In some embodiments, the executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0181] As an example, the executable instructions may or may not correspond to a file in the file system, and may be stored as part of a file that stores other programs or data. For example, they may be stored in one or more scripts in a hypertext markup language (HTML) document, stored in a single file dedicated to the program under discussion, or stored in multiple cooperating files (such as files that store one or more modules, subroutines, or code portions).

[0182] As an example, the executable instructions may be deployed to execute on one computing device, or on multiple computing devices located at one location, or on multiple computing devices distributed at multiple locations and interconnected through a communication network.

[0183] The above computer-readable storage medium has the following beneficial effects:

[0184] (1) According to the relative effects of three components, namely phytoplankton, yellow substance, and suspended matter, which affect the optical properties of seawater, a classification method based on remote sensing reflectance is established, and classification thresholds are determined to divide water bodies into clear water bodies and turbid water bodies, realizing a large-scale remote sensing water body classification method based on the optical properties of seawater.

[0185] (2) For clean water bodies and turbid water bodies, relationship models between remote sensing reflectance and light attenuation coefficient based on remote sensing basic parameters are established respectively, which can meet the needs of different water bodies, have higher portability and wider applicability. The directly adopted remote sensing reflectance avoids the use of any intermediate optical variables, thus effectively controlling the error sources of the model. The method is simple and easy to operate, more suitable for actual satellite remote sensing applications and easy to promote.

[0186] (3) A method for calculating the seawater transmittance based on the radiative transfer theory is established. From remote sensing reflectance to light attenuation coefficient and then to seawater transmittance, there is only one intermediate optical variable, with relatively small errors. At the same time, through the seawater transmittance, the euphotic layer and the water depth surfaces under different ratios of seawater transmittance such as Z_(0.5%), Z_(10%), Z_(20%) can be further inversely deduced, which can meet the selection of light suitable areas for various plants such as seagrass and seaweed, is more suitable for actual satellite remote sensing applications and has a wider application range.

[0187] (4) The inversion method is simple and easy to operate. The algorithm involves fewer intermediate variables, the determination method of the optimal spectral characterization factor is simple, and it makes full use of the existing public and standardized data, with convenient operation and easy promotion.

[0188] In several embodiments provided in the present application, it should be understood that the disclosed methods and electronic devices can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical, or other forms.

[0189] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0191] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs.

[0192] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for remotely sensing and extracting seawater transmittance based on ocean optical classification, characterized in that, The method includes: Obtaining measured data and remote sensing image data for the target water body, constructing a measured data set based on the measured data, and constructing a remote sensing data set based on the remote sensing image data; Performing classification processing on the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type, where the target type includes clean water body or turbid water body; Constructing a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculating the seawater transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient; The measured data set is constructed by the following method: Measuring the target water body with a digital optical quantum meter and an underwater radiometer to obtain the remote sensing reflectance, light attenuation coefficient, non-pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body, where the measurement wavelength range is 380-710 nm; Constructing the measured data set based on the measured remote sensing reflectance, light attenuation coefficient, non-pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body; The constructing a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculating the seawater transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient includes: Construct the remote sensing estimation model of the light attenuation coefficient based on the relationship between the remote sensing reflectance and the light attenuation coefficient , where Kdpar is the light attenuation coefficient, with the unit of m -1 , X is the spectral characterization factor of the remote sensing reflectance, and the model coefficients include the first model coefficient a, the second model coefficient b, and the third model coefficient c. The values of the model coefficients match the target type of the target water body; Based on the distribution characteristics of remote sensing reflectance at different wavelengths of different water bodies in the measured dataset, combined with the band settings of remote sensing data, sensitive wavelengths are selected, and single-band models are constructed in sequence , dual-band models , band ratio models , logarithmic models , normalization models , where λ1 and λ2 represent different wavelengths respectively, represents remote sensing reflectance; Based on the measured data set, obtaining the accuracy data of the single-band model, the double-band model, the band ratio model, the logarithmic model, and the normalization model under different wavelength combinations, and determining the optimal spectral characterization factor based on the highest accuracy data, and determining the model coefficients; Based on the radiation transfer principle, the seawater light transmittance at different depths of the target water body is obtained. , where Fr is the seawater light transmittance, h is the depth from the sea surface to the measurement point, with the unit of m. is the photosynthetically active radiation intensity at depth h. is the photosynthetically active radiation intensity below the water surface, with the unit of μmol·m －2 ·s －1 .

2. The method according to claim 1, wherein The method further includes: Applying the remote sensing estimation model of the light attenuation coefficient to a long-term satellite remote sensing data set to obtain the spatio-temporal distribution of the seawater transmittance of the water body in the target sea area.

3. The method according to claim 1, wherein The remote sensing data set is constructed by the following method: Screening satellite remote sensing images with the same time window and space window as the measured data set, and preprocessing the satellite remote sensing images to obtain a remote sensing data set, where the preprocessing includes at least one of radiometric calibration, atmospheric correction, and normalization processing of the remote sensing reflectance.

4. The method according to claim 1, wherein The performing classification processing on the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type includes: Based on the distribution characteristics of remote sensing reflectance at different wavelengths and the band settings of the remote sensing image data, sensitive wavelengths of suspended solids, yellow substances, and phytoplankton in water bodies are selected to establish a water body classification criterion, where the water body classification criterion is the ratio of remote sensing reflectance ; λ1 and λ2 respectively represent different wavelengths, represents the remote sensing reflectance; Classifying the remote sensing data set based on the water body classification criterion to determine the target type of the target water body.

5. The method according to claim 1, wherein The method further includes: Comparing the accuracy of the inversion results of the remote sensing estimation model of the light attenuation coefficient based on the measured data set, and estimating the light attenuation coefficient corresponding to all satellite remote sensing reflectances in the remote sensing data set through the remote sensing estimation model of the light attenuation coefficient based on the remote sensing data set, and then comparing it with the light attenuation coefficient in the measured data set to determine the error of the remote sensing estimation model of the light attenuation coefficient.

6. The method according to claim 1, wherein When Fr is 1%, directly determine the water depth h as the euphotic layer depth; when Fr ≥ 20%, determine that the area where the target water body is located meets the seagrass light requirement.

7. A seawater transmissivity remote sensing extraction device based on ocean optical classification, characterized in that, The device includes: An acquisition module, configured to acquire measured data and remote sensing image data for a target water body, construct a measured data set based on the measured data, and construct a remote sensing data set based on the remote sensing image data; the measured data set is constructed in the following manner: measure the target water body by a digital quantum photometer and an underwater radiometer to obtain the remote sensing reflectance, light attenuation coefficient, non - pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body, where the measurement wavelength range is 380 - 710 nm; construct the measured data set based on the measured remote sensing reflectance, light attenuation coefficient, non - pigment particulate matter light absorption coefficient, yellow substance light absorption coefficient, phytoplankton pigment light absorption coefficient, and total water body absorption coefficient of the target water body; A classification module, configured to perform classification processing on the target water body based on the measured data set and the remote sensing data set to obtain the target water body of the target type, where the target type includes clean water body or turbid water body; A calculation module, configured to construct a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculate the seawater transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient; the constructing a remote sensing estimation model of the light attenuation coefficient for the target type based on the target type and the measured data set, and calculating the seawater transmittance of the target water body based on the remote sensing estimation model of the light attenuation coefficient includes: Construct the remote sensing estimation model of the light attenuation coefficient based on the relationship between the remote sensing reflectance and the light attenuation coefficient , where Kdpar is the light attenuation coefficient, with the unit of m -1 , X is the spectral characterization factor of the remote sensing reflectance ratio, and the model coefficients include the first model coefficient a, the second model coefficient b, and the third model coefficient c. The values of the model coefficients match the target type of the target water body; Based on the distribution characteristics of remote sensing reflectance at different wavelengths of different water bodies in the measured dataset, combined with the band settings of remote sensing data, sensitive wavelengths are screened, and single-band models are constructed in sequence , dual-band models , band ratio models , logarithmic models , normalization models , where λ1 and λ2 represent different wavelengths respectively, represents remote sensing reflectance; Based on the measured data set, obtain the accuracy data of the single - band model, the double - band model, the band ratio model, the logarithm model, and the normalization model under different wavelength combinations, determine the optimal spectral characterization factor based on the highest accuracy data, and determine the model coefficients; Based on the radiation transfer principle, the seawater transmittance at different depths of the target water body is obtained. , where Fr is the seawater transmittance, h is the depth from the sea surface to the measurement point, with the unit of m. is the photosynthetically active radiation intensity at depth h. is the photosynthetically active radiation intensity below the water surface, with the unit of μmol·m －2 ·s －1 .

8. An electronic device, characterized in that, Including: A processor, a storage medium, and a bus, where the storage medium stores machine - readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine - readable instructions to execute the seawater transmittance remote sensing extraction method based on ocean optical classification according to any one of claims 1 to 6.

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